repo_name
stringlengths 6
92
| path
stringlengths 7
220
| copies
stringclasses 78
values | size
stringlengths 2
9
| content
stringlengths 15
1.05M
⌀ | license
stringclasses 15
values |
|---|---|---|---|---|---|
p0licat/university | Experiments/Crawling/Jupyter Notebooks/Chisalita Cretu.ipynb | 1 | 50583 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"data = \"\"\"\n",
"Chisăliţă-Creţu, M.-C.: Refactoring in Object-Oriented Modeling, Todesco Publisher House, Cluj-Napoca, 2011, ISBN 978-606-595-014-6, P.234.2.Book Chapters 2.1.Chisăliţă-Creţu, M.-C.: The Multi-Objective Refactoring Set Selection Problem - A Solution Representation Analysis, book title: Advances in Computer Science and Engineering, Edited by: Matthias Schmidt, InTech Publisher House, March 2011 2011, ISBN 978-953-307-173-2, P. 441-462. 3.International Journals 3.1.Chisăliţă-Creţu M.-C.: Conceptual Modeling Evolution. A Formal Approach, MathSciNet, http://www.ams.org/mathscinet, Studia Universitatis Babeş-Bolyai, Series Informatica, Categ CNCSIS B+, XVI(1), 2011, P.62 – 83.3.2.Chisăliţă-Creţu M.-C.: An evolutionary approach for the entity refactoring setselection problem, Scopus, http://www.scopus.com/home.url, Journal ofInformation Technology Review, 2010, P.107-118.3.3.Chisăliţă-Creţu, M.-C., Vescan (Fanea), A.:The multi-objective refactoring selection problem, MathSciNet, http://www.ams.org/mathscinet, Studia Universitatis Babeş-Bolyai, Series Informatica, 2009, P.249-253.4.ISI Conferences 4.1.Chisăliţă-Creţu, M.-C., Vescan (Fanea), A.,The multiobjective refactoring selection problem, International Conference ”Knowledge Engineering: Principles and Techniques”, Presa Universitară Clujeană, www.cs.ubbcluj.ro/kept2009/, 2009, P. 291-298.4.2.Chisăliţă-Creţu, M.-C.: The Entity Refactoring Set Selection Problem - Practical Experiments for an Evolutionary Approach, The World Congress on Engineering and Computer Science (WCECS2009), October 20-22, 2009, San Francisco, USA, Newswood Limited, Editor: S. I. Ao, Craig Douglas, W. S. Grundfest, Jon Burgstone, 978-988-17012-6-8, http://www.iaeng.org/WCECS2009/ ICCSA2009.html, 2009, P. 285-290.4.3.Chisăliţă-Creţu, M.-C. A Multi-Objective Approach for Entity Refactoring Set Selection Problem, the 2nd International Conference on the Applications of Digital \n",
"Maria-Camelia Chisăliţă-Creţu 2/4 Information and Web Technologies (ICADIWT 2009), August 4-6, 2009, London, UK, Scopus, 2009, P. 100-105.5.International Conferences 5.1.Chisăliţă-Creţu, M.-C.: Refactoring Impact Formal Representation on the Internal Program Structure, The 6th International Conference on virtual Learning, October 28-29, 2011, Cluj-Napoca, România, Editura Universităţii Bucureşti, ISSN 1844 - 8933, http://c3.icvl.eu/2011, 2011, P. 500-510.5.2.Chisăliţă-Creţu, M.-C.: The refactoring plan configuration. A formal model, The 5thInternational Conference on virtual Learning, October 29-31, 2010, Târgu Mureş, România, Bucharest University Publisher House, ISSN 1844 - 8933, http://c3.icvl.eu/2010, 2010, P. 418-424.5.3.Chisăliţă-Creţu, M.-C.: The optimal refactoring selection problem - a multi-objective evolutionary approach, The 5th International Conference on virtual Learning, October 29-31, 2010, Târgu Mureş, România, Editura Universităţii Bucureşti, ISSN 1844 - 8933, http://c3.icvl.eu/2010, 2010, P. 410-417.5.4.Chisăliţă-Creţu, M.-C., Mihiş, A.-D.:A model for conceptual modeling evolution, International Conference on Applied Mathematics (ICAM 7), September 1-4, 2010, Baia-Mare, Romania, 2010, P. 100-107.5.5.Chisăliţă-Creţu, M.-C.: A refactoring impact based approach for the internal quality assessment, International Conference on Applied Mathematics (ICAM 7), September 1-4, 2010, Baia-Mare, Romania, 2010, P. 108-115.5.6.Chisăliţă-Creţu, M.-C.: Solution Representation Analysis For The EvolutionaryApproach of the Entity Refactoring Set Selection Problem, the 12th International Multiconference \"Information Society\" (IS 2009), October 12–16, 2009, Ljubljana, Slovenia, Informacijska družba, Editor: Marko Bohanec, Matjaž Gams, Vladislav Rajkovič, 978-961-264-010-1, Inspec, Scopus, 2009, P. 269-272.5.7.Chisăliţă-Creţu, M.-C.: First Results of an Evolutionary Approach for the Entity Refactoring Set Selection Problem, the 4th International Conference \"Interdisciplinarity in Engineering\" (INTER-ENG 2009), November 12-13, 2009, Târgu Mureş, România, 2009, P. 200-205.5.8.Chisăliţă-Creţu M.-C.: Search-Based Software Entity Refactoring – A New Solution Representation For The Multi-Objective Evolutionary Approach Of The Entity Set Selection Refactoring Problem, the 12th International Scientific and Professional Conference (DidMatTech 2009), September 10-11, 2009, Trnava, Slovakia, Editor: Veronika Stoffová, 2009, P. 100-103.5.9.Chisăliţă-Creţu, M.-C.: Identifying Patterns to Solve Low-Level Problems, International Conference on Competitiveness and European Integration (ICCEI 2007), October 26-27, 2007, Risoprint, 2007, P. 89-96.5.10.Chisăliţă-Creţu, M.-C.: Hidden Relations Between Code Duplication and Change Couplings, International Conference on Competitiveness and European Integration (ICCEI 2007), October 26-27, 2007, Risoprint, 2007, P. 80-88.\n",
"Maria-Camelia Chisăliţă-Creţu 3/4 5.11.Chisăliţă-Creţu, M.-C.: Applying Graph Transformation Rules for Refactoring, International Conference on Competitiveness and European Integration (ICCEI 2007), October 26-27, 2007, Risoprint, 2007, P. 73-79.5.12.Mihiş, A.-D., Chisăliţă-Creţu, M.-C., Mihăilă, C., Şerban, C.-A.:A Tool That Supports Simplifying Conditional Expressions Using Boolean Functions, International Conference of Mathematics and Informatics (ICMI45), Studii şi cercetări ştiinţifice, Editor: Mocanu Marcelina & Nimineţ Valer, 2006, P. 493-502.6.National Journals 6.1.Chisăliţă-Creţu, M.-C.: Describing low level problems as patterns and solving them via refactorings, Studii şi Cercetări Ştiinţifice, Seria Matematică, Bacău, Categ CNCSIS B+, 17, 2007, P.29 – 48.6.2.Chisăliţă-Creţu, M.-C.: Refactorizarea automată a codului sursă prin aplicarea regulilor limbajului Constraint Java , Universitatea Creştină „Dimitrie Cantemir” Bucureşti, Facultatea de Ştiinţe Economice Cluj-Napoca, „Analele Facultăţii”, Seria Ştiinţe Economice, 2007, P.192-201.6.3.Chisăliţă-Creţu, M.-C.: Modele eficiente de descriere pentru anti-şabloanele de refactorizare a codului sursă , Universitatea Creştină „Dimitrie Cantemir” Bucureşti, Facultatea de Ştiinţe Economice Cluj-Napoca, „Analele Facultăţii”, Seria Ştiinţe Economice, 2007, P.181-191.6.4.Chisăliţă-Creţu, M.-C.: Problema redundanţei în codul sursă. Definiţie, cauze,consecinţe şi soluţii , Universitatea Creştină „Dimitrie Cantemir” Bucureşti,Facultatea de Ştiinţe Economice Cluj-Napoca, „Analele Facultăţii”, Seria Ştiinţe Economice, 2006, P.183-189.6.5.Chisăliţă-Creţu, M.-C.: Efecte ale refactorizării asupra structurii interne a codului , Universitatea Creştină „Dimitrie Cantemir” Bucureşti, Facultatea de Ştiinţe Economice Cluj-Napoca, „Analele Facultăţii”, Seria Ştiinţe Economice, 2005, P.214-230.7.National Conferences 7.1.Chisăliţă-Creţu, M.-C.: Introducing Composition Strategies for the Refactoring Plan Building Problem, Symposium ”Zilele Academice Clujene”, Presa Universitară Clujeană, 2012, P. 56-63.7.2.Chisăliţă-Creţu, M.-C.: Formalizing the refactoring impact on internal program quality, Symposium ”Zilele Academice Clujene”, Presa Universitară Clujeană, 2010, P. 86-91.7.3.Chisăliţă-Creţu, M.-C.: Introducing Open-Closed Principle In Object Oriented Design Via Refactorings, Zilele Informaticii Economice Clujene, 2008, P. 104-115.7.4.Chisăliţă-Creţu, M.-C.: Describing Low-Level Problems as Patterns and Solving Them via Refactorings, Proceedings of the Symposium „Zilele Academice Clujene”, 2008, P. 75-86.7.5.Chisăliţă-Creţu, M.-C.: Describing Low Level Problems As Patterns And Solving Them Via Refactorings, Conferinţa Naţională de Matematică şi Informatică (CNMI 2007), 16-17 Noiembrie, 2007, P. 10-23.\n",
"Maria-Camelia Chisăliţă-Creţu 4/4 7.6.Chisăliţă-Creţu, M.-C.: Consecinţe asupra proiectării orientate obiect prin aplicarea refactorizărilor, folosind metrici soft, Sesiunea Ştiinţifică a Universităţii Creştine „Dimitrie Cantemir” Bucureşti, Facultatea de Ştiinţe Economice Cluj-Napoca „Probleme actuale ale gândirii, ştiinţei şi practicii economico-sociale”, Risoprint, 2007, P. 212-224.7.7.Chisăliţă-Creţu, M.-C.: Utilizarea analizei conceptelor formale în refactorizare, Sesiunea Ştiinţifică a Universităţii Creştine „Dimitrie Cantemir” Bucureşti, Facultatea de Ştiinţe Economice Cluj-Napoca „Probleme actuale ale gândirii, ştiinţei şi practicii economico-sociale”, Risoprint, 2007, P. 205-211.7.8.Chisăliţă-Creţu, M.-C., Şerban, C.-A.:Impact on Design Quality of Refactorings on Code via Metrics, Proceedings of the Symposium „Zilele Academice Clujene”, 2006, P. 39-44.7.9.Chisăliţă-Creţu, M.-C., Cheia publică şi aplicaţiile ei, Sesiunea Ştiinţifică a Universităţii Creştine „Dimitrie Cantemir” Bucureşti, Facultatea de Ştiinţe Economice Cluj-Napoca „Probleme actuale ale gândirii, ştiinţei şi practicii economico-sociale”, Risoprint, 2006, P. 258-268.7.10.Chisăliţă-Creţu, M.-C.: Program Internal Structure View with Formal Concept Analysis, Proceedings of the Symposium „Zilele Academice Clujene”, 2006, P. 33-38.7.11.Chisăliţă-Creţu, M.-C.: Direcţii de cercetare în aplicarea refactorizării, Sesiunea Ştiinţifică a Universităţii Creştine „Dimitrie Cantemir” Bucureşti, Facultatea de Ştiinţe Economice Cluj-Napoca „Probleme actuale ale gândirii, ştiinţei şi practicii economico-sociale”, Risoprint, 2005, P. 287-292.7.12.Chisăliţă-Creţu, M.-C.: General Aspects of Refactoring Applicability to Conceptual Models, Proceedings of the Symposium „Colocviul Academic Clujean de INFORMATICĂ”, 2005, P. 99-104.7.13.Chisăliţă-Creţu, M.-C.: Current Problems In Refactoring Activities, Proceedings of the Symposium „Zilele Academice Clujene”, 2004, P. 27-32.7.14.Chisăliţă-Creţu, M.-C.: Rolul refactorizării în procesul de dezvoltare a produselor soft, Sesiunea Ştiinţifică a Universităţii Creştine „Dimitrie Cantemir” Bucureşti, Facultatea de Ştiinţe Economice Cluj-Napoca „Probleme actuale ale gândirii, ştiinţei şi practicii economico-sociale”, Ecoexpert, 2004, P. 429-432.7.15.Chisăliţă-Creţu, M.-C., Mihiş, A.-D., Guran, A.-M.:3D Modeling vs. 2D Modeling, Proceedings of the Symposium „Colocviul Academic Clujean de INFORMATICĂ, 2003, P. 59-64.7.16.Barticel, S., Bretan, H., Costea, C., Chisăliţă-Creţu, M.-C., Mara, A., Măgeruşan, C., Mărcuş, G., Mihiş, A.-D., Mureşan, R., Petraşcu, D., Pitiş, A., Tompa, A.:An Application for Higher Education Admission, Proceedings of the Symposium „Zilele Academice Clujene, 2002, P. 163-170.7.17.Barticel, S., Chisăliţă-Creţu, M.-C., Mara, A., Măgeruşan, C., Marcuş, G., Mihiş, A.-D., Petraşcu, D., Tompa, A.:A Documentation Server, Research Seminars, Seminar on Computer Science, 2001, P. 65-74\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import re"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"class HelperMethods:\n",
" @staticmethod\n",
" def IsDate(text):\n",
"# print(\"text\")\n",
"# print(text)\n",
" for c in text.lstrip():\n",
" if c not in \"1234567890 \":\n",
" return False\n",
" return True"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"def ProcessLine(line):\n",
" authors = \"\"\n",
" title = \"\"\n",
" \n",
" if len(line.split()) < 5:\n",
" return False, authors, title\n",
" \n",
" #re.search(\"\", line)\n",
" authors = line.split(':')[0]\n",
" title = line.split(':')[1].split(',')[0]\n",
" date = [date for date in line.split(',') if HelperMethods.IsDate(date.lstrip())][0]\n",
" return True, authors, title, date"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"def ProcessLineB(line):\n",
" \n",
" return False, \"\", \"\""
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Chisăliţă-Creţu, M.-C.: Refactoring in Object-Oriented Modeling, Todesco Publisher House, Cluj-Napoca, 2011, ISBN 978-606-595-014-6, P.23\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: Refactoring in Object-Oriented Modeling\n",
"date: 2011\n",
"\n",
"Book Chapters \n",
"\n",
"Chisăliţă-Creţu, M.-C.: The Multi-Objective Refactoring Set Selection Problem - A Solution Representation Analysis, book title: Advances in Computer Science and Engineering, Edited by: Matthias Schmidt, InTech Publisher House, March 2011 2011, ISBN 978-953-307-173-2, P. 441-46\n",
" \n",
"International Journals \n",
"\n",
"Chisăliţă-Creţu M.-C.: Conceptual Modeling Evolution. A Formal Approach, MathSciNet, http://www.ams.org/mathscinet, Studia Universitatis Babeş-Bolyai, Series Informatica, Categ CNCSIS B+, XVI(1), 2011, P.62 – 8\n",
"authors: Chisăliţă-Creţu M.-C.\n",
"title: Conceptual Modeling Evolution. A Formal Approach\n",
"date: 2011\n",
"\n",
"\n",
"Chisăliţă-Creţu M.-C.: An evolutionary approach for the entity refactoring setselection problem, Scopus, http://www.scopus.com/home.url, Journal ofInformation Technology Review, 2010, P.107-11\n",
"authors: Chisăliţă-Creţu M.-C.\n",
"title: An evolutionary approach for the entity refactoring setselection problem\n",
"date: 2010\n",
"\n",
"\n",
"Chisăliţă-Creţu, M.-C., Vescan (Fanea), A.:The multi-objective refactoring selection problem, MathSciNet, http://www.ams.org/mathscinet, Studia Universitatis Babeş-Bolyai, Series Informatica, 2009, P.249-25\n",
"authors: Chisăliţă-Creţu, M.-C., Vescan (Fanea), A.\n",
"title: The multi-objective refactoring selection problem\n",
"date: 2009\n",
"\n",
"ISI Conferences \n",
"\n",
"Chisăliţă-Creţu, M.-C., Vescan (Fanea), A.,The multiobjective refactoring selection problem, International Conference ”Knowledge Engineering: Principles and Techniques”, Presa Universitară Clujeană, www.cs.ubbcluj.ro/kept2009/, 2009, P. 291-29\n",
"authors: Chisăliţă-Creţu, M.-C., Vescan (Fanea), A.,The multiobjective refactoring selection problem, International Conference ”Knowledge Engineering\n",
"title: Principles and Techniques”\n",
"date: 2009\n",
"\n",
"\n",
"Chisăliţă-Creţu, M.-C.: The Entity Refactoring Set Selection Problem - Practical Experiments for an Evolutionary Approach, The World Congress on Engineering and Computer Science (WCECS2009), October 20-22, 2009, San Francisco, USA, Newswood Limited, Editor: S. I. Ao, Craig Douglas, W. S. Grundfest, Jon Burgstone, 978-988-17012-6-8, http://www.iaeng.org/WCECS2009/ ICCSA200\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: The Entity Refactoring Set Selection Problem - Practical Experiments for an Evolutionary Approach\n",
"date: 2009\n",
"html, 2009, P. 285-29\n",
"\n",
"\n",
"Chisăliţă-Creţu, M.-C. A Multi-Objective Approach for Entity Refactoring Set Selection Problem, the 2nd International Conference on the Applications of Digital \n",
"Maria-Camelia Chisăliţă-Creţu 2/4 Information and Web Technologies (ICADIWT 2009), August 4-6, 2009, London, UK, Scopus, 2009, P. 100-10\n",
"\n",
"International Conferences \n",
"\n",
"Chisăliţă-Creţu, M.-C.: Refactoring Impact Formal Representation on the Internal Program Structure, The 6th International Conference on virtual Learning, October 28-29, 2011, Cluj-Napoca, România, Editura Universităţii Bucureşti, ISSN 1844 - 8933, http://c\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: Refactoring Impact Formal Representation on the Internal Program Structure\n",
"date: 2011\n",
"icvl.eu/2011, 2011, P. 500-51\n",
"\n",
"\n",
"Chisăliţă-Creţu, M.-C.: The refactoring plan configuration. A formal model, The 5thInternational Conference on virtual Learning, October 29-31, 2010, Târgu Mureş, România, Bucharest University Publisher House, ISSN 1844 - 8933, http://c\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: The refactoring plan configuration. A formal model\n",
"date: 2010\n",
"icvl.eu/2010, 2010, P. 418-42\n",
"\n",
"\n",
"Chisăliţă-Creţu, M.-C.: The optimal refactoring selection problem - a multi-objective evolutionary approach, The 5th International Conference on virtual Learning, October 29-31, 2010, Târgu Mureş, România, Editura Universităţii Bucureşti, ISSN 1844 - 8933, http://c\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: The optimal refactoring selection problem - a multi-objective evolutionary approach\n",
"date: 2010\n",
"icvl.eu/2010, 2010, P. 410-41\n",
"\n",
"\n",
"Chisăliţă-Creţu, M.-C., Mihiş, A.-D.:A model for conceptual modeling evolution, International Conference on Applied Mathematics (ICAM 7), September 1-4, 2010, Baia-Mare, Romania, 2010, P. 100-10\n",
"authors: Chisăliţă-Creţu, M.-C., Mihiş, A.-D.\n",
"title: A model for conceptual modeling evolution\n",
"date: 2010\n",
"\n",
"\n",
"Chisăliţă-Creţu, M.-C.: A refactoring impact based approach for the internal quality assessment, International Conference on Applied Mathematics (ICAM 7), September 1-4, 2010, Baia-Mare, Romania, 2010, P. 108-11\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: A refactoring impact based approach for the internal quality assessment\n",
"date: 2010\n",
"\n",
"\n",
"Chisăliţă-Creţu, M.-C.: Solution Representation Analysis For The EvolutionaryApproach of the Entity Refactoring Set Selection Problem, the 12th International Multiconference \"Information Society\" (IS 2009), October 12–16, 2009, Ljubljana, Slovenia, Informacijska družba, Editor: Marko Bohanec, Matjaž Gams, Vladislav Rajkovič, 978-961-264-010-1, Inspec, Scopus, 2009, P. 269-27\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: Solution Representation Analysis For The EvolutionaryApproach of the Entity Refactoring Set Selection Problem\n",
"date: 2009\n",
"\n",
"\n",
"Chisăliţă-Creţu, M.-C.: First Results of an Evolutionary Approach for the Entity Refactoring Set Selection Problem, the 4th International Conference \"Interdisciplinarity in Engineering\" (INTER-ENG 2009), November 12-13, 2009, Târgu Mureş, România, 2009, P. 200-20\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: First Results of an Evolutionary Approach for the Entity Refactoring Set Selection Problem\n",
"date: 2009\n",
"\n",
"\n",
"Chisăliţă-Creţu M.-C.: Search-Based Software Entity Refactoring – A New Solution Representation For The Multi-Objective Evolutionary Approach Of The Entity Set Selection Refactoring Problem, the 12th International Scientific and Professional Conference (DidMatTech 2009), September 10-11, 2009, Trnava, Slovakia, Editor: Veronika Stoffová, 2009, P. 100-10\n",
"authors: Chisăliţă-Creţu M.-C.\n",
"title: Search-Based Software Entity Refactoring – A New Solution Representation For The Multi-Objective Evolutionary Approach Of The Entity Set Selection Refactoring Problem\n",
"date: 2009\n",
"\n",
"\n",
"Chisăliţă-Creţu, M.-C.: Identifying Patterns to Solve Low-Level Problems, International Conference on Competitiveness and European Integration (ICCEI 2007), October 26-27, 2007, Risoprint, 2007, P. 89-9\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: Identifying Patterns to Solve Low-Level Problems\n",
"date: 2007\n",
"\n",
"1\n",
"Chisăliţă-Creţu, M.-C.: Hidden Relations Between Code Duplication and Change Couplings, International Conference on Competitiveness and European Integration (ICCEI 2007), October 26-27, 2007, Risoprint, 2007, P. 80-8\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: Hidden Relations Between Code Duplication and Change Couplings\n",
"date: 2007\n",
"\n",
"Maria-Camelia Chisăliţă-Creţu 3/4 \n",
"1\n",
"Chisăliţă-Creţu, M.-C.: Applying Graph Transformation Rules for Refactoring, International Conference on Competitiveness and European Integration (ICCEI 2007), October 26-27, 2007, Risoprint, 2007, P. 73-7\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: Applying Graph Transformation Rules for Refactoring\n",
"date: 2007\n",
"\n",
"1\n",
"Mihiş, A.-D., Chisăliţă-Creţu, M.-C., Mihăilă, C., Şerban, C.-A.:A Tool That Supports Simplifying Conditional Expressions Using Boolean Functions, International Conference of Mathematics and Informatics (ICMI45), Studii şi cercetări ştiinţifice, Editor: Mocanu Marcelina & Nimineţ Valer, 2006, P. 493-50\n",
"authors: Mihiş, A.-D., Chisăliţă-Creţu, M.-C., Mihăilă, C., Şerban, C.-A.\n",
"title: A Tool That Supports Simplifying Conditional Expressions Using Boolean Functions\n",
"date: 2006\n",
"\n",
"National Journals \n",
"\n",
"Chisăliţă-Creţu, M.-C.: Describing low level problems as patterns and solving them via refactorings, Studii şi Cercetări Ştiinţifice, Seria Matematică, Bacău, Categ CNCSIS B+, 17, 2007, P.29 – 4\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: Describing low level problems as patterns and solving them via refactorings\n",
"date: 17\n",
"\n",
"\n",
"Chisăliţă-Creţu, M.-C.: Refactorizarea automată a codului sursă prin aplicarea regulilor limbajului Constraint Java , Universitatea Creştină „Dimitrie Cantemir” Bucureşti, Facultatea de Ştiinţe Economice Cluj-Napoca, „Analele Facultăţii”, Seria Ştiinţe Economice, 2007, P.192-20\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: Refactorizarea automată a codului sursă prin aplicarea regulilor limbajului Constraint Java \n",
"date: 2007\n",
"\n",
"\n",
"Chisăliţă-Creţu, M.-C.: Modele eficiente de descriere pentru anti-şabloanele de refactorizare a codului sursă , Universitatea Creştină „Dimitrie Cantemir” Bucureşti, Facultatea de Ştiinţe Economice Cluj-Napoca, „Analele Facultăţii”, Seria Ştiinţe Economice, 2007, P.181-19\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: Modele eficiente de descriere pentru anti-şabloanele de refactorizare a codului sursă \n",
"date: 2007\n",
"\n",
"\n",
"Chisăliţă-Creţu, M.-C.: Problema redundanţei în codul sursă. Definiţie, cauze,consecinţe şi soluţii , Universitatea Creştină „Dimitrie Cantemir” Bucureşti,Facultatea de Ştiinţe Economice Cluj-Napoca, „Analele Facultăţii”, Seria Ştiinţe Economice, 2006, P.183-18\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: Problema redundanţei în codul sursă. Definiţie\n",
"date: 2006\n",
"\n",
"\n",
"Chisăliţă-Creţu, M.-C.: Efecte ale refactorizării asupra structurii interne a codului , Universitatea Creştină „Dimitrie Cantemir” Bucureşti, Facultatea de Ştiinţe Economice Cluj-Napoca, „Analele Facultăţii”, Seria Ştiinţe Economice, 2005, P.214-23\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: Efecte ale refactorizării asupra structurii interne a codului \n",
"date: 2005\n",
"\n",
"National Conferences \n",
"\n",
"Chisăliţă-Creţu, M.-C.: Introducing Composition Strategies for the Refactoring Plan Building Problem, Symposium ”Zilele Academice Clujene”, Presa Universitară Clujeană, 2012, P. 56-6\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: Introducing Composition Strategies for the Refactoring Plan Building Problem\n",
"date: 2012\n",
"\n",
"\n",
"Chisăliţă-Creţu, M.-C.: Formalizing the refactoring impact on internal program quality, Symposium ”Zilele Academice Clujene”, Presa Universitară Clujeană, 2010, P. 86-9\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: Formalizing the refactoring impact on internal program quality\n",
"date: 2010\n",
"\n",
"\n",
"Chisăliţă-Creţu, M.-C.: Introducing Open-Closed Principle In Object Oriented Design Via Refactorings, Zilele Informaticii Economice Clujene, 2008, P. 104-11\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: Introducing Open-Closed Principle In Object Oriented Design Via Refactorings\n",
"date: 2008\n",
"\n",
"\n",
"Chisăliţă-Creţu, M.-C.: Describing Low-Level Problems as Patterns and Solving Them via Refactorings, Proceedings of the Symposium „Zilele Academice Clujene”, 2008, P. 75-8\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: Describing Low-Level Problems as Patterns and Solving Them via Refactorings\n",
"date: 2008\n",
"\n",
"\n",
"Chisăliţă-Creţu, M.-C.: Describing Low Level Problems As Patterns And Solving Them Via Refactorings, Conferinţa Naţională de Matematică şi Informatică (CNMI 2007), 16-17 Noiembrie, 2007, P. 10-2\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: Describing Low Level Problems As Patterns And Solving Them Via Refactorings\n",
"date: 2007\n",
"\n",
"Maria-Camelia Chisăliţă-Creţu 4/4 \n",
"\n",
"Chisăliţă-Creţu, M.-C.: Consecinţe asupra proiectării orientate obiect prin aplicarea refactorizărilor, folosind metrici soft, Sesiunea Ştiinţifică a Universităţii Creştine „Dimitrie Cantemir” Bucureşti, Facultatea de Ştiinţe Economice Cluj-Napoca „Probleme actuale ale gândirii, ştiinţei şi practicii economico-sociale”, Risoprint, 2007, P. 212-22\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: Consecinţe asupra proiectării orientate obiect prin aplicarea refactorizărilor\n",
"date: 2007\n",
"\n",
"\n",
"Chisăliţă-Creţu, M.-C.: Utilizarea analizei conceptelor formale în refactorizare, Sesiunea Ştiinţifică a Universităţii Creştine „Dimitrie Cantemir” Bucureşti, Facultatea de Ştiinţe Economice Cluj-Napoca „Probleme actuale ale gândirii, ştiinţei şi practicii economico-sociale”, Risoprint, 2007, P. 205-21\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: Utilizarea analizei conceptelor formale în refactorizare\n",
"date: 2007\n",
"\n",
"\n",
"Chisăliţă-Creţu, M.-C., Şerban, C.-A.:Impact on Design Quality of Refactorings on Code via Metrics, Proceedings of the Symposium „Zilele Academice Clujene”, 2006, P. 39-4\n",
"authors: Chisăliţă-Creţu, M.-C., Şerban, C.-A.\n",
"title: Impact on Design Quality of Refactorings on Code via Metrics\n",
"date: 2006\n",
"\n",
"\n",
"Chisăliţă-Creţu, M.-C., Cheia publică şi aplicaţiile ei, Sesiunea Ştiinţifică a Universităţii Creştine „Dimitrie Cantemir” Bucureşti, Facultatea de Ştiinţe Economice Cluj-Napoca „Probleme actuale ale gândirii, ştiinţei şi practicii economico-sociale”, Risoprint, 2006, P. 258-26\n",
"\n",
"1\n",
"Chisăliţă-Creţu, M.-C.: Program Internal Structure View with Formal Concept Analysis, Proceedings of the Symposium „Zilele Academice Clujene”, 2006, P. 33-3\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: Program Internal Structure View with Formal Concept Analysis\n",
"date: 2006\n",
"\n",
"1\n",
"Chisăliţă-Creţu, M.-C.: Direcţii de cercetare în aplicarea refactorizării, Sesiunea Ştiinţifică a Universităţii Creştine „Dimitrie Cantemir” Bucureşti, Facultatea de Ştiinţe Economice Cluj-Napoca „Probleme actuale ale gândirii, ştiinţei şi practicii economico-sociale”, Risoprint, 2005, P. 287-29\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: Direcţii de cercetare în aplicarea refactorizării\n",
"date: 2005\n",
"\n",
"1\n",
"Chisăliţă-Creţu, M.-C.: General Aspects of Refactoring Applicability to Conceptual Models, Proceedings of the Symposium „Colocviul Academic Clujean de INFORMATICĂ”, 2005, P. 99-10\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: General Aspects of Refactoring Applicability to Conceptual Models\n",
"date: 2005\n",
"\n",
"1\n",
"Chisăliţă-Creţu, M.-C.: Current Problems In Refactoring Activities, Proceedings of the Symposium „Zilele Academice Clujene”, 2004, P. 27-3\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: Current Problems In Refactoring Activities\n",
"date: 2004\n",
"\n",
"1\n",
"Chisăliţă-Creţu, M.-C.: Rolul refactorizării în procesul de dezvoltare a produselor soft, Sesiunea Ştiinţifică a Universităţii Creştine „Dimitrie Cantemir” Bucureşti, Facultatea de Ştiinţe Economice Cluj-Napoca „Probleme actuale ale gândirii, ştiinţei şi practicii economico-sociale”, Ecoexpert, 2004, P. 429-43\n",
"authors: Chisăliţă-Creţu, M.-C.\n",
"title: Rolul refactorizării în procesul de dezvoltare a produselor soft\n",
"date: 2004\n",
"\n",
"1\n",
"Chisăliţă-Creţu, M.-C., Mihiş, A.-D., Guran, A.-M.:3D Modeling vs. 2D Modeling, Proceedings of the Symposium „Colocviul Academic Clujean de INFORMATICĂ, 2003, P. 59-6\n",
"authors: Chisăliţă-Creţu, M.-C., Mihiş, A.-D., Guran, A.-M.\n",
"title: 3D Modeling vs. 2D Modeling\n",
"date: 2003\n",
"\n",
"1\n",
"Barticel, S., Bretan, H., Costea, C., Chisăliţă-Creţu, M.-C., Mara, A., Măgeruşan, C., Mărcuş, G., Mihiş, A.-D., Mureşan, R., Petraşcu, D., Pitiş, A., Tompa, A.:An Application for Higher Education Admission, Proceedings of the Symposium „Zilele Academice Clujene, 2002, P. 163-17\n",
"authors: Barticel, S., Bretan, H., Costea, C., Chisăliţă-Creţu, M.-C., Mara, A., Măgeruşan, C., Mărcuş, G., Mihiş, A.-D., Mureşan, R., Petraşcu, D., Pitiş, A., Tompa, A.\n",
"title: An Application for Higher Education Admission\n",
"date: 2002\n",
"\n",
"1\n",
"Barticel, S., Chisăliţă-Creţu, M.-C., Mara, A., Măgeruşan, C., Marcuş, G., Mihiş, A.-D., Petraşcu, D., Tompa, A.:A Documentation Server, Research Seminars, Seminar on Computer Science, 2001, P. 65-74\n",
"\n",
"authors: Barticel, S., Chisăliţă-Creţu, M.-C., Mara, A., Măgeruşan, C., Marcuş, G., Mihiş, A.-D., Petraşcu, D., Tompa, A.\n",
"title: A Documentation Server\n",
"date: 2001\n"
]
}
],
"source": [
"pubs = []\n",
"for i in re.split(\"[0-9]{1}\\.\", data):\n",
" print(i)\n",
" try:\n",
" rv, authors, title, date = ProcessLine(i)\n",
" except:\n",
" rv, authors, title = ProcessLineB(i)\n",
" date = \"\"\n",
" if rv:\n",
" print(\"authors: \", authors.lstrip())\n",
" print(\"title: \", title)\n",
" print(\"date: \", date)\n",
" pubs.append((authors.lstrip(), title, date))"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('Chisăliţă-Creţu, M.-C.', ' Refactoring in Object-Oriented Modeling', ' 2011')\n",
"('Chisăliţă-Creţu M.-C.', ' Conceptual Modeling Evolution. A Formal Approach', ' 2011')\n",
"('Chisăliţă-Creţu M.-C.', ' An evolutionary approach for the entity refactoring setselection problem', ' 2010')\n",
"('Chisăliţă-Creţu, M.-C., Vescan (Fanea), A.', 'The multi-objective refactoring selection problem', ' 2009')\n",
"('Chisăliţă-Creţu, M.-C., Vescan (Fanea), A.,The multiobjective refactoring selection problem, International Conference ”Knowledge Engineering', ' Principles and Techniques”', ' 2009')\n",
"('Chisăliţă-Creţu, M.-C.', ' The Entity Refactoring Set Selection Problem - Practical Experiments for an Evolutionary Approach', ' 2009')\n",
"('Chisăliţă-Creţu, M.-C.', ' Refactoring Impact Formal Representation on the Internal Program Structure', ' 2011')\n",
"('Chisăliţă-Creţu, M.-C.', ' The refactoring plan configuration. A formal model', ' 2010')\n",
"('Chisăliţă-Creţu, M.-C.', ' The optimal refactoring selection problem - a multi-objective evolutionary approach', ' 2010')\n",
"('Chisăliţă-Creţu, M.-C., Mihiş, A.-D.', 'A model for conceptual modeling evolution', ' 2010')\n",
"('Chisăliţă-Creţu, M.-C.', ' A refactoring impact based approach for the internal quality assessment', ' 2010')\n",
"('Chisăliţă-Creţu, M.-C.', ' Solution Representation Analysis For The EvolutionaryApproach of the Entity Refactoring Set Selection Problem', ' 2009')\n",
"('Chisăliţă-Creţu, M.-C.', ' First Results of an Evolutionary Approach for the Entity Refactoring Set Selection Problem', ' 2009')\n",
"('Chisăliţă-Creţu M.-C.', ' Search-Based Software Entity Refactoring – A New Solution Representation For The Multi-Objective Evolutionary Approach Of The Entity Set Selection Refactoring Problem', ' 2009')\n",
"('Chisăliţă-Creţu, M.-C.', ' Identifying Patterns to Solve Low-Level Problems', ' 2007')\n",
"('Chisăliţă-Creţu, M.-C.', ' Hidden Relations Between Code Duplication and Change Couplings', ' 2007')\n",
"('Chisăliţă-Creţu, M.-C.', ' Applying Graph Transformation Rules for Refactoring', ' 2007')\n",
"('Mihiş, A.-D., Chisăliţă-Creţu, M.-C., Mihăilă, C., Şerban, C.-A.', 'A Tool That Supports Simplifying Conditional Expressions Using Boolean Functions', ' 2006')\n",
"('Chisăliţă-Creţu, M.-C.', ' Describing low level problems as patterns and solving them via refactorings', ' 17')\n",
"('Chisăliţă-Creţu, M.-C.', ' Refactorizarea automată a codului sursă prin aplicarea regulilor limbajului Constraint Java ', ' 2007')\n",
"('Chisăliţă-Creţu, M.-C.', ' Modele eficiente de descriere pentru anti-şabloanele de refactorizare a codului sursă ', ' 2007')\n",
"('Chisăliţă-Creţu, M.-C.', ' Problema redundanţei în codul sursă. Definiţie', ' 2006')\n",
"('Chisăliţă-Creţu, M.-C.', ' Efecte ale refactorizării asupra structurii interne a codului ', ' 2005')\n",
"('Chisăliţă-Creţu, M.-C.', ' Introducing Composition Strategies for the Refactoring Plan Building Problem', ' 2012')\n",
"('Chisăliţă-Creţu, M.-C.', ' Formalizing the refactoring impact on internal program quality', ' 2010')\n",
"('Chisăliţă-Creţu, M.-C.', ' Introducing Open-Closed Principle In Object Oriented Design Via Refactorings', ' 2008')\n",
"('Chisăliţă-Creţu, M.-C.', ' Describing Low-Level Problems as Patterns and Solving Them via Refactorings', ' 2008')\n",
"('Chisăliţă-Creţu, M.-C.', ' Describing Low Level Problems As Patterns And Solving Them Via Refactorings', ' 2007')\n",
"('Chisăliţă-Creţu, M.-C.', ' Consecinţe asupra proiectării orientate obiect prin aplicarea refactorizărilor', ' 2007')\n",
"('Chisăliţă-Creţu, M.-C.', ' Utilizarea analizei conceptelor formale în refactorizare', ' 2007')\n",
"('Chisăliţă-Creţu, M.-C., Şerban, C.-A.', 'Impact on Design Quality of Refactorings on Code via Metrics', ' 2006')\n",
"('Chisăliţă-Creţu, M.-C.', ' Program Internal Structure View with Formal Concept Analysis', ' 2006')\n",
"('Chisăliţă-Creţu, M.-C.', ' Direcţii de cercetare în aplicarea refactorizării', ' 2005')\n",
"('Chisăliţă-Creţu, M.-C.', ' General Aspects of Refactoring Applicability to Conceptual Models', ' 2005')\n",
"('Chisăliţă-Creţu, M.-C.', ' Current Problems In Refactoring Activities', ' 2004')\n",
"('Chisăliţă-Creţu, M.-C.', ' Rolul refactorizării în procesul de dezvoltare a produselor soft', ' 2004')\n",
"('Chisăliţă-Creţu, M.-C., Mihiş, A.-D., Guran, A.-M.', '3D Modeling vs. 2D Modeling', ' 2003')\n",
"('Barticel, S., Bretan, H., Costea, C., Chisăliţă-Creţu, M.-C., Mara, A., Măgeruşan, C., Mărcuş, G., Mihiş, A.-D., Mureşan, R., Petraşcu, D., Pitiş, A., Tompa, A.', 'An Application for Higher Education Admission', ' 2002')\n",
"('Barticel, S., Chisăliţă-Creţu, M.-C., Mara, A., Măgeruşan, C., Marcuş, G., Mihiş, A.-D., Petraşcu, D., Tompa, A.', 'A Documentation Server', ' 2001')\n"
]
}
],
"source": [
"for pub in pubs:\n",
" print(pub)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"import mariadb\n",
"import json"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"with open('../credentials.json', 'r') as crd_json_fd:\n",
" json_text = crd_json_fd.read()\n",
" json_obj = json.loads(json_text)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"credentials = json_obj[\"Credentials\"]\n",
"username = credentials[\"username\"]\n",
"password = credentials[\"password\"]\n",
"table_name = \"publications_cache\"\n",
"db_name = \"ubbcluj\""
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"mariadb_connection = mariadb.connect(user=username, password=password, database=db_name)\n",
"mariadb_cursor = mariadb_connection.cursor()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INSERT INTO publications_cache SET Title='Refactoring in Object-Oriented Modeling', ProfessorId='21', PublicationDate='2011-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='Conceptual Modeling Evolution. A Formal Approach', ProfessorId='21', PublicationDate='2011-01-01', Authors='Chisăliţă-Creţu M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='An evolutionary approach for the entity refactoring setselection problem', ProfessorId='21', PublicationDate='2010-01-01', Authors='Chisăliţă-Creţu M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='The multi-objective refactoring selection problem', ProfessorId='21', PublicationDate='2009-01-01', Authors='Chisăliţă-Creţu, M.-C., Vescan (Fanea), A.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='Principles and Techniques”', ProfessorId='21', PublicationDate='2009-01-01', Authors='Chisăliţă-Creţu, M.-C., Vescan (Fanea), A.,The multiobjective refactoring selection problem, International Conference ”Knowledge Engineering', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='The Entity Refactoring Set Selection Problem - Practical Experiments for an Evolutionary Approach', ProfessorId='21', PublicationDate='2009-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='Refactoring Impact Formal Representation on the Internal Program Structure', ProfessorId='21', PublicationDate='2011-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='The refactoring plan configuration. A formal model', ProfessorId='21', PublicationDate='2010-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='The optimal refactoring selection problem - a multi-objective evolutionary approach', ProfessorId='21', PublicationDate='2010-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='A model for conceptual modeling evolution', ProfessorId='21', PublicationDate='2010-01-01', Authors='Chisăliţă-Creţu, M.-C., Mihiş, A.-D.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='A refactoring impact based approach for the internal quality assessment', ProfessorId='21', PublicationDate='2010-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='Solution Representation Analysis For The EvolutionaryApproach of the Entity Refactoring Set Selection Problem', ProfessorId='21', PublicationDate='2009-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='First Results of an Evolutionary Approach for the Entity Refactoring Set Selection Problem', ProfessorId='21', PublicationDate='2009-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='Search-Based Software Entity Refactoring – A New Solution Representation For The Multi-Objective Evolutionary Approach Of The Entity Set Selection Refactoring Problem', ProfessorId='21', PublicationDate='2009-01-01', Authors='Chisăliţă-Creţu M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='Identifying Patterns to Solve Low-Level Problems', ProfessorId='21', PublicationDate='2007-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='Hidden Relations Between Code Duplication and Change Couplings', ProfessorId='21', PublicationDate='2007-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='Applying Graph Transformation Rules for Refactoring', ProfessorId='21', PublicationDate='2007-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='A Tool That Supports Simplifying Conditional Expressions Using Boolean Functions', ProfessorId='21', PublicationDate='2006-01-01', Authors='Mihiş, A.-D., Chisăliţă-Creţu, M.-C., Mihăilă, C., Şerban, C.-A.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='Describing low level problems as patterns and solving them via refactorings', ProfessorId='21', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='Refactorizarea automată a codului sursă prin aplicarea regulilor limbajului Constraint Java ', ProfessorId='21', PublicationDate='2007-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='Modele eficiente de descriere pentru anti-şabloanele de refactorizare a codului sursă ', ProfessorId='21', PublicationDate='2007-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='Problema redundanţei în codul sursă. Definiţie', ProfessorId='21', PublicationDate='2006-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='Efecte ale refactorizării asupra structurii interne a codului ', ProfessorId='21', PublicationDate='2005-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='Introducing Composition Strategies for the Refactoring Plan Building Problem', ProfessorId='21', PublicationDate='2012-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='Formalizing the refactoring impact on internal program quality', ProfessorId='21', PublicationDate='2010-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='Introducing Open-Closed Principle In Object Oriented Design Via Refactorings', ProfessorId='21', PublicationDate='2008-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='Describing Low-Level Problems as Patterns and Solving Them via Refactorings', ProfessorId='21', PublicationDate='2008-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='Describing Low Level Problems As Patterns And Solving Them Via Refactorings', ProfessorId='21', PublicationDate='2007-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='Consecinţe asupra proiectării orientate obiect prin aplicarea refactorizărilor', ProfessorId='21', PublicationDate='2007-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='Utilizarea analizei conceptelor formale în refactorizare', ProfessorId='21', PublicationDate='2007-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='Impact on Design Quality of Refactorings on Code via Metrics', ProfessorId='21', PublicationDate='2006-01-01', Authors='Chisăliţă-Creţu, M.-C., Şerban, C.-A.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='Program Internal Structure View with Formal Concept Analysis', ProfessorId='21', PublicationDate='2006-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='Direcţii de cercetare în aplicarea refactorizării', ProfessorId='21', PublicationDate='2005-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='General Aspects of Refactoring Applicability to Conceptual Models', ProfessorId='21', PublicationDate='2005-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='Current Problems In Refactoring Activities', ProfessorId='21', PublicationDate='2004-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='Rolul refactorizării în procesul de dezvoltare a produselor soft', ProfessorId='21', PublicationDate='2004-01-01', Authors='Chisăliţă-Creţu, M.-C.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='3D Modeling vs. 2D Modeling', ProfessorId='21', PublicationDate='2003-01-01', Authors='Chisăliţă-Creţu, M.-C., Mihiş, A.-D., Guran, A.-M.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='An Application for Higher Education Admission', ProfessorId='21', PublicationDate='2002-01-01', Authors='Barticel, S., Bretan, H., Costea, C., Chisăliţă-Creţu, M.-C., Mara, A., Măgeruşan, C., Mărcuş, G., Mihiş, A.-D., Mureşan, R., Petraşcu, D., Pitiş, A., Tompa, A.', Affiliations='' \n",
"INSERT INTO publications_cache SET Title='A Documentation Server', ProfessorId='21', PublicationDate='2001-01-01', Authors='Barticel, S., Chisăliţă-Creţu, M.-C., Mara, A., Măgeruşan, C., Marcuş, G., Mihiş, A.-D., Petraşcu, D., Tompa, A.', Affiliations='' \n"
]
}
],
"source": [
"for paper in pubs:\n",
" \n",
" title = \"\"\n",
" pub_date = \"\"\n",
" affiliations = \"\"\n",
" authors = \"\"\n",
" \n",
" try:\n",
" pub_date = paper[2].lstrip()\n",
" pub_date = str(pub_date) + \"-01-01\"\n",
" if len(pub_date) != 10:\n",
" pub_date = \"\"\n",
" except:\n",
" pass\n",
" \n",
" try:\n",
" affiliations = paper[2].lstrip().split('\\'')[0]\n",
" except:\n",
" pass\n",
" \n",
" try:\n",
" title = paper[1].lstrip().split('\\'')[0]\n",
" except:\n",
" pass\n",
" \n",
" try:\n",
" authors = paper[0].lstrip().split('\\'')[0]\n",
" except AttributeError:\n",
" pass\n",
" \n",
" table_name = \"publications_cache\"\n",
" \n",
" insert_string = \"INSERT INTO {0} SET \".format(table_name)\n",
" insert_string += \"Title=\\'{0}\\', \".format(title)\n",
" insert_string += \"ProfessorId=\\'{0}\\', \".format(21)\n",
" if pub_date != \"\":\n",
" insert_string += \"PublicationDate=\\'{0}\\', \".format(str(pub_date))\n",
" insert_string += \"Authors=\\'{0}\\', \".format(authors)\n",
" insert_string += \"Affiliations=\\'{0}\\' \".format(\"\")\n",
" print(insert_string)\n",
" \n",
" #print(pub_date)\n",
" \n",
" try:\n",
" mariadb_cursor.execute(insert_string)\n",
" except mariadb.ProgrammingError as pe:\n",
" print(\"Error\")\n",
" raise pe\n",
" except mariadb.IntegrityError:\n",
" continue"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.8"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
LogicWang/ml | deep/tf/basics/.ipynb_checkpoints/iris-checkpoint.ipynb | 1 | 7077 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
" \"This module will be removed in 0.20.\", DeprecationWarning)\n"
]
}
],
"source": [
"\"\"\"Example of DNNClassifier for Iris plant dataset.\"\"\"\n",
"\n",
"from __future__ import absolute_import\n",
"from __future__ import division\n",
"from __future__ import print_function\n",
"from sklearn import cross_validation\n",
"from sklearn import metrics\n",
"import tensorflow as tf"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.\n",
"WARNING:tensorflow:Using temporary folder as model directory: /var/folders/4n/dvmcx9mx3sgcp8mk0r_3xkhm0000gn/T/tmpxxetbc9i\n",
"INFO:tensorflow:Using default config.\n",
"INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_evaluation_master': '', '_save_summary_steps': 100, '_task_id': 0, '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_keep_checkpoint_max': 5, '_tf_random_seed': None, '_num_ps_replicas': 0, '_is_chief': True, '_master': '', '_environment': 'local', '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x111691550>, '_task_type': None, '_tf_config': gpu_options {\n",
" per_process_gpu_memory_fraction: 1\n",
"}\n",
"}\n",
"WARNING:tensorflow:From <ipython-input-7-61190a75637f>:11: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.\n",
"Instructions for updating:\n",
"Estimator is decoupled from Scikit Learn interface by moving into\n",
"separate class SKCompat. Arguments x, y and batch_size are only\n",
"available in the SKCompat class, Estimator will only accept input_fn.\n",
"Example conversion:\n",
" est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
"WARNING:tensorflow:From <ipython-input-7-61190a75637f>:11: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
"Instructions for updating:\n",
"Estimator is decoupled from Scikit Learn interface by moving into\n",
"separate class SKCompat. Arguments x, y and batch_size are only\n",
"available in the SKCompat class, Estimator will only accept input_fn.\n",
"Example conversion:\n",
" est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
"WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:1362: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30.\n",
"Instructions for updating:\n",
"Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/site-packages/tensorflow/python/util/deprecation.py:247: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n",
" equality = a == b\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Create CheckpointSaverHook.\n",
"INFO:tensorflow:Saving checkpoints for 1 into /var/folders/4n/dvmcx9mx3sgcp8mk0r_3xkhm0000gn/T/tmpxxetbc9i/model.ckpt.\n",
"INFO:tensorflow:step = 1, loss = 1.13631\n",
"INFO:tensorflow:global_step/sec: 639.255\n",
"INFO:tensorflow:step = 101, loss = 0.141485\n",
"INFO:tensorflow:Saving checkpoints for 200 into /var/folders/4n/dvmcx9mx3sgcp8mk0r_3xkhm0000gn/T/tmpxxetbc9i/model.ckpt.\n",
"INFO:tensorflow:Loss for final step: 0.132648.\n",
"WARNING:tensorflow:From /usr/local/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/dnn.py:374: calling BaseEstimator.predict (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
"Instructions for updating:\n",
"Estimator is decoupled from Scikit Learn interface by moving into\n",
"separate class SKCompat. Arguments x, y and batch_size are only\n",
"available in the SKCompat class, Estimator will only accept input_fn.\n",
"Example conversion:\n",
" est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
"WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.\n",
"Accurary: 0.933333\n"
]
}
],
"source": [
"# load data\n",
"iris = tf.contrib.learn.datasets.load_dataset('iris')\n",
"x_train, x_test, y_train, y_test = cross_validation.train_test_split(\n",
" iris.data, iris.target, test_size=0.2, random_state=42)\n",
"\n",
"# build 3 layers DNN with 10, 20, 10 units respectively.\n",
"feature_columns = tf.contrib.learn.infer_real_valued_columns_from_input(x_train)\n",
"classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns, hidden_units=[20,30,10,10], n_classes=3)\n",
"\n",
"# Fit and predict\n",
"classifier.fit(x_train, y_train, steps=200)\n",
"predictions = list(classifier.predict(x_test, as_iterable=True))\n",
"score = metrics.accuracy_score(y_test, predictions)\n",
"\n",
"print('Accurary: {0:f}'.format(score))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
yunfeiz/py_learnt | sample_code/date_utils.ipynb | 1 | 11834 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import tushare as ts\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"from xpinyin import Pinyin"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"df=ts.get_stock_basics()\n",
"df.head(5)\n",
"att=df.columns.values.tolist()\n",
"#clommun_show = ['name', 'pe', 'outstanding', 'totals', 'totalAssets', 'liquidAssets', 'fixedAssets',\n",
"#'esp', 'bvps', 'pb', 'perundp', 'rev', 'profit', 'gpr', 'npr', 'holders']\n",
"\n",
"pin=Pinyin()\n",
"df['UP'] = None\n",
"for index, row in df.iterrows():\n",
" name_str = df.name[index]\n",
" #print(name_str)\n",
" up_letter = pin.get_initials(name_str,u'')\n",
" #print(up_letter)\n",
" df.at[index,['UP']]=up_letter\n",
"#df[df['UP']=='HTGD']\n",
"df['code']=df.index\n",
"#print(df.UP)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"code,代码\n",
"name,名称\n",
"industry,所属行业\n",
"area,地区\n",
"pe,市盈率\n",
"outstanding,流通股本(亿)\n",
"totals,总股本(亿)\n",
"totalAssets,总资产(万)\n",
"liquidAssets,流动资产\n",
"fixedAssets,固定资产\n",
"reserved,公积金\n",
"reservedPerShare,每股公积金\n",
"esp,每股收益\n",
"bvps,每股净资\n",
"pb,市净率\n",
"timeToMarket,上市日期\n",
"undp,未分利润\n",
"perundp, 每股未分配\n",
"rev,收入同比(%)\n",
"profit,利润同比(%)\n",
"gpr,毛利率(%)\n",
"npr,净利润率(%)\n",
"holders,股东人数\n",
"['name', 'pe', 'outstanding', 'totals', 'totalAssets', 'liquidAssets', 'fixedAssets', 'esp', 'bvps', 'pb', 'perundp', 'rev', 'profit', 'gpr', 'npr', 'holders']"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>open</th>\n",
" <th>pre_close</th>\n",
" <th>price</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>volume</th>\n",
" <th>amount</th>\n",
" <th>time</th>\n",
" <th>code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>亨通光电</td>\n",
" <td>21.500</td>\n",
" <td>22.000</td>\n",
" <td>21.960</td>\n",
" <td>23.110</td>\n",
" <td>21.200</td>\n",
" <td>98727052</td>\n",
" <td>2198642229.000</td>\n",
" <td>15:00:00</td>\n",
" <td>600487</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>欧菲科技</td>\n",
" <td>14.000</td>\n",
" <td>14.740</td>\n",
" <td>14.110</td>\n",
" <td>14.940</td>\n",
" <td>13.800</td>\n",
" <td>170908883</td>\n",
" <td>2454933765.380</td>\n",
" <td>15:00:03</td>\n",
" <td>002456</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>长电科技</td>\n",
" <td>14.300</td>\n",
" <td>14.700</td>\n",
" <td>15.900</td>\n",
" <td>16.170</td>\n",
" <td>14.020</td>\n",
" <td>165795445</td>\n",
" <td>2632525261.000</td>\n",
" <td>15:00:00</td>\n",
" <td>600584</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>中际旭创</td>\n",
" <td>56.990</td>\n",
" <td>59.370</td>\n",
" <td>58.400</td>\n",
" <td>64.390</td>\n",
" <td>56.000</td>\n",
" <td>9156938</td>\n",
" <td>549740196.910</td>\n",
" <td>15:00:03</td>\n",
" <td>300308</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>光迅科技</td>\n",
" <td>31.000</td>\n",
" <td>31.620</td>\n",
" <td>32.580</td>\n",
" <td>34.500</td>\n",
" <td>30.680</td>\n",
" <td>47640638</td>\n",
" <td>1578763475.520</td>\n",
" <td>15:00:03</td>\n",
" <td>002281</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>烽火通信</td>\n",
" <td>31.700</td>\n",
" <td>32.370</td>\n",
" <td>32.290</td>\n",
" <td>33.460</td>\n",
" <td>31.250</td>\n",
" <td>50910497</td>\n",
" <td>1661789943.000</td>\n",
" <td>15:00:00</td>\n",
" <td>600498</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>大族激光</td>\n",
" <td>40.600</td>\n",
" <td>42.440</td>\n",
" <td>42.550</td>\n",
" <td>45.100</td>\n",
" <td>39.800</td>\n",
" <td>56482328</td>\n",
" <td>2428853657.020</td>\n",
" <td>15:00:03</td>\n",
" <td>002008</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name open pre_close price high low volume amount \\\n",
"0 亨通光电 21.500 22.000 21.960 23.110 21.200 98727052 2198642229.000 \n",
"1 欧菲科技 14.000 14.740 14.110 14.940 13.800 170908883 2454933765.380 \n",
"2 长电科技 14.300 14.700 15.900 16.170 14.020 165795445 2632525261.000 \n",
"3 中际旭创 56.990 59.370 58.400 64.390 56.000 9156938 549740196.910 \n",
"4 光迅科技 31.000 31.620 32.580 34.500 30.680 47640638 1578763475.520 \n",
"5 烽火通信 31.700 32.370 32.290 33.460 31.250 50910497 1661789943.000 \n",
"6 大族激光 40.600 42.440 42.550 45.100 39.800 56482328 2428853657.020 \n",
"\n",
" time code \n",
"0 15:00:00 600487 \n",
"1 15:00:03 002456 \n",
"2 15:00:00 600584 \n",
"3 15:00:03 300308 \n",
"4 15:00:03 002281 \n",
"5 15:00:00 600498 \n",
"6 15:00:03 002008 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"col_show = ['name', 'open', 'pre_close', 'price', 'high', 'low', 'volume', 'amount', 'time', 'code']\n",
"initial_letter = ['HTGD','OFKJ','CDKJ','ZJXC','GXKJ','FHTX','DZJG']\n",
"code =[]\n",
"for letter in initial_letter:\n",
" code.append(df[df['UP']==letter].code[0])\n",
" #print(code)\n",
"if code != '': #not empty != ''\n",
" df_price = ts.get_realtime_quotes(code)\n",
" #print(df_price)\n",
" #df_price.columns.values.tolist()\n",
"df_price[col_show]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TO-DO\n",
"#Add the map from initial to code\n",
"#build up a dataframe with fundamental and indicotors\n",
"#For Leadings, need cache more data for the begining data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from matplotlib.mlab import csv2rec"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/yunfeiz/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: MatplotlibDeprecationWarning: The csv2rec function was deprecated in Matplotlib 2.2 and will be removed in 3.1.\n",
" This is separate from the ipykernel package so we can avoid doing imports until\n"
]
}
],
"source": [
"df=ts.get_k_data(\"002456\",start='2018-01-05',end='2018-01-09')\n",
"df.to_csv(\"temp.csv\")\n",
"r=csv2rec(\"temp.csv\")\n",
"#r.date"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"当前的日期和时间是 2019-03-10 11:13:55.914648\n",
"ISO格式的日期和时间是 2019-03-10T11:13:55.914648\n",
"当前的年份是 2019\n",
"当前的月份是 3\n",
"当前的日期是 10\n",
"dd/mm/yyyy 格式是 10/3/2019\n",
"当前小时是 11\n",
"当前分钟是 13\n",
"当前秒是 55\n"
]
}
],
"source": [
"import time, datetime\n",
"\n",
"#str = df[df.code == '600487'][clommun_show].name.values\n",
"#print(str)\n",
"today=datetime.date.today()\n",
"yesterday = today - datetime.timedelta(1)\n",
"#print(today, yesterday)\n",
"i = datetime.datetime.now()\n",
"print (\"当前的日期和时间是 %s\" % i)\n",
"print (\"ISO格式的日期和时间是 %s\" % i.isoformat() )\n",
"print (\"当前的年份是 %s\" %i.year)\n",
"print (\"当前的月份是 %s\" %i.month)\n",
"print (\"当前的日期是 %s\" %i.day)\n",
"print (\"dd/mm/yyyy 格式是 %s/%s/%s\" % (i.day, i.month, i.year) )\n",
"print (\"当前小时是 %s\" %i.hour)\n",
"print (\"当前分钟是 %s\" %i.minute)\n",
"print (\"当前秒是 %s\" %i.second)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"本地时间为 : time.struct_time(tm_year=2019, tm_mon=3, tm_mday=10, tm_hour=11, tm_min=13, tm_sec=55, tm_wday=6, tm_yday=69, tm_isdst=0)\n",
"2019-03-10 11:13:55\n",
"Sun Mar 10 11:13:55 2019\n"
]
}
],
"source": [
"import time\n",
" \n",
"localtime = time.localtime(time.time())\n",
"print(\"本地时间为 :\", localtime)\n",
"\n",
"# 格式化成2016-03-20 11:45:39形式\n",
"print(time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime()))\n",
" \n",
"# 格式化成Sat Mar 28 22:24:24 2016形式\n",
"print(time.strftime(\"%a %b %d %H:%M:%S %Y\", time.localtime()))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"#!/usr/bin/python\n",
"# -*- coding: UTF-8 -*-\n",
" \n",
"import calendar \n",
"cal = calendar.month(2019, 3)\n",
"#print (cal)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
jason9263/JasonAI | torch/1_get_started.ipynb | 1 | 2896928 | null | apache-2.0 |
ioos/system-test | content/downloads/notebooks/2015-09-28-OpeningPost.ipynb | 2 | 4217 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"title = \"Opening post\"\n",
"name = '2015-09-28-OpeningPost'"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os\n",
"from datetime import datetime\n",
"from IPython.core.display import HTML\n",
"\n",
"# Metadata and markdown generation.\n",
"hour = datetime.utcnow().strftime('%H:%M')\n",
"comments = \"true\"\n",
"\n",
"date = '-'.join(name.split('-')[:3])\n",
"slug = '-'.join(name.split('-')[3:])\n",
"\n",
"metadata = dict(title=title,\n",
" date=date,\n",
" hour=hour,\n",
" comments=comments,\n",
" slug=slug,\n",
" name=name)\n",
"\n",
"markdown = \"\"\"Title: {title}\n",
"date: {date} {hour}\n",
"comments: {comments}\n",
"slug: {slug}\n",
"\n",
"{{% notebook {name}.ipynb cells[2:] %}}\n",
"\"\"\".format(**metadata)\n",
"\n",
"content = os.path.abspath(os.path.join(os.getcwd(), os.pardir,\n",
" os.pardir, '{}.md'.format(name)))\n",
"\n",
"with open('{}'.format(content), 'w') as f:\n",
" f.writelines(markdown)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This blog is a \"notebook digest\" for all, but not only, notebooks created\n",
"during the\n",
"[IOOS](http://www.ioos.noaa.gov/)\n",
"[DMAC](http://www.ioos.noaa.gov/data/dmac/welcome.html)\n",
"[System Integration Test](https://github.com/ioos/system-test/wiki) exercise.\n",
"\n",
"The goal is to post readable, bite size, and\n",
"[executable(!)](http://mybinder.org/) notebooks.\n",
"\n",
"Wait... Before we dive into the notebooks:\n",
"\n",
"### What is IOOS?\n",
"\n",
"The US Integrated Ocean Observing System (IOOS)\n",
"is a collaboration between Federal, State, Local, Academic and\n",
"Commercial partners to manage and/or provide access to a wide\n",
"range of ocean observing assets and data feeds, including *in-situ*\n",
"buoys, drifters, gliders, radar, satellite data, and numerical models\n",
"and meet the needs of the ocean data community.\n",
"\n",
"### What is DMAC?\n",
"\n",
"The Data Management and Communications (DMAC) is a subsystem of IOOS\n",
"that provides the procedures, protocols and technology solutions to allow\n",
"integration of the disparate observational data feeds within and amongst the\n",
"regional associations and other IOOS data providers. Much of the data\n",
"delivery and access technologies implemented by the DMAC subsystem are\n",
"leveraged from the atmospheric community, where these technologies had become\n",
"community practice.\n",
"\n",
"### What is System Integration Test?\n",
"\n",
"The System Integration Test (SIT) is an effort to stress-testing DMAC by\n",
"evaluating how they scale across geographies, data types,\n",
"very large datasets, and long term archives.\n",
"\n",
"The SIT project has been organized into three themes\n",
"\n",
"- 1) Baseline Assessment,\n",
"- 2) Extreme Events, and\n",
"- 3) Species Protection and Marine Habitat Classification.\n",
"\n",
"\n",
"Click in the binder badge below to load the notebook binder.\n",
"You can browse, open, modify, and run any of the notebooks available here.\n",
"\n",
"[](http://mybinder.org/repo/ioos/system-test/)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| unlicense |
mykespb/jupyters | mp-nettemp1-fru.ipynb | 1 | 6816 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Mikhail Kolodin. \n",
"Project: Internet temperature.\n",
"2015-12-15 1.1.2\n",
"\n",
"IPython research for internet temperature. \n",
"We use now only fontanka.ru website, \n",
"later other sites and methods will be added."
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import requests\n",
"import lxml.html as lh\n",
"\n",
"import datetime\n",
"now = datetime.datetime.now()"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"url = \"http://www.fontanka.ru/fontanka/\""
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Getting data from http://www.fontanka.ru/fontanka/2015/12/15/all.html\n"
]
}
],
"source": [
"myyear, mymonth, myday = now.year, now.month, now.day\n",
"plus = \"{0:04d}/{1:02d}/{2:02d}\" .format (myyear, mymonth, myday)\n",
"fullurl = url + plus + '/all.html'\n",
"print (\"Getting data from {}\" .format(fullurl))"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"page = requests.get(fullurl)\n",
"tree = lh.fromstring(page.text)\n",
"#print(tree.text_content())"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bloks_spb = tree.xpath(\"//div[@class='entry article switcher-all-news switcher-spb-news']\")\n",
"bloks_rus = tree.xpath(\"//div[@class='entry article switcher-all-news switcher-russian-news']\")\n",
"bloks_world = tree.xpath(\"//div[@class='entry article switcher-all-news switcher-world-news']\")\n",
"bloks = bloks_spb + bloks_rus + bloks_world"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"blogs_spb = []\n",
"for b in bloks_spb:\n",
" blogs_spb.append ((\"spb\", b))\n",
"blogs_rus = []\n",
"for b in bloks_rus:\n",
" blogs_rus.append ((\"rus\", b))\n",
"blogs_world = []\n",
"for b in bloks_world:\n",
" blogs_world.append ((\"mir\", b))\n",
"blogs = blogs_spb + blogs_rus + blogs_world\n",
"#print (blogs)"
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def procref (addr):\n",
" \"\"\"get full text of news\"\"\"\n",
" if addr == \"\": return\n",
" page = requests.get(addr)\n",
" tree = lh.fromstring(page.text)\n",
" try:\n",
" full = tree.xpath(\"//div[@class='article_fulltext']\")\n",
" print (full[0].xpath(\"./p\"))\n",
"# print (full[0].text.strip())\n",
" except:\n",
" print (\"None\")"
]
},
{
"cell_type": "code",
"execution_count": 184,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"spb 2015/12/15 14:00 text = [«ДП»: Последний актив экс-владельца «Балтимора» банкротится второй раз за год] goto = [http://www.dp.ru/a/2015/12/14/Millioni_roz_Alekseja_Anti/]\n",
"spb 2015/12/15 13:22 text = [О рождении российского кино расскажут в музее Фаберже] goto = [http://calendar.fontanka.ru/articles/3125/]\n",
"spb 2015/12/15 13:09 text = [Улицу Зодчего Росси перекроют на два дня] goto = [http://spbvoditel.ru/2015/12/15/019/]\n",
"spb 2015/12/15 12:40 text = [В Эрмитаже покажут фильм про ООН и Путина] goto = [http://www.fontanka.ru/fontanka//2015/12/15/062/]\n",
"spb 2015/12/15 12:26 text = [Губернатор Ленобласти запретил подчинённым летать зарубежными авиалиниями] goto = [http://47news.ru/articles/97118/]\n",
"spb 2015/12/15 12:14 text = [Петербуржец получил с ЖКС 950 тысяч за выброшенную антенну] goto = [http://www.fontanka.ru/fontanka//2015/12/15/059/]\n",
"spb 2015/12/15 12:06 text = [Сыр, колбасу и икру нашли на границе в тайниках питерского микроавтобуса] goto = [http://fontanka.fi/articles/24897/]\n",
"spb 2015/12/15 11:53 text = [Дворник нашел завернутого в одеяло младенца на улице Есенина] goto = [http://www.fontanka.ru/fontanka//2015/12/15/053/]\n",
"spb 2015/12/15 11:50 text = [Метрополитен потратит 8 млн на анализ пропускной способности третьей линии] goto = [http://www.fontanka.ru/fontanka//2015/12/15/052/]\n",
"spb 2015/12/15 11:37 text = [Очевидцы: В ДТП на дамбе пострадали двое] goto = [http://www.fontanka.ru/fontanka//2015/12/15/050/]\n",
"...\n",
"Total records: 66\n"
]
}
],
"source": [
"for blog in blogs[:10]:\n",
" blok = blog[1]\n",
" dt = blok.xpath(\"div[@class='entry_date']\")\n",
" if dt[0].text.strip()[2] != \":\": continue\n",
" print (blog[0], plus, dt[0].text.strip(), end=\" \")\n",
" tit = blok.xpath(\"div[@class='entry_title']\")\n",
" ref = tit[0].xpath(\"a[@href]\")\n",
" print (\"text = [{}]\" .format (ref[0].text.strip()), end=\" \")\n",
" goes = tit[0].xpath(\"a/@href\")[0]\n",
" if goes.startswith('/'):\n",
" goes = url + goes\n",
" print (\"goto = [{}]\" .format(goes))\n",
"# procref(goes)\n",
"print (\"...\\nTotal records: {}\" .format(len(blogs)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
xiaoxiaoyao/MyApp | jupyter_notebook/datascience.ipynb | 2 | 15762 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# [如何用Python从海量文本抽取主题？](https://zhuanlan.zhihu.com/p/28992175)\n",
"\n",
"你在工作、学习中是否曾因信息过载叫苦不迭？有一种方法能够替你读海量文章，并将不同的主题和对应的关键词抽取出来，让你谈笑间观其大略。本文使用Python对超过1000条文本做主题抽取，一步步带你体会非监督机器学习LDA方法的魅力。想不想试试呢？\n",
"\n",
"每个现代人，几乎都体会过信息过载的痛苦。文章读不过来，音乐听不过来，视频看不过来。可是现实的压力，使你又不能轻易放弃掉。\n",
"\n",
"## 准备\n",
"\n",
"`pip install jieba\n",
"pip install pyldavis\n",
"pip install pandas,sklearn`\n",
"\n",
"为了处理表格数据，我们依然使用数据框工具Pandas。先调用它,然后读入我们的数据文件datascience.csv."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>title</th>\n",
" <th>author</th>\n",
" <th>content</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>大数据产业迎政策暖风 最新大数据概念股一览</td>\n",
" <td>财经热点扒客</td>\n",
" <td>大数据产业发展受到国家重视，而大数据已经上升为国家战略，未来发展前景很广阔。大数据产业“十三...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Google发布机器学习平台Tensorflow游乐场～带你一起玩神经网络！</td>\n",
" <td>硅谷周边</td>\n",
" <td>点击上方“硅谷周边”关注我，收到最新的文章哦！昨天，Google发布了Tensorflow游...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>李克强：中国大数据和云计算产业是开放的</td>\n",
" <td>苏州高新区金融办</td>\n",
" <td>国务院总理李克强当地时间20日上午在纽约下榻饭店同美国经济、金融、智库、媒体等各界人士座谈，...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>全峰集团持续挖掘大数据</td>\n",
" <td>快递物流网</td>\n",
" <td>2016年，全峰集团持续挖掘大数据、云计算、“互联网+”等前沿技术和物流快递的融合，并通过优...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>第366期【微理工】贵州理工学院召开大数据分析与应用专题分享会</td>\n",
" <td>贵州理工学院</td>\n",
" <td>贵州理工学院召开大数据分析与应用专题分享会 借“创响中国”贵安站巡回接力活动暨2016贵安大...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" title author \\\n",
"0 大数据产业迎政策暖风 最新大数据概念股一览 财经热点扒客 \n",
"1 Google发布机器学习平台Tensorflow游乐场～带你一起玩神经网络！ 硅谷周边 \n",
"2 李克强：中国大数据和云计算产业是开放的 苏州高新区金融办 \n",
"3 全峰集团持续挖掘大数据 快递物流网 \n",
"4 第366期【微理工】贵州理工学院召开大数据分析与应用专题分享会 贵州理工学院 \n",
"\n",
" content \n",
"0 大数据产业发展受到国家重视，而大数据已经上升为国家战略，未来发展前景很广阔。大数据产业“十三... \n",
"1 点击上方“硅谷周边”关注我，收到最新的文章哦！昨天，Google发布了Tensorflow游... \n",
"2 国务院总理李克强当地时间20日上午在纽约下榻饭店同美国经济、金融、智库、媒体等各界人士座谈，... \n",
"3 2016年，全峰集团持续挖掘大数据、云计算、“互联网+”等前沿技术和物流快递的融合，并通过优... \n",
"4 贵州理工学院召开大数据分析与应用专题分享会 借“创响中国”贵安站巡回接力活动暨2016贵安大... "
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"df = pd.read_csv(\"datascience.csv\", encoding='gb18030') #注意它的编码是中文GB18030，不是Pandas默认设置的编码，所以此处需要显式指定编码类型，以免出现乱码错误。\n",
"# 之后看看数据框的头几行，以确认读取是否正确。\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1024, 3)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#我们看看数据框的长度，以确认数据是否读取完整。\n",
"df.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`(1024, 3)`\n",
"行列数都与我们爬取到的数量一致，通过。\n",
"# 分词\n",
"下面我们需要做一件重要工作——分词\n",
"\n",
"我们首先调用jieba分词包。\n",
"\n",
"我们此次需要处理的，不是单一文本数据，而是1000多条文本数据，因此我们需要把这项工作并行化。这就需要首先编写一个函数，处理单一文本的分词。\n",
"\n",
"有了这个函数之后，我们就可以不断调用它来批量处理数据框里面的全部文本（正文）信息了。你当然可以自己写个循环来做这项工作。但这里我们使用更为高效的apply函数。如果你对这个函数有兴趣，可以点击这段教学视频查看具体的介绍。\n",
"\n",
"下面这一段代码执行起来，可能需要一小段时间。请耐心等候。"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Building prefix dict from the default dictionary ...\n",
"Dumping model to file cache /var/folders/sg/3xqgzjkd4rq85xbf4g1tg37w0000gn/T/jieba.cache\n",
"Loading model cost 1.546 seconds.\n",
"Prefix dict has been built succesfully.\n"
]
}
],
"source": [
"import jieba\n",
"def chinese_word_cut(mytext):\n",
" return \" \".join(jieba.cut(mytext))\n",
"df[\"content_cutted\"] = df.content.apply(chinese_word_cut)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 大 数据 产业 发展 受到 国家 重视 ， 而 大 数据 已经 上升 为 国家 战略 ， 未...\n",
"1 点击 上方 “ 硅谷 周边 ” 关注 我 ， 收到 最新 的 文章 哦 ！ 昨天 ， Goo...\n",
"2 国务院 总理 李克强 当地 时间 20 日 上午 在 纽约 下榻 饭店 同 美国 经济 、 ...\n",
"3 2016 年 ， 全峰 集团 持续 挖掘 大 数据 、 云 计算 、 “ 互联网 + ” 等...\n",
"4 贵州 理工学院 召开 大 数据分析 与 应用 专题 分享 会 借 “ 创响 中国 ” 贵...\n",
"Name: content_cutted, dtype: object"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#执行完毕之后，我们需要查看一下，文本是否已经被正确分词。\n",
"df.content_cutted.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"#文本向量化\n",
"from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
"n_features = 1000\n",
"tf_vectorizer = CountVectorizer(strip_accents = 'unicode',\n",
" max_features=n_features,\n",
" stop_words='english',\n",
" max_df = 0.5,\n",
" min_df = 10)\n",
"tf = tf_vectorizer.fit_transform(df.content_cutted)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"我们需要人为设定主题的数量。这个要求让很多人大跌眼镜——我怎么知道这一堆文章里面多少主题？！\n",
"\n",
"别着急。应用LDA方法，指定（或者叫瞎猜）主题个数是必须的。如果你只需要把文章粗略划分成几个大类，就可以把数字设定小一些；相反，如果你希望能够识别出非常细分的主题，就增大主题个数。\n",
"\n",
"对划分的结果，如果你觉得不够满意，可以通过继续迭代，调整主题数量来优化。\n",
"\n",
"这里我们先设定为5个分类试试。"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/decomposition/online_lda.py:314: DeprecationWarning: n_topics has been renamed to n_components in version 0.19 and will be removed in 0.21\n",
" DeprecationWarning)\n"
]
}
],
"source": [
"#应用LDA方法\n",
"from sklearn.decomposition import LatentDirichletAllocation\n",
"n_topics = 5\n",
"lda = LatentDirichletAllocation(n_topics=n_topics, max_iter=50,\n",
" learning_method='online',\n",
" learning_offset=50.,\n",
" random_state=0)\n",
"\n",
"#这一部分工作量较大，程序会执行一段时间，Jupyter Notebook在执行中可能暂时没有响应。等待一会儿就好，不要着急。\n",
"lda.fit(tf)\n",
"\n",
"#主题没有一个确定的名称，而是用一系列关键词刻画的。我们定义以下的函数，把每个主题里面的前若干个关键词显示出来：\n",
"def print_top_words(model, feature_names, n_top_words):\n",
" for topic_idx, topic in enumerate(model.components_):\n",
" print(\"Topic #%d:\" % topic_idx)\n",
" print(\" \".join([feature_names[i]\n",
" for i in topic.argsort()[:-n_top_words - 1:-1]]))\n",
" print()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Topic #0:\n",
"学习 模型 使用 算法 方法 机器 可视化 神经网络 特征 处理 不同 计算 用户 数据库 系统 如果 分类 训练 一种 基于\n",
"Topic #1:\n",
"这个 就是 可能 没有 如果 他们 自己 很多 什么 不是 但是 或者 因为 时候 这样 现在 电子 一些 所以 孩子\n",
"Topic #2:\n",
"企业 平台 服务 管理 互联网 数据分析 公司 产品 用户 业务 行业 客户 金融 创新 实现 价值 系统 能力 工作 需求\n",
"Topic #3:\n",
"中国 2016 市场 增长 10 城市 用户 2015 关注 行业 其中 30 人口 检索 阅读 大众 投资 全国 美国 20\n",
"Topic #4:\n",
"人工智能 学习 领域 智能 机器人 机器 人类 公司 深度 研究 未来 识别 已经 系统 计算机 目前 医疗 语音 方面 服务\n",
"\n"
]
}
],
"source": [
"#定义好函数之后，我们暂定每个主题输出前20个关键词。\n",
"n_top_words = 20\n",
"\n",
"#以下命令会帮助我们依次输出每个主题的关键词表：\n",
"tf_feature_names = tf_vectorizer.get_feature_names()\n",
"print_top_words(lda, tf_feature_names, n_top_words)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"到这里，LDA已经成功帮我们完成了主题抽取。但是我知道你不是很满意，因为结果不够直观。\n",
"\n",
"那咱们就让它直观一些好了。\n",
"\n",
"执行以下命令，会有有趣的事情发生。"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'pyLDAvis'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-02bd018c9a0e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpyLDAvis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpyLDAvis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msklearn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mpyLDAvis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_notebook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mpyLDAvis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprepare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlda\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtf_vectorizer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pyLDAvis'"
]
}
],
"source": [
"import pyLDAvis\n",
"import pyLDAvis.sklearn\n",
"pyLDAvis.enable_notebook()\n",
"pyLDAvis.sklearn.prepare(lda, tf, tf_vectorizer)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"祝探索旅程愉快！"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| unlicense |
maxkoe/budgeter | accessing-google-spreadsheet.ipynb | 1 | 136430 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import gspread, gspread_dataframe\n",
"from oauth2client.service_account import ServiceAccountCredentials\n",
"\n",
"import pandas as pd\n",
"import re\n",
"\n",
"def num_to_col_letters(num) :\n",
" letters = ''\n",
" while num:\n",
" mod = (num - 1) % 26\n",
" letters += chr(mod + 65)\n",
" num = (num - 1) // 26\n",
" return ''.join(reversed(letters))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def connect_to_sheets(spreadsheet='GemeinsameBilanzierung_16_17', sheetname='') :\n",
" \n",
" if type(spreadsheet) is str : \n",
" scope = ['https://spreadsheets.google.com/feeds']\n",
" creds = ServiceAccountCredentials.from_json_keyfile_name('client-secret.json', scope)\n",
" client = gspread.authorize(creds)\n",
" spreadsheet = client.open(spreadsheet)\n",
"\n",
" # Find a workbook by name and open the first sheet\n",
" # Make sure you use the right name here.\n",
" if sheetname == '' :\n",
" return spreadsheet\n",
" else :\n",
" worksheet = spreadsheet.worksheet(sheetname)\n",
" return worksheet"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"res = { # re for at all valid\n",
" 'valid_range' : r'^[A-Z]+\\d*(:[A-Z]+\\d*)?$',\n",
" # re for complete range\n",
" 'normal_range' : r'^[A-Z]+\\d+:[A-Z]+\\d+$',\n",
" # re for single cell\n",
" 'single_cell' : r'^[A-Z]+\\d+$',\n",
" # re for only letters\n",
" 'only_cols' : r'^[A-Z]+:[A-Z]+$',\n",
" # re for open end\n",
" 'missing_start' : r'^[A-Z]+\\d+:[A-Z]+$',\n",
" # re for open beginning\n",
" 'missing_end' : r'^[A-Z]+:[A-Z]+\\d+$'}\n",
"\n",
"def normalize_range(range_string, worksheet) :\n",
" re_strings = [\n",
" # re for at all valid\n",
" r'^[A-Z]+\\d*(:[A-Z]+\\d*)?$', \n",
" # re for complete range\n",
" r'^[A-Z]+\\d+:[A-Z]+\\d+$',\n",
" # re for single cell\n",
" r'^[A-Z]+\\d+$',\n",
" # re for only letters\n",
" r'^[A-Z]+:[A-Z]+$',\n",
" # re for open end\n",
" r'^[A-Z]+\\d+:[A-Z]+$',\n",
" # re for open beginning\n",
" r'^[A-Z]+:[A-Z]+\\d+$']\n",
" res = [re.compile(re_string) for re_string in re_strings]\n",
" if not res[0].match(range_string) :\n",
" raise RuntimeError('This is not a valid descriptor of ranges or cells')\n",
" if res[1].match(range_string) or res[2].match(range_string) :\n",
" return range_string\n",
" if res[5].match(range_string) :\n",
" pos_colon = range_string.find(':')\n",
" return range_string[0:pos_colon] + '1' + range_string[pos_colon:]\n",
" if res[4].match(range_string) :\n",
" return range_string + str(worksheet.row_count)\n",
" if res[3].match(range_string) :\n",
" pos_colon = range_string.find(':')\n",
" return range_string[0:pos_colon] + '1' + range_string[pos_colon:] + str(worksheet.row_count)\n",
" \n",
"# def get_rowcol_range(range_string, worksheet) :"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def read_gsheet_into_df(range_string, worksheet, date_col=None) :\n",
" range_string = normalize_range(range_string, worksheet)\n",
" cell_range = worksheet.range(range_string)\n",
" result = pd.DataFrame()\n",
" for cell in cell_range :\n",
" if pd.isnull(cell.numeric_value) :\n",
" if cell.value == '' :\n",
" value = cell.numeric_value\n",
" else :\n",
" value = cell.value\n",
" else :\n",
" value = round(cell.numeric_value, 2)\n",
" result.loc[cell.row, num_to_col_letters(cell.col)] = value\n",
" \n",
" if date_col is not None and date_col in result.columns :\n",
" result[date_col] = pd.to_datetime('1899-12-30') + pd.to_timedelta(result[date_col], 'D')\n",
" return result.dropna(how='all')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (<ipython-input-7-44da3d80670c>, line 3)",
"output_type": "error",
"traceback": [
"\u001b[0;36m File \u001b[0;32m\"<ipython-input-7-44da3d80670c>\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m gspread.models.\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
"source": [
"def read_gsheet_into_df(range_string, worksheet, date_col=None) :\n",
" range_string = normalize_range(range_string, worksheet)\n",
" gspread.models.\n",
" \n",
" if date_col is not None and date_col in result.columns :\n",
" result[date_col] = pd.to_datetime('1899-12-30') + pd.to_timedelta(result[date_col], 'D')\n",
" return result.dropna(how='all')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"workbook = connect_to_sheets()\n",
"sheet = connect_to_sheets(workbook, 'August')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>budget_type</th>\n",
" <th>description</th>\n",
" <th>date</th>\n",
" <th>amount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>R</td>\n",
" <td>Miete</td>\n",
" <td>2017-08-01</td>\n",
" <td>-568.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>R</td>\n",
" <td>Miete FFM</td>\n",
" <td>2017-08-02</td>\n",
" <td>-450.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>R</td>\n",
" <td>Berufsunfähigkeitsversicherung</td>\n",
" <td>2017-08-01</td>\n",
" <td>-49.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>R</td>\n",
" <td>Strom EnviaM</td>\n",
" <td>NaT</td>\n",
" <td>-51.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>R</td>\n",
" <td>Vodafone</td>\n",
" <td>NaT</td>\n",
" <td>-19.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>M</td>\n",
" <td>Telefonie</td>\n",
" <td>NaT</td>\n",
" <td>-6.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>R</td>\n",
" <td>Rechtsschutzversicherung</td>\n",
" <td>2017-01-30</td>\n",
" <td>-13.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>R</td>\n",
" <td>Haftpflichtversicherung</td>\n",
" <td>2017-08-01</td>\n",
" <td>-7.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>G</td>\n",
" <td>GEW</td>\n",
" <td>NaT</td>\n",
" <td>-2.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>R</td>\n",
" <td>Semestergebühr Paul</td>\n",
" <td>2016-06-21</td>\n",
" <td>-46.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>R</td>\n",
" <td>GEZ</td>\n",
" <td>2017-03-31</td>\n",
" <td>-17.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>R</td>\n",
" <td>Handy Max</td>\n",
" <td>2017-08-10</td>\n",
" <td>-7.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>R</td>\n",
" <td>Handy Paul</td>\n",
" <td>2017-08-10</td>\n",
" <td>-7.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>G</td>\n",
" <td>Spotify Max</td>\n",
" <td>2017-08-15</td>\n",
" <td>-4.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>G</td>\n",
" <td>Apple Music Paul</td>\n",
" <td>NaT</td>\n",
" <td>-4.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>G</td>\n",
" <td>Backblaze Max</td>\n",
" <td>2017-04-18</td>\n",
" <td>-3.77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>G</td>\n",
" <td>Backblaze Paul</td>\n",
" <td>2017-04-18</td>\n",
" <td>-3.77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>G</td>\n",
" <td>Fitnessstudio</td>\n",
" <td>2017-08-03</td>\n",
" <td>-39.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>L</td>\n",
" <td>Aldi</td>\n",
" <td>2017-07-31</td>\n",
" <td>-23.97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>R</td>\n",
" <td>Monatskarte FFM</td>\n",
" <td>2017-07-31</td>\n",
" <td>-87.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>L</td>\n",
" <td>Netto</td>\n",
" <td>2017-07-31</td>\n",
" <td>-9.44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>L</td>\n",
" <td>Rewe</td>\n",
" <td>2017-08-01</td>\n",
" <td>-8.71</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>D</td>\n",
" <td>Rossmann</td>\n",
" <td>2017-08-01</td>\n",
" <td>-9.17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>D</td>\n",
" <td>DM</td>\n",
" <td>2017-08-01</td>\n",
" <td>-9.45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>D</td>\n",
" <td>DM</td>\n",
" <td>2017-08-01</td>\n",
" <td>-12.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>AM</td>\n",
" <td>Mittagessen Casino</td>\n",
" <td>2017-08-01</td>\n",
" <td>-2.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>L</td>\n",
" <td>Türkischer Supermarkt</td>\n",
" <td>2017-08-01</td>\n",
" <td>-10.37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>L</td>\n",
" <td>Brot</td>\n",
" <td>2017-08-01</td>\n",
" <td>-0.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>AM</td>\n",
" <td>Doppelter Kaffee Trianon</td>\n",
" <td>2017-08-02</td>\n",
" <td>-1.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>AM</td>\n",
" <td>Kaffee</td>\n",
" <td>2017-08-02</td>\n",
" <td>-0.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>AM</td>\n",
" <td>Mr Tom</td>\n",
" <td>2017-08-07</td>\n",
" <td>-0.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>AM</td>\n",
" <td>Kaffee</td>\n",
" <td>2017-08-07</td>\n",
" <td>-0.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>L</td>\n",
" <td>Brot</td>\n",
" <td>2017-08-08</td>\n",
" <td>-1.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>AM</td>\n",
" <td>Mittagssalat</td>\n",
" <td>2017-08-10</td>\n",
" <td>-2.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>S</td>\n",
" <td>Rossmann Zahnbürste</td>\n",
" <td>2017-08-10</td>\n",
" <td>-15.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>D</td>\n",
" <td>Rossmann</td>\n",
" <td>2017-08-10</td>\n",
" <td>-4.54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67</th>\n",
" <td>L</td>\n",
" <td>Edeka</td>\n",
" <td>2017-08-10</td>\n",
" <td>-3.17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>AM</td>\n",
" <td>Miitag Zwiebelsuppe</td>\n",
" <td>2017-08-11</td>\n",
" <td>-0.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>AM</td>\n",
" <td>Espresso</td>\n",
" <td>2017-08-11</td>\n",
" <td>-0.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>AM</td>\n",
" <td>Mr Tom</td>\n",
" <td>2017-08-11</td>\n",
" <td>-0.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>L</td>\n",
" <td>Aldi</td>\n",
" <td>2017-08-11</td>\n",
" <td>-19.31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>D</td>\n",
" <td>Schirm Rossmann</td>\n",
" <td>2017-08-11</td>\n",
" <td>-2.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>A</td>\n",
" <td>Eisessen</td>\n",
" <td>2017-08-13</td>\n",
" <td>-4.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>L</td>\n",
" <td>Wasser</td>\n",
" <td>2017-08-13</td>\n",
" <td>-1.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>L</td>\n",
" <td>Rewe</td>\n",
" <td>2017-08-13</td>\n",
" <td>-34.42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>A</td>\n",
" <td>Eisessen</td>\n",
" <td>2017-08-13</td>\n",
" <td>-4.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>A</td>\n",
" <td>Wasser</td>\n",
" <td>2017-08-13</td>\n",
" <td>-1.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>AM</td>\n",
" <td>Balisto</td>\n",
" <td>2017-08-14</td>\n",
" <td>-0.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>L</td>\n",
" <td>Netto</td>\n",
" <td>2017-08-14</td>\n",
" <td>-13.42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>T</td>\n",
" <td>Fahrkarte Frankfurt</td>\n",
" <td>2017-08-14</td>\n",
" <td>-2.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>AM</td>\n",
" <td>Mittagessen Casino</td>\n",
" <td>2017-08-15</td>\n",
" <td>-4.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>AM</td>\n",
" <td>KitKat</td>\n",
" <td>2017-08-15</td>\n",
" <td>-1.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>L</td>\n",
" <td>Rewe</td>\n",
" <td>2017-08-16</td>\n",
" <td>-17.57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>AM</td>\n",
" <td>Kakao</td>\n",
" <td>2017-08-16</td>\n",
" <td>-0.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>L</td>\n",
" <td>Real</td>\n",
" <td>2017-08-18</td>\n",
" <td>-10.19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>L</td>\n",
" <td>Rewe</td>\n",
" <td>2017-08-18</td>\n",
" <td>-2.69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>AM</td>\n",
" <td>Suppe</td>\n",
" <td>2017-08-18</td>\n",
" <td>-0.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>T</td>\n",
" <td>Sitzplatz reservierung</td>\n",
" <td>2017-08-18</td>\n",
" <td>-4.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>A</td>\n",
" <td>Eisessen</td>\n",
" <td>2017-08-17</td>\n",
" <td>-1.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>M</td>\n",
" <td>Toilette</td>\n",
" <td>2017-08-18</td>\n",
" <td>-0.25</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>85 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" budget_type description date amount\n",
"6 R Miete 2017-08-01 -568.00\n",
"7 R Miete FFM 2017-08-02 -450.00\n",
"8 R Berufsunfähigkeitsversicherung 2017-08-01 -49.05\n",
"9 R Strom EnviaM NaT -51.00\n",
"10 R Vodafone NaT -19.99\n",
"11 M Telefonie NaT -6.00\n",
"12 R Rechtsschutzversicherung 2017-01-30 -13.90\n",
"13 R Haftpflichtversicherung 2017-08-01 -7.50\n",
"14 G GEW NaT -2.50\n",
"15 R Semestergebühr Paul 2016-06-21 -46.15\n",
"16 R GEZ 2017-03-31 -17.50\n",
"17 R Handy Max 2017-08-10 -7.99\n",
"18 R Handy Paul 2017-08-10 -7.99\n",
"19 G Spotify Max 2017-08-15 -4.99\n",
"20 G Apple Music Paul NaT -4.99\n",
"21 G Backblaze Max 2017-04-18 -3.77\n",
"22 G Backblaze Paul 2017-04-18 -3.77\n",
"23 G Fitnessstudio 2017-08-03 -39.80\n",
"24 L Aldi 2017-07-31 -23.97\n",
"25 R Monatskarte FFM 2017-07-31 -87.40\n",
"26 L Netto 2017-07-31 -9.44\n",
"27 L Rewe 2017-08-01 -8.71\n",
"28 D Rossmann 2017-08-01 -9.17\n",
"29 D DM 2017-08-01 -9.45\n",
"30 D DM 2017-08-01 -12.25\n",
"31 AM Mittagessen Casino 2017-08-01 -2.80\n",
"32 L Türkischer Supermarkt 2017-08-01 -10.37\n",
"33 L Brot 2017-08-01 -0.95\n",
"34 AM Doppelter Kaffee Trianon 2017-08-02 -1.50\n",
"35 AM Kaffee 2017-08-02 -0.70\n",
".. ... ... ... ...\n",
"61 AM Mr Tom 2017-08-07 -0.40\n",
"62 AM Kaffee 2017-08-07 -0.70\n",
"63 L Brot 2017-08-08 -1.40\n",
"64 AM Mittagssalat 2017-08-10 -2.26\n",
"65 S Rossmann Zahnbürste 2017-08-10 -15.99\n",
"66 D Rossmann 2017-08-10 -4.54\n",
"67 L Edeka 2017-08-10 -3.17\n",
"68 AM Miitag Zwiebelsuppe 2017-08-11 -0.60\n",
"69 AM Espresso 2017-08-11 -0.80\n",
"70 AM Mr Tom 2017-08-11 -0.40\n",
"71 L Aldi 2017-08-11 -19.31\n",
"72 D Schirm Rossmann 2017-08-11 -2.95\n",
"73 A Eisessen 2017-08-13 -4.80\n",
"74 L Wasser 2017-08-13 -1.30\n",
"75 L Rewe 2017-08-13 -34.42\n",
"76 A Eisessen 2017-08-13 -4.80\n",
"77 A Wasser 2017-08-13 -1.30\n",
"78 AM Balisto 2017-08-14 -0.50\n",
"79 L Netto 2017-08-14 -13.42\n",
"80 T Fahrkarte Frankfurt 2017-08-14 -2.90\n",
"81 AM Mittagessen Casino 2017-08-15 -4.30\n",
"82 AM KitKat 2017-08-15 -1.00\n",
"83 L Rewe 2017-08-16 -17.57\n",
"84 AM Kakao 2017-08-16 -0.40\n",
"85 L Real 2017-08-18 -10.19\n",
"86 L Rewe 2017-08-18 -2.69\n",
"87 AM Suppe 2017-08-18 -0.60\n",
"88 T Sitzplatz reservierung 2017-08-18 -4.50\n",
"89 A Eisessen 2017-08-17 -1.20\n",
"90 M Toilette 2017-08-18 -0.25\n",
"\n",
"[85 rows x 4 columns]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>description</th>\n",
" <th>date</th>\n",
" <th>amount</th>\n",
" <th>money_pot</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Geld aufladen Mitarbeiter ausweis</td>\n",
" <td>2017-08-01</td>\n",
" <td>-20.00</td>\n",
" <td>BM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Geld abheben</td>\n",
" <td>2017-08-03</td>\n",
" <td>30.00</td>\n",
" <td>BM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Geld aufladen Trianon</td>\n",
" <td>2017-08-02</td>\n",
" <td>-20.00</td>\n",
" <td>BM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Geld aus Schatulle</td>\n",
" <td>2017-08-05</td>\n",
" <td>1.30</td>\n",
" <td>BM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Betriebsausflug</td>\n",
" <td>2017-08-07</td>\n",
" <td>-18.50</td>\n",
" <td>BM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Geldfund</td>\n",
" <td>2017-08-12</td>\n",
" <td>0.05</td>\n",
" <td>BM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Eisessen</td>\n",
" <td>2017-08-13</td>\n",
" <td>-4.80</td>\n",
" <td>BM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Wasser</td>\n",
" <td>2017-08-13</td>\n",
" <td>-1.30</td>\n",
" <td>BM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>BBk Karte Aufladen</td>\n",
" <td>2017-08-14</td>\n",
" <td>-10.00</td>\n",
" <td>BM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Eisessen</td>\n",
" <td>2017-08-17</td>\n",
" <td>-1.20</td>\n",
" <td>BM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Toilette</td>\n",
" <td>2017-08-18</td>\n",
" <td>-0.25</td>\n",
" <td>BM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Geld aus Schatulle</td>\n",
" <td>NaT</td>\n",
" <td>1.00</td>\n",
" <td>BP</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Brot</td>\n",
" <td>2017-08-01</td>\n",
" <td>-0.95</td>\n",
" <td>BP</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Brot</td>\n",
" <td>2017-08-08</td>\n",
" <td>-1.40</td>\n",
" <td>BP</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Geld aus Schatulle</td>\n",
" <td>2017-08-08</td>\n",
" <td>1.35</td>\n",
" <td>BP</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Budgetbeitrag von BMS</td>\n",
" <td>2017-07-28</td>\n",
" <td>1468.00</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Aldi</td>\n",
" <td>2017-07-31</td>\n",
" <td>-23.97</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Monatskarte FFM</td>\n",
" <td>2017-07-31</td>\n",
" <td>-87.40</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Netto</td>\n",
" <td>2017-07-31</td>\n",
" <td>-9.44</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Haftpflichtversicherung</td>\n",
" <td>2017-08-01</td>\n",
" <td>-7.50</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Berufsunfähigkeitsversicherung</td>\n",
" <td>2017-08-01</td>\n",
" <td>-49.05</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Miete</td>\n",
" <td>2017-08-01</td>\n",
" <td>-568.00</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Rewe</td>\n",
" <td>2017-08-01</td>\n",
" <td>-8.71</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Rossmann</td>\n",
" <td>2017-08-01</td>\n",
" <td>-9.17</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>DM</td>\n",
" <td>2017-08-01</td>\n",
" <td>-9.45</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>DM</td>\n",
" <td>2017-08-01</td>\n",
" <td>-12.25</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Türkischer Supermarkt</td>\n",
" <td>2017-08-01</td>\n",
" <td>-10.37</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Miete FFM auf Max Konto</td>\n",
" <td>2017-08-02</td>\n",
" <td>450.00</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Miete FFM</td>\n",
" <td>2017-08-02</td>\n",
" <td>-450.00</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Ausgehen mit Kollegen</td>\n",
" <td>2017-08-02</td>\n",
" <td>-20.60</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>H&M</td>\n",
" <td>2017-08-05</td>\n",
" <td>-55.08</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Rewe Wasser</td>\n",
" <td>2017-08-05</td>\n",
" <td>-1.17</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>Sommerticket DB</td>\n",
" <td>2017-08-05</td>\n",
" <td>-96.00</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>Sommerticket DB</td>\n",
" <td>2017-08-05</td>\n",
" <td>-96.00</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>Edeka</td>\n",
" <td>2017-08-05</td>\n",
" <td>-5.43</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>Amazon Bestellung</td>\n",
" <td>2017-08-06</td>\n",
" <td>-39.24</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>Sitzplatz reservierung</td>\n",
" <td>2017-08-06</td>\n",
" <td>-4.50</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>Rewe</td>\n",
" <td>2017-08-07</td>\n",
" <td>-21.00</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>H&M Hose</td>\n",
" <td>2017-08-07</td>\n",
" <td>-30.14</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>Briefmarken</td>\n",
" <td>2017-08-07</td>\n",
" <td>-21.50</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>Netto</td>\n",
" <td>2017-08-07</td>\n",
" <td>-16.60</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>Real</td>\n",
" <td>2017-08-07</td>\n",
" <td>-3.99</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>Amazon Festplatte Paul</td>\n",
" <td>2017-08-06</td>\n",
" <td>-52.94</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>Amazon Fail Topf</td>\n",
" <td>2017-08-06</td>\n",
" <td>-4.61</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>Handy Max</td>\n",
" <td>2017-08-10</td>\n",
" <td>-7.99</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>Handy Paul</td>\n",
" <td>2017-08-10</td>\n",
" <td>-13.99</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>Rossmann</td>\n",
" <td>2017-08-10</td>\n",
" <td>-20.53</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>Edeka</td>\n",
" <td>2017-08-10</td>\n",
" <td>-3.17</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>Aldi</td>\n",
" <td>2017-08-11</td>\n",
" <td>-19.31</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>Schirm Rossmann</td>\n",
" <td>2017-08-11</td>\n",
" <td>-2.95</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>Rewe</td>\n",
" <td>2017-08-13</td>\n",
" <td>-34.42</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>Sommerticket DB</td>\n",
" <td>2017-08-14</td>\n",
" <td>-192.00</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>Vorschuss für Sparticket</td>\n",
" <td>2017-08-14</td>\n",
" <td>192.00</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>Netto</td>\n",
" <td>2017-08-14</td>\n",
" <td>-13.42</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>Spotify Max</td>\n",
" <td>2017-08-15</td>\n",
" <td>-4.99</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>Rewe</td>\n",
" <td>2017-08-16</td>\n",
" <td>-17.57</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>Fahrkarte Frankfurt</td>\n",
" <td>2017-08-14</td>\n",
" <td>-2.90</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>Real</td>\n",
" <td>2017-08-18</td>\n",
" <td>-10.19</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>Rewe</td>\n",
" <td>2017-08-18</td>\n",
" <td>-2.69</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>Sitzplatz reservierung</td>\n",
" <td>2017-08-18</td>\n",
" <td>-4.50</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>65 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" description date amount money_pot\n",
"7 Geld aufladen Mitarbeiter ausweis 2017-08-01 -20.00 BM\n",
"8 Geld abheben 2017-08-03 30.00 BM\n",
"9 Geld aufladen Trianon 2017-08-02 -20.00 BM\n",
"10 Geld aus Schatulle 2017-08-05 1.30 BM\n",
"11 Betriebsausflug 2017-08-07 -18.50 BM\n",
"12 Geldfund 2017-08-12 0.05 BM\n",
"13 Eisessen 2017-08-13 -4.80 BM\n",
"14 Wasser 2017-08-13 -1.30 BM\n",
"15 BBk Karte Aufladen 2017-08-14 -10.00 BM\n",
"16 Eisessen 2017-08-17 -1.20 BM\n",
"17 Toilette 2017-08-18 -0.25 BM\n",
"7 Geld aus Schatulle NaT 1.00 BP\n",
"8 Brot 2017-08-01 -0.95 BP\n",
"9 Brot 2017-08-08 -1.40 BP\n",
"10 Geld aus Schatulle 2017-08-08 1.35 BP\n",
"7 Budgetbeitrag von BMS 2017-07-28 1468.00 KG\n",
"8 Aldi 2017-07-31 -23.97 KG\n",
"9 Monatskarte FFM 2017-07-31 -87.40 KG\n",
"10 Netto 2017-07-31 -9.44 KG\n",
"11 Haftpflichtversicherung 2017-08-01 -7.50 KG\n",
"12 Berufsunfähigkeitsversicherung 2017-08-01 -49.05 KG\n",
"13 Miete 2017-08-01 -568.00 KG\n",
"14 Rewe 2017-08-01 -8.71 KG\n",
"15 Rossmann 2017-08-01 -9.17 KG\n",
"16 DM 2017-08-01 -9.45 KG\n",
"17 DM 2017-08-01 -12.25 KG\n",
"18 Türkischer Supermarkt 2017-08-01 -10.37 KG\n",
"19 Miete FFM auf Max Konto 2017-08-02 450.00 KG\n",
"20 Miete FFM 2017-08-02 -450.00 KG\n",
"21 Ausgehen mit Kollegen 2017-08-02 -20.60 KG\n",
".. ... ... ... ...\n",
"27 H&M 2017-08-05 -55.08 KG\n",
"28 Rewe Wasser 2017-08-05 -1.17 KG\n",
"29 Sommerticket DB 2017-08-05 -96.00 KG\n",
"30 Sommerticket DB 2017-08-05 -96.00 KG\n",
"31 Edeka 2017-08-05 -5.43 KG\n",
"32 Amazon Bestellung 2017-08-06 -39.24 KG\n",
"33 Sitzplatz reservierung 2017-08-06 -4.50 KG\n",
"34 Rewe 2017-08-07 -21.00 KG\n",
"35 H&M Hose 2017-08-07 -30.14 KG\n",
"36 Briefmarken 2017-08-07 -21.50 KG\n",
"37 Netto 2017-08-07 -16.60 KG\n",
"38 Real 2017-08-07 -3.99 KG\n",
"39 Amazon Festplatte Paul 2017-08-06 -52.94 KG\n",
"40 Amazon Fail Topf 2017-08-06 -4.61 KG\n",
"41 Handy Max 2017-08-10 -7.99 KG\n",
"42 Handy Paul 2017-08-10 -13.99 KG\n",
"43 Rossmann 2017-08-10 -20.53 KG\n",
"44 Edeka 2017-08-10 -3.17 KG\n",
"45 Aldi 2017-08-11 -19.31 KG\n",
"46 Schirm Rossmann 2017-08-11 -2.95 KG\n",
"47 Rewe 2017-08-13 -34.42 KG\n",
"48 Sommerticket DB 2017-08-14 -192.00 KG\n",
"49 Vorschuss für Sparticket 2017-08-14 192.00 KG\n",
"50 Netto 2017-08-14 -13.42 KG\n",
"51 Spotify Max 2017-08-15 -4.99 KG\n",
"52 Rewe 2017-08-16 -17.57 KG\n",
"53 Fahrkarte Frankfurt 2017-08-14 -2.90 KG\n",
"54 Real 2017-08-18 -10.19 KG\n",
"55 Rewe 2017-08-18 -2.69 KG\n",
"56 Sitzplatz reservierung 2017-08-18 -4.50 KG\n",
"\n",
"[65 rows x 4 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"col_titles = ['description', 'date', 'amount']\n",
"\n",
"budgeting = read_gsheet_into_df('A6:D', sheet, 'C')\n",
"budgeting.columns = ['budget_type'] + col_titles\n",
"\n",
"max_bargeld = read_gsheet_into_df('H7:J', sheet, 'I')\n",
"max_bargeld.columns = col_titles\n",
"max_bargeld['money_pot'] = 'BM'\n",
"\n",
"paul_bargeld = read_gsheet_into_df('K7:M', sheet, 'L')\n",
"paul_bargeld.columns = col_titles\n",
"paul_bargeld['money_pot'] = 'BP'\n",
"\n",
"konto = read_gsheet_into_df('N7:P', sheet, 'O')\n",
"konto.columns = col_titles\n",
"konto['money_pot'] = 'KG'\n",
"\n",
"conjoined = pd.concat([max_bargeld, paul_bargeld, konto])\n",
"\n",
"display(budgeting, conjoined)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>budget_type</th>\n",
" <th>description</th>\n",
" <th>date</th>\n",
" <th>amount</th>\n",
" <th>money_pot</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>R</td>\n",
" <td>Miete</td>\n",
" <td>2017-08-01</td>\n",
" <td>-568.00</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>R</td>\n",
" <td>Miete FFM</td>\n",
" <td>2017-08-02</td>\n",
" <td>-450.00</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>R</td>\n",
" <td>Berufsunfähigkeitsversicherung</td>\n",
" <td>2017-08-01</td>\n",
" <td>-49.05</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>R</td>\n",
" <td>Strom EnviaM</td>\n",
" <td>NaT</td>\n",
" <td>-51.00</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>R</td>\n",
" <td>Vodafone</td>\n",
" <td>NaT</td>\n",
" <td>-19.99</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>M</td>\n",
" <td>Telefonie</td>\n",
" <td>NaT</td>\n",
" <td>-6.00</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>R</td>\n",
" <td>Rechtsschutzversicherung</td>\n",
" <td>2017-01-30</td>\n",
" <td>-13.90</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>R</td>\n",
" <td>Haftpflichtversicherung</td>\n",
" <td>2017-08-01</td>\n",
" <td>-7.50</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>G</td>\n",
" <td>GEW</td>\n",
" <td>NaT</td>\n",
" <td>-2.50</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>R</td>\n",
" <td>Semestergebühr Paul</td>\n",
" <td>2016-06-21</td>\n",
" <td>-46.15</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>R</td>\n",
" <td>GEZ</td>\n",
" <td>2017-03-31</td>\n",
" <td>-17.50</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>R</td>\n",
" <td>Handy Max</td>\n",
" <td>2017-08-10</td>\n",
" <td>-7.99</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>R</td>\n",
" <td>Handy Paul</td>\n",
" <td>2017-08-10</td>\n",
" <td>-7.99</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>G</td>\n",
" <td>Spotify Max</td>\n",
" <td>NaT</td>\n",
" <td>-4.99</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>G</td>\n",
" <td>Apple Music Paul</td>\n",
" <td>NaT</td>\n",
" <td>-4.99</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>G</td>\n",
" <td>Backblaze Max</td>\n",
" <td>2017-04-18</td>\n",
" <td>-3.77</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>G</td>\n",
" <td>Backblaze Paul</td>\n",
" <td>2017-04-18</td>\n",
" <td>-3.77</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>G</td>\n",
" <td>Fitnessstudio</td>\n",
" <td>2017-08-03</td>\n",
" <td>-39.80</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>L</td>\n",
" <td>Aldi</td>\n",
" <td>2017-07-31</td>\n",
" <td>-23.97</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>R</td>\n",
" <td>Monatskarte FFM</td>\n",
" <td>2017-07-31</td>\n",
" <td>-87.40</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>L</td>\n",
" <td>Netto</td>\n",
" <td>2017-07-31</td>\n",
" <td>-9.44</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>L</td>\n",
" <td>Rewe</td>\n",
" <td>2017-08-01</td>\n",
" <td>-8.71</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>D</td>\n",
" <td>Rossmann</td>\n",
" <td>2017-08-01</td>\n",
" <td>-9.17</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>D</td>\n",
" <td>DM</td>\n",
" <td>2017-08-01</td>\n",
" <td>-9.45</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>D</td>\n",
" <td>DM</td>\n",
" <td>2017-08-01</td>\n",
" <td>-12.25</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>AM</td>\n",
" <td>Mittagessen Casino</td>\n",
" <td>2017-08-01</td>\n",
" <td>-2.80</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>L</td>\n",
" <td>Türkischer Supermarkt</td>\n",
" <td>2017-08-01</td>\n",
" <td>-10.37</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>L</td>\n",
" <td>Brot</td>\n",
" <td>2017-08-01</td>\n",
" <td>-0.95</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>AM</td>\n",
" <td>Doppelter Kaffee Trianon</td>\n",
" <td>2017-08-02</td>\n",
" <td>-1.50</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>AM</td>\n",
" <td>Kaffee</td>\n",
" <td>2017-08-02</td>\n",
" <td>-0.70</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>AM</td>\n",
" <td>Kaffee</td>\n",
" <td>2017-08-07</td>\n",
" <td>-0.70</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>AM</td>\n",
" <td>Mr Tom</td>\n",
" <td>2017-08-07</td>\n",
" <td>-0.40</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>AM</td>\n",
" <td>Mittagssalat</td>\n",
" <td>2017-08-10</td>\n",
" <td>-2.26</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>S</td>\n",
" <td>Rossmann Zahnbürste</td>\n",
" <td>2017-08-10</td>\n",
" <td>-15.99</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>D</td>\n",
" <td>Rossmann</td>\n",
" <td>2017-08-10</td>\n",
" <td>-4.54</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>AM</td>\n",
" <td>Miitag Zwiebelsuppe</td>\n",
" <td>2017-08-11</td>\n",
" <td>-0.60</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>AM</td>\n",
" <td>Espresso</td>\n",
" <td>2017-08-11</td>\n",
" <td>-0.80</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>AM</td>\n",
" <td>Mr Tom</td>\n",
" <td>2017-08-11</td>\n",
" <td>-0.40</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>A</td>\n",
" <td>Eisessen</td>\n",
" <td>2017-08-13</td>\n",
" <td>-4.80</td>\n",
" <td>BM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>L</td>\n",
" <td>Wasser</td>\n",
" <td>2017-08-13</td>\n",
" <td>-1.30</td>\n",
" <td>BM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>L</td>\n",
" <td>Aldi</td>\n",
" <td>2017-08-11</td>\n",
" <td>-19.31</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>D</td>\n",
" <td>Schirm Rossmann</td>\n",
" <td>2017-08-11</td>\n",
" <td>-2.95</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>L</td>\n",
" <td>Rewe</td>\n",
" <td>2017-08-13</td>\n",
" <td>-34.42</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67</th>\n",
" <td>NaN</td>\n",
" <td>Geld aufladen Mitarbeiter ausweis</td>\n",
" <td>2017-08-01</td>\n",
" <td>-20.00</td>\n",
" <td>BM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>NaN</td>\n",
" <td>Geld abheben</td>\n",
" <td>2017-08-03</td>\n",
" <td>30.00</td>\n",
" <td>BM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>NaN</td>\n",
" <td>Geld aufladen Trianon</td>\n",
" <td>2017-08-02</td>\n",
" <td>-20.00</td>\n",
" <td>BM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>NaN</td>\n",
" <td>Geld aus Schatulle</td>\n",
" <td>2017-08-05</td>\n",
" <td>1.30</td>\n",
" <td>BM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>NaN</td>\n",
" <td>Betriebsausflug</td>\n",
" <td>2017-08-07</td>\n",
" <td>-18.50</td>\n",
" <td>BM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>NaN</td>\n",
" <td>Geldfund</td>\n",
" <td>2017-08-12</td>\n",
" <td>0.05</td>\n",
" <td>BM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>NaN</td>\n",
" <td>Geld aus Schatulle</td>\n",
" <td>NaT</td>\n",
" <td>1.00</td>\n",
" <td>BP</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>NaN</td>\n",
" <td>Brot</td>\n",
" <td>NaT</td>\n",
" <td>-0.95</td>\n",
" <td>BP</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>NaN</td>\n",
" <td>Budgetbeitrag von BMS</td>\n",
" <td>2017-07-28</td>\n",
" <td>1468.00</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>NaN</td>\n",
" <td>Miete FFM auf Max Konto</td>\n",
" <td>2017-08-02</td>\n",
" <td>450.00</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>NaN</td>\n",
" <td>Ausgehen mit Kollegen</td>\n",
" <td>2017-08-02</td>\n",
" <td>-20.60</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>NaN</td>\n",
" <td>Abheben</td>\n",
" <td>2017-08-03</td>\n",
" <td>-30.00</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>NaN</td>\n",
" <td>Sommerticket DB</td>\n",
" <td>2017-08-05</td>\n",
" <td>-96.00</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>NaN</td>\n",
" <td>Sommerticket DB</td>\n",
" <td>2017-08-05</td>\n",
" <td>-96.00</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>NaN</td>\n",
" <td>Amazon Fail Topf</td>\n",
" <td>2017-08-06</td>\n",
" <td>-4.61</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>NaN</td>\n",
" <td>Handy Paul</td>\n",
" <td>2017-08-10</td>\n",
" <td>-13.99</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>NaN</td>\n",
" <td>Rossmann</td>\n",
" <td>2017-08-10</td>\n",
" <td>-20.53</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>84 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" budget_type description date amount \\\n",
"0 R Miete 2017-08-01 -568.00 \n",
"1 R Miete FFM 2017-08-02 -450.00 \n",
"2 R Berufsunfähigkeitsversicherung 2017-08-01 -49.05 \n",
"3 R Strom EnviaM NaT -51.00 \n",
"4 R Vodafone NaT -19.99 \n",
"5 M Telefonie NaT -6.00 \n",
"6 R Rechtsschutzversicherung 2017-01-30 -13.90 \n",
"7 R Haftpflichtversicherung 2017-08-01 -7.50 \n",
"8 G GEW NaT -2.50 \n",
"9 R Semestergebühr Paul 2016-06-21 -46.15 \n",
"10 R GEZ 2017-03-31 -17.50 \n",
"11 R Handy Max 2017-08-10 -7.99 \n",
"12 R Handy Paul 2017-08-10 -7.99 \n",
"13 G Spotify Max NaT -4.99 \n",
"14 G Apple Music Paul NaT -4.99 \n",
"15 G Backblaze Max 2017-04-18 -3.77 \n",
"16 G Backblaze Paul 2017-04-18 -3.77 \n",
"17 G Fitnessstudio 2017-08-03 -39.80 \n",
"18 L Aldi 2017-07-31 -23.97 \n",
"19 R Monatskarte FFM 2017-07-31 -87.40 \n",
"20 L Netto 2017-07-31 -9.44 \n",
"21 L Rewe 2017-08-01 -8.71 \n",
"22 D Rossmann 2017-08-01 -9.17 \n",
"23 D DM 2017-08-01 -9.45 \n",
"24 D DM 2017-08-01 -12.25 \n",
"25 AM Mittagessen Casino 2017-08-01 -2.80 \n",
"26 L Türkischer Supermarkt 2017-08-01 -10.37 \n",
"27 L Brot 2017-08-01 -0.95 \n",
"28 AM Doppelter Kaffee Trianon 2017-08-02 -1.50 \n",
"29 AM Kaffee 2017-08-02 -0.70 \n",
".. ... ... ... ... \n",
"54 AM Kaffee 2017-08-07 -0.70 \n",
"55 AM Mr Tom 2017-08-07 -0.40 \n",
"56 AM Mittagssalat 2017-08-10 -2.26 \n",
"57 S Rossmann Zahnbürste 2017-08-10 -15.99 \n",
"58 D Rossmann 2017-08-10 -4.54 \n",
"59 AM Miitag Zwiebelsuppe 2017-08-11 -0.60 \n",
"60 AM Espresso 2017-08-11 -0.80 \n",
"61 AM Mr Tom 2017-08-11 -0.40 \n",
"62 A Eisessen 2017-08-13 -4.80 \n",
"63 L Wasser 2017-08-13 -1.30 \n",
"64 L Aldi 2017-08-11 -19.31 \n",
"65 D Schirm Rossmann 2017-08-11 -2.95 \n",
"66 L Rewe 2017-08-13 -34.42 \n",
"67 NaN Geld aufladen Mitarbeiter ausweis 2017-08-01 -20.00 \n",
"68 NaN Geld abheben 2017-08-03 30.00 \n",
"69 NaN Geld aufladen Trianon 2017-08-02 -20.00 \n",
"70 NaN Geld aus Schatulle 2017-08-05 1.30 \n",
"71 NaN Betriebsausflug 2017-08-07 -18.50 \n",
"72 NaN Geldfund 2017-08-12 0.05 \n",
"73 NaN Geld aus Schatulle NaT 1.00 \n",
"74 NaN Brot NaT -0.95 \n",
"75 NaN Budgetbeitrag von BMS 2017-07-28 1468.00 \n",
"76 NaN Miete FFM auf Max Konto 2017-08-02 450.00 \n",
"77 NaN Ausgehen mit Kollegen 2017-08-02 -20.60 \n",
"78 NaN Abheben 2017-08-03 -30.00 \n",
"79 NaN Sommerticket DB 2017-08-05 -96.00 \n",
"80 NaN Sommerticket DB 2017-08-05 -96.00 \n",
"81 NaN Amazon Fail Topf 2017-08-06 -4.61 \n",
"82 NaN Handy Paul 2017-08-10 -13.99 \n",
"83 NaN Rossmann 2017-08-10 -20.53 \n",
"\n",
" money_pot \n",
"0 KG \n",
"1 KG \n",
"2 KG \n",
"3 NaN \n",
"4 NaN \n",
"5 NaN \n",
"6 NaN \n",
"7 KG \n",
"8 NaN \n",
"9 NaN \n",
"10 NaN \n",
"11 KG \n",
"12 NaN \n",
"13 NaN \n",
"14 NaN \n",
"15 NaN \n",
"16 NaN \n",
"17 KG \n",
"18 KG \n",
"19 KG \n",
"20 KG \n",
"21 KG \n",
"22 KG \n",
"23 KG \n",
"24 KG \n",
"25 NaN \n",
"26 KG \n",
"27 NaN \n",
"28 NaN \n",
"29 NaN \n",
".. ... \n",
"54 NaN \n",
"55 NaN \n",
"56 NaN \n",
"57 NaN \n",
"58 NaN \n",
"59 NaN \n",
"60 NaN \n",
"61 NaN \n",
"62 BM \n",
"63 BM \n",
"64 KG \n",
"65 KG \n",
"66 KG \n",
"67 BM \n",
"68 BM \n",
"69 BM \n",
"70 BM \n",
"71 BM \n",
"72 BM \n",
"73 BP \n",
"74 BP \n",
"75 KG \n",
"76 KG \n",
"77 KG \n",
"78 KG \n",
"79 KG \n",
"80 KG \n",
"81 KG \n",
"82 KG \n",
"83 KG \n",
"\n",
"[84 rows x 5 columns]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>budget_type</th>\n",
" <th>description</th>\n",
" <th>date</th>\n",
" <th>amount</th>\n",
" <th>money_pot</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>R</td>\n",
" <td>Miete</td>\n",
" <td>2017-08-01</td>\n",
" <td>-568.00</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>R</td>\n",
" <td>Miete FFM</td>\n",
" <td>2017-08-02</td>\n",
" <td>-450.00</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>R</td>\n",
" <td>Berufsunfähigkeitsversicherung</td>\n",
" <td>2017-08-01</td>\n",
" <td>-49.05</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>R</td>\n",
" <td>Haftpflichtversicherung</td>\n",
" <td>2017-08-01</td>\n",
" <td>-7.50</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>R</td>\n",
" <td>Handy Max</td>\n",
" <td>2017-08-10</td>\n",
" <td>-7.99</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>G</td>\n",
" <td>Fitnessstudio</td>\n",
" <td>2017-08-03</td>\n",
" <td>-39.80</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>L</td>\n",
" <td>Aldi</td>\n",
" <td>2017-07-31</td>\n",
" <td>-23.97</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>R</td>\n",
" <td>Monatskarte FFM</td>\n",
" <td>2017-07-31</td>\n",
" <td>-87.40</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>L</td>\n",
" <td>Netto</td>\n",
" <td>2017-07-31</td>\n",
" <td>-9.44</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>L</td>\n",
" <td>Rewe</td>\n",
" <td>2017-08-01</td>\n",
" <td>-8.71</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>D</td>\n",
" <td>Rossmann</td>\n",
" <td>2017-08-01</td>\n",
" <td>-9.17</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>D</td>\n",
" <td>DM</td>\n",
" <td>2017-08-01</td>\n",
" <td>-9.45</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>D</td>\n",
" <td>DM</td>\n",
" <td>2017-08-01</td>\n",
" <td>-12.25</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>L</td>\n",
" <td>Türkischer Supermarkt</td>\n",
" <td>2017-08-01</td>\n",
" <td>-10.37</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>L</td>\n",
" <td>Real</td>\n",
" <td>2017-08-02</td>\n",
" <td>-6.86</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>L</td>\n",
" <td>Kaufland</td>\n",
" <td>2017-08-04</td>\n",
" <td>-13.95</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>A</td>\n",
" <td>McDonalds</td>\n",
" <td>2017-08-05</td>\n",
" <td>-2.59</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>S</td>\n",
" <td>H&M</td>\n",
" <td>2017-08-05</td>\n",
" <td>-55.08</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>L</td>\n",
" <td>Rewe Wasser</td>\n",
" <td>2017-08-05</td>\n",
" <td>-1.17</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>L</td>\n",
" <td>Edeka</td>\n",
" <td>2017-08-05</td>\n",
" <td>-5.43</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>T</td>\n",
" <td>Sitzplatz reservierung</td>\n",
" <td>2017-08-06</td>\n",
" <td>-4.50</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>S</td>\n",
" <td>Amazon Bestellung</td>\n",
" <td>2017-08-06</td>\n",
" <td>-39.24</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>L</td>\n",
" <td>Rewe</td>\n",
" <td>2017-08-07</td>\n",
" <td>-21.00</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>S</td>\n",
" <td>H&M Hose</td>\n",
" <td>2017-08-07</td>\n",
" <td>-30.14</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>M</td>\n",
" <td>Briefmarken</td>\n",
" <td>2017-08-07</td>\n",
" <td>-21.50</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>L</td>\n",
" <td>Netto</td>\n",
" <td>2017-08-07</td>\n",
" <td>-16.60</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>L</td>\n",
" <td>Real</td>\n",
" <td>2017-08-07</td>\n",
" <td>-3.99</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>S</td>\n",
" <td>Amazon Festplatte Paul</td>\n",
" <td>2017-08-06</td>\n",
" <td>-52.94</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>A</td>\n",
" <td>Eisessen</td>\n",
" <td>2017-08-13</td>\n",
" <td>-4.80</td>\n",
" <td>BM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>L</td>\n",
" <td>Wasser</td>\n",
" <td>2017-08-13</td>\n",
" <td>-1.30</td>\n",
" <td>BM</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>L</td>\n",
" <td>Aldi</td>\n",
" <td>2017-08-11</td>\n",
" <td>-19.31</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>D</td>\n",
" <td>Schirm Rossmann</td>\n",
" <td>2017-08-11</td>\n",
" <td>-2.95</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>L</td>\n",
" <td>Rewe</td>\n",
" <td>2017-08-13</td>\n",
" <td>-34.42</td>\n",
" <td>KG</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" budget_type description date amount money_pot\n",
"0 R Miete 2017-08-01 -568.00 KG\n",
"1 R Miete FFM 2017-08-02 -450.00 KG\n",
"2 R Berufsunfähigkeitsversicherung 2017-08-01 -49.05 KG\n",
"7 R Haftpflichtversicherung 2017-08-01 -7.50 KG\n",
"11 R Handy Max 2017-08-10 -7.99 KG\n",
"17 G Fitnessstudio 2017-08-03 -39.80 KG\n",
"18 L Aldi 2017-07-31 -23.97 KG\n",
"19 R Monatskarte FFM 2017-07-31 -87.40 KG\n",
"20 L Netto 2017-07-31 -9.44 KG\n",
"21 L Rewe 2017-08-01 -8.71 KG\n",
"22 D Rossmann 2017-08-01 -9.17 KG\n",
"23 D DM 2017-08-01 -9.45 KG\n",
"24 D DM 2017-08-01 -12.25 KG\n",
"26 L Türkischer Supermarkt 2017-08-01 -10.37 KG\n",
"31 L Real 2017-08-02 -6.86 KG\n",
"38 L Kaufland 2017-08-04 -13.95 KG\n",
"40 A McDonalds 2017-08-05 -2.59 KG\n",
"41 S H&M 2017-08-05 -55.08 KG\n",
"42 L Rewe Wasser 2017-08-05 -1.17 KG\n",
"43 L Edeka 2017-08-05 -5.43 KG\n",
"45 T Sitzplatz reservierung 2017-08-06 -4.50 KG\n",
"46 S Amazon Bestellung 2017-08-06 -39.24 KG\n",
"47 L Rewe 2017-08-07 -21.00 KG\n",
"48 S H&M Hose 2017-08-07 -30.14 KG\n",
"49 M Briefmarken 2017-08-07 -21.50 KG\n",
"50 L Netto 2017-08-07 -16.60 KG\n",
"51 L Real 2017-08-07 -3.99 KG\n",
"52 S Amazon Festplatte Paul 2017-08-06 -52.94 KG\n",
"62 A Eisessen 2017-08-13 -4.80 BM\n",
"63 L Wasser 2017-08-13 -1.30 BM\n",
"64 L Aldi 2017-08-11 -19.31 KG\n",
"65 D Schirm Rossmann 2017-08-11 -2.95 KG\n",
"66 L Rewe 2017-08-13 -34.42 KG"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"all_info = pd.merge(budgeting, conjoined, how='outer', \n",
" on=['description', 'date', 'amount'])\n",
"\n",
"display(all_info)\n",
"\n",
"perfect_result = all_info.dropna()\n",
"display(perfect_result)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sqlite3 as sql\n",
"db = sql.connect('my-budget-v0.sqlite')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def id_generation(date) :\n",
" date_int = int(date.strftime('%Y%m%d'))\n",
" crsr = db.cursor()\n",
" crsr.execute('SELECT id FROM money_events WHERE id BETWEEN {} AND {}'.format(\n",
" date_int, date_int + 99))\n",
" results = [row[0] for row in crsr.fetchall()]\n",
"\n",
" current_id = date_int + 1\n",
" while current_id in results :\n",
" current_id += 1\n",
" if current_id > date_int + 99 :\n",
" raise IndexError('Encountered to many ids for the date {}'.format(date))\n",
"\n",
" return current_id"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def simple_payment(date, description, amount, budget_type, transaction_type, money_pot): \n",
" new_id = id_generation(date)\n",
" crsr = db.cursor()\n",
"\n",
" crsr.execute(\"INSERT INTO money_events VALUES ({}, '{}', '{}', DATE('{}'), NULL, NULL, 'auto-gen');\".format(\n",
" new_id, transaction_type, description, date))\n",
" crsr.execute(\"INSERT INTO budget_events VALUES ({}, '{}', {}, NULL, DATE('{}'), NULL, NULL, 'auto-gen');\".format(\n",
" new_id, budget_type, amount, date))\n",
" crsr.execute(\"INSERT INTO payments VALUES ({}, '{}', {}, NULL, NULL, DATE('{}'), NULL, NULL, 'auto-gen');\".format(\n",
" new_id, money_pot, amount, date))\n",
"\n",
" db.commit()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2017-08-01 00:00:00\n",
"2017-08-02 00:00:00\n",
"2017-08-01 00:00:00\n",
"2017-08-01 00:00:00\n",
"2017-08-10 00:00:00\n",
"2017-08-03 00:00:00\n",
"2017-07-31 00:00:00\n",
"2017-07-31 00:00:00\n",
"2017-07-31 00:00:00\n",
"2017-08-01 00:00:00\n",
"2017-08-01 00:00:00\n",
"2017-08-01 00:00:00\n",
"2017-08-01 00:00:00\n",
"2017-08-01 00:00:00\n",
"2017-08-02 00:00:00\n",
"2017-08-04 00:00:00\n",
"2017-08-05 00:00:00\n",
"2017-08-05 00:00:00\n",
"2017-08-05 00:00:00\n",
"2017-08-05 00:00:00\n",
"2017-08-06 00:00:00\n",
"2017-08-06 00:00:00\n",
"2017-08-07 00:00:00\n",
"2017-08-07 00:00:00\n",
"2017-08-07 00:00:00\n",
"2017-08-07 00:00:00\n",
"2017-08-07 00:00:00\n",
"2017-08-06 00:00:00\n",
"2017-08-13 00:00:00\n",
"2017-08-13 00:00:00\n",
"2017-08-11 00:00:00\n",
"2017-08-11 00:00:00\n",
"2017-08-13 00:00:00\n"
]
}
],
"source": [
"for index, row in perfect_result.iterrows() :\n",
" simple_payment(row['date'], row['description'], row['amount'], row['budget_type'], \n",
" 'Kartenzahlung' if row['money_pot'] == 'KG' else 'Barzahlung', row['money_pot'])"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>type</th>\n",
" <th>description</th>\n",
" <th>creation_date</th>\n",
" <th>modification_dates</th>\n",
" <th>comments</th>\n",
" <th>complete</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20170732</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Aldi</td>\n",
" <td>2017-07-31</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20170733</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Monatskarte FFM</td>\n",
" <td>2017-07-31</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>20170734</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Netto</td>\n",
" <td>2017-07-31</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>20170802</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Miete</td>\n",
" <td>2017-08-01</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>20170803</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Miete FFM</td>\n",
" <td>2017-08-02</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>20170804</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Berufsunfähigkeitsversicherung</td>\n",
" <td>2017-08-01</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>20170805</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Haftpflichtversicherung</td>\n",
" <td>2017-08-01</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>20170806</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Fitnessstudio</td>\n",
" <td>2017-08-03</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>20170807</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Rewe</td>\n",
" <td>2017-08-01</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>20170808</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Rossmann</td>\n",
" <td>2017-08-01</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>20170809</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>DM</td>\n",
" <td>2017-08-01</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>20170810</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>DM</td>\n",
" <td>2017-08-01</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>20170811</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Handy Max</td>\n",
" <td>2017-08-10</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>20170812</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Türkischer Supermarkt</td>\n",
" <td>2017-08-01</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>20170813</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Real</td>\n",
" <td>2017-08-02</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>20170814</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Kaufland</td>\n",
" <td>2017-08-04</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>20170815</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>McDonalds</td>\n",
" <td>2017-08-05</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>20170816</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>H&M</td>\n",
" <td>2017-08-05</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>20170817</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Rewe Wasser</td>\n",
" <td>2017-08-05</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20170818</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Edeka</td>\n",
" <td>2017-08-05</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>20170819</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Sitzplatz reservierung</td>\n",
" <td>2017-08-06</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>20170820</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Amazon Bestellung</td>\n",
" <td>2017-08-06</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>20170821</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Rewe</td>\n",
" <td>2017-08-07</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>20170822</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>H&M Hose</td>\n",
" <td>2017-08-07</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>20170823</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Briefmarken</td>\n",
" <td>2017-08-07</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>20170824</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Netto</td>\n",
" <td>2017-08-07</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>20170825</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Real</td>\n",
" <td>2017-08-07</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>20170826</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Amazon Festplatte Paul</td>\n",
" <td>2017-08-06</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>20170827</td>\n",
" <td>Barzahlung</td>\n",
" <td>Eisessen</td>\n",
" <td>2017-08-13</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>20170828</td>\n",
" <td>Barzahlung</td>\n",
" <td>Wasser</td>\n",
" <td>2017-08-13</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>20170829</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Aldi</td>\n",
" <td>2017-08-11</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>20170830</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Schirm Rossmann</td>\n",
" <td>2017-08-11</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>20170831</td>\n",
" <td>Kartenzahlung</td>\n",
" <td>Rewe</td>\n",
" <td>2017-08-13</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id type description creation_date \\\n",
"0 20170732 Kartenzahlung Aldi 2017-07-31 \n",
"1 20170733 Kartenzahlung Monatskarte FFM 2017-07-31 \n",
"2 20170734 Kartenzahlung Netto 2017-07-31 \n",
"3 20170802 Kartenzahlung Miete 2017-08-01 \n",
"4 20170803 Kartenzahlung Miete FFM 2017-08-02 \n",
"5 20170804 Kartenzahlung Berufsunfähigkeitsversicherung 2017-08-01 \n",
"6 20170805 Kartenzahlung Haftpflichtversicherung 2017-08-01 \n",
"7 20170806 Kartenzahlung Fitnessstudio 2017-08-03 \n",
"8 20170807 Kartenzahlung Rewe 2017-08-01 \n",
"9 20170808 Kartenzahlung Rossmann 2017-08-01 \n",
"10 20170809 Kartenzahlung DM 2017-08-01 \n",
"11 20170810 Kartenzahlung DM 2017-08-01 \n",
"12 20170811 Kartenzahlung Handy Max 2017-08-10 \n",
"13 20170812 Kartenzahlung Türkischer Supermarkt 2017-08-01 \n",
"14 20170813 Kartenzahlung Real 2017-08-02 \n",
"15 20170814 Kartenzahlung Kaufland 2017-08-04 \n",
"16 20170815 Kartenzahlung McDonalds 2017-08-05 \n",
"17 20170816 Kartenzahlung H&M 2017-08-05 \n",
"18 20170817 Kartenzahlung Rewe Wasser 2017-08-05 \n",
"19 20170818 Kartenzahlung Edeka 2017-08-05 \n",
"20 20170819 Kartenzahlung Sitzplatz reservierung 2017-08-06 \n",
"21 20170820 Kartenzahlung Amazon Bestellung 2017-08-06 \n",
"22 20170821 Kartenzahlung Rewe 2017-08-07 \n",
"23 20170822 Kartenzahlung H&M Hose 2017-08-07 \n",
"24 20170823 Kartenzahlung Briefmarken 2017-08-07 \n",
"25 20170824 Kartenzahlung Netto 2017-08-07 \n",
"26 20170825 Kartenzahlung Real 2017-08-07 \n",
"27 20170826 Kartenzahlung Amazon Festplatte Paul 2017-08-06 \n",
"28 20170827 Barzahlung Eisessen 2017-08-13 \n",
"29 20170828 Barzahlung Wasser 2017-08-13 \n",
"30 20170829 Kartenzahlung Aldi 2017-08-11 \n",
"31 20170830 Kartenzahlung Schirm Rossmann 2017-08-11 \n",
"32 20170831 Kartenzahlung Rewe 2017-08-13 \n",
"\n",
" modification_dates comments complete \n",
"0 None None YES \n",
"1 None None YES \n",
"2 None None YES \n",
"3 None None YES \n",
"4 None None YES \n",
"5 None None YES \n",
"6 None None YES \n",
"7 None None YES \n",
"8 None None YES \n",
"9 None None YES \n",
"10 None None YES \n",
"11 None None YES \n",
"12 None None YES \n",
"13 None None YES \n",
"14 None None YES \n",
"15 None None YES \n",
"16 None None YES \n",
"17 None None YES \n",
"18 None None YES \n",
"19 None None YES \n",
"20 None None YES \n",
"21 None None YES \n",
"22 None None YES \n",
"23 None None YES \n",
"24 None None YES \n",
"25 None None YES \n",
"26 None None YES \n",
"27 None None YES \n",
"28 None None YES \n",
"29 None None YES \n",
"30 None None YES \n",
"31 None None YES \n",
"32 None None YES "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>budget_pot</th>\n",
" <th>amount</th>\n",
" <th>additional_description</th>\n",
" <th>creation_date</th>\n",
" <th>modification_dates</th>\n",
" <th>comments</th>\n",
" <th>complete</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20170802</td>\n",
" <td>R</td>\n",
" <td>-568.00</td>\n",
" <td>None</td>\n",
" <td>2017-08-01</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20170803</td>\n",
" <td>R</td>\n",
" <td>-450.00</td>\n",
" <td>None</td>\n",
" <td>2017-08-02</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>20170804</td>\n",
" <td>R</td>\n",
" <td>-49.05</td>\n",
" <td>None</td>\n",
" <td>2017-08-01</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>20170805</td>\n",
" <td>R</td>\n",
" <td>-7.50</td>\n",
" <td>None</td>\n",
" <td>2017-08-01</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>20170811</td>\n",
" <td>R</td>\n",
" <td>-7.99</td>\n",
" <td>None</td>\n",
" <td>2017-08-10</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>20170806</td>\n",
" <td>G</td>\n",
" <td>-39.80</td>\n",
" <td>None</td>\n",
" <td>2017-08-03</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>20170732</td>\n",
" <td>L</td>\n",
" <td>-23.97</td>\n",
" <td>None</td>\n",
" <td>2017-07-31</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>20170733</td>\n",
" <td>R</td>\n",
" <td>-87.40</td>\n",
" <td>None</td>\n",
" <td>2017-07-31</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>20170734</td>\n",
" <td>L</td>\n",
" <td>-9.44</td>\n",
" <td>None</td>\n",
" <td>2017-07-31</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>20170807</td>\n",
" <td>L</td>\n",
" <td>-8.71</td>\n",
" <td>None</td>\n",
" <td>2017-08-01</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>20170808</td>\n",
" <td>D</td>\n",
" <td>-9.17</td>\n",
" <td>None</td>\n",
" <td>2017-08-01</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>20170809</td>\n",
" <td>D</td>\n",
" <td>-9.45</td>\n",
" <td>None</td>\n",
" <td>2017-08-01</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>20170810</td>\n",
" <td>D</td>\n",
" <td>-12.25</td>\n",
" <td>None</td>\n",
" <td>2017-08-01</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>20170812</td>\n",
" <td>L</td>\n",
" <td>-10.37</td>\n",
" <td>None</td>\n",
" <td>2017-08-01</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>20170813</td>\n",
" <td>L</td>\n",
" <td>-6.86</td>\n",
" <td>None</td>\n",
" <td>2017-08-02</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>20170814</td>\n",
" <td>L</td>\n",
" <td>-13.95</td>\n",
" <td>None</td>\n",
" <td>2017-08-04</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>20170815</td>\n",
" <td>A</td>\n",
" <td>-2.59</td>\n",
" <td>None</td>\n",
" <td>2017-08-05</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>20170816</td>\n",
" <td>S</td>\n",
" <td>-55.08</td>\n",
" <td>None</td>\n",
" <td>2017-08-05</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>20170817</td>\n",
" <td>L</td>\n",
" <td>-1.17</td>\n",
" <td>None</td>\n",
" <td>2017-08-05</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20170818</td>\n",
" <td>L</td>\n",
" <td>-5.43</td>\n",
" <td>None</td>\n",
" <td>2017-08-05</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>20170819</td>\n",
" <td>T</td>\n",
" <td>-4.50</td>\n",
" <td>None</td>\n",
" <td>2017-08-06</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>20170820</td>\n",
" <td>S</td>\n",
" <td>-39.24</td>\n",
" <td>None</td>\n",
" <td>2017-08-06</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>20170821</td>\n",
" <td>L</td>\n",
" <td>-21.00</td>\n",
" <td>None</td>\n",
" <td>2017-08-07</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>20170822</td>\n",
" <td>S</td>\n",
" <td>-30.14</td>\n",
" <td>None</td>\n",
" <td>2017-08-07</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>20170823</td>\n",
" <td>M</td>\n",
" <td>-21.50</td>\n",
" <td>None</td>\n",
" <td>2017-08-07</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>20170824</td>\n",
" <td>L</td>\n",
" <td>-16.60</td>\n",
" <td>None</td>\n",
" <td>2017-08-07</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>20170825</td>\n",
" <td>L</td>\n",
" <td>-3.99</td>\n",
" <td>None</td>\n",
" <td>2017-08-07</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>20170826</td>\n",
" <td>S</td>\n",
" <td>-52.94</td>\n",
" <td>None</td>\n",
" <td>2017-08-06</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>20170827</td>\n",
" <td>A</td>\n",
" <td>-4.80</td>\n",
" <td>None</td>\n",
" <td>2017-08-13</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>20170828</td>\n",
" <td>L</td>\n",
" <td>-1.30</td>\n",
" <td>None</td>\n",
" <td>2017-08-13</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>20170829</td>\n",
" <td>L</td>\n",
" <td>-19.31</td>\n",
" <td>None</td>\n",
" <td>2017-08-11</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>20170830</td>\n",
" <td>D</td>\n",
" <td>-2.95</td>\n",
" <td>None</td>\n",
" <td>2017-08-11</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>20170831</td>\n",
" <td>L</td>\n",
" <td>-34.42</td>\n",
" <td>None</td>\n",
" <td>2017-08-13</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id budget_pot amount additional_description creation_date \\\n",
"0 20170802 R -568.00 None 2017-08-01 \n",
"1 20170803 R -450.00 None 2017-08-02 \n",
"2 20170804 R -49.05 None 2017-08-01 \n",
"3 20170805 R -7.50 None 2017-08-01 \n",
"4 20170811 R -7.99 None 2017-08-10 \n",
"5 20170806 G -39.80 None 2017-08-03 \n",
"6 20170732 L -23.97 None 2017-07-31 \n",
"7 20170733 R -87.40 None 2017-07-31 \n",
"8 20170734 L -9.44 None 2017-07-31 \n",
"9 20170807 L -8.71 None 2017-08-01 \n",
"10 20170808 D -9.17 None 2017-08-01 \n",
"11 20170809 D -9.45 None 2017-08-01 \n",
"12 20170810 D -12.25 None 2017-08-01 \n",
"13 20170812 L -10.37 None 2017-08-01 \n",
"14 20170813 L -6.86 None 2017-08-02 \n",
"15 20170814 L -13.95 None 2017-08-04 \n",
"16 20170815 A -2.59 None 2017-08-05 \n",
"17 20170816 S -55.08 None 2017-08-05 \n",
"18 20170817 L -1.17 None 2017-08-05 \n",
"19 20170818 L -5.43 None 2017-08-05 \n",
"20 20170819 T -4.50 None 2017-08-06 \n",
"21 20170820 S -39.24 None 2017-08-06 \n",
"22 20170821 L -21.00 None 2017-08-07 \n",
"23 20170822 S -30.14 None 2017-08-07 \n",
"24 20170823 M -21.50 None 2017-08-07 \n",
"25 20170824 L -16.60 None 2017-08-07 \n",
"26 20170825 L -3.99 None 2017-08-07 \n",
"27 20170826 S -52.94 None 2017-08-06 \n",
"28 20170827 A -4.80 None 2017-08-13 \n",
"29 20170828 L -1.30 None 2017-08-13 \n",
"30 20170829 L -19.31 None 2017-08-11 \n",
"31 20170830 D -2.95 None 2017-08-11 \n",
"32 20170831 L -34.42 None 2017-08-13 \n",
"\n",
" modification_dates comments complete \n",
"0 None None YES \n",
"1 None None YES \n",
"2 None None YES \n",
"3 None None YES \n",
"4 None None YES \n",
"5 None None YES \n",
"6 None None YES \n",
"7 None None YES \n",
"8 None None YES \n",
"9 None None YES \n",
"10 None None YES \n",
"11 None None YES \n",
"12 None None YES \n",
"13 None None YES \n",
"14 None None YES \n",
"15 None None YES \n",
"16 None None YES \n",
"17 None None YES \n",
"18 None None YES \n",
"19 None None YES \n",
"20 None None YES \n",
"21 None None YES \n",
"22 None None YES \n",
"23 None None YES \n",
"24 None None YES \n",
"25 None None YES \n",
"26 None None YES \n",
"27 None None YES \n",
"28 None None YES \n",
"29 None None YES \n",
"30 None None YES \n",
"31 None None YES \n",
"32 None None YES "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>money_pot</th>\n",
" <th>amount</th>\n",
" <th>additional_description</th>\n",
" <th>budget_effect_date</th>\n",
" <th>creation_date</th>\n",
" <th>modification_dates</th>\n",
" <th>comments</th>\n",
" <th>complete</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20170802</td>\n",
" <td>KG</td>\n",
" <td>-568.00</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-01</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20170803</td>\n",
" <td>KG</td>\n",
" <td>-450.00</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-02</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>20170804</td>\n",
" <td>KG</td>\n",
" <td>-49.05</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-01</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>20170805</td>\n",
" <td>KG</td>\n",
" <td>-7.50</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-01</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>20170811</td>\n",
" <td>KG</td>\n",
" <td>-7.99</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-10</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>20170806</td>\n",
" <td>KG</td>\n",
" <td>-39.80</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-03</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>20170732</td>\n",
" <td>KG</td>\n",
" <td>-23.97</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-07-31</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>20170733</td>\n",
" <td>KG</td>\n",
" <td>-87.40</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-07-31</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>20170734</td>\n",
" <td>KG</td>\n",
" <td>-9.44</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-07-31</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>20170807</td>\n",
" <td>KG</td>\n",
" <td>-8.71</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-01</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>20170808</td>\n",
" <td>KG</td>\n",
" <td>-9.17</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-01</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>20170809</td>\n",
" <td>KG</td>\n",
" <td>-9.45</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-01</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>20170810</td>\n",
" <td>KG</td>\n",
" <td>-12.25</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-01</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>20170812</td>\n",
" <td>KG</td>\n",
" <td>-10.37</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-01</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>20170813</td>\n",
" <td>KG</td>\n",
" <td>-6.86</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-02</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>20170814</td>\n",
" <td>KG</td>\n",
" <td>-13.95</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-04</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>20170815</td>\n",
" <td>KG</td>\n",
" <td>-2.59</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-05</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>20170816</td>\n",
" <td>KG</td>\n",
" <td>-55.08</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-05</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>20170817</td>\n",
" <td>KG</td>\n",
" <td>-1.17</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-05</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20170818</td>\n",
" <td>KG</td>\n",
" <td>-5.43</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-05</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>20170819</td>\n",
" <td>KG</td>\n",
" <td>-4.50</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-06</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>20170820</td>\n",
" <td>KG</td>\n",
" <td>-39.24</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-06</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>20170821</td>\n",
" <td>KG</td>\n",
" <td>-21.00</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-07</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>20170822</td>\n",
" <td>KG</td>\n",
" <td>-30.14</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-07</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>20170823</td>\n",
" <td>KG</td>\n",
" <td>-21.50</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-07</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>20170824</td>\n",
" <td>KG</td>\n",
" <td>-16.60</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-07</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>20170825</td>\n",
" <td>KG</td>\n",
" <td>-3.99</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-07</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>20170826</td>\n",
" <td>KG</td>\n",
" <td>-52.94</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-06</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>20170827</td>\n",
" <td>BM</td>\n",
" <td>-4.80</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-13</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>20170828</td>\n",
" <td>BM</td>\n",
" <td>-1.30</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-13</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>20170829</td>\n",
" <td>KG</td>\n",
" <td>-19.31</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-11</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>20170830</td>\n",
" <td>KG</td>\n",
" <td>-2.95</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-11</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>20170831</td>\n",
" <td>KG</td>\n",
" <td>-34.42</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>2017-08-13</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>YES</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id money_pot amount additional_description budget_effect_date \\\n",
"0 20170802 KG -568.00 None None \n",
"1 20170803 KG -450.00 None None \n",
"2 20170804 KG -49.05 None None \n",
"3 20170805 KG -7.50 None None \n",
"4 20170811 KG -7.99 None None \n",
"5 20170806 KG -39.80 None None \n",
"6 20170732 KG -23.97 None None \n",
"7 20170733 KG -87.40 None None \n",
"8 20170734 KG -9.44 None None \n",
"9 20170807 KG -8.71 None None \n",
"10 20170808 KG -9.17 None None \n",
"11 20170809 KG -9.45 None None \n",
"12 20170810 KG -12.25 None None \n",
"13 20170812 KG -10.37 None None \n",
"14 20170813 KG -6.86 None None \n",
"15 20170814 KG -13.95 None None \n",
"16 20170815 KG -2.59 None None \n",
"17 20170816 KG -55.08 None None \n",
"18 20170817 KG -1.17 None None \n",
"19 20170818 KG -5.43 None None \n",
"20 20170819 KG -4.50 None None \n",
"21 20170820 KG -39.24 None None \n",
"22 20170821 KG -21.00 None None \n",
"23 20170822 KG -30.14 None None \n",
"24 20170823 KG -21.50 None None \n",
"25 20170824 KG -16.60 None None \n",
"26 20170825 KG -3.99 None None \n",
"27 20170826 KG -52.94 None None \n",
"28 20170827 BM -4.80 None None \n",
"29 20170828 BM -1.30 None None \n",
"30 20170829 KG -19.31 None None \n",
"31 20170830 KG -2.95 None None \n",
"32 20170831 KG -34.42 None None \n",
"\n",
" creation_date modification_dates comments complete \n",
"0 2017-08-01 None None YES \n",
"1 2017-08-02 None None YES \n",
"2 2017-08-01 None None YES \n",
"3 2017-08-01 None None YES \n",
"4 2017-08-10 None None YES \n",
"5 2017-08-03 None None YES \n",
"6 2017-07-31 None None YES \n",
"7 2017-07-31 None None YES \n",
"8 2017-07-31 None None YES \n",
"9 2017-08-01 None None YES \n",
"10 2017-08-01 None None YES \n",
"11 2017-08-01 None None YES \n",
"12 2017-08-01 None None YES \n",
"13 2017-08-01 None None YES \n",
"14 2017-08-02 None None YES \n",
"15 2017-08-04 None None YES \n",
"16 2017-08-05 None None YES \n",
"17 2017-08-05 None None YES \n",
"18 2017-08-05 None None YES \n",
"19 2017-08-05 None None YES \n",
"20 2017-08-06 None None YES \n",
"21 2017-08-06 None None YES \n",
"22 2017-08-07 None None YES \n",
"23 2017-08-07 None None YES \n",
"24 2017-08-07 None None YES \n",
"25 2017-08-07 None None YES \n",
"26 2017-08-07 None None YES \n",
"27 2017-08-06 None None YES \n",
"28 2017-08-13 None None YES \n",
"29 2017-08-13 None None YES \n",
"30 2017-08-11 None None YES \n",
"31 2017-08-11 None None YES \n",
"32 2017-08-13 None None YES "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def print_action_tables() :\n",
" crsr = db.cursor()\n",
"\n",
" for table in ['money_events', 'budget_events', 'payments'] :\n",
" display(pd.read_sql_query('SELECT * FROM {};'.format(table), db))\n",
" \n",
"print_action_tables()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
qutip/qutip-notebooks | docs/TableOfContents.ipynb | 2 | 15839 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<center><img src='images/TableOfContents/logo.png'></center>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1 style='font-size:42px'>QuTiP: Quantum Toolbox in Python</h1>\n",
"\n",
"<h3>\n",
"<p>P. D. Nation & R. J. Johansson</p>\n",
"<p>Version 3.2</p>\n",
"<p>July XX, 2015</p>\n",
"</h3>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Table of Contents"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- [Frontmatter](FrontMatter.ipynb)\n",
" \n",
" - [About QuTiP](FrontMatter.ipynb#about)\n",
" - [About This Documentation](FrontMatter.ipynb#doc)\n",
" - [Citing QuTiP](FrontMatter.ipynb#cite)\n",
" - [Funding](FrontMatter.ipynb#funding)\n",
" - [Contributing](FrontMatter.ipynb#contributing)\n",
" \n",
"- [Installation](Installation.ipynb)\n",
" - [General Requirements](Installation.ipynb#require)\n",
" - [Basic Installation](Installation.ipynb#basic)\n",
" - [Installing From Source](Installation.ipynb#source)\n",
" - [Installing on Linux](Installation.ipynb#linux)\n",
" - [Installing on Mac](Installation.ipynb#mac)\n",
" - [Installing on Windows](Installation.ipynb#win)\n",
" - [Optional Install Items](Installation.ipynb#optional)\n",
" - [Verifying Installation](Installation.ipynb#verify)\n",
" - [Checking Version Information](Installation.ipynb#about)\n",
"- [Users Guide](guide/UsersGuide.ipynb)\n",
" - [Guide Overview]()\n",
" - [Basic Operations on Quantum Objects](guide/BasicOperations.ipynb)\n",
" - [Manipulating Quantum States & Operators](guide/StatesOperators.ipynb)\n",
" - [Tensor Products and Partial Traces](guide/TensorPtrace.ipynb)\n",
" - [Time Evolution and Quantum Dynamics]()\n",
" - [Steady State Solutions](guide/SteadyState.ipynb)\n",
" - [Overview of Eseries Class](guide/Eseries.ipynb)\n",
" - [Correlation Functions](guide/CorrelationFunctions.ipynb)\n",
" - [Bloch Sphere]()\n",
" - [Visualizing Quantum States and Processes](guide/Visualization.ipynb)\n",
" - [Parallel Computations](guide/Parallel.ipynb)\n",
" - [Saving QuTiP Objects and Data Sets]()\n",
" - [Random Quantum Objects]()\n",
" - [Modifying Internal Settings](guide/Settings.ipynb)\n",
"- [Change Log](ChangeLog.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<style>\n",
"@import url('http://fonts.googleapis.com/css?family=Source+Sans+Pro');\n",
"@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
"\n",
"body {\n",
" -webkit-font-smoothing: antialiased;\n",
" font-family: \"Source Sans Pro\", Helvetica, Arial, Verdana, sans-serif;\n",
"}\n",
"\n",
"\n",
"div.cell{\n",
" width:768px;\n",
" margin-left:10% !important;\n",
" margin-right:auto;\n",
"}\n",
"h1 {\n",
" font-family: \"Source Sans Pro\", ,Helvetica, Arial, serif;\n",
"\n",
"}\n",
"\n",
"h4{\n",
" font-family: \"Source Sans Pro\", Helvetica, Arial, serif;\n",
"\tmargin-top:12px;\n",
" margin-bottom: 3px;\n",
" }\n",
"\n",
"\n",
"div.text_cell_render{\n",
" font-family: \"Source Sans Pro\", Helvetica, Arial, sans-serif;\n",
" line-height: 125%;\n",
" font-size: 110%;\n",
" width:768px;\n",
" margin-left:auto;\n",
" margin-right:auto;\n",
"}\n",
"\n",
"div.cell.code_cell { /* area that contains code + output */\n",
" background: #fff;\n",
" border: none;\n",
" border-radius: 10px;\n",
" padding-top: 1ex;\n",
"}\n",
"\n",
"div.input_area { /* box around box with code */\n",
" border: none;\n",
" background: #f5f5f5;\n",
" border: 1px solid #ccc;\n",
" border-radius: 10px;\n",
" padding-top: 0.5ex;\n",
" padding-bottom: 0.5ex;\n",
" padding-left: 0.5em;\n",
"}\n",
"\n",
"div.prompt { /* remove In/Out prompt */\n",
" display: none;\n",
"}\n",
"\n",
"div.cell.border-box-sizing.code_cell.running { \n",
" /* draw border around running cells */\n",
" border: 3px dotted #f33;\n",
"}\n",
"\n",
".CodeMirror{\n",
" font-family: \"Source Code Pro\", Consolas, monospace;\n",
"}\n",
".prompt{\n",
" display: None;\n",
"}\n",
".text_cell_render h5 {\n",
" font-family: \"Source Sans Pro\", Helvetica, Arial, serif;\n",
" font-size: 20pt;\n",
" color: #0001E0;\n",
" margin-bottom: .5em;\n",
" margin-top: 0.5em;\n",
" display: block;\n",
"}\n",
"\n",
".warning{\n",
" color: rgb( 240, 20, 20 )\n",
" } \n",
"\n",
"a {\n",
" color: #0080FF;\n",
" text-decoration: none;\n",
" -webkit-transition: color 0.2s ease-in-out;\n",
" -moz-transition: color 0.2s ease-in-out;\n",
" -o-transition: color 0.2s ease-in-out;\n",
" -ms-transition: color 0.2s ease-in-out;\n",
" transition: color 0.2s ease-in-out;\n",
"}\n",
"a:hover {\n",
" color: #8C0028;\n",
"}\n",
"\n",
"li li{\n",
" font-size:14px;\n",
"}\n",
"\n",
"div.danger { \n",
" background-color: #F7A7AA;\n",
" border-color: #F1595F;\n",
" border: 2px solid #F1595F;\n",
" border-radius: 5px;\n",
" padding-top: 0.5ex;\n",
" padding-bottom: 0.5ex;\n",
" padding-left: 0.5em;\n",
" }\n",
"\n",
"div.warn { \n",
"background-color: #FBD1A7;\n",
"border-color: #F9A65A;\n",
"border: 2px solid #F9A65A;\n",
"border-radius: 5px;\n",
"padding-top: 0.5ex;\n",
"padding-bottom: 0.5ex;\n",
"padding-left: 0.5em;\n",
"}\n",
"\n",
"div.info { \n",
"background-color: #A6CBE9;\n",
"border-color: #599AD3;\n",
"border: 2px solid #599AD3;\n",
"border-radius: 5px;\n",
"padding-top: 0.5ex;\n",
"padding-bottom: 0.5ex;\n",
"padding-left: 0.5em;\n",
"}\n",
"\n",
"div.success { \n",
"background-color: #B9E0B0;\n",
"border-color: #79C36A;\n",
"border: 2px solid #79C36A;\n",
"border-radius: 5px;\n",
"padding-top: 0.5ex;\n",
"padding-bottom: 0.5ex;\n",
"padding-left: 0.5em;\n",
"}\n",
"\n",
"table a:link {\n",
" color: #666;\n",
" font-weight: bold;\n",
" text-decoration:none;\n",
"}\n",
"table a:visited {\n",
" color: #999999;\n",
" font-weight:bold;\n",
" text-decoration:none;\n",
"}\n",
"table a:active,\n",
"table a:hover {\n",
" color: #bd5a35;\n",
" text-decoration:underline;\n",
"}\n",
"table {\n",
" font-family:\"Source Sans Pro\", Helvetica, Arial, serif;\n",
" color:#666;\n",
" font-size:14px;\n",
" text-shadow: 1px 1px 0px #fff;\n",
" background:#eaebec;\n",
" margin:20px;\n",
" border:#ccc 1px solid;\n",
" border-spacing: 0;\n",
" -moz-border-radius:3px;\n",
" -webkit-border-radius:3px;\n",
" border-radius:3px;\n",
"\n",
" -moz-box-shadow: 0 1px 2px #d1d1d1;\n",
" -webkit-box-shadow: 0 1px 2px #d1d1d1;\n",
" box-shadow: 0 1px 2px #d1d1d1;\n",
"}\n",
"table th {\n",
" padding:21px 25px 22px 25px;\n",
" border-top:1px solid #fafafa;\n",
" border-bottom:1px solid #e0e0e0;\n",
"\n",
" background: #ededed;\n",
" background: -webkit-gradient(linear, left top, left bottom, from(#ededed), to(#ebebeb));\n",
" background: -moz-linear-gradient(top, #ededed, #ebebeb);\n",
"}\n",
"table th:first-child{\n",
" text-align: left;\n",
" padding-left:20px;\n",
"}\n",
"table tr:first-child th:first-child{\n",
" -moz-border-radius-topleft:3px;\n",
" -webkit-border-top-left-radius:3px;\n",
" border-top-left-radius:3px;\n",
"}\n",
"table tr:first-child th:last-child{\n",
" -moz-border-radius-topright:3px;\n",
" -webkit-border-top-right-radius:3px;\n",
" border-top-right-radius:3px;\n",
"}\n",
"table tr{\n",
" text-align: center;\n",
" padding-left:20px;\n",
"}\n",
"table tr td:first-child{\n",
" text-align: left;\n",
" padding-left:20px;\n",
" border-left: 0;\n",
"}\n",
"table tr td {\n",
" padding:18px;\n",
" border-top: 1px solid #ffffff;\n",
" border-bottom:1px solid #e0e0e0;\n",
" border-left: 1px solid #e0e0e0;\n",
"\n",
" background: #fafafa;\n",
" background: -webkit-gradient(linear, left top, left bottom, from(#fbfbfb), to(#fafafa));\n",
" background: -moz-linear-gradient(top, #fbfbfb, #fafafa);\n",
"}\n",
"\n",
"table tr:nth-child(2n) td {\n",
" background: #f6f6f6;\n",
" background: -webkit-gradient(linear, left top, left bottom, from(#f8f8f8), to(#f6f6f6));\n",
" background: -moz-linear-gradient(top, #f8f8f8, #f6f6f6);\n",
"}\n",
"\n",
"table tr:last-child td{\n",
" border-bottom:0;\n",
"}\n",
"table tr:last-child td:first-child{\n",
" -moz-border-radius-bottomleft:3px;\n",
" -webkit-border-bottom-left-radius:3px;\n",
" border-bottom-left-radius:3px;\n",
"}\n",
"table tr:last-child td:last-child{\n",
" -moz-border-radius-bottomright:3px;\n",
" -webkit-border-bottom-right-radius:3px;\n",
" border-bottom-right-radius:3px;\n",
"}\n",
"table tr:hover td{\n",
" background: #f2f2f2;\n",
" background: -webkit-gradient(linear, left top, left bottom, from(#f2f2f2), to(#f0f0f0));\n",
" background: -moz-linear-gradient(top, #f2f2f2, #f0f0f0);\t\n",
"}\n",
"\n",
"\n",
"caption {\n",
" display: table-caption;\n",
" font-weight: 700;\n",
"}\n",
"\n",
"figure {\n",
" display: inline-block;\n",
" position: relative;\n",
" margin: 1em 0 2em;\n",
"}\n",
"figcaption {\n",
" font-style: italic;\n",
" text-align: center;\n",
" background: white;\n",
" color: #666;\n",
" position: absolute;\n",
" left: 0;\n",
" bottom: -24px;\n",
" width: 98%;\n",
" padding: 1%;\n",
" -webkit-transition: all 0.2s ease-in-out;\n",
" -moz-transition: all 0.2s ease-in-out;\n",
" -o-transition: all 0.2s ease-in-out;\n",
" -ms-transition: all 0.2s ease-in-out;\n",
" transition: all 0.2s ease-in-out;\n",
"}\n",
"\n",
".prompt.input_prompt {\n",
" color: rgba(0,0,0,0.5);\n",
"}\n",
"\n",
".cell.command_mode.selected {\n",
" border-color: rgba(0,0,0,0.1);\n",
"}\n",
"\n",
".cell.edit_mode.selected {\n",
" border-color: rgba(0,0,0,0.15);\n",
" box-shadow: 0px 0px 5px #f0f0f0;\n",
" -webkit-box-shadow: 0px 0px 5px #f0f0f0;\n",
"}\n",
"\n",
"div.output_scroll {\n",
" -webkit-box-shadow: inset 0 2px 8px rgba(0,0,0,0.1);\n",
" box-shadow: inset 0 2px 8px rgba(0,0,0,0.1);\n",
" border-radious: 2px;\n",
"}\n",
"\n",
"#menubar .navbar-inner {\n",
" background: #fff;\n",
" -webkit-box-shadow: none;\n",
" box-shadow: none;\n",
" border-radius: 0;\n",
" border: none;\n",
" font-family: \"Source Sans Pro\", Helvetica, Arial, serif;\n",
" font-weight: 400;\n",
"}\n",
"\n",
".navbar-fixed-top .navbar-inner,\n",
".navbar-static-top .navbar-inner {\n",
" box-shadow: none;\n",
" -webkit-box-shadow: none;\n",
" border: none;\n",
"}\n",
"\n",
"div#notebook_panel {\n",
" box-shadow: none;\n",
" -webkit-box-shadow: none;\n",
" border-top: none;\n",
"}\n",
"\n",
"div#notebook {\n",
" border-top: 1px solid rgba(0,0,0,0.15);\n",
"}\n",
"\n",
"#menubar .navbar .navbar-inner,\n",
".toolbar-inner {\n",
" padding-left: 0;\n",
" padding-right: 0;\n",
"}\n",
"\n",
"#checkpoint_status,\n",
"#autosave_status {\n",
" color: rgba(0,0,0,0.5);\n",
"}\n",
"\n",
"#header {\n",
" font-family: \"Source Sans Pro\", Helvetica, Arial, serif;\n",
"}\n",
"\n",
"#notebook_name {\n",
" font-weight: 200;\n",
"}\n",
"\n",
"/* \n",
" This is a lazy fix, we *should* fix the \n",
" background for each Bootstrap button type\n",
"*/\n",
"#site * .btn {\n",
" background: #fafafa;\n",
" -webkit-box-shadow: none;\n",
" box-shadow: none;\n",
"}\n",
"\n",
"</style>\n",
"\n",
"<script>\n",
" MathJax.Hub.Config({\n",
" TeX: {equationNumbers: { autoNumber: \"AMS\" }, \n",
" extensions: [\"AMSmath.js\"]\n",
" },\n",
" tex2jax: {\n",
" inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
" displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
" },\n",
" displayAlign: 'center', // Change this to 'center' to center equations.\n",
" \"HTML-CSS\": {\n",
" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
" }\n",
" });\n",
"</script>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.core.display import HTML\n",
"def css_styling():\n",
" styles = open(\"styles/guide.css\", \"r\").read()\n",
" return HTML(styles)\n",
"css_styling()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| lgpl-3.0 |
Phononic/Sean_Lubner_250HWs | hw3/.ipynb_checkpoints/hw3_b_Lubner-checkpoint.ipynb | 1 | 3037 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"##### HW 3, problem 2 #####\n",
"# Sean Lubner\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"# import the data\n",
"google = np.loadtxt('hw_3_data/google_data.txt', skiprows=1)\n",
"ny = np.loadtxt('hw_3_data/ny_temps.txt', skiprows=1)\n",
"yahoo = np.loadtxt('hw_3_data/yahoo_data.txt', skiprows=1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Generate the initial plot (figure, axes, lines)\n",
"f1, ax1 = plt.subplots() \n",
"f1.set_size_inches(11.2,8.56)\n",
"yahoo_line, = ax1.plot(yahoo[:,0], yahoo[:,1], c='purple', label='Yahoo! Stock Value') \n",
"google_line, = ax1.plot(google[:,0], google[:,1], c='blue', label='Google Stock Value') \n",
"ny_line, = ax1.plot(ny[:,0], ny[:,1], c='red', label='NY Mon. High Temp') \n",
"ax1.set_title('Measured Thermal Conductivities: Water and \\nAgar Gel', size=20, fontweight='bold') \n",
"ax1.set_xlabel('Temperature, T ($^{\\circ}$C)', size=18, fontweight='bold') \n",
"ax1.set_ylabel('Thermal Conductivity, k (W/m-K)', size=18, fontweight='bold')\n",
"\n",
"# Format ticks, axes & labels\n",
"ax1.axis([-30, 35, 0, 4])\n",
"ax1.set_yticks([0,1,2,3,4])\n",
"ax1.yaxis.tick_left() # remove ticks at right\n",
"ax1.xaxis.tick_bottom() # remove ticks at top\n",
"plt.setp(ax1.get_xticklabels(), fontsize=14, weight='bold')\n",
"plt.setp(ax1.get_yticklabels(), fontsize=14, weight='bold')\n",
"ax1.tick_params('both', width=2, which='major')\n",
"\n",
"\n",
"# Format the lines\n",
"plt.setp(agar_line, ls=\"none\", marker='^', ms=11, mfc='lightgreen', mew=1.5, mec='green')\n",
"plt.setp(ice_line, ls=\"none\", marker='s', ms=11, mfc='lightblue', mew=1.5, mec='blue')\n",
"\n",
"# Render and format legend\n",
"l = ax1.legend([ice_line, agar_line, water_line], # control order of legend\n",
" [ice_line.get_label(), agar_line.get_label(), water_line.get_label()], \n",
" bbox_to_anchor=(0.98,0.8),\n",
" prop={'size':18}) # control location of legend\n",
"l.draw_frame(False) # no box\n",
"\n",
"# Add in \"a\" label\n",
"plt.annotate(\"a\", weight='bold', xy=(0.1, .9), xycoords='axes fraction', size=42, va='center',\n",
" bbox=dict(fc='white', ec='black', lw=2, pad=20))\n",
"\n",
"plt.savefig('WaterAgarFigure_matplotlib.pdf') # Save the figure"
],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
dssg/wikienergy | proto/FHMM/nameless_appliance_modeling.ipynb | 1 | 214328 | {
"metadata": {
"name": "",
"signature": "sha256:93bef355945ebbb297abb567fcd94ab167d10fb71763a11155e89d3b56c53a48"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"import sys\n",
"import os.path\n",
"sys.path.append(os.path.join(os.pardir,os.pardir))\n",
"import disaggregator.GreenButtonDatasetAdapter as gbda\n",
"import matplotlib.pyplot as plt\n",
"import pymc"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xml_path = '../../../xml_data.xml'\n",
"with open(xml_path,'r') as f:\n",
" xml_string = f.read()\n",
"\n",
"trace = gbda.get_trace_from_xml(xml_string)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(trace.series)\n",
"plt.show()\n",
"hist = plt.hist(trace.series.astype(float),bins=150)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEACAYAAACpoOGTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10c930650>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEACAYAAABcXmojAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x111f84650>"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a,a,a = plt.hist(trace.series.astype(float),bins=150)\n",
"plt.yscale('log')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEDCAYAAADdpATdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4gAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x106378510>"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a,a,a = plt.hist(trace.series.diff().abs().fillna(0).astype(float),bins=150)\n",
"plt.yscale('log')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEDCAYAAADdpATdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJEmSJFX1/wW4uZbTROouAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10c9a9b50>"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data = trace.series.astype(float).values[:100]/1000\n",
"print data"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ 1.714 0.362 0.382 0.17 0.114 0.169 0.132 0.156 0.12 0.144\n",
" 0.117 0.148 0.121 0.161 0.113 0.155 0.099 0.315 0.233 0.53\n",
" 1.841 3.232 1.934 0.958 1.002 1.107 1.28 1.38 0.121 0.483\n",
" 0.79 1. 0.929 0.911 1.113 1.125 1.032 0.857 0.795 0.851\n",
" 0.791 0.492 0.669 0.375 0.382 0.317 0.132 2.35 0.129 0.081\n",
" 0.135 0.079 0.14 0.092 0.137 0.092 0.174 0.123 0.154 0.113\n",
" 0.113 0.113 0.103 0.116 0.096 0.119 0.091 0.127 0.093 0.417\n",
" 0.387 0.773 0.695 0.829 0.96 1.166 1.311 1.263 1.209 1.41\n",
" 1.356 1.27 1.511 1.173 1.205 1.281 1.073 0.926 0.812 0.651\n",
" 0.777 0.428 0.63 0.34 0.402 0.095 0.162 0.357 0.136 0.322]\n"
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"n_states = 3\n",
"mean_lower_bounds = [0,0,0]\n",
"mean_upper_bounds = [1,1,1]\n",
"mean_bounds = zip(mean_lower_bounds,mean_upper_bounds)\n",
"beta_lower_bounds = [0,0,0]\n",
"beta_upper_bounds = [1,1,1]\n",
"beta_bounds = zip(beta_lower_bounds,beta_upper_bounds)\n",
"prob_priors = [.9,.5,.1]\n",
"\n",
"\n",
"\n",
"alpha_params = [pymc.Uniform('mean_param_{}'.format(i),lower=lower, upper=upper) for i,(lower,upper) in enumerate(mean_bounds)]\n",
"beta_params = [pymc.Uniform('std_param_{}'.format(i),lower=lower, upper=upper) for i,(lower,upper) in enumerate(beta_bounds)]\n",
"appliance_ab = zip(alpha_params,beta_params)\n",
"\n",
"appliances = [pymc.Gamma('appliance_params_{}'.format(i),alpha=alpha,beta=beta) for i,(alpha,beta) in enumerate(appliance_ab)]\n",
"\n",
"prob_params = [pymc.Bernoulli('prob_param_{}'.format(i),p=p) for i,p in enumerate(prob_priors)]\n",
"\n",
"@pymc.stochastic\n",
"def actual_output(appliances=appliances,probs=prob_params,value=data,observed=True):\n",
" out = 0\n",
" for appliance,prob in zip(appliances,probs):\n",
" if prob:\n",
" out = out + appliance\n",
" print out\n",
" return out\n",
"\n",
"@pymc.stochastic\n",
"def pred_output(appliances=appliances,probs=prob_params,value=.2):\n",
" out = 0\n",
" for appliance,prob in zip(appliances,probs):\n",
" if prob:\n",
" out = out + appliance\n",
" return out\n",
"\n",
"M = pymc.MCMC(pymc.Model([pred_output,actual_output]))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0.547678669603\n"
]
}
],
"prompt_number": 41
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"M.sample(1000,500,10)\n",
"print"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pymc.Matplot.plot(M)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Plotting appliance_params_2\n",
"Plotting"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" prob_param_0\n",
"Plotting"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" appliance_params_0\n",
"Plotting"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" appliance_params_1\n",
"Plotting"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" prob_param_1\n",
"Plotting"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" pred_output\n",
"Plotting"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" prob_param_2\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAncAAAFwCAYAAADaJ11HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSZIkSZIkSZIkSZIkSZIkSZIkSZKk+ft/jZgwxrd1lA0AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10d2b3890>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAFwCAYAAAAWvmesAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xu8VHW9+P/XCJQVB5FUvEBiiKgdJUEuKuFQXhBR/Onv\nq/krtejir0StzLydb236PeyoXbxRSoXiKX8qVsfjSUVBGT15BUXUdAco+MMLYBqaQgmyfn981t57\nGGZvhjVr9prZ+/V8PNZjz7rMmvcahjXv+Xze67NAkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJakgT\ngGZgKXBBO9tcE69fDBxUtLwv8DvgBeB5YEztwpSkxFYAzwCLgCfiZf2AucAS4D7C+UySMtEDWAYM\nAnoBTwP7lWwzEbg7fjwaeKxo3U3AlPhxT2CHWgUqSVVYTkjAil0BfC9+fAFwWadGJElFDgHmFM1f\nGE/FrgdOKZpvBvoTkq+XahqdJKVjOfDxkmUt5zKAXeN5SUpkuyqfvwewsmj+lXjZ1rYZAOwFvAHc\nCDwF/Ar4aJXxSFItRMA8YCHwtXhZf2B1/Hg1bcmZJG2zahOyqMLtcmWe1xMYDvwi/vseW7auSVI9\nOIxQ/3oMcBbwmZL1EZWfDyVpCz2rfP6rwMCi+YGEFrCOthkQL8vF2y6Il/+OMgnZ4MGDoxdffLHK\nMCU1mBeBvbMOosjr8d83gP8ERhFaxXYFVgG7AWvKPdFzmNTtJDp/VdtCthAYQijq/xChVuzOkm3u\nBE6PH48B1hJOZKsIXZn7xOuOAP5c+gIvvvgiURR1iekHP/hB5jF4LF3zOLrasQCDqzw3pemjwL/E\njz8GHAU8Szi3nREvPwO4o9yT0z6H7bPPKMK1UVHK0w9qsM9aTI0QZ4us4+hK72kt4ryWKVPOqpvz\nV7UtZBuBqcC9hCsuZxKGsDgzXj+DcIXlRMLVmO8BXy56/tnAzYRk7sWSdZJUD/oTWsUgnDNvJgxz\nsRCYDXyFMCzGyVkEp/rT1NTE/PnzefDBrCNRI6k2IQO4J56KzSiZn9rOcxcDI1OIQZJqZTnw6TLL\n3yK07EubaWpqyjoENaBquyy1DfL5fNYhpKarHEtXOQ7oWseiLOSzDqBC+awD6ILyWQdQoXzWAdSU\nCVkn6kpfmF3lWLrKcUDXOhZlIZ91ABXKZx1AF5TPOoAK5bMOoKbS6LKUJEmxli5Ley61LUzIJElK\nUVsN2Q+yDEMNxi5LSZKkjJmQSZIkZcwuS0mSUmQNmZIwIZMkKUXWkCkJuywlSZIyZkImSZKUMbss\nJUlKkTVkSsKETJKkFFlDpiS6bZdlU1MTd911V9ZhdOiDDz5gypQpjBs3jm9/+9tbrF+8eDELFizI\nIDJJkpSmLp+QRVFUdnkul+v019xWf/zjHxkwYAAPPfQQ7733Ho899thm6xctWsQTTzxRs9eXJEmd\no+ETslmzZnHCCSdw7LHHMm7cOF577TUA9t9/f6ZMmcJ3vvMdnn32WcaOHcvYsWO57LLLWp978803\nc8wxx3DMMcfw7rvvlt3/8OHDOfPMMzn00EP5yU9+AsDcuXPJ5/OMGjWKyy+/vDWOz3/+8xx//PHM\nmTOH8847j3w+z+jRo1m8eDEQbv583nnnccghh3DZZZfxrW99i5EjR3L11VeXfe1HH32Uo446CoAJ\nEybw8MMPb7b++uuv5+qrr2bChAm8/PLLjBs3js9//vNcfvnl/Pa3v2X8+PGMGDGC3/72twC88cYb\nTJo0iXw+z2mnnQbAnDlzGDduHIcddhi33npron8DSfXgS8Dvsw6iSnOAfYEhwOXtbPMlyh/na8D/\n6mDfbwPXVRNcxZqamoq6LaWuI+rIrFmzoi984QtRFEXRnDlzonPOOSeKoijq06dPtHbt2iiKoui4\n446LmpuboyiKoqOOOipasWJF1NTUFF1yySVRFEXR9ddfH/3sZz8ru/+99torWrJkSbRp06Zo3Lhx\n0Zo1a6J169ZFURRFH3zwQTRy5Mho/fr10Y033hidfvrprc9r2eapp55qjS+fz0ePPPJItGnTpmjg\nwIHR4sWLo40bN0YjRowo+9pf//rXo6effjqKoiiaN29edPHFF29x7D//+c+jKIqi5cuXR4MHD442\nbNiw2euvW7cuGj58eBRFUfTtb387+sMf/tD6/E2bNkWHHXZYtGHDhmjjxo3RYYcdFn3wwQcdvd1S\npwC6UjNvau/Lhg0bon32GRXBYxFEJdOXIvhdmeW1mD6owT43RjA4guURvB/BsAieT/E4l0fwr530\n/hBPnfFaTsmna6MpU85K7f9ni/jff5s1fAsZhFYsgIMPPpilS5cCsPfee7PDDjsAsGrVKoYOHdq6\n7YsvvrjZ80aOHNn6vFK9e/dmyJAh5HI5hg0bxvLly1m4cCFHHnkkn/3sZ3n55ZdZs2YNuVyOgw8+\nuPV5V1xxBePGjePcc8/l9ddfb11+4IEHksvl6N+/PwceeCA9evSgV69eZV+7b9++vPPOOwCsXbuW\nfv36bbFN+LcPhg0bRs+e4TqNOXPmMH78eI455pjW421ububwww9v3f6NN95gyZIlHHnkkRxxxBG8\n/fbb/PWvfy3/JkuqqXw+z7e+9S0OOuggDjjggNb60KamJk477TTGjh3LGWecwYYN/wTOAoYBRwAr\ni/YyDxgJDAU6qpGdBUwGxgP7AD8sWvd/AAcD/wr8qmh5b+C7wKeBR4H/BxgFHACcWXwkwHfiOPYD\nFsT73Af43x3E9ASwNzAI6AV8HvivdrZ9CDgMGExba9mKOBaAPwOjgYPieJcBFwIvxssu6CAOKRtp\nJGQTgGZgKe1/yq+J1y8m/G8o1gNYBPx3khePoohFixYBsHDhQoYMGQLAdtu1HVr//v1pbm4miiKe\neuopBg8evNnzFixY0Pq8Uu+++y7Lli0jiiKeeeYZBg0axI9//GNmzJjBAw88wO67796aFLW85ptv\nvsm8efN46KGHuPLKK9m0aVPr/raldu3QQw9l3rx5ANx3332MHTt2s/W9evXigw8+aJ0vPuZLL72U\nu+++m7vvvpuPfOQjAOy33348+OCDre/bTjvtxL777st9993H/PnzWbRoEbvsskvF8UlKTy6XY/36\n9SxatIhf/OIXTJkypXVdc3Mz999/PzfffDNr1rwMTCKcTr8AnBNvFQEvExKgu4D/G3i/g1dcAPwB\neAa4HXgyXn4DsDBefw3wt3j5OmAM8DQhGZpKSKKeBdYDf2w5EuDD8fO/QUj8rgeeIySCLfsr9Sow\nsGh+QLysVASsAh6OX/PCMttcD5xL+GpZGO/rckICt4j2u0Ol7FQ77EUPYDrhZ9qrhP+BdwIvFG0z\nkfCzZwjhJ8t1hP/VLc4Fngf+JUkAuVyO999/n2OOOYb33nuPW265pXV5i0svvZSvfvWrRFHEpEmT\n2HPPPcnlcqxcuZKjjz6a7bbbjttvv73s/nfccUeuuuoqnnzySU488UR22WUXTjrpJE444QQOOOAA\n+vTps1ksAP369aNfv36MHz+eMWPGlE3Cipe1l6RNmjSJO+64g3HjxjF8+HBGjx692fpDDjmE008/\nnSeeeIJLL710s3UnnngiY8eOZfjw4a0taxdddBFf+tKXuPrqqxk4cCC/+c1v+Ld/+zeOPPJItttu\nO3beeWduu+22dt9rSbV16qmnAvCZz3yGd955h7fffptcLsfxxx/Phz/8YQD+8Y93gaPiZ3wR+F78\nOAecHD/eG/gk4VQ8rJ1XOwrYMX58IvAnYARwNXBHvHwl4bf0KMLp/qSi5z8A/JiQqL1FaFGbFK87\nPv77r/HUP57/JPD/Fb1usUp/rOaAE+LH+wGry2xzKHAp8Ep8bHuTsBcpEcchUxYOIVRhtriQLX+u\nXA+cUjTfTNv/zgGENvbxtN9C1mFf7axZs6Lp06en3gfc4uCDD67ZviWVR2d+e9ZeRcecz+ej+fPn\nt85/4hOfiN5+++2oqakp+slPftK6vEePnhH8Ka6BeT+CneLHX4rgxqL6mHERPNNO7cysCM4omv/f\nEVwdwfwIxkawPl6ej+DB+HHvou3XR9A/glfi+aYIphU958n48fwIJhU9r3hd6fRoBEcXzf8ogsvK\nbFdaQ9YS1/Jo8xqxlyK4JoIhETxQZn0tJ2vIGmOqrxqyalvI9mDzAoZXCK1gW9tmD8LPmiuB84E+\nVCGtISwmTpzI+vXrW+fPP//8mg6PUWz27Nlcd13bFUA777wzs2fP7pTXllQfbrvtNvL5PH/605/o\n27cvffr0IZzf22y/fW/ee28uodvwZmBcvCYidD2eAbwUT0PbeaUImEvoPtyeUKt1I+H0vGO8rBl4\nrJ3n/yP++3Hg3fh1T25n20odTGiNWwHsDtwG3JJwXy8RWuPOJrTIPUtoKfx7lTFKtVNtQlZpFlia\n1eQIbdtrCB36+Y6eXHz5cD6fJ59v2/yMM86oMIStu/vuu7dYNnHixNT235GTTz6Zk0+u9oQmNaZC\noUChUMg6jMxtv/32DB8+nI0bN3LDDTcA4Qdn8Q/DXXbZk+XL7wL+E9iFkEhBOK1+gtC9+A4wA/hQ\nO6+Ui7c7iZCEnQYMJ3QvXg/sT0jmDil5Tou+wNfi7Xdly9/hxc+p9EdtT0IFzNHAB8BXCF2S7e23\no8ezgd8SLg7YDbgkjvkwQuH/RKwjU72ptvlnDNBEKOwHuAjYxOaf9OuBAtAyyFUzIQE7h3AW2Ej4\nOdaHcLnM6SWvEZX+QpTUtcUJSOc0T9deReew8ePH89Of/rT16u/2DB06miVLrqH9JKgSswhF/NdW\nsQ+1p6lpWvz3BxlHoo5NZ8qUZmbOnJ7qXpOev6ptIVtIKNYfRBiV7xTg1JJt7iRcjnMrIYFbS7hE\n5uJ4AjiccD11aTImSUrdtrRcaVt5L0slUW1CtpGQbN1LuARnJuGynpZBaWYAdxPah5cB7wFfbmdf\nNoNJ6rbmz59fg73ey5bXWX2S0BmRXrnHtnmTcGF+qfuBLcdalLqLahMygHviqdiMkvmpW9nHg/Ek\nSUrN0fFUTz5OKB2WVCyNhEySJMUch0xJmJBJkpQia8iURJe4l6UkSVIjMyGTJEnKmF2WkiSlyBoy\nJWFCJklSiqwhUxJ2WUqSJGXMhEySJCljdllKkpQia8iUhAmZJEkpsoZMSdhlKUmSlDETMkmSpIzZ\nZSlJUoqsIVMSJmSSJKXIGjIlYZelJElSxkzIJEmSMpZGQjYBaAaWAhe0s8018frFwEHxsoHAfODP\nwHPAOSnEIklSppqamoq6LaXKVFtD1gOYDhwBvAosAO4EXijaZiKwNzAEGA1cB4wBNgDfBp4GegNP\nAnNLnitJUkOxhkxJVNtCNgpYBqwgJFi3ApNLtjkeuCl+/DjQF+gPrCIkYwDvEhKx3auMR5IkqeFU\nm5DtAawsmn8lXra1bQaUbDOI0JX5eJXxSJIkNZxquyyjCrfLdfC83sDvgHMJLWWSJDUsxyFTEtUm\nZK8SivNbDCS0gHW0zYB4GUAv4PfAb4E72nuR4uLIfD5PPp9PGq+kOlQoFCgUClmHIaXCGjIlUdpy\nta16An8BPge8BjwBnMqWRf1T479jgKvivzlCbdmbhOL+9kRRVGlDnKSuIJfLQfXnp3qR6jls6NDR\nLFlyDeEaKdWnlo+u3131bTpTpjQzc+b0VPea9PxVbQvZRkKydS/hisuZhGTszHj9DOBuQjK2DHgP\n+HK87jDgi8AzwKJ42UXAnCpjkiRJaihp3DrpnngqNqNkfmqZ5/0JB6aVJHUx1pApCe9lKUlSiqwh\nUxK2UEmSJGXMhEySJCljdllKkpQia8iUhAmZJEkpsoZMSdhlKUmSlDETMkmSpIzZZSlJlekBLCTc\nHu44oB9wG7AnsAI4GVibVXCqH9aQKQlbyCSpMucCz9N2P5wLgbnAPsD98bxEU1PTZvdgliphQiZJ\nWzeAcAu4X9N2j7rjCffjJf57QgZxSeoiTMgkaeuuBM4HNhUt6w+sjh+vjuclKRFryCSpY5OANcAi\nIN/ONhFtXZnq5qwhUxImZJLUsUMJ3ZMTge2BPsBvCK1iuwKrgN0ISVtZxfVE+XyefD5fs2CVPcch\n614KhQKFQqHq/eS2vknmoijyh6fUneRyOajP89PhwHcJV1leAbwJXE4o6O9L+cL+VM9hQ4eOZsmS\na4DRqe1TaWv56PrdVd+mM2VKMzNnTk91r0nPX9aQSdK2afmWvQw4ElgCfDael6RE7LKUpMo9GE8A\nbwFHZBiL6pQ1ZEoijRayCUAzsBS4oJ1tronXLwYO2sbnSpLUMByHTElUm5D1AKYTEqv9gVOB/Uq2\nmQjsDQwBvg5ctw3PlSRJ6vKqTchGAcsItw3ZANwKTC7ZpnjwxMcJha+7VvhcSZKkLq/aGrI9gJVF\n86+w5aU/5bbZA9i9gudKktRQrCFTEtUmZJVe01uPl69LkpQ6xyFTEtUmZK8CA4vmBxJaujraZkC8\nTa8KngtALtdUNJen/cGyJTWi+fPTGVhRkhpVtQnZQkKx/iDgNeAUQnF+sTuBqYQasTHAWsII129W\n8FwAoqipyjAl1bfNR6+fNm1adqFIUgaqTcg2EpKtewlXTc4EXgDOjNfPAO4mXGm5DHgP+PJWnitJ\nUsOyhkxJpDEw7D3xVGxGyfzUbXiuJEkNyxoyJeGtkyRJkjJmQiZJkpQx72UpSVKKrCFTEiZkkiSl\nyBoyJWGXpSRJUsZMyCRJkjJml6UkSSmyhkxJmJBJkpQia8iUhF2WkiRJGTMhkyRJyphdlpIkpcga\nMiVhQiZJUoqsIVMSdllKkiRlzIRMkiQpY3ZZSpKUImvIlIQJmSRJKbKGTEnYZSlJkpSxahOyfsBc\nYAlwH9C3ne0mAM3AUuCCouU/Bl4AFgN/AHaoMh5JkqSGU21CdiEhIdsHuD+eL9UDmE5IyvYHTgX2\ni9fdB3wKGEZI6i6qMh5JkjLV1NRU1G0pVabaGrLjgcPjxzcBBbZMykYBy4AV8fytwGRCy9jcou0e\nB06qMh5JkjJlDZmSqLaFrD+wOn68Op4vtQewsmj+lXhZqSnA3VXGI0mS1HAqaSGbC+xaZvklJfNR\nPJUqt6zcvt4H/t9yK4ubfvP5PPl8voJdSmoUhUKBQqGQdRiSlJlKErIjO1i3mpCsrQJ2A9aU2eZV\nYGDR/EBCK1mLLwETgc+19yL2xUtdW+kPrWnTpmUXjFQlxyFTEtXWkN0JnAFcHv+9o8w2C4EhwCDg\nNeAUQmE/hEL/8wl1aP+oMhZJkjJnDZmSqLaG7DJCC9oS4LPxPMDuwF3x443AVOBe4HngNkJBP8C1\nQG9Ct+gi4BdVxiNJktRwqm0hews4oszy14Bji+bviadSQ6p8fUmSpIbnrZMkSUqRNWRKwoRMkqQU\nWUOmJLyXpSRJUsZMyCRJkjJml6UkSSmyhkxJmJBJkpQia8iUhF2WkiRJGTMhkyRJyphdlpIkpcga\nMiVhQiZJUoqsIVMSdllKkiRlzIRMkiQpY3ZZSpKUImvIlIQJmSRJKbKGTEnYZSlJkpQxEzJJkqSM\nVZOQ9QPmAkuA+4C+7Ww3AWgGlgIXlFl/HrAp3p8kSQ2tqampqNtSqkw1CdmFhIRsH+D+eL5UD2A6\nISnbHzgV2K9o/UDgSODlKuKQJKlumJApiWoSsuOBm+LHNwEnlNlmFLAMWAFsAG4FJhet/xnwvSpi\nkCRJanjVJGT9gdXx49XxfKk9gJVF86/EyyAkZq8Az1QRgyRJUsPb2rAXc4Fdyyy/pGQ+iqdS5ZYB\nfAS4mNBd2SK3lVgkSap7jkOmJLaWkB3ZwbrVhGRtFbAbsKbMNq8S6sRaDCS0ig0GBgGL4+UDgCcJ\nXZxb7Ke4Lz6fz5PP57cStqRGUigUKBQKWYchpcJxyJRENa1SVwBvApcTCvr7smVhf0/gL8DngNeA\nJwiF/S+UbLccGAG8VeZ1oihqr6FNUleUy+Wg67Sap3oOGzp0NEuWXAOMTm2fSlvLR9fvrvo2nSlT\nmpk5c3qqe016/qqmhuwyQgvaEuCz8TzA7sBd8eONwFTgXuB54Da2TMbAT62k+rU98DjwNOE89u/x\n8kqH/pGkrarm1klvAUeUWf4acGzR/D3x1JFPVhGHJNXSP4DxwDrCOfNPwFjCleZzCb0FFxB6CMoN\n/6NuxhoyJeG9LCVp69bFfz9EGF/xb4SE7PB4+U1AARMyYQ2ZkvHWSZK0ddsRuixXA/OBP1PZ0D+S\nVBFbyCRp6zYBnwZ2INTEji9Z397QP5JUERMySarc24SLlkZQ2dA/gEP3dDfWkHUvaQ3b0wiXlTvs\nhdTN1NmwFzsRrhhfSxjU+l5gGnA0Wx/6Bxz2ohty2IvGUF/DXthCJkkd241QtL9dPP0GuB9YBMwG\nvkK4X+/JGcUnqQswIZOkjj0LDC+zvL2hfyRpm5mQSZKUImvIlIQJmSRJKXIcMiXhOGSSJEkZMyGT\nJEnKmF2WkiSlyBoyJWFCJklSiqwhUxJ2WUqSJGXMhEySJCljdllKkpQia8iURDUtZP2AucAS4D7C\nfdzKmQA0A0uBC0rWnQ28ADxHuB+cJEkNrampabMbykuVqCYhu5CQkO1DuK9buZvq9gCmE5Ky/YFT\ngf3ideOB44EDgX8FflJFLA0hjbvB14uucixd5Tigax2LJHU31SRkxxNuuEv894Qy24wClhFuvLsB\nuBWYHK/7BvDv8XKAN6qIpSF0pS/MrnIsXeU4oGsdiyR1N9UkZP2B1fHj1fF8qT2AlUXzr8TLAIYA\n44DHgAJwcBWxSJJUF+yyVBJbK+qfC+xaZvklJfNRPJUqt6z4tXcExgAjgdnAJ7cSjySl4T+AW4B7\nsg5EXY/jkKmzNdOWrO0Wz5caA8wpmr+ItsL+e4DDi9YtAz5eZh/LaEv4nJycuse0jNr6MHA6cBtw\nLvCxGr5WlKZ99hkVwWMRRE51O7V8jrOOw6nj6dpoypSzUv3/GUWt//7brJouyzuBM+LHZwB3lNlm\nIaFrchDwIeCU+HnE2382frxPvP7NMvvYG8g5OTl1q2lvauvjhBb5twklFzfU+PUkqUPVjEN2GaGb\n8SuEov2T4+W7A78CjgU2AlOBewlXXM4kDHMB4QR4A/As8D7h16okdYbzgF8AL8bzKzvYVtomjkOm\nJKpJyN4Cjiiz/DVCMtbiHsrXaWwATqvi9SUpqQJtydixwF3ZhaKuxhoyJVHvt07qaFDZencDoSvk\n2aJllQ6mW08GAvOBPxMG8D0nXt6Ix7I98DjwNPA8YdgVaMxjgdDqvAj473i+UY9jBfAM4VieiJfV\n+lgOL3r8mZT3LUnbrJ4Tso4GlW0ENxJiL1bJYLr1ZgPwbeBThIs0ziL8OzTisfyDMCDxpwkDEo8H\nxtKYxwKhGP152gpIG/U4IiAPHEQYuxBqfyw7A58j1LGWG7JHkjpVPSdkHQ0q2wj+B/hbybJKBtOt\nN6sILUoA7xJqAPegMY8FYF3890OEpP9vNOaxDAAmAr8mFMFDYx5Hi1zJfK2P5RxCsrcv8K2U961u\nznHIlEQ9J2QdDSrbqCoZTLeeDSK0YjxO4x7LdoQEczVtXbGNeCxXAucDm4qWNeJxQGghm0e4Kvtr\n8bJaH8sngB0ILWXnprxvdXMmZEqimqL+Wks0jkcDSTxWSUZ6A78nfHn9vWRdIx3LJkKX5Q6Eq3/H\nl6xvhGOZBKwh1Fzl29mmEY6jxWHA64TkaC5bjmlYi2P5DvBT2m7dJkmZqueE7FVCQXmLgYRWska2\nmjCY7irCYLprsg2nYr0IydhvaBtvrlGPpcXbhCvrRtB4x3IooUtvIuFChT6Ef5tGO44Wr8d/3wD+\nk1CuUOtjeS6eJKku1HOXZUeDyjaqSgbTrTc5wvhxzwNXFS1vxGPZibar9T4CHEloZWq0Y7mY8ANl\nL+DzwAOEIWQa7TgAPgr8S/z4Y8BRhCuTa30s4wlXp94eT1Jq7LJUV3QM8BdCcf9FGceyrW4hjMn2\nPqEW7suES/nn0VjDEowldPM9TUheFhGuHm3EYzkAeIpwLM8QarCgMY+lxeG0/VBpxOPYi/Dv8TSh\nxarl/3mtj6U34R66EC6QqKVUb8virZMaYfLWSY0x1detk+q5yxLaH1S2EZzazvJyg+nWsz/Rfktq\nox3Ls8DwMsvbG+S4ETwYT9CYx7GcUNNXqtbHciXhx9ICQovjN2v4WpK0VfWekElSLbxL27A067MM\nRJLAhExS9/RXwgj9P2XzoUOkqnkvSyVhQiapO7qUMCjsdoQLVqTUeC9LJWFCJqk7uiX++5H4byPd\n1UBSF2RCJqk7arnoJke4V6skZcqETFJ39CnCpem94sdSaqwhUxImZJK6o/8z/vtP4JosA1HXYw2Z\nkjAhk9QdLSx6PCCe7sooFkkyIZPULX0VeJjQbTmWxrjNlKQuzIRMUnfUDPwkfrwzcFOGsaiLsYZM\nSZiQSequZhJayFZnHYi6FmvIlIQJmaTu6BJC3dhaQmG/JGWqvZtGS1JXdhWh+eId4NqMY5EkW8gk\ndUubgJfjx2uzDERdjzVkSsKETFJ39E9gf+BsYMeMY1EXYw2ZkjAhk9Td5IDfATvFj3+RbTiSZEIm\nqfuJgPHAFVkHIkktTMgkdTeT4+lo4K142f/KLhx1NdaQKYm6T8iGDRsWLV68OOswJHWuB4F8jfY9\nATgMuA74Ro1eQ92YNWRKou6HvVi8eDFRFHWZ6Qc/+EHmMXg83eN4GvlYgMNreFr5BHBs/HdiPElS\npuq+hUySUnY7oaB/NuG2SZKUORMySd3NrKwDUNdmDZmSMCHrZPl8PusQUuXx1K+udCxSI7GGTEnU\nfQ1ZV9PVviQ9nvrVlY5Fkro6EzJJkqSM1TIhuwFYDTzbwTbXAEuBxcBBNYxFkqRO0dTUVNRtKVWm\nljVkNwLXAv/RzvqJwN7AEGA0YUygMTWMR5KkmrOGTEnUsoXsf4C/dbD+eOCm+PHjQF+gfw3jkSRJ\nqktZ1pDtAawsmn8FGJBRLJIkSZnJetiLXMl8lEkUkiSlxHHIlESWCdmrwMCi+QHxsi0UF0fm83kv\n55e6mEItMW2oAAAVeUlEQVShQKFQyDoMKRXWkCmJLBOyO4GpwK2EYv61hKsyt+DVKlLXVvpDa9q0\nadkFs6WBhIuTdiG04v+ScIV4P+A2YE9gBXAy4TwmSdusljVktwCPAEMJtWJTgDPjCeBu4CVgGTAD\n+GYNY5GkpDYA3wY+RfjxeBawH3AhMBfYB7g/npekRGrZQnZqBdtMreHrS1IaVsUTwLvAC4SLko4H\nDo+X3wQUMCkT1pApmayL+jPT1NTEyJEjOfbYY7MOpV0ffPABX/va11i2bBkjRozgyiuvzDokqbsb\nRBjE+nHCMD0tZRarcdgexawhUxJd/tZJUVT+ws1crvQCz9q/5rb64x//yIABA3jooYd47733eOyx\nx1LZb6nieNOKXeqCegO/B84F/l6yLsKrxCVVoeFbyGbNmsUdd9zBhg0b+Pvf/86tt97K7rvvzv77\n78+YMWPYYYcdmDJlCt/4xjcAmDRpEhdeGHoVbr75ZqZPnw7A7bffTu/evbfY//Dhwxk5ciTPPvss\nJ554It/97neZO3cul156KevWreOkk07iggsuYNasWcyZM4d169bxjW98g3nz5vHkk0+yfv16fvnL\nXzJs2DDy+TwjRozgkUceYfLkyaxatYqHH36YL37xi5x77rlbvPajjz7KpEmTAJgwYQIPP/wwY8a0\n3czgueeeY+rUqbz//vuMGDGCa6+9liiKmDp1Ks8++yw9e/Zk9uzZvP7663zzm98kiqLW429qamLF\nihW88cYb/OhHP+Lss89m991359Of/nTr+yOpVS9CMvYb4I542WpgV0J35m7Amvae7JXiUtfVna4S\njzoya9as6Atf+EIURVE0Z86c6JxzzomiKIr69OkTrV27NoqiKDruuOOi5ubmKIqi6KijjopWrFgR\nNTU1RZdcckkURVF0/fXXRz/72c/K7n+vvfaKlixZEm3atCkaN25ctGbNmmjdunVRFEXRBx98EI0c\nOTJav359dOONN0ann3566/Natnnqqada48vn89EjjzwSbdq0KRo4cGC0ePHiaOPGjdGIESPKvvbX\nv/716Omnn46iKIrmzZsXXXzxxZutX79+fevjyZMnR0uXLo3+67/+Kzr77LNbl2/atKnd4//+978f\nRVEULV++PBo8eHC0YcOGDt5pqfNQX61NOcJVlqU1A1cAF8SPLwQua+f5qb43++wzKoLHIoic6nRq\namqKmpqaMo/DaWvTtdGUKWel+v8ziqIIkp2/Gr6FDEIrFsDBBx/M1VdfDcDee+/NDjvsAMCqVasY\nOnRo67YvvvjiZs8bOXIkv/71r8vuu3fv3gwZMgSAYcOGsXz5cv75z3/ywx/+kA0bNvDyyy+zZs0a\ncrkcBx98cOvzrrjiCu6//34AevXq1br8wAMPJJfL0b9/fw488MAt1hfr27cv77zzDgBr166lX79+\nm61/6aWX+O53v8u6det46aWXeO2112hububwww9v3SaXy7V7/MXxDhs2jJ49u8THQUrbYcAXgWeA\nRfGyiwgJ2GzgK7QNeyFZQ6ZEGr6GLIoiFi0K58iFCxe2Jk/bbdd2aP3796e5uZkoinjqqacYPHjw\nZs9bsGBB6/NKvfvuuyxbtowoinjmmWcYNGgQP/7xj5kxYwYPPPAAu+++OyEhbnvNN998k3nz5vHQ\nQw9x5ZVXsmnTptb9bUvt2qGHHsq8efMAuO+++xg7duxm66+//nrOO+88CoUCBx10EFEUsd9++/HQ\nQw+1brNp06ayx1/6HhU/lrSZPxHOlZ8mFPQfBMwB3gKOIAx7cRSOQSapCg3fJJLL5Xj//fc55phj\neO+997jllltal7e49NJL+epXv0oUhRqqPffck1wux8qVKzn66KPZbrvtuP3228vuf8cdd+Sqq67i\nySef5MQTT2SXXXbhpJNO4oQTTuCAAw6gT58+m8UC0K9fP/r168f48eMZM2ZM2SSseFl7SdqkSZO4\n4447GDduHMOHD2f06NGbrT/uuOM499xz2XfffYmiiFwux3HHHcecOXP4zGc+Q69evZg9e3bZ4y9+\n3VwuV9OLHCRJUsca4Vs4ammBKuemm27i3Xff5ayzzqrJi48cOZIFCxbUZN+Syot/IDTC+akSHZ7D\nttXQoaNZsuQaYPRWt1U2mpqmxX/tsqxv05kypZmZM6enutek56+GbyGD9IawmDhxIuvXr2+dP//8\n8zut5Wj27Nlcd911rfM777wzs2fP7pTXliSlxxoyJdEIv0BT/XUpqf7ZQtY+W8gaQctH1++u+lZf\nLWRWckuSJGWsS3RZSpJUL7yXpZIwIZMkKUXWkCkJuywlSZIyZkImSZKUMbssJUlKkTVkSsKETJKk\nFFlDpiRq3WU5AWgGlgIXlFm/E+GecE8DzwFfqnE8kiRJdaeWCVkPYDohKdsfOBXYr2SbqcAiwk17\n88BPsdVOkiR1M7VMfkYBy4AV8fytwGTghaJtXgcOjB/3Ad4ENtYwJkmSasoaMiVRy4RsD2Bl0fwr\nbHmvj18BDwCvAf8CnFzDeCRJqjlryJRELbssK7mJ18WE+rHdCd2WPyckZpIkSd1GLVvIXgUGFs0P\nJLSSFTsUuDR+/CKwHBgKLCzeqKmo3Tefz5PP59ONVFKmCoUChUIh6zAkKTO1TMgWAkOAQYQuyVMI\nhf3FmoEjgIeB/oRk7KXSHTXZES91aaU/tKZNm5ZdMFKVrCFTErVMyDYSrqK8l3DF5UxCQf+Z8foZ\nwI+AG4HFhO7T7wFv1TAmSZJqyhoyJVHrISbuiadiM4oe/xU4rsYxSJIk1TXvZSlJkpQxB2GVJClF\n1pApCRMySZJSZA2ZkrDLUpIkKWMmZJIkSRmzy1KSpBRZQ6YkTMgkSUqRNWRKwi5LSZKkjJmQSZIk\nZcwuS0mSUmQNmZIwIZMkKUXWkCkJuywlSZIyZkImSZKUMbssJUlKkTVkSsKETJKkFFlDpiTsspQk\nScpYLROyCUAzsBS4oJ1t8sAi4DmgUMNYJEmS6latuix7ANOBI4BXgQXAncALRdv0BX4OHA28AuxU\no1gkSeo01pApiVolZKOAZcCKeP5WYDKbJ2T/F/B7QjIG8NcaxSJJUqexhkxJ1KrLcg9gZdH8K/Gy\nYkOAfsB8YCFwWo1ikSRJqmu1aiGLKtimFzAc+BzwUeBR4DFCzZkkSVK3UauE7FVgYNH8QNq6Jlus\nJHRTro+nh4BhlEnImoo64vP5PPl8PtVgJWWrUChQKBSyDkNKhTVkSiJXo/32BP5CaP16DXgCOJXN\na8j2JRT+Hw18GHgcOAV4vmRfURRV0uAmqavI5XJQu/NTZ0v1HDZ06GiWLLkGGJ3aPpW2lo+u3131\nbTpTpjQzc+b0VPea9PxVqxayjcBU4F7CFZczCcnYmfH6GYQhMeYAzwCbgF+xZTImSZLU5dVypP57\n4qnYjJL5n8STJElSt+WtkyRJSpE1ZErChEySpBQ5DpmS8F6WkiRJGTMhkyRJyphdlpIkpcgaMiVh\nQiZJUoqsIVMSdllKkiRlzIRMkiQpY3ZZSpKUImvIlIQJmSRJKbKGTEnYZSlJkpQxEzJJkqSM2WUp\nSVKKrCFTEiZkkrR1NwDHAmuAA+Jl/YDbgD2BFcDJwNosglN9sYZMSdhlKUlbdyMwoWTZhcBcYB/g\n/nhekhIxIZOkrfsf4G8ly44Hboof3wSc0KkRSepS7LKUpGT6A6vjx6vjeckaMiVSyxayCUAzsBS4\noIPtRgIbgRNrGIsk1VIUTxJNTU1FdWRSZWrVQtYDmA4cAbwKLADuBF4os93lwBwgV6NYJKkWVgO7\nAquA3QgF/2UVfznn83ny+XyNQ5PUWQqFAoVCoer91CohGwUsI1x5BHArMJktE7Kzgd8RWskkqZHc\nCZxB+FF5BnBHexvaWiJ1XaU/sqZNm5ZoP7VKyPYAVhbNvwKMLrPNZOCzhITM5n5J9eoW4HBgJ8K5\n7fvAZcBs4Cu0DXshWUOmRGqVkFWSXF1FuEw8InRXtttlaXO/1LWl1eRfQ6e2s/yITo1CDcFxyJRE\nrRKyV4GBRfMDCa1kxUYQujIh/Oo8BthA6AbYjM39UteWVpO/JDWqWiVkC4EhwCDgNeAUtvyF+cmi\nxzcC/02ZZEySJKmrq1VCthGYCtxLuJJyJqGg/8x4/Ywava4kSZmyhkxJ1HJg2HviqVh7idiXaxiH\nJEmdxhoyJeGtkyRJkjJmQiZJkpQx72UpSVKKrCFTEiZkkiSlyBoyJWGXpSRJUsZMyCRJkjJml6Uk\nSSmyhkxJmJBJkpQia8iUhF2WkiRJGTMhkyRJyphdlpIkpcgaMiVhQiZJUoqsIVMSdllKkiRlzIRM\nkiQpY3ZZSpKUImvIlIQJmSRJKbKGTEnUustyAtAMLAUuKLP+C8Bi4BngYeDAGscjSZJUd2rZQtYD\nmA4cAbwKLADuBF4o2uYlYBzwNiF5+yUwpoYxSZIk1Z1aJmSjgGXAinj+VmAymydkjxY9fhwYUMN4\nJEmqOWvIlEQtE7I9gJVF868AozvY/ivA3TWMR5KkmrOGTEnUMiGLtmHb8cAU4LAaxSJJklS3apmQ\nvQoMLJofSGglK3Ug8CtCDdnfyu2oqajdN5/Pk8/n04pRUh0oFAoUCoWsw5CkzNQyIVsIDAEGAa8B\npwCnlmzzCeAPwBcJ9WZlNdkRL3VppT+0pk2bll0wUpWsIVMStUzINgJTgXsJV1zOJBT0nxmvnwF8\nH9gRuC5etoFwMYAkSQ3JGjIlUeuBYe+Jp2Izih5/NZ4kSZK6Le9lKUmSlDFvnSRJUoqsIVMSJmSS\nJKXIGjIlYZelJElSxkzIJEmSMmaXpSRJKbKGTEmYkEmSlCJryJSEXZaSJEkZMyGTJEnKmF2WkiSl\nyBoyJWFCJklSiqwhUxJ2WUqSJGXMhEySJCljdllKkpQia8iUhAmZJEkpsoZMSdhlKUmSlLFaJ2QT\ngGZgKXBBO9tcE69fDBxU43gkSZLqTi27LHsA04EjgFeBBcCdwAtF20wE9gaGAKOB64AxNYxJkqSa\nsoZMSdQyIRsFLANWxPO3ApPZPCE7Hrgpfvw40BfoD6yuYVySJNWMNWRKopZdlnsAK4vmX4mXbW2b\nATWMSZIkqe7UMiGLKtwul/B5kiRJXUItuyxfBQYWzQ8ktIB1tM2AeNlmcrmmorl8PEnqOgrxJDU+\na8iURC0TsoWEYv1BwGvAKcCpJdvcCUwl1JeNAdZSpn4sippqGKak7OUp/qGVy03LKhCpataQKYla\nJmQbCcnWvYQrLmcSCvrPjNfPAO4mXGm5DHgP+HIN45EkSapLtR6p/554KjajZH5qjWOQJEmqa946\nSZKkFFlDpiRMyCRJSpE1ZErCe1lKkiRlzIRMkiQpYyZkklSdCUAzsBS4IONYVAeampqKui2lypiQ\ndbJCoZB1CKnyeOpXVzqWOtYDmE5IyvYnjLW4X6YRJVbIOoAKFbIOYKsaLyErZB1AhQpZB1BTJmSd\nrKt9SXo89asrHUsdG0UYR3EFsIEwyPXkLANKrpB1ABUqZB1AF1TIOoAKFbIOoKZMyCQpuT2AlUXz\nr8TLJGmbOOyFJCUXdfYL9uq1HR/72Hn06LFjqvv9xz/+wvbbP5nqPmuhEeL8zneamD9/PosWHZd1\nKBVphPcU0o/z/feX07PnZ1PbX7VyWQdQgaeBYVkHIalTPUjxzS3r1xigiVBDBnARsAm4vGibZcDg\nzg1LUoZeBPbOOghJ6k56Ek6+g4APEX5ANmhRvyRJUuM6BvgLoSXsooxjkSRJkiR1RY0+4OINwGrg\n2aJl/YC5wBLgPqBvBnElMRCYD/wZeA44J17eqMezPfA4oYvpeeDf4+WNejwQxsRaBPx3PN/Ix9Ko\nKn3P+wK/A14gfP7GdEp0m9uWz0fpZ6szVRJne+enzlDJ99Q18frFwEGdFFeprcX5BUJ8zwAPAwd2\nXmhbqPS7fySwETixM4Iqo5I484T/O8/RwON29CB0AQwCetGYtRmfIfznK07IrgC+Fz++ALiss4NK\naFfg0/Hj3oQumv1o3OMB+Gj8tyfwGDCWxj6e7wA3A3fG8418LI2q0vf8JmBK/LgnsEON4ypnWz4f\npZ+tzlRJnO2dn2qtku+picDd8ePRhHNNZ6skzkNo+xxOIJs4ofLv/h7AA8AfgZM6K7iS199anH0J\nPxIGxPM7dVZwaTsEmFM0f2E8NZpBbJ6QNQP948e7xvON6A7gCLrG8XwUWAB8isY9ngHAPGA8ba0Y\njXosjayS93wH4KVOi6h9lX4+yn22OlOSz/EdwOdqFlGbSr6nrgdOKZovPp7Osq3fpzsSxtTLQqWx\nfgv4JnAj2SRklcT5TeCHle6wngeG7aoDLvYndGMS/+3s/5hpGERo+Xucxj6e7Qi/albT1t3RqMdz\nJXA+YciFFo16LI2skvd8L+ANwhfJU8CvaGut7UyVfj7KfbY607Z+jgfRdn6qtUq+p8ptM4DOta3f\np1+hrVWvs1X6nk4GrovnO308QCqLcwihy30+sBA4raMd1vPAsFm8wZ0tovGOszfwe+Bc4O8l6xrt\neDYRujl2AO4ltAAUa5TjmQSsIdQp5NvZplGOpRHMJbTUlLqkZL6997wnMByYSmiZvYrwy/r7KcbY\notpYK/lspaHaOFv0JtTmnQu8m05oHar0/1TpmJ+d/X9xW15vPKE7/bAaxbI1lcTa8n8mIry3WYyp\nWkmcvQj/1z9H+NH1KKEreGm5jes5IXuVUKjZYiDZNaGmaTXhxLMK2I1wsmsUvQjJ2G8IXQLQ2MfT\n4m3gLmAEjXk8hwLHE2pVtgf6EP6NGvFYGsGRHayr5D1/JZ4WxPO/o3blGNXGWu6z9R/A6emGWXWc\n0HZ++i1t56daq+R7qnSbAfGyzlTp9+mBhBbbCcDfOiGuciqJdQThvrEQ6rKOIdxLtjNrHCuJcyXw\nV2B9PD1EGOi+bEJWz7rKgIuD2LKov+VqjAtpnELrHOFEfGXJ8kY9np1ou1rrI4T/KJ+jcY+nxeG0\n1fk0+rE0okrf84eAfeLHTWw+sn9n2dbPR/FnqzNVEmd756daq+R7qriofwzZFMtXEucnCEXqWVzx\nW2xbv/tvJJurLCuJc19C/WUPQgvZs8D+nRdiuhp9wMVbgNeA9wmZ8pcJ/cnzaLyhCMYSuvieJnRf\nLCL8imrU4zmAUL/zNOEy7/Pj5Y16PC0Op+1XYqMfSyNq7z3fndAK22IYoYVsMfAHsrnKstJYWxR/\ntjpTJXG2d37qDOW+p86MpxbT4/WLCV1YWdhanL8G3qTt/XuiswMsUsl72iKrhAwqi/O7hPrkZ+nc\n4VgkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSVvz/wONixaSA7Q7ogAAAABJRU5ErkJg\ngg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10f6bce90>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAnUAAAFwCAYAAADe0o16AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10ca15490>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAm0AAAFwCAYAAADqq0xmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiRJkiRJkiRJkiRJqs7/ARd9dUa8ENYJAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1050bc550>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAFwCAYAAAAWvmesAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiRJkiRJkiRJkiRJkiRJkiRJkqSd+f8B92HxKQi4zqcAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10d1a0fd0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAm8AAAFwCAYAAADuXpxbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSZIkSZIkSZIkSZIkSZIkSZIkSdJg+D+0gRyiG+wpKQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10d200ed0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAlwAAAFwCAYAAABtmqVgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10d542310>"
]
}
],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
longyangking/ML | Statistics/Conditional Probability.ipynb | 1 | 8224 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conditional Probability"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Basic Concepts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Definition\n",
"$$ P(A|B) = \\frac {P(A,B)}{P(B)} $$\n",
"Basically, when $P(A) \\ne 0$, $P(B) \\ne 0$:\n",
"$$P(A) = \\sum_B P(A|B)P(B) = \\sum_B P(A,B)$$\n",
"$$ 1 = \\sum_A P(A|B) = \\sum_A \\frac {P(A,B)}{P(B)} = \\frac {\\sum_A P(A,B)}{P(B)} = \\frac {P(B)}{P(B)}$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Chain Rule\n",
"In probability theory, the chain rule permits the calculation of any member of the joint distribution of a set of random variables using only conditional probabilities. The rule is useful in the study of Bayesian networks, which describe a probability distribution in terms of conditional probabilities.\n",
"\n",
"Consider an indexed set of sets $A_1,...,A_n$. To find the value of this member of the joint distribution, we can apply the definition of conditional probability to obtain:\n",
"$$P(A_n,...,A_1) = P(A_n|A_{n-1},...,A_1)P(A_{n-1},...,A_1)$$\n",
"\n",
"Repeating this process with each final term creates the product:\n",
"$$P(\\bigcap^n_{k=1}A_k) = \\prod_{k=1}^n P(A_k|\\bigcap_{j=1}^{k-1} A_j) $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Factorization\n",
"Factorize the probability in a trail, for example\n",
"$$ P(A,B,C) = P(A|B,C)P(B|C)P(C) $$\n",
"This factorization correspond a Bayesian map: \n",
"$$ C \\to B \\to A $$\n",
"$$ C \\to A $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Statistical independence\n",
"Events $A$ and $B$ are defined to be statistically independent if $ P(A,B)=P(A)P(B) $, so we have:\n",
"$$ P(A|B) = P(A) $$\n",
"$$ P(B|A) = P(B) $$\n",
"The notations of independence is $ P \\models A \\perp B$, where $\\models$ means \"satisfy\", and $\\perp$ means \"independent\".\n",
"\n",
"For random variables $X$,$Y$, $ P \\models X \\perp Y$, similarly, we have:\n",
"$$ P(X,Y) = P(X)P(Y) $$\n",
"$$ P(X|Y) = P(X) $$\n",
"$$ P(Y|X) = P(Y) $$\n",
"\n",
"## Conditional Independence\n",
"For (sets of) random variables $X$,$Y$,$Z$, $P \\models (X \\perp Y | Z)$ if:\n",
"$$ P(X,Y|Z) = P(X|Z)P(Y|Z)$$\n",
"$$ P(X|Y,Z) = P(X|Z) $$\n",
"$$ P(Y|X,Z) = P(Y|Z) $$\n",
"The conditional independence means that the $X$ and $Y$ are conditionally independent given $Z$, but reminder that this statement may not work given another variables other than $Z$.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bayes' theorem\n",
"$$ P(A|B)=\\frac{P(B|A)P(A)}{P(B)}$$\n",
"Actually, they follow as the form $ P(A,B)=P(A|B)P(B) = P(B|A)P(A)$. In general, it cannot be assumed that $P(A|B) \\approx P(B|A)$.\n",
"\n",
"$$ \\frac {P(B|A)}{P(A|B)} = \\frac {P(B)}{P(A)} $$\n",
"\n",
"Sometimes, for $A$ is a binary variable, we can write the probability in alternative form as $ P(B) = P(B|A)P(A) + P(B|\\overline{A})P(\\overline{A})$, thus we have:\n",
"$$ P(A|B)=\\frac{P(B|A)P(A)} {P(B|A)P(A) + P(B|\\overline{A})P(\\overline{A})}$$\n",
"\n",
"Often, for some partition ${A_j}$ of the sample space, the event space is given or conceptualized in terms of $P(A_j)$ and $P(B|A_j)$. It is then useful to compute $P(B)$ using the law of total probability $P(B) = \\sum_j P(B|A_j)P(A_j)$:\n",
"$$ P(A_i|B) = \\frac{P(B|A_i)P(A_i)}{\\sum_j P(B|A_j)P(A_j)}$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multi-conditional probability\n",
"$$ P(Y|X_1,X_2) = \\frac {P(Y,X_1,X_2)} {P(X_1,X_2)}$$\n",
"$$ P(Y_1,Y_2|X) = \\frac {P(Y_1,Y_2,X)} {P(X)} $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Influence Flow\n",
"$$ P(X_1|X_2) = \\frac {P(X_1,X_2)} {P(X_2)} = \\frac{P(Y,X_1,X_2)} {P(Y|X_1,X_2)P(X_2)}$$\n",
"$X_1$ and $X_2$ are independent, but conditionally dependent given $Y$, which activates the V-structure, $ P(X_1|X_2) \\ne P(X_1) $."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## D-separation\n",
"Definition: $X$ and $Y$ are d-separated in G given $Z$ if there is no active trail in G (likely, V-structure) betweeb $X$ and $Y$ given $Z$, notation: $d-sep_G(X,Y|Z)$.\n",
"\n",
">Theorem: If $P$ factorizes over $G$, and $d-sep_G(X,Y|Z)$ then $P$ satisfies $(X \\perp Y |Z)$\n",
"\n",
"For example, $ P(D,I,G,S,L) = P(D)P(I)P(G|D,I)P(S|I)P(L|G)$, so\n",
"$$ P(D,S) = \\sum_{G,L,I} P(D)P(I)P(G|D,I)P(S|I)P(L|G) $$\n",
"$$ P(D,S) = \\sum_I P(D)P(I)P(S|I) \\sum_G (P(G|D,I)\\sum_L P(L|G)) $$\n",
"\n",
"we have $1 = \\sum_L P(L|G)$, $1 = \\sum_G P(G|D,I)$, and $P(S) = \\sum_I P(I)P(S|I)$, so \n",
"\n",
"$$ P(D,S) = P(D)P(S) $$\n",
"Thus, $D$ and $S$ are independent.\n",
"\n",
">Any node is d-separated from its non-descendants given its parents"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## I-maps\n",
"+ Definition: d-separation in $G \\to P$ satisfies corresonding independence statement $I(G)={(X \\perp Y|Z):d-sep_G(X,Y|Z)}$.\n",
"+ Definition: If $P$ satisfies $I(G)$, we say that $G$ is an I-map(independency map) of $P$.\n",
"\n",
"> If $P$ factorizes over $G$, then $G$ is an I-map for $P$, reversely, if $G$ is an I-map for $P$, then $P$ factorizes over $G$.\n",
"\n",
"Basically, according to the chain rule, the factorization shall be written as:\n",
"$$P(D,I,G,S,L)=P(D)P(I|D)P(G|D,I)P(S|D,I,G)P(L|D,I,G,S)$$\n",
"because $P(S|D,I,G) = P(S|I)$, $P(L|D,I,G,S) = P(L|G)$, $P(I) = P(I|D)$\n",
"$$P(D,I,G,S,L)=P(D)P(I|D)P(G|D,I)P(S|I)P(L|G)$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bayesian Network"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Naive Bayes Classifier\n",
"For class variables ${C_k}$, with features ${x_n}$, we have conditional probability as:\n",
"$$P(C_k,x_1,...,x_n) = P(x_1|x_2,...,x_n,C_k)P(x_2|x_3,...,x_n,C_k)...P(x_{n-1}|x_n,C_k)P(x_n|C_k)P(C_k)$$\n",
"\n",
"The \"naive\" conditional independence assumptions come into play: assume that each feature $x_i$ is conditionally independent of every other feature $x_j$ for $j \\ne i$, given the category $C$. This means that\n",
"$$P(x_i|x_{i+1},...,x_n,C_k)=P(x_i|C_k)$$\n",
"\n",
"Thus, the joint model can be expressed as:\n",
"$$P(C_k|x_1,...,x_n) = P(C_k)\\prod_{i=1}^{n} P(x_i|C_k)$$\n",
"\n",
"## Constructing a classifier from the probability model\n",
"The naive Bayes classifier combines this model with a decision rule. One common rule is to pick the hypothesis that is most probalbe, this is known as the [maximum a posteriori](https://en.wikipedia.org/wiki/Maximum_a_posteriori_estimation) or MAP decision rule. The corresponding classifier, a Bayes classifier, is the function that assigns a class label $y=C_k$ for some $k$ as follows:\n",
"$$ y = \\underset{k\\in\\{1,...K\\}}{argmax} P(C_k)\\prod_{i=1}^n P(x_i|C_k)$$\n",
"\n",
"It's surprisingly effective in domains with many weakly relevant features. Strong independence assumptions reduce performance when many features strongly correlated."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"+ Bernoulli Naive Bayes\n",
"+ Multinomial Naive Bayes"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| lgpl-3.0 |
penny4860/object-detector | readme.ipynb | 1 | 3027 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# object-detector\n",
"\n",
"This project implements basic object detection framework using python.\n",
"\n",
"References for building this project is [pyimagesearch](https://gurus.pyimagesearch.com/).\n",
"\n",
"## 1. Quick Start with default configuration file\n",
"\n",
"### 1) Download dataset\n",
"\n",
"* Download [Caltech-101](http://www.vision.caltech.edu/Image_Datasets/Caltech101/101_ObjectCategories.tar.gz)\n",
"* Download [houses](http://www.robots.ox.ac.uk/~vgg/data/houses/houses.tar) : This dataset will be used as the negative images\n",
"\n",
"### 2) Extract dataset files\n",
"\n",
"* Extract dataset files.\n",
"* Locate dataset directory as the following structure.\n",
"\n",
"```\n",
"|--- [Project Directory]\n",
"|--- [datasets]\n",
" |--- [caltech101]\n",
" |--- [101_ObjectCategories]\n",
" |--- [Annotations]\n",
" |--- [houses]\n",
"```\n",
"\n",
"### 3) Confirm the path of the dataset\n",
"\n",
"Run \"0_check_dataset_path.py\". If you can find the following message, you can go to the next step.\n",
"\n",
"```\n",
"c:\\object-detector>python 0_check_dataset_path.py\n",
"Positive dataset location is correct\n",
"Negative dataset location is correct\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4) Run the main driver\n",
"\n",
"Run \"main.py\". It will automatically build object-detector with the 7-step framework.\n",
"\n",
"#### 4.1) Displayed Test Image\n",
"\n",
"After finishing step 6, you can see the following test image and recognized bounding box.\n",
"<img src=\"examples/car_side.png\"> \n",
"\n",
"In the case of using **faces.conf** we can see the following test image and recognized bounding box.\n",
"<img src=\"examples/faces.png\">\n",
"\n",
"#### 4.2) Average-Precision Evaluation\n",
"\n",
"AP(Average Precision) is a measure of evaluating object detector. After finishing step 7, object detector's average precision score will be printed like this\n",
"\n",
"```\n",
"Average Precision : 0.937441470843\n",
"```\n",
"\n",
"It is also displayed [precision-recall curve](https://en.wikipedia.org/wiki/Precision_and_recall).\n",
"\n",
"<img src=\"examples/precision-recall.png\">\n",
"\n"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
tpin3694/tpin3694.github.io | statistics/.ipynb_checkpoints/Multi-Armed Bandit Problem-checkpoint.ipynb | 1 | 56699 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Title: Multi-Armed Bandit Problem \n",
"Slug: multi-armed-bandit \n",
"Summary: Exploring and implementing some of the approaches found to solving the multi-armed bandit problem. \n",
"Date: 2018-01-22 10:49 \n",
"Category: Statistcs \n",
"Tags: Basics \n",
"Authors: Thomas Pinder"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I first encountered the multi-armed bandit problem when learning about reinforcement learning. The problem is classic problem within machine learning and statistics, concerning itself with multiple slot machines, all with varying success probabilities. The problem is deciding which slot machine to choose, so as to give yourself the best probability of success, whilst losing the smallest amount of money. The notes here are based upon content from the Udemy course [here](https://www.udemy.com/artificial-intelligence-reinforcement-learning-in-python/learn/v4/) by the [Lazy Programmer](https://lazyprogrammer.me/deep-learning-courses/) with my own interpretations and annotations added in, so as to aid my own learning. \n",
"\n",
"### Epsilon-Greedy\n",
"An initial approach to solving this problem can be defined through an epsilon greedy strategy. Using this strategy, a small value of epsilon is chosen, this is our probability of exploitation. We then make random draws from a uniform probability distribution and if the value of this draw is less than epsilon we explore, meaning that we pull the arm of a random machine. If the draw is greater than epsilon, then we simply pull the arm of the best performing machine at present. To demonstrate this better we should implement this in the setting of three machines.\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEECAYAAADK0VhyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f18727de588>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bandit 1's Mean: 0.9945925152354567\n",
"Bandit 2's Mean: 2.031884525867273\n",
"Bandit 3's Mean: 3.0027190916428332\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEECAYAAAAmiP8hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMff/AYxQF5rwpXH9AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1898d74518>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bandit 1's Mean: 0.9232646374694955\n",
"Bandit 2's Mean: 2.0148010108311833\n",
"Bandit 3's Mean: 3.004720907443339\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEECAYAAAAmiP8hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1872a7ae80>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bandit 1's Mean: 0.9495607424791751\n",
"Bandit 2's Mean: 1.0253274264675913\n",
"Bandit 3's Mean: 3.0044380931662\n"
]
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"class Bandit:\n",
" def __init__(self, m):\n",
" self.m = m \n",
" self.mean = 0\n",
" self.N = 0\n",
" \n",
" def pull(self):\n",
" return np.random.randn() + self.m\n",
"\n",
" def update(self, x):\n",
" self.N += 1\n",
" self.mean = (1-1.0/self.N)*self.mean + 1.0/self.N*x\n",
" \n",
"\n",
"def run_simulation(m1, m2, m3, epsilon, N):\n",
" bandits = [Bandit(m1), Bandit(m2), Bandit(m3)]\n",
" data = np.empty(N)\n",
" for i in range(N):\n",
" draw = np.random.random()\n",
" if draw < epsilon:\n",
" j = np.random.choice(3)\n",
" else:\n",
" j = np.argmax([b.mean for b in bandits])\n",
" x = bandits[j].pull()\n",
" bandits[j].update(x)\n",
" data[i] = x\n",
" cumulative_avg = np.cumsum(data)/(np.arange(N) + 1)\n",
"\n",
" plt.plot(cumulative_avg)\n",
" plt.plot(np.ones(N)*m1)\n",
" plt.plot(np.ones(N)*m2)\n",
" plt.plot(np.ones(N)*m3)\n",
" plt.xscale('log')\n",
" plt.show()\n",
" \n",
" i = 1\n",
" for b in bandits:\n",
" print(\"Bandit {}'s Mean: {}\".format(i, b.mean))\n",
" i += 1\n",
" \n",
" return cumulative_avg\n",
"\n",
"if __name__ == \"__main__\":\n",
" c_1 = run_simulation(1.0, 2.0, 3.0, 0.1, 10000)\n",
" c_05 = run_simulation(1.0, 2.0, 3.0, 0.05, 10000)\n",
" c_01 = run_simulation(1.0, 2.0, 3.0, 0.001, 10000)\n",
" \n",
" plt.plot(c_1, label = 'eps=0.1')\n",
" plt.plot(c_05, label = 'eps=0.05')\n",
" plt.plot(c_01, label = 'eps=0.01')\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So what's going on here? Well, we first create a bandit object and assign to it a couple of methods that will be needed, firstly the pulling motion in method *pull()*. This method replicates the act of pulling the bandit's arm and takes a random draw that will later be compared to our value of epsilon. The second method we've created, *update()* takes a single argument, *x* the bandit's draw and updates the sample mean along with incrementing the counter N. \n",
"\n",
"With a bandit object constructed, we can run the simulations from the *run_simulation()* function. This function takes in five arguments, the first three of which is true means for our bandits, i.e. the true probability of success from the bandit. The fourth function is our epsilon and this will determine the probability of eploiting or exploring. Finally we pass the function N, the number of simulations to run. From here, an empty matrix is initialised for us to store our bandits pulls. We then go ahead and loop through each of iterations, each time taking a random draw and comparing it to epsilon. If the draw is less than epsilon we randomly select on of our three bandit machines, else we choose the optimal machine. Once a machine has been selected, we pull the machine and then update the machine's sample mean and plot the results. The function returns a cumulative average that we have calculated by calculating the cumulative sum of the machine's outputs and dividing by the number of iterations."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
mitliagkas/graphs | randomwalks/WDC Random Walk.ipynb | 1 | 18243 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import networkx as nx\n",
"import math\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from IPython.display import Javascript"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Load data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lastnode = 5000"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"datafile = open('/var/datasets/wdc/small-pld-arc')\n",
"\n",
"G = nx.DiGraph()\n",
"\n",
"for line in datafile:\n",
" ijstr = line.split('\\t')\n",
" \n",
" i=int(ijstr[0])\n",
" j=int(ijstr[1])\n",
" \n",
" if i>lastnode:\n",
" break\n",
" if j>lastnode:\n",
" continue\n",
" G.add_edge(i,j)\n",
" \n",
"datafile.close()\n",
"Gorig = G.copy()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"indexfile = open('/var/datasets/wdc/small-pld-index')\n",
"index = {}\n",
"\n",
"for line in indexfile:\n",
" namei = line.split('\\t')\n",
" \n",
" name=namei[0]\n",
" i=int(namei[1])\n",
" \n",
" if i>lastnode:\n",
" break\n",
"\n",
" index[i]=name\n",
" \n",
"indexfile.close()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def cleanupgraph(G):\n",
" comp = nx.weakly_connected_components(G.copy())\n",
" for c in comp:\n",
" if len(c)<4:\n",
" G.remove_nodes_from(c)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def graphcleanup(G):\n",
" for (node, deg) in G.degree_iter():\n",
" if deg==0:\n",
" G.remove_node(node)\n",
" elif deg==1:\n",
" if G.degree((G.predecessors(node) + G.successors(node))[0]) == 1:\n",
" G.remove_node(node)\n",
" elif deg==2 and G.in_degree(node)==1:\n",
" if (G.predecessors(node) == G.successors(node)) and G.degree((G.predecessors(node) + G.successors(node))[0]) == 2:\n",
" G.remove_node(node)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cleanupgraph(G)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"232"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"G.size()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"402"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Gorig.number_of_nodes()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"409"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Gorig.size()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Convert to Javascript for interactivity"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Adapted from:\n",
"http://nbviewer.ipython.org/github/ipython-books/cookbook-code/blob/master/notebooks/chapter06_viz/04_d3.ipynb"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From:\n",
"http://networkx.github.io/documentation/latest/examples/javascript/force.html"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#from IPython.core.display import display_javascript\n",
"import json\n",
"from networkx.readwrite import json_graph"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"d = json_graph.node_link_data(G)\n",
"for node in d['nodes']:\n",
" node['name']=node['id']\n",
" node['value']=G.degree(node['id'])\n",
" if True:\n",
" node['group'] = node['id'] % 4\n",
" else:\n",
" if node['id']<10:\n",
" node['group']=0#node['id'] % 4\n",
" else:\n",
" node['group']=1#node['id'] % 4\n",
" \n",
"d['adjacency'] = json_graph.adjacency_data(G)['adjacency']\n",
"json.dump(d, open('rwgraph.json','w'))"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div id=\"d3-example\"></div>\n",
"<style>\n",
".node {stroke: #fff; stroke-width: 1.5px;}\n",
".link {stroke: #999; stroke-opacity: .3;}\n",
"</style>\n",
"<script src=\"randomwalk.js\"></script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%html\n",
"<div id=\"d3-example\"></div>\n",
"<style>\n",
".node {stroke: #fff; stroke-width: 1.5px;}\n",
".link {stroke: #999; stroke-opacity: .3;}\n",
"</style>\n",
"<script src=\"randomwalk.js\"></script>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Uses:\n",
"https://github.com/mbostock/d3/wiki/Force-Layout\n",
"\n",
"http://bl.ocks.org/mbostock/4062045"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "IOError",
"evalue": "[Errno 2] No such file or directory: u'force.js'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIOError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-17-76c543eb261d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mJavascript\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'force.js'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/migish/anaconda/lib/python2.7/site-packages/IPython/core/display.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, url, filename, lib, css)\u001b[0m\n\u001b[1;32m 589\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlib\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 590\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 591\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mJavascript\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 592\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 593\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_repr_javascript_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/migish/anaconda/lib/python2.7/site-packages/IPython/core/display.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, url, filename)\u001b[0m\n\u001b[1;32m 384\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfilename\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0municode_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 385\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 386\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 387\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/migish/anaconda/lib/python2.7/site-packages/IPython/core/display.pyc\u001b[0m in \u001b[0;36mreload\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 402\u001b[0m \u001b[0;34m\"\"\"Reload the raw data from file or URL.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 403\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilename\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 404\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_flags\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 405\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 406\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: u'force.js'"
]
}
],
"source": [
"Javascript(filename='force.js')"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"L = nx.linalg.laplacianmatrix.directed_laplacian_matrix(G)\n",
"Linv = np.linalg.inv(L)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(138, 138)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L.shape"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"n = L.shape[0]\n",
"Reff = np.zeros((n,n))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Gsparse = G.copy()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"graphcleanup(Gsparse)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nodelookup={Gsparse.nodes()[idx]:idx for idx in range(len(Gsparse.nodes()))}"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"edge = np.zeros((n,1))\n",
"for (i,j) in Gsparse.edges_iter():\n",
" edge[nodelookup[i]] = 1\n",
" edge[nodelookup[j]] = -1\n",
" Reff[nodelookup[i],nodelookup[j]] = edge.T.dot(Linv.dot(edge))\n",
" edge[[nodelookup[i]]] = 0\n",
" edge[[nodelookup[j]]] = 0"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ReffAbs=np.abs(Reff)+np.abs(Reff.T)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you call\n",
"\n",
"arr.argsort()[:3]\n",
"It will give you the indices of the 3 smallest elements.\n",
"\n",
"array([0, 2, 1], dtype=int64)\n",
"So, for n, you should call\n",
"\n",
"arr.argsort()[:n]"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"res = ReffAbs.reshape(n**2)\n",
"argp = np.argpartition(res,n**2-n)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mask = (ReffAbs < res[argp[-int(0.5*Gsparse.number_of_nodes())]]) & (ReffAbs >0)\n",
"for (i,j) in Gsparse.edges():\n",
" if mask[nodelookup[i],nodelookup[j]]:\n",
" Gsparse.remove_edge(i,j)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cleanupgraph(Gsparse)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"d = json_graph.node_link_data(Gsparse)\n",
"for node in d['nodes']:\n",
" node['name']=index[node['id']]\n",
" node['value']=Gsparse.degree(node['id'])\n",
" node['group']=index[node['id']][-3:]\n",
"\n",
"json.dump(d, open('graph.json','w'))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"409"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Gorig.number_of_edges()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"17"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Gsparse.number_of_edges()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"16"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Gsparse.number_of_nodes()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"GsparseAdj = nx.linalg.adjacency_matrix(Gorig).toarray()\n",
"GsparseAdj = nx.to_numpy_matrix(Gorig)\n",
"GsparseAdj[ReffAbs < res[argp[-300]]] = 0\n",
"Gsparse = nx.from_numpy_matrix?\n",
"Gsparse = nx.from_numpy_matrix\n",
"Gsparse = nx.from_numpy_matrix(GsparseAdj, create_using=nx.DiGraph())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Gsparse = nx.from_numpy_matrix"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 %\n",
"10 %\n",
"20 %\n",
"30 %\n",
"40 %\n",
"50 %\n",
"60 %\n",
"71 %\n",
"81 %\n",
"91 %\n"
]
}
],
"source": [
"edge = np.zeros((n,1))\n",
"for i in range(n):\n",
" if i % int(math.ceil((float(10)/100)*n)) == 0: \n",
" print int(math.floor(100*float(i)/n)), '%'\n",
" \n",
" edge[i] = 1\n",
"\n",
" for j in range(i+1, n):\n",
" edge[j] = -1\n",
" Reff[i,j] = edge.T.dot(Linv.dot(edge))\n",
" edge[j] = 0\n",
" \n",
" edge[i] = 0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
jmhsi/justin_tinker | data_science/music/testing_wavenet.ipynb | 1 | 3555 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import torchaudio\n",
"import pydub\n",
"import librosa\n",
"import librosa.display\n",
"\n",
"from IPython.display import FileLink, FileLinks\n",
"import IPython.display as ipd\n",
"\n",
"%matplotlib notebook"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"path = os.path.abspath('/home/justin/rsync_dl_rig/mp3s/Kaskade - Santa Baby ft. Jane.mp3')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ipd.Audio(filename=path, autoplay=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data, sampling_rate = librosa.load(path, sr=44100, mono=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=(12, 4))\n",
"librosa.display.waveplot(data, sr=sampling_rate)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sampling_rate"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ipd.Audio(data, rate=sampling_rate, autoplay=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"librosa.output.write_wav('foo.wav', data, sampling_rate)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Below is how to convert the .wav to .mp3"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"librosa.output.write_wav('foo.wav', data, sampling_rate)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!sox -r 44100 -c 2 foo.wav foo.mp3"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ipd.Audio('foo.mp3', autoplay=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
],
"window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
ioam/holoviews | examples/reference/streams/bokeh/Selection1D_tap.ipynb | 1 | 3175 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### **Title**: Regression selection\n",
"\n",
"**Description**: A linked streams example demonstrating how to the Selection1D stream to tap on a datapoint and reveal a regression plot. Highlights how custom interactivity can be used to reveal more information about a dataset.\n",
"\n",
"**Dependencies**: Bokeh, SciPy\n",
"\n",
"**Backends**: [Bokeh](./Selection1D_tap.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import holoviews as hv\n",
"from holoviews import opts\n",
"from holoviews.streams import Selection1D\n",
"from scipy import stats\n",
"hv.extension('bokeh')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def gen_samples(N, corr=0.8):\n",
" xx = np.array([-0.51, 51.2])\n",
" yy = np.array([0.33, 51.6])\n",
" means = [xx.mean(), yy.mean()] \n",
" stds = [xx.std() / 3, yy.std() / 3]\n",
" covs = [[stds[0]**2 , stds[0]*stds[1]*corr], \n",
" [stds[0]*stds[1]*corr, stds[1]**2]] \n",
"\n",
" return np.random.multivariate_normal(means, covs, N)\n",
"\n",
"data = [('Week %d' % (i%10), np.random.rand(), chr(65+np.random.randint(5)), i) for i in range(100)]\n",
"sample_data = hv.NdOverlay({i: hv.Points(gen_samples(np.random.randint(1000, 5000), r2))\n",
" for _, r2, _, i in data})\n",
"points = hv.Scatter(data, 'Date', ['r2', 'block', 'id']).redim.range(r2=(0., 1))\n",
"stream = Selection1D(source=points)\n",
"empty = (hv.Points(np.random.rand(0, 2)) * hv.Slope(0, 0)).relabel('No selection')\n",
"\n",
"def regression(index):\n",
" if not index:\n",
" return empty\n",
" scatter = sample_data[index[0]]\n",
" xs, ys = scatter['x'], scatter['y']\n",
" slope, intercep, rval, pval, std = stats.linregress(xs, ys)\n",
" return (scatter * hv.Slope(slope, intercep)).relabel('r2: %.3f' % slope)\n",
"\n",
"reg = hv.DynamicMap(regression, kdims=[], streams=[stream])\n",
"\n",
"average = hv.Curve(points, 'Date', 'r2').aggregate(function=np.mean)\n",
"layout = points * average + reg\n",
"layout.opts(\n",
" opts.Curve(color='black'),\n",
" opts.Slope(color='black', framewise=True),\n",
" opts.Scatter(color='block', tools=['tap', 'hover'], width=600, \n",
" marker='triangle', cmap='Set1', size=10, framewise=True),\n",
" opts.Points(frame_width=250),\n",
" opts.Overlay(toolbar='above', legend_position='right')\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<center><img src=\"https://assets.holoviews.org/gifs/examples/streams/bokeh/regression_tap.gif\" width=800></center>"
]
}
],
"metadata": {
"language_info": {
"name": "python",
"pygments_lexer": "ipython3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| bsd-3-clause |
ODZ-UJF-AV-CR/osciloskop | micsig.ipynb | 1 | 4405 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Oscilloskope USBTCM utility"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Micsig, TO1072, 311070077, 6.11\n",
"\n",
"-1\n",
"\n",
"0\n",
"\n"
]
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import sys\n",
"import os\n",
"import time\n",
"import h5py\n",
"import numpy as np\n",
"\n",
"\n",
"# TRYING TO ADAPT THE SCRIPT FOR THE MICSIG oscilloscope\n",
"\n",
"# Create ownership rule as /etc/udev/rules.d/99-micsig.rules\n",
"# SUBSYSTEMS==\"usb\", ATTRS{idVendor}==\"18d1\", ATTRS{idProduct}==\"0303\", GROUP=\"medved\", MODE=\"0666\"\n",
"\n",
"class UsbTmcDriver:\n",
"\n",
" def __init__(self, device):\n",
" self.device = device\n",
" self.FILE = os.open(device, os.O_RDWR)\n",
" \n",
" def write(self, command):\n",
" os.write(self.FILE, command);\n",
" \n",
" def read(self, length = 2048):\n",
" return os.read(self.FILE, length)\n",
" \n",
" def getName(self):\n",
" self.write(\"*IDN?\")\n",
" return self.read(300)\n",
" \n",
" def sendReset(self):\n",
" self.write(\"*RST\") # Be carefull, this real resets an oscilloscope\n",
"\n",
"# Looking for USBTMC device\n",
"def getDeviceList(): \n",
" dirList=os.listdir(\"/dev\")\n",
" result=list()\n",
"\n",
" for fname in dirList:\n",
" if(fname.startswith(\"usbtmc\")):\n",
" result.append(\"/dev/\" + fname)\n",
"\n",
" return result\n",
"\n",
"# looking for oscilloscope\n",
"devices = getDeviceList()\n",
"# initiate oscilloscope\n",
"osc = UsbTmcDriver(devices[0])\n",
"\n",
"print osc.getName()\n",
"osc.write(\":STOP\")\n",
"time.sleep(.2)\n",
"osc.write(':WAV:SOUR CHAN1')\n",
"time.sleep(.2)\n",
"osc.write(':WAV:XINC?')\n",
"time.sleep(.2)\n",
"data = bytearray(osc.read(500))\n",
"print data\n",
"osc.write(':WAV:YINC?')\n",
"time.sleep(.2)\n",
"data = bytearray(osc.read(500))\n",
"print data\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"osc.write(\"MENU:RUN\")\n",
"time.sleep(1)\n",
"\n",
"'''\n",
"osc.write(':WAV:SOUR CHAN1')\n",
"time.sleep(.2)\n",
"osc.write(':WAV:MODE NORM')\n",
"time.sleep(.2)\n",
"osc.write(':WAV:DATA?')\n",
"time.sleep(.2)\n",
"wave1 = bytearray(osc.read(500))\n",
"print wave1\n",
"time.sleep(.2)\n",
"osc.write(\"MENU:STOP\")\n",
"time.sleep(.2)\n",
"osc.write(':WAV:SOUR CHAN1')\n",
"time.sleep(.2)\n",
"osc.write(':WAV:MODE RAW')\n",
"time.sleep(.2)\n",
"osc.write(':WAV:RESet')\n",
"time.sleep(.2)\n",
"osc.write(':WAV:BEGin')\n",
"time.sleep(.2)\n",
"osc.write(':WAV:STATus?')\n",
"time.sleep(.2)\n",
"wave1 = bytearray(osc.read(500))\n",
"print wave1\n",
"'''\n",
"osc.write(\"MENU:STOP\")\n",
"time.sleep(1)\n",
"\n",
"#osc.write(':STORage:SAVECH1,UDISK')\n",
"osc.write(':STORage:CAPTure')\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"osc.write(':WAV:DATA?')\n",
"time.sleep(.2)\n",
"wave1 = bytearray(osc.read(500))\n",
"print wave1\n",
"\n",
"#osc.write(':WAV:END')\n",
"#time.sleep(.2)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.15+"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
kmunve/APS | aps/notebooks/ml_varsom/preprocessing.ipynb | 1 | 846214 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"pycharm": {}
},
"source": [
"# Pre-processing of avalanche warning data for machine learning\n"
]
},
{
"cell_type": "code",
"execution_count": 237,
"metadata": {
"collapsed": false,
"pycharm": {
"is_executing": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"D:\\Dev\\APS\\aps\\notebooks\\ml_varsom\n"
]
}
],
"source": [
"import sys\n",
"import pandas as pd # check out Modin https://towardsdatascience.com/get-faster-pandas-with-modin-even-on-your-laptops-b527a2eeda74\n",
"import numpy as np\n",
"import json\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from pathlib import Path\n",
"import datetime\n",
"\n",
"# Add path to APS modules\n",
"aps_pth = Path('.').absolute()\n",
"print(aps_pth)\n",
"if aps_pth not in sys.path:\n",
" sys.path.append(aps_pth)\n",
"sns.set(style=\"white\")\n",
"#from sklearn.preprocessing import LabelEncoder\n",
"#from pprint import pprint\n",
"\n",
"#pd.set_option(\"display.max_rows\",6)\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 238,
"metadata": {
"collapsed": false,
"pycharm": {
"is_executing": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>author</th>\n",
" <th>avalanche_danger</th>\n",
" <th>avalanche_problem_1_advice</th>\n",
" <th>avalanche_problem_1_cause_id</th>\n",
" <th>avalanche_problem_1_cause_name</th>\n",
" <th>avalanche_problem_1_destructive_size_ext_id</th>\n",
" <th>avalanche_problem_1_destructive_size_ext_name</th>\n",
" <th>avalanche_problem_1_distribution_id</th>\n",
" <th>avalanche_problem_1_distribution_name</th>\n",
" <th>avalanche_problem_1_exposed_height_1</th>\n",
" <th>...</th>\n",
" <th>region_id</th>\n",
" <th>region_name</th>\n",
" <th>region_type_id</th>\n",
" <th>region_type_name</th>\n",
" <th>snow_surface</th>\n",
" <th>utm_east</th>\n",
" <th>utm_north</th>\n",
" <th>utm_zone</th>\n",
" <th>valid_from</th>\n",
" <th>valid_to</th>\n",
" </tr>\n",
" <tr>\n",
" <th>index</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>3003</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>3007</td>\n",
" <td>Vest-Finnmark</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>3009</td>\n",
" <td>Nord-Troms</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>3010</td>\n",
" <td>Lyngen</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>3011</td>\n",
" <td>Tromsø</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 102 columns</p>\n",
"</div>"
],
"text/plain": [
" author avalanche_danger avalanche_problem_1_advice \\\n",
"index \n",
"0 NaN NaN Not given \n",
"1 NaN NaN Not given \n",
"2 NaN NaN Not given \n",
"3 NaN NaN Not given \n",
"4 NaN NaN Not given \n",
"\n",
" avalanche_problem_1_cause_id avalanche_problem_1_cause_name \\\n",
"index \n",
"0 0 Not given \n",
"1 0 Not given \n",
"2 0 Not given \n",
"3 0 Not given \n",
"4 0 Not given \n",
"\n",
" avalanche_problem_1_destructive_size_ext_id \\\n",
"index \n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
" avalanche_problem_1_destructive_size_ext_name \\\n",
"index \n",
"0 Not given \n",
"1 Not given \n",
"2 Not given \n",
"3 Not given \n",
"4 Not given \n",
"\n",
" avalanche_problem_1_distribution_id \\\n",
"index \n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
" avalanche_problem_1_distribution_name \\\n",
"index \n",
"0 Not given \n",
"1 Not given \n",
"2 Not given \n",
"3 Not given \n",
"4 Not given \n",
"\n",
" avalanche_problem_1_exposed_height_1 ... \\\n",
"index ... \n",
"0 0 ... \n",
"1 0 ... \n",
"2 0 ... \n",
"3 0 ... \n",
"4 0 ... \n",
"\n",
" region_id region_name region_type_id region_type_name \\\n",
"index \n",
"0 3003 Nordenskiöld Land 10 A \n",
"1 3007 Vest-Finnmark 10 A \n",
"2 3009 Nord-Troms 10 A \n",
"3 3010 Lyngen 10 A \n",
"4 3011 Tromsø 10 A \n",
"\n",
" snow_surface utm_east utm_north utm_zone valid_from \\\n",
"index \n",
"0 NaN 0 0 0 2016-12-01 00:00:00.000 \n",
"1 NaN 0 0 0 2016-12-01 00:00:00.000 \n",
"2 NaN 0 0 0 2016-12-01 00:00:00.000 \n",
"3 NaN 0 0 0 2016-12-01 00:00:00.000 \n",
"4 NaN 0 0 0 2016-12-01 00:00:00.000 \n",
"\n",
" valid_to \n",
"index \n",
"0 2016-12-01 23:59:59.000 \n",
"1 2016-12-01 23:59:59.000 \n",
"2 2016-12-01 23:59:59.000 \n",
"3 2016-12-01 23:59:59.000 \n",
"4 2016-12-01 23:59:59.000 \n",
"\n",
"[5 rows x 102 columns]"
]
},
"execution_count": 238,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# analysis of entire data set - collected using varsomdata2.varsomscripts.avalanchewarningscomplete.get_season_17_18()\n",
"#data_pth = Path(r'.\\aps\\data\\varsom\\norwegian_avalanche_warnings_season_17_18.csv')\n",
"data_pth = Path(r'D:\\Dev\\APS\\aps\\data\\varsom\\norwegian_avalanche_warnings_season_16_19.csv')\n",
"\n",
"#varsom_df = pd.read_csv(aps_pth / data_pth, index_col=0)\n",
"varsom_df = pd.read_csv(data_pth, index_col=0)\n",
"varsom_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 239,
"metadata": {
"collapsed": false,
"pycharm": {
"is_executing": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"array(['author', 'avalanche_danger', 'avalanche_problem_1_advice',\n",
" 'avalanche_problem_1_cause_id', 'avalanche_problem_1_cause_name',\n",
" 'avalanche_problem_1_destructive_size_ext_id',\n",
" 'avalanche_problem_1_destructive_size_ext_name',\n",
" 'avalanche_problem_1_distribution_id',\n",
" 'avalanche_problem_1_distribution_name',\n",
" 'avalanche_problem_1_exposed_height_1',\n",
" 'avalanche_problem_1_exposed_height_2',\n",
" 'avalanche_problem_1_exposed_height_fill',\n",
" 'avalanche_problem_1_ext_id', 'avalanche_problem_1_ext_name',\n",
" 'avalanche_problem_1_probability_id',\n",
" 'avalanche_problem_1_probability_name',\n",
" 'avalanche_problem_1_problem_id',\n",
" 'avalanche_problem_1_problem_type_id',\n",
" 'avalanche_problem_1_problem_type_name',\n",
" 'avalanche_problem_1_trigger_simple_id',\n",
" 'avalanche_problem_1_trigger_simple_name',\n",
" 'avalanche_problem_1_type_id', 'avalanche_problem_1_type_name',\n",
" 'avalanche_problem_1_valid_expositions',\n",
" 'avalanche_problem_2_advice', 'avalanche_problem_2_cause_id',\n",
" 'avalanche_problem_2_cause_name',\n",
" 'avalanche_problem_2_destructive_size_ext_id',\n",
" 'avalanche_problem_2_destructive_size_ext_name',\n",
" 'avalanche_problem_2_distribution_id',\n",
" 'avalanche_problem_2_distribution_name',\n",
" 'avalanche_problem_2_exposed_height_1',\n",
" 'avalanche_problem_2_exposed_height_2',\n",
" 'avalanche_problem_2_exposed_height_fill',\n",
" 'avalanche_problem_2_ext_id', 'avalanche_problem_2_ext_name',\n",
" 'avalanche_problem_2_probability_id',\n",
" 'avalanche_problem_2_probability_name',\n",
" 'avalanche_problem_2_problem_id',\n",
" 'avalanche_problem_2_problem_type_id',\n",
" 'avalanche_problem_2_problem_type_name',\n",
" 'avalanche_problem_2_trigger_simple_id',\n",
" 'avalanche_problem_2_trigger_simple_name',\n",
" 'avalanche_problem_2_type_id', 'avalanche_problem_2_type_name',\n",
" 'avalanche_problem_2_valid_expositions',\n",
" 'avalanche_problem_3_advice', 'avalanche_problem_3_cause_id',\n",
" 'avalanche_problem_3_cause_name',\n",
" 'avalanche_problem_3_destructive_size_ext_id',\n",
" 'avalanche_problem_3_destructive_size_ext_name',\n",
" 'avalanche_problem_3_distribution_id',\n",
" 'avalanche_problem_3_distribution_name',\n",
" 'avalanche_problem_3_exposed_height_1',\n",
" 'avalanche_problem_3_exposed_height_2',\n",
" 'avalanche_problem_3_exposed_height_fill',\n",
" 'avalanche_problem_3_ext_id', 'avalanche_problem_3_ext_name',\n",
" 'avalanche_problem_3_probability_id',\n",
" 'avalanche_problem_3_probability_name',\n",
" 'avalanche_problem_3_problem_id',\n",
" 'avalanche_problem_3_problem_type_id',\n",
" 'avalanche_problem_3_problem_type_name',\n",
" 'avalanche_problem_3_trigger_simple_id',\n",
" 'avalanche_problem_3_trigger_simple_name',\n",
" 'avalanche_problem_3_type_id', 'avalanche_problem_3_type_name',\n",
" 'avalanche_problem_3_valid_expositions', 'current_weak_layers',\n",
" 'danger_level', 'danger_level_name', 'date_valid',\n",
" 'emergency_warning', 'latest_avalanche_activity',\n",
" 'latest_observations', 'main_text',\n",
" 'mountain_weather_change_hour_of_day_start',\n",
" 'mountain_weather_change_hour_of_day_stop',\n",
" 'mountain_weather_change_wind_direction',\n",
" 'mountain_weather_change_wind_speed',\n",
" 'mountain_weather_fl_hour_of_day_start',\n",
" 'mountain_weather_fl_hour_of_day_stop',\n",
" 'mountain_weather_freezing_level',\n",
" 'mountain_weather_precip_most_exposed',\n",
" 'mountain_weather_precip_region',\n",
" 'mountain_weather_temperature_elevation',\n",
" 'mountain_weather_temperature_max',\n",
" 'mountain_weather_temperature_min',\n",
" 'mountain_weather_wind_direction', 'mountain_weather_wind_speed',\n",
" 'publish_time', 'reg_id', 'region_id', 'region_name',\n",
" 'region_type_id', 'region_type_name', 'snow_surface', 'utm_east',\n",
" 'utm_north', 'utm_zone', 'valid_from', 'valid_to'], dtype=object)"
]
},
"execution_count": 239,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"varsom_df.columns.values"
]
},
{
"cell_type": "code",
"execution_count": 240,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>avalanche_problem_1_cause_id</th>\n",
" <th>avalanche_problem_1_cause_name</th>\n",
" <th>avalanche_problem_1_destructive_size_ext_id</th>\n",
" <th>avalanche_problem_1_destructive_size_ext_name</th>\n",
" <th>avalanche_problem_1_distribution_id</th>\n",
" <th>avalanche_problem_1_distribution_name</th>\n",
" <th>avalanche_problem_1_exposed_height_1</th>\n",
" <th>avalanche_problem_1_exposed_height_2</th>\n",
" <th>avalanche_problem_1_exposed_height_fill</th>\n",
" <th>avalanche_problem_1_ext_id</th>\n",
" <th>avalanche_problem_1_ext_name</th>\n",
" <th>avalanche_problem_1_probability_id</th>\n",
" <th>avalanche_problem_1_probability_name</th>\n",
" <th>avalanche_problem_1_problem_id</th>\n",
" <th>avalanche_problem_1_problem_type_id</th>\n",
" <th>avalanche_problem_1_problem_type_name</th>\n",
" <th>avalanche_problem_1_trigger_simple_id</th>\n",
" <th>avalanche_problem_1_trigger_simple_name</th>\n",
" </tr>\n",
" <tr>\n",
" <th>index</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>600</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>20</td>\n",
" <td>Tørre flakskred</td>\n",
" <td>3</td>\n",
" <td>Mulig</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>Fokksnø (flakskred)</td>\n",
" <td>21</td>\n",
" <td>Liten tilleggsbelastning</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>700</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>20</td>\n",
" <td>Tørre flakskred</td>\n",
" <td>3</td>\n",
" <td>Mulig</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>Fokksnø (flakskred)</td>\n",
" <td>10</td>\n",
" <td>Stor tilleggsbelastning</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>3</td>\n",
" <td>Mange bratte heng</td>\n",
" <td>400</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>20</td>\n",
" <td>Tørre flakskred</td>\n",
" <td>3</td>\n",
" <td>Mulig</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>Nysnø (flakskred)</td>\n",
" <td>21</td>\n",
" <td>Liten tilleggsbelastning</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>3</td>\n",
" <td>Mange bratte heng</td>\n",
" <td>400</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>20</td>\n",
" <td>Tørre flakskred</td>\n",
" <td>3</td>\n",
" <td>Mulig</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>Nysnø (flakskred)</td>\n",
" <td>21</td>\n",
" <td>Liten tilleggsbelastning</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>3</td>\n",
" <td>3 - Store</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>500</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>20</td>\n",
" <td>Tørre flakskred</td>\n",
" <td>3</td>\n",
" <td>Mulig</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>Nysnø (flakskred)</td>\n",
" <td>21</td>\n",
" <td>Liten tilleggsbelastning</td>\n",
" </tr>\n",
" <tr>\n",
" <th>131</th>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>3</td>\n",
" <td>3 - Store</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>700</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>20</td>\n",
" <td>Tørre flakskred</td>\n",
" <td>3</td>\n",
" <td>Mulig</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>Nysnø (flakskred)</td>\n",
" <td>22</td>\n",
" <td>Naturlig utløst</td>\n",
" </tr>\n",
" <tr>\n",
" <th>152</th>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>700</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>20</td>\n",
" <td>Tørre flakskred</td>\n",
" <td>3</td>\n",
" <td>Mulig</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>Nysnø (flakskred)</td>\n",
" <td>21</td>\n",
" <td>Liten tilleggsbelastning</td>\n",
" </tr>\n",
" <tr>\n",
" <th>173</th>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>700</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>20</td>\n",
" <td>Tørre flakskred</td>\n",
" <td>3</td>\n",
" <td>Mulig</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>Fokksnø (flakskred)</td>\n",
" <td>10</td>\n",
" <td>Stor tilleggsbelastning</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>14</td>\n",
" <td>Dårlig binding mellom glatt skare og overligge...</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>600</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>20</td>\n",
" <td>Tørre flakskred</td>\n",
" <td>3</td>\n",
" <td>Mulig</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>Nysnø (flakskred)</td>\n",
" <td>21</td>\n",
" <td>Liten tilleggsbelastning</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" avalanche_problem_1_cause_id \\\n",
"index \n",
"5 0 \n",
"26 10 \n",
"47 10 \n",
"68 10 \n",
"89 10 \n",
"110 10 \n",
"131 10 \n",
"152 10 \n",
"173 10 \n",
"194 14 \n",
"\n",
" avalanche_problem_1_cause_name \\\n",
"index \n",
"5 Not given \n",
"26 Nedføyket svakt lag med nysnø \n",
"47 Nedføyket svakt lag med nysnø \n",
"68 Nedføyket svakt lag med nysnø \n",
"89 Nedføyket svakt lag med nysnø \n",
"110 Nedføyket svakt lag med nysnø \n",
"131 Nedføyket svakt lag med nysnø \n",
"152 Nedføyket svakt lag med nysnø \n",
"173 Nedføyket svakt lag med nysnø \n",
"194 Dårlig binding mellom glatt skare og overligge... \n",
"\n",
" avalanche_problem_1_destructive_size_ext_id \\\n",
"index \n",
"5 0 \n",
"26 2 \n",
"47 2 \n",
"68 2 \n",
"89 2 \n",
"110 3 \n",
"131 3 \n",
"152 2 \n",
"173 2 \n",
"194 2 \n",
"\n",
" avalanche_problem_1_destructive_size_ext_name \\\n",
"index \n",
"5 Not given \n",
"26 2 - Middels \n",
"47 2 - Middels \n",
"68 2 - Middels \n",
"89 2 - Middels \n",
"110 3 - Store \n",
"131 3 - Store \n",
"152 2 - Middels \n",
"173 2 - Middels \n",
"194 2 - Middels \n",
"\n",
" avalanche_problem_1_distribution_id \\\n",
"index \n",
"5 0 \n",
"26 2 \n",
"47 2 \n",
"68 3 \n",
"89 3 \n",
"110 2 \n",
"131 2 \n",
"152 2 \n",
"173 2 \n",
"194 2 \n",
"\n",
" avalanche_problem_1_distribution_name \\\n",
"index \n",
"5 Not given \n",
"26 Noen bratte heng \n",
"47 Noen bratte heng \n",
"68 Mange bratte heng \n",
"89 Mange bratte heng \n",
"110 Noen bratte heng \n",
"131 Noen bratte heng \n",
"152 Noen bratte heng \n",
"173 Noen bratte heng \n",
"194 Noen bratte heng \n",
"\n",
" avalanche_problem_1_exposed_height_1 \\\n",
"index \n",
"5 0 \n",
"26 600 \n",
"47 700 \n",
"68 400 \n",
"89 400 \n",
"110 500 \n",
"131 700 \n",
"152 700 \n",
"173 700 \n",
"194 600 \n",
"\n",
" avalanche_problem_1_exposed_height_2 \\\n",
"index \n",
"5 0 \n",
"26 0 \n",
"47 0 \n",
"68 0 \n",
"89 0 \n",
"110 0 \n",
"131 0 \n",
"152 0 \n",
"173 0 \n",
"194 0 \n",
"\n",
" avalanche_problem_1_exposed_height_fill avalanche_problem_1_ext_id \\\n",
"index \n",
"5 0 0 \n",
"26 1 20 \n",
"47 1 20 \n",
"68 1 20 \n",
"89 1 20 \n",
"110 1 20 \n",
"131 1 20 \n",
"152 1 20 \n",
"173 1 20 \n",
"194 2 20 \n",
"\n",
" avalanche_problem_1_ext_name avalanche_problem_1_probability_id \\\n",
"index \n",
"5 Not given 0 \n",
"26 Tørre flakskred 3 \n",
"47 Tørre flakskred 3 \n",
"68 Tørre flakskred 3 \n",
"89 Tørre flakskred 3 \n",
"110 Tørre flakskred 3 \n",
"131 Tørre flakskred 3 \n",
"152 Tørre flakskred 3 \n",
"173 Tørre flakskred 3 \n",
"194 Tørre flakskred 3 \n",
"\n",
" avalanche_problem_1_probability_name avalanche_problem_1_problem_id \\\n",
"index \n",
"5 Not given 0 \n",
"26 Mulig 1 \n",
"47 Mulig 1 \n",
"68 Mulig 1 \n",
"89 Mulig 1 \n",
"110 Mulig 1 \n",
"131 Mulig 1 \n",
"152 Mulig 1 \n",
"173 Mulig 1 \n",
"194 Mulig 1 \n",
"\n",
" avalanche_problem_1_problem_type_id \\\n",
"index \n",
"5 0 \n",
"26 10 \n",
"47 10 \n",
"68 7 \n",
"89 7 \n",
"110 7 \n",
"131 7 \n",
"152 7 \n",
"173 10 \n",
"194 7 \n",
"\n",
" avalanche_problem_1_problem_type_name \\\n",
"index \n",
"5 Not given \n",
"26 Fokksnø (flakskred) \n",
"47 Fokksnø (flakskred) \n",
"68 Nysnø (flakskred) \n",
"89 Nysnø (flakskred) \n",
"110 Nysnø (flakskred) \n",
"131 Nysnø (flakskred) \n",
"152 Nysnø (flakskred) \n",
"173 Fokksnø (flakskred) \n",
"194 Nysnø (flakskred) \n",
"\n",
" avalanche_problem_1_trigger_simple_id \\\n",
"index \n",
"5 0 \n",
"26 21 \n",
"47 10 \n",
"68 21 \n",
"89 21 \n",
"110 21 \n",
"131 22 \n",
"152 21 \n",
"173 10 \n",
"194 21 \n",
"\n",
" avalanche_problem_1_trigger_simple_name \n",
"index \n",
"5 Not given \n",
"26 Liten tilleggsbelastning \n",
"47 Stor tilleggsbelastning \n",
"68 Liten tilleggsbelastning \n",
"89 Liten tilleggsbelastning \n",
"110 Liten tilleggsbelastning \n",
"131 Naturlig utløst \n",
"152 Liten tilleggsbelastning \n",
"173 Stor tilleggsbelastning \n",
"194 Liten tilleggsbelastning "
]
},
"execution_count": 240,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"varsom_df[varsom_df['region_id']==3012].filter(['avalanche_problem_1_cause_id', 'avalanche_problem_1_cause_name',\n",
" 'avalanche_problem_1_destructive_size_ext_id',\n",
" 'avalanche_problem_1_destructive_size_ext_name',\n",
" 'avalanche_problem_1_distribution_id',\n",
" 'avalanche_problem_1_distribution_name',\n",
" 'avalanche_problem_1_exposed_height_1',\n",
" 'avalanche_problem_1_exposed_height_2',\n",
" 'avalanche_problem_1_exposed_height_fill',\n",
" 'avalanche_problem_1_ext_id', 'avalanche_problem_1_ext_name',\n",
" 'avalanche_problem_1_probability_id',\n",
" 'avalanche_problem_1_probability_name',\n",
" 'avalanche_problem_1_problem_id',\n",
" 'avalanche_problem_1_problem_type_id',\n",
" 'avalanche_problem_1_problem_type_name',\n",
" 'avalanche_problem_1_trigger_simple_id',\n",
" 'avalanche_problem_1_trigger_simple_name',]).head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {}
},
"source": [
"Check if there are missing values."
]
},
{
"cell_type": "code",
"execution_count": 241,
"metadata": {
"collapsed": true,
"pycharm": {
"is_executing": false
}
},
"outputs": [],
"source": [
"# for col in varsom_df.columns.values:\n",
"# print(f'{col}: {varsom_df[col].unique()} \\n')"
]
},
{
"cell_type": "code",
"execution_count": 242,
"metadata": {
"collapsed": false,
"pycharm": {
"is_executing": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mountain_weather_change_hour_of_day_start 14614\n",
"mountain_weather_change_hour_of_day_stop 14614\n",
"mountain_weather_change_wind_speed 14602\n",
"mountain_weather_fl_hour_of_day_stop 13670\n",
"mountain_weather_fl_hour_of_day_start 13670\n",
"latest_avalanche_activity 12971\n",
"current_weak_layers 12022\n",
"mountain_weather_freezing_level 11902\n",
"mountain_weather_temperature_max 11533\n",
"mountain_weather_temperature_min 11532\n",
"mountain_weather_temperature_elevation 11515\n",
"mountain_weather_precip_most_exposed 11491\n",
"snow_surface 11490\n",
"mountain_weather_precip_region 11489\n",
"mountain_weather_wind_speed 11486\n",
"latest_observations 9157\n",
"avalanche_danger 7676\n",
"emergency_warning 7657\n",
"danger_level_name 7657\n",
"author 7657\n",
"mountain_weather_change_wind_direction 3144\n",
"mountain_weather_wind_direction 77\n",
"main_text 40\n",
"avalanche_problem_2_advice 6\n",
"avalanche_problem_1_advice 3\n",
"avalanche_problem_2_distribution_id 0\n",
"avalanche_problem_1_type_name 0\n",
"avalanche_problem_1_valid_expositions 0\n",
"avalanche_problem_2_cause_id 0\n",
"avalanche_problem_2_cause_name 0\n",
" ... \n",
"publish_time 0\n",
"reg_id 0\n",
"region_id 0\n",
"region_name 0\n",
"region_type_id 0\n",
"region_type_name 0\n",
"utm_east 0\n",
"utm_north 0\n",
"utm_zone 0\n",
"avalanche_problem_3_problem_type_id 0\n",
"avalanche_problem_3_problem_id 0\n",
"avalanche_problem_3_probability_name 0\n",
"avalanche_problem_3_destructive_size_ext_id 0\n",
"avalanche_problem_2_trigger_simple_name 0\n",
"avalanche_problem_2_type_id 0\n",
"avalanche_problem_2_type_name 0\n",
"avalanche_problem_2_valid_expositions 0\n",
"avalanche_problem_3_advice 0\n",
"avalanche_problem_3_cause_id 0\n",
"avalanche_problem_3_cause_name 0\n",
"valid_from 0\n",
"avalanche_problem_3_probability_id 0\n",
"avalanche_problem_3_distribution_id 0\n",
"avalanche_problem_3_distribution_name 0\n",
"avalanche_problem_3_exposed_height_1 0\n",
"avalanche_problem_3_exposed_height_2 0\n",
"avalanche_problem_3_exposed_height_fill 0\n",
"avalanche_problem_3_ext_id 0\n",
"avalanche_problem_3_ext_name 0\n",
"avalanche_problem_3_destructive_size_ext_name 0\n",
"Length: 102, dtype: int64\n"
]
}
],
"source": [
"# Find the amount of NaN values in each column\n",
"print(varsom_df.isnull().sum().sort_values(ascending=False))"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {}
},
"source": [
"Fill missing values where necessary."
]
},
{
"cell_type": "code",
"execution_count": 243,
"metadata": {
"collapsed": false,
"pycharm": {
"is_executing": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mountain_weather_change_hour_of_day_start 14614\n",
"mountain_weather_change_hour_of_day_stop 14614\n",
"mountain_weather_change_wind_speed 14602\n",
"mountain_weather_fl_hour_of_day_start 13670\n",
"mountain_weather_fl_hour_of_day_stop 13670\n",
"latest_avalanche_activity 12971\n",
"current_weak_layers 12022\n",
"mountain_weather_freezing_level 11902\n",
"mountain_weather_temperature_max 11533\n",
"mountain_weather_temperature_min 11532\n",
"mountain_weather_temperature_elevation 11515\n",
"mountain_weather_precip_most_exposed 11491\n",
"snow_surface 11490\n",
"mountain_weather_precip_region 11489\n",
"latest_observations 9157\n",
"avalanche_danger 7676\n",
"emergency_warning 7657\n",
"danger_level_name 7657\n",
"author 7657\n",
"mountain_weather_change_wind_direction 3144\n",
"main_text 40\n",
"avalanche_problem_2_advice 6\n",
"avalanche_problem_1_advice 3\n",
"avalanche_problem_2_distribution_name 0\n",
"avalanche_problem_2_problem_id 0\n",
"avalanche_problem_1_valid_expositions 0\n",
"avalanche_problem_2_cause_id 0\n",
"avalanche_problem_2_cause_name 0\n",
"avalanche_problem_2_destructive_size_ext_id 0\n",
"avalanche_problem_2_destructive_size_ext_name 0\n",
" ... \n",
"reg_id 0\n",
"region_id 0\n",
"region_name 0\n",
"region_type_id 0\n",
"region_type_name 0\n",
"utm_east 0\n",
"utm_north 0\n",
"utm_zone 0\n",
"avalanche_problem_3_trigger_simple_id 0\n",
"avalanche_problem_3_problem_type_id 0\n",
"avalanche_problem_2_trigger_simple_name 0\n",
"avalanche_problem_3_problem_id 0\n",
"avalanche_problem_2_type_id 0\n",
"avalanche_problem_2_type_name 0\n",
"avalanche_problem_2_valid_expositions 0\n",
"avalanche_problem_3_advice 0\n",
"avalanche_problem_3_cause_id 0\n",
"avalanche_problem_3_cause_name 0\n",
"avalanche_problem_3_destructive_size_ext_id 0\n",
"valid_from 0\n",
"avalanche_problem_3_distribution_id 0\n",
"avalanche_problem_3_distribution_name 0\n",
"avalanche_problem_3_exposed_height_1 0\n",
"avalanche_problem_3_exposed_height_2 0\n",
"avalanche_problem_3_exposed_height_fill 0\n",
"avalanche_problem_3_ext_id 0\n",
"avalanche_problem_3_ext_name 0\n",
"avalanche_problem_3_probability_id 0\n",
"avalanche_problem_3_probability_name 0\n",
"avalanche_problem_3_destructive_size_ext_name 0\n",
"Length: 102, dtype: int64\n"
]
}
],
"source": [
"varsom_df['mountain_weather_wind_speed'] = varsom_df['mountain_weather_wind_speed'].fillna('None')\n",
"varsom_df['mountain_weather_wind_direction'] = varsom_df['mountain_weather_wind_direction'].fillna('None')\n",
"print(varsom_df.isnull().sum().sort_values(ascending=False))"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {}
},
"source": [
"## Feature engineering\n",
"Re-label og -classifiy variables where necessary.\n",
"\n",
"Add an avalanche problem severity index - based on its attributes size, distribution and sensitivity.\n",
"\n",
"When using _shift_ or filling values using _mean_ or similar, make sure to first sort individual regions and seasons by date.\n"
]
},
{
"cell_type": "code",
"execution_count": 244,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"varsom_df['date'] = pd.to_datetime(varsom_df['date_valid'], infer_datetime_format=True)"
]
},
{
"cell_type": "code",
"execution_count": 245,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def add_prevday_features(df):\n",
" ### danger level\n",
" df['danger_level_prev1day'] = df['danger_level'].shift(1)\n",
" df['danger_level_name_prev1day'] = df['danger_level_name'].shift(1)\n",
" df['danger_level_prev2day'] = df['danger_level'].shift(2)\n",
" df['danger_level_name_prev2day'] = df['danger_level_name'].shift(2)\n",
" df['danger_level_prev3day'] = df['danger_level'].shift(3)\n",
" df['danger_level_name_prev3day'] = df['danger_level_name'].shift(3)\n",
"\n",
" ### avalanche problem\n",
" df['avalanche_problem_1_cause_id_prev1day'] = df['avalanche_problem_1_cause_id'].shift(1)\n",
" df['avalanche_problem_1_problem_type_id_prev1day'] = df['avalanche_problem_1_problem_type_id'].shift(1)\n",
" df['avalanche_problem_1_cause_id_prev2day'] = df['avalanche_problem_1_cause_id'].shift(2)\n",
" df['avalanche_problem_1_problem_type_id_prev2day'] = df['avalanche_problem_1_problem_type_id'].shift(2)\n",
" df['avalanche_problem_1_cause_id_prev3day'] = df['avalanche_problem_1_cause_id'].shift(3)\n",
" df['avalanche_problem_1_problem_type_id_prev3day'] = df['avalanche_problem_1_problem_type_id'].shift(3)\n",
"\n",
" df['avalanche_problem_2_cause_id_prev1day'] = df['avalanche_problem_2_cause_id'].shift(1)\n",
" df['avalanche_problem_2_problem_type_id_prev1day'] = df['avalanche_problem_2_problem_type_id'].shift(1)\n",
" df['avalanche_problem_2_cause_id_prev2day'] = df['avalanche_problem_2_cause_id'].shift(2)\n",
" df['avalanche_problem_2_problem_type_id_prev2day'] = df['avalanche_problem_2_problem_type_id'].shift(2)\n",
" df['avalanche_problem_2_cause_id_prev3day'] = df['avalanche_problem_2_cause_id'].shift(3)\n",
" df['avalanche_problem_2_problem_type_id_prev3day'] = df['avalanche_problem_2_problem_type_id'].shift(3)\n",
"\n",
" ### weather\n",
" df['mountain_weather_temperature_max_prev1day'] = df['mountain_weather_temperature_max'].shift(1)\n",
" df['mountain_weather_temperature_max_prev2day'] = df['mountain_weather_temperature_max'].shift(2)\n",
" df['mountain_weather_temperature_max_prev3day'] = df['mountain_weather_temperature_max'].shift(3)\n",
"\n",
" df['mountain_weather_temperature_min_prev1day'] = df['mountain_weather_temperature_min'].shift(1)\n",
" df['mountain_weather_temperature_min_prev2day'] = df['mountain_weather_temperature_min'].shift(2)\n",
" df['mountain_weather_temperature_min_prev3day'] = df['mountain_weather_temperature_min'].shift(3)\n",
"\n",
" df['mountain_weather_precip_region_prev1day'] = df['mountain_weather_precip_region'].shift(1)\n",
" df['mountain_weather_precip_most_exposed_prev1day'] = df['mountain_weather_precip_most_exposed'].shift(1)\n",
" df['mountain_weather_precip_region_prev3daysum'] = df['mountain_weather_precip_region'].shift(1) + df['mountain_weather_precip_region'].shift(2) + df['mountain_weather_precip_region'].shift(3)\n",
"\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 246,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:1: FutureWarning: Comparing Series of datetimes with 'datetime.date'. Currently, the\n",
"'datetime.date' is coerced to a datetime. In the future pandas will\n",
"not coerce, and a TypeError will be raised. To retain the current\n",
"behavior, convert the 'datetime.date' to a datetime with\n",
"'pd.Timestamp'.\n",
" if __name__ == '__main__':\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>author</th>\n",
" <th>avalanche_danger</th>\n",
" <th>avalanche_problem_1_advice</th>\n",
" <th>avalanche_problem_1_cause_id</th>\n",
" <th>avalanche_problem_1_cause_name</th>\n",
" <th>avalanche_problem_1_destructive_size_ext_id</th>\n",
" <th>avalanche_problem_1_destructive_size_ext_name</th>\n",
" <th>avalanche_problem_1_distribution_id</th>\n",
" <th>avalanche_problem_1_distribution_name</th>\n",
" <th>avalanche_problem_1_exposed_height_1</th>\n",
" <th>...</th>\n",
" <th>region_name</th>\n",
" <th>region_type_id</th>\n",
" <th>region_type_name</th>\n",
" <th>snow_surface</th>\n",
" <th>utm_east</th>\n",
" <th>utm_north</th>\n",
" <th>utm_zone</th>\n",
" <th>valid_from</th>\n",
" <th>valid_to</th>\n",
" <th>date</th>\n",
" </tr>\n",
" <tr>\n",
" <th>index</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" <td>2016-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>Vest-Finnmark</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" <td>2016-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>Nord-Troms</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" <td>2016-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>Lyngen</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" <td>2016-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>Tromsø</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" <td>2016-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>Sør-Troms</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" <td>2016-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>Indre Troms</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" <td>2016-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>Lofoten og Vesterålen</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" <td>2016-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>Ofoten</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" <td>2016-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>Salten</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" <td>2016-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>Svartisen</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" <td>2016-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>Trollheimen</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" <td>2016-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>Romsdal</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" <td>2016-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>Sunnmøre</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" <td>2016-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>Indre Fjordane</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" <td>2016-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>Jotunheimen</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" <td>2016-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>Indre Sogn</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" <td>2016-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>Voss</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" <td>2016-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>Hallingdal</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" <td>2016-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>Hardanger</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" <td>2016-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>Not given</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>2016-12-01 23:59:59.000</td>\n",
" <td>2016-12-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>JonasD@ObsKorps</td>\n",
" <td>Snøen i leområdene er generelt sett stabile og...</td>\n",
" <td>Vær forsiktig i områder brattere enn 30 grader...</td>\n",
" <td>15</td>\n",
" <td>Dårlig binding mellom lag i fokksnøen</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>800</td>\n",
" <td>...</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>520332</td>\n",
" <td>8663904</td>\n",
" <td>33</td>\n",
" <td>2016-12-02 00:00:00.000</td>\n",
" <td>2016-12-02 23:59:59.000</td>\n",
" <td>2016-12-02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>emma@nve</td>\n",
" <td>Litt påfyll av nysnø i kombinasjon med vind fø...</td>\n",
" <td>Vær forsiktig i bratte heng under og etter sn...</td>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>400</td>\n",
" <td>...</td>\n",
" <td>Vest-Finnmark</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>802123</td>\n",
" <td>7794717</td>\n",
" <td>33</td>\n",
" <td>2016-12-02 00:00:00.000</td>\n",
" <td>2016-12-02 23:59:59.000</td>\n",
" <td>2016-12-02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>emma@nve</td>\n",
" <td>Litt påfyll av nysnø i kombinasjon med vind fø...</td>\n",
" <td>Vær forsiktig i bratte heng under og etter sn...</td>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>400</td>\n",
" <td>...</td>\n",
" <td>Nord-Troms</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>750984</td>\n",
" <td>7742562</td>\n",
" <td>33</td>\n",
" <td>2016-12-02 00:00:00.000</td>\n",
" <td>2016-12-02 23:59:59.000</td>\n",
" <td>2016-12-02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>emma@nve</td>\n",
" <td>Påfyll av nysnø i kombinasjon med vind føre ti...</td>\n",
" <td>Vær forsiktig i bratte heng under og etter sn...</td>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>400</td>\n",
" <td>...</td>\n",
" <td>Lyngen</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>692056</td>\n",
" <td>7719872</td>\n",
" <td>33</td>\n",
" <td>2016-12-02 00:00:00.000</td>\n",
" <td>2016-12-02 23:59:59.000</td>\n",
" <td>2016-12-02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>emma@nve</td>\n",
" <td>Påfyll av nysnø i kombinasjon med vind føre ti...</td>\n",
" <td>Vær forsiktig i bratte heng under og etter sn...</td>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>400</td>\n",
" <td>...</td>\n",
" <td>Tromsø</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>656496</td>\n",
" <td>7764237</td>\n",
" <td>33</td>\n",
" <td>2016-12-02 00:00:00.000</td>\n",
" <td>2016-12-02 23:59:59.000</td>\n",
" <td>2016-12-02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>JonasD@ObsKorps</td>\n",
" <td>I høyfjellet kan det lokalt ha samlet seg nok ...</td>\n",
" <td>Vær forsiktig i områder brattere enn 30 grader...</td>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>600</td>\n",
" <td>...</td>\n",
" <td>Sør-Troms</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>594858</td>\n",
" <td>7642656</td>\n",
" <td>33</td>\n",
" <td>2016-12-02 00:00:00.000</td>\n",
" <td>2016-12-02 23:59:59.000</td>\n",
" <td>2016-12-02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>emma@nve</td>\n",
" <td>Litt påfyll av nysnø i kombinasjon med vind fø...</td>\n",
" <td>Vær forsiktig i bratte heng under og etter sn...</td>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>400</td>\n",
" <td>...</td>\n",
" <td>Indre Troms</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>647352</td>\n",
" <td>7647736</td>\n",
" <td>33</td>\n",
" <td>2016-12-02 00:00:00.000</td>\n",
" <td>2016-12-02 23:59:59.000</td>\n",
" <td>2016-12-02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>JonasD@ObsKorps</td>\n",
" <td>I høyfjellet kan det lokalt ha samlet seg nok ...</td>\n",
" <td>Vær forsiktig i bratte heng under og etter sn...</td>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>600</td>\n",
" <td>...</td>\n",
" <td>Lofoten og Vesterålen</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>527125</td>\n",
" <td>7620981</td>\n",
" <td>33</td>\n",
" <td>2016-12-02 00:00:00.000</td>\n",
" <td>2016-12-02 23:59:59.000</td>\n",
" <td>2016-12-02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>JonasD@ObsKorps</td>\n",
" <td>I høyfjellet kan det lokalt ha samlet seg nok ...</td>\n",
" <td>Vær forsiktig i områder brattere enn 30 grader...</td>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>900</td>\n",
" <td>...</td>\n",
" <td>Ofoten</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>602309</td>\n",
" <td>7578309</td>\n",
" <td>33</td>\n",
" <td>2016-12-02 00:00:00.000</td>\n",
" <td>2016-12-02 23:59:59.000</td>\n",
" <td>2016-12-02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3792</th>\n",
" <td>[email protected]</td>\n",
" <td>Varme og sol vil gi fortsatt god snøsmelting. ...</td>\n",
" <td>Vær varsom der skredproblemet er å finne i kom...</td>\n",
" <td>24</td>\n",
" <td>Ubunden snø</td>\n",
" <td>1</td>\n",
" <td>1 - Små</td>\n",
" <td>1</td>\n",
" <td>Få bratte heng</td>\n",
" <td>1000</td>\n",
" <td>...</td>\n",
" <td>Romsdal</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>123434</td>\n",
" <td>6960580</td>\n",
" <td>33</td>\n",
" <td>2017-05-30 00:00:00.000</td>\n",
" <td>2017-05-30 23:59:59.000</td>\n",
" <td>2017-05-30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3793</th>\n",
" <td>[email protected]</td>\n",
" <td>Varme og sol vil gi fortsatt god snøsmelting. ...</td>\n",
" <td>Vær varsom der skredproblemet er å finne i kom...</td>\n",
" <td>24</td>\n",
" <td>Ubunden snø</td>\n",
" <td>1</td>\n",
" <td>1 - Små</td>\n",
" <td>1</td>\n",
" <td>Få bratte heng</td>\n",
" <td>1000</td>\n",
" <td>...</td>\n",
" <td>Sunnmøre</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>62473</td>\n",
" <td>6916553</td>\n",
" <td>33</td>\n",
" <td>2017-05-30 00:00:00.000</td>\n",
" <td>2017-05-30 23:59:59.000</td>\n",
" <td>2017-05-30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3794</th>\n",
" <td>[email protected]</td>\n",
" <td>Varmt vær vil fortsatt gi fortsatt god snøsmel...</td>\n",
" <td>Vær varsom der skredproblemet er å finne i kom...</td>\n",
" <td>24</td>\n",
" <td>Ubunden snø</td>\n",
" <td>1</td>\n",
" <td>1 - Små</td>\n",
" <td>1</td>\n",
" <td>Få bratte heng</td>\n",
" <td>1000</td>\n",
" <td>...</td>\n",
" <td>Indre Fjordane</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>34025</td>\n",
" <td>6868801</td>\n",
" <td>33</td>\n",
" <td>2017-05-30 00:00:00.000</td>\n",
" <td>2017-05-30 23:59:59.000</td>\n",
" <td>2017-05-30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3795</th>\n",
" <td>jostein@nve</td>\n",
" <td>Det er framleis mogelegheiter for at det kan l...</td>\n",
" <td>Unngå lengre opphold i og nedenfor heng med gl...</td>\n",
" <td>20</td>\n",
" <td>Vann ved bakken/smelting fra bakken</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>1</td>\n",
" <td>Få bratte heng</td>\n",
" <td>900</td>\n",
" <td>...</td>\n",
" <td>Jotunheimen</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>155607</td>\n",
" <td>6844417</td>\n",
" <td>33</td>\n",
" <td>2017-05-30 00:00:00.000</td>\n",
" <td>2017-05-30 23:59:59.000</td>\n",
" <td>2017-05-30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3796</th>\n",
" <td>jostein@nve</td>\n",
" <td>Det er framleis mogelegheiter for at det kan l...</td>\n",
" <td>Unngå lengre opphold i og nedenfor heng med gl...</td>\n",
" <td>20</td>\n",
" <td>Vann ved bakken/smelting fra bakken</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>1</td>\n",
" <td>Få bratte heng</td>\n",
" <td>900</td>\n",
" <td>...</td>\n",
" <td>Indre Sogn</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>96001</td>\n",
" <td>6816985</td>\n",
" <td>33</td>\n",
" <td>2017-05-30 00:00:00.000</td>\n",
" <td>2017-05-30 23:59:59.000</td>\n",
" <td>2017-05-30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3797</th>\n",
" <td>[email protected]</td>\n",
" <td>Varme og sol vil gi fortsatt god snøsmelting. ...</td>\n",
" <td>Vær varsom der skredproblemet er å finne i kom...</td>\n",
" <td>24</td>\n",
" <td>Ubunden snø</td>\n",
" <td>1</td>\n",
" <td>1 - Små</td>\n",
" <td>1</td>\n",
" <td>Få bratte heng</td>\n",
" <td>1000</td>\n",
" <td>...</td>\n",
" <td>Voss</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>28607</td>\n",
" <td>6779054</td>\n",
" <td>33</td>\n",
" <td>2017-05-30 00:00:00.000</td>\n",
" <td>2017-05-30 23:59:59.000</td>\n",
" <td>2017-05-30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3798</th>\n",
" <td>jostein@nve</td>\n",
" <td>Generelt stabile forhold. Smeltevatn nede i sn...</td>\n",
" <td>Unngå lengre opphold i og nedenfor heng med gl...</td>\n",
" <td>20</td>\n",
" <td>Vann ved bakken/smelting fra bakken</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>1</td>\n",
" <td>Få bratte heng</td>\n",
" <td>1100</td>\n",
" <td>...</td>\n",
" <td>Hallingdal</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>150188</td>\n",
" <td>6763814</td>\n",
" <td>33</td>\n",
" <td>2017-05-30 00:00:00.000</td>\n",
" <td>2017-05-30 23:59:59.000</td>\n",
" <td>2017-05-30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3799</th>\n",
" <td>jostein@nve</td>\n",
" <td>Det er framleis mogelegheiter for at det kan l...</td>\n",
" <td>Unngå lengre opphold i og nedenfor heng med gl...</td>\n",
" <td>20</td>\n",
" <td>Vann ved bakken/smelting fra bakken</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>1</td>\n",
" <td>Få bratte heng</td>\n",
" <td>1000</td>\n",
" <td>...</td>\n",
" <td>Hardanger</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>62473</td>\n",
" <td>6692016</td>\n",
" <td>33</td>\n",
" <td>2017-05-30 00:00:00.000</td>\n",
" <td>2017-05-30 23:59:59.000</td>\n",
" <td>2017-05-30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3800</th>\n",
" <td>jostein@nve</td>\n",
" <td>Generelt stabile forhold. Smeltevatn nede i sn...</td>\n",
" <td>Unngå lengre opphold i og nedenfor heng med gl...</td>\n",
" <td>20</td>\n",
" <td>Vann ved bakken/smelting fra bakken</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>1</td>\n",
" <td>Få bratte heng</td>\n",
" <td>1100</td>\n",
" <td>...</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>131223</td>\n",
" <td>6642571</td>\n",
" <td>33</td>\n",
" <td>2017-05-30 00:00:00.000</td>\n",
" <td>2017-05-30 23:59:59.000</td>\n",
" <td>2017-05-30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3801</th>\n",
" <td>jostein@nve</td>\n",
" <td>Det vert framleis danna fersk fokksnø i leområ...</td>\n",
" <td>Vær forsiktig i områder brattere enn 30 grader...</td>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>100</td>\n",
" <td>...</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>520332</td>\n",
" <td>8663904</td>\n",
" <td>33</td>\n",
" <td>2017-05-31 00:00:00.000</td>\n",
" <td>2017-05-31 23:59:59.000</td>\n",
" <td>2017-05-31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3802</th>\n",
" <td>HåvardT@met</td>\n",
" <td>Snøbyger og noe vind vil føre til at det kan d...</td>\n",
" <td>Vær forsiktig i områder brattere enn 30 grader...</td>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>300</td>\n",
" <td>...</td>\n",
" <td>Vest-Finnmark</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>802123</td>\n",
" <td>7794717</td>\n",
" <td>33</td>\n",
" <td>2017-05-31 00:00:00.000</td>\n",
" <td>2017-05-31 23:59:59.000</td>\n",
" <td>2017-05-31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3803</th>\n",
" <td>HåvardT@met</td>\n",
" <td>Snøbyger og noe vind vil føre til at det kan d...</td>\n",
" <td>Vær forsiktig i områder brattere enn 30 grader...</td>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>500</td>\n",
" <td>...</td>\n",
" <td>Nord-Troms</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>750984</td>\n",
" <td>7742562</td>\n",
" <td>33</td>\n",
" <td>2017-05-31 00:00:00.000</td>\n",
" <td>2017-05-31 23:59:59.000</td>\n",
" <td>2017-05-31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3804</th>\n",
" <td>HåvardT@met</td>\n",
" <td>Snøbyger og noe vind vil føre til at det kan d...</td>\n",
" <td>Vær forsiktig i områder brattere enn 30 grader...</td>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>500</td>\n",
" <td>...</td>\n",
" <td>Lyngen</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>692056</td>\n",
" <td>7719872</td>\n",
" <td>33</td>\n",
" <td>2017-05-31 00:00:00.000</td>\n",
" <td>2017-05-31 23:59:59.000</td>\n",
" <td>2017-05-31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3805</th>\n",
" <td>HåvardT@met</td>\n",
" <td>Nysnø og noe vind vil føre til at det kan dann...</td>\n",
" <td>Vær forsiktig i områder brattere enn 30 grader...</td>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>500</td>\n",
" <td>...</td>\n",
" <td>Tromsø</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>656496</td>\n",
" <td>7764237</td>\n",
" <td>33</td>\n",
" <td>2017-05-31 00:00:00.000</td>\n",
" <td>2017-05-31 23:59:59.000</td>\n",
" <td>2017-05-31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3806</th>\n",
" <td>HåvardT@met</td>\n",
" <td>Snøbyger og noe vind vil føre til at det kan d...</td>\n",
" <td>Vær varsom der skredproblemet er å finne i ko...</td>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>1</td>\n",
" <td>1 - Små</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>500</td>\n",
" <td>...</td>\n",
" <td>Sør-Troms</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>594858</td>\n",
" <td>7642656</td>\n",
" <td>33</td>\n",
" <td>2017-05-31 00:00:00.000</td>\n",
" <td>2017-05-31 23:59:59.000</td>\n",
" <td>2017-05-31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3807</th>\n",
" <td>HåvardT@met</td>\n",
" <td>Snøbyger og noe vind vil føre til at det kan d...</td>\n",
" <td>Vær varsom der skredproblemet er å finne i ko...</td>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>1</td>\n",
" <td>1 - Små</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>500</td>\n",
" <td>...</td>\n",
" <td>Indre Troms</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>647352</td>\n",
" <td>7647736</td>\n",
" <td>33</td>\n",
" <td>2017-05-31 00:00:00.000</td>\n",
" <td>2017-05-31 23:59:59.000</td>\n",
" <td>2017-05-31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3808</th>\n",
" <td>HåvardT@met</td>\n",
" <td>Kjølig vær med litt nysnø og vind vil føre til...</td>\n",
" <td>Vær varsom der skredproblemet er å finne i ko...</td>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>1</td>\n",
" <td>1 - Små</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>600</td>\n",
" <td>...</td>\n",
" <td>Lofoten og Vesterålen</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>527125</td>\n",
" <td>7620981</td>\n",
" <td>33</td>\n",
" <td>2017-05-31 00:00:00.000</td>\n",
" <td>2017-05-31 23:59:59.000</td>\n",
" <td>2017-05-31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3809</th>\n",
" <td>knutinge@svv</td>\n",
" <td>Nysnø og vind vil på nytt danne nysnøflak som ...</td>\n",
" <td>Vær forsiktig i bratte heng under og etter sn...</td>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>700</td>\n",
" <td>...</td>\n",
" <td>Ofoten</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>602309</td>\n",
" <td>7578309</td>\n",
" <td>33</td>\n",
" <td>2017-05-31 00:00:00.000</td>\n",
" <td>2017-05-31 23:59:59.000</td>\n",
" <td>2017-05-31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3810</th>\n",
" <td>knutinge@svv</td>\n",
" <td>Nysnø og vind vil på nytt danne nysnøflak som ...</td>\n",
" <td>Vær forsiktig i bratte heng under og etter sn...</td>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>800</td>\n",
" <td>...</td>\n",
" <td>Salten</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>533221</td>\n",
" <td>7497029</td>\n",
" <td>33</td>\n",
" <td>2017-05-31 00:00:00.000</td>\n",
" <td>2017-05-31 23:59:59.000</td>\n",
" <td>2017-05-31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3811</th>\n",
" <td>knutinge@svv</td>\n",
" <td>Nysnø og vind vil på nytt danne nysnøflak som ...</td>\n",
" <td>Vær forsiktig i bratte heng under og etter sn...</td>\n",
" <td>10</td>\n",
" <td>Nedføyket svakt lag med nysnø</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>2</td>\n",
" <td>Noen bratte heng</td>\n",
" <td>800</td>\n",
" <td>...</td>\n",
" <td>Svartisen</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>464133</td>\n",
" <td>7381882</td>\n",
" <td>33</td>\n",
" <td>2017-05-31 00:00:00.000</td>\n",
" <td>2017-05-31 23:59:59.000</td>\n",
" <td>2017-05-31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3812</th>\n",
" <td>knutinge@svv</td>\n",
" <td>Det er generelt stabile forhold i regionen. De...</td>\n",
" <td>Unngå lengre opphold i og nedenfor heng med gl...</td>\n",
" <td>20</td>\n",
" <td>Vann ved bakken/smelting fra bakken</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>1</td>\n",
" <td>Få bratte heng</td>\n",
" <td>1000</td>\n",
" <td>...</td>\n",
" <td>Trollheimen</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>210810</td>\n",
" <td>6991060</td>\n",
" <td>33</td>\n",
" <td>2017-05-31 00:00:00.000</td>\n",
" <td>2017-05-31 23:59:59.000</td>\n",
" <td>2017-05-31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3813</th>\n",
" <td>knutinge@svv</td>\n",
" <td>Det er generelt stabile forhold i regionen. De...</td>\n",
" <td>Unngå lengre opphold i og nedenfor heng med gl...</td>\n",
" <td>20</td>\n",
" <td>Vann ved bakken/smelting fra bakken</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>1</td>\n",
" <td>Få bratte heng</td>\n",
" <td>1000</td>\n",
" <td>...</td>\n",
" <td>Romsdal</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>123434</td>\n",
" <td>6960580</td>\n",
" <td>33</td>\n",
" <td>2017-05-31 00:00:00.000</td>\n",
" <td>2017-05-31 23:59:59.000</td>\n",
" <td>2017-05-31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3814</th>\n",
" <td>knutinge@svv</td>\n",
" <td>Det er generelt stabile forhold i regionen. De...</td>\n",
" <td>Unngå lengre opphold i og nedenfor heng med gl...</td>\n",
" <td>20</td>\n",
" <td>Vann ved bakken/smelting fra bakken</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>1</td>\n",
" <td>Få bratte heng</td>\n",
" <td>1000</td>\n",
" <td>...</td>\n",
" <td>Sunnmøre</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>62473</td>\n",
" <td>6916553</td>\n",
" <td>33</td>\n",
" <td>2017-05-31 00:00:00.000</td>\n",
" <td>2017-05-31 23:59:59.000</td>\n",
" <td>2017-05-31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3815</th>\n",
" <td>knutinge@svv</td>\n",
" <td>Varme og sol vil gi fortsatt god snøsmelting, ...</td>\n",
" <td>Vær varsom der skredproblemet er å finne i kom...</td>\n",
" <td>24</td>\n",
" <td>Ubunden snø</td>\n",
" <td>1</td>\n",
" <td>1 - Små</td>\n",
" <td>1</td>\n",
" <td>Få bratte heng</td>\n",
" <td>1000</td>\n",
" <td>...</td>\n",
" <td>Indre Fjordane</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>34025</td>\n",
" <td>6868801</td>\n",
" <td>33</td>\n",
" <td>2017-05-31 00:00:00.000</td>\n",
" <td>2017-05-31 23:59:59.000</td>\n",
" <td>2017-05-31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3816</th>\n",
" <td>jostein@nve</td>\n",
" <td>Det kan gå glideskred ved bakken og våte flaks...</td>\n",
" <td>Unngå lengre opphold i og nedenfor heng med gl...</td>\n",
" <td>20</td>\n",
" <td>Vann ved bakken/smelting fra bakken</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>1</td>\n",
" <td>Få bratte heng</td>\n",
" <td>900</td>\n",
" <td>...</td>\n",
" <td>Jotunheimen</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>155607</td>\n",
" <td>6844417</td>\n",
" <td>33</td>\n",
" <td>2017-05-31 00:00:00.000</td>\n",
" <td>2017-05-31 23:59:59.000</td>\n",
" <td>2017-05-31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3817</th>\n",
" <td>jostein@nve</td>\n",
" <td>Det er framleis mogelegheiter for at det kan l...</td>\n",
" <td>Unngå lengre opphold i og nedenfor heng med gl...</td>\n",
" <td>20</td>\n",
" <td>Vann ved bakken/smelting fra bakken</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>1</td>\n",
" <td>Få bratte heng</td>\n",
" <td>900</td>\n",
" <td>...</td>\n",
" <td>Indre Sogn</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>96001</td>\n",
" <td>6816985</td>\n",
" <td>33</td>\n",
" <td>2017-05-31 00:00:00.000</td>\n",
" <td>2017-05-31 23:59:59.000</td>\n",
" <td>2017-05-31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3818</th>\n",
" <td>knutinge@svv</td>\n",
" <td>Varme og sol vil gi fortsatt god snøsmelting. ...</td>\n",
" <td>Vær varsom der skredproblemet er å finne i kom...</td>\n",
" <td>24</td>\n",
" <td>Ubunden snø</td>\n",
" <td>1</td>\n",
" <td>1 - Små</td>\n",
" <td>1</td>\n",
" <td>Få bratte heng</td>\n",
" <td>1000</td>\n",
" <td>...</td>\n",
" <td>Voss</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>28607</td>\n",
" <td>6779054</td>\n",
" <td>33</td>\n",
" <td>2017-05-31 00:00:00.000</td>\n",
" <td>2017-05-31 23:59:59.000</td>\n",
" <td>2017-05-31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3819</th>\n",
" <td>jostein@nve</td>\n",
" <td>Generelt stabile forhold. Smeltevatn nede i sn...</td>\n",
" <td>Unngå lengre opphold i og nedenfor heng med gl...</td>\n",
" <td>20</td>\n",
" <td>Vann ved bakken/smelting fra bakken</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>1</td>\n",
" <td>Få bratte heng</td>\n",
" <td>1100</td>\n",
" <td>...</td>\n",
" <td>Hallingdal</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>150188</td>\n",
" <td>6763814</td>\n",
" <td>33</td>\n",
" <td>2017-05-31 00:00:00.000</td>\n",
" <td>2017-05-31 23:59:59.000</td>\n",
" <td>2017-05-31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3820</th>\n",
" <td>jostein@nve</td>\n",
" <td>Det er framleis mogelegheiter for at det kan l...</td>\n",
" <td>Unngå lengre opphold i og nedenfor heng med gl...</td>\n",
" <td>20</td>\n",
" <td>Vann ved bakken/smelting fra bakken</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>1</td>\n",
" <td>Få bratte heng</td>\n",
" <td>1000</td>\n",
" <td>...</td>\n",
" <td>Hardanger</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>62473</td>\n",
" <td>6692016</td>\n",
" <td>33</td>\n",
" <td>2017-05-31 00:00:00.000</td>\n",
" <td>2017-05-31 23:59:59.000</td>\n",
" <td>2017-05-31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3821</th>\n",
" <td>jostein@nve</td>\n",
" <td>Generelt stabile forhold. Smeltevatn nede i sn...</td>\n",
" <td>Unngå lengre opphold i og nedenfor heng med gl...</td>\n",
" <td>20</td>\n",
" <td>Vann ved bakken/smelting fra bakken</td>\n",
" <td>2</td>\n",
" <td>2 - Middels</td>\n",
" <td>1</td>\n",
" <td>Få bratte heng</td>\n",
" <td>1100</td>\n",
" <td>...</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>10</td>\n",
" <td>A</td>\n",
" <td>NaN</td>\n",
" <td>131223</td>\n",
" <td>6642571</td>\n",
" <td>33</td>\n",
" <td>2017-05-31 00:00:00.000</td>\n",
" <td>2017-05-31 23:59:59.000</td>\n",
" <td>2017-05-31</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3822 rows × 103 columns</p>\n",
"</div>"
],
"text/plain": [
" author avalanche_danger \\\n",
"index \n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"5 NaN NaN \n",
"6 NaN NaN \n",
"7 NaN NaN \n",
"8 NaN NaN \n",
"9 NaN NaN \n",
"10 NaN NaN \n",
"11 NaN NaN \n",
"12 NaN NaN \n",
"13 NaN NaN \n",
"14 NaN NaN \n",
"15 NaN NaN \n",
"16 NaN NaN \n",
"17 NaN NaN \n",
"18 NaN NaN \n",
"19 NaN NaN \n",
"20 NaN NaN \n",
"21 JonasD@ObsKorps Snøen i leområdene er generelt sett stabile og... \n",
"22 emma@nve Litt påfyll av nysnø i kombinasjon med vind fø... \n",
"23 emma@nve Litt påfyll av nysnø i kombinasjon med vind fø... \n",
"24 emma@nve Påfyll av nysnø i kombinasjon med vind føre ti... \n",
"25 emma@nve Påfyll av nysnø i kombinasjon med vind føre ti... \n",
"26 JonasD@ObsKorps I høyfjellet kan det lokalt ha samlet seg nok ... \n",
"27 emma@nve Litt påfyll av nysnø i kombinasjon med vind fø... \n",
"28 JonasD@ObsKorps I høyfjellet kan det lokalt ha samlet seg nok ... \n",
"29 JonasD@ObsKorps I høyfjellet kan det lokalt ha samlet seg nok ... \n",
"... ... ... \n",
"3792 [email protected] Varme og sol vil gi fortsatt god snøsmelting. ... \n",
"3793 [email protected] Varme og sol vil gi fortsatt god snøsmelting. ... \n",
"3794 [email protected] Varmt vær vil fortsatt gi fortsatt god snøsmel... \n",
"3795 jostein@nve Det er framleis mogelegheiter for at det kan l... \n",
"3796 jostein@nve Det er framleis mogelegheiter for at det kan l... \n",
"3797 [email protected] Varme og sol vil gi fortsatt god snøsmelting. ... \n",
"3798 jostein@nve Generelt stabile forhold. Smeltevatn nede i sn... \n",
"3799 jostein@nve Det er framleis mogelegheiter for at det kan l... \n",
"3800 jostein@nve Generelt stabile forhold. Smeltevatn nede i sn... \n",
"3801 jostein@nve Det vert framleis danna fersk fokksnø i leområ... \n",
"3802 HåvardT@met Snøbyger og noe vind vil føre til at det kan d... \n",
"3803 HåvardT@met Snøbyger og noe vind vil føre til at det kan d... \n",
"3804 HåvardT@met Snøbyger og noe vind vil føre til at det kan d... \n",
"3805 HåvardT@met Nysnø og noe vind vil føre til at det kan dann... \n",
"3806 HåvardT@met Snøbyger og noe vind vil føre til at det kan d... \n",
"3807 HåvardT@met Snøbyger og noe vind vil føre til at det kan d... \n",
"3808 HåvardT@met Kjølig vær med litt nysnø og vind vil føre til... \n",
"3809 knutinge@svv Nysnø og vind vil på nytt danne nysnøflak som ... \n",
"3810 knutinge@svv Nysnø og vind vil på nytt danne nysnøflak som ... \n",
"3811 knutinge@svv Nysnø og vind vil på nytt danne nysnøflak som ... \n",
"3812 knutinge@svv Det er generelt stabile forhold i regionen. De... \n",
"3813 knutinge@svv Det er generelt stabile forhold i regionen. De... \n",
"3814 knutinge@svv Det er generelt stabile forhold i regionen. De... \n",
"3815 knutinge@svv Varme og sol vil gi fortsatt god snøsmelting, ... \n",
"3816 jostein@nve Det kan gå glideskred ved bakken og våte flaks... \n",
"3817 jostein@nve Det er framleis mogelegheiter for at det kan l... \n",
"3818 knutinge@svv Varme og sol vil gi fortsatt god snøsmelting. ... \n",
"3819 jostein@nve Generelt stabile forhold. Smeltevatn nede i sn... \n",
"3820 jostein@nve Det er framleis mogelegheiter for at det kan l... \n",
"3821 jostein@nve Generelt stabile forhold. Smeltevatn nede i sn... \n",
"\n",
" avalanche_problem_1_advice \\\n",
"index \n",
"0 Not given \n",
"1 Not given \n",
"2 Not given \n",
"3 Not given \n",
"4 Not given \n",
"5 Not given \n",
"6 Not given \n",
"7 Not given \n",
"8 Not given \n",
"9 Not given \n",
"10 Not given \n",
"11 Not given \n",
"12 Not given \n",
"13 Not given \n",
"14 Not given \n",
"15 Not given \n",
"16 Not given \n",
"17 Not given \n",
"18 Not given \n",
"19 Not given \n",
"20 Not given \n",
"21 Vær forsiktig i områder brattere enn 30 grader... \n",
"22 Vær forsiktig i bratte heng under og etter sn... \n",
"23 Vær forsiktig i bratte heng under og etter sn... \n",
"24 Vær forsiktig i bratte heng under og etter sn... \n",
"25 Vær forsiktig i bratte heng under og etter sn... \n",
"26 Vær forsiktig i områder brattere enn 30 grader... \n",
"27 Vær forsiktig i bratte heng under og etter sn... \n",
"28 Vær forsiktig i bratte heng under og etter sn... \n",
"29 Vær forsiktig i områder brattere enn 30 grader... \n",
"... ... \n",
"3792 Vær varsom der skredproblemet er å finne i kom... \n",
"3793 Vær varsom der skredproblemet er å finne i kom... \n",
"3794 Vær varsom der skredproblemet er å finne i kom... \n",
"3795 Unngå lengre opphold i og nedenfor heng med gl... \n",
"3796 Unngå lengre opphold i og nedenfor heng med gl... \n",
"3797 Vær varsom der skredproblemet er å finne i kom... \n",
"3798 Unngå lengre opphold i og nedenfor heng med gl... \n",
"3799 Unngå lengre opphold i og nedenfor heng med gl... \n",
"3800 Unngå lengre opphold i og nedenfor heng med gl... \n",
"3801 Vær forsiktig i områder brattere enn 30 grader... \n",
"3802 Vær forsiktig i områder brattere enn 30 grader... \n",
"3803 Vær forsiktig i områder brattere enn 30 grader... \n",
"3804 Vær forsiktig i områder brattere enn 30 grader... \n",
"3805 Vær forsiktig i områder brattere enn 30 grader... \n",
"3806 Vær varsom der skredproblemet er å finne i ko... \n",
"3807 Vær varsom der skredproblemet er å finne i ko... \n",
"3808 Vær varsom der skredproblemet er å finne i ko... \n",
"3809 Vær forsiktig i bratte heng under og etter sn... \n",
"3810 Vær forsiktig i bratte heng under og etter sn... \n",
"3811 Vær forsiktig i bratte heng under og etter sn... \n",
"3812 Unngå lengre opphold i og nedenfor heng med gl... \n",
"3813 Unngå lengre opphold i og nedenfor heng med gl... \n",
"3814 Unngå lengre opphold i og nedenfor heng med gl... \n",
"3815 Vær varsom der skredproblemet er å finne i kom... \n",
"3816 Unngå lengre opphold i og nedenfor heng med gl... \n",
"3817 Unngå lengre opphold i og nedenfor heng med gl... \n",
"3818 Vær varsom der skredproblemet er å finne i kom... \n",
"3819 Unngå lengre opphold i og nedenfor heng med gl... \n",
"3820 Unngå lengre opphold i og nedenfor heng med gl... \n",
"3821 Unngå lengre opphold i og nedenfor heng med gl... \n",
"\n",
" avalanche_problem_1_cause_id avalanche_problem_1_cause_name \\\n",
"index \n",
"0 0 Not given \n",
"1 0 Not given \n",
"2 0 Not given \n",
"3 0 Not given \n",
"4 0 Not given \n",
"5 0 Not given \n",
"6 0 Not given \n",
"7 0 Not given \n",
"8 0 Not given \n",
"9 0 Not given \n",
"10 0 Not given \n",
"11 0 Not given \n",
"12 0 Not given \n",
"13 0 Not given \n",
"14 0 Not given \n",
"15 0 Not given \n",
"16 0 Not given \n",
"17 0 Not given \n",
"18 0 Not given \n",
"19 0 Not given \n",
"20 0 Not given \n",
"21 15 Dårlig binding mellom lag i fokksnøen \n",
"22 10 Nedføyket svakt lag med nysnø \n",
"23 10 Nedføyket svakt lag med nysnø \n",
"24 10 Nedføyket svakt lag med nysnø \n",
"25 10 Nedføyket svakt lag med nysnø \n",
"26 10 Nedføyket svakt lag med nysnø \n",
"27 10 Nedføyket svakt lag med nysnø \n",
"28 10 Nedføyket svakt lag med nysnø \n",
"29 10 Nedføyket svakt lag med nysnø \n",
"... ... ... \n",
"3792 24 Ubunden snø \n",
"3793 24 Ubunden snø \n",
"3794 24 Ubunden snø \n",
"3795 20 Vann ved bakken/smelting fra bakken \n",
"3796 20 Vann ved bakken/smelting fra bakken \n",
"3797 24 Ubunden snø \n",
"3798 20 Vann ved bakken/smelting fra bakken \n",
"3799 20 Vann ved bakken/smelting fra bakken \n",
"3800 20 Vann ved bakken/smelting fra bakken \n",
"3801 10 Nedføyket svakt lag med nysnø \n",
"3802 10 Nedføyket svakt lag med nysnø \n",
"3803 10 Nedføyket svakt lag med nysnø \n",
"3804 10 Nedføyket svakt lag med nysnø \n",
"3805 10 Nedføyket svakt lag med nysnø \n",
"3806 10 Nedføyket svakt lag med nysnø \n",
"3807 10 Nedføyket svakt lag med nysnø \n",
"3808 10 Nedføyket svakt lag med nysnø \n",
"3809 10 Nedføyket svakt lag med nysnø \n",
"3810 10 Nedføyket svakt lag med nysnø \n",
"3811 10 Nedføyket svakt lag med nysnø \n",
"3812 20 Vann ved bakken/smelting fra bakken \n",
"3813 20 Vann ved bakken/smelting fra bakken \n",
"3814 20 Vann ved bakken/smelting fra bakken \n",
"3815 24 Ubunden snø \n",
"3816 20 Vann ved bakken/smelting fra bakken \n",
"3817 20 Vann ved bakken/smelting fra bakken \n",
"3818 24 Ubunden snø \n",
"3819 20 Vann ved bakken/smelting fra bakken \n",
"3820 20 Vann ved bakken/smelting fra bakken \n",
"3821 20 Vann ved bakken/smelting fra bakken \n",
"\n",
" avalanche_problem_1_destructive_size_ext_id \\\n",
"index \n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"5 0 \n",
"6 0 \n",
"7 0 \n",
"8 0 \n",
"9 0 \n",
"10 0 \n",
"11 0 \n",
"12 0 \n",
"13 0 \n",
"14 0 \n",
"15 0 \n",
"16 0 \n",
"17 0 \n",
"18 0 \n",
"19 0 \n",
"20 0 \n",
"21 2 \n",
"22 2 \n",
"23 2 \n",
"24 2 \n",
"25 2 \n",
"26 2 \n",
"27 2 \n",
"28 2 \n",
"29 2 \n",
"... ... \n",
"3792 1 \n",
"3793 1 \n",
"3794 1 \n",
"3795 2 \n",
"3796 2 \n",
"3797 1 \n",
"3798 2 \n",
"3799 2 \n",
"3800 2 \n",
"3801 2 \n",
"3802 2 \n",
"3803 2 \n",
"3804 2 \n",
"3805 2 \n",
"3806 1 \n",
"3807 1 \n",
"3808 1 \n",
"3809 2 \n",
"3810 2 \n",
"3811 2 \n",
"3812 2 \n",
"3813 2 \n",
"3814 2 \n",
"3815 1 \n",
"3816 2 \n",
"3817 2 \n",
"3818 1 \n",
"3819 2 \n",
"3820 2 \n",
"3821 2 \n",
"\n",
" avalanche_problem_1_destructive_size_ext_name \\\n",
"index \n",
"0 Not given \n",
"1 Not given \n",
"2 Not given \n",
"3 Not given \n",
"4 Not given \n",
"5 Not given \n",
"6 Not given \n",
"7 Not given \n",
"8 Not given \n",
"9 Not given \n",
"10 Not given \n",
"11 Not given \n",
"12 Not given \n",
"13 Not given \n",
"14 Not given \n",
"15 Not given \n",
"16 Not given \n",
"17 Not given \n",
"18 Not given \n",
"19 Not given \n",
"20 Not given \n",
"21 2 - Middels \n",
"22 2 - Middels \n",
"23 2 - Middels \n",
"24 2 - Middels \n",
"25 2 - Middels \n",
"26 2 - Middels \n",
"27 2 - Middels \n",
"28 2 - Middels \n",
"29 2 - Middels \n",
"... ... \n",
"3792 1 - Små \n",
"3793 1 - Små \n",
"3794 1 - Små \n",
"3795 2 - Middels \n",
"3796 2 - Middels \n",
"3797 1 - Små \n",
"3798 2 - Middels \n",
"3799 2 - Middels \n",
"3800 2 - Middels \n",
"3801 2 - Middels \n",
"3802 2 - Middels \n",
"3803 2 - Middels \n",
"3804 2 - Middels \n",
"3805 2 - Middels \n",
"3806 1 - Små \n",
"3807 1 - Små \n",
"3808 1 - Små \n",
"3809 2 - Middels \n",
"3810 2 - Middels \n",
"3811 2 - Middels \n",
"3812 2 - Middels \n",
"3813 2 - Middels \n",
"3814 2 - Middels \n",
"3815 1 - Små \n",
"3816 2 - Middels \n",
"3817 2 - Middels \n",
"3818 1 - Små \n",
"3819 2 - Middels \n",
"3820 2 - Middels \n",
"3821 2 - Middels \n",
"\n",
" avalanche_problem_1_distribution_id \\\n",
"index \n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"5 0 \n",
"6 0 \n",
"7 0 \n",
"8 0 \n",
"9 0 \n",
"10 0 \n",
"11 0 \n",
"12 0 \n",
"13 0 \n",
"14 0 \n",
"15 0 \n",
"16 0 \n",
"17 0 \n",
"18 0 \n",
"19 0 \n",
"20 0 \n",
"21 2 \n",
"22 2 \n",
"23 2 \n",
"24 2 \n",
"25 2 \n",
"26 2 \n",
"27 2 \n",
"28 2 \n",
"29 2 \n",
"... ... \n",
"3792 1 \n",
"3793 1 \n",
"3794 1 \n",
"3795 1 \n",
"3796 1 \n",
"3797 1 \n",
"3798 1 \n",
"3799 1 \n",
"3800 1 \n",
"3801 2 \n",
"3802 2 \n",
"3803 2 \n",
"3804 2 \n",
"3805 2 \n",
"3806 2 \n",
"3807 2 \n",
"3808 2 \n",
"3809 2 \n",
"3810 2 \n",
"3811 2 \n",
"3812 1 \n",
"3813 1 \n",
"3814 1 \n",
"3815 1 \n",
"3816 1 \n",
"3817 1 \n",
"3818 1 \n",
"3819 1 \n",
"3820 1 \n",
"3821 1 \n",
"\n",
" avalanche_problem_1_distribution_name \\\n",
"index \n",
"0 Not given \n",
"1 Not given \n",
"2 Not given \n",
"3 Not given \n",
"4 Not given \n",
"5 Not given \n",
"6 Not given \n",
"7 Not given \n",
"8 Not given \n",
"9 Not given \n",
"10 Not given \n",
"11 Not given \n",
"12 Not given \n",
"13 Not given \n",
"14 Not given \n",
"15 Not given \n",
"16 Not given \n",
"17 Not given \n",
"18 Not given \n",
"19 Not given \n",
"20 Not given \n",
"21 Noen bratte heng \n",
"22 Noen bratte heng \n",
"23 Noen bratte heng \n",
"24 Noen bratte heng \n",
"25 Noen bratte heng \n",
"26 Noen bratte heng \n",
"27 Noen bratte heng \n",
"28 Noen bratte heng \n",
"29 Noen bratte heng \n",
"... ... \n",
"3792 Få bratte heng \n",
"3793 Få bratte heng \n",
"3794 Få bratte heng \n",
"3795 Få bratte heng \n",
"3796 Få bratte heng \n",
"3797 Få bratte heng \n",
"3798 Få bratte heng \n",
"3799 Få bratte heng \n",
"3800 Få bratte heng \n",
"3801 Noen bratte heng \n",
"3802 Noen bratte heng \n",
"3803 Noen bratte heng \n",
"3804 Noen bratte heng \n",
"3805 Noen bratte heng \n",
"3806 Noen bratte heng \n",
"3807 Noen bratte heng \n",
"3808 Noen bratte heng \n",
"3809 Noen bratte heng \n",
"3810 Noen bratte heng \n",
"3811 Noen bratte heng \n",
"3812 Få bratte heng \n",
"3813 Få bratte heng \n",
"3814 Få bratte heng \n",
"3815 Få bratte heng \n",
"3816 Få bratte heng \n",
"3817 Få bratte heng \n",
"3818 Få bratte heng \n",
"3819 Få bratte heng \n",
"3820 Få bratte heng \n",
"3821 Få bratte heng \n",
"\n",
" avalanche_problem_1_exposed_height_1 ... region_name \\\n",
"index ... \n",
"0 0 ... Nordenskiöld Land \n",
"1 0 ... Vest-Finnmark \n",
"2 0 ... Nord-Troms \n",
"3 0 ... Lyngen \n",
"4 0 ... Tromsø \n",
"5 0 ... Sør-Troms \n",
"6 0 ... Indre Troms \n",
"7 0 ... Lofoten og Vesterålen \n",
"8 0 ... Ofoten \n",
"9 0 ... Salten \n",
"10 0 ... Svartisen \n",
"11 0 ... Trollheimen \n",
"12 0 ... Romsdal \n",
"13 0 ... Sunnmøre \n",
"14 0 ... Indre Fjordane \n",
"15 0 ... Jotunheimen \n",
"16 0 ... Indre Sogn \n",
"17 0 ... Voss \n",
"18 0 ... Hallingdal \n",
"19 0 ... Hardanger \n",
"20 0 ... Vest-Telemark \n",
"21 800 ... Nordenskiöld Land \n",
"22 400 ... Vest-Finnmark \n",
"23 400 ... Nord-Troms \n",
"24 400 ... Lyngen \n",
"25 400 ... Tromsø \n",
"26 600 ... Sør-Troms \n",
"27 400 ... Indre Troms \n",
"28 600 ... Lofoten og Vesterålen \n",
"29 900 ... Ofoten \n",
"... ... ... ... \n",
"3792 1000 ... Romsdal \n",
"3793 1000 ... Sunnmøre \n",
"3794 1000 ... Indre Fjordane \n",
"3795 900 ... Jotunheimen \n",
"3796 900 ... Indre Sogn \n",
"3797 1000 ... Voss \n",
"3798 1100 ... Hallingdal \n",
"3799 1000 ... Hardanger \n",
"3800 1100 ... Vest-Telemark \n",
"3801 100 ... Nordenskiöld Land \n",
"3802 300 ... Vest-Finnmark \n",
"3803 500 ... Nord-Troms \n",
"3804 500 ... Lyngen \n",
"3805 500 ... Tromsø \n",
"3806 500 ... Sør-Troms \n",
"3807 500 ... Indre Troms \n",
"3808 600 ... Lofoten og Vesterålen \n",
"3809 700 ... Ofoten \n",
"3810 800 ... Salten \n",
"3811 800 ... Svartisen \n",
"3812 1000 ... Trollheimen \n",
"3813 1000 ... Romsdal \n",
"3814 1000 ... Sunnmøre \n",
"3815 1000 ... Indre Fjordane \n",
"3816 900 ... Jotunheimen \n",
"3817 900 ... Indre Sogn \n",
"3818 1000 ... Voss \n",
"3819 1100 ... Hallingdal \n",
"3820 1000 ... Hardanger \n",
"3821 1100 ... Vest-Telemark \n",
"\n",
" region_type_id region_type_name snow_surface utm_east utm_north \\\n",
"index \n",
"0 10 A NaN 0 0 \n",
"1 10 A NaN 0 0 \n",
"2 10 A NaN 0 0 \n",
"3 10 A NaN 0 0 \n",
"4 10 A NaN 0 0 \n",
"5 10 A NaN 0 0 \n",
"6 10 A NaN 0 0 \n",
"7 10 A NaN 0 0 \n",
"8 10 A NaN 0 0 \n",
"9 10 A NaN 0 0 \n",
"10 10 A NaN 0 0 \n",
"11 10 A NaN 0 0 \n",
"12 10 A NaN 0 0 \n",
"13 10 A NaN 0 0 \n",
"14 10 A NaN 0 0 \n",
"15 10 A NaN 0 0 \n",
"16 10 A NaN 0 0 \n",
"17 10 A NaN 0 0 \n",
"18 10 A NaN 0 0 \n",
"19 10 A NaN 0 0 \n",
"20 10 A NaN 0 0 \n",
"21 10 A NaN 520332 8663904 \n",
"22 10 A NaN 802123 7794717 \n",
"23 10 A NaN 750984 7742562 \n",
"24 10 A NaN 692056 7719872 \n",
"25 10 A NaN 656496 7764237 \n",
"26 10 A NaN 594858 7642656 \n",
"27 10 A NaN 647352 7647736 \n",
"28 10 A NaN 527125 7620981 \n",
"29 10 A NaN 602309 7578309 \n",
"... ... ... ... ... ... \n",
"3792 10 A NaN 123434 6960580 \n",
"3793 10 A NaN 62473 6916553 \n",
"3794 10 A NaN 34025 6868801 \n",
"3795 10 A NaN 155607 6844417 \n",
"3796 10 A NaN 96001 6816985 \n",
"3797 10 A NaN 28607 6779054 \n",
"3798 10 A NaN 150188 6763814 \n",
"3799 10 A NaN 62473 6692016 \n",
"3800 10 A NaN 131223 6642571 \n",
"3801 10 A NaN 520332 8663904 \n",
"3802 10 A NaN 802123 7794717 \n",
"3803 10 A NaN 750984 7742562 \n",
"3804 10 A NaN 692056 7719872 \n",
"3805 10 A NaN 656496 7764237 \n",
"3806 10 A NaN 594858 7642656 \n",
"3807 10 A NaN 647352 7647736 \n",
"3808 10 A NaN 527125 7620981 \n",
"3809 10 A NaN 602309 7578309 \n",
"3810 10 A NaN 533221 7497029 \n",
"3811 10 A NaN 464133 7381882 \n",
"3812 10 A NaN 210810 6991060 \n",
"3813 10 A NaN 123434 6960580 \n",
"3814 10 A NaN 62473 6916553 \n",
"3815 10 A NaN 34025 6868801 \n",
"3816 10 A NaN 155607 6844417 \n",
"3817 10 A NaN 96001 6816985 \n",
"3818 10 A NaN 28607 6779054 \n",
"3819 10 A NaN 150188 6763814 \n",
"3820 10 A NaN 62473 6692016 \n",
"3821 10 A NaN 131223 6642571 \n",
"\n",
" utm_zone valid_from valid_to date \n",
"index \n",
"0 0 2016-12-01 00:00:00.000 2016-12-01 23:59:59.000 2016-12-01 \n",
"1 0 2016-12-01 00:00:00.000 2016-12-01 23:59:59.000 2016-12-01 \n",
"2 0 2016-12-01 00:00:00.000 2016-12-01 23:59:59.000 2016-12-01 \n",
"3 0 2016-12-01 00:00:00.000 2016-12-01 23:59:59.000 2016-12-01 \n",
"4 0 2016-12-01 00:00:00.000 2016-12-01 23:59:59.000 2016-12-01 \n",
"5 0 2016-12-01 00:00:00.000 2016-12-01 23:59:59.000 2016-12-01 \n",
"6 0 2016-12-01 00:00:00.000 2016-12-01 23:59:59.000 2016-12-01 \n",
"7 0 2016-12-01 00:00:00.000 2016-12-01 23:59:59.000 2016-12-01 \n",
"8 0 2016-12-01 00:00:00.000 2016-12-01 23:59:59.000 2016-12-01 \n",
"9 0 2016-12-01 00:00:00.000 2016-12-01 23:59:59.000 2016-12-01 \n",
"10 0 2016-12-01 00:00:00.000 2016-12-01 23:59:59.000 2016-12-01 \n",
"11 0 2016-12-01 00:00:00.000 2016-12-01 23:59:59.000 2016-12-01 \n",
"12 0 2016-12-01 00:00:00.000 2016-12-01 23:59:59.000 2016-12-01 \n",
"13 0 2016-12-01 00:00:00.000 2016-12-01 23:59:59.000 2016-12-01 \n",
"14 0 2016-12-01 00:00:00.000 2016-12-01 23:59:59.000 2016-12-01 \n",
"15 0 2016-12-01 00:00:00.000 2016-12-01 23:59:59.000 2016-12-01 \n",
"16 0 2016-12-01 00:00:00.000 2016-12-01 23:59:59.000 2016-12-01 \n",
"17 0 2016-12-01 00:00:00.000 2016-12-01 23:59:59.000 2016-12-01 \n",
"18 0 2016-12-01 00:00:00.000 2016-12-01 23:59:59.000 2016-12-01 \n",
"19 0 2016-12-01 00:00:00.000 2016-12-01 23:59:59.000 2016-12-01 \n",
"20 0 2016-12-01 00:00:00.000 2016-12-01 23:59:59.000 2016-12-01 \n",
"21 33 2016-12-02 00:00:00.000 2016-12-02 23:59:59.000 2016-12-02 \n",
"22 33 2016-12-02 00:00:00.000 2016-12-02 23:59:59.000 2016-12-02 \n",
"23 33 2016-12-02 00:00:00.000 2016-12-02 23:59:59.000 2016-12-02 \n",
"24 33 2016-12-02 00:00:00.000 2016-12-02 23:59:59.000 2016-12-02 \n",
"25 33 2016-12-02 00:00:00.000 2016-12-02 23:59:59.000 2016-12-02 \n",
"26 33 2016-12-02 00:00:00.000 2016-12-02 23:59:59.000 2016-12-02 \n",
"27 33 2016-12-02 00:00:00.000 2016-12-02 23:59:59.000 2016-12-02 \n",
"28 33 2016-12-02 00:00:00.000 2016-12-02 23:59:59.000 2016-12-02 \n",
"29 33 2016-12-02 00:00:00.000 2016-12-02 23:59:59.000 2016-12-02 \n",
"... ... ... ... ... \n",
"3792 33 2017-05-30 00:00:00.000 2017-05-30 23:59:59.000 2017-05-30 \n",
"3793 33 2017-05-30 00:00:00.000 2017-05-30 23:59:59.000 2017-05-30 \n",
"3794 33 2017-05-30 00:00:00.000 2017-05-30 23:59:59.000 2017-05-30 \n",
"3795 33 2017-05-30 00:00:00.000 2017-05-30 23:59:59.000 2017-05-30 \n",
"3796 33 2017-05-30 00:00:00.000 2017-05-30 23:59:59.000 2017-05-30 \n",
"3797 33 2017-05-30 00:00:00.000 2017-05-30 23:59:59.000 2017-05-30 \n",
"3798 33 2017-05-30 00:00:00.000 2017-05-30 23:59:59.000 2017-05-30 \n",
"3799 33 2017-05-30 00:00:00.000 2017-05-30 23:59:59.000 2017-05-30 \n",
"3800 33 2017-05-30 00:00:00.000 2017-05-30 23:59:59.000 2017-05-30 \n",
"3801 33 2017-05-31 00:00:00.000 2017-05-31 23:59:59.000 2017-05-31 \n",
"3802 33 2017-05-31 00:00:00.000 2017-05-31 23:59:59.000 2017-05-31 \n",
"3803 33 2017-05-31 00:00:00.000 2017-05-31 23:59:59.000 2017-05-31 \n",
"3804 33 2017-05-31 00:00:00.000 2017-05-31 23:59:59.000 2017-05-31 \n",
"3805 33 2017-05-31 00:00:00.000 2017-05-31 23:59:59.000 2017-05-31 \n",
"3806 33 2017-05-31 00:00:00.000 2017-05-31 23:59:59.000 2017-05-31 \n",
"3807 33 2017-05-31 00:00:00.000 2017-05-31 23:59:59.000 2017-05-31 \n",
"3808 33 2017-05-31 00:00:00.000 2017-05-31 23:59:59.000 2017-05-31 \n",
"3809 33 2017-05-31 00:00:00.000 2017-05-31 23:59:59.000 2017-05-31 \n",
"3810 33 2017-05-31 00:00:00.000 2017-05-31 23:59:59.000 2017-05-31 \n",
"3811 33 2017-05-31 00:00:00.000 2017-05-31 23:59:59.000 2017-05-31 \n",
"3812 33 2017-05-31 00:00:00.000 2017-05-31 23:59:59.000 2017-05-31 \n",
"3813 33 2017-05-31 00:00:00.000 2017-05-31 23:59:59.000 2017-05-31 \n",
"3814 33 2017-05-31 00:00:00.000 2017-05-31 23:59:59.000 2017-05-31 \n",
"3815 33 2017-05-31 00:00:00.000 2017-05-31 23:59:59.000 2017-05-31 \n",
"3816 33 2017-05-31 00:00:00.000 2017-05-31 23:59:59.000 2017-05-31 \n",
"3817 33 2017-05-31 00:00:00.000 2017-05-31 23:59:59.000 2017-05-31 \n",
"3818 33 2017-05-31 00:00:00.000 2017-05-31 23:59:59.000 2017-05-31 \n",
"3819 33 2017-05-31 00:00:00.000 2017-05-31 23:59:59.000 2017-05-31 \n",
"3820 33 2017-05-31 00:00:00.000 2017-05-31 23:59:59.000 2017-05-31 \n",
"3821 33 2017-05-31 00:00:00.000 2017-05-31 23:59:59.000 2017-05-31 \n",
"\n",
"[3822 rows x 103 columns]"
]
},
"execution_count": 246,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"varsom_df[(varsom_df['date']>=datetime.date(year=2016, month=12, day=1)) & (varsom_df['date']<datetime.date(year=2017, month=6, day=1))]"
]
},
{
"cell_type": "code",
"execution_count": 247,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"empty\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:11: FutureWarning: Comparing Series of datetimes with 'datetime.date'. Currently, the\n",
"'datetime.date' is coerced to a datetime. In the future pandas will\n",
"not coerce, and a TypeError will be raised. To retain the current\n",
"behavior, convert the 'datetime.date' to a datetime with\n",
"'pd.Timestamp'.\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>valid_from</th>\n",
" <th>region_name</th>\n",
" <th>region_id</th>\n",
" <th>avalanche_problem_1_problem_type_id</th>\n",
" <th>avalanche_problem_1_problem_type_id_prev2day</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2016-12-01 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2016-12-02 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>10</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2016-12-03 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>10</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2016-12-04 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>10</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2016-12-05 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>7</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2016-12-06 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>7</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2016-12-07 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>7</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2016-12-08 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>7</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2016-12-09 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>10</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2016-12-10 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>10</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2016-12-11 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>10</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2016-12-12 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>10</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2016-12-13 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>37</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2016-12-14 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>10</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2016-12-15 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>10</td>\n",
" <td>37.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2016-12-16 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>10</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2016-12-17 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>10</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2016-12-18 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>10</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2016-12-19 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>10</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2016-12-20 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>7</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>2016-12-21 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>10</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2016-12-22 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>10</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2016-12-23 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>10</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>2016-12-24 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>10</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>2016-12-25 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>10</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>2016-12-26 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>37</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>2016-12-27 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>37</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2016-12-28 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>37</td>\n",
" <td>37.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>2016-12-29 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>37</td>\n",
" <td>37.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2016-12-30 00:00:00.000</td>\n",
" <td>Nordenskiöld Land</td>\n",
" <td>3003</td>\n",
" <td>37</td>\n",
" <td>37.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33234</th>\n",
" <td>2019-01-02 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>10</td>\n",
" <td>45.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33235</th>\n",
" <td>2019-01-03 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>10</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33236</th>\n",
" <td>2019-01-04 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>45</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33237</th>\n",
" <td>2019-01-05 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>10</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33238</th>\n",
" <td>2019-01-06 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>10</td>\n",
" <td>45.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33239</th>\n",
" <td>2019-01-07 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>7</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33240</th>\n",
" <td>2019-01-08 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>7</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33241</th>\n",
" <td>2019-01-09 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>7</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33242</th>\n",
" <td>2019-01-10 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>7</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33243</th>\n",
" <td>2019-01-11 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>10</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33244</th>\n",
" <td>2019-01-12 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>10</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33245</th>\n",
" <td>2019-01-13 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>30</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33246</th>\n",
" <td>2019-01-14 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>30</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33247</th>\n",
" <td>2019-01-15 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>30</td>\n",
" <td>30.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33248</th>\n",
" <td>2019-01-16 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>10</td>\n",
" <td>30.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33249</th>\n",
" <td>2019-01-17 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>10</td>\n",
" <td>30.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33250</th>\n",
" <td>2019-01-18 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>10</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33251</th>\n",
" <td>2019-01-19 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>10</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33252</th>\n",
" <td>2019-01-20 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>10</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33253</th>\n",
" <td>2019-01-21 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>10</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33254</th>\n",
" <td>2019-01-22 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>10</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33255</th>\n",
" <td>2019-01-23 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>10</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33256</th>\n",
" <td>2019-01-24 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>10</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33257</th>\n",
" <td>2019-01-25 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>7</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33258</th>\n",
" <td>2019-01-26 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>7</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33259</th>\n",
" <td>2019-01-27 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>7</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33260</th>\n",
" <td>2019-01-28 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>7</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33261</th>\n",
" <td>2019-01-29 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>7</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33262</th>\n",
" <td>2019-01-30 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>7</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33263</th>\n",
" <td>2019-01-31 00:00:00.000</td>\n",
" <td>Vest-Telemark</td>\n",
" <td>3035</td>\n",
" <td>7</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>33264 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" valid_from region_name region_id \\\n",
"0 2016-12-01 00:00:00.000 Nordenskiöld Land 3003 \n",
"1 2016-12-02 00:00:00.000 Nordenskiöld Land 3003 \n",
"2 2016-12-03 00:00:00.000 Nordenskiöld Land 3003 \n",
"3 2016-12-04 00:00:00.000 Nordenskiöld Land 3003 \n",
"4 2016-12-05 00:00:00.000 Nordenskiöld Land 3003 \n",
"5 2016-12-06 00:00:00.000 Nordenskiöld Land 3003 \n",
"6 2016-12-07 00:00:00.000 Nordenskiöld Land 3003 \n",
"7 2016-12-08 00:00:00.000 Nordenskiöld Land 3003 \n",
"8 2016-12-09 00:00:00.000 Nordenskiöld Land 3003 \n",
"9 2016-12-10 00:00:00.000 Nordenskiöld Land 3003 \n",
"10 2016-12-11 00:00:00.000 Nordenskiöld Land 3003 \n",
"11 2016-12-12 00:00:00.000 Nordenskiöld Land 3003 \n",
"12 2016-12-13 00:00:00.000 Nordenskiöld Land 3003 \n",
"13 2016-12-14 00:00:00.000 Nordenskiöld Land 3003 \n",
"14 2016-12-15 00:00:00.000 Nordenskiöld Land 3003 \n",
"15 2016-12-16 00:00:00.000 Nordenskiöld Land 3003 \n",
"16 2016-12-17 00:00:00.000 Nordenskiöld Land 3003 \n",
"17 2016-12-18 00:00:00.000 Nordenskiöld Land 3003 \n",
"18 2016-12-19 00:00:00.000 Nordenskiöld Land 3003 \n",
"19 2016-12-20 00:00:00.000 Nordenskiöld Land 3003 \n",
"20 2016-12-21 00:00:00.000 Nordenskiöld Land 3003 \n",
"21 2016-12-22 00:00:00.000 Nordenskiöld Land 3003 \n",
"22 2016-12-23 00:00:00.000 Nordenskiöld Land 3003 \n",
"23 2016-12-24 00:00:00.000 Nordenskiöld Land 3003 \n",
"24 2016-12-25 00:00:00.000 Nordenskiöld Land 3003 \n",
"25 2016-12-26 00:00:00.000 Nordenskiöld Land 3003 \n",
"26 2016-12-27 00:00:00.000 Nordenskiöld Land 3003 \n",
"27 2016-12-28 00:00:00.000 Nordenskiöld Land 3003 \n",
"28 2016-12-29 00:00:00.000 Nordenskiöld Land 3003 \n",
"29 2016-12-30 00:00:00.000 Nordenskiöld Land 3003 \n",
"... ... ... ... \n",
"33234 2019-01-02 00:00:00.000 Vest-Telemark 3035 \n",
"33235 2019-01-03 00:00:00.000 Vest-Telemark 3035 \n",
"33236 2019-01-04 00:00:00.000 Vest-Telemark 3035 \n",
"33237 2019-01-05 00:00:00.000 Vest-Telemark 3035 \n",
"33238 2019-01-06 00:00:00.000 Vest-Telemark 3035 \n",
"33239 2019-01-07 00:00:00.000 Vest-Telemark 3035 \n",
"33240 2019-01-08 00:00:00.000 Vest-Telemark 3035 \n",
"33241 2019-01-09 00:00:00.000 Vest-Telemark 3035 \n",
"33242 2019-01-10 00:00:00.000 Vest-Telemark 3035 \n",
"33243 2019-01-11 00:00:00.000 Vest-Telemark 3035 \n",
"33244 2019-01-12 00:00:00.000 Vest-Telemark 3035 \n",
"33245 2019-01-13 00:00:00.000 Vest-Telemark 3035 \n",
"33246 2019-01-14 00:00:00.000 Vest-Telemark 3035 \n",
"33247 2019-01-15 00:00:00.000 Vest-Telemark 3035 \n",
"33248 2019-01-16 00:00:00.000 Vest-Telemark 3035 \n",
"33249 2019-01-17 00:00:00.000 Vest-Telemark 3035 \n",
"33250 2019-01-18 00:00:00.000 Vest-Telemark 3035 \n",
"33251 2019-01-19 00:00:00.000 Vest-Telemark 3035 \n",
"33252 2019-01-20 00:00:00.000 Vest-Telemark 3035 \n",
"33253 2019-01-21 00:00:00.000 Vest-Telemark 3035 \n",
"33254 2019-01-22 00:00:00.000 Vest-Telemark 3035 \n",
"33255 2019-01-23 00:00:00.000 Vest-Telemark 3035 \n",
"33256 2019-01-24 00:00:00.000 Vest-Telemark 3035 \n",
"33257 2019-01-25 00:00:00.000 Vest-Telemark 3035 \n",
"33258 2019-01-26 00:00:00.000 Vest-Telemark 3035 \n",
"33259 2019-01-27 00:00:00.000 Vest-Telemark 3035 \n",
"33260 2019-01-28 00:00:00.000 Vest-Telemark 3035 \n",
"33261 2019-01-29 00:00:00.000 Vest-Telemark 3035 \n",
"33262 2019-01-30 00:00:00.000 Vest-Telemark 3035 \n",
"33263 2019-01-31 00:00:00.000 Vest-Telemark 3035 \n",
"\n",
" avalanche_problem_1_problem_type_id \\\n",
"0 0 \n",
"1 10 \n",
"2 10 \n",
"3 10 \n",
"4 7 \n",
"5 7 \n",
"6 7 \n",
"7 7 \n",
"8 10 \n",
"9 10 \n",
"10 10 \n",
"11 10 \n",
"12 37 \n",
"13 10 \n",
"14 10 \n",
"15 10 \n",
"16 10 \n",
"17 10 \n",
"18 10 \n",
"19 7 \n",
"20 10 \n",
"21 10 \n",
"22 10 \n",
"23 10 \n",
"24 10 \n",
"25 37 \n",
"26 37 \n",
"27 37 \n",
"28 37 \n",
"29 37 \n",
"... ... \n",
"33234 10 \n",
"33235 10 \n",
"33236 45 \n",
"33237 10 \n",
"33238 10 \n",
"33239 7 \n",
"33240 7 \n",
"33241 7 \n",
"33242 7 \n",
"33243 10 \n",
"33244 10 \n",
"33245 30 \n",
"33246 30 \n",
"33247 30 \n",
"33248 10 \n",
"33249 10 \n",
"33250 10 \n",
"33251 10 \n",
"33252 10 \n",
"33253 10 \n",
"33254 10 \n",
"33255 10 \n",
"33256 10 \n",
"33257 7 \n",
"33258 7 \n",
"33259 7 \n",
"33260 7 \n",
"33261 7 \n",
"33262 7 \n",
"33263 7 \n",
"\n",
" avalanche_problem_1_problem_type_id_prev2day \n",
"0 NaN \n",
"1 NaN \n",
"2 0.0 \n",
"3 10.0 \n",
"4 10.0 \n",
"5 10.0 \n",
"6 7.0 \n",
"7 7.0 \n",
"8 7.0 \n",
"9 7.0 \n",
"10 10.0 \n",
"11 10.0 \n",
"12 10.0 \n",
"13 10.0 \n",
"14 37.0 \n",
"15 10.0 \n",
"16 10.0 \n",
"17 10.0 \n",
"18 10.0 \n",
"19 10.0 \n",
"20 10.0 \n",
"21 7.0 \n",
"22 10.0 \n",
"23 10.0 \n",
"24 10.0 \n",
"25 10.0 \n",
"26 10.0 \n",
"27 37.0 \n",
"28 37.0 \n",
"29 37.0 \n",
"... ... \n",
"33234 45.0 \n",
"33235 10.0 \n",
"33236 10.0 \n",
"33237 10.0 \n",
"33238 45.0 \n",
"33239 10.0 \n",
"33240 10.0 \n",
"33241 7.0 \n",
"33242 7.0 \n",
"33243 7.0 \n",
"33244 7.0 \n",
"33245 10.0 \n",
"33246 10.0 \n",
"33247 30.0 \n",
"33248 30.0 \n",
"33249 30.0 \n",
"33250 10.0 \n",
"33251 10.0 \n",
"33252 10.0 \n",
"33253 10.0 \n",
"33254 10.0 \n",
"33255 10.0 \n",
"33256 10.0 \n",
"33257 10.0 \n",
"33258 10.0 \n",
"33259 7.0 \n",
"33260 7.0 \n",
"33261 7.0 \n",
"33262 7.0 \n",
"33263 7.0 \n",
"\n",
"[33264 rows x 5 columns]"
]
},
"execution_count": 247,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# grouping by region and season\n",
"grouped_df = pd.DataFrame()\n",
"\n",
"for id in varsom_df['region_id'].unique():\n",
"#for id in [3003, 3011, 3014, 3028]:\n",
" _tmp_df = varsom_df[varsom_df['region_id']==id].copy()\n",
" _tmp_df.sort_values(by='valid_from')\n",
" \n",
" start, stop = int(_tmp_df['date_valid'].min()[:4]), int(_tmp_df['date_valid'].max()[:4])\n",
" for yr in range(start, stop-1):\n",
" _tmp_df[(_tmp_df['date']>=datetime.date(year=yr, month=12, day=1)) & (_tmp_df['date']<datetime.date(year=yr+1, month=6, day=1))]\n",
" _tmp_df = add_prevday_features(_tmp_df)\n",
" #print(len(_tmp_df), _tmp_df['region_id'].unique())\n",
" if grouped_df.empty:\n",
" print('empty')\n",
" grouped_df = _tmp_df.copy()\n",
" else:\n",
" grouped_df = pd.concat([grouped_df, _tmp_df], ignore_index=True).copy()\n",
" \n",
" #print('g', len(grouped_df), grouped_df['region_id'].unique())\n",
" \n",
"\n",
"grouped_df.filter(['valid_from', 'region_name', 'region_id', 'avalanche_problem_1_problem_type_id', 'avalanche_problem_1_problem_type_id_prev2day'])\n"
]
},
{
"cell_type": "code",
"execution_count": 248,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"varsom_df = grouped_df.copy()"
]
},
{
"cell_type": "code",
"execution_count": 249,
"metadata": {
"collapsed": false,
"pycharm": {
"is_executing": false
}
},
"outputs": [],
"source": [
"#from aps.notebooks.ml_varsom.regroup_forecast import regroup\n",
"from regroup_forecast import regroup\n",
"varsom_df = regroup(varsom_df)"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {}
},
"source": [
"Add historical values, e.g. yesterdays precipitation\n",
"\n",
"Add a tag to the feature name to indicate if it is categorical (c) or numerical (n).\n",
"Add a target tag (t).\n",
"Add a modelled (m) or observed (o) tag.\n",
"\n",
"_prev1day\n",
"_prev3day\n",
"\n",
"n_f_Next24HourChangeInTempFromPrev3DayMax - change of temperature over a certain period.\n",
"n_r_Prev7dayMinTemp2InPast - ???\n",
"n_r_SNOWDAS_SnowpackAveTemp_k2InPast - modelled average temperature from model SNOWDAS (? https://nsidc.org/data/g02158)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 250,
"metadata": {
"collapsed": false,
"pycharm": {
"is_executing": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 15462\n",
"3 10396\n",
"2 3234\n",
"5 3038\n",
"6 500\n",
"1 370\n",
"4 264\n",
"Name: avalanche_problem_1_sensitivity_id_class, dtype: int64\n"
]
}
],
"source": [
"# Check if sensitivity transformation worked...\n",
"print(varsom_df['avalanche_problem_1_sensitivity_id_class'].value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 251,
"metadata": {
"collapsed": false,
"pycharm": {
"is_executing": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mountain_weather_precip_region</th>\n",
" <th>mountain_weather_precip_region_prev3daysum</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mountain_weather_precip_region mountain_weather_precip_region_prev3daysum\n",
"0 NaN NaN\n",
"1 NaN NaN\n",
"2 NaN NaN\n",
"3 NaN NaN\n",
"4 NaN NaN\n",
"5 NaN NaN\n",
"6 NaN NaN\n",
"7 NaN NaN\n",
"8 NaN NaN\n",
"9 NaN NaN\n",
"10 NaN NaN\n",
"11 NaN NaN"
]
},
"execution_count": 251,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"varsom_df.filter(['mountain_weather_precip_region', 'mountain_weather_precip_region_prev3daysum']).head(12)"
]
},
{
"cell_type": "code",
"execution_count": 252,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>region_id</th>\n",
" <th>danger_level</th>\n",
" <th>danger_level_prev1day</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>7920</th>\n",
" <td>3012</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7921</th>\n",
" <td>3012</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7922</th>\n",
" <td>3012</td>\n",
" <td>2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7923</th>\n",
" <td>3012</td>\n",
" <td>3</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7924</th>\n",
" <td>3012</td>\n",
" <td>3</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7925</th>\n",
" <td>3012</td>\n",
" <td>3</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7926</th>\n",
" <td>3012</td>\n",
" <td>3</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7927</th>\n",
" <td>3012</td>\n",
" <td>2</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7928</th>\n",
" <td>3012</td>\n",
" <td>2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7929</th>\n",
" <td>3012</td>\n",
" <td>2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7930</th>\n",
" <td>3012</td>\n",
" <td>3</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7931</th>\n",
" <td>3012</td>\n",
" <td>3</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7932</th>\n",
" <td>3012</td>\n",
" <td>2</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7933</th>\n",
" <td>3012</td>\n",
" <td>2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7934</th>\n",
" <td>3012</td>\n",
" <td>2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7935</th>\n",
" <td>3012</td>\n",
" <td>2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7936</th>\n",
" <td>3012</td>\n",
" <td>2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7937</th>\n",
" <td>3012</td>\n",
" <td>2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7938</th>\n",
" <td>3012</td>\n",
" <td>2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7939</th>\n",
" <td>3012</td>\n",
" <td>1</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7940</th>\n",
" <td>3012</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7941</th>\n",
" <td>3012</td>\n",
" <td>2</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7942</th>\n",
" <td>3012</td>\n",
" <td>2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7943</th>\n",
" <td>3012</td>\n",
" <td>2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7944</th>\n",
" <td>3012</td>\n",
" <td>2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7945</th>\n",
" <td>3012</td>\n",
" <td>2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7946</th>\n",
" <td>3012</td>\n",
" <td>3</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7947</th>\n",
" <td>3012</td>\n",
" <td>3</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7948</th>\n",
" <td>3012</td>\n",
" <td>2</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7949</th>\n",
" <td>3012</td>\n",
" <td>3</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7950</th>\n",
" <td>3012</td>\n",
" <td>3</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7951</th>\n",
" <td>3012</td>\n",
" <td>3</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7952</th>\n",
" <td>3012</td>\n",
" <td>2</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7953</th>\n",
" <td>3012</td>\n",
" <td>3</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7954</th>\n",
" <td>3012</td>\n",
" <td>2</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7955</th>\n",
" <td>3012</td>\n",
" <td>2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7956</th>\n",
" <td>3012</td>\n",
" <td>3</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7957</th>\n",
" <td>3012</td>\n",
" <td>3</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7958</th>\n",
" <td>3012</td>\n",
" <td>3</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7959</th>\n",
" <td>3012</td>\n",
" <td>3</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" region_id danger_level danger_level_prev1day\n",
"7920 3012 0 NaN\n",
"7921 3012 2 0.0\n",
"7922 3012 2 2.0\n",
"7923 3012 3 2.0\n",
"7924 3012 3 3.0\n",
"7925 3012 3 3.0\n",
"7926 3012 3 3.0\n",
"7927 3012 2 3.0\n",
"7928 3012 2 2.0\n",
"7929 3012 2 2.0\n",
"7930 3012 3 2.0\n",
"7931 3012 3 3.0\n",
"7932 3012 2 3.0\n",
"7933 3012 2 2.0\n",
"7934 3012 2 2.0\n",
"7935 3012 2 2.0\n",
"7936 3012 2 2.0\n",
"7937 3012 2 2.0\n",
"7938 3012 2 2.0\n",
"7939 3012 1 2.0\n",
"7940 3012 1 1.0\n",
"7941 3012 2 1.0\n",
"7942 3012 2 2.0\n",
"7943 3012 2 2.0\n",
"7944 3012 2 2.0\n",
"7945 3012 2 2.0\n",
"7946 3012 3 2.0\n",
"7947 3012 3 3.0\n",
"7948 3012 2 3.0\n",
"7949 3012 3 2.0\n",
"7950 3012 3 3.0\n",
"7951 3012 3 3.0\n",
"7952 3012 2 3.0\n",
"7953 3012 3 2.0\n",
"7954 3012 2 3.0\n",
"7955 3012 2 2.0\n",
"7956 3012 3 2.0\n",
"7957 3012 3 3.0\n",
"7958 3012 3 3.0\n",
"7959 3012 3 3.0"
]
},
"execution_count": 252,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"varsom_df[varsom_df['region_id']==3012].filter(['region_id', 'danger_level', 'danger_level_prev1day']).head(40)"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {}
},
"source": [
"Combine avalanche problem attributes into single parameter"
]
},
{
"cell_type": "code",
"execution_count": 253,
"metadata": {
"collapsed": false,
"pycharm": {
"is_executing": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wind slab:Specific:Stubborn:Isolated\n",
"[ 0 6212 6202 5232 5342 6232 6222 7212 6233 5332 7313 7363 7333 7233\n",
" 6262 5231 7223 7253 5233 6333 6332 6352 7133 7123 4352 6132 6253 6363\n",
" 6243 7203 5252 5352 5242 6223 7232 6242 7213 7222 6252 6122 5222 6231\n",
" 4362 7322 7323 7132 5131 5132 4253 4152 2231 2131 6263 5253 5230 4252\n",
" 6230 4354 7153 2252 2232 2251 7332 7263 6112 6221 6131 6121 6353 7353\n",
" 7324 7224 7134 7234 5262 2152 4153 2362 2151 6133 6331 5363 4263 7242\n",
" 7252 7254 4233 2262 2122 4363 6220 7122 7231 6362 7124 7154 4254 4154\n",
" 4134 4353 6200 5122 6211 4133 2121 6111 4232 5333 3253 2361 2352 5221\n",
" 7113 7112 2130 3152 5331 5353 5223 7243 7131 6323 7214 6102 4151 7352\n",
" 3153 6303 6123 6354 4264 2153 2261 232 4463 6322 5152 5133 6152 2332\n",
" 3263 2253 2221 2111 6254 7121 5153 5243 5343 4332 6101 1351 4223 4123\n",
" 1152 6342 2132 4121 5362 4222 2250 3252 1232 2263 5111 4262 5263 2351\n",
" 4132 6343 3132 122 2212 2353 7102 7342 2141 6264 7152 5121] 180\n",
"0 15428\n",
"6232 2830\n",
"5232 1522\n",
"6222 1366\n",
"6233 912\n",
"7233 678\n",
"2252 582\n",
"6122 548\n",
"6132 524\n",
"2131 498\n",
"5233 480\n",
"7232 442\n",
"6332 412\n",
"5332 326\n",
"6253 268\n",
"6121 262\n",
"6131 250\n",
"4253 222\n",
"7223 222\n",
"2251 204\n",
"5132 202\n",
"5253 188\n",
"6223 184\n",
"3152 170\n",
"2232 168\n",
"2152 146\n",
"5231 144\n",
"7132 144\n",
"4153 134\n",
"7253 132\n",
" ... \n",
"6264 2\n",
"2141 2\n",
"5263 2\n",
"7352 2\n",
"3132 2\n",
"6303 2\n",
"4463 2\n",
"2250 2\n",
"1152 2\n",
"5262 2\n",
"2111 2\n",
"6211 2\n",
"5121 2\n",
"5362 2\n",
"6200 2\n",
"4121 2\n",
"122 2\n",
"6362 2\n",
"7121 2\n",
"2263 2\n",
"5230 2\n",
"4133 2\n",
"4151 2\n",
"5221 2\n",
"6220 2\n",
"6323 2\n",
"7152 2\n",
"1232 2\n",
"2130 2\n",
"2221 2\n",
"Name: aval_problem_1_combined, Length: 180, dtype: int64\n",
"0 15434\n",
"6 8502\n",
"5 3530\n",
"7 2530\n",
"2 2140\n",
"4 896\n",
"3 222\n",
"1 10\n",
"Name: avalanche_problem_1_problem_type_id_class, dtype: int64\n"
]
}
],
"source": [
"def get_aval_problem_combined(type_, dist_, sens_, size_):\n",
" return int(\"{0}{1}{2}{3}\".format(type_, dist_, sens_, size_))\n",
"\n",
"\n",
"def print_aval_problem_combined(aval_combined_int):\n",
" aval_combined_str = str(aval_combined_int)\n",
" #with open(aps_pth / r'aps/config/snoskred_keys.json') as jdata:\n",
" with open(r'D:\\Dev\\APS\\aps\\config\\snoskred_keys.json') as jdata:\n",
" snoskred_keys = json.load(jdata)\n",
" type_ = snoskred_keys[\"Class_AvalancheProblemTypeName\"][aval_combined_str[0]]\n",
" dist_ = snoskred_keys[\"Class_AvalDistributionName\"][aval_combined_str[1]]\n",
" sens_ = snoskred_keys[\"Class_AvalSensitivityId\"][aval_combined_str[2]]\n",
" size_ = snoskred_keys[\"DestructiveSizeId\"][aval_combined_str[3]]\n",
" \n",
" return f\"{type_}:{dist_}:{sens_}:{size_}\"\n",
"\n",
"print(print_aval_problem_combined(6221))\n",
" \n",
" \n",
" \n",
"varsom_df['aval_problem_1_combined'] = varsom_df.apply(lambda row: get_aval_problem_combined(row['avalanche_problem_1_problem_type_id_class'],\n",
" row['avalanche_problem_1_distribution_id'],\n",
" row['avalanche_problem_1_sensitivity_id_class'], #avalanche_problem_1_trigger_simple_id_class / avalanche_problem_1_sensitivity_id_class\n",
" row['avalanche_problem_1_destructive_size_ext_id']), axis=1)\n",
"\n",
"aval_uni = varsom_df['aval_problem_1_combined'].unique()\n",
"print(aval_uni, len(aval_uni))\n",
"print(varsom_df['aval_problem_1_combined'].value_counts())\n",
"print(varsom_df['avalanche_problem_1_problem_type_id_class'].value_counts())"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {}
},
"source": [
"Hot encode categorical variables where necessary."
]
},
{
"cell_type": "code",
"execution_count": 254,
"metadata": {
"collapsed": false,
"pycharm": {
"is_executing": false
}
},
"outputs": [],
"source": [
"# hot encode\n",
"hot_encode_ = ['emergency_warning', 'author', 'mountain_weather_wind_direction']\n",
"varsom_df = pd.get_dummies(varsom_df, columns=hot_encode_)"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {}
},
"source": [
"Check if there are no weired or missing values."
]
},
{
"cell_type": "code",
"execution_count": 255,
"metadata": {
"collapsed": false,
"pycharm": {
"is_executing": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"avalanche_danger: [nan\n",
" 'Snøen i leområdene er generelt sett stabile og det er ujevn fordeling av snøen i terrenget. Litt nysnø ventes ikke å øke skredfaren.\\r\\n \\r\\nSkredfaren vurderes som 2-moderat, og så vidt det da det er lite snø i regionen.\\r\\n\\r\\nNB: Tidlig på sesongen baserer varslet seg på få observasjoner og varslet kan derfor være noe usikkert.'\n",
" 'Snøen i leområdene er generelt sett stabile og det er ujevn fordeling av snøen i terrenget. Den varslede nysnøen ventes ikke å øke skredfaren, men vær oppmerksom i områder der det ligger fersk vindtransportert snø.\\r\\n \\r\\nTil tross for lite snø i regionen vurderes skredfaren til 2-moderat.\\r\\n'\n",
" ...\n",
" 'Nysnø og vind fra SØ i helga har gitt flakdannelse i terreng som samler snø og leheng mot V og N. Stabilisering av nysnøflak går sakte i kaldt vær, men mindre vind og ubetydelig med ny nedbør gjør at skredproblemet blir mindre utbredd tirsdag. I noen heng vil det være lett å løse ut middels store skred (str 2) og det kan heller ikke utelukkes store skred (str 3) i noen få heng. Ferske skred og sprekker i snødekket er tydelige faretegn. Eldre fokksnø i S og Ø ansees i all hovedsak som stabil. Det er generelt vanskelig å løse ut skred i kantkornlag, men vær forsiktig i områder med tynt snødekke og nysnøflak.'\n",
" 'Litt vind og nysnø vil danne nysnøflak i fjellet. Flakene ventes ikke å bli store og selv om det er mye løs snø tilgjengelig er det få steder at vinden er kraftig nok til å flytte denne gamle snøen. En skiløper kan påvirke nysnøflakene og gi skred opp i str 2. Eldre fokksnø i S og Ø ansees i all hovedsak som stabil. Det er generelt vanskelig å løse ut skred i kantkornlag, men vær forsiktig i områder med tynt snødekke og nysnøflak.'\n",
" 'Nysnø kan noen steder flyttes av vind og danne myke nysnøflak i heng som samler snø, hovedsakelig mot NV. Disse vil være lette å påvirke av en skiløper, særlig S og Ø i regionen som får mest snø. Lengre nord mot Hardangervidda kan sterkere vind gi hardere fokksnøflak hvis det er løs snø tilgjengelig. Det er generelt vanskelig å løse ut skred i kantkornlag, men vær forsiktig i områder med tynt snødekke og nysnøflak.'] \n",
"\n",
"avalanche_problem_1_advice: ['Not given'\n",
" 'Vær forsiktig i områder brattere enn 30 grader med fokksnø til den har fått stabilisert seg. Hold avstand mellom hverandre ved ferdsel i bratt terreng. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk. Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk tegn.'\n",
" 'Vær forsiktig i bratte heng under og etter snøfallet til nysnøen har stabilisert seg. Hold avstand til hverandre ved ferdsel i brat terreng. Unngå ferdsel i / ved terrengfeller. Skredproblemet finnes overalt hvor det ligger mye nysnø i bratt terreng. Se etter nysnø som binder seg sammen til myke flak og sprekker opp eller fester seg dårlig til den eldre snøen under. Snø som sprekker opp rundt skiene/brettet er et typisk tegn.'\n",
" 'Unngå bratte heng og terrengfeller under og etter snøfallet til nysnøen har stabilisert seg. Skredproblemet finnes overalt hvor det ligger mye nysnø i bratt terreng. Se etter nysnø som binder seg sammen til myke flak og sprekker opp eller fester seg dårlig til den eldre snøen under. Snø som sprekker opp rundt skiene/brettet er et typisk tegn.'\n",
" 'Hold god avstand til hverandre og til løsneområdene. Fjernutløsning er mulig. Vær ekstra forsiktig og tenk konsekvens når du gjør vegvalg, særlig i ukjent terreng, etter snøfall eller vind og i perioder med temperaturstigning. Når det svake laget ligger dypt i snødekket eller det overliggende laget er hardt, trengs det som regel stor tilleggsbelastning for å påvirke dette. Det krever mye kunnskap å gjenkjenne svake lag i snødekket.'\n",
" 'Unngå terreng brattere enn 30 grader med fersk fokksnø til den har fått stabilisert seg. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk.Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk tegn.'\n",
" 'For friluftsliv: Hold deg unna skredterreng (brattere enn 30 grader) og utløpssoner for skred. For infrastruktur: Dype svake lag kan gi store snøskred som kan nå ned til vei / bebyggelse.'\n",
" 'Unngå ferdsel i skredterreng brattere enn 30 grader og i utløpsområder. Vær oppmerksom på fare for fjernutløsning. Når det svake laget ligg dypt i snødekket eller det overliggende laget er hardt, trengs det som regel stor tilleggsbelastning for å påvirke dette. Vær ekstra forsiktig og tenk konsekvens når du gjør vegvalg, særlig i ukjent terreng, etter snøfall eller vind og i perioder med temperaturstigning. Det krever mye kunnskap å gjenkjenne svake lag i snødekket.'\n",
" 'Unngå ferdsel i skredterreng og i utløpsområder. Det er størst sjanse for å løse ut skred der snødekket er tynt, nær rygger og framstikkende steiner. Fjernutløsning er sannsynlig. Det krever mye kunnskap å gjenkjenne svake lag i snødekket. Drønnelyder og skytende sprekker er tydelige tegn, men fravær av slike tegn betyr ikke at det er trygt. Vær ekstra forsiktig etter snøfall eller vind og i perioder med temperaturstigning.'\n",
" 'Hold god avstand til hverandre ved ferdsel i skredterreng. Det er størst sjanse for å løse ut skred der snødekket er tynt, nær rygger og framstikkende steiner. Fjernutløsning er mulig. Det krever mye kunnskap for å gjenkjenne svake lag i snødekket. Drønnelyder og skytende sprekker er tydelige tegn, men fravær av slike tegn betyr ikke at det er trygt. Vær ekstra forsiktig og tenk konsekvens når du gjør vegvalg, særlig i ukjent terreng, etter snøfall eller vind og i perioder med temperaturstigning.'\n",
" 'Unngå langvarige opphold i løsneområder og utløpsområder for skred. Husk at selv små skred er tunge og kan skade deg, særlig nært terrengfeller. Vær bevisst på når på døgnet du beveger deg i bratt terreng, skredfaren kan variere mye gjennom døgnet. Vær ekstra forsiktig når det regner og/eller når temperaturen stiger. Følg med når snøoverflaten blir våt.'\n",
" 'Unngå ferdsel i løsneområder og utløpsområder for skred når snøoverflaten blir våt. Vær bevisst på når på døgnet du beveger deg i bratt terreng, skredfaren kan variere mye gjennom døgnet. Vær ekstra forsiktig når det regner og/eller når temperaturen stiger.'\n",
" 'For friluftsliv: Hold deg unna skredterreng (brattere enn 30 grader) og utløpssoner for skred. For infrastruktur: Områder i le for hovedvindretningen vil være mest utsatt for snøskred.'\n",
" 'Vær varsom der skredproblemet er å finne i kombinasjon med terrengfeller. Kun enkelte spesielt utsatte områder er skredutsatt. Se etter områder der vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk.'\n",
" 'Vær varsom der skredproblemet er å finne i kombinasjon med terrengfeller. Kun enkelte spesielt utsatte områder er skredutsatt. Se etter områder der vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk.'\n",
" 'Vær forsiktig i bratte heng under og etter snøfallet til nysnøen har stabilisert seg. Hold avstand til hverandre ved ferdsel i bratt terreng. Unngå ferdsel i / ved terrengfeller. Skredproblemet finnes overalt hvor det ligger mye nysnø i bratt terreng. Se etter nysnø som binder seg sammen til myke flak og sprekker opp eller fester seg dårlig til den eldre snøen under. Snø som sprekker opp rundt skiene/brettet er et typisk tegn.'\n",
" 'Hold deg unna skredterreng (brattere enn 30 grader) og utløpssoner for skred. '\n",
" 'Vær forsiktig i bratte heng under og etter snøfallet til nysnøen har stabilisert seg. Hold avstand til hverandre ved ferdsel i bratt terreng. Unngå ferdsel i / ved terrengfeller. Skredproblemet finnes overalt hvor det ligger mye nysnø i bratt terreng. Se etter nysnø som binder seg sammen til myke flak og sprekker opp eller fester seg dårlig til den eldre snøen under. Snø som sprekker opp rundt skiene/brettet er et typisk tegn.'\n",
" 'Unngå terreng brattere enn 30 grader med fersk fokksnø til den har fått stabilisert seg. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk.Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk faretegn.'\n",
" 'Vær forsiktig i områder brattere enn 30 grader med fokksnø til den har fått stabilisert seg. Hold avstand mellom hverandre ved ferdsel i bratt terreng. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk. Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk faretegn.'\n",
" 'Vær forsiktig i bratte heng under og etter snøfallet til nysnøen har stabilisert seg. Hold avstand til hverandre ved ferdsel i bratt terreng. Unngå ferdsel i / ved terrengfeller. Skredproblemet finnes overalt hvor det ligger mye nysnø i bratt terreng. Se etter nysnø som binder seg sammen til myke flak og sprekker opp eller fester seg dårlig til den eldre snøen under. Snø som sprekker opp rundt skiene/brettet er et typisk faretegn.'\n",
" 'Hold god avstand til hverandre ved ferdsel i skredterreng. Det er størst sjanse for å løse ut skred der snødekket er tynt, nær rygger og framstikkende steiner. Fjernutløsning er mulig. Det krever mye kunnskap for å gjenkjenne svake lag i snødekket. Drønnelyder og skytende sprekker er tydelige tegn som betyr at dere påvirker svake lag og bør unngå skredterreng, men fravær av slike tegn betyr ikke at det er trygt. Vær ekstra forsiktig og tenk konsekvens når du gjør vegvalg, særlig i ukjent terreng, etter snøfall eller vind og i perioder med temperaturstigning.'\n",
" 'Husk at selv små skred er tunge og kan skade deg, særlig ved skred i terrengfeller. Når du er på vei ned store heng bør du gradvis bevege deg ut av fallinja for å unngå å bli tatt igjen av små skred som du selv har utløst. Følg med når snøoverflaten blir våt og myk. Skredfaren øker i takt med temperaturstigning og nedbørintensitet. Vær ekstra forsiktig når det regner og/eller når temperaturen stiger.'\n",
" 'Unngå ferdsel i skredterreng (løsne og utløpsområder). Det er størst sjanse for å løse ut skred der snødekket er tynt, nær rygger og framstikkende steiner. Fjernutløsning er sannsynlig. Det krever mye kunnskap å gjenkjenne svake lag i snødekket. Drønnelyder og skytende sprekker er tydelige tegn, men fravær av slike tegn betyr ikke at det er trygt. Vær ekstra forsiktig etter snøfall eller vind og i perioder med temperaturstigning.'\n",
" 'Unngå bratte heng og terrengfeller under og etter snøfallet til nysnøen har stabilisert seg. Skredproblemet finnes overalt hvor det ligger mye nysnø i bratt terreng. Se etter nysnø som binder seg sammen til myke flak og sprekker opp eller fester seg dårlig til den eldre snøen under. Snø som sprekker opp rundt skiene/brettet er et typisk faretegn.'\n",
" 'Kun enkelte spesielt utsatte områder er skredutsatt. Vær varsom der skredproblemet er å finne i kombinasjon med terrengfeller. Slike skred blir vanligvis utløst av deg selv eller av andre skiløpere. Skredproblemet finnes der det ligger nysnø som danner myke flak i bratt terreng.'\n",
" 'Unngå terreng brattere enn 30 grader med fersk fokksnø til den har fått stabilisert seg. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk. Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk tegn.'\n",
" 'Hold deg unna skredterreng (brattere enn 30 grader) og utløpssoner for skred. For infrastruktur: Store naturlig utløste skred kan forekomme og nå ned til vei/bebyggelse'\n",
" 'Unngå terreng brattere enn 30 grader med fersk fokksnø til den har fått stabilisert seg. Det er størst sannsynlighet for å løse ut skred på konvekse formasjoner i terrenget og der fokksnøen er myk. Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Snø som sprekker opp rundt skiene/brettet er et typisk tegn.'\n",
" 'Vær varsom der skredproblemet er å finne i kombinasjon med terrengfeller. Kun enkelte spesielt utsatte områder er skredutsatt.'\n",
" 'For friluftsliv: Hold deg unna skredterreng (brattere enn 30 grader) og utløpssoner for skred. For infrastruktur: Ved stor utbredelse av det svake laget kan snøskredene bli store og nå ned til vei/bebyggelse.'\n",
" 'For friluftsliv: Hold deg unna alt skredterreng (løsneområder brattere enn 30 grader og utløpssoner for skred). For infrastruktur: Ved stor utbredelse av det svake laget kan snøskredene bli store og nå ned til vei/bebyggelse.'\n",
" 'Husk at selv små skred er tunge og kan skade deg, særlig ved skred i terrengfeller. Når du er på vei ned store heng bør du gradvis bevege deg ut av fallinja for å unngå å bli tatt igjen av små skred som du selv har utløst. Følg med når snøoverflaten blir våt og myk. Skredfaren øker i takt med temperaturstigning og nedbørintensitet. Vær ekstra forsiktig når temperaturen stiger.'\n",
" 'Unngå ferdsel i bratt terreng der skredproblemet finnes og i utløpssoner. Naturlig utløste skred er forventet. Følg med når snøoverflaten blir våt og myk. Skredfaren øker i takt med temperaturstigning og nedbørintensitet. Vær ekstra forsiktig når det regner og/eller når temperaturen stiger.'\n",
" 'Unngå terreng brattere enn 30 grader med fersk fokksnø til den har fått stabilisert seg. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk. Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk faretegn.'\n",
" 'Unngå terreng brattere enn 30 grader med fersk fokksnø og nysnøflak til den har fått stabilisert seg. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der snøen er myk. Se etter områder hvor vinden nylig har lagt fra seg snø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor snø legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk tegn.'\n",
" 'For friluftsliv: Hold deg unna skredterreng (brattere enn 30 grader) og utløpssoner for skred. For infrastruktur: Områder i le for hovedvindretningen vil være mest utsatt for snøskred.'\n",
" 'Unngå ferdsel i skredterreng (løsne og utløpsområder). Det er størst sjanse for å løse ut skred der snødekket er tynt, nær rygger og framstikkende steiner. Fjernutløsning er mulig. Det krever mye kunnskap å gjenkjenne svake lag i snødekket. Drønnelyder og skytende sprekker er tydelige tegn, men fravær av slike tegn betyr ikke at det er trygt. Vær ekstra forsiktig etter snøfall eller vind og i perioder med temperaturstigning.'\n",
" 'Unngå skredterreng med fersk fokksnø til den har fått stabilisert seg. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk. Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk faretegn.'\n",
" 'Vær svært forsiktig ved ferdsel i skredterreng og i utløpsområder. Det krever mye kunnskap å gjenkjenne svake lag i snødekket, uten kunnskap og oversikt om dette anbefales ikke ferdsel i skredterreng. Det er størst sjanse for å løse ut skred der snødekket er tynt og kantkorn har fått utviklet seg, som f.eks. nær rygger. Fjernutløsning er mulig. Drønnelyder og skytende sprekker er tydelige tegn, men fravær av slike tegn betyr ikke at det er trygt.'\n",
" 'Unngå terreng brattere enn 30 grader med fersk fokksnø til den har fått stabilisert seg, unngå også utløpsområder i utsatte himmelretninger. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk.Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk tegn.'\n",
" 'Kun enkelte spesielt utsatte områder er skredutsatt. Vær varsom der skredproblemet er å finne i kombinasjon med terrengfeller.'\n",
" 'Unngå skredterreng (løsne og utløpsområder) med fersk fokksnø til den har fått stabilisert seg. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk. Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk faretegn.'\n",
" 'Vær forsiktig i områder brattere enn 30 grader med dagsfersk fokksnø til den har fått stabilisert seg. Hold avstand mellom hverandre ved ferdsel i bratt terreng. Det er størst sannsynlighet for å løse ut skred på konvekse formasjoner i terrenget og der fokksnøen er myk. Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk tegn.'\n",
" 'Husk at selv små skred er tunge og kan skade deg, særlig ved skred i terrengfeller. Når du er på vei ned store heng bør du gradvis bevege deg ut av fallinja for å unngå å bli tatt igjen av små skred som du selv har utløst. Følg med når snøoverflaten blir våt og myk. Skredfaren øker i takt med temperaturstigning og nedbørintensitet. Vær ekstra forsiktig når temperaturen stiger.'\n",
" 'Unngå terreng brattere enn 30 grader med fersk fokksnø til den har fått stabilisert seg. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk.Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk faretegn.'\n",
" 'Unngå ferdsel i og nedenfor fjellsider med ferske glidesprekker. Det er vanskelig å forutse når slike skred vil løsne.'\n",
" 'Husk at selv små skred er tunge og kan skade deg, særlig ved skred i terrengfeller. Når du er på vei ned store heng bør du gradvis bevege deg ut av fallinja for å unngå å bli tatt igjen av små skred som du selv har utløst. Følg med når snøoverflaten blir våt og myk. Skredfaren øker i takt med temperaturstigning og økt solinnstråling. '\n",
" 'Unngå lengre opphold i og nedenfor fjellsider med ferske glidesprekker. Det er vanskelig å forutse når slike skred vil løsne.'\n",
" 'Vær varsom der skredproblemet er å finne i kombinasjon med terrengfeller. Følg med når snøoverflaten blir våt. Kun enkelte spesielt utsatte områder er skredutsatt.'\n",
" 'Husk at selv små skred er tunge og kan skade deg, særlig ved skred i terrengfeller. Når du er på vei ned store heng bør du gradvis bevege deg ut av fallinja for å unngå å bli tatt igjen av små skred som du selv har utløst. Følg med når snøoverflaten blir våt og myk. Skredfaren øker i takt med temperaturstigning og økt solinnstråling.'\n",
" 'Unngå lengre opphold i og nedenfor heng med glidesprekker. Kun enkelte spesielt utsatte områder er skredutsatt.'\n",
" 'Vær forsiktig i bratte heng under og etter snøfallet til nysnøen har stabilisert seg. Hold avstand til hverandre ved ferdsel i brat terreng. Unngå ferdsel i / ved terrengfeller. Skredproblemet finnes overalt hvor det ligger mye nysnø i bratt terreng. Se etter nysnø som binder seg sammen til myke flak og sprekker opp eller fester seg dårlig til den eldre snøen under. Snø som sprekker opp rundt skiene/brettet er et typisk tegn.'\n",
" 'Vær forsiktig i bratte heng under og etter snøfallet til nysnøen har stabilisert seg. Hold avstand til hverandre ved ferdsel i bratt terreng. Unngå ferdsel i / ved terrengfeller. Skredproblemet finnes overalt hvor det ligger mye nysnø i bratt terreng. Se etter nysnø som binder seg sammen til myke flak og sprekker opp eller fester seg dårlig til den eldre snøen under. Snø som sprekker opp rundt skiene/brettet er et typisk faretegn.'\n",
" 'Husk at selv små skred er tunge og kan skade deg, særlig ved skred i terrengfeller. Når du er på vei ned store heng bør du gradvis bevege deg ut av fallinja for å unngå å bli tatt igjen av små skred som du selv har utløst. Følg med når snøoverflaten blir våt og myk. Skredfaren øker i takt med temperaturstigning og økende solinnstråling. '\n",
" 'Unngå terreng brattere enn 30 grader med fersk fokksnø til den har fått stabilisert seg. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk. Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk faretegn.'\n",
" nan\n",
" 'Hold god avstand til hverandre ved ferdsel i skredterreng. Det er størst sjanse for å løse ut skred der snødekket er tynt, nær rygger og framstikkende steiner. Det krever mye kunnskap for å gjenkjenne svake lag i snødekket. Drønnelyder og skytende sprekker er tydelige tegn, men fravær av slike tegn betyr ikke at det er trygt. Vær ekstra forsiktig og tenk konsekvens når du gjør vegvalg, særlig i ukjent terreng, etter snøfall eller vind og i perioder med temperaturstigning.'\n",
" 'Unngå ferdsel i skredterreng og i utløpsområder. Det er størst sjanse for å løse ut skred der snødekket er tynt, nær rygger og framstikkende steiner. Fjernutløsning er mulig. Det krever mye kunnskap å gjenkjenne svake lag i snødekket. Drønnelyder og skytende sprekker er tydelige tegn, men fravær av slike tegn betyr ikke at det er trygt. Vær ekstra forsiktig etter snøfall eller vind og i perioder med temperaturstigning.'\n",
" 'Unngå ferdsel i skredterreng (løsne og utløpsområder) spesiellt når snøoverflaten blir våt. Det er størst sjanse for å løse ut skred der snødekket er tynt, nær rygger og framstikkende steiner. Fjernutløsning er sannsynlig. Det krever mye kunnskap å gjenkjenne svake lag i snødekket. Drønnelyder og skytende sprekker er tydelige tegn, men fravær av slike tegn betyr ikke at det er trygt. Vær bevisst på når på døgnet du beveger deg i bratt terreng, skredfaren kan variere mye gjennom døgnet. Vær ekstra forsiktig når temperaturen stiger.'\n",
" 'Unngå bratte heng og terrengfeller under og etter snøfallet til nysnøen har stabilisert seg. Skredproblemet finnes overalt hvor det ligger mye nysnø i bratt terreng. Se etter nysnø som binder seg sammen til myke flak og sprekker opp eller fester seg dårlig til den eldre snøen under. Snø som sprekker opp rundt skiene/brettet er et typisk faretegn. '\n",
" 'Vær varsom der skredproblemet er å finne i kombinasjon med terrengfeller. Kun enkelte spesielt utsatte områder er skredutsatt. Se etter områder der vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. '\n",
" 'Fokksnøflaka som blei danna tidlegare i veka har fått tid til å stabilisere seg, men prosessen går seinare i kulda. Flaka er i hovudsak harde, og det skal mykje til for å løyse ut skred. Der flaka er mjuke og tynne er det forøvrig lettare å påverke svake lag i fokksnøen, og i eventuelle rim- og kantkornlag lenger ned i snødekket. Der det er avblåst kan skara vere hard og fall i bratte heng kan gi alvorlige konsekvensar. Snøskredfara er vurdert som moderat (2). '\n",
" 'Stay away from avalanche terrain and runout zones for avalanches.'\n",
" 'Kun områder der det legger seg nysnøflak på de vedvarende svake lagene er skredutsatt. Vær varsom der skredproblemet er å finne i kombinasjon med terrengfeller.'\n",
" 'Kun områder der det legger seg fokksnø/nysnøflak på de vedvarende svake lagene er skredutsatt. Vær spesielt forsiktig der skredproblemet er å finne i kombinasjon med terrengfeller.'\n",
" 'Unngå terreng brattere enn 30 grader med flak av nysnø. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der nysnøflaken er myke. Se etter områder hvor vinden har lagt fra seg snø, typisk bak rygger, i renneformasjoner og søkk. Snø som sprekker opp rundt skiene/brettet er et typisk tegn. Fjernutløsning er mulig.'\n",
" 'Eventuelle skred bli vanligvis utløst av deg selv eller andre som ferdest i terrenget. Vurder utløpsområder og gjør sporvalg i forhold til dette. Når du er på vei ned store heng bør du gradvis bevege deg ut av fallinja for å unngå å bli tatt igjen av små løssnøskred som du selv har utløst. Husk at selv et lite skred kan få konsekvenser nært terrengfeller.Skredproblemet finnes overalt hvor det ligger nysnø i bratt terreng.'\n",
" 'Unngå ferdsel i løsneområder og utløpsområder for skred når snøoverflaten blir våt. Vær bevisst på når på døgnet du beveger deg i bratt terreng, skredfaren kan variere mye gjennom døgnet. Vær ekstra forsiktig når det regner og/eller når temperaturen stiger.'\n",
" 'Vær varsom der skredproblemet er å finne i kombinasjon med terrengfeller. Kun områder der det ligger fokksnø er skredutsatt. Se etter områder der vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk.'\n",
" 'Kun områder der det ligger harde eller myke flak av fokksnø er skredutsatt. Vær varsom der skredproblemet er å finne i kombinasjon med terrengfeller.'\n",
" 'Unngå terreng brattere enn 30 grader med flak av fokksnø. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk. Se etter områder hvor vinden har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk tegn. Fjernutløsning er mulig.'\n",
" 'Vær forsiktig i områder brattere enn 30 grader med fokksnø til den har fått stabilisert seg. Hold avstand mellom hverandre ved ferdsel i bratt terreng. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk. Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Se også etter om overflaterim kan ha blitt dekket av snø som har drevet inn i nordvendte hellinger med sønnavinden søndag kveld. Snø som sprekker opp rundt skiene/brettet er et typisk faretegn. '\n",
" 'Vær varsom der skredproblemet er å finne i kombinasjon med terrengfeller. Kun enkelte spesielt utsatte områder er skredutsatt. Se etter områder der vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk.\\r\\n'\n",
" 'Kun områder der det ligger fokksnø er skredutsatt. Vær varsom der skredproblemet er å finne i kombinasjon med terrengfeller.'\n",
" 'Kun områder der det ligger flak av eldre fokksnø eller bundet nysnø er skredutsatt. Vær varsom der skredproblemet er å finne i kombinasjon med terrengfeller.'\n",
" 'Lave temperaturer gir generelt mindre smelting enn forrige uke og det blir mindre fritt vatn i snødekket. Dette er gunstig for snøskredfaren og gir stabilisering i det gamle snødekket. \\r\\nVær forsiktig under glidesprekker og der snødekket ligger på glatt underlag. Det er vanskelig å forutse når glideskred løsner og disse kan gå selv om temperaturen synker.\\r\\nOver nysnøgrensa kan vinden danne små flak som kan påvirkes av en skiløper, men stabilisering skjer raskt og evt skred ventes å være små. \\r\\nVær oppmerksom der nysnøen blir fuktig for første gang, for eksempel hvis sola kommer frem.'\n",
" 'Unngå ferdsel i bratt terreng der skredproblemet finnes og i utløpssoner. Naturlig utløste skred er forventet. Følg med når snøoverflaten blir våt og myk. Skredfaren øker i takt med temperaturstigning. '\n",
" 'Vær forsiktig i områder brattere enn 30 grader med fokksnø til den har fått stabilisert seg. Hold avstand mellom hverandre ved ferdsel i bratt terreng. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk/tynn. Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk tegn.'\n",
" 'Unngå bratte heng og terrengfeller under og etter snøfallet til nysnøen har stabilisert seg. Skredproblemet finnes overalt hvor det ligger mye nysnø i bratt terreng. Se etter nysnø som binder seg sammen til myke flak og sprekker opp eller fester seg dårlig til den eldre snøen under. Snø som sprekker opp rundt skiene/brettet er et typisk tegn. En kan også finne eldre nysnøflak i vestlig sektor fra mandag og i går.'\n",
" 'Unngå bratte heng og terrengfeller under og etter snøfallet til nysnøen har stabilisert seg. Skredproblemet finnes overalt hvor det ligger mye nysnø i bratt terreng. Se etter nysnø som binder seg sammen til myke flak og sprekker opp eller fester seg dårlig til den eldre snøen under. Snø som sprekker opp rundt skiene/brettet er et typisk tegn. '\n",
" 'Når tåke og skyer forsvinner vil skredfaren variere mer gjennom døgnet. Utstråling og nattefrost gir stabilt snødekke inntil sola kommer frem og overflata blir myk. Da er det mulig å løse ut små løssnøskred. Husk at glideskred som løsner ved bakken kan gå selv om overflata er hard og gjenfrosset. Vær forsiktig under glidesprekker.'\n",
" 'Vær forsiktig i områder brattere enn 30 grader med fokksnø til den har fått stabilisert seg. Hold avstand mellom hverandre ved ferdsel i bratt terreng. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk. Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet eller drønn i snødekket er typiske faretegn.'\n",
" 'Vær forsiktig i bratte heng til nysnøen har stabilisert seg. Hold avstand til hverandre ved ferdsel i bratt terreng. Unngå ferdsel i / ved terrengfeller. Skredproblemet finnes overalt hvor det ligger mye nysnø i bratt terreng. Se etter nysnø som binder seg sammen til myke flak og sprekker opp eller fester seg dårlig til den eldre snøen under. Snø som sprekker opp rundt skiene/brettet er et typisk faretegn.'] \n",
"\n",
"avalanche_problem_1_cause_id: [ 0 15 10 18 11 19 22 13 16 24 14 20] \n",
"\n",
"avalanche_problem_1_cause_name: ['Not given' 'Dårlig binding mellom lag i fokksnøen'\n",
" 'Nedføyket svakt lag med nysnø' 'Kantkornet snø over skarelag'\n",
" 'Nedsnødd eller nedføyket overflaterim' 'Kantkornet snø under skarelag'\n",
" 'Opphopning av vann i/over lag i snødekket'\n",
" 'Nedsnødd eller nedføyket kantkornet snø' 'Kantkornet snø ved bakken'\n",
" 'Ubunden snø' 'Dårlig binding mellom glatt skare og overliggende snø'\n",
" 'Vann ved bakken/smelting fra bakken'] \n",
"\n",
"avalanche_problem_1_destructive_size_ext_id: [0 2 3 1 4] \n",
"\n",
"avalanche_problem_1_destructive_size_ext_name: ['Not given' '2 - Middels' '3 - Store' '1 - Små' 'Ikke gitt'\n",
" '4 - Svært store'] \n",
"\n",
"avalanche_problem_1_distribution_id: [0 2 3 1 4] \n",
"\n",
"avalanche_problem_1_distribution_name: ['Not given' 'Noen bratte heng' 'Mange bratte heng' 'Få bratte heng'\n",
" 'De fleste bratte heng'] \n",
"\n",
"avalanche_problem_1_exposed_height_1: [ 0 800 400 300 200 100 700 600 500 1000 900 1200 1300 1100\n",
" 1400 1600 1500 1900 2000 1700 2100] \n",
"\n",
"avalanche_problem_1_exposed_height_2: [ 0 600 100 200 300 400 800 1000 500 700 1300 1200 900 1100\n",
" 2000 1500 1400 1900] \n",
"\n",
"avalanche_problem_1_exposed_height_fill: [0 1 2 4] \n",
"\n",
"avalanche_problem_1_ext_id: [ 0 20 25 15 10] \n",
"\n",
"avalanche_problem_1_ext_name: ['Not given' 'Tørre flakskred' 'Våte flakskred' 'Våte løssnøskred '\n",
" 'Ikke gitt ' 'Tørre løssnøskred '] \n",
"\n",
"avalanche_problem_1_probability_id: [0 2 3 5] \n",
"\n",
"avalanche_problem_1_probability_name: ['Not given' 'Lite sannsynlig ' 'Mulig ' 'Sannsynlig ' 'Ikke gitt '] \n",
"\n",
"avalanche_problem_1_problem_id: [0 1] \n",
"\n",
"avalanche_problem_1_problem_type_id: [ 0 10 7 37 30 45 5 50 3] \n",
"\n",
"avalanche_problem_1_problem_type_name: ['Not given' 'Fokksnø (flakskred)' 'Nysnø (flakskred)'\n",
" 'Dypt vedvarende svakt lag' 'Vedvarende svakt lag (flakskred)'\n",
" 'Våt snø (flakskred)' 'Våt snø (løssnøskred)' 'Glideskred' 'Ikke gitt'\n",
" 'Nysnø (løssnøskred)'] \n",
"\n",
"avalanche_problem_1_trigger_simple_id: [ 0 10 21 22] \n",
"\n",
"avalanche_problem_1_trigger_simple_name: ['Not given' 'Stor tilleggsbelastning' 'Liten tilleggsbelastning'\n",
" 'Naturlig utløst' 'Ikke gitt'] \n",
"\n",
"avalanche_problem_1_type_id: [ 0 10 20] \n",
"\n",
"avalanche_problem_1_type_name: ['Not given' 'Flakskred' 'Løssnøskred' 'Ikke gitt'] \n",
"\n",
"avalanche_problem_1_valid_expositions: [ 0 10001111 10000111 111110 11111111 11000111 11111001 11110001\n",
" 11000011 11100011 10000011 1111 1110 11110 11111 1111110\n",
" 11100001 111000 1111000 11110000 111100 11001111 11111000 1111100\n",
" 111111 111 1110000 1100 11100000 10011111 11111101 11100111\n",
" 11000001 11110111 11100 11111100 11110011 11111110 1111111 11111011\n",
" 110 11000000 10111111 10000001] \n",
"\n",
"avalanche_problem_2_advice: ['Not given'\n",
" 'Hold god avstand til hverandre ved ferdsel i skredterreng. Det er størst sjanse for å løse ut skred der snødekket er tynt, nær rygger og framstikkende steiner. Fjernutløsning er mulig. Det krever mye kunnskap for å gjenkjenne svake lag i snødekket. Drønnelyder og skytende sprekker er tydelige tegn, men fravær av slike tegn betyr ikke at det er trygt. Vær ekstra forsiktig og tenk konsekvens når du gjør vegvalg, særlig i ukjent terreng, etter snøfall eller vind og i perioder med temperaturstigning.'\n",
" 'Unngå ferdsel i skredterreng og i utløpsområder. Det er størst sjanse for å løse ut skred der snødekket er tynt, nær rygger og framstikkende steiner. Fjernutløsning er sannsynlig. Det krever mye kunnskap å gjenkjenne svake lag i snødekket. Drønnelyder og skytende sprekker er tydelige tegn, men fravær av slike tegn betyr ikke at det er trygt. Vær ekstra forsiktig etter snøfall eller vind og i perioder med temperaturstigning.'\n",
" 'Hold god avstand til hverandre og til løsneområdene. Fjernutløsning er mulig. Vær ekstra forsiktig og tenk konsekvens når du gjør vegvalg, særlig i ukjent terreng, etter snøfall eller vind og i perioder med temperaturstigning. Når det svake laget ligger dypt i snødekket eller det overliggende laget er hardt, trengs det som regel stor tilleggsbelastning for å påvirke dette. Det krever mye kunnskap å gjenkjenne svake lag i snødekket.'\n",
" 'Unngå ferdsel i skredterreng brattere enn 30 grader og i utløpsområder. Vær oppmerksom på fare for fjernutløsning. Når det svake laget ligg dypt i snødekket eller det overliggende laget er hardt, trengs det som regel stor tilleggsbelastning for å påvirke dette. Vær ekstra forsiktig og tenk konsekvens når du gjør vegvalg, særlig i ukjent terreng, etter snøfall eller vind og i perioder med temperaturstigning. Det krever mye kunnskap å gjenkjenne svake lag i snødekket.'\n",
" 'Vær forsiktig i områder brattere enn 30 grader med fokksnø til den har fått stabilisert seg. Hold avstand mellom hverandre ved ferdsel i bratt terreng. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk. Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk tegn.'\n",
" 'For friluftsliv: Hold deg unna skredterreng (brattere enn 30 grader) og utløpssoner for skred. For infrastruktur: Områder i le for hovedvindretningen vil være mest utsatt for snøskred.'\n",
" 'Unngå terreng brattere enn 30 grader med fersk fokksnø til den har fått stabilisert seg. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk.Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk tegn.'\n",
" 'Vær forsiktig i bratte heng under og etter snøfallet til nysnøen har stabilisert seg. Hold avstand til hverandre ved ferdsel i brat terreng. Unngå ferdsel i / ved terrengfeller. Skredproblemet finnes overalt hvor det ligger mye nysnø i bratt terreng. Se etter nysnø som binder seg sammen til myke flak og sprekker opp eller fester seg dårlig til den eldre snøen under. Snø som sprekker opp rundt skiene/brettet er et typisk tegn.'\n",
" 'Unngå bratte heng og terrengfeller under og etter snøfallet til nysnøen har stabilisert seg. Skredproblemet finnes overalt hvor det ligger mye nysnø i bratt terreng. Se etter nysnø som binder seg sammen til myke flak og sprekker opp eller fester seg dårlig til den eldre snøen under. Snø som sprekker opp rundt skiene/brettet er et typisk tegn.'\n",
" 'Kun enkelte spesielt utsatte områder er skredutsatt. Vær varsom der skredproblemet er å finne i kombinasjon med terrengfeller.'\n",
" 'For friluftsliv: Hold deg unna skredterreng (brattere enn 30 grader) og utløpssoner for skred. '\n",
" 'Vær forsiktig i områder brattere enn 30 grader med fokksnø til den har fått stabilisert seg. Hold avstand mellom hverandre ved ferdsel i bratt terreng. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk. Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk faretegn.'\n",
" 'Vær forsiktig i bratte heng under og etter snøfallet til nysnøen har stabilisert seg. Hold avstand til hverandre ved ferdsel i bratt terreng. Unngå ferdsel i / ved terrengfeller. Skredproblemet finnes overalt hvor det ligger mye nysnø i bratt terreng. Se etter nysnø som binder seg sammen til myke flak og sprekker opp eller fester seg dårlig til den eldre snøen under. Snø som sprekker opp rundt skiene/brettet er et typisk faretegn.'\n",
" 'Unngå ferdsel i skredterreng (løsne og utløpsområder). Det er størst sjanse for å løse ut skred der snødekket er tynt, nær rygger og framstikkende steiner. Fjernutløsning er sannsynlig. Det krever mye kunnskap å gjenkjenne svake lag i snødekket. Drønnelyder og skytende sprekker er tydelige tegn, men fravær av slike tegn betyr ikke at det er trygt. Vær ekstra forsiktig etter snøfall eller vind og i perioder med temperaturstigning.'\n",
" 'Hold god avstand til hverandre ved ferdsel i skredterreng. Det er størst sjanse for å løse ut skred der snødekket er tynt, nær rygger og framstikkende steiner. Fjernutløsning er mulig. Det krever mye kunnskap for å gjenkjenne svake lag i snødekket. Drønnelyder og skytende sprekker er tydelige tegn som betyr at dere påvirker svake lag og bør unngå skredterreng, men fravær av slike tegn betyr ikke at det er trygt. Vær ekstra forsiktig og tenk konsekvens når du gjør vegvalg, særlig i ukjent terreng, etter snøfall eller vind og i perioder med temperaturstigning.'\n",
" 'Unngå terreng brattere enn 30 grader med fersk fokksnø til den har fått stabilisert seg. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk.Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk faretegn.'\n",
" 'Unngå langvarige opphold i løsneområder og utløpsområder for skred. Husk at selv små skred er tunge og kan skade deg, særlig nært terrengfeller. Vær bevisst på når på døgnet du beveger deg i bratt terreng, skredfaren kan variere mye gjennom døgnet. Vær ekstra forsiktig når det regner og/eller når temperaturen stiger. Følg med når snøoverflaten blir våt.'\n",
" 'Unngå ferdsel i løsneområder og utløpsområder for skred når snøoverflaten blir våt. Vær bevisst på når på døgnet du beveger deg i bratt terreng, skredfaren kan variere mye gjennom døgnet. Vær ekstra forsiktig når det regner og/eller når temperaturen stiger.'\n",
" 'Unngå ferdsel i bratt terreng der skredproblemet finnes og i utløpssoner. Naturlig utløste skred er forventet. Følg med når snøoverflaten blir våt og myk. Skredfaren øker i takt med temperaturstigning og nedbørintensitet. Vær ekstra forsiktig når det regner og/eller når temperaturen stiger.'\n",
" 'Husk at selv små skred er tunge og kan skade deg, særlig ved skred i terrengfeller. Når du er på vei ned store heng bør du gradvis bevege deg ut av fallinja for å unngå å bli tatt igjen av små skred som du selv har utløst. Følg med når snøoverflaten blir våt og myk. Skredfaren øker i takt med temperaturstigning og nedbørintensitet. Vær ekstra forsiktig når det regner og/eller når temperaturen stiger.'\n",
" 'Unngå bratte heng og terrengfeller under og etter snøfallet til nysnøen har stabilisert seg. Skredproblemet finnes overalt hvor det ligger mye nysnø i bratt terreng. Se etter nysnø som binder seg sammen til myke flak og sprekker opp eller fester seg dårlig til den eldre snøen under. Snø som sprekker opp rundt skiene/brettet er et typisk faretegn.'\n",
" 'For friluftsliv: Hold deg unna alt skredterreng (løsneområder brattere enn 30 grader og utløpssoner for skred). For infrastruktur: Ved stor utbredelse av det svake laget kan snøskredene bli store og nå ned til vei/bebyggelse.'\n",
" 'Hold deg unna skredterreng (brattere enn 30 grader) og utløpssoner for skred. For infrastruktur: Store naturlig utløste skred kan forekomme og nå ned til vei/bebyggelse'\n",
" 'Unngå ferdsel i skredterreng og i utløpsområder. Det er størst sjanse for å løse ut skred der snødekket er tynt, nær rygger og framstikkende steiner. Fjernutløsning er sannsynlig. Det krever mye kunnskap å gjenkjenne svake lag i snødekket. Drønnelyder og skytende sprekker er tydelige tegn, men fravær av slike tegn betyr ikke at det er trygt. Vær ekstra forsiktig etter snøfall eller vind og i perioder med temperaturstigning. '\n",
" 'Vær varsom der skredproblemet er å finne i kombinasjon med terrengfeller. Følg med når snøoverflaten blir våt. Kun enkelte spesielt utsatte områder er skredutsatt.'\n",
" 'Vær varsom der skredproblemet er å finne i kombinasjon med terrengfeller. Kun enkelte spesielt utsatte områder er skredutsatt. Se etter områder der vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk.'\n",
" 'Vær varsom der skredproblemet er å finne i kombinasjon med terrengfeller. Kun enkelte spesielt utsatte områder er skredutsatt.'\n",
" 'Unngå lengre opphold i og nedenfor heng med glidesprekker. Kun enkelte spesielt utsatte områder er skredutsatt.'\n",
" 'For friluftsliv: Hold deg unna skredterreng (brattere enn 30 grader) og utløpssoner for skred. For infrastruktur: Ved stor utbredelse av det svake laget kan snøskredene bli store og nå ned til vei/bebyggelse.'\n",
" 'For friluftsliv: Hold deg unna skredterreng (brattere enn 30 grader) og utløpssoner for skred. For infrastruktur: Områder i le for hovedvindretningen vil være mest utsatt for snøskred.'\n",
" 'Hold god avstand til hverandre ved ferdsel i skredterreng. Det er størst sjanse for å løse ut skred der snødekket er tynt, nær rygger og framstikkende steiner. Fjernutløsning er mulig. Det krever mye kunnskap for å gjenkjenne svake lag i snødekket. Drønnelyder og skytende sprekker er tydelige tegn, men fravær av slike tegn betyr ikke at det er trygt. Vær ekstra forsiktig og tenk konsekvens når du gjør vegvalg, særlig i ukjent terreng, etter snøfall eller vind og i perioder med temperaturstigning.\\r\\n'\n",
" 'Det er størst sjanse for å løse ut skred der snødekket er tynt, nær rygger og framstikkende steiner. Fjernutløsning er sannsynlig. Det krever mye kunnskap å gjenkjenne svake lag i snødekket. Drønnelyder og skytende sprekker er tydelige tegn, men fravær av slike tegn betyr ikke at det er trygt. Vær ekstra forsiktig etter snøfall eller vind og i perioder med temperaturstigning.'\n",
" 'Hold god avstand til hverandre og til løsneområdene. Fjernutløsning er mulig. Vær ekstra forsiktig og tenk konsekvens når du gjør vegvalg, særlig i ukjent terreng, etter snøfall eller vind og i perioder med temperaturstigning. Når det svake laget ligger dypt i snødekket eller det overliggende laget er hardt, trengs det som regel stor tilleggsbelastning for å påvirke dette. Det krever mye kunnskap å gjenkjenne svake lag i snødekket.'\n",
" nan\n",
" 'Unngå ferdsel i skredterreng (løsne og utløpsområder). Det er størst sjanse for å løse ut skred der snødekket er tynt, nær rygger og framstikkende steiner. Fjernutløsning er mulig. Det krever mye kunnskap å gjenkjenne svake lag i snødekket. Drønnelyder og skytende sprekker er tydelige tegn, men fravær av slike tegn betyr ikke at det er trygt. Vær ekstra forsiktig etter snøfall eller vind og i perioder med temperaturstigning.'\n",
" 'Unngå ferdsel i bratt terreng der skredproblemet finnes. Naturlig utløste skred er forventet. Følg med når snøoverflaten blir våt og myk. Skredfaren øker i takt med temperaturstigning og nedbørintensitet. Vær ekstra forsiktig når det regner og/eller når temperaturen stiger.'\n",
" 'Vær svært forsiktig ved ferdsel i skredterreng og i utløpsområder. Det krever mye kunnskap å gjenkjenne svake lag i snødekket, uten kunnskap og oversikt om dette anbefales ikke ferdsel i skredterreng. Det er størst sjanse for å løse ut skred der snødekket er tynt og kantkorn har fått utviklet seg, som f.eks. nær rygger. Fjernutløsning er mulig. Drønnelyder og skytende sprekker er tydelige tegn, men fravær av slike tegn betyr ikke at det er trygt.'\n",
" 'Det ventes noe vær fra N-NV med nedbør natt til lørdag. Det kan legge seg opp nye ustabile flak av nysnø og fokksnø i lesider. Fokksnøflakene som ble dannet torsdag med kraftig vind har nå stort sett stabilisert seg, men det kan fortsatt finnes enkelte ustabile flak i høyden. Det usikkert om økt pålagring med nedbør er nok til å påvirke eventuelle vedvarende svake lag av kantkorn i høyden. Faregraden vurderes foreløpig til å gå ned til faregrad 2 - moderat. Følge med på oppdateringer fredag ettermiddag da det kan komme justeringer.'\n",
" 'For friluftsliv: Hold deg unna skredterreng (brattere enn 30 grader) og utløpssoner for skred. For infrastruktur: Våte løssnøskred kan nå vei / bebyggelse. Slike skred følger vanligvis faste skredløp.'\n",
" 'Hold god avstand til hverandre ved ferdsel i skredterreng i høyfjellet. Det er størst sjanse for å løse ut skred der snødekket er tynt, nær rygger og framstikkende steiner. Fjernutløsning er også mulig. Det krever mye kunnskap for å gjenkjenne svake lag i snødekket. Drønnelyder og skytende sprekker er tydelige tegn, men fravær av slike tegn betyr ikke at det er trygt. Vær ekstra forsiktig og tenk konsekvens når du gjør vegvalg, særlig i ukjent terreng.'\n",
" 'Vær forsiktig i bratte heng under og etter snøfallet til nysnøen har stabilisert seg. Hold avstand til hverandre ved ferdsel i bratt terreng. Unngå ferdsel i / ved terrengfeller. Skredproblemet finnes overalt hvor det ligger mye nysnø i bratt terreng. Se etter nysnø som binder seg sammen til myke flak og sprekker opp eller fester seg dårlig til den eldre snøen under. Snø som sprekker opp rundt skiene/brettet er et typisk tegn.'\n",
" 'Kun enkelte spesielt utsatte områder er skredutsatt. Vær varsom der skredproblemet er å finne i kombinasjon med terrengfeller. Slike skred blir vanligvis utløst av deg selv eller av andre skiløpere. Skredproblemet finnes der det ligger nysnø som danner myke flak i bratt terreng.'\n",
" 'Unngå lengre opphold i og nedenfor fjellsider med ferske glidesprekker. Det er vanskelig å forutse når slike skred vil løsne.'\n",
" 'Unngå terreng brattere enn 30 grader med fersk fokksnø til den har fått stabilisert seg. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk. Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk tegn.'\n",
" 'Eventuelle skred bli vanligvis utløst av deg selv eller andre som ferdest i terrenget. Vurder utløpsområder og gjør sporvalg i forhold til dette. Når du er på vei ned store heng bør du gradvis bevege deg ut av fallinja for å unngå å bli tatt igjen av små løssnøskred som du selv har utløst. Husk at selv et lite skred kan få konsekvenser nært terrengfeller.Skredproblemet finnes overalt hvor det ligger nysnø i bratt terreng.'\n",
" 'Unngå ferdsel i bratt terreng. Skredproblemet finnes overalt hvor det ligger nysnø i bratt terreng. Vurder størrelse på heng, snømengde og utløpsområder og gjør sporvalg i forhold til dette.'\n",
" 'Hold deg unna skredterreng (brattere enn 30 grader) og utløpssoner for skred. For infrastruktur: Store naturlig utløste skred kan forekomme og nå ned til vei/bebyggelse.'\n",
" 'Unngå ferdsel i løsneområder og utløpsområder for skred når snøoverflaten blir våt. Vær bevisst på når på døgnet du beveger deg i bratt terreng, skredfaren kan variere mye gjennom døgnet. '\n",
" 'Unngå terreng brattere enn 30 grader med fersk fokksnø til den har fått stabilisert seg. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk. Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk faretegn.'\n",
" 'Unngå ferdsel i skredterreng og i utløpsområder. Det er størst sjanse for å løse ut skred der snødekket er tynt, nær rygger og framstikkende steiner. Fjernutløsning er mulig. Det krever mye kunnskap å gjenkjenne svake lag i snødekket. Drønnelyder og skytende sprekker er tydelige tegn, men fravær av slike tegn betyr ikke at det er trygt. Vær ekstra forsiktig etter snøfall eller vind og i perioder med temperaturstigning.'\n",
" 'Unngå ferdsel i bratt terreng der skredproblemet finnes og i utløpssoner. Naturlig utløste skred er forventet. Følg med når snøoverflaten blir våt og myk. Skredfaren øker i takt med temperaturstigning. Vær ekstra forsiktig når temperaturen stiger.'\n",
" 'Unngå ferdsel i og nedenfor fjellsider med ferske glidesprekker. Det er vanskelig å forutse når slike skred vil løsne.'\n",
" 'Eventuelle skred blir vanligvis utløst av deg selv eller av andre som ferdes i terrenget. Vurder utløpsområder og gjør sporvalg i forhold til dette der skredproblemet er å finne i kombinasjon med terrengfeller. Kun enkelte spesielt utsatte områder er skredutsatt.'\n",
" 'Unngå ferdsel i skredterreng (løsne og utløpsområder) spesiellt når snøoverflaten blir våt. Det er størst sjanse for å løse ut skred der snødekket er tynt, nær rygger og framstikkende steiner. Fjernutløsning er sannsynlig. Det krever mye kunnskap å gjenkjenne svake lag i snødekket. Drønnelyder og skytende sprekker er tydelige tegn, men fravær av slike tegn betyr ikke at det er trygt. Vær bevisst på når på døgnet du beveger deg i bratt terreng, skredfaren kan variere mye gjennom døgnet. Vær ekstra forsiktig når temperaturen stiger.'\n",
" 'Unngå terreng brattere enn 30 grader med flak av fokksnø. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk. Se etter områder hvor vinden har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk tegn. Fjernutløsning er mulig.'\n",
" 'Hold god avstand til hverandre ved ferdsel i skredterreng. Det er størst sjanse for å løse ut skred der snødekket er tynt, nær rygger og framstikkende steiner. Fjernutløsning er mulig. Det krever mye kunnskap for å gjenkjenne svake lag i snødekket. Drønnelyder og skytende sprekker er tydelige tegn som betyr at dere påvirker svake lag og bør unngå skredterreng, men fravær av slike tegn betyr ikke at det er trygt. Vær ekstra forsiktig og tenk konsekvens når du gjør vegvalg, særlig i ukjent terreng, etter snøfall eller vind og i perioder med temperaturstigning. Skredproblemet ser ut til å være mest utbredt øst i reginen.'] \n",
"\n",
"avalanche_problem_2_cause_id: [ 0 11 18 15 10 16 19 13 24 22 14 20] \n",
"\n",
"avalanche_problem_2_cause_name: ['Not given' 'Nedsnødd eller nedføyket overflaterim'\n",
" 'Kantkornet snø over skarelag' 'Dårlig binding mellom lag i fokksnøen'\n",
" 'Nedføyket svakt lag med nysnø' 'Kantkornet snø ved bakken'\n",
" 'Kantkornet snø under skarelag' 'Nedsnødd eller nedføyket kantkornet snø'\n",
" 'Ubunden snø' 'Opphopning av vann i/over lag i snødekket'\n",
" 'Dårlig binding mellom glatt skare og overliggende snø'\n",
" 'Vann ved bakken/smelting fra bakken'] \n",
"\n",
"avalanche_problem_2_destructive_size_ext_id: [0 2 3 1 4] \n",
"\n",
"avalanche_problem_2_destructive_size_ext_name: ['Not given' '2 - Middels' '3 - Store' '1 - Små' 'Ikke gitt'\n",
" '4 - Svært store'] \n",
"\n",
"avalanche_problem_2_distribution_id: [0 2 1 3] \n",
"\n",
"avalanche_problem_2_distribution_name: ['Not given' 'Noen bratte heng' 'Få bratte heng' 'Mange bratte heng'\n",
" 'Ikke gitt'] \n",
"\n",
"avalanche_problem_2_exposed_height_1: [ 0 400 200 300 700 100 600 500 800 1000 900 1100 1300 1400\n",
" 1200 1500 1600 1900 1700 2000 1800 2100 2300] \n",
"\n",
"avalanche_problem_2_exposed_height_2: [ 0 300 600 200 100 400 700 500 800 900 1000 1200 1100 1300\n",
" 1500 1400] \n",
"\n",
"avalanche_problem_2_exposed_height_fill: [0 1 2 4 3] \n",
"\n",
"avalanche_problem_2_ext_id: [ 0 20 25 15 10] \n",
"\n",
"avalanche_problem_2_ext_name: ['Not given' 'Tørre flakskred' 'Våte flakskred' 'Våte løssnøskred '\n",
" 'Ikke gitt ' 'Tørre løssnøskred '] \n",
"\n",
"avalanche_problem_2_probability_id: [0 2 3 5] \n",
"\n",
"avalanche_problem_2_probability_name: ['Not given' 'Lite sannsynlig ' 'Mulig ' 'Sannsynlig ' 'Ikke gitt '] \n",
"\n",
"avalanche_problem_2_problem_id: [0 2] \n",
"\n",
"avalanche_problem_2_problem_type_id: [ 0 30 37 10 7 45 5 50 3] \n",
"\n",
"avalanche_problem_2_problem_type_name: ['Not given' 'Vedvarende svakt lag (flakskred)'\n",
" 'Dypt vedvarende svakt lag' 'Fokksnø (flakskred)' 'Nysnø (flakskred)'\n",
" 'Våt snø (flakskred)' 'Våt snø (løssnøskred)' 'Glideskred' 'Ikke gitt'\n",
" 'Nysnø (løssnøskred)'] \n",
"\n",
"avalanche_problem_2_trigger_simple_id: [ 0 10 21 22] \n",
"\n",
"avalanche_problem_2_trigger_simple_name: ['Not given' 'Stor tilleggsbelastning' 'Liten tilleggsbelastning'\n",
" 'Naturlig utløst' 'Ikke gitt'] \n",
"\n",
"avalanche_problem_2_type_id: [ 0 10 20] \n",
"\n",
"avalanche_problem_2_type_name: ['Not given' 'Flakskred' 'Løssnøskred' 'Ikke gitt'] \n",
"\n",
"avalanche_problem_2_valid_expositions: [ 0 10001111 11111111 111 10000011 11000111 11100011 1111000\n",
" 11110000 1111 11000011 11100001 11110001 11001111 10000111 11111000\n",
" 111110 1111110 11100000 1110 11110 111100 1111100 11000001\n",
" 11111001 1100 11100 111111 11111 11110011 11111100 1\n",
" 1110000 111000 10011111 11100111 11111101 11110111 1100000 10000001] \n",
"\n",
"avalanche_problem_3_advice: ['Not given'\n",
" 'Unngå ferdsel i skredterreng brattere enn 30 grader og i utløpsområder. Vær oppmerksom på fare for fjernutløsning. Når det svake laget ligg dypt i snødekket eller det overliggende laget er hardt, trengs det som regel stor tilleggsbelastning for å påvirke dette. Vær ekstra forsiktig og tenk konsekvens når du gjør vegvalg, særlig i ukjent terreng, etter snøfall eller vind og i perioder med temperaturstigning. Det krever mye kunnskap å gjenkjenne svake lag i snødekket.'\n",
" 'Hold god avstand til hverandre ved ferdsel i skredterreng. Det er størst sjanse for å løse ut skred der snødekket er tynt, nær rygger og framstikkende steiner. Fjernutløsning er mulig. Det krever mye kunnskap for å gjenkjenne svake lag i snødekket. Drønnelyder og skytende sprekker er tydelige tegn, men fravær av slike tegn betyr ikke at det er trygt. Vær ekstra forsiktig og tenk konsekvens når du gjør vegvalg, særlig i ukjent terreng, etter snøfall eller vind og i perioder med temperaturstigning.'\n",
" 'Unngå ferdsel i skredterreng og i utløpsområder. Det er størst sjanse for å løse ut skred der snødekket er tynt, nær rygger og framstikkende steiner. Fjernutløsning er sannsynlig. Det krever mye kunnskap å gjenkjenne svake lag i snødekket. Drønnelyder og skytende sprekker er tydelige tegn, men fravær av slike tegn betyr ikke at det er trygt. Vær ekstra forsiktig etter snøfall eller vind og i perioder med temperaturstigning.'\n",
" 'Vær forsiktig i områder brattere enn 30 grader med fokksnø til den har fått stabilisert seg. Hold avstand mellom hverandre ved ferdsel i bratt terreng. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk. Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk tegn.'\n",
" 'For friluftsliv: Hold deg unna skredterreng (brattere enn 30 grader) og utløpssoner for skred. '\n",
" 'Vær forsiktig i områder brattere enn 30 grader med fokksnø til den har fått stabilisert seg. Hold avstand mellom hverandre ved ferdsel i bratt terreng. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk. Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk faretegn.'\n",
" 'Hold god avstand til hverandre ved ferdsel i skredterreng. Det er størst sjanse for å løse ut skred der snødekket er tynt, nær rygger og framstikkende steiner. Fjernutløsning er mulig. Det krever mye kunnskap for å gjenkjenne svake lag i snødekket. Drønnelyder og skytende sprekker er tydelige tegn som betyr at dere påvirker svake lag og bør unngå skredterreng, men fravær av slike tegn betyr ikke at det er trygt. Vær ekstra forsiktig og tenk konsekvens når du gjør vegvalg, særlig i ukjent terreng, etter snøfall eller vind og i perioder med temperaturstigning.'\n",
" 'For friluftsliv: Hold deg unna skredterreng (brattere enn 30 grader) og utløpssoner for skred. For infrastruktur: Områder i le for hovedvindretningen vil være mest utsatt for snøskred.'\n",
" 'Unngå bratte heng og terrengfeller under og etter snøfallet til nysnøen har stabilisert seg. Skredproblemet finnes overalt hvor det ligger mye nysnø i bratt terreng. Se etter nysnø som binder seg sammen til myke flak og sprekker opp eller fester seg dårlig til den eldre snøen under. Snø som sprekker opp rundt skiene/brettet er et typisk tegn.'\n",
" 'Husk at selv små skred er tunge og kan skade deg, særlig ved skred i terrengfeller. Når du er på vei ned store heng bør du gradvis bevege deg ut av fallinja for å unngå å bli tatt igjen av små skred som du selv har utløst. Følg med når snøoverflaten blir våt og myk. Skredfaren øker i takt med temperaturstigning og nedbørintensitet. Vær ekstra forsiktig når det regner og/eller når temperaturen stiger.'\n",
" 'For friluftsliv: Hold deg unna skredterreng (brattere enn 30 grader) og utløpssoner for skred. For infrastruktur: Våte løssnøskred kan nå vei / bebyggelse. Slike skred følger vanligvis faste skredløp.'\n",
" 'Unngå ferdsel i bratt terreng der skredproblemet finnes og i utløpssoner. Naturlig utløste skred er forventet. Følg med når snøoverflaten blir våt og myk. Skredfaren øker i takt med temperaturstigning og nedbørintensitet. Vær ekstra forsiktig når det regner og/eller når temperaturen stiger.'\n",
" 'Unngå terreng brattere enn 30 grader med fersk fokksnø til den har fått stabilisert seg. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk.Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk faretegn.'\n",
" 'Unngå ferdsel i løsneområder og utløpsområder for skred når snøoverflaten blir våt. Vær bevisst på når på døgnet du beveger deg i bratt terreng, skredfaren kan variere mye gjennom døgnet. Vær ekstra forsiktig når det regner og/eller når temperaturen stiger.'\n",
" 'Unngå bratte heng og terrengfeller under og etter snøfallet til nysnøen har stabilisert seg. Skredproblemet finnes overalt hvor det ligger mye nysnø i bratt terreng. Se etter nysnø som binder seg sammen til myke flak og sprekker opp eller fester seg dårlig til den eldre snøen under. Snø som sprekker opp rundt skiene/brettet er et typisk faretegn.'\n",
" 'Unngå ferdsel i skredterreng (løsne og utløpsområder). Det er størst sjanse for å løse ut skred der snødekket er tynt, nær rygger og framstikkende steiner. Fjernutløsning er sannsynlig. Det krever mye kunnskap å gjenkjenne svake lag i snødekket. Drønnelyder og skytende sprekker er tydelige tegn, men fravær av slike tegn betyr ikke at det er trygt. Vær ekstra forsiktig etter snøfall eller vind og i perioder med temperaturstigning.'\n",
" 'Hold deg unna skredterreng (brattere enn 30 grader) og utløpssoner for skred. For infrastruktur: Store naturlig utløste skred kan forekomme og nå ned til vei/bebyggelse'\n",
" 'For friluftsliv: Hold deg unna skredterreng (brattere enn 30 grader) og utløpssoner for skred. For infrastruktur: Ved stor utbredelse av det svake laget kan snøskredene bli store og nå ned til vei/bebyggelse.'\n",
" 'Unngå ferdsel i skredterreng (løsne og utløpsområder). Det er størst sjanse for å løse ut skred der snødekket er tynt, nær rygger og framstikkende steiner. Fjernutløsning er mulig. Det krever mye kunnskap å gjenkjenne svake lag i snødekket. Drønnelyder og skytende sprekker er tydelige tegn, men fravær av slike tegn betyr ikke at det er trygt. Vær ekstra forsiktig etter snøfall eller vind og i perioder med temperaturstigning.'\n",
" 'For friluftsliv: Hold deg unna skredterreng (brattere enn 30 grader) og utløpssoner for skred. For infrastruktur: Områder i le for hovedvindretningen vil være mest utsatt for snøskred.'\n",
" 'For friluftsliv: Hold deg unna alt skredterreng (løsneområder brattere enn 30 grader og utløpssoner for skred). For infrastruktur: Ved stor utbredelse av det svake laget kan snøskredene bli store og nå ned til vei/bebyggelse.'\n",
" 'Unngå langvarige opphold i løsneområder og utløpsområder for skred. Husk at selv små skred er tunge og kan skade deg, særlig nært terrengfeller. Vær bevisst på når på døgnet du beveger deg i bratt terreng, skredfaren kan variere mye gjennom døgnet. Vær ekstra forsiktig når det regner og/eller når temperaturen stiger. Følg med når snøoverflaten blir våt.'\n",
" 'Unngå lengre opphold i og nedenfor fjellsider med ferske glidesprekker. Det er vanskelig å forutse når slike skred vil løsne.'\n",
" 'Eventuelle skred bli vanligvis utløst av deg selv eller andre som ferdest i terrenget. Vurder utløpsområder og gjør sporvalg i forhold til dette. Når du er på vei ned store heng bør du gradvis bevege deg ut av fallinja for å unngå å bli tatt igjen av små løssnøskred som du selv har utløst. Husk at selv et lite skred kan få konsekvenser nært terrengfeller.Skredproblemet finnes overalt hvor det ligger nysnø i bratt terreng.'\n",
" 'Unngå ferdsel i bratt terreng. Skredproblemet finnes overalt hvor det ligger nysnø i bratt terreng. Vurder størrelse på heng, snømengde og utløpsområder og gjør sporvalg i forhold til dette.'\n",
" 'Unngå ferdsel i og nedenfor fjellsider med ferske glidesprekker. Det er vanskelig å forutse når slike skred vil løsne.'\n",
" 'Vær forsiktig i bratte heng under og etter snøfallet til nysnøen har stabilisert seg. Hold avstand til hverandre ved ferdsel i bratt terreng. Unngå ferdsel i / ved terrengfeller. Skredproblemet finnes overalt hvor det ligger mye nysnø i bratt terreng. Se etter nysnø som binder seg sammen til myke flak og sprekker opp eller fester seg dårlig til den eldre snøen under. Snø som sprekker opp rundt skiene/brettet er et typisk faretegn.'\n",
" 'Unngå terreng brattere enn 30 grader med fersk fokksnø til den har fått stabilisert seg. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk. Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk faretegn.'\n",
" 'Vær forsiktig i bratte heng under og etter snøfallet til nysnøen har stabilisert seg. Hold avstand til hverandre ved ferdsel i brat terreng. Unngå ferdsel i / ved terrengfeller. Skredproblemet finnes overalt hvor det ligger mye nysnø i bratt terreng. Se etter nysnø som binder seg sammen til myke flak og sprekker opp eller fester seg dårlig til den eldre snøen under. Snø som sprekker opp rundt skiene/brettet er et typisk tegn.'\n",
" 'Kun enkelte spesielt utsatte områder er skredutsatt. Vær varsom der skredproblemet er å finne i kombinasjon med terrengfeller.'\n",
" 'Unngå terreng brattere enn 30 grader med fersk fokksnø til den har fått stabilisert seg. Det er størst sannsynlighet for å løse ut skred på kul-formasjoner i terrenget og der fokksnøen er myk.Se etter områder hvor vinden nylig har lagt fra seg fokksnø, typisk bak rygger, i renneformasjoner og søkk. Lokale vindeffekter og skiftende vindretning kan gi stor variasjon i hvor fokksnøen legger seg. Snø som sprekker opp rundt skiene/brettet er et typisk tegn.'] \n",
"\n",
"avalanche_problem_3_cause_id: [ 0 16 19 11 18 15 10 24 13 22 20 14] \n",
"\n",
"avalanche_problem_3_cause_name: ['Not given' 'Kantkornet snø ved bakken' 'Kantkornet snø under skarelag'\n",
" 'Nedsnødd eller nedføyket overflaterim' 'Kantkornet snø over skarelag'\n",
" 'Dårlig binding mellom lag i fokksnøen' 'Nedføyket svakt lag med nysnø'\n",
" 'Ubunden snø' 'Nedsnødd eller nedføyket kantkornet snø'\n",
" 'Opphopning av vann i/over lag i snødekket'\n",
" 'Vann ved bakken/smelting fra bakken'\n",
" 'Dårlig binding mellom glatt skare og overliggende snø'] \n",
"\n",
"avalanche_problem_3_destructive_size_ext_id: [0 3 2 4 1] \n",
"\n",
"avalanche_problem_3_destructive_size_ext_name: ['Not given' '3 - Store' '2 - Middels' '4 - Svært store' '1 - Små'\n",
" 'Ikke gitt'] \n",
"\n",
"avalanche_problem_3_distribution_id: [0 3 2 1] \n",
"\n",
"avalanche_problem_3_distribution_name: ['Not given' 'Mange bratte heng' 'Noen bratte heng' 'Få bratte heng'\n",
" 'Ikke gitt'] \n",
"\n",
"avalanche_problem_3_exposed_height_1: [ 0 600 200 400 100 500 700 800 1000 300 900 1200 1100 2000\n",
" 1400 1600 1300 1900 1800 1500] \n",
"\n",
"avalanche_problem_3_exposed_height_2: [ 0 500 300 700 400 100 800 200 600 900 1000 1100] \n",
"\n",
"avalanche_problem_3_exposed_height_fill: [0 1 2 4] \n",
"\n",
"avalanche_problem_3_ext_id: [ 0 20 15 25 10] \n",
"\n",
"avalanche_problem_3_ext_name: ['Not given' 'Tørre flakskred' 'Våte løssnøskred ' 'Våte flakskred'\n",
" 'Tørre løssnøskred '] \n",
"\n",
"avalanche_problem_3_probability_id: [0 3 2 5] \n",
"\n",
"avalanche_problem_3_probability_name: ['Not given' 'Mulig ' 'Lite sannsynlig ' 'Sannsynlig ' 'Ikke gitt '] \n",
"\n",
"avalanche_problem_3_problem_id: [0 3] \n",
"\n",
"avalanche_problem_3_problem_type_id: [ 0 37 30 10 7 5 45 50 3] \n",
"\n",
"avalanche_problem_3_problem_type_name: ['Not given' 'Dypt vedvarende svakt lag'\n",
" 'Vedvarende svakt lag (flakskred)' 'Fokksnø (flakskred)'\n",
" 'Nysnø (flakskred)' 'Våt snø (løssnøskred)' 'Våt snø (flakskred)'\n",
" 'Glideskred' 'Nysnø (løssnøskred)'] \n",
"\n",
"avalanche_problem_3_trigger_simple_id: [ 0 10 21 22] \n",
"\n",
"avalanche_problem_3_trigger_simple_name: ['Not given' 'Stor tilleggsbelastning' 'Liten tilleggsbelastning'\n",
" 'Naturlig utløst' 'Ikke gitt'] \n",
"\n",
"avalanche_problem_3_type_id: [ 0 10 20] \n",
"\n",
"avalanche_problem_3_type_name: ['Not given' 'Flakskred' 'Løssnøskred'] \n",
"\n",
"avalanche_problem_3_valid_expositions: [ 0 11111111 1111 11111000 11100000 11110001 10000111 111\n",
" 11110000 111110 11110 11000001 11100001 11000011 11000111 11100\n",
" 11100011 111100 11111001 10000011 1111100 111111 1111000 11111011\n",
" 1110000 11111100 11110011] \n",
"\n",
"current_weak_layers: [nan 'Det har dannet seg kantkorn ved bakken i isolerte områder.'\n",
" 'Det har enkelte steder dannet seg kantkorn ved bakken.' ...\n",
" 'Kantkorn er tidligere påvist i snødekket, men det har sannsynligvis liten utbredelse og skal ha stor tilleggsbelastning for å kunne gå til brudd.'\n",
" 'Det finnes vedvarende svakt lag av kantkorn i snødekket, men det er stort sett vanskelig å løse ut skred på det.'\n",
" 'Det finnes vedvarende svakt lag av kantkorn i snødekket, men de er stort sett vanskelige å påvirke.'] \n",
"\n",
"danger_level: [0 2 3 4 1] \n",
"\n",
"danger_level_name: [nan '2 Moderat' '3 Betydelig' '4 Stor' '1 Liten' '0 Ikke vurdert'] \n",
"\n",
"date_valid: ['2016-12-01' '2016-12-02' '2016-12-03' '2016-12-04' '2016-12-05'\n",
" '2016-12-06' '2016-12-07' '2016-12-08' '2016-12-09' '2016-12-10'\n",
" '2016-12-11' '2016-12-12' '2016-12-13' '2016-12-14' '2016-12-15'\n",
" '2016-12-16' '2016-12-17' '2016-12-18' '2016-12-19' '2016-12-20'\n",
" '2016-12-21' '2016-12-22' '2016-12-23' '2016-12-24' '2016-12-25'\n",
" '2016-12-26' '2016-12-27' '2016-12-28' '2016-12-29' '2016-12-30'\n",
" '2016-12-31' '2017-01-01' '2017-01-02' '2017-01-03' '2017-01-04'\n",
" '2017-01-05' '2017-01-06' '2017-01-07' '2017-01-08' '2017-01-09'\n",
" '2017-01-10' '2017-01-11' '2017-01-12' '2017-01-13' '2017-01-14'\n",
" '2017-01-15' '2017-01-16' '2017-01-17' '2017-01-18' '2017-01-19'\n",
" '2017-01-20' '2017-01-21' '2017-01-22' '2017-01-23' '2017-01-24'\n",
" '2017-01-25' '2017-01-26' '2017-01-27' '2017-01-28' '2017-01-29'\n",
" '2017-01-30' '2017-01-31' '2017-02-01' '2017-02-02' '2017-02-03'\n",
" '2017-02-04' '2017-02-05' '2017-02-06' '2017-02-07' '2017-02-08'\n",
" '2017-02-09' '2017-02-10' '2017-02-11' '2017-02-12' '2017-02-13'\n",
" '2017-02-14' '2017-02-15' '2017-02-16' '2017-02-17' '2017-02-18'\n",
" '2017-02-19' '2017-02-20' '2017-02-21' '2017-02-22' '2017-02-23'\n",
" '2017-02-24' '2017-02-25' '2017-02-26' '2017-02-27' '2017-02-28'\n",
" '2017-03-01' '2017-03-02' '2017-03-03' '2017-03-04' '2017-03-05'\n",
" '2017-03-06' '2017-03-07' '2017-03-08' '2017-03-09' '2017-03-10'\n",
" '2017-03-11' '2017-03-12' '2017-03-13' '2017-03-14' '2017-03-15'\n",
" '2017-03-16' '2017-03-17' '2017-03-18' '2017-03-19' '2017-03-20'\n",
" '2017-03-21' '2017-03-22' '2017-03-23' '2017-03-24' '2017-03-25'\n",
" '2017-03-26' '2017-03-27' '2017-03-28' '2017-03-29' '2017-03-30'\n",
" '2017-03-31' '2017-04-01' '2017-04-02' '2017-04-03' '2017-04-04'\n",
" '2017-04-05' '2017-04-06' '2017-04-07' '2017-04-08' '2017-04-09'\n",
" '2017-04-10' '2017-04-11' '2017-04-12' '2017-04-13' '2017-04-14'\n",
" '2017-04-15' '2017-04-16' '2017-04-17' '2017-04-18' '2017-04-19'\n",
" '2017-04-20' '2017-04-21' '2017-04-22' '2017-04-23' '2017-04-24'\n",
" '2017-04-25' '2017-04-26' '2017-04-27' '2017-04-28' '2017-04-29'\n",
" '2017-04-30' '2017-05-01' '2017-05-02' '2017-05-03' '2017-05-04'\n",
" '2017-05-05' '2017-05-06' '2017-05-07' '2017-05-08' '2017-05-09'\n",
" '2017-05-10' '2017-05-11' '2017-05-12' '2017-05-13' '2017-05-14'\n",
" '2017-05-15' '2017-05-16' '2017-05-17' '2017-05-18' '2017-05-19'\n",
" '2017-05-20' '2017-05-21' '2017-05-22' '2017-05-23' '2017-05-24'\n",
" '2017-05-25' '2017-05-26' '2017-05-27' '2017-05-28' '2017-05-29'\n",
" '2017-05-30' '2017-05-31' '2017-06-01' '2017-06-02' '2017-06-03'\n",
" '2017-06-04' '2017-06-05' '2017-06-06' '2017-06-07' '2017-06-08'\n",
" '2017-06-09' '2017-06-10' '2017-06-11' '2017-06-12' '2017-06-13'\n",
" '2017-06-14' '2017-06-15' '2017-06-16' '2017-06-17' '2017-06-18'\n",
" '2017-06-19' '2017-06-20' '2017-06-21' '2017-06-22' '2017-06-23'\n",
" '2017-06-24' '2017-06-25' '2017-06-26' '2017-06-27' '2017-06-28'\n",
" '2017-06-29' '2017-06-30' '2017-07-01' '2017-07-02' '2017-07-03'\n",
" '2017-07-04' '2017-07-05' '2017-07-06' '2017-07-07' '2017-07-08'\n",
" '2017-07-09' '2017-07-10' '2017-07-11' '2017-07-12' '2017-07-13'\n",
" '2017-07-14' '2017-07-15' '2017-07-16' '2017-07-17' '2017-07-18'\n",
" '2017-07-19' '2017-07-20' '2017-07-21' '2017-07-22' '2017-07-23'\n",
" '2017-07-24' '2017-07-25' '2017-07-26' '2017-07-27' '2017-07-28'\n",
" '2017-07-29' '2017-07-30' '2017-07-31' '2017-08-01' '2017-08-02'\n",
" '2017-08-03' '2017-08-04' '2017-08-05' '2017-08-06' '2017-08-07'\n",
" '2017-08-08' '2017-08-09' '2017-08-10' '2017-08-11' '2017-08-12'\n",
" '2017-08-13' '2017-08-14' '2017-08-15' '2017-08-16' '2017-08-17'\n",
" '2017-08-18' '2017-08-19' '2017-08-20' '2017-08-21' '2017-08-22'\n",
" '2017-08-23' '2017-08-24' '2017-08-25' '2017-08-26' '2017-08-27'\n",
" '2017-08-28' '2017-08-29' '2017-08-30' '2017-08-31' '2017-09-01'\n",
" '2017-09-02' '2017-09-03' '2017-09-04' '2017-09-05' '2017-09-06'\n",
" '2017-09-07' '2017-09-08' '2017-09-09' '2017-09-10' '2017-09-11'\n",
" '2017-09-12' '2017-09-13' '2017-09-14' '2017-09-15' '2017-09-16'\n",
" '2017-09-17' '2017-09-18' '2017-09-19' '2017-09-20' '2017-09-21'\n",
" '2017-09-22' '2017-09-23' '2017-09-24' '2017-09-25' '2017-09-26'\n",
" '2017-09-27' '2017-09-28' '2017-09-29' '2017-09-30' '2017-10-01'\n",
" '2017-10-02' '2017-10-03' '2017-10-04' '2017-10-05' '2017-10-06'\n",
" '2017-10-07' '2017-10-08' '2017-10-09' '2017-10-10' '2017-10-11'\n",
" '2017-10-12' '2017-10-13' '2017-10-14' '2017-10-15' '2017-10-16'\n",
" '2017-10-17' '2017-10-18' '2017-10-19' '2017-10-20' '2017-10-21'\n",
" '2017-10-22' '2017-10-23' '2017-10-24' '2017-10-25' '2017-10-26'\n",
" '2017-10-27' '2017-10-28' '2017-10-29' '2017-10-30' '2017-10-31'\n",
" '2017-11-01' '2017-11-02' '2017-11-03' '2017-11-04' '2017-11-05'\n",
" '2017-11-06' '2017-11-07' '2017-11-08' '2017-11-09' '2017-11-10'\n",
" '2017-11-11' '2017-11-12' '2017-11-13' '2017-11-14' '2017-11-15'\n",
" '2017-11-16' '2017-11-17' '2017-11-18' '2017-11-19' '2017-11-20'\n",
" '2017-11-21' '2017-11-22' '2017-11-23' '2017-11-24' '2017-11-25'\n",
" '2017-11-26' '2017-11-27' '2017-11-28' '2017-11-29' '2017-11-30'\n",
" '2017-12-01' '2017-12-02' '2017-12-03' '2017-12-04' '2017-12-05'\n",
" '2017-12-06' '2017-12-07' '2017-12-08' '2017-12-09' '2017-12-10'\n",
" '2017-12-11' '2017-12-12' '2017-12-13' '2017-12-14' '2017-12-15'\n",
" '2017-12-16' '2017-12-17' '2017-12-18' '2017-12-19' '2017-12-20'\n",
" '2017-12-21' '2017-12-22' '2017-12-23' '2017-12-24' '2017-12-25'\n",
" '2017-12-26' '2017-12-27' '2017-12-28' '2017-12-29' '2017-12-30'\n",
" '2017-12-31' '2018-01-01' '2018-01-02' '2018-01-03' '2018-01-04'\n",
" '2018-01-05' '2018-01-06' '2018-01-07' '2018-01-08' '2018-01-09'\n",
" '2018-01-10' '2018-01-11' '2018-01-12' '2018-01-13' '2018-01-14'\n",
" '2018-01-15' '2018-01-16' '2018-01-17' '2018-01-18' '2018-01-19'\n",
" '2018-01-20' '2018-01-21' '2018-01-22' '2018-01-23' '2018-01-24'\n",
" '2018-01-25' '2018-01-26' '2018-01-27' '2018-01-28' '2018-01-29'\n",
" '2018-01-30' '2018-01-31' '2018-02-01' '2018-02-02' '2018-02-03'\n",
" '2018-02-04' '2018-02-05' '2018-02-06' '2018-02-07' '2018-02-08'\n",
" '2018-02-09' '2018-02-10' '2018-02-11' '2018-02-12' '2018-02-13'\n",
" '2018-02-14' '2018-02-15' '2018-02-16' '2018-02-17' '2018-02-18'\n",
" '2018-02-19' '2018-02-20' '2018-02-21' '2018-02-22' '2018-02-23'\n",
" '2018-02-24' '2018-02-25' '2018-02-26' '2018-02-27' '2018-02-28'\n",
" '2018-03-01' '2018-03-02' '2018-03-03' '2018-03-04' '2018-03-05'\n",
" '2018-03-06' '2018-03-07' '2018-03-08' '2018-03-09' '2018-03-10'\n",
" '2018-03-11' '2018-03-12' '2018-03-13' '2018-03-14' '2018-03-15'\n",
" '2018-03-16' '2018-03-17' '2018-03-18' '2018-03-19' '2018-03-20'\n",
" '2018-03-21' '2018-03-22' '2018-03-23' '2018-03-24' '2018-03-25'\n",
" '2018-03-26' '2018-03-27' '2018-03-28' '2018-03-29' '2018-03-30'\n",
" '2018-03-31' '2018-04-01' '2018-04-02' '2018-04-03' '2018-04-04'\n",
" '2018-04-05' '2018-04-06' '2018-04-07' '2018-04-08' '2018-04-09'\n",
" '2018-04-10' '2018-04-11' '2018-04-12' '2018-04-13' '2018-04-14'\n",
" '2018-04-15' '2018-04-16' '2018-04-17' '2018-04-18' '2018-04-19'\n",
" '2018-04-20' '2018-04-21' '2018-04-22' '2018-04-23' '2018-04-24'\n",
" '2018-04-25' '2018-04-26' '2018-04-27' '2018-04-28' '2018-04-29'\n",
" '2018-04-30' '2018-05-01' '2018-05-02' '2018-05-03' '2018-05-04'\n",
" '2018-05-05' '2018-05-06' '2018-05-07' '2018-05-08' '2018-05-09'\n",
" '2018-05-10' '2018-05-11' '2018-05-12' '2018-05-13' '2018-05-14'\n",
" '2018-05-15' '2018-05-16' '2018-05-17' '2018-05-18' '2018-05-19'\n",
" '2018-05-20' '2018-05-21' '2018-05-22' '2018-05-23' '2018-05-24'\n",
" '2018-05-25' '2018-05-26' '2018-05-27' '2018-05-28' '2018-05-29'\n",
" '2018-05-30' '2018-05-31' '2018-06-01' '2018-06-02' '2018-06-03'\n",
" '2018-06-04' '2018-06-05' '2018-06-06' '2018-06-07' '2018-06-08'\n",
" '2018-06-09' '2018-06-10' '2018-06-11' '2018-06-12' '2018-06-13'\n",
" '2018-06-14' '2018-06-15' '2018-06-16' '2018-06-17' '2018-06-18'\n",
" '2018-06-19' '2018-06-20' '2018-06-21' '2018-06-22' '2018-06-23'\n",
" '2018-06-24' '2018-06-25' '2018-06-26' '2018-06-27' '2018-06-28'\n",
" '2018-06-29' '2018-06-30' '2018-07-01' '2018-07-02' '2018-07-03'\n",
" '2018-07-04' '2018-07-05' '2018-07-06' '2018-07-07' '2018-07-08'\n",
" '2018-07-09' '2018-07-10' '2018-07-11' '2018-07-12' '2018-07-13'\n",
" '2018-07-14' '2018-07-15' '2018-07-16' '2018-07-17' '2018-07-18'\n",
" '2018-07-19' '2018-07-20' '2018-07-21' '2018-07-22' '2018-07-23'\n",
" '2018-07-24' '2018-07-25' '2018-07-26' '2018-07-27' '2018-07-28'\n",
" '2018-07-29' '2018-07-30' '2018-07-31' '2018-08-01' '2018-08-02'\n",
" '2018-08-03' '2018-08-04' '2018-08-05' '2018-08-06' '2018-08-07'\n",
" '2018-08-08' '2018-08-09' '2018-08-10' '2018-08-11' '2018-08-12'\n",
" '2018-08-13' '2018-08-14' '2018-08-15' '2018-08-16' '2018-08-17'\n",
" '2018-08-18' '2018-08-19' '2018-08-20' '2018-08-21' '2018-08-22'\n",
" '2018-08-23' '2018-08-24' '2018-08-25' '2018-08-26' '2018-08-27'\n",
" '2018-08-28' '2018-08-29' '2018-08-30' '2018-08-31' '2018-09-01'\n",
" '2018-09-02' '2018-09-03' '2018-09-04' '2018-09-05' '2018-09-06'\n",
" '2018-09-07' '2018-09-08' '2018-09-09' '2018-09-10' '2018-09-11'\n",
" '2018-09-12' '2018-09-13' '2018-09-14' '2018-09-15' '2018-09-16'\n",
" '2018-09-17' '2018-09-18' '2018-09-19' '2018-09-20' '2018-09-21'\n",
" '2018-09-22' '2018-09-23' '2018-09-24' '2018-09-25' '2018-09-26'\n",
" '2018-09-27' '2018-09-28' '2018-09-29' '2018-09-30' '2018-10-01'\n",
" '2018-10-02' '2018-10-03' '2018-10-04' '2018-10-05' '2018-10-06'\n",
" '2018-10-07' '2018-10-08' '2018-10-09' '2018-10-10' '2018-10-11'\n",
" '2018-10-12' '2018-10-13' '2018-10-14' '2018-10-15' '2018-10-16'\n",
" '2018-10-17' '2018-10-18' '2018-10-19' '2018-10-20' '2018-10-21'\n",
" '2018-10-22' '2018-10-23' '2018-10-24' '2018-10-25' '2018-10-26'\n",
" '2018-10-27' '2018-10-28' '2018-10-29' '2018-10-30' '2018-10-31'\n",
" '2018-11-01' '2018-11-02' '2018-11-03' '2018-11-04' '2018-11-05'\n",
" '2018-11-06' '2018-11-07' '2018-11-08' '2018-11-09' '2018-11-10'\n",
" '2018-11-11' '2018-11-12' '2018-11-13' '2018-11-14' '2018-11-15'\n",
" '2018-11-16' '2018-11-17' '2018-11-18' '2018-11-19' '2018-11-20'\n",
" '2018-11-21' '2018-11-22' '2018-11-23' '2018-11-24' '2018-11-25'\n",
" '2018-11-26' '2018-11-27' '2018-11-28' '2018-11-29' '2018-11-30'\n",
" '2018-12-01' '2018-12-02' '2018-12-03' '2018-12-04' '2018-12-05'\n",
" '2018-12-06' '2018-12-07' '2018-12-08' '2018-12-09' '2018-12-10'\n",
" '2018-12-11' '2018-12-12' '2018-12-13' '2018-12-14' '2018-12-15'\n",
" '2018-12-16' '2018-12-17' '2018-12-18' '2018-12-19' '2018-12-20'\n",
" '2018-12-21' '2018-12-22' '2018-12-23' '2018-12-24' '2018-12-25'\n",
" '2018-12-26' '2018-12-27' '2018-12-28' '2018-12-29' '2018-12-30'\n",
" '2018-12-31' '2019-01-01' '2019-01-02' '2019-01-03' '2019-01-04'\n",
" '2019-01-05' '2019-01-06' '2019-01-07' '2019-01-08' '2019-01-09'\n",
" '2019-01-10' '2019-01-11' '2019-01-12' '2019-01-13' '2019-01-14'\n",
" '2019-01-15' '2019-01-16' '2019-01-17' '2019-01-18' '2019-01-19'\n",
" '2019-01-20' '2019-01-21' '2019-01-22' '2019-01-23' '2019-01-24'\n",
" '2019-01-25' '2019-01-26' '2019-01-27' '2019-01-28' '2019-01-29'\n",
" '2019-01-30' '2019-01-31'] \n",
"\n",
"latest_avalanche_activity: [nan 'Det er ikke meldt om skredaktivitet de siste dagene.'\n",
" 'Det er ikke meldt om skredaktivitet de siste dagene, men det var lett å løse ut mindre utglidninger i små heng.'\n",
" ...\n",
" 'Det ble rapportert om tre skred Haukelifjell skisenter lørdag. Skred har sannsynligvis løsnet i nysnøflak, men det ene skredet skal ha gått helt ned på skarelaget.'\n",
" 'Det ble rapportert om tre skred Haukelifjell skisenter lørdag. Skred har sannsynligvis løsnet i nysnøflak, men det ene skredet skal ha gått helt ned på et skarelag.'\n",
" 'Det ble rapportert om tre skred Haukelifjell skisenter lørdag. Skred har sannsynligvis løsnet i nysnøflak. Det er også observert str 1 skred i Bykle mandag'] \n",
"\n",
"latest_observations: [nan\n",
" 'Det er generelt kun leområder i høyfjellet som har snø nok til å gi skredfare. Snøen er ujevnt fordelt i terrenget.\\r\\n\\r\\nMot bakken er et lag med kantkorn under oppbygging, men utgjør ikke noe problem foreløpig.\\r\\n\\r\\nTorsdag er det opphold og en liten trekk fra Ø.\\r\\n'\n",
" 'Det er generelt lite snø i regionen og snødekket er ujevnt fordelt over 400 moh, mest i leområder i vestlig sektor. Snøprofil fra Larsbreen på torsdag viser et nedsnødd lag med rim som går i brudd relativt lett ved testing. Ellers melder observatør om generelt stabile forhold og ingen faretegn.\\r\\n\\r\\nFredag er det -8 grader og 7 m/s vind fra SV ved Svalbard lufthavn.\\r\\n\\r\\n'\n",
" ...\n",
" 'Mandag blåser det bris på de mest utsatte toppene, for eksempel Honnegrasnuten (1344 moh). Det har snødd mye i løpet av helga og spesielt søndag. 30-50 cm økning i snødybden på flere stasjoner siden fredag.'\n",
" 'Tirsdag er det rolige vindforhold i regionen, ingen målestasjoner indikerer snøtransport. Det har ikke kommet nedbør siste døgn, men det er mye løs snø tilgjengelig rundt skoggrensa.'\n",
" 'Onsdag rapporteres det om lett snøvær ved Haukeliseter og lite vind. '] \n",
"\n",
"main_text: ['Ikke vurdert'\n",
" 'Lokalt ustabile forhold i leområder. Lite snø i regionen.'\n",
" 'Vær varsom i områder med ustabile fokksnøflak. Det finnes også vedvarende svake lag lenger ned i snødekket som kan påvirkes der snødekket er tynt.'\n",
" ...\n",
" 'Vær varsom i bratt terreng med fersk fokksnø. Lokalt kan det finnes vedvarende svake lag i snødekket.'\n",
" 'Vær forsiktig i bratt terreng med ferske nysnøflak.'\n",
" 'Unngå leområder med fersk fokksnø. Det finnes vedvarende svake lag i snødekket, vær oppmerksom i områder med tynt snødekke.'] \n",
"\n",
"mountain_weather_change_hour_of_day_start: [nan 6. 12. 18. 0.] \n",
"\n",
"mountain_weather_change_hour_of_day_stop: [nan 12. 18. 24. 6.] \n",
"\n",
"mountain_weather_change_wind_direction: ['Not given' 'SE' nan 'E' 'S' 'N' 'NE' 'NW' 'W' 'SW'] \n",
"\n",
"mountain_weather_change_wind_speed: [nan 'Frisk bris' 'Stiv kuling' 'Bris' 'Storm' 'Liten kuling'\n",
" 'Sterk kuling' 'Liten storm' 'Stille/svak vind'] \n",
"\n",
"mountain_weather_fl_hour_of_day_start: [nan 18. 6. 0. 12.] \n",
"\n",
"mountain_weather_fl_hour_of_day_stop: [nan 24. 12. 6. 18. 23.] \n",
"\n",
"mountain_weather_freezing_level: [ nan 0. 300. 100. 400. 200. 500. 700. 150. 350. 600. 1200.\n",
" 1000. 800. 2000. 2500. 1800. 900. 1600. 1400. 2400. 1500. 1300. 1100.\n",
" 1700. 2800. 2200. 1900. 2300. 2100. 20.] \n",
"\n",
"mountain_weather_precip_most_exposed: [ nan 4. 5. 10. 8. 3. 1. 2. 0. 20. 6. 50. 12. 7.\n",
" 15. 13. 14. 9. 18. 25. 40. 35. 16. 30. 17. 22. 11. 45.\n",
" 60. 55. 70. 65. 75. 80. 85. 120. 90. 160. 100.] \n",
"\n",
"mountain_weather_precip_region: [nan 2. 5. 0. 1. 4. 15. 25. 8. 12. 10. 3. 6. 7. 9. 16. 20. 30.\n",
" 14. 18. 35. 40. 45. 50. 65. 60. 55. 70. 90.] \n",
"\n",
"mountain_weather_temperature_elevation: [ nan 500. 600. 700. 800. 1100. 1400. 1800.] \n",
"\n",
"mountain_weather_temperature_max: [ nan -9. -8. -4. -10. -6. -5. -7. -13. -18. -17. -20.\n",
" -16. -15. -11. -3. -2. 0. -14. -19. -1. -12. -21. -22.\n",
" 1. 20. 2. 3. 8. 10. 5. 7. 6. 4. 11. 9.\n",
" 13. 12. 14. 15. 16. 18. 17. 19. 0.9] \n",
"\n",
"mountain_weather_temperature_min: [ nan -14. -12. -8. -10. -11. -20. -19. -24. -30. -18. -16. -9. -7.\n",
" -13. -23. -17. -22. -15. -4. -5. -2. -21. -25. -32. -3. -6. -1.\n",
" 0. -28. -27. -26. 1. 2. 3. -29. 4. 5. 6. 7. 10. 8.\n",
" 9.] \n",
"\n",
"mountain_weather_wind_speed: ['None' 'Bris' 'Frisk bris' 'Stiv kuling' 'Sterk kuling' 'Liten kuling'\n",
" 'Stille/svak vind' 'Liten storm' 'Storm'] \n",
"\n",
"publish_time: ['2016-12-01 00:00:00.000' '2016-12-01 15:34:32.493'\n",
" '2016-12-02 15:50:09.580' ... '2019-01-28 15:29:57.597'\n",
" '2019-01-29 15:34:32.720' '2019-01-30 15:20:35.667'] \n",
"\n",
"reg_id: [ 0 104166 104482 ... 178028 178172 178307] \n",
"\n",
"region_id: [3003 3007 3009 3010 3011 3012 3013 3014 3015 3016 3017 3022 3023 3024\n",
" 3027 3028 3029 3031 3032 3034 3035] \n",
"\n",
"region_name: ['Nordenskiöld Land' 'Vest-Finnmark' 'Nord-Troms' 'Lyngen' 'Tromsø'\n",
" 'Sør-Troms' 'Indre Troms' 'Lofoten og Vesterålen' 'Ofoten' 'Salten'\n",
" 'Svartisen' 'Trollheimen' 'Romsdal' 'Sunnmøre' 'Indre Fjordane'\n",
" 'Jotunheimen' 'Indre Sogn' 'Voss' 'Hallingdal' 'Hardanger'\n",
" 'Vest-Telemark'] \n",
"\n",
"region_type_id: [10] \n",
"\n",
"region_type_name: ['A'] \n",
"\n",
"snow_surface: [nan\n",
" 'Det er generelt lite snø i terrenget. Rygger er avblåst. I renner, forsenkninger og andre terrengformasjoner som samler snø ligger det rundt 20-30 cm med snø. De øverste cm er løse og kan flyttes av vinden.'\n",
" 'Det kom et dryss av nysnø i løpet av lørdagen, men det er generelt lite snø i terrenget. Rygger er avblåst. I renner, forsenkninger og andre terrengformasjoner som samler snø ligger det rundt 20-30 cm med snø. De øverste cm er løse og kan flyttes av vinden.'\n",
" ...\n",
" 'Snøoverflaten består nå av løs nysnø eller myke flak i leheng der vinden har tatt. \\r\\nEldre snødekke og gammel fokksnø ligger hovedsakelig i heng mot S og Ø.\\r\\nUnder tregrensa er det skarelag med tørr og ubunden snø over. Regionen har fremdeles noe mindre snø enn normalt, men det har stedvis kommet godt med nysnø de siste dagene.\\r\\n'\n",
" 'Snøoverflaten består nå av løs nysnø eller myke flak i leheng der vinden har tatt. I høyfjellet er det mange steder hard fokksnø eller avblåst ned til gammelt snødekke.\\r\\nEldre snødekke og gammel fokksnø ligger hovedsakelig i heng mot S og Ø.\\r\\nUnder tregrensa er det skarelag med tørr og ubunden snø over. Regionen har fremdeles noe mindre snø enn normalt, men det har stedvis kommet godt med nysnø de siste dagene.\\r\\n'\n",
" 'Snøoverflaten består nå av løs nysnø eller myke flak i leheng der vinden har tatt. I høyfjellet er det mange steder hard fokksnø eller avblåst ned til gammelt snødekke.\\r\\nEldre snødekke og gammel fokksnø ligger hovedsakelig i heng mot S og Ø.\\r\\nUnder tregrensa er det skarelag med tørr og ubunden snø over. Det er fortsatt mindre snø enn normal i regionen, spesielt i fjellet.\\r\\n'] \n",
"\n",
"utm_east: [ 0 520332 802123 750984 692056 656496 594858 647352 527125 602309\n",
" 533221 464133 210810 123434 62473 34025 155607 96001 28607 150188\n",
" 131223] \n",
"\n",
"utm_north: [ 0 8663904 7794717 7742562 7719872 7764237 7642656 7647736 7620981\n",
" 7578309 7497029 7381882 6991060 6960580 6916553 6868801 6844417 6816985\n",
" 6779054 6763814 6692016 6642571] \n",
"\n",
"utm_zone: [ 0 33] \n",
"\n",
"valid_from: ['2016-12-01 00:00:00.000' '2016-12-02 00:00:00.000'\n",
" '2016-12-03 00:00:00.000' '2016-12-04 00:00:00.000'\n",
" '2016-12-05 00:00:00.000' '2016-12-06 00:00:00.000'\n",
" '2016-12-07 00:00:00.000' '2016-12-08 00:00:00.000'\n",
" '2016-12-09 00:00:00.000' '2016-12-10 00:00:00.000'\n",
" '2016-12-11 00:00:00.000' '2016-12-12 00:00:00.000'\n",
" '2016-12-13 00:00:00.000' '2016-12-14 00:00:00.000'\n",
" '2016-12-15 00:00:00.000' '2016-12-16 00:00:00.000'\n",
" '2016-12-17 00:00:00.000' '2016-12-18 00:00:00.000'\n",
" '2016-12-19 00:00:00.000' '2016-12-20 00:00:00.000'\n",
" '2016-12-21 00:00:00.000' '2016-12-22 00:00:00.000'\n",
" '2016-12-23 00:00:00.000' '2016-12-24 00:00:00.000'\n",
" '2016-12-25 00:00:00.000' '2016-12-26 00:00:00.000'\n",
" '2016-12-27 00:00:00.000' '2016-12-28 00:00:00.000'\n",
" '2016-12-29 00:00:00.000' '2016-12-30 00:00:00.000'\n",
" '2016-12-31 00:00:00.000' '2017-01-01 00:00:00.000'\n",
" '2017-01-02 00:00:00.000' '2017-01-03 00:00:00.000'\n",
" '2017-01-04 00:00:00.000' '2017-01-05 00:00:00.000'\n",
" '2017-01-06 00:00:00.000' '2017-01-07 00:00:00.000'\n",
" '2017-01-08 00:00:00.000' '2017-01-09 00:00:00.000'\n",
" '2017-01-10 00:00:00.000' '2017-01-11 00:00:00.000'\n",
" '2017-01-12 00:00:00.000' '2017-01-13 00:00:00.000'\n",
" '2017-01-14 00:00:00.000' '2017-01-15 00:00:00.000'\n",
" '2017-01-16 00:00:00.000' '2017-01-17 00:00:00.000'\n",
" '2017-01-18 00:00:00.000' '2017-01-19 00:00:00.000'\n",
" '2017-01-20 00:00:00.000' '2017-01-21 00:00:00.000'\n",
" '2017-01-22 00:00:00.000' '2017-01-23 00:00:00.000'\n",
" '2017-01-24 00:00:00.000' '2017-01-25 00:00:00.000'\n",
" '2017-01-26 00:00:00.000' '2017-01-27 00:00:00.000'\n",
" '2017-01-28 00:00:00.000' '2017-01-29 00:00:00.000'\n",
" '2017-01-30 00:00:00.000' '2017-01-31 00:00:00.000'\n",
" '2017-02-01 00:00:00.000' '2017-02-02 00:00:00.000'\n",
" '2017-02-03 00:00:00.000' '2017-02-04 00:00:00.000'\n",
" '2017-02-05 00:00:00.000' '2017-02-06 00:00:00.000'\n",
" '2017-02-07 00:00:00.000' '2017-02-08 00:00:00.000'\n",
" '2017-02-09 00:00:00.000' '2017-02-10 00:00:00.000'\n",
" '2017-02-11 00:00:00.000' '2017-02-12 00:00:00.000'\n",
" '2017-02-13 00:00:00.000' '2017-02-14 00:00:00.000'\n",
" '2017-02-15 00:00:00.000' '2017-02-16 00:00:00.000'\n",
" '2017-02-17 00:00:00.000' '2017-02-18 00:00:00.000'\n",
" '2017-02-19 00:00:00.000' '2017-02-20 00:00:00.000'\n",
" '2017-02-21 00:00:00.000' '2017-02-22 00:00:00.000'\n",
" '2017-02-23 00:00:00.000' '2017-02-24 00:00:00.000'\n",
" '2017-02-25 00:00:00.000' '2017-02-26 00:00:00.000'\n",
" '2017-02-27 00:00:00.000' '2017-02-28 00:00:00.000'\n",
" '2017-03-01 00:00:00.000' '2017-03-02 00:00:00.000'\n",
" '2017-03-03 00:00:00.000' '2017-03-04 00:00:00.000'\n",
" '2017-03-05 00:00:00.000' '2017-03-06 00:00:00.000'\n",
" '2017-03-07 00:00:00.000' '2017-03-08 00:00:00.000'\n",
" '2017-03-09 00:00:00.000' '2017-03-10 00:00:00.000'\n",
" '2017-03-11 00:00:00.000' '2017-03-12 00:00:00.000'\n",
" '2017-03-13 00:00:00.000' '2017-03-14 00:00:00.000'\n",
" '2017-03-15 00:00:00.000' '2017-03-16 00:00:00.000'\n",
" '2017-03-17 00:00:00.000' '2017-03-18 00:00:00.000'\n",
" '2017-03-19 00:00:00.000' '2017-03-20 00:00:00.000'\n",
" '2017-03-21 00:00:00.000' '2017-03-22 00:00:00.000'\n",
" '2017-03-23 00:00:00.000' '2017-03-24 00:00:00.000'\n",
" '2017-03-25 00:00:00.000' '2017-03-26 00:00:00.000'\n",
" '2017-03-27 00:00:00.000' '2017-03-28 00:00:00.000'\n",
" '2017-03-29 00:00:00.000' '2017-03-30 00:00:00.000'\n",
" '2017-03-31 00:00:00.000' '2017-04-01 00:00:00.000'\n",
" '2017-04-02 00:00:00.000' '2017-04-03 00:00:00.000'\n",
" '2017-04-04 00:00:00.000' '2017-04-05 00:00:00.000'\n",
" '2017-04-06 00:00:00.000' '2017-04-07 00:00:00.000'\n",
" '2017-04-08 00:00:00.000' '2017-04-09 00:00:00.000'\n",
" '2017-04-10 00:00:00.000' '2017-04-11 00:00:00.000'\n",
" '2017-04-12 00:00:00.000' '2017-04-13 00:00:00.000'\n",
" '2017-04-14 00:00:00.000' '2017-04-15 00:00:00.000'\n",
" '2017-04-16 00:00:00.000' '2017-04-17 00:00:00.000'\n",
" '2017-04-18 00:00:00.000' '2017-04-19 00:00:00.000'\n",
" '2017-04-20 00:00:00.000' '2017-04-21 00:00:00.000'\n",
" '2017-04-22 00:00:00.000' '2017-04-23 00:00:00.000'\n",
" '2017-04-24 00:00:00.000' '2017-04-25 00:00:00.000'\n",
" '2017-04-26 00:00:00.000' '2017-04-27 00:00:00.000'\n",
" '2017-04-28 00:00:00.000' '2017-04-29 00:00:00.000'\n",
" '2017-04-30 00:00:00.000' '2017-05-01 00:00:00.000'\n",
" '2017-05-02 00:00:00.000' '2017-05-03 00:00:00.000'\n",
" '2017-05-04 00:00:00.000' '2017-05-05 00:00:00.000'\n",
" '2017-05-06 00:00:00.000' '2017-05-07 00:00:00.000'\n",
" '2017-05-08 00:00:00.000' '2017-05-09 00:00:00.000'\n",
" '2017-05-10 00:00:00.000' '2017-05-11 00:00:00.000'\n",
" '2017-05-12 00:00:00.000' '2017-05-13 00:00:00.000'\n",
" '2017-05-14 00:00:00.000' '2017-05-15 00:00:00.000'\n",
" '2017-05-16 00:00:00.000' '2017-05-17 00:00:00.000'\n",
" '2017-05-18 00:00:00.000' '2017-05-19 00:00:00.000'\n",
" '2017-05-20 00:00:00.000' '2017-05-21 00:00:00.000'\n",
" '2017-05-22 00:00:00.000' '2017-05-23 00:00:00.000'\n",
" '2017-05-24 00:00:00.000' '2017-05-25 00:00:00.000'\n",
" '2017-05-26 00:00:00.000' '2017-05-27 00:00:00.000'\n",
" '2017-05-28 00:00:00.000' '2017-05-29 00:00:00.000'\n",
" '2017-05-30 00:00:00.000' '2017-05-31 00:00:00.000'\n",
" '2017-06-01 00:00:00.000' '2017-06-02 00:00:00.000'\n",
" '2017-06-03 00:00:00.000' '2017-06-04 00:00:00.000'\n",
" '2017-06-05 00:00:00.000' '2017-06-06 00:00:00' '2017-06-07 00:00:00'\n",
" '2017-06-08 00:00:00' '2017-06-09 00:00:00' '2017-06-10 00:00:00'\n",
" '2017-06-11 00:00:00' '2017-06-12 00:00:00' '2017-06-13 00:00:00'\n",
" '2017-06-14 00:00:00' '2017-06-15 00:00:00' '2017-06-16 00:00:00'\n",
" '2017-06-17 00:00:00' '2017-06-18 00:00:00' '2017-06-19 00:00:00'\n",
" '2017-06-20 00:00:00' '2017-06-21 00:00:00' '2017-06-22 00:00:00'\n",
" '2017-06-23 00:00:00' '2017-06-24 00:00:00' '2017-06-25 00:00:00'\n",
" '2017-06-26 00:00:00' '2017-06-27 00:00:00' '2017-06-28 00:00:00'\n",
" '2017-06-29 00:00:00' '2017-06-30 00:00:00' '2017-07-01 00:00:00'\n",
" '2017-07-02 00:00:00' '2017-07-03 00:00:00' '2017-07-04 00:00:00'\n",
" '2017-07-05 00:00:00' '2017-07-06 00:00:00' '2017-07-07 00:00:00'\n",
" '2017-07-08 00:00:00' '2017-07-09 00:00:00' '2017-07-10 00:00:00'\n",
" '2017-07-11 00:00:00' '2017-07-12 00:00:00' '2017-07-13 00:00:00'\n",
" '2017-07-14 00:00:00' '2017-07-15 00:00:00' '2017-07-16 00:00:00'\n",
" '2017-07-17 00:00:00' '2017-07-18 00:00:00' '2017-07-19 00:00:00'\n",
" '2017-07-20 00:00:00' '2017-07-21 00:00:00' '2017-07-22 00:00:00'\n",
" '2017-07-23 00:00:00' '2017-07-24 00:00:00' '2017-07-25 00:00:00'\n",
" '2017-07-26 00:00:00' '2017-07-27 00:00:00' '2017-07-28 00:00:00'\n",
" '2017-07-29 00:00:00' '2017-07-30 00:00:00' '2017-07-31 00:00:00'\n",
" '2017-08-01 00:00:00' '2017-08-02 00:00:00' '2017-08-03 00:00:00'\n",
" '2017-08-04 00:00:00' '2017-08-05 00:00:00' '2017-08-06 00:00:00'\n",
" '2017-08-07 00:00:00' '2017-08-08 00:00:00' '2017-08-09 00:00:00'\n",
" '2017-08-10 00:00:00' '2017-08-11 00:00:00' '2017-08-12 00:00:00'\n",
" '2017-08-13 00:00:00' '2017-08-14 00:00:00' '2017-08-15 00:00:00'\n",
" '2017-08-16 00:00:00' '2017-08-17 00:00:00' '2017-08-18 00:00:00'\n",
" '2017-08-19 00:00:00' '2017-08-20 00:00:00' '2017-08-21 00:00:00'\n",
" '2017-08-22 00:00:00' '2017-08-23 00:00:00' '2017-08-24 00:00:00'\n",
" '2017-08-25 00:00:00' '2017-08-26 00:00:00' '2017-08-27 00:00:00'\n",
" '2017-08-28 00:00:00' '2017-08-29 00:00:00' '2017-08-30 00:00:00'\n",
" '2017-08-31 00:00:00' '2017-09-01 00:00:00' '2017-09-02 00:00:00'\n",
" '2017-09-03 00:00:00' '2017-09-04 00:00:00' '2017-09-05 00:00:00'\n",
" '2017-09-06 00:00:00' '2017-09-07 00:00:00' '2017-09-08 00:00:00'\n",
" '2017-09-09 00:00:00' '2017-09-10 00:00:00' '2017-09-11 00:00:00'\n",
" '2017-09-12 00:00:00' '2017-09-13 00:00:00' '2017-09-14 00:00:00'\n",
" '2017-09-15 00:00:00' '2017-09-16 00:00:00' '2017-09-17 00:00:00'\n",
" '2017-09-18 00:00:00' '2017-09-19 00:00:00' '2017-09-20 00:00:00'\n",
" '2017-09-21 00:00:00' '2017-09-22 00:00:00' '2017-09-23 00:00:00'\n",
" '2017-09-24 00:00:00' '2017-09-25 00:00:00' '2017-09-26 00:00:00'\n",
" '2017-09-27 00:00:00' '2017-09-28 00:00:00' '2017-09-29 00:00:00'\n",
" '2017-09-30 00:00:00' '2017-10-01 00:00:00' '2017-10-02 00:00:00'\n",
" '2017-10-03 00:00:00' '2017-10-04 00:00:00' '2017-10-05 00:00:00'\n",
" '2017-10-06 00:00:00' '2017-10-07 00:00:00' '2017-10-08 00:00:00'\n",
" '2017-10-09 00:00:00' '2017-10-10 00:00:00' '2017-10-11 00:00:00'\n",
" '2017-10-12 00:00:00' '2017-10-13 00:00:00' '2017-10-14 00:00:00'\n",
" '2017-10-15 00:00:00' '2017-10-16 00:00:00' '2017-10-17 00:00:00'\n",
" '2017-10-18 00:00:00' '2017-10-19 00:00:00' '2017-10-20 00:00:00'\n",
" '2017-10-21 00:00:00' '2017-10-22 00:00:00' '2017-10-23 00:00:00'\n",
" '2017-10-24 00:00:00.000' '2017-10-25 00:00:00.000'\n",
" '2017-10-26 00:00:00.000' '2017-10-27 00:00:00.000'\n",
" '2017-10-28 00:00:00.000' '2017-10-29 00:00:00.000'\n",
" '2017-10-30 00:00:00.000' '2017-10-31 00:00:00.000'\n",
" '2017-11-01 00:00:00.000' '2017-11-02 00:00:00.000'\n",
" '2017-11-03 00:00:00.000' '2017-11-04 00:00:00.000'\n",
" '2017-11-05 00:00:00.000' '2017-11-06 00:00:00.000'\n",
" '2017-11-07 00:00:00.000' '2017-11-08 00:00:00.000'\n",
" '2017-11-09 00:00:00.000' '2017-11-10 00:00:00.000'\n",
" '2017-11-11 00:00:00.000' '2017-11-12 00:00:00.000'\n",
" '2017-11-13 00:00:00.000' '2017-11-14 00:00:00.000'\n",
" '2017-11-15 00:00:00.000' '2017-11-16 00:00:00.000'\n",
" '2017-11-17 00:00:00.000' '2017-11-18 00:00:00.000'\n",
" '2017-11-19 00:00:00.000' '2017-11-20 00:00:00.000'\n",
" '2017-11-21 00:00:00.000' '2017-11-22 00:00:00.000'\n",
" '2017-11-23 00:00:00.000' '2017-11-24 00:00:00.000'\n",
" '2017-11-25 00:00:00.000' '2017-11-26 00:00:00.000'\n",
" '2017-11-27 00:00:00.000' '2017-11-28 00:00:00.000'\n",
" '2017-11-29 00:00:00.000' '2017-11-30 00:00:00.000'\n",
" '2017-12-01 00:00:00.000' '2017-12-02 00:00:00.000'\n",
" '2017-12-03 00:00:00.000' '2017-12-04 00:00:00.000'\n",
" '2017-12-05 00:00:00.000' '2017-12-06 00:00:00.000'\n",
" '2017-12-07 00:00:00.000' '2017-12-08 00:00:00.000'\n",
" '2017-12-09 00:00:00.000' '2017-12-10 00:00:00.000'\n",
" '2017-12-11 00:00:00.000' '2017-12-12 00:00:00.000'\n",
" '2017-12-13 00:00:00.000' '2017-12-14 00:00:00.000'\n",
" '2017-12-15 00:00:00.000' '2017-12-16 00:00:00.000'\n",
" '2017-12-17 00:00:00.000' '2017-12-18 00:00:00.000'\n",
" '2017-12-19 00:00:00.000' '2017-12-20 00:00:00.000'\n",
" '2017-12-21 00:00:00.000' '2017-12-22 00:00:00.000'\n",
" '2017-12-23 00:00:00.000' '2017-12-24 00:00:00.000'\n",
" '2017-12-25 00:00:00.000' '2017-12-26 00:00:00.000'\n",
" '2017-12-27 00:00:00.000' '2017-12-28 00:00:00.000'\n",
" '2017-12-29 00:00:00.000' '2017-12-30 00:00:00.000'\n",
" '2017-12-31 00:00:00.000' '2018-01-01 00:00:00.000'\n",
" '2018-01-02 00:00:00.000' '2018-01-03 00:00:00.000'\n",
" '2018-01-04 00:00:00.000' '2018-01-05 00:00:00.000'\n",
" '2018-01-06 00:00:00.000' '2018-01-07 00:00:00.000'\n",
" '2018-01-08 00:00:00.000' '2018-01-09 00:00:00.000'\n",
" '2018-01-10 00:00:00.000' '2018-01-11 00:00:00.000'\n",
" '2018-01-12 00:00:00.000' '2018-01-13 00:00:00.000'\n",
" '2018-01-14 00:00:00.000' '2018-01-15 00:00:00.000'\n",
" '2018-01-16 00:00:00.000' '2018-01-17 00:00:00.000'\n",
" '2018-01-18 00:00:00.000' '2018-01-19 00:00:00.000'\n",
" '2018-01-20 00:00:00.000' '2018-01-21 00:00:00.000'\n",
" '2018-01-22 00:00:00.000' '2018-01-23 00:00:00.000'\n",
" '2018-01-24 00:00:00.000' '2018-01-25 00:00:00.000'\n",
" '2018-01-26 00:00:00.000' '2018-01-27 00:00:00.000'\n",
" '2018-01-28 00:00:00.000' '2018-01-29 00:00:00.000'\n",
" '2018-01-30 00:00:00.000' '2018-01-31 00:00:00.000'\n",
" '2018-02-01 00:00:00.000' '2018-02-02 00:00:00.000'\n",
" '2018-02-03 00:00:00.000' '2018-02-04 00:00:00.000'\n",
" '2018-02-05 00:00:00.000' '2018-02-06 00:00:00.000'\n",
" '2018-02-07 00:00:00.000' '2018-02-08 00:00:00.000'\n",
" '2018-02-09 00:00:00.000' '2018-02-10 00:00:00.000'\n",
" '2018-02-11 00:00:00.000' '2018-02-12 00:00:00.000'\n",
" '2018-02-13 00:00:00.000' '2018-02-14 00:00:00.000'\n",
" '2018-02-15 00:00:00.000' '2018-02-16 00:00:00.000'\n",
" '2018-02-17 00:00:00.000' '2018-02-18 00:00:00.000'\n",
" '2018-02-19 00:00:00.000' '2018-02-20 00:00:00.000'\n",
" '2018-02-21 00:00:00.000' '2018-02-22 00:00:00.000'\n",
" '2018-02-23 00:00:00.000' '2018-02-24 00:00:00.000'\n",
" '2018-02-25 00:00:00.000' '2018-02-26 00:00:00.000'\n",
" '2018-02-27 00:00:00.000' '2018-02-28 00:00:00.000'\n",
" '2018-03-01 00:00:00.000' '2018-03-02 00:00:00.000'\n",
" '2018-03-03 00:00:00.000' '2018-03-04 00:00:00.000'\n",
" '2018-03-05 00:00:00.000' '2018-03-06 00:00:00.000'\n",
" '2018-03-07 00:00:00.000' '2018-03-08 00:00:00.000'\n",
" '2018-03-09 00:00:00.000' '2018-03-10 00:00:00.000'\n",
" '2018-03-11 00:00:00.000' '2018-03-12 00:00:00.000'\n",
" '2018-03-13 00:00:00.000' '2018-03-14 00:00:00.000'\n",
" '2018-03-15 00:00:00.000' '2018-03-16 00:00:00.000'\n",
" '2018-03-17 00:00:00.000' '2018-03-18 00:00:00.000'\n",
" '2018-03-19 00:00:00.000' '2018-03-20 00:00:00.000'\n",
" '2018-03-21 00:00:00.000' '2018-03-22 00:00:00.000'\n",
" '2018-03-23 00:00:00.000' '2018-03-24 00:00:00.000'\n",
" '2018-03-25 00:00:00.000' '2018-03-26 00:00:00.000'\n",
" '2018-03-27 00:00:00.000' '2018-03-28 00:00:00.000'\n",
" '2018-03-29 00:00:00.000' '2018-03-30 00:00:00.000'\n",
" '2018-03-31 00:00:00.000' '2018-04-01 00:00:00.000'\n",
" '2018-04-02 00:00:00.000' '2018-04-03 00:00:00.000'\n",
" '2018-04-04 00:00:00.000' '2018-04-05 00:00:00.000'\n",
" '2018-04-06 00:00:00.000' '2018-04-07 00:00:00.000'\n",
" '2018-04-08 00:00:00.000' '2018-04-09 00:00:00.000'\n",
" '2018-04-10 00:00:00.000' '2018-04-11 00:00:00.000'\n",
" '2018-04-12 00:00:00.000' '2018-04-13 00:00:00.000'\n",
" '2018-04-14 00:00:00.000' '2018-04-15 00:00:00.000'\n",
" '2018-04-16 00:00:00.000' '2018-04-17 00:00:00.000'\n",
" '2018-04-18 00:00:00.000' '2018-04-19 00:00:00.000'\n",
" '2018-04-20 00:00:00.000' '2018-04-21 00:00:00.000'\n",
" '2018-04-22 00:00:00.000' '2018-04-23 00:00:00.000'\n",
" '2018-04-24 00:00:00.000' '2018-04-25 00:00:00.000'\n",
" '2018-04-26 00:00:00.000' '2018-04-27 00:00:00.000'\n",
" '2018-04-28 00:00:00.000' '2018-04-29 00:00:00.000'\n",
" '2018-04-30 00:00:00.000' '2018-05-01 00:00:00.000'\n",
" '2018-05-02 00:00:00.000' '2018-05-03 00:00:00.000'\n",
" '2018-05-04 00:00:00.000' '2018-05-05 00:00:00.000'\n",
" '2018-05-06 00:00:00.000' '2018-05-07 00:00:00.000'\n",
" '2018-05-08 00:00:00.000' '2018-05-09 00:00:00.000'\n",
" '2018-05-10 00:00:00.000' '2018-05-11 00:00:00.000'\n",
" '2018-05-12 00:00:00.000' '2018-05-13 00:00:00.000'\n",
" '2018-05-14 00:00:00.000' '2018-05-15 00:00:00.000'\n",
" '2018-05-16 00:00:00.000' '2018-05-17 00:00:00.000'\n",
" '2018-05-18 00:00:00.000' '2018-05-19 00:00:00.000'\n",
" '2018-05-20 00:00:00.000' '2018-05-21 00:00:00.000'\n",
" '2018-05-22 00:00:00.000' '2018-05-23 00:00:00.000'\n",
" '2018-05-24 00:00:00.000' '2018-05-25 00:00:00.000'\n",
" '2018-05-26 00:00:00.000' '2018-05-27 00:00:00.000'\n",
" '2018-05-28 00:00:00.000' '2018-05-29 00:00:00.000'\n",
" '2018-05-30 00:00:00.000' '2018-05-31 00:00:00.000'\n",
" '2018-06-01 00:00:00.000' '2018-06-02 00:00:00.000'\n",
" '2018-06-03 00:00:00.000' '2018-06-04 00:00:00.000'\n",
" '2018-06-05 00:00:00.000' '2018-06-06 00:00:00.000'\n",
" '2018-06-07 00:00:00.000' '2018-06-08 00:00:00.000'\n",
" '2018-06-09 00:00:00.000' '2018-06-10 00:00:00.000'\n",
" '2018-06-11 00:00:00.000' '2018-06-12 00:00:00.000'\n",
" '2018-06-13 00:00:00.000' '2018-06-14 00:00:00' '2018-06-15 00:00:00'\n",
" '2018-06-16 00:00:00' '2018-06-17 00:00:00' '2018-06-18 00:00:00'\n",
" '2018-06-19 00:00:00' '2018-06-20 00:00:00' '2018-06-21 00:00:00'\n",
" '2018-06-22 00:00:00' '2018-06-23 00:00:00' '2018-06-24 00:00:00'\n",
" '2018-06-25 00:00:00' '2018-06-26 00:00:00' '2018-06-27 00:00:00'\n",
" '2018-06-28 00:00:00' '2018-06-29 00:00:00' '2018-06-30 00:00:00'\n",
" '2018-07-01 00:00:00' '2018-07-02 00:00:00' '2018-07-03 00:00:00'\n",
" '2018-07-04 00:00:00' '2018-07-05 00:00:00' '2018-07-06 00:00:00'\n",
" '2018-07-07 00:00:00' '2018-07-08 00:00:00' '2018-07-09 00:00:00'\n",
" '2018-07-10 00:00:00' '2018-07-11 00:00:00' '2018-07-12 00:00:00'\n",
" '2018-07-13 00:00:00' '2018-07-14 00:00:00' '2018-07-15 00:00:00'\n",
" '2018-07-16 00:00:00' '2018-07-17 00:00:00' '2018-07-18 00:00:00'\n",
" '2018-07-19 00:00:00' '2018-07-20 00:00:00' '2018-07-21 00:00:00'\n",
" '2018-07-22 00:00:00' '2018-07-23 00:00:00' '2018-07-24 00:00:00'\n",
" '2018-07-25 00:00:00' '2018-07-26 00:00:00' '2018-07-27 00:00:00'\n",
" '2018-07-28 00:00:00' '2018-07-29 00:00:00' '2018-07-30 00:00:00'\n",
" '2018-07-31 00:00:00' '2018-08-01 00:00:00' '2018-08-02 00:00:00'\n",
" '2018-08-03 00:00:00' '2018-08-04 00:00:00' '2018-08-05 00:00:00'\n",
" '2018-08-06 00:00:00' '2018-08-07 00:00:00' '2018-08-08 00:00:00'\n",
" '2018-08-09 00:00:00' '2018-08-10 00:00:00' '2018-08-11 00:00:00'\n",
" '2018-08-12 00:00:00' '2018-08-13 00:00:00' '2018-08-14 00:00:00'\n",
" '2018-08-15 00:00:00' '2018-08-16 00:00:00' '2018-08-17 00:00:00'\n",
" '2018-08-18 00:00:00' '2018-08-19 00:00:00' '2018-08-20 00:00:00'\n",
" '2018-08-21 00:00:00' '2018-08-22 00:00:00' '2018-08-23 00:00:00'\n",
" '2018-08-24 00:00:00' '2018-08-25 00:00:00' '2018-08-26 00:00:00'\n",
" '2018-08-27 00:00:00' '2018-08-28 00:00:00' '2018-08-29 00:00:00'\n",
" '2018-08-30 00:00:00' '2018-08-31 00:00:00' '2018-09-01 00:00:00'\n",
" '2018-09-02 00:00:00' '2018-09-03 00:00:00' '2018-09-04 00:00:00'\n",
" '2018-09-05 00:00:00' '2018-09-06 00:00:00' '2018-09-07 00:00:00'\n",
" '2018-09-08 00:00:00' '2018-09-09 00:00:00' '2018-09-10 00:00:00'\n",
" '2018-09-11 00:00:00' '2018-09-12 00:00:00' '2018-09-13 00:00:00'\n",
" '2018-09-14 00:00:00' '2018-09-15 00:00:00' '2018-09-16 00:00:00'\n",
" '2018-09-17 00:00:00' '2018-09-18 00:00:00' '2018-09-19 00:00:00'\n",
" '2018-09-20 00:00:00' '2018-09-21 00:00:00' '2018-09-22 00:00:00'\n",
" '2018-09-23 00:00:00' '2018-09-24 00:00:00' '2018-09-25 00:00:00'\n",
" '2018-09-26 00:00:00' '2018-09-27 00:00:00' '2018-09-28 00:00:00'\n",
" '2018-09-29 00:00:00' '2018-09-30 00:00:00' '2018-10-01 00:00:00'\n",
" '2018-10-02 00:00:00' '2018-10-03 00:00:00' '2018-10-04 00:00:00'\n",
" '2018-10-05 00:00:00' '2018-10-06 00:00:00' '2018-10-07 00:00:00'\n",
" '2018-10-08 00:00:00' '2018-10-09 00:00:00' '2018-10-10 00:00:00'\n",
" '2018-10-11 00:00:00' '2018-10-12 00:00:00' '2018-10-13 00:00:00'\n",
" '2018-10-14 00:00:00' '2018-10-15 00:00:00' '2018-10-16 00:00:00'\n",
" '2018-10-17 00:00:00' '2018-10-18 00:00:00' '2018-10-19 00:00:00'\n",
" '2018-10-20 00:00:00' '2018-10-21 00:00:00' '2018-10-22 00:00:00'\n",
" '2018-10-23 00:00:00' '2018-10-24 00:00:00' '2018-10-25 00:00:00'\n",
" '2018-10-26 00:00:00' '2018-10-27 00:00:00' '2018-10-28 00:00:00'\n",
" '2018-10-29 00:00:00' '2018-10-30 00:00:00' '2018-10-31 00:00:00'\n",
" '2018-11-01 00:00:00.000' '2018-11-02 00:00:00.000'\n",
" '2018-11-03 00:00:00.000' '2018-11-04 00:00:00.000'\n",
" '2018-11-05 00:00:00.000' '2018-11-06 00:00:00.000'\n",
" '2018-11-07 00:00:00.000' '2018-11-08 00:00:00.000'\n",
" '2018-11-09 00:00:00.000' '2018-11-10 00:00:00.000'\n",
" '2018-11-11 00:00:00.000' '2018-11-12 00:00:00.000'\n",
" '2018-11-13 00:00:00.000' '2018-11-14 00:00:00.000'\n",
" '2018-11-15 00:00:00.000' '2018-11-16 00:00:00.000'\n",
" '2018-11-17 00:00:00.000' '2018-11-18 00:00:00.000'\n",
" '2018-11-19 00:00:00.000' '2018-11-20 00:00:00.000'\n",
" '2018-11-21 00:00:00.000' '2018-11-22 00:00:00.000'\n",
" '2018-11-23 00:00:00.000' '2018-11-24 00:00:00.000'\n",
" '2018-11-25 00:00:00.000' '2018-11-26 00:00:00.000'\n",
" '2018-11-27 00:00:00.000' '2018-11-28 00:00:00.000'\n",
" '2018-11-29 00:00:00.000' '2018-11-30 00:00:00.000'\n",
" '2018-12-01 00:00:00.000' '2018-12-02 00:00:00.000'\n",
" '2018-12-03 00:00:00.000' '2018-12-04 00:00:00.000'\n",
" '2018-12-05 00:00:00.000' '2018-12-06 00:00:00.000'\n",
" '2018-12-07 00:00:00.000' '2018-12-08 00:00:00.000'\n",
" '2018-12-09 00:00:00.000' '2018-12-10 00:00:00.000'\n",
" '2018-12-11 00:00:00.000' '2018-12-12 00:00:00.000'\n",
" '2018-12-13 00:00:00.000' '2018-12-14 00:00:00.000'\n",
" '2018-12-15 00:00:00.000' '2018-12-16 00:00:00.000'\n",
" '2018-12-17 00:00:00.000' '2018-12-18 00:00:00.000'\n",
" '2018-12-19 00:00:00.000' '2018-12-20 00:00:00.000'\n",
" '2018-12-21 00:00:00.000' '2018-12-22 00:00:00.000'\n",
" '2018-12-23 00:00:00.000' '2018-12-24 00:00:00.000'\n",
" '2018-12-25 00:00:00.000' '2018-12-26 00:00:00.000'\n",
" '2018-12-27 00:00:00.000' '2018-12-28 00:00:00.000'\n",
" '2018-12-29 00:00:00.000' '2018-12-30 00:00:00.000'\n",
" '2018-12-31 00:00:00.000' '2019-01-01 00:00:00.000'\n",
" '2019-01-02 00:00:00.000' '2019-01-03 00:00:00.000'\n",
" '2019-01-04 00:00:00.000' '2019-01-05 00:00:00.000'\n",
" '2019-01-06 00:00:00.000' '2019-01-07 00:00:00.000'\n",
" '2019-01-08 00:00:00.000' '2019-01-09 00:00:00.000'\n",
" '2019-01-10 00:00:00.000' '2019-01-11 00:00:00.000'\n",
" '2019-01-12 00:00:00.000' '2019-01-13 00:00:00.000'\n",
" '2019-01-14 00:00:00.000' '2019-01-15 00:00:00.000'\n",
" '2019-01-16 00:00:00.000' '2019-01-17 00:00:00.000'\n",
" '2019-01-18 00:00:00.000' '2019-01-19 00:00:00.000'\n",
" '2019-01-20 00:00:00.000' '2019-01-21 00:00:00.000'\n",
" '2019-01-22 00:00:00.000' '2019-01-23 00:00:00.000'\n",
" '2019-01-24 00:00:00.000' '2019-01-25 00:00:00.000'\n",
" '2019-01-26 00:00:00.000' '2019-01-27 00:00:00.000'\n",
" '2019-01-28 00:00:00.000' '2019-01-29 00:00:00.000'\n",
" '2019-01-30 00:00:00.000' '2019-01-31 00:00:00.000' '2017-06-05 00:00:00'\n",
" '2017-10-23 00:00:00.000'] \n",
"\n",
"valid_to: ['2016-12-01 23:59:59.000' '2016-12-02 23:59:59.000'\n",
" '2016-12-03 23:59:59.000' '2016-12-04 23:59:59.000'\n",
" '2016-12-05 23:59:59.000' '2016-12-06 23:59:59.000'\n",
" '2016-12-07 23:59:59.000' '2016-12-08 23:59:59.000'\n",
" '2016-12-09 23:59:59.000' '2016-12-10 23:59:59.000'\n",
" '2016-12-11 23:59:59.000' '2016-12-12 23:59:59.000'\n",
" '2016-12-13 23:59:59.000' '2016-12-14 23:59:59.000'\n",
" '2016-12-15 23:59:59.000' '2016-12-16 23:59:59.000'\n",
" '2016-12-17 23:59:59.000' '2016-12-18 23:59:59.000'\n",
" '2016-12-19 23:59:59.000' '2016-12-20 23:59:59.000'\n",
" '2016-12-21 23:59:59.000' '2016-12-22 23:59:59.000'\n",
" '2016-12-23 23:59:59.000' '2016-12-24 23:59:59.000'\n",
" '2016-12-25 23:59:59.000' '2016-12-26 23:59:59.000'\n",
" '2016-12-27 23:59:59.000' '2016-12-28 23:59:59.000'\n",
" '2016-12-29 23:59:59.000' '2016-12-30 23:59:59.000'\n",
" '2016-12-31 23:59:59.000' '2017-01-01 23:59:59.000'\n",
" '2017-01-02 23:59:59.000' '2017-01-03 23:59:59.000'\n",
" '2017-01-04 23:59:59.000' '2017-01-05 23:59:59.000'\n",
" '2017-01-06 23:59:59.000' '2017-01-07 23:59:59.000'\n",
" '2017-01-08 23:59:59.000' '2017-01-09 23:59:59.000'\n",
" '2017-01-10 23:59:59.000' '2017-01-11 23:59:59.000'\n",
" '2017-01-12 23:59:59.000' '2017-01-13 23:59:59.000'\n",
" '2017-01-14 23:59:59.000' '2017-01-15 23:59:59.000'\n",
" '2017-01-16 23:59:59.000' '2017-01-17 23:59:59.000'\n",
" '2017-01-18 23:59:59.000' '2017-01-19 23:59:59.000'\n",
" '2017-01-20 23:59:59.000' '2017-01-21 23:59:59.000'\n",
" '2017-01-22 23:59:59.000' '2017-01-23 23:59:59.000'\n",
" '2017-01-24 23:59:59.000' '2017-01-25 23:59:59.000'\n",
" '2017-01-26 23:59:59.000' '2017-01-27 23:59:59.000'\n",
" '2017-01-28 23:59:59.000' '2017-01-29 23:59:59.000'\n",
" '2017-01-30 23:59:59.000' '2017-01-31 23:59:59.000'\n",
" '2017-02-01 23:59:59.000' '2017-02-02 23:59:59.000'\n",
" '2017-02-03 23:59:59.000' '2017-02-04 23:59:59.000'\n",
" '2017-02-05 23:59:59.000' '2017-02-06 23:59:59.000'\n",
" '2017-02-07 23:59:59.000' '2017-02-08 23:59:59.000'\n",
" '2017-02-09 23:59:59.000' '2017-02-10 23:59:59.000'\n",
" '2017-02-11 23:59:59.000' '2017-02-12 23:59:59.000'\n",
" '2017-02-13 23:59:59.000' '2017-02-14 23:59:59.000'\n",
" '2017-02-15 23:59:59.000' '2017-02-16 23:59:59.000'\n",
" '2017-02-17 23:59:59.000' '2017-02-18 23:59:59.000'\n",
" '2017-02-19 23:59:59.000' '2017-02-20 23:59:59.000'\n",
" '2017-02-21 23:59:59.000' '2017-02-22 23:59:59.000'\n",
" '2017-02-23 23:59:59.000' '2017-02-24 23:59:59.000'\n",
" '2017-02-25 23:59:59.000' '2017-02-26 23:59:59.000'\n",
" '2017-02-27 23:59:59.000' '2017-02-28 23:59:59.000'\n",
" '2017-03-01 23:59:59.000' '2017-03-02 23:59:59.000'\n",
" '2017-03-03 23:59:59.000' '2017-03-04 23:59:59.000'\n",
" '2017-03-05 23:59:59.000' '2017-03-06 23:59:59.000'\n",
" '2017-03-07 23:59:59.000' '2017-03-08 23:59:59.000'\n",
" '2017-03-09 23:59:59.000' '2017-03-10 23:59:59.000'\n",
" '2017-03-11 23:59:59.000' '2017-03-12 23:59:59.000'\n",
" '2017-03-13 23:59:59.000' '2017-03-14 23:59:59.000'\n",
" '2017-03-15 23:59:59.000' '2017-03-16 23:59:59.000'\n",
" '2017-03-17 23:59:59.000' '2017-03-18 23:59:59.000'\n",
" '2017-03-19 23:59:59.000' '2017-03-20 23:59:59.000'\n",
" '2017-03-21 23:59:59.000' '2017-03-22 23:59:59.000'\n",
" '2017-03-23 23:59:59.000' '2017-03-24 23:59:59.000'\n",
" '2017-03-25 23:59:59.000' '2017-03-26 23:59:59.000'\n",
" '2017-03-27 23:59:59.000' '2017-03-28 23:59:59.000'\n",
" '2017-03-29 23:59:59.000' '2017-03-30 23:59:59.000'\n",
" '2017-03-31 23:59:59.000' '2017-04-01 23:59:59.000'\n",
" '2017-04-02 23:59:59.000' '2017-04-03 23:59:59.000'\n",
" '2017-04-04 23:59:59.000' '2017-04-05 23:59:59.000'\n",
" '2017-04-06 23:59:59.000' '2017-04-07 23:59:59.000'\n",
" '2017-04-08 23:59:59.000' '2017-04-09 23:59:59.000'\n",
" '2017-04-10 23:59:59.000' '2017-04-11 23:59:59.000'\n",
" '2017-04-12 23:59:59.000' '2017-04-13 23:59:59.000'\n",
" '2017-04-14 23:59:59.000' '2017-04-15 23:59:59.000'\n",
" '2017-04-16 23:59:59.000' '2017-04-17 23:59:59.000'\n",
" '2017-04-18 23:59:59.000' '2017-04-19 23:59:59.000'\n",
" '2017-04-20 23:59:59.000' '2017-04-21 23:59:59.000'\n",
" '2017-04-22 23:59:59.000' '2017-04-23 23:59:59.000'\n",
" '2017-04-24 23:59:59.000' '2017-04-25 23:59:59.000'\n",
" '2017-04-26 23:59:59.000' '2017-04-27 23:59:59.000'\n",
" '2017-04-28 23:59:59.000' '2017-04-29 23:59:59.000'\n",
" '2017-04-30 23:59:59.000' '2017-05-01 23:59:59.000'\n",
" '2017-05-02 23:59:59.000' '2017-05-03 23:59:59.000'\n",
" '2017-05-04 23:59:59.000' '2017-05-05 23:59:59.000'\n",
" '2017-05-06 23:59:59.000' '2017-05-07 23:59:59.000'\n",
" '2017-05-08 23:59:59.000' '2017-05-09 23:59:59.000'\n",
" '2017-05-10 23:59:59.000' '2017-05-11 23:59:59.000'\n",
" '2017-05-12 23:59:59.000' '2017-05-13 23:59:59.000'\n",
" '2017-05-14 23:59:59.000' '2017-05-15 23:59:59.000'\n",
" '2017-05-16 23:59:59.000' '2017-05-17 23:59:59.000'\n",
" '2017-05-18 23:59:59.000' '2017-05-19 23:59:59.000'\n",
" '2017-05-20 23:59:59.000' '2017-05-21 23:59:59.000'\n",
" '2017-05-22 23:59:59.000' '2017-05-23 23:59:59.000'\n",
" '2017-05-24 23:59:59.000' '2017-05-25 23:59:59.000'\n",
" '2017-05-26 23:59:59.000' '2017-05-27 23:59:59.000'\n",
" '2017-05-28 23:59:59.000' '2017-05-29 23:59:59.000'\n",
" '2017-05-30 23:59:59.000' '2017-05-31 23:59:59.000'\n",
" '2017-06-01 23:59:59.000' '2017-06-02 23:59:59.000'\n",
" '2017-06-03 23:59:59.000' '2017-06-04 23:59:59.000'\n",
" '2017-06-05 23:59:59.000' '2017-06-06 23:59:59' '2017-06-07 23:59:59'\n",
" '2017-06-08 23:59:59' '2017-06-09 23:59:59' '2017-06-10 23:59:59'\n",
" '2017-06-11 23:59:59' '2017-06-12 23:59:59' '2017-06-13 23:59:59'\n",
" '2017-06-14 23:59:59' '2017-06-15 23:59:59' '2017-06-16 23:59:59'\n",
" '2017-06-17 23:59:59' '2017-06-18 23:59:59' '2017-06-19 23:59:59'\n",
" '2017-06-20 23:59:59' '2017-06-21 23:59:59' '2017-06-22 23:59:59'\n",
" '2017-06-23 23:59:59' '2017-06-24 23:59:59' '2017-06-25 23:59:59'\n",
" '2017-06-26 23:59:59' '2017-06-27 23:59:59' '2017-06-28 23:59:59'\n",
" '2017-06-29 23:59:59' '2017-06-30 23:59:59' '2017-07-01 23:59:59'\n",
" '2017-07-02 23:59:59' '2017-07-03 23:59:59' '2017-07-04 23:59:59'\n",
" '2017-07-05 23:59:59' '2017-07-06 23:59:59' '2017-07-07 23:59:59'\n",
" '2017-07-08 23:59:59' '2017-07-09 23:59:59' '2017-07-10 23:59:59'\n",
" '2017-07-11 23:59:59' '2017-07-12 23:59:59' '2017-07-13 23:59:59'\n",
" '2017-07-14 23:59:59' '2017-07-15 23:59:59' '2017-07-16 23:59:59'\n",
" '2017-07-17 23:59:59' '2017-07-18 23:59:59' '2017-07-19 23:59:59'\n",
" '2017-07-20 23:59:59' '2017-07-21 23:59:59' '2017-07-22 23:59:59'\n",
" '2017-07-23 23:59:59' '2017-07-24 23:59:59' '2017-07-25 23:59:59'\n",
" '2017-07-26 23:59:59' '2017-07-27 23:59:59' '2017-07-28 23:59:59'\n",
" '2017-07-29 23:59:59' '2017-07-30 23:59:59' '2017-07-31 23:59:59'\n",
" '2017-08-01 23:59:59' '2017-08-02 23:59:59' '2017-08-03 23:59:59'\n",
" '2017-08-04 23:59:59' '2017-08-05 23:59:59' '2017-08-06 23:59:59'\n",
" '2017-08-07 23:59:59' '2017-08-08 23:59:59' '2017-08-09 23:59:59'\n",
" '2017-08-10 23:59:59' '2017-08-11 23:59:59' '2017-08-12 23:59:59'\n",
" '2017-08-13 23:59:59' '2017-08-14 23:59:59' '2017-08-15 23:59:59'\n",
" '2017-08-16 23:59:59' '2017-08-17 23:59:59' '2017-08-18 23:59:59'\n",
" '2017-08-19 23:59:59' '2017-08-20 23:59:59' '2017-08-21 23:59:59'\n",
" '2017-08-22 23:59:59' '2017-08-23 23:59:59' '2017-08-24 23:59:59'\n",
" '2017-08-25 23:59:59' '2017-08-26 23:59:59' '2017-08-27 23:59:59'\n",
" '2017-08-28 23:59:59' '2017-08-29 23:59:59' '2017-08-30 23:59:59'\n",
" '2017-08-31 23:59:59' '2017-09-01 23:59:59' '2017-09-02 23:59:59'\n",
" '2017-09-03 23:59:59' '2017-09-04 23:59:59' '2017-09-05 23:59:59'\n",
" '2017-09-06 23:59:59' '2017-09-07 23:59:59' '2017-09-08 23:59:59'\n",
" '2017-09-09 23:59:59' '2017-09-10 23:59:59' '2017-09-11 23:59:59'\n",
" '2017-09-12 23:59:59' '2017-09-13 23:59:59' '2017-09-14 23:59:59'\n",
" '2017-09-15 23:59:59' '2017-09-16 23:59:59' '2017-09-17 23:59:59'\n",
" '2017-09-18 23:59:59' '2017-09-19 23:59:59' '2017-09-20 23:59:59'\n",
" '2017-09-21 23:59:59' '2017-09-22 23:59:59' '2017-09-23 23:59:59'\n",
" '2017-09-24 23:59:59' '2017-09-25 23:59:59' '2017-09-26 23:59:59'\n",
" '2017-09-27 23:59:59' '2017-09-28 23:59:59' '2017-09-29 23:59:59'\n",
" '2017-09-30 23:59:59' '2017-10-01 23:59:59' '2017-10-02 23:59:59'\n",
" '2017-10-03 23:59:59' '2017-10-04 23:59:59' '2017-10-05 23:59:59'\n",
" '2017-10-06 23:59:59' '2017-10-07 23:59:59' '2017-10-08 23:59:59'\n",
" '2017-10-09 23:59:59' '2017-10-10 23:59:59' '2017-10-11 23:59:59'\n",
" '2017-10-12 23:59:59' '2017-10-13 23:59:59' '2017-10-14 23:59:59'\n",
" '2017-10-15 23:59:59' '2017-10-16 23:59:59' '2017-10-17 23:59:59'\n",
" '2017-10-18 23:59:59' '2017-10-19 23:59:59' '2017-10-20 23:59:59'\n",
" '2017-10-21 23:59:59' '2017-10-22 23:59:59' '2017-10-23 23:59:59'\n",
" '2017-10-24 23:59:59.000' '2017-10-25 23:59:59.000'\n",
" '2017-10-26 23:59:59.000' '2017-10-27 23:59:59.000'\n",
" '2017-10-28 23:59:59.000' '2017-10-29 23:59:59.000'\n",
" '2017-10-30 23:59:59.000' '2017-10-31 23:59:59.000'\n",
" '2017-11-01 23:59:59.000' '2017-11-02 23:59:59.000'\n",
" '2017-11-03 23:59:59.000' '2017-11-04 23:59:59.000'\n",
" '2017-11-05 23:59:59.000' '2017-11-06 23:59:59.000'\n",
" '2017-11-07 23:59:59.000' '2017-11-08 23:59:59.000'\n",
" '2017-11-09 23:59:59.000' '2017-11-10 23:59:59.000'\n",
" '2017-11-11 23:59:59.000' '2017-11-12 23:59:59.000'\n",
" '2017-11-13 23:59:59.000' '2017-11-14 23:59:59.000'\n",
" '2017-11-15 23:59:59.000' '2017-11-16 23:59:59.000'\n",
" '2017-11-17 23:59:59.000' '2017-11-18 23:59:59.000'\n",
" '2017-11-19 23:59:59.000' '2017-11-20 23:59:59.000'\n",
" '2017-11-21 23:59:59.000' '2017-11-22 23:59:59.000'\n",
" '2017-11-23 23:59:59.000' '2017-11-24 23:59:59.000'\n",
" '2017-11-25 23:59:59.000' '2017-11-26 23:59:59.000'\n",
" '2017-11-27 23:59:59.000' '2017-11-28 23:59:59.000'\n",
" '2017-11-29 23:59:59.000' '2017-11-30 23:59:59.000'\n",
" '2017-12-01 23:59:59.000' '2017-12-02 23:59:59.000'\n",
" '2017-12-03 23:59:59.000' '2017-12-04 23:59:59.000'\n",
" '2017-12-05 23:59:59.000' '2017-12-06 23:59:59.000'\n",
" '2017-12-07 23:59:59.000' '2017-12-08 23:59:59.000'\n",
" '2017-12-09 23:59:59.000' '2017-12-10 23:59:59.000'\n",
" '2017-12-11 23:59:59.000' '2017-12-12 23:59:59.000'\n",
" '2017-12-13 23:59:59.000' '2017-12-14 23:59:59.000'\n",
" '2017-12-15 23:59:59.000' '2017-12-16 23:59:59.000'\n",
" '2017-12-17 23:59:59.000' '2017-12-18 23:59:59.000'\n",
" '2017-12-19 23:59:59.000' '2017-12-20 23:59:59.000'\n",
" '2017-12-21 23:59:59.000' '2017-12-22 23:59:59.000'\n",
" '2017-12-23 23:59:59.000' '2017-12-24 23:59:59.000'\n",
" '2017-12-25 23:59:59.000' '2017-12-26 23:59:59.000'\n",
" '2017-12-27 23:59:59.000' '2017-12-28 23:59:59.000'\n",
" '2017-12-29 23:59:59.000' '2017-12-30 23:59:59.000'\n",
" '2017-12-31 23:59:59.000' '2018-01-01 23:59:59.000'\n",
" '2018-01-02 23:59:59.000' '2018-01-03 23:59:59.000'\n",
" '2018-01-04 23:59:59.000' '2018-01-05 23:59:59.000'\n",
" '2018-01-06 23:59:59.000' '2018-01-07 23:59:59.000'\n",
" '2018-01-08 23:59:59.000' '2018-01-09 23:59:59.000'\n",
" '2018-01-10 23:59:59.000' '2018-01-11 23:59:59.000'\n",
" '2018-01-12 23:59:59.000' '2018-01-13 23:59:59.000'\n",
" '2018-01-14 23:59:59.000' '2018-01-15 23:59:59.000'\n",
" '2018-01-16 23:59:59.000' '2018-01-17 23:59:59.000'\n",
" '2018-01-18 23:59:59.000' '2018-01-19 23:59:59.000'\n",
" '2018-01-20 23:59:59.000' '2018-01-21 23:59:59.000'\n",
" '2018-01-22 23:59:59.000' '2018-01-23 23:59:59.000'\n",
" '2018-01-24 23:59:59.000' '2018-01-25 23:59:59.000'\n",
" '2018-01-26 23:59:59.000' '2018-01-27 23:59:59.000'\n",
" '2018-01-28 23:59:59.000' '2018-01-29 23:59:59.000'\n",
" '2018-01-30 23:59:59.000' '2018-01-31 23:59:59.000'\n",
" '2018-02-01 23:59:59.000' '2018-02-02 23:59:59.000'\n",
" '2018-02-03 23:59:59.000' '2018-02-04 23:59:59.000'\n",
" '2018-02-05 23:59:59.000' '2018-02-06 23:59:59.000'\n",
" '2018-02-07 23:59:59.000' '2018-02-08 23:59:59.000'\n",
" '2018-02-09 23:59:59.000' '2018-02-10 23:59:59.000'\n",
" '2018-02-11 23:59:59.000' '2018-02-12 23:59:59.000'\n",
" '2018-02-13 23:59:59.000' '2018-02-14 23:59:59.000'\n",
" '2018-02-15 23:59:59.000' '2018-02-16 23:59:59.000'\n",
" '2018-02-17 23:59:59.000' '2018-02-18 23:59:59.000'\n",
" '2018-02-19 23:59:59.000' '2018-02-20 23:59:59.000'\n",
" '2018-02-21 23:59:59.000' '2018-02-22 23:59:59.000'\n",
" '2018-02-23 23:59:59.000' '2018-02-24 23:59:59.000'\n",
" '2018-02-25 23:59:59.000' '2018-02-26 23:59:59.000'\n",
" '2018-02-27 23:59:59.000' '2018-02-28 23:59:59.000'\n",
" '2018-03-01 23:59:59.000' '2018-03-02 23:59:59.000'\n",
" '2018-03-03 23:59:59.000' '2018-03-04 23:59:59.000'\n",
" '2018-03-05 23:59:59.000' '2018-03-06 23:59:59.000'\n",
" '2018-03-07 23:59:59.000' '2018-03-08 23:59:59.000'\n",
" '2018-03-09 23:59:59.000' '2018-03-10 23:59:59.000'\n",
" '2018-03-11 23:59:59.000' '2018-03-12 23:59:59.000'\n",
" '2018-03-13 23:59:59.000' '2018-03-14 23:59:59.000'\n",
" '2018-03-15 23:59:59.000' '2018-03-16 23:59:59.000'\n",
" '2018-03-17 23:59:59.000' '2018-03-18 23:59:59.000'\n",
" '2018-03-19 23:59:59.000' '2018-03-20 23:59:59.000'\n",
" '2018-03-21 23:59:59.000' '2018-03-22 23:59:59.000'\n",
" '2018-03-23 23:59:59.000' '2018-03-24 23:59:59.000'\n",
" '2018-03-25 23:59:59.000' '2018-03-26 23:59:59.000'\n",
" '2018-03-27 23:59:59.000' '2018-03-28 23:59:59.000'\n",
" '2018-03-29 23:59:59.000' '2018-03-30 23:59:59.000'\n",
" '2018-03-31 23:59:59.000' '2018-04-01 23:59:59.000'\n",
" '2018-04-02 23:59:59.000' '2018-04-03 23:59:59.000'\n",
" '2018-04-04 23:59:59.000' '2018-04-05 23:59:59.000'\n",
" '2018-04-06 23:59:59.000' '2018-04-07 23:59:59.000'\n",
" '2018-04-08 23:59:59.000' '2018-04-09 23:59:59.000'\n",
" '2018-04-10 23:59:59.000' '2018-04-11 23:59:59.000'\n",
" '2018-04-12 23:59:59.000' '2018-04-13 23:59:59.000'\n",
" '2018-04-14 23:59:59.000' '2018-04-15 23:59:59.000'\n",
" '2018-04-16 23:59:59.000' '2018-04-17 23:59:59.000'\n",
" '2018-04-18 23:59:59.000' '2018-04-19 23:59:59.000'\n",
" '2018-04-20 23:59:59.000' '2018-04-21 23:59:59.000'\n",
" '2018-04-22 23:59:59.000' '2018-04-23 23:59:59.000'\n",
" '2018-04-24 23:59:59.000' '2018-04-25 23:59:59.000'\n",
" '2018-04-26 23:59:59.000' '2018-04-27 23:59:59.000'\n",
" '2018-04-28 23:59:59.000' '2018-04-29 23:59:59.000'\n",
" '2018-04-30 23:59:59.000' '2018-05-01 23:59:59.000'\n",
" '2018-05-02 23:59:59.000' '2018-05-03 23:59:59.000'\n",
" '2018-05-04 23:59:59.000' '2018-05-05 23:59:59.000'\n",
" '2018-05-06 23:59:59.000' '2018-05-07 23:59:59.000'\n",
" '2018-05-08 23:59:59.000' '2018-05-09 23:59:59.000'\n",
" '2018-05-10 23:59:59.000' '2018-05-11 23:59:59.000'\n",
" '2018-05-12 23:59:59.000' '2018-05-13 23:59:59.000'\n",
" '2018-05-14 23:59:59.000' '2018-05-15 23:59:59.000'\n",
" '2018-05-16 23:59:59.000' '2018-05-17 23:59:59.000'\n",
" '2018-05-18 23:59:59.000' '2018-05-19 23:59:59.000'\n",
" '2018-05-20 23:59:59.000' '2018-05-21 23:59:59.000'\n",
" '2018-05-22 23:59:59.000' '2018-05-23 23:59:59.000'\n",
" '2018-05-24 23:59:59.000' '2018-05-25 23:59:59.000'\n",
" '2018-05-26 23:59:59.000' '2018-05-27 23:59:59.000'\n",
" '2018-05-28 23:59:59.000' '2018-05-29 23:59:59.000'\n",
" '2018-05-30 23:59:59.000' '2018-05-31 23:59:59.000'\n",
" '2018-06-01 23:59:59.000' '2018-06-02 23:59:59.000'\n",
" '2018-06-03 23:59:59.000' '2018-06-04 23:59:59.000'\n",
" '2018-06-05 23:59:59.000' '2018-06-06 23:59:59.000'\n",
" '2018-06-07 23:59:59.000' '2018-06-08 23:59:59.000'\n",
" '2018-06-09 23:59:59.000' '2018-06-10 23:59:59.000'\n",
" '2018-06-11 23:59:59.000' '2018-06-12 23:59:59.000'\n",
" '2018-06-13 23:59:59.000' '2018-06-14 23:59:59' '2018-06-15 23:59:59'\n",
" '2018-06-16 23:59:59' '2018-06-17 23:59:59' '2018-06-18 23:59:59'\n",
" '2018-06-19 23:59:59' '2018-06-20 23:59:59' '2018-06-21 23:59:59'\n",
" '2018-06-22 23:59:59' '2018-06-23 23:59:59' '2018-06-24 23:59:59'\n",
" '2018-06-25 23:59:59' '2018-06-26 23:59:59' '2018-06-27 23:59:59'\n",
" '2018-06-28 23:59:59' '2018-06-29 23:59:59' '2018-06-30 23:59:59'\n",
" '2018-07-01 23:59:59' '2018-07-02 23:59:59' '2018-07-03 23:59:59'\n",
" '2018-07-04 23:59:59' '2018-07-05 23:59:59' '2018-07-06 23:59:59'\n",
" '2018-07-07 23:59:59' '2018-07-08 23:59:59' '2018-07-09 23:59:59'\n",
" '2018-07-10 23:59:59' '2018-07-11 23:59:59' '2018-07-12 23:59:59'\n",
" '2018-07-13 23:59:59' '2018-07-14 23:59:59' '2018-07-15 23:59:59'\n",
" '2018-07-16 23:59:59' '2018-07-17 23:59:59' '2018-07-18 23:59:59'\n",
" '2018-07-19 23:59:59' '2018-07-20 23:59:59' '2018-07-21 23:59:59'\n",
" '2018-07-22 23:59:59' '2018-07-23 23:59:59' '2018-07-24 23:59:59'\n",
" '2018-07-25 23:59:59' '2018-07-26 23:59:59' '2018-07-27 23:59:59'\n",
" '2018-07-28 23:59:59' '2018-07-29 23:59:59' '2018-07-30 23:59:59'\n",
" '2018-07-31 23:59:59' '2018-08-01 23:59:59' '2018-08-02 23:59:59'\n",
" '2018-08-03 23:59:59' '2018-08-04 23:59:59' '2018-08-05 23:59:59'\n",
" '2018-08-06 23:59:59' '2018-08-07 23:59:59' '2018-08-08 23:59:59'\n",
" '2018-08-09 23:59:59' '2018-08-10 23:59:59' '2018-08-11 23:59:59'\n",
" '2018-08-12 23:59:59' '2018-08-13 23:59:59' '2018-08-14 23:59:59'\n",
" '2018-08-15 23:59:59' '2018-08-16 23:59:59' '2018-08-17 23:59:59'\n",
" '2018-08-18 23:59:59' '2018-08-19 23:59:59' '2018-08-20 23:59:59'\n",
" '2018-08-21 23:59:59' '2018-08-22 23:59:59' '2018-08-23 23:59:59'\n",
" '2018-08-24 23:59:59' '2018-08-25 23:59:59' '2018-08-26 23:59:59'\n",
" '2018-08-27 23:59:59' '2018-08-28 23:59:59' '2018-08-29 23:59:59'\n",
" '2018-08-30 23:59:59' '2018-08-31 23:59:59' '2018-09-01 23:59:59'\n",
" '2018-09-02 23:59:59' '2018-09-03 23:59:59' '2018-09-04 23:59:59'\n",
" '2018-09-05 23:59:59' '2018-09-06 23:59:59' '2018-09-07 23:59:59'\n",
" '2018-09-08 23:59:59' '2018-09-09 23:59:59' '2018-09-10 23:59:59'\n",
" '2018-09-11 23:59:59' '2018-09-12 23:59:59' '2018-09-13 23:59:59'\n",
" '2018-09-14 23:59:59' '2018-09-15 23:59:59' '2018-09-16 23:59:59'\n",
" '2018-09-17 23:59:59' '2018-09-18 23:59:59' '2018-09-19 23:59:59'\n",
" '2018-09-20 23:59:59' '2018-09-21 23:59:59' '2018-09-22 23:59:59'\n",
" '2018-09-23 23:59:59' '2018-09-24 23:59:59' '2018-09-25 23:59:59'\n",
" '2018-09-26 23:59:59' '2018-09-27 23:59:59' '2018-09-28 23:59:59'\n",
" '2018-09-29 23:59:59' '2018-09-30 23:59:59' '2018-10-01 23:59:59'\n",
" '2018-10-02 23:59:59' '2018-10-03 23:59:59' '2018-10-04 23:59:59'\n",
" '2018-10-05 23:59:59' '2018-10-06 23:59:59' '2018-10-07 23:59:59'\n",
" '2018-10-08 23:59:59' '2018-10-09 23:59:59' '2018-10-10 23:59:59'\n",
" '2018-10-11 23:59:59' '2018-10-12 23:59:59' '2018-10-13 23:59:59'\n",
" '2018-10-14 23:59:59' '2018-10-15 23:59:59' '2018-10-16 23:59:59'\n",
" '2018-10-17 23:59:59' '2018-10-18 23:59:59' '2018-10-19 23:59:59'\n",
" '2018-10-20 23:59:59' '2018-10-21 23:59:59' '2018-10-22 23:59:59'\n",
" '2018-10-23 23:59:59' '2018-10-24 23:59:59' '2018-10-25 23:59:59'\n",
" '2018-10-26 23:59:59' '2018-10-27 23:59:59' '2018-10-28 23:59:59'\n",
" '2018-10-29 23:59:59' '2018-10-30 23:59:59' '2018-10-31 23:59:59'\n",
" '2018-11-01 23:59:59.000' '2018-11-02 23:59:59.000'\n",
" '2018-11-03 23:59:59.000' '2018-11-04 23:59:59.000'\n",
" '2018-11-05 23:59:59.000' '2018-11-06 23:59:59.000'\n",
" '2018-11-07 23:59:59.000' '2018-11-08 23:59:59.000'\n",
" '2018-11-09 23:59:59.000' '2018-11-10 23:59:59.000'\n",
" '2018-11-11 23:59:59.000' '2018-11-12 23:59:59.000'\n",
" '2018-11-13 23:59:59.000' '2018-11-14 23:59:59.000'\n",
" '2018-11-15 23:59:59.000' '2018-11-16 23:59:59.000'\n",
" '2018-11-17 23:59:59.000' '2018-11-18 23:59:59.000'\n",
" '2018-11-19 23:59:59.000' '2018-11-20 23:59:59.000'\n",
" '2018-11-21 23:59:59.000' '2018-11-22 23:59:59.000'\n",
" '2018-11-23 23:59:59.000' '2018-11-24 23:59:59.000'\n",
" '2018-11-25 23:59:59.000' '2018-11-26 23:59:59.000'\n",
" '2018-11-27 23:59:59.000' '2018-11-28 23:59:59.000'\n",
" '2018-11-29 23:59:59.000' '2018-11-30 23:59:59.000'\n",
" '2018-12-01 23:59:59.000' '2018-12-02 23:59:59.000'\n",
" '2018-12-03 23:59:59.000' '2018-12-04 23:59:59.000'\n",
" '2018-12-05 23:59:59.000' '2018-12-06 23:59:59.000'\n",
" '2018-12-07 23:59:59.000' '2018-12-08 23:59:59.000'\n",
" '2018-12-09 23:59:59.000' '2018-12-10 23:59:59.000'\n",
" '2018-12-11 23:59:59.000' '2018-12-12 23:59:59.000'\n",
" '2018-12-13 23:59:59.000' '2018-12-14 23:59:59.000'\n",
" '2018-12-15 23:59:59.000' '2018-12-16 23:59:59.000'\n",
" '2018-12-17 23:59:59.000' '2018-12-18 23:59:59.000'\n",
" '2018-12-19 23:59:59.000' '2018-12-20 23:59:59.000'\n",
" '2018-12-21 23:59:59.000' '2018-12-22 23:59:59.000'\n",
" '2018-12-23 23:59:59.000' '2018-12-24 23:59:59.000'\n",
" '2018-12-25 23:59:59.000' '2018-12-26 23:59:59.000'\n",
" '2018-12-27 23:59:59.000' '2018-12-28 23:59:59.000'\n",
" '2018-12-29 23:59:59.000' '2018-12-30 23:59:59.000'\n",
" '2018-12-31 23:59:59.000' '2019-01-01 23:59:59.000'\n",
" '2019-01-02 23:59:59.000' '2019-01-03 23:59:59.000'\n",
" '2019-01-04 23:59:59.000' '2019-01-05 23:59:59.000'\n",
" '2019-01-06 23:59:59.000' '2019-01-07 23:59:59.000'\n",
" '2019-01-08 23:59:59.000' '2019-01-09 23:59:59.000'\n",
" '2019-01-10 23:59:59.000' '2019-01-11 23:59:59.000'\n",
" '2019-01-12 23:59:59.000' '2019-01-13 23:59:59.000'\n",
" '2019-01-14 23:59:59.000' '2019-01-15 23:59:59.000'\n",
" '2019-01-16 23:59:59.000' '2019-01-17 23:59:59.000'\n",
" '2019-01-18 23:59:59.000' '2019-01-19 23:59:59.000'\n",
" '2019-01-20 23:59:59.000' '2019-01-21 23:59:59.000'\n",
" '2019-01-22 23:59:59.000' '2019-01-23 23:59:59.000'\n",
" '2019-01-24 23:59:59.000' '2019-01-25 23:59:59.000'\n",
" '2019-01-26 23:59:59.000' '2019-01-27 23:59:59.000'\n",
" '2019-01-28 23:59:59.000' '2019-01-29 23:59:59.000'\n",
" '2019-01-30 23:59:59.000' '2019-01-31 23:59:59.000' '2017-06-05 23:59:59'\n",
" '2017-10-23 23:59:59.000'] \n",
"\n",
"date: ['2016-12-01T00:00:00.000000000' '2016-12-02T00:00:00.000000000'\n",
" '2016-12-03T00:00:00.000000000' '2016-12-04T00:00:00.000000000'\n",
" '2016-12-05T00:00:00.000000000' '2016-12-06T00:00:00.000000000'\n",
" '2016-12-07T00:00:00.000000000' '2016-12-08T00:00:00.000000000'\n",
" '2016-12-09T00:00:00.000000000' '2016-12-10T00:00:00.000000000'\n",
" '2016-12-11T00:00:00.000000000' '2016-12-12T00:00:00.000000000'\n",
" '2016-12-13T00:00:00.000000000' '2016-12-14T00:00:00.000000000'\n",
" '2016-12-15T00:00:00.000000000' '2016-12-16T00:00:00.000000000'\n",
" '2016-12-17T00:00:00.000000000' '2016-12-18T00:00:00.000000000'\n",
" '2016-12-19T00:00:00.000000000' '2016-12-20T00:00:00.000000000'\n",
" '2016-12-21T00:00:00.000000000' '2016-12-22T00:00:00.000000000'\n",
" '2016-12-23T00:00:00.000000000' '2016-12-24T00:00:00.000000000'\n",
" '2016-12-25T00:00:00.000000000' '2016-12-26T00:00:00.000000000'\n",
" '2016-12-27T00:00:00.000000000' '2016-12-28T00:00:00.000000000'\n",
" '2016-12-29T00:00:00.000000000' '2016-12-30T00:00:00.000000000'\n",
" '2016-12-31T00:00:00.000000000' '2017-01-01T00:00:00.000000000'\n",
" '2017-01-02T00:00:00.000000000' '2017-01-03T00:00:00.000000000'\n",
" '2017-01-04T00:00:00.000000000' '2017-01-05T00:00:00.000000000'\n",
" '2017-01-06T00:00:00.000000000' '2017-01-07T00:00:00.000000000'\n",
" '2017-01-08T00:00:00.000000000' '2017-01-09T00:00:00.000000000'\n",
" '2017-01-10T00:00:00.000000000' '2017-01-11T00:00:00.000000000'\n",
" '2017-01-12T00:00:00.000000000' '2017-01-13T00:00:00.000000000'\n",
" '2017-01-14T00:00:00.000000000' '2017-01-15T00:00:00.000000000'\n",
" '2017-01-16T00:00:00.000000000' '2017-01-17T00:00:00.000000000'\n",
" '2017-01-18T00:00:00.000000000' '2017-01-19T00:00:00.000000000'\n",
" '2017-01-20T00:00:00.000000000' '2017-01-21T00:00:00.000000000'\n",
" '2017-01-22T00:00:00.000000000' '2017-01-23T00:00:00.000000000'\n",
" '2017-01-24T00:00:00.000000000' '2017-01-25T00:00:00.000000000'\n",
" '2017-01-26T00:00:00.000000000' '2017-01-27T00:00:00.000000000'\n",
" '2017-01-28T00:00:00.000000000' '2017-01-29T00:00:00.000000000'\n",
" '2017-01-30T00:00:00.000000000' '2017-01-31T00:00:00.000000000'\n",
" '2017-02-01T00:00:00.000000000' '2017-02-02T00:00:00.000000000'\n",
" '2017-02-03T00:00:00.000000000' '2017-02-04T00:00:00.000000000'\n",
" '2017-02-05T00:00:00.000000000' '2017-02-06T00:00:00.000000000'\n",
" '2017-02-07T00:00:00.000000000' '2017-02-08T00:00:00.000000000'\n",
" '2017-02-09T00:00:00.000000000' '2017-02-10T00:00:00.000000000'\n",
" '2017-02-11T00:00:00.000000000' '2017-02-12T00:00:00.000000000'\n",
" '2017-02-13T00:00:00.000000000' '2017-02-14T00:00:00.000000000'\n",
" '2017-02-15T00:00:00.000000000' '2017-02-16T00:00:00.000000000'\n",
" '2017-02-17T00:00:00.000000000' '2017-02-18T00:00:00.000000000'\n",
" '2017-02-19T00:00:00.000000000' '2017-02-20T00:00:00.000000000'\n",
" '2017-02-21T00:00:00.000000000' '2017-02-22T00:00:00.000000000'\n",
" '2017-02-23T00:00:00.000000000' '2017-02-24T00:00:00.000000000'\n",
" '2017-02-25T00:00:00.000000000' '2017-02-26T00:00:00.000000000'\n",
" '2017-02-27T00:00:00.000000000' '2017-02-28T00:00:00.000000000'\n",
" '2017-03-01T00:00:00.000000000' '2017-03-02T00:00:00.000000000'\n",
" '2017-03-03T00:00:00.000000000' '2017-03-04T00:00:00.000000000'\n",
" '2017-03-05T00:00:00.000000000' '2017-03-06T00:00:00.000000000'\n",
" '2017-03-07T00:00:00.000000000' '2017-03-08T00:00:00.000000000'\n",
" '2017-03-09T00:00:00.000000000' '2017-03-10T00:00:00.000000000'\n",
" '2017-03-11T00:00:00.000000000' '2017-03-12T00:00:00.000000000'\n",
" '2017-03-13T00:00:00.000000000' '2017-03-14T00:00:00.000000000'\n",
" '2017-03-15T00:00:00.000000000' '2017-03-16T00:00:00.000000000'\n",
" '2017-03-17T00:00:00.000000000' '2017-03-18T00:00:00.000000000'\n",
" '2017-03-19T00:00:00.000000000' '2017-03-20T00:00:00.000000000'\n",
" '2017-03-21T00:00:00.000000000' '2017-03-22T00:00:00.000000000'\n",
" '2017-03-23T00:00:00.000000000' '2017-03-24T00:00:00.000000000'\n",
" '2017-03-25T00:00:00.000000000' '2017-03-26T00:00:00.000000000'\n",
" '2017-03-27T00:00:00.000000000' '2017-03-28T00:00:00.000000000'\n",
" '2017-03-29T00:00:00.000000000' '2017-03-30T00:00:00.000000000'\n",
" '2017-03-31T00:00:00.000000000' '2017-04-01T00:00:00.000000000'\n",
" '2017-04-02T00:00:00.000000000' '2017-04-03T00:00:00.000000000'\n",
" '2017-04-04T00:00:00.000000000' '2017-04-05T00:00:00.000000000'\n",
" '2017-04-06T00:00:00.000000000' '2017-04-07T00:00:00.000000000'\n",
" '2017-04-08T00:00:00.000000000' '2017-04-09T00:00:00.000000000'\n",
" '2017-04-10T00:00:00.000000000' '2017-04-11T00:00:00.000000000'\n",
" '2017-04-12T00:00:00.000000000' '2017-04-13T00:00:00.000000000'\n",
" '2017-04-14T00:00:00.000000000' '2017-04-15T00:00:00.000000000'\n",
" '2017-04-16T00:00:00.000000000' '2017-04-17T00:00:00.000000000'\n",
" '2017-04-18T00:00:00.000000000' '2017-04-19T00:00:00.000000000'\n",
" '2017-04-20T00:00:00.000000000' '2017-04-21T00:00:00.000000000'\n",
" '2017-04-22T00:00:00.000000000' '2017-04-23T00:00:00.000000000'\n",
" '2017-04-24T00:00:00.000000000' '2017-04-25T00:00:00.000000000'\n",
" '2017-04-26T00:00:00.000000000' '2017-04-27T00:00:00.000000000'\n",
" '2017-04-28T00:00:00.000000000' '2017-04-29T00:00:00.000000000'\n",
" '2017-04-30T00:00:00.000000000' '2017-05-01T00:00:00.000000000'\n",
" '2017-05-02T00:00:00.000000000' '2017-05-03T00:00:00.000000000'\n",
" '2017-05-04T00:00:00.000000000' '2017-05-05T00:00:00.000000000'\n",
" '2017-05-06T00:00:00.000000000' '2017-05-07T00:00:00.000000000'\n",
" '2017-05-08T00:00:00.000000000' '2017-05-09T00:00:00.000000000'\n",
" '2017-05-10T00:00:00.000000000' '2017-05-11T00:00:00.000000000'\n",
" '2017-05-12T00:00:00.000000000' '2017-05-13T00:00:00.000000000'\n",
" '2017-05-14T00:00:00.000000000' '2017-05-15T00:00:00.000000000'\n",
" '2017-05-16T00:00:00.000000000' '2017-05-17T00:00:00.000000000'\n",
" '2017-05-18T00:00:00.000000000' '2017-05-19T00:00:00.000000000'\n",
" '2017-05-20T00:00:00.000000000' '2017-05-21T00:00:00.000000000'\n",
" '2017-05-22T00:00:00.000000000' '2017-05-23T00:00:00.000000000'\n",
" '2017-05-24T00:00:00.000000000' '2017-05-25T00:00:00.000000000'\n",
" '2017-05-26T00:00:00.000000000' '2017-05-27T00:00:00.000000000'\n",
" '2017-05-28T00:00:00.000000000' '2017-05-29T00:00:00.000000000'\n",
" '2017-05-30T00:00:00.000000000' '2017-05-31T00:00:00.000000000'\n",
" '2017-06-01T00:00:00.000000000' '2017-06-02T00:00:00.000000000'\n",
" '2017-06-03T00:00:00.000000000' '2017-06-04T00:00:00.000000000'\n",
" '2017-06-05T00:00:00.000000000' '2017-06-06T00:00:00.000000000'\n",
" '2017-06-07T00:00:00.000000000' '2017-06-08T00:00:00.000000000'\n",
" '2017-06-09T00:00:00.000000000' '2017-06-10T00:00:00.000000000'\n",
" '2017-06-11T00:00:00.000000000' '2017-06-12T00:00:00.000000000'\n",
" '2017-06-13T00:00:00.000000000' '2017-06-14T00:00:00.000000000'\n",
" '2017-06-15T00:00:00.000000000' '2017-06-16T00:00:00.000000000'\n",
" '2017-06-17T00:00:00.000000000' '2017-06-18T00:00:00.000000000'\n",
" '2017-06-19T00:00:00.000000000' '2017-06-20T00:00:00.000000000'\n",
" '2017-06-21T00:00:00.000000000' '2017-06-22T00:00:00.000000000'\n",
" '2017-06-23T00:00:00.000000000' '2017-06-24T00:00:00.000000000'\n",
" '2017-06-25T00:00:00.000000000' '2017-06-26T00:00:00.000000000'\n",
" '2017-06-27T00:00:00.000000000' '2017-06-28T00:00:00.000000000'\n",
" '2017-06-29T00:00:00.000000000' '2017-06-30T00:00:00.000000000'\n",
" '2017-07-01T00:00:00.000000000' '2017-07-02T00:00:00.000000000'\n",
" '2017-07-03T00:00:00.000000000' '2017-07-04T00:00:00.000000000'\n",
" '2017-07-05T00:00:00.000000000' '2017-07-06T00:00:00.000000000'\n",
" '2017-07-07T00:00:00.000000000' '2017-07-08T00:00:00.000000000'\n",
" '2017-07-09T00:00:00.000000000' '2017-07-10T00:00:00.000000000'\n",
" '2017-07-11T00:00:00.000000000' '2017-07-12T00:00:00.000000000'\n",
" '2017-07-13T00:00:00.000000000' '2017-07-14T00:00:00.000000000'\n",
" '2017-07-15T00:00:00.000000000' '2017-07-16T00:00:00.000000000'\n",
" '2017-07-17T00:00:00.000000000' '2017-07-18T00:00:00.000000000'\n",
" '2017-07-19T00:00:00.000000000' '2017-07-20T00:00:00.000000000'\n",
" '2017-07-21T00:00:00.000000000' '2017-07-22T00:00:00.000000000'\n",
" '2017-07-23T00:00:00.000000000' '2017-07-24T00:00:00.000000000'\n",
" '2017-07-25T00:00:00.000000000' '2017-07-26T00:00:00.000000000'\n",
" '2017-07-27T00:00:00.000000000' '2017-07-28T00:00:00.000000000'\n",
" '2017-07-29T00:00:00.000000000' '2017-07-30T00:00:00.000000000'\n",
" '2017-07-31T00:00:00.000000000' '2017-08-01T00:00:00.000000000'\n",
" '2017-08-02T00:00:00.000000000' '2017-08-03T00:00:00.000000000'\n",
" '2017-08-04T00:00:00.000000000' '2017-08-05T00:00:00.000000000'\n",
" '2017-08-06T00:00:00.000000000' '2017-08-07T00:00:00.000000000'\n",
" '2017-08-08T00:00:00.000000000' '2017-08-09T00:00:00.000000000'\n",
" '2017-08-10T00:00:00.000000000' '2017-08-11T00:00:00.000000000'\n",
" '2017-08-12T00:00:00.000000000' '2017-08-13T00:00:00.000000000'\n",
" '2017-08-14T00:00:00.000000000' '2017-08-15T00:00:00.000000000'\n",
" '2017-08-16T00:00:00.000000000' '2017-08-17T00:00:00.000000000'\n",
" '2017-08-18T00:00:00.000000000' '2017-08-19T00:00:00.000000000'\n",
" '2017-08-20T00:00:00.000000000' '2017-08-21T00:00:00.000000000'\n",
" '2017-08-22T00:00:00.000000000' '2017-08-23T00:00:00.000000000'\n",
" '2017-08-24T00:00:00.000000000' '2017-08-25T00:00:00.000000000'\n",
" '2017-08-26T00:00:00.000000000' '2017-08-27T00:00:00.000000000'\n",
" '2017-08-28T00:00:00.000000000' '2017-08-29T00:00:00.000000000'\n",
" '2017-08-30T00:00:00.000000000' '2017-08-31T00:00:00.000000000'\n",
" '2017-09-01T00:00:00.000000000' '2017-09-02T00:00:00.000000000'\n",
" '2017-09-03T00:00:00.000000000' '2017-09-04T00:00:00.000000000'\n",
" '2017-09-05T00:00:00.000000000' '2017-09-06T00:00:00.000000000'\n",
" '2017-09-07T00:00:00.000000000' '2017-09-08T00:00:00.000000000'\n",
" '2017-09-09T00:00:00.000000000' '2017-09-10T00:00:00.000000000'\n",
" '2017-09-11T00:00:00.000000000' '2017-09-12T00:00:00.000000000'\n",
" '2017-09-13T00:00:00.000000000' '2017-09-14T00:00:00.000000000'\n",
" '2017-09-15T00:00:00.000000000' '2017-09-16T00:00:00.000000000'\n",
" '2017-09-17T00:00:00.000000000' '2017-09-18T00:00:00.000000000'\n",
" '2017-09-19T00:00:00.000000000' '2017-09-20T00:00:00.000000000'\n",
" '2017-09-21T00:00:00.000000000' '2017-09-22T00:00:00.000000000'\n",
" '2017-09-23T00:00:00.000000000' '2017-09-24T00:00:00.000000000'\n",
" '2017-09-25T00:00:00.000000000' '2017-09-26T00:00:00.000000000'\n",
" '2017-09-27T00:00:00.000000000' '2017-09-28T00:00:00.000000000'\n",
" '2017-09-29T00:00:00.000000000' '2017-09-30T00:00:00.000000000'\n",
" '2017-10-01T00:00:00.000000000' '2017-10-02T00:00:00.000000000'\n",
" '2017-10-03T00:00:00.000000000' '2017-10-04T00:00:00.000000000'\n",
" '2017-10-05T00:00:00.000000000' '2017-10-06T00:00:00.000000000'\n",
" '2017-10-07T00:00:00.000000000' '2017-10-08T00:00:00.000000000'\n",
" '2017-10-09T00:00:00.000000000' '2017-10-10T00:00:00.000000000'\n",
" '2017-10-11T00:00:00.000000000' '2017-10-12T00:00:00.000000000'\n",
" '2017-10-13T00:00:00.000000000' '2017-10-14T00:00:00.000000000'\n",
" '2017-10-15T00:00:00.000000000' '2017-10-16T00:00:00.000000000'\n",
" '2017-10-17T00:00:00.000000000' '2017-10-18T00:00:00.000000000'\n",
" '2017-10-19T00:00:00.000000000' '2017-10-20T00:00:00.000000000'\n",
" '2017-10-21T00:00:00.000000000' '2017-10-22T00:00:00.000000000'\n",
" '2017-10-23T00:00:00.000000000' '2017-10-24T00:00:00.000000000'\n",
" '2017-10-25T00:00:00.000000000' '2017-10-26T00:00:00.000000000'\n",
" '2017-10-27T00:00:00.000000000' '2017-10-28T00:00:00.000000000'\n",
" '2017-10-29T00:00:00.000000000' '2017-10-30T00:00:00.000000000'\n",
" '2017-10-31T00:00:00.000000000' '2017-11-01T00:00:00.000000000'\n",
" '2017-11-02T00:00:00.000000000' '2017-11-03T00:00:00.000000000'\n",
" '2017-11-04T00:00:00.000000000' '2017-11-05T00:00:00.000000000'\n",
" '2017-11-06T00:00:00.000000000' '2017-11-07T00:00:00.000000000'\n",
" '2017-11-08T00:00:00.000000000' '2017-11-09T00:00:00.000000000'\n",
" '2017-11-10T00:00:00.000000000' '2017-11-11T00:00:00.000000000'\n",
" '2017-11-12T00:00:00.000000000' '2017-11-13T00:00:00.000000000'\n",
" '2017-11-14T00:00:00.000000000' '2017-11-15T00:00:00.000000000'\n",
" '2017-11-16T00:00:00.000000000' '2017-11-17T00:00:00.000000000'\n",
" '2017-11-18T00:00:00.000000000' '2017-11-19T00:00:00.000000000'\n",
" '2017-11-20T00:00:00.000000000' '2017-11-21T00:00:00.000000000'\n",
" '2017-11-22T00:00:00.000000000' '2017-11-23T00:00:00.000000000'\n",
" '2017-11-24T00:00:00.000000000' '2017-11-25T00:00:00.000000000'\n",
" '2017-11-26T00:00:00.000000000' '2017-11-27T00:00:00.000000000'\n",
" '2017-11-28T00:00:00.000000000' '2017-11-29T00:00:00.000000000'\n",
" '2017-11-30T00:00:00.000000000' '2017-12-01T00:00:00.000000000'\n",
" '2017-12-02T00:00:00.000000000' '2017-12-03T00:00:00.000000000'\n",
" '2017-12-04T00:00:00.000000000' '2017-12-05T00:00:00.000000000'\n",
" '2017-12-06T00:00:00.000000000' '2017-12-07T00:00:00.000000000'\n",
" '2017-12-08T00:00:00.000000000' '2017-12-09T00:00:00.000000000'\n",
" '2017-12-10T00:00:00.000000000' '2017-12-11T00:00:00.000000000'\n",
" '2017-12-12T00:00:00.000000000' '2017-12-13T00:00:00.000000000'\n",
" '2017-12-14T00:00:00.000000000' '2017-12-15T00:00:00.000000000'\n",
" '2017-12-16T00:00:00.000000000' '2017-12-17T00:00:00.000000000'\n",
" '2017-12-18T00:00:00.000000000' '2017-12-19T00:00:00.000000000'\n",
" '2017-12-20T00:00:00.000000000' '2017-12-21T00:00:00.000000000'\n",
" '2017-12-22T00:00:00.000000000' '2017-12-23T00:00:00.000000000'\n",
" '2017-12-24T00:00:00.000000000' '2017-12-25T00:00:00.000000000'\n",
" '2017-12-26T00:00:00.000000000' '2017-12-27T00:00:00.000000000'\n",
" '2017-12-28T00:00:00.000000000' '2017-12-29T00:00:00.000000000'\n",
" '2017-12-30T00:00:00.000000000' '2017-12-31T00:00:00.000000000'\n",
" '2018-01-01T00:00:00.000000000' '2018-01-02T00:00:00.000000000'\n",
" '2018-01-03T00:00:00.000000000' '2018-01-04T00:00:00.000000000'\n",
" '2018-01-05T00:00:00.000000000' '2018-01-06T00:00:00.000000000'\n",
" '2018-01-07T00:00:00.000000000' '2018-01-08T00:00:00.000000000'\n",
" '2018-01-09T00:00:00.000000000' '2018-01-10T00:00:00.000000000'\n",
" '2018-01-11T00:00:00.000000000' '2018-01-12T00:00:00.000000000'\n",
" '2018-01-13T00:00:00.000000000' '2018-01-14T00:00:00.000000000'\n",
" '2018-01-15T00:00:00.000000000' '2018-01-16T00:00:00.000000000'\n",
" '2018-01-17T00:00:00.000000000' '2018-01-18T00:00:00.000000000'\n",
" '2018-01-19T00:00:00.000000000' '2018-01-20T00:00:00.000000000'\n",
" '2018-01-21T00:00:00.000000000' '2018-01-22T00:00:00.000000000'\n",
" '2018-01-23T00:00:00.000000000' '2018-01-24T00:00:00.000000000'\n",
" '2018-01-25T00:00:00.000000000' '2018-01-26T00:00:00.000000000'\n",
" '2018-01-27T00:00:00.000000000' '2018-01-28T00:00:00.000000000'\n",
" '2018-01-29T00:00:00.000000000' '2018-01-30T00:00:00.000000000'\n",
" '2018-01-31T00:00:00.000000000' '2018-02-01T00:00:00.000000000'\n",
" '2018-02-02T00:00:00.000000000' '2018-02-03T00:00:00.000000000'\n",
" '2018-02-04T00:00:00.000000000' '2018-02-05T00:00:00.000000000'\n",
" '2018-02-06T00:00:00.000000000' '2018-02-07T00:00:00.000000000'\n",
" '2018-02-08T00:00:00.000000000' '2018-02-09T00:00:00.000000000'\n",
" '2018-02-10T00:00:00.000000000' '2018-02-11T00:00:00.000000000'\n",
" '2018-02-12T00:00:00.000000000' '2018-02-13T00:00:00.000000000'\n",
" '2018-02-14T00:00:00.000000000' '2018-02-15T00:00:00.000000000'\n",
" '2018-02-16T00:00:00.000000000' '2018-02-17T00:00:00.000000000'\n",
" '2018-02-18T00:00:00.000000000' '2018-02-19T00:00:00.000000000'\n",
" '2018-02-20T00:00:00.000000000' '2018-02-21T00:00:00.000000000'\n",
" '2018-02-22T00:00:00.000000000' '2018-02-23T00:00:00.000000000'\n",
" '2018-02-24T00:00:00.000000000' '2018-02-25T00:00:00.000000000'\n",
" '2018-02-26T00:00:00.000000000' '2018-02-27T00:00:00.000000000'\n",
" '2018-02-28T00:00:00.000000000' '2018-03-01T00:00:00.000000000'\n",
" '2018-03-02T00:00:00.000000000' '2018-03-03T00:00:00.000000000'\n",
" '2018-03-04T00:00:00.000000000' '2018-03-05T00:00:00.000000000'\n",
" '2018-03-06T00:00:00.000000000' '2018-03-07T00:00:00.000000000'\n",
" '2018-03-08T00:00:00.000000000' '2018-03-09T00:00:00.000000000'\n",
" '2018-03-10T00:00:00.000000000' '2018-03-11T00:00:00.000000000'\n",
" '2018-03-12T00:00:00.000000000' '2018-03-13T00:00:00.000000000'\n",
" '2018-03-14T00:00:00.000000000' '2018-03-15T00:00:00.000000000'\n",
" '2018-03-16T00:00:00.000000000' '2018-03-17T00:00:00.000000000'\n",
" '2018-03-18T00:00:00.000000000' '2018-03-19T00:00:00.000000000'\n",
" '2018-03-20T00:00:00.000000000' '2018-03-21T00:00:00.000000000'\n",
" '2018-03-22T00:00:00.000000000' '2018-03-23T00:00:00.000000000'\n",
" '2018-03-24T00:00:00.000000000' '2018-03-25T00:00:00.000000000'\n",
" '2018-03-26T00:00:00.000000000' '2018-03-27T00:00:00.000000000'\n",
" '2018-03-28T00:00:00.000000000' '2018-03-29T00:00:00.000000000'\n",
" '2018-03-30T00:00:00.000000000' '2018-03-31T00:00:00.000000000'\n",
" '2018-04-01T00:00:00.000000000' '2018-04-02T00:00:00.000000000'\n",
" '2018-04-03T00:00:00.000000000' '2018-04-04T00:00:00.000000000'\n",
" '2018-04-05T00:00:00.000000000' '2018-04-06T00:00:00.000000000'\n",
" '2018-04-07T00:00:00.000000000' '2018-04-08T00:00:00.000000000'\n",
" '2018-04-09T00:00:00.000000000' '2018-04-10T00:00:00.000000000'\n",
" '2018-04-11T00:00:00.000000000' '2018-04-12T00:00:00.000000000'\n",
" '2018-04-13T00:00:00.000000000' '2018-04-14T00:00:00.000000000'\n",
" '2018-04-15T00:00:00.000000000' '2018-04-16T00:00:00.000000000'\n",
" '2018-04-17T00:00:00.000000000' '2018-04-18T00:00:00.000000000'\n",
" '2018-04-19T00:00:00.000000000' '2018-04-20T00:00:00.000000000'\n",
" '2018-04-21T00:00:00.000000000' '2018-04-22T00:00:00.000000000'\n",
" '2018-04-23T00:00:00.000000000' '2018-04-24T00:00:00.000000000'\n",
" '2018-04-25T00:00:00.000000000' '2018-04-26T00:00:00.000000000'\n",
" '2018-04-27T00:00:00.000000000' '2018-04-28T00:00:00.000000000'\n",
" '2018-04-29T00:00:00.000000000' '2018-04-30T00:00:00.000000000'\n",
" '2018-05-01T00:00:00.000000000' '2018-05-02T00:00:00.000000000'\n",
" '2018-05-03T00:00:00.000000000' '2018-05-04T00:00:00.000000000'\n",
" '2018-05-05T00:00:00.000000000' '2018-05-06T00:00:00.000000000'\n",
" '2018-05-07T00:00:00.000000000' '2018-05-08T00:00:00.000000000'\n",
" '2018-05-09T00:00:00.000000000' '2018-05-10T00:00:00.000000000'\n",
" '2018-05-11T00:00:00.000000000' '2018-05-12T00:00:00.000000000'\n",
" '2018-05-13T00:00:00.000000000' '2018-05-14T00:00:00.000000000'\n",
" '2018-05-15T00:00:00.000000000' '2018-05-16T00:00:00.000000000'\n",
" '2018-05-17T00:00:00.000000000' '2018-05-18T00:00:00.000000000'\n",
" '2018-05-19T00:00:00.000000000' '2018-05-20T00:00:00.000000000'\n",
" '2018-05-21T00:00:00.000000000' '2018-05-22T00:00:00.000000000'\n",
" '2018-05-23T00:00:00.000000000' '2018-05-24T00:00:00.000000000'\n",
" '2018-05-25T00:00:00.000000000' '2018-05-26T00:00:00.000000000'\n",
" '2018-05-27T00:00:00.000000000' '2018-05-28T00:00:00.000000000'\n",
" '2018-05-29T00:00:00.000000000' '2018-05-30T00:00:00.000000000'\n",
" '2018-05-31T00:00:00.000000000' '2018-06-01T00:00:00.000000000'\n",
" '2018-06-02T00:00:00.000000000' '2018-06-03T00:00:00.000000000'\n",
" '2018-06-04T00:00:00.000000000' '2018-06-05T00:00:00.000000000'\n",
" '2018-06-06T00:00:00.000000000' '2018-06-07T00:00:00.000000000'\n",
" '2018-06-08T00:00:00.000000000' '2018-06-09T00:00:00.000000000'\n",
" '2018-06-10T00:00:00.000000000' '2018-06-11T00:00:00.000000000'\n",
" '2018-06-12T00:00:00.000000000' '2018-06-13T00:00:00.000000000'\n",
" '2018-06-14T00:00:00.000000000' '2018-06-15T00:00:00.000000000'\n",
" '2018-06-16T00:00:00.000000000' '2018-06-17T00:00:00.000000000'\n",
" '2018-06-18T00:00:00.000000000' '2018-06-19T00:00:00.000000000'\n",
" '2018-06-20T00:00:00.000000000' '2018-06-21T00:00:00.000000000'\n",
" '2018-06-22T00:00:00.000000000' '2018-06-23T00:00:00.000000000'\n",
" '2018-06-24T00:00:00.000000000' '2018-06-25T00:00:00.000000000'\n",
" '2018-06-26T00:00:00.000000000' '2018-06-27T00:00:00.000000000'\n",
" '2018-06-28T00:00:00.000000000' '2018-06-29T00:00:00.000000000'\n",
" '2018-06-30T00:00:00.000000000' '2018-07-01T00:00:00.000000000'\n",
" '2018-07-02T00:00:00.000000000' '2018-07-03T00:00:00.000000000'\n",
" '2018-07-04T00:00:00.000000000' '2018-07-05T00:00:00.000000000'\n",
" '2018-07-06T00:00:00.000000000' '2018-07-07T00:00:00.000000000'\n",
" '2018-07-08T00:00:00.000000000' '2018-07-09T00:00:00.000000000'\n",
" '2018-07-10T00:00:00.000000000' '2018-07-11T00:00:00.000000000'\n",
" '2018-07-12T00:00:00.000000000' '2018-07-13T00:00:00.000000000'\n",
" '2018-07-14T00:00:00.000000000' '2018-07-15T00:00:00.000000000'\n",
" '2018-07-16T00:00:00.000000000' '2018-07-17T00:00:00.000000000'\n",
" '2018-07-18T00:00:00.000000000' '2018-07-19T00:00:00.000000000'\n",
" '2018-07-20T00:00:00.000000000' '2018-07-21T00:00:00.000000000'\n",
" '2018-07-22T00:00:00.000000000' '2018-07-23T00:00:00.000000000'\n",
" '2018-07-24T00:00:00.000000000' '2018-07-25T00:00:00.000000000'\n",
" '2018-07-26T00:00:00.000000000' '2018-07-27T00:00:00.000000000'\n",
" '2018-07-28T00:00:00.000000000' '2018-07-29T00:00:00.000000000'\n",
" '2018-07-30T00:00:00.000000000' '2018-07-31T00:00:00.000000000'\n",
" '2018-08-01T00:00:00.000000000' '2018-08-02T00:00:00.000000000'\n",
" '2018-08-03T00:00:00.000000000' '2018-08-04T00:00:00.000000000'\n",
" '2018-08-05T00:00:00.000000000' '2018-08-06T00:00:00.000000000'\n",
" '2018-08-07T00:00:00.000000000' '2018-08-08T00:00:00.000000000'\n",
" '2018-08-09T00:00:00.000000000' '2018-08-10T00:00:00.000000000'\n",
" '2018-08-11T00:00:00.000000000' '2018-08-12T00:00:00.000000000'\n",
" '2018-08-13T00:00:00.000000000' '2018-08-14T00:00:00.000000000'\n",
" '2018-08-15T00:00:00.000000000' '2018-08-16T00:00:00.000000000'\n",
" '2018-08-17T00:00:00.000000000' '2018-08-18T00:00:00.000000000'\n",
" '2018-08-19T00:00:00.000000000' '2018-08-20T00:00:00.000000000'\n",
" '2018-08-21T00:00:00.000000000' '2018-08-22T00:00:00.000000000'\n",
" '2018-08-23T00:00:00.000000000' '2018-08-24T00:00:00.000000000'\n",
" '2018-08-25T00:00:00.000000000' '2018-08-26T00:00:00.000000000'\n",
" '2018-08-27T00:00:00.000000000' '2018-08-28T00:00:00.000000000'\n",
" '2018-08-29T00:00:00.000000000' '2018-08-30T00:00:00.000000000'\n",
" '2018-08-31T00:00:00.000000000' '2018-09-01T00:00:00.000000000'\n",
" '2018-09-02T00:00:00.000000000' '2018-09-03T00:00:00.000000000'\n",
" '2018-09-04T00:00:00.000000000' '2018-09-05T00:00:00.000000000'\n",
" '2018-09-06T00:00:00.000000000' '2018-09-07T00:00:00.000000000'\n",
" '2018-09-08T00:00:00.000000000' '2018-09-09T00:00:00.000000000'\n",
" '2018-09-10T00:00:00.000000000' '2018-09-11T00:00:00.000000000'\n",
" '2018-09-12T00:00:00.000000000' '2018-09-13T00:00:00.000000000'\n",
" '2018-09-14T00:00:00.000000000' '2018-09-15T00:00:00.000000000'\n",
" '2018-09-16T00:00:00.000000000' '2018-09-17T00:00:00.000000000'\n",
" '2018-09-18T00:00:00.000000000' '2018-09-19T00:00:00.000000000'\n",
" '2018-09-20T00:00:00.000000000' '2018-09-21T00:00:00.000000000'\n",
" '2018-09-22T00:00:00.000000000' '2018-09-23T00:00:00.000000000'\n",
" '2018-09-24T00:00:00.000000000' '2018-09-25T00:00:00.000000000'\n",
" '2018-09-26T00:00:00.000000000' '2018-09-27T00:00:00.000000000'\n",
" '2018-09-28T00:00:00.000000000' '2018-09-29T00:00:00.000000000'\n",
" '2018-09-30T00:00:00.000000000' '2018-10-01T00:00:00.000000000'\n",
" '2018-10-02T00:00:00.000000000' '2018-10-03T00:00:00.000000000'\n",
" '2018-10-04T00:00:00.000000000' '2018-10-05T00:00:00.000000000'\n",
" '2018-10-06T00:00:00.000000000' '2018-10-07T00:00:00.000000000'\n",
" '2018-10-08T00:00:00.000000000' '2018-10-09T00:00:00.000000000'\n",
" '2018-10-10T00:00:00.000000000' '2018-10-11T00:00:00.000000000'\n",
" '2018-10-12T00:00:00.000000000' '2018-10-13T00:00:00.000000000'\n",
" '2018-10-14T00:00:00.000000000' '2018-10-15T00:00:00.000000000'\n",
" '2018-10-16T00:00:00.000000000' '2018-10-17T00:00:00.000000000'\n",
" '2018-10-18T00:00:00.000000000' '2018-10-19T00:00:00.000000000'\n",
" '2018-10-20T00:00:00.000000000' '2018-10-21T00:00:00.000000000'\n",
" '2018-10-22T00:00:00.000000000' '2018-10-23T00:00:00.000000000'\n",
" '2018-10-24T00:00:00.000000000' '2018-10-25T00:00:00.000000000'\n",
" '2018-10-26T00:00:00.000000000' '2018-10-27T00:00:00.000000000'\n",
" '2018-10-28T00:00:00.000000000' '2018-10-29T00:00:00.000000000'\n",
" '2018-10-30T00:00:00.000000000' '2018-10-31T00:00:00.000000000'\n",
" '2018-11-01T00:00:00.000000000' '2018-11-02T00:00:00.000000000'\n",
" '2018-11-03T00:00:00.000000000' '2018-11-04T00:00:00.000000000'\n",
" '2018-11-05T00:00:00.000000000' '2018-11-06T00:00:00.000000000'\n",
" '2018-11-07T00:00:00.000000000' '2018-11-08T00:00:00.000000000'\n",
" '2018-11-09T00:00:00.000000000' '2018-11-10T00:00:00.000000000'\n",
" '2018-11-11T00:00:00.000000000' '2018-11-12T00:00:00.000000000'\n",
" '2018-11-13T00:00:00.000000000' '2018-11-14T00:00:00.000000000'\n",
" '2018-11-15T00:00:00.000000000' '2018-11-16T00:00:00.000000000'\n",
" '2018-11-17T00:00:00.000000000' '2018-11-18T00:00:00.000000000'\n",
" '2018-11-19T00:00:00.000000000' '2018-11-20T00:00:00.000000000'\n",
" '2018-11-21T00:00:00.000000000' '2018-11-22T00:00:00.000000000'\n",
" '2018-11-23T00:00:00.000000000' '2018-11-24T00:00:00.000000000'\n",
" '2018-11-25T00:00:00.000000000' '2018-11-26T00:00:00.000000000'\n",
" '2018-11-27T00:00:00.000000000' '2018-11-28T00:00:00.000000000'\n",
" '2018-11-29T00:00:00.000000000' '2018-11-30T00:00:00.000000000'\n",
" '2018-12-01T00:00:00.000000000' '2018-12-02T00:00:00.000000000'\n",
" '2018-12-03T00:00:00.000000000' '2018-12-04T00:00:00.000000000'\n",
" '2018-12-05T00:00:00.000000000' '2018-12-06T00:00:00.000000000'\n",
" '2018-12-07T00:00:00.000000000' '2018-12-08T00:00:00.000000000'\n",
" '2018-12-09T00:00:00.000000000' '2018-12-10T00:00:00.000000000'\n",
" '2018-12-11T00:00:00.000000000' '2018-12-12T00:00:00.000000000'\n",
" '2018-12-13T00:00:00.000000000' '2018-12-14T00:00:00.000000000'\n",
" '2018-12-15T00:00:00.000000000' '2018-12-16T00:00:00.000000000'\n",
" '2018-12-17T00:00:00.000000000' '2018-12-18T00:00:00.000000000'\n",
" '2018-12-19T00:00:00.000000000' '2018-12-20T00:00:00.000000000'\n",
" '2018-12-21T00:00:00.000000000' '2018-12-22T00:00:00.000000000'\n",
" '2018-12-23T00:00:00.000000000' '2018-12-24T00:00:00.000000000'\n",
" '2018-12-25T00:00:00.000000000' '2018-12-26T00:00:00.000000000'\n",
" '2018-12-27T00:00:00.000000000' '2018-12-28T00:00:00.000000000'\n",
" '2018-12-29T00:00:00.000000000' '2018-12-30T00:00:00.000000000'\n",
" '2018-12-31T00:00:00.000000000' '2019-01-01T00:00:00.000000000'\n",
" '2019-01-02T00:00:00.000000000' '2019-01-03T00:00:00.000000000'\n",
" '2019-01-04T00:00:00.000000000' '2019-01-05T00:00:00.000000000'\n",
" '2019-01-06T00:00:00.000000000' '2019-01-07T00:00:00.000000000'\n",
" '2019-01-08T00:00:00.000000000' '2019-01-09T00:00:00.000000000'\n",
" '2019-01-10T00:00:00.000000000' '2019-01-11T00:00:00.000000000'\n",
" '2019-01-12T00:00:00.000000000' '2019-01-13T00:00:00.000000000'\n",
" '2019-01-14T00:00:00.000000000' '2019-01-15T00:00:00.000000000'\n",
" '2019-01-16T00:00:00.000000000' '2019-01-17T00:00:00.000000000'\n",
" '2019-01-18T00:00:00.000000000' '2019-01-19T00:00:00.000000000'\n",
" '2019-01-20T00:00:00.000000000' '2019-01-21T00:00:00.000000000'\n",
" '2019-01-22T00:00:00.000000000' '2019-01-23T00:00:00.000000000'\n",
" '2019-01-24T00:00:00.000000000' '2019-01-25T00:00:00.000000000'\n",
" '2019-01-26T00:00:00.000000000' '2019-01-27T00:00:00.000000000'\n",
" '2019-01-28T00:00:00.000000000' '2019-01-29T00:00:00.000000000'\n",
" '2019-01-30T00:00:00.000000000' '2019-01-31T00:00:00.000000000'] \n",
"\n",
"danger_level_prev1day: [nan 0. 2. 3. 4. 1.] \n",
"\n",
"danger_level_name_prev1day: [nan '2 Moderat' '3 Betydelig' '4 Stor' '1 Liten' '0 Ikke vurdert'] \n",
"\n",
"danger_level_prev2day: [nan 0. 2. 3. 4. 1.] \n",
"\n",
"danger_level_name_prev2day: [nan '2 Moderat' '3 Betydelig' '4 Stor' '1 Liten' '0 Ikke vurdert'] \n",
"\n",
"danger_level_prev3day: [nan 0. 2. 3. 4. 1.] \n",
"\n",
"danger_level_name_prev3day: [nan '2 Moderat' '3 Betydelig' '4 Stor' '1 Liten' '0 Ikke vurdert'] \n",
"\n",
"avalanche_problem_1_cause_id_prev1day: [nan 0. 15. 10. 18. 11. 19. 22. 13. 16. 24. 14. 20.] \n",
"\n",
"avalanche_problem_1_problem_type_id_prev1day: [nan 0. 10. 7. 37. 30. 45. 5. 50. 3.] \n",
"\n",
"avalanche_problem_1_cause_id_prev2day: [nan 0. 15. 10. 18. 11. 19. 22. 13. 16. 24. 14. 20.] \n",
"\n",
"avalanche_problem_1_problem_type_id_prev2day: [nan 0. 10. 7. 37. 30. 45. 5. 50. 3.] \n",
"\n",
"avalanche_problem_1_cause_id_prev3day: [nan 0. 15. 10. 18. 11. 19. 22. 13. 16. 24. 14. 20.] \n",
"\n",
"avalanche_problem_1_problem_type_id_prev3day: [nan 0. 10. 7. 37. 30. 45. 5. 50. 3.] \n",
"\n",
"avalanche_problem_2_cause_id_prev1day: [nan 0. 11. 18. 15. 10. 16. 19. 13. 24. 22. 14. 20.] \n",
"\n",
"avalanche_problem_2_problem_type_id_prev1day: [nan 0. 30. 37. 10. 7. 45. 5. 50. 3.] \n",
"\n",
"avalanche_problem_2_cause_id_prev2day: [nan 0. 11. 18. 15. 10. 16. 19. 13. 24. 22. 14. 20.] \n",
"\n",
"avalanche_problem_2_problem_type_id_prev2day: [nan 0. 30. 37. 10. 7. 45. 5. 50. 3.] \n",
"\n",
"avalanche_problem_2_cause_id_prev3day: [nan 0. 11. 18. 15. 10. 16. 19. 13. 24. 22. 14. 20.] \n",
"\n",
"avalanche_problem_2_problem_type_id_prev3day: [nan 0. 30. 37. 10. 7. 45. 5. 50. 3.] \n",
"\n",
"mountain_weather_temperature_max_prev1day: [ nan -9. -8. -4. -10. -6. -5. -7. -13. -18. -17. -20.\n",
" -16. -15. -11. -3. -2. 0. -14. -19. -1. -12. -21. -22.\n",
" 1. 20. 2. 3. 8. 10. 5. 7. 6. 4. 11. 9.\n",
" 13. 12. 14. 15. 16. 18. 17. 19. 0.9] \n",
"\n",
"mountain_weather_temperature_max_prev2day: [ nan -9. -8. -4. -10. -6. -5. -7. -13. -18. -17. -20.\n",
" -16. -15. -11. -3. -2. 0. -14. -19. -1. -12. -21. -22.\n",
" 1. 20. 2. 3. 8. 10. 5. 7. 6. 4. 11. 9.\n",
" 13. 12. 14. 15. 16. 18. 17. 19. 0.9] \n",
"\n",
"mountain_weather_temperature_max_prev3day: [ nan -9. -8. -4. -10. -6. -5. -7. -13. -18. -17. -20.\n",
" -16. -15. -11. -3. -2. 0. -14. -19. -1. -12. -21. -22.\n",
" 1. 20. 2. 3. 8. 10. 5. 7. 6. 4. 11. 9.\n",
" 13. 12. 14. 15. 16. 18. 17. 19. 0.9] \n",
"\n",
"mountain_weather_temperature_min_prev1day: [ nan -14. -12. -8. -10. -11. -20. -19. -24. -30. -18. -16. -9. -7.\n",
" -13. -23. -17. -22. -15. -4. -5. -2. -21. -25. -32. -3. -6. -1.\n",
" 0. -28. -27. -26. 1. 2. 3. -29. 4. 5. 6. 7. 10. 8.\n",
" 9.] \n",
"\n",
"mountain_weather_temperature_min_prev2day: [ nan -14. -12. -8. -10. -11. -20. -19. -24. -30. -18. -16. -9. -7.\n",
" -13. -23. -17. -22. -15. -4. -5. -2. -21. -25. -32. -3. -6. -1.\n",
" 0. -28. -27. -26. 1. 2. 3. -29. 4. 5. 6. 7. 10. 8.\n",
" 9.] \n",
"\n",
"mountain_weather_temperature_min_prev3day: [ nan -14. -12. -8. -10. -11. -20. -19. -24. -30. -18. -16. -9. -7.\n",
" -13. -23. -17. -22. -15. -4. -5. -2. -21. -25. -32. -3. -6. -1.\n",
" 0. -28. -27. -26. 1. 2. 3. -29. 4. 5. 6. 7. 10. 8.\n",
" 9.] \n",
"\n",
"mountain_weather_precip_region_prev1day: [nan 2. 5. 0. 1. 4. 15. 25. 8. 12. 10. 3. 6. 7. 9. 16. 20. 30.\n",
" 14. 18. 35. 40. 45. 50. 65. 60. 55. 70. 90.] \n",
"\n",
"mountain_weather_precip_most_exposed_prev1day: [ nan 4. 5. 10. 8. 3. 1. 2. 0. 20. 6. 50. 12. 7.\n",
" 15. 13. 14. 9. 18. 25. 40. 35. 16. 30. 17. 22. 11. 45.\n",
" 60. 55. 70. 65. 75. 80. 85. 120. 90. 160. 100.] \n",
"\n",
"mountain_weather_precip_region_prev3daysum: [ nan 9. 7. 5. 0. 1. 3. 2. 4. 15. 19. 21. 8. 6.\n",
" 30. 35. 38. 14. 16. 28. 24. 11. 10. 13. 12. 17. 20. 23.\n",
" 22. 34. 26. 18. 39. 52. 42. 31. 37. 33. 25. 27. 32. 47.\n",
" 55. 29. 45. 40. 36. 46. 61. 48. 53. 60. 65. 44. 50. 41.\n",
" 51. 43. 58. 71. 49. 68. 69. 56. 63. 54. 87. 83. 57. 73.\n",
" 64. 92. 86. 59. 95. 105. 110. 80. 100. 85. 75. 62. 67. 72.\n",
" 112. 104. 94. 74. 88. 140. 130. 115. 76. 122. 165. 135. 109. 90.] \n",
"\n",
"mountain_weather_wind_speed_num: [ 0 4 5 7 8 6 2 9 10] \n",
"\n",
"mountain_weather_wind_direction_num: [0 1 3 4 2 5 6 8 7] \n",
"\n",
"avalanche_problem_1_problem_type_id_class: [0 6 5 7 4 2 3 1] \n",
"\n",
"avalanche_problem_1_sensitivity_id_class: [0 1 3 4 2 6 5] \n",
"\n",
"avalanche_problem_1_trigger_simple_id_class: [0 1 2 3] \n",
"\n",
"avalanche_problem_2_problem_type_id_class: [0 7 6 5 4 2 3 1] \n",
"\n",
"avalanche_problem_2_sensitivity_id_class: [0 1 3 2 6 5 4] \n",
"\n",
"avalanche_problem_2_trigger_simple_id_class: [0 1 2 3] \n",
"\n",
"avalanche_problem_3_problem_type_id_class: [0 7 6 5 2 4 3 1] \n",
"\n",
"avalanche_problem_3_sensitivity_id_class: [0 2 3 1 5 6 4] \n",
"\n",
"avalanche_problem_3_trigger_simple_id_class: [0 1 2 3] \n",
"\n",
"region_group_id: [0 1 2 3 5 7 6] \n",
"\n",
"aval_problem_1_combined: [ 0 6212 6202 5232 5342 6232 6222 7212 6233 5332 7313 7363 7333 7233\n",
" 6262 5231 7223 7253 5233 6333 6332 6352 7133 7123 4352 6132 6253 6363\n",
" 6243 7203 5252 5352 5242 6223 7232 6242 7213 7222 6252 6122 5222 6231\n",
" 4362 7322 7323 7132 5131 5132 4253 4152 2231 2131 6263 5253 5230 4252\n",
" 6230 4354 7153 2252 2232 2251 7332 7263 6112 6221 6131 6121 6353 7353\n",
" 7324 7224 7134 7234 5262 2152 4153 2362 2151 6133 6331 5363 4263 7242\n",
" 7252 7254 4233 2262 2122 4363 6220 7122 7231 6362 7124 7154 4254 4154\n",
" 4134 4353 6200 5122 6211 4133 2121 6111 4232 5333 3253 2361 2352 5221\n",
" 7113 7112 2130 3152 5331 5353 5223 7243 7131 6323 7214 6102 4151 7352\n",
" 3153 6303 6123 6354 4264 2153 2261 232 4463 6322 5152 5133 6152 2332\n",
" 3263 2253 2221 2111 6254 7121 5153 5243 5343 4332 6101 1351 4223 4123\n",
" 1152 6342 2132 4121 5362 4222 2250 3252 1232 2263 5111 4262 5263 2351\n",
" 4132 6343 3132 122 2212 2353 7102 7342 2141 6264 7152 5121] \n",
"\n",
"emergency_warning_Ikke gitt: [0 1] \n",
"\n",
"emergency_warning_Naturlig utløste skred: [0 1] \n",
"\n",
"author_Andreas@nve: [0 1] \n",
"\n",
"author_Eldbjorg@MET: [0 1] \n",
"\n",
"author_Espen Granan: [0 1] \n",
"\n",
"author_EspenN: [0 1] \n",
"\n",
"author_Halvor@NVE: [0 1] \n",
"\n",
"author_HåvardT@met: [0 1] \n",
"\n",
"author_Ida@met: [0 1] \n",
"\n",
"author_Ingrid@NVE: [0 1] \n",
"\n",
"author_John Smits: [0 1] \n",
"\n",
"author_JonasD@ObsKorps: [0 1] \n",
"\n",
"author_Julie@SVV: [0 1] \n",
"\n",
"author_Jørgen@obskorps: [0 1] \n",
"\n",
"author_Karsten@NVE: [0 1] \n",
"\n",
"author_MSA@nortind: [0 1] \n",
"\n",
"author_Matilda@MET: [0 1] \n",
"\n",
"author_Odd-Arne@NVE: [0 1] \n",
"\n",
"author_Ragnar@NVE: [0 1] \n",
"\n",
"author_Ronny@NVE: [0 1] \n",
"\n",
"author_Silje@svv: [0 1] \n",
"\n",
"author_Tommy@NVE: [0 1] \n",
"\n",
"author_ToreV@met: [0 1] \n",
"\n",
"author_anitaaw@met: [0 1] \n",
"\n",
"author_emma@nve: [0 1] \n",
"\n",
"[email protected]: [0 1] \n",
"\n",
"[email protected]: [0 1] \n",
"\n",
"author_jan arild@obskorps: [0 1] \n",
"\n",
"author_jegu@NVE: [0 1] \n",
"\n",
"author_jostein@nve: [0 1] \n",
"\n",
"author_knutinge@svv: [0 1] \n",
"\n",
"author_magnush@met: [0 1] \n",
"\n",
"author_martin@svv: [0 1] \n",
"\n",
"author_ragnhildn@met: [0 1] \n",
"\n",
"author_rue@nve: [0 1] \n",
"\n",
"author_siri@met: [0 1] \n",
"\n",
"author_solveig@NVE: [0 1] \n",
"\n",
"author_torehum@svv: [0 1] \n",
"\n",
"author_torolav@obskorps: [0 1] \n",
"\n",
"mountain_weather_wind_direction_E: [0 1] \n",
"\n",
"mountain_weather_wind_direction_N: [0 1] \n",
"\n",
"mountain_weather_wind_direction_NE: [0 1] \n",
"\n",
"mountain_weather_wind_direction_NW: [0 1] \n",
"\n",
"mountain_weather_wind_direction_None: [0 1] \n",
"\n",
"mountain_weather_wind_direction_Not given: [1 0] \n",
"\n",
"mountain_weather_wind_direction_S: [0 1] \n",
"\n",
"mountain_weather_wind_direction_SE: [0 1] \n",
"\n",
"mountain_weather_wind_direction_SW: [0 1] \n",
"\n",
"mountain_weather_wind_direction_W: [0 1] \n",
"\n"
]
}
],
"source": [
"# Check if there are no weired or missing values.\n",
"for col in varsom_df.columns.values:\n",
" print(f'{col}: {varsom_df[col].unique()} \\n')"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {}
},
"source": [
"Remove variables we know we do not need. In this case mainly because they are redundant like the _avalanche\\_problem\\_1\\_ext\\_name_ and _avalanche\\_problem\\_1\\_ext\\_id_ - in this case we only keep the numeric _id_ variable."
]
},
{
"cell_type": "code",
"execution_count": 256,
"metadata": {
"collapsed": false,
"pycharm": {
"is_executing": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"[0 0\n",
" 1 33\n",
" 2 33\n",
" 3 33\n",
" 4 33\n",
" 5 33\n",
" 6 33\n",
" 7 33\n",
" 8 33\n",
" 9 33\n",
" 10 33\n",
" 11 33\n",
" 12 33\n",
" 13 33\n",
" 14 33\n",
" 15 33\n",
" 16 33\n",
" 17 33\n",
" 18 33\n",
" 19 33\n",
" 20 33\n",
" 21 33\n",
" 22 33\n",
" 23 33\n",
" 24 33\n",
" 25 33\n",
" 26 33\n",
" 27 33\n",
" 28 33\n",
" 29 33\n",
" ..\n",
" 33234 33\n",
" 33235 33\n",
" 33236 33\n",
" 33237 33\n",
" 33238 33\n",
" 33239 33\n",
" 33240 33\n",
" 33241 33\n",
" 33242 33\n",
" 33243 33\n",
" 33244 33\n",
" 33245 33\n",
" 33246 33\n",
" 33247 33\n",
" 33248 33\n",
" 33249 33\n",
" 33250 33\n",
" 33251 33\n",
" 33252 33\n",
" 33253 33\n",
" 33254 33\n",
" 33255 33\n",
" 33256 33\n",
" 33257 33\n",
" 33258 33\n",
" 33259 33\n",
" 33260 33\n",
" 33261 33\n",
" 33262 33\n",
" 33263 33\n",
" Name: utm_zone, Length: 33264, dtype: int64, 0 0\n",
" 1 520332\n",
" 2 520332\n",
" 3 520332\n",
" 4 520332\n",
" 5 520332\n",
" 6 520332\n",
" 7 520332\n",
" 8 520332\n",
" 9 520332\n",
" 10 520332\n",
" 11 520332\n",
" 12 520332\n",
" 13 520332\n",
" 14 520332\n",
" 15 520332\n",
" 16 520332\n",
" 17 520332\n",
" 18 520332\n",
" 19 520332\n",
" 20 520332\n",
" 21 520332\n",
" 22 520332\n",
" 23 520332\n",
" 24 520332\n",
" 25 520332\n",
" 26 520332\n",
" 27 520332\n",
" 28 520332\n",
" 29 520332\n",
" ... \n",
" 33234 131223\n",
" 33235 131223\n",
" 33236 131223\n",
" 33237 131223\n",
" 33238 131223\n",
" 33239 131223\n",
" 33240 131223\n",
" 33241 131223\n",
" 33242 131223\n",
" 33243 131223\n",
" 33244 131223\n",
" 33245 131223\n",
" 33246 131223\n",
" 33247 131223\n",
" 33248 131223\n",
" 33249 131223\n",
" 33250 131223\n",
" 33251 131223\n",
" 33252 131223\n",
" 33253 131223\n",
" 33254 131223\n",
" 33255 131223\n",
" 33256 131223\n",
" 33257 131223\n",
" 33258 131223\n",
" 33259 131223\n",
" 33260 131223\n",
" 33261 131223\n",
" 33262 131223\n",
" 33263 131223\n",
" Name: utm_east, Length: 33264, dtype: int64, 0 0\n",
" 1 8663904\n",
" 2 8663904\n",
" 3 8663904\n",
" 4 8663904\n",
" 5 8663904\n",
" 6 8663904\n",
" 7 8663904\n",
" 8 8663904\n",
" 9 8663904\n",
" 10 8663904\n",
" 11 8663904\n",
" 12 8663904\n",
" 13 8663904\n",
" 14 8663904\n",
" 15 8663904\n",
" 16 8663904\n",
" 17 8663904\n",
" 18 8663904\n",
" 19 8663904\n",
" 20 8663904\n",
" 21 8663904\n",
" 22 8663904\n",
" 23 8663904\n",
" 24 8663904\n",
" 25 8663904\n",
" 26 8663904\n",
" 27 8663904\n",
" 28 8663904\n",
" 29 8663904\n",
" ... \n",
" 33234 6642571\n",
" 33235 6642571\n",
" 33236 6642571\n",
" 33237 6642571\n",
" 33238 6642571\n",
" 33239 6642571\n",
" 33240 6642571\n",
" 33241 6642571\n",
" 33242 6642571\n",
" 33243 6642571\n",
" 33244 6642571\n",
" 33245 6642571\n",
" 33246 6642571\n",
" 33247 6642571\n",
" 33248 6642571\n",
" 33249 6642571\n",
" 33250 6642571\n",
" 33251 6642571\n",
" 33252 6642571\n",
" 33253 6642571\n",
" 33254 6642571\n",
" 33255 6642571\n",
" 33256 6642571\n",
" 33257 6642571\n",
" 33258 6642571\n",
" 33259 6642571\n",
" 33260 6642571\n",
" 33261 6642571\n",
" 33262 6642571\n",
" 33263 6642571\n",
" Name: utm_north, Length: 33264, dtype: int64, 0 NaN\n",
" 1 2 Moderat\n",
" 2 2 Moderat\n",
" 3 2 Moderat\n",
" 4 2 Moderat\n",
" 5 3 Betydelig\n",
" 6 2 Moderat\n",
" 7 2 Moderat\n",
" 8 2 Moderat\n",
" 9 2 Moderat\n",
" 10 2 Moderat\n",
" 11 2 Moderat\n",
" 12 2 Moderat\n",
" 13 2 Moderat\n",
" 14 2 Moderat\n",
" 15 2 Moderat\n",
" 16 2 Moderat\n",
" 17 2 Moderat\n",
" 18 3 Betydelig\n",
" 19 3 Betydelig\n",
" 20 3 Betydelig\n",
" 21 3 Betydelig\n",
" 22 2 Moderat\n",
" 23 2 Moderat\n",
" 24 2 Moderat\n",
" 25 2 Moderat\n",
" 26 2 Moderat\n",
" 27 4 Stor\n",
" 28 4 Stor\n",
" 29 3 Betydelig\n",
" ... \n",
" 33234 2 Moderat\n",
" 33235 2 Moderat\n",
" 33236 2 Moderat\n",
" 33237 1 Liten\n",
" 33238 1 Liten\n",
" 33239 1 Liten\n",
" 33240 1 Liten\n",
" 33241 1 Liten\n",
" 33242 1 Liten\n",
" 33243 1 Liten\n",
" 33244 1 Liten\n",
" 33245 1 Liten\n",
" 33246 2 Moderat\n",
" 33247 2 Moderat\n",
" 33248 2 Moderat\n",
" 33249 2 Moderat\n",
" 33250 2 Moderat\n",
" 33251 2 Moderat\n",
" 33252 2 Moderat\n",
" 33253 2 Moderat\n",
" 33254 2 Moderat\n",
" 33255 1 Liten\n",
" 33256 1 Liten\n",
" 33257 2 Moderat\n",
" 33258 2 Moderat\n",
" 33259 2 Moderat\n",
" 33260 3 Betydelig\n",
" 33261 2 Moderat\n",
" 33262 2 Moderat\n",
" 33263 2 Moderat\n",
" Name: danger_level_name, Length: 33264, dtype: object, 0 0\n",
" 1 1\n",
" 2 1\n",
" 3 1\n",
" 4 1\n",
" 5 1\n",
" 6 1\n",
" 7 1\n",
" 8 1\n",
" 9 1\n",
" 10 1\n",
" 11 1\n",
" 12 1\n",
" 13 1\n",
" 14 1\n",
" 15 1\n",
" 16 1\n",
" 17 1\n",
" 18 1\n",
" 19 1\n",
" 20 1\n",
" 21 1\n",
" 22 1\n",
" 23 1\n",
" 24 1\n",
" 25 1\n",
" 26 1\n",
" 27 2\n",
" 28 2\n",
" 29 2\n",
" ..\n",
" 33234 1\n",
" 33235 1\n",
" 33236 1\n",
" 33237 1\n",
" 33238 1\n",
" 33239 1\n",
" 33240 1\n",
" 33241 1\n",
" 33242 1\n",
" 33243 1\n",
" 33244 1\n",
" 33245 1\n",
" 33246 1\n",
" 33247 1\n",
" 33248 1\n",
" 33249 1\n",
" 33250 1\n",
" 33251 1\n",
" 33252 1\n",
" 33253 1\n",
" 33254 1\n",
" 33255 1\n",
" 33256 1\n",
" 33257 1\n",
" 33258 1\n",
" 33259 1\n",
" 33260 1\n",
" 33261 1\n",
" 33262 1\n",
" 33263 1\n",
" Name: avalanche_problem_1_exposed_height_fill, Length: 33264, dtype: int64, 0 0\n",
" 1 0\n",
" 2 0\n",
" 3 1\n",
" 4 1\n",
" 5 1\n",
" 6 1\n",
" 7 1\n",
" 8 1\n",
" 9 1\n",
" 10 1\n",
" 11 0\n",
" 12 0\n",
" 13 1\n",
" 14 1\n",
" 15 1\n",
" 16 1\n",
" 17 1\n",
" 18 1\n",
" 19 1\n",
" 20 1\n",
" 21 1\n",
" 22 1\n",
" 23 1\n",
" 24 1\n",
" 25 1\n",
" 26 1\n",
" 27 1\n",
" 28 1\n",
" 29 1\n",
" ..\n",
" 33234 0\n",
" 33235 0\n",
" 33236 0\n",
" 33237 0\n",
" 33238 0\n",
" 33239 0\n",
" 33240 0\n",
" 33241 0\n",
" 33242 0\n",
" 33243 0\n",
" 33244 0\n",
" 33245 1\n",
" 33246 1\n",
" 33247 1\n",
" 33248 1\n",
" 33249 1\n",
" 33250 1\n",
" 33251 1\n",
" 33252 1\n",
" 33253 1\n",
" 33254 1\n",
" 33255 1\n",
" 33256 1\n",
" 33257 1\n",
" 33258 1\n",
" 33259 1\n",
" 33260 1\n",
" 33261 1\n",
" 33262 1\n",
" 33263 1\n",
" Name: avalanche_problem_2_exposed_height_fill, Length: 33264, dtype: int64, 0 0\n",
" 1 0\n",
" 2 0\n",
" 3 0\n",
" 4 0\n",
" 5 0\n",
" 6 0\n",
" 7 0\n",
" 8 0\n",
" 9 0\n",
" 10 0\n",
" 11 0\n",
" 12 0\n",
" 13 0\n",
" 14 0\n",
" 15 0\n",
" 16 0\n",
" 17 0\n",
" 18 0\n",
" 19 0\n",
" 20 0\n",
" 21 0\n",
" 22 0\n",
" 23 0\n",
" 24 0\n",
" 25 0\n",
" 26 0\n",
" 27 0\n",
" 28 0\n",
" 29 0\n",
" ..\n",
" 33234 0\n",
" 33235 0\n",
" 33236 0\n",
" 33237 0\n",
" 33238 0\n",
" 33239 0\n",
" 33240 0\n",
" 33241 0\n",
" 33242 0\n",
" 33243 0\n",
" 33244 0\n",
" 33245 0\n",
" 33246 0\n",
" 33247 0\n",
" 33248 0\n",
" 33249 0\n",
" 33250 0\n",
" 33251 0\n",
" 33252 0\n",
" 33253 0\n",
" 33254 0\n",
" 33255 0\n",
" 33256 0\n",
" 33257 0\n",
" 33258 0\n",
" 33259 0\n",
" 33260 0\n",
" 33261 0\n",
" 33262 0\n",
" 33263 0\n",
" Name: avalanche_problem_3_exposed_height_fill, Length: 33264, dtype: int64, 0 0\n",
" 1 10001111\n",
" 2 10001111\n",
" 3 10000111\n",
" 4 10001111\n",
" 5 10001111\n",
" 6 111110\n",
" 7 111110\n",
" 8 111110\n",
" 9 111110\n",
" 10 111110\n",
" 11 111110\n",
" 12 11111111\n",
" 13 11000111\n",
" 14 11000111\n",
" 15 11000111\n",
" 16 11000111\n",
" 17 11111001\n",
" 18 11110001\n",
" 19 11110001\n",
" 20 11000011\n",
" 21 11100011\n",
" 22 11000011\n",
" 23 11000011\n",
" 24 10000011\n",
" 25 11111111\n",
" 26 11111111\n",
" 27 11000111\n",
" 28 11000111\n",
" 29 11000111\n",
" ... \n",
" 33234 11111100\n",
" 33235 11111100\n",
" 33236 11111100\n",
" 33237 1111100\n",
" 33238 1111100\n",
" 33239 11111111\n",
" 33240 111110\n",
" 33241 111110\n",
" 33242 111110\n",
" 33243 111100\n",
" 33244 111100\n",
" 33245 1111100\n",
" 33246 1111100\n",
" 33247 1111100\n",
" 33248 1111100\n",
" 33249 1111000\n",
" 33250 1111000\n",
" 33251 1111000\n",
" 33252 1111000\n",
" 33253 1111000\n",
" 33254 11110000\n",
" 33255 11110000\n",
" 33256 11111000\n",
" 33257 10000011\n",
" 33258 10000011\n",
" 33259 10000111\n",
" 33260 11000111\n",
" 33261 11000111\n",
" 33262 11000111\n",
" 33263 11000111\n",
" Name: avalanche_problem_1_valid_expositions, Length: 33264, dtype: int64, 0 0\n",
" 1 0\n",
" 2 0\n",
" 3 10001111\n",
" 4 10001111\n",
" 5 10001111\n",
" 6 10001111\n",
" 7 11111111\n",
" 8 11111111\n",
" 9 11111111\n",
" 10 11111111\n",
" 11 0\n",
" 12 0\n",
" 13 11111111\n",
" 14 11111111\n",
" 15 10001111\n",
" 16 111\n",
" 17 111\n",
" 18 111\n",
" 19 11111111\n",
" 20 11111111\n",
" 21 11111111\n",
" 22 11111111\n",
" 23 11111111\n",
" 24 11111111\n",
" 25 10000011\n",
" 26 10000011\n",
" 27 10000011\n",
" 28 10000011\n",
" 29 10000011\n",
" ... \n",
" 33234 0\n",
" 33235 0\n",
" 33236 0\n",
" 33237 0\n",
" 33238 0\n",
" 33239 0\n",
" 33240 0\n",
" 33241 0\n",
" 33242 0\n",
" 33243 0\n",
" 33244 0\n",
" 33245 1111100\n",
" 33246 1111100\n",
" 33247 1111100\n",
" 33248 11111111\n",
" 33249 11111111\n",
" 33250 11111111\n",
" 33251 11111111\n",
" 33252 11111111\n",
" 33253 11111111\n",
" 33254 11111111\n",
" 33255 11111111\n",
" 33256 11111111\n",
" 33257 11111111\n",
" 33258 11111111\n",
" 33259 11111111\n",
" 33260 11111111\n",
" 33261 11111111\n",
" 33262 11111111\n",
" 33263 11111111\n",
" Name: avalanche_problem_2_valid_expositions, Length: 33264, dtype: int64, 0 0\n",
" 1 0\n",
" 2 0\n",
" 3 0\n",
" 4 0\n",
" 5 0\n",
" 6 0\n",
" 7 0\n",
" 8 0\n",
" 9 0\n",
" 10 0\n",
" 11 0\n",
" 12 0\n",
" 13 0\n",
" 14 0\n",
" 15 0\n",
" 16 0\n",
" 17 0\n",
" 18 0\n",
" 19 0\n",
" 20 0\n",
" 21 0\n",
" 22 0\n",
" 23 0\n",
" 24 0\n",
" 25 0\n",
" 26 0\n",
" 27 0\n",
" 28 0\n",
" 29 0\n",
" ..\n",
" 33234 0\n",
" 33235 0\n",
" 33236 0\n",
" 33237 0\n",
" 33238 0\n",
" 33239 0\n",
" 33240 0\n",
" 33241 0\n",
" 33242 0\n",
" 33243 0\n",
" 33244 0\n",
" 33245 0\n",
" 33246 0\n",
" 33247 0\n",
" 33248 0\n",
" 33249 0\n",
" 33250 0\n",
" 33251 0\n",
" 33252 0\n",
" 33253 0\n",
" 33254 0\n",
" 33255 0\n",
" 33256 0\n",
" 33257 0\n",
" 33258 0\n",
" 33259 0\n",
" 33260 0\n",
" 33261 0\n",
" 33262 0\n",
" 33263 0\n",
" Name: avalanche_problem_3_valid_expositions, Length: 33264, dtype: int64, 0 Not given\n",
" 1 Dårlig binding mellom lag i fokksnøen\n",
" 2 Dårlig binding mellom lag i fokksnøen\n",
" 3 Dårlig binding mellom lag i fokksnøen\n",
" 4 Nedføyket svakt lag med nysnø\n",
" 5 Nedføyket svakt lag med nysnø\n",
" 6 Nedføyket svakt lag med nysnø\n",
" 7 Nedføyket svakt lag med nysnø\n",
" 8 Dårlig binding mellom lag i fokksnøen\n",
" 9 Dårlig binding mellom lag i fokksnøen\n",
" 10 Dårlig binding mellom lag i fokksnøen\n",
" 11 Dårlig binding mellom lag i fokksnøen\n",
" 12 Kantkornet snø over skarelag\n",
" 13 Nedføyket svakt lag med nysnø\n",
" 14 Nedføyket svakt lag med nysnø\n",
" 15 Nedføyket svakt lag med nysnø\n",
" 16 Nedføyket svakt lag med nysnø\n",
" 17 Nedføyket svakt lag med nysnø\n",
" 18 Nedføyket svakt lag med nysnø\n",
" 19 Nedføyket svakt lag med nysnø\n",
" 20 Dårlig binding mellom lag i fokksnøen\n",
" 21 Dårlig binding mellom lag i fokksnøen\n",
" 22 Dårlig binding mellom lag i fokksnøen\n",
" 23 Dårlig binding mellom lag i fokksnøen\n",
" 24 Dårlig binding mellom lag i fokksnøen\n",
" 25 Kantkornet snø over skarelag\n",
" 26 Kantkornet snø over skarelag\n",
" 27 Kantkornet snø over skarelag\n",
" 28 Kantkornet snø over skarelag\n",
" 29 Kantkornet snø over skarelag\n",
" ... \n",
" 33234 Nedføyket svakt lag med nysnø\n",
" 33235 Nedføyket svakt lag med nysnø\n",
" 33236 Opphopning av vann i/over lag i snødekket\n",
" 33237 Nedføyket svakt lag med nysnø\n",
" 33238 Nedføyket svakt lag med nysnø\n",
" 33239 Nedføyket svakt lag med nysnø\n",
" 33240 Nedføyket svakt lag med nysnø\n",
" 33241 Nedføyket svakt lag med nysnø\n",
" 33242 Nedføyket svakt lag med nysnø\n",
" 33243 Nedføyket svakt lag med nysnø\n",
" 33244 Nedføyket svakt lag med nysnø\n",
" 33245 Kantkornet snø over skarelag\n",
" 33246 Kantkornet snø over skarelag\n",
" 33247 Kantkornet snø over skarelag\n",
" 33248 Dårlig binding mellom lag i fokksnøen\n",
" 33249 Dårlig binding mellom lag i fokksnøen\n",
" 33250 Dårlig binding mellom lag i fokksnøen\n",
" 33251 Dårlig binding mellom lag i fokksnøen\n",
" 33252 Dårlig binding mellom lag i fokksnøen\n",
" 33253 Dårlig binding mellom lag i fokksnøen\n",
" 33254 Dårlig binding mellom lag i fokksnøen\n",
" 33255 Dårlig binding mellom lag i fokksnøen\n",
" 33256 Dårlig binding mellom lag i fokksnøen\n",
" 33257 Nedføyket svakt lag med nysnø\n",
" 33258 Nedføyket svakt lag med nysnø\n",
" 33259 Nedføyket svakt lag med nysnø\n",
" 33260 Nedføyket svakt lag med nysnø\n",
" 33261 Nedføyket svakt lag med nysnø\n",
" 33262 Nedføyket svakt lag med nysnø\n",
" 33263 Nedføyket svakt lag med nysnø\n",
" Name: avalanche_problem_1_cause_name, Length: 33264, dtype: object, 0 Not given\n",
" 1 Fokksnø (flakskred)\n",
" 2 Fokksnø (flakskred)\n",
" 3 Fokksnø (flakskred)\n",
" 4 Nysnø (flakskred)\n",
" 5 Nysnø (flakskred)\n",
" 6 Nysnø (flakskred)\n",
" 7 Nysnø (flakskred)\n",
" 8 Fokksnø (flakskred)\n",
" 9 Fokksnø (flakskred)\n",
" 10 Fokksnø (flakskred)\n",
" 11 Fokksnø (flakskred)\n",
" 12 Dypt vedvarende svakt lag\n",
" 13 Fokksnø (flakskred)\n",
" 14 Fokksnø (flakskred)\n",
" 15 Fokksnø (flakskred)\n",
" 16 Fokksnø (flakskred)\n",
" 17 Fokksnø (flakskred)\n",
" 18 Fokksnø (flakskred)\n",
" 19 Nysnø (flakskred)\n",
" 20 Fokksnø (flakskred)\n",
" 21 Fokksnø (flakskred)\n",
" 22 Fokksnø (flakskred)\n",
" 23 Fokksnø (flakskred)\n",
" 24 Fokksnø (flakskred)\n",
" 25 Dypt vedvarende svakt lag\n",
" 26 Dypt vedvarende svakt lag\n",
" 27 Dypt vedvarende svakt lag\n",
" 28 Dypt vedvarende svakt lag\n",
" 29 Dypt vedvarende svakt lag\n",
" ... \n",
" 33234 Fokksnø (flakskred)\n",
" 33235 Fokksnø (flakskred)\n",
" 33236 Våt snø (flakskred)\n",
" 33237 Fokksnø (flakskred)\n",
" 33238 Fokksnø (flakskred)\n",
" 33239 Nysnø (flakskred)\n",
" 33240 Nysnø (flakskred)\n",
" 33241 Nysnø (flakskred)\n",
" 33242 Nysnø (flakskred)\n",
" 33243 Fokksnø (flakskred)\n",
" 33244 Fokksnø (flakskred)\n",
" 33245 Vedvarende svakt lag (flakskred)\n",
" 33246 Vedvarende svakt lag (flakskred)\n",
" 33247 Vedvarende svakt lag (flakskred)\n",
" 33248 Fokksnø (flakskred)\n",
" 33249 Fokksnø (flakskred)\n",
" 33250 Fokksnø (flakskred)\n",
" 33251 Fokksnø (flakskred)\n",
" 33252 Fokksnø (flakskred)\n",
" 33253 Fokksnø (flakskred)\n",
" 33254 Fokksnø (flakskred)\n",
" 33255 Fokksnø (flakskred)\n",
" 33256 Fokksnø (flakskred)\n",
" 33257 Nysnø (flakskred)\n",
" 33258 Nysnø (flakskred)\n",
" 33259 Nysnø (flakskred)\n",
" 33260 Nysnø (flakskred)\n",
" 33261 Nysnø (flakskred)\n",
" 33262 Nysnø (flakskred)\n",
" 33263 Nysnø (flakskred)\n",
" Name: avalanche_problem_1_problem_type_name, Length: 33264, dtype: object, 0 Not given\n",
" 1 2 - Middels\n",
" 2 2 - Middels\n",
" 3 2 - Middels\n",
" 4 2 - Middels\n",
" 5 2 - Middels\n",
" 6 2 - Middels\n",
" 7 2 - Middels\n",
" 8 2 - Middels\n",
" 9 2 - Middels\n",
" 10 2 - Middels\n",
" 11 2 - Middels\n",
" 12 2 - Middels\n",
" 13 2 - Middels\n",
" 14 2 - Middels\n",
" 15 2 - Middels\n",
" 16 2 - Middels\n",
" 17 2 - Middels\n",
" 18 3 - Store\n",
" 19 2 - Middels\n",
" 20 3 - Store\n",
" 21 3 - Store\n",
" 22 2 - Middels\n",
" 23 2 - Middels\n",
" 24 2 - Middels\n",
" 25 3 - Store\n",
" 26 3 - Store\n",
" 27 3 - Store\n",
" 28 3 - Store\n",
" 29 3 - Store\n",
" ... \n",
" 33234 2 - Middels\n",
" 33235 2 - Middels\n",
" 33236 2 - Middels\n",
" 33237 2 - Middels\n",
" 33238 2 - Middels\n",
" 33239 1 - Små\n",
" 33240 1 - Små\n",
" 33241 1 - Små\n",
" 33242 1 - Små\n",
" 33243 1 - Små\n",
" 33244 1 - Små\n",
" 33245 2 - Middels\n",
" 33246 2 - Middels\n",
" 33247 2 - Middels\n",
" 33248 2 - Middels\n",
" 33249 2 - Middels\n",
" 33250 2 - Middels\n",
" 33251 2 - Middels\n",
" 33252 2 - Middels\n",
" 33253 2 - Middels\n",
" 33254 2 - Middels\n",
" 33255 2 - Middels\n",
" 33256 2 - Middels\n",
" 33257 2 - Middels\n",
" 33258 2 - Middels\n",
" 33259 2 - Middels\n",
" 33260 2 - Middels\n",
" 33261 2 - Middels\n",
" 33262 2 - Middels\n",
" 33263 2 - Middels\n",
" Name: avalanche_problem_1_destructive_size_ext_name, Length: 33264, dtype: object, 0 Not given\n",
" 1 Noen bratte heng\n",
" 2 Noen bratte heng\n",
" 3 Noen bratte heng\n",
" 4 Noen bratte heng\n",
" 5 Mange bratte heng\n",
" 6 Noen bratte heng\n",
" 7 Noen bratte heng\n",
" 8 Noen bratte heng\n",
" 9 Noen bratte heng\n",
" 10 Noen bratte heng\n",
" 11 Noen bratte heng\n",
" 12 Noen bratte heng\n",
" 13 Noen bratte heng\n",
" 14 Noen bratte heng\n",
" 15 Noen bratte heng\n",
" 16 Noen bratte heng\n",
" 17 Noen bratte heng\n",
" 18 Noen bratte heng\n",
" 19 Mange bratte heng\n",
" 20 Noen bratte heng\n",
" 21 Noen bratte heng\n",
" 22 Noen bratte heng\n",
" 23 Noen bratte heng\n",
" 24 Noen bratte heng\n",
" 25 Mange bratte heng\n",
" 26 Mange bratte heng\n",
" 27 Mange bratte heng\n",
" 28 Mange bratte heng\n",
" 29 Mange bratte heng\n",
" ... \n",
" 33234 Noen bratte heng\n",
" 33235 Noen bratte heng\n",
" 33236 Noen bratte heng\n",
" 33237 Få bratte heng\n",
" 33238 Få bratte heng\n",
" 33239 Få bratte heng\n",
" 33240 Noen bratte heng\n",
" 33241 Noen bratte heng\n",
" 33242 Noen bratte heng\n",
" 33243 Noen bratte heng\n",
" 33244 Noen bratte heng\n",
" 33245 Få bratte heng\n",
" 33246 Få bratte heng\n",
" 33247 Få bratte heng\n",
" 33248 Noen bratte heng\n",
" 33249 Noen bratte heng\n",
" 33250 Noen bratte heng\n",
" 33251 Noen bratte heng\n",
" 33252 Noen bratte heng\n",
" 33253 Noen bratte heng\n",
" 33254 Noen bratte heng\n",
" 33255 Få bratte heng\n",
" 33256 Få bratte heng\n",
" 33257 Noen bratte heng\n",
" 33258 Noen bratte heng\n",
" 33259 Noen bratte heng\n",
" 33260 Mange bratte heng\n",
" 33261 Noen bratte heng\n",
" 33262 Noen bratte heng\n",
" 33263 Noen bratte heng\n",
" Name: avalanche_problem_1_distribution_name, Length: 33264, dtype: object, 0 Not given\n",
" 1 Tørre flakskred\n",
" 2 Tørre flakskred\n",
" 3 Tørre flakskred\n",
" 4 Tørre flakskred\n",
" 5 Tørre flakskred\n",
" 6 Tørre flakskred\n",
" 7 Tørre flakskred\n",
" 8 Tørre flakskred\n",
" 9 Tørre flakskred\n",
" 10 Tørre flakskred\n",
" 11 Tørre flakskred\n",
" 12 Tørre flakskred\n",
" 13 Tørre flakskred\n",
" 14 Tørre flakskred\n",
" 15 Tørre flakskred\n",
" 16 Tørre flakskred\n",
" 17 Tørre flakskred\n",
" 18 Tørre flakskred\n",
" 19 Tørre flakskred\n",
" 20 Tørre flakskred\n",
" 21 Tørre flakskred\n",
" 22 Tørre flakskred\n",
" 23 Tørre flakskred\n",
" 24 Tørre flakskred\n",
" 25 Tørre flakskred\n",
" 26 Tørre flakskred\n",
" 27 Tørre flakskred\n",
" 28 Tørre flakskred\n",
" 29 Tørre flakskred\n",
" ... \n",
" 33234 Tørre flakskred\n",
" 33235 Tørre flakskred\n",
" 33236 Våte flakskred\n",
" 33237 Tørre flakskred\n",
" 33238 Tørre flakskred\n",
" 33239 Tørre flakskred\n",
" 33240 Tørre flakskred\n",
" 33241 Tørre flakskred\n",
" 33242 Tørre flakskred\n",
" 33243 Tørre flakskred\n",
" 33244 Tørre flakskred\n",
" 33245 Tørre flakskred\n",
" 33246 Tørre flakskred\n",
" 33247 Tørre flakskred\n",
" 33248 Tørre flakskred\n",
" 33249 Tørre flakskred\n",
" 33250 Tørre flakskred\n",
" 33251 Tørre flakskred\n",
" 33252 Tørre flakskred\n",
" 33253 Tørre flakskred\n",
" 33254 Tørre flakskred\n",
" 33255 Tørre flakskred\n",
" 33256 Tørre flakskred\n",
" 33257 Tørre flakskred\n",
" 33258 Tørre flakskred\n",
" 33259 Tørre flakskred\n",
" 33260 Tørre flakskred\n",
" 33261 Tørre flakskred\n",
" 33262 Tørre flakskred\n",
" 33263 Tørre flakskred\n",
" Name: avalanche_problem_1_ext_name, Length: 33264, dtype: object, 0 Not given\n",
" 1 Lite sannsynlig \n",
" 2 Lite sannsynlig \n",
" 3 Lite sannsynlig \n",
" 4 Mulig \n",
" 5 Sannsynlig \n",
" 6 Mulig \n",
" 7 Mulig \n",
" 8 Mulig \n",
" 9 Mulig \n",
" 10 Mulig \n",
" 11 Mulig \n",
" 12 Lite sannsynlig \n",
" 13 Mulig \n",
" 14 Mulig \n",
" 15 Mulig \n",
" 16 Mulig \n",
" 17 Mulig \n",
" 18 Mulig \n",
" 19 Mulig \n",
" 20 Mulig \n",
" 21 Mulig \n",
" 22 Mulig \n",
" 23 Mulig \n",
" 24 Lite sannsynlig \n",
" 25 Lite sannsynlig \n",
" 26 Lite sannsynlig \n",
" 27 Sannsynlig \n",
" 28 Sannsynlig \n",
" 29 Mulig \n",
" ... \n",
" 33234 Mulig \n",
" 33235 Mulig \n",
" 33236 Mulig \n",
" 33237 Mulig \n",
" 33238 Mulig \n",
" 33239 Mulig \n",
" 33240 Mulig \n",
" 33241 Mulig \n",
" 33242 Mulig \n",
" 33243 Mulig \n",
" 33244 Mulig \n",
" 33245 Mulig \n",
" 33246 Mulig \n",
" 33247 Mulig \n",
" 33248 Mulig \n",
" 33249 Mulig \n",
" 33250 Mulig \n",
" 33251 Mulig \n",
" 33252 Mulig \n",
" 33253 Mulig \n",
" 33254 Mulig \n",
" 33255 Mulig \n",
" 33256 Mulig \n",
" 33257 Mulig \n",
" 33258 Mulig \n",
" 33259 Mulig \n",
" 33260 Mulig \n",
" 33261 Mulig \n",
" 33262 Mulig \n",
" 33263 Mulig \n",
" Name: avalanche_problem_1_probability_name, Length: 33264, dtype: object, 0 Not given\n",
" 1 Stor tilleggsbelastning\n",
" 2 Stor tilleggsbelastning\n",
" 3 Liten tilleggsbelastning\n",
" 4 Liten tilleggsbelastning\n",
" 5 Liten tilleggsbelastning\n",
" 6 Liten tilleggsbelastning\n",
" 7 Liten tilleggsbelastning\n",
" 8 Liten tilleggsbelastning\n",
" 9 Liten tilleggsbelastning\n",
" 10 Stor tilleggsbelastning\n",
" 11 Stor tilleggsbelastning\n",
" 12 Stor tilleggsbelastning\n",
" 13 Liten tilleggsbelastning\n",
" 14 Liten tilleggsbelastning\n",
" 15 Liten tilleggsbelastning\n",
" 16 Liten tilleggsbelastning\n",
" 17 Liten tilleggsbelastning\n",
" 18 Liten tilleggsbelastning\n",
" 19 Liten tilleggsbelastning\n",
" 20 Liten tilleggsbelastning\n",
" 21 Liten tilleggsbelastning\n",
" 22 Liten tilleggsbelastning\n",
" 23 Stor tilleggsbelastning\n",
" 24 Stor tilleggsbelastning\n",
" 25 Stor tilleggsbelastning\n",
" 26 Stor tilleggsbelastning\n",
" 27 Naturlig utløst\n",
" 28 Naturlig utløst\n",
" 29 Liten tilleggsbelastning\n",
" ... \n",
" 33234 Liten tilleggsbelastning\n",
" 33235 Stor tilleggsbelastning\n",
" 33236 Stor tilleggsbelastning\n",
" 33237 Stor tilleggsbelastning\n",
" 33238 Stor tilleggsbelastning\n",
" 33239 Stor tilleggsbelastning\n",
" 33240 Liten tilleggsbelastning\n",
" 33241 Liten tilleggsbelastning\n",
" 33242 Liten tilleggsbelastning\n",
" 33243 Liten tilleggsbelastning\n",
" 33244 Liten tilleggsbelastning\n",
" 33245 Stor tilleggsbelastning\n",
" 33246 Stor tilleggsbelastning\n",
" 33247 Stor tilleggsbelastning\n",
" 33248 Liten tilleggsbelastning\n",
" 33249 Liten tilleggsbelastning\n",
" 33250 Liten tilleggsbelastning\n",
" 33251 Stor tilleggsbelastning\n",
" 33252 Stor tilleggsbelastning\n",
" 33253 Stor tilleggsbelastning\n",
" 33254 Stor tilleggsbelastning\n",
" 33255 Stor tilleggsbelastning\n",
" 33256 Stor tilleggsbelastning\n",
" 33257 Liten tilleggsbelastning\n",
" 33258 Liten tilleggsbelastning\n",
" 33259 Liten tilleggsbelastning\n",
" 33260 Liten tilleggsbelastning\n",
" 33261 Liten tilleggsbelastning\n",
" 33262 Liten tilleggsbelastning\n",
" 33263 Liten tilleggsbelastning\n",
" Name: avalanche_problem_1_trigger_simple_name, Length: 33264, dtype: object, 0 Not given\n",
" 1 Flakskred\n",
" 2 Flakskred\n",
" 3 Flakskred\n",
" 4 Flakskred\n",
" 5 Flakskred\n",
" 6 Flakskred\n",
" 7 Flakskred\n",
" 8 Flakskred\n",
" 9 Flakskred\n",
" 10 Flakskred\n",
" 11 Flakskred\n",
" 12 Flakskred\n",
" 13 Flakskred\n",
" 14 Flakskred\n",
" 15 Flakskred\n",
" 16 Flakskred\n",
" 17 Flakskred\n",
" 18 Flakskred\n",
" 19 Flakskred\n",
" 20 Flakskred\n",
" 21 Flakskred\n",
" 22 Flakskred\n",
" 23 Flakskred\n",
" 24 Flakskred\n",
" 25 Flakskred\n",
" 26 Flakskred\n",
" 27 Flakskred\n",
" 28 Flakskred\n",
" 29 Flakskred\n",
" ... \n",
" 33234 Flakskred\n",
" 33235 Flakskred\n",
" 33236 Flakskred\n",
" 33237 Flakskred\n",
" 33238 Flakskred\n",
" 33239 Flakskred\n",
" 33240 Flakskred\n",
" 33241 Flakskred\n",
" 33242 Flakskred\n",
" 33243 Flakskred\n",
" 33244 Flakskred\n",
" 33245 Flakskred\n",
" 33246 Flakskred\n",
" 33247 Flakskred\n",
" 33248 Flakskred\n",
" 33249 Flakskred\n",
" 33250 Flakskred\n",
" 33251 Flakskred\n",
" 33252 Flakskred\n",
" 33253 Flakskred\n",
" 33254 Flakskred\n",
" 33255 Flakskred\n",
" 33256 Flakskred\n",
" 33257 Flakskred\n",
" 33258 Flakskred\n",
" 33259 Flakskred\n",
" 33260 Flakskred\n",
" 33261 Flakskred\n",
" 33262 Flakskred\n",
" 33263 Flakskred\n",
" Name: avalanche_problem_1_type_name, Length: 33264, dtype: object, 0 Not given\n",
" 1 Not given\n",
" 2 Not given\n",
" 3 Nedsnødd eller nedføyket overflaterim\n",
" 4 Nedsnødd eller nedføyket overflaterim\n",
" 5 Nedsnødd eller nedføyket overflaterim\n",
" 6 Nedsnødd eller nedføyket overflaterim\n",
" 7 Nedsnødd eller nedføyket overflaterim\n",
" 8 Nedsnødd eller nedføyket overflaterim\n",
" 9 Nedsnødd eller nedføyket overflaterim\n",
" 10 Nedsnødd eller nedføyket overflaterim\n",
" 11 Not given\n",
" 12 Not given\n",
" 13 Kantkornet snø over skarelag\n",
" 14 Kantkornet snø over skarelag\n",
" 15 Kantkornet snø over skarelag\n",
" 16 Kantkornet snø over skarelag\n",
" 17 Kantkornet snø over skarelag\n",
" 18 Kantkornet snø over skarelag\n",
" 19 Kantkornet snø over skarelag\n",
" 20 Kantkornet snø over skarelag\n",
" 21 Kantkornet snø over skarelag\n",
" 22 Kantkornet snø over skarelag\n",
" 23 Kantkornet snø over skarelag\n",
" 24 Kantkornet snø over skarelag\n",
" 25 Dårlig binding mellom lag i fokksnøen\n",
" 26 Dårlig binding mellom lag i fokksnøen\n",
" 27 Dårlig binding mellom lag i fokksnøen\n",
" 28 Dårlig binding mellom lag i fokksnøen\n",
" 29 Dårlig binding mellom lag i fokksnøen\n",
" ... \n",
" 33234 Not given\n",
" 33235 Not given\n",
" 33236 Not given\n",
" 33237 Not given\n",
" 33238 Not given\n",
" 33239 Not given\n",
" 33240 Not given\n",
" 33241 Not given\n",
" 33242 Not given\n",
" 33243 Not given\n",
" 33244 Not given\n",
" 33245 Dårlig binding mellom lag i fokksnøen\n",
" 33246 Dårlig binding mellom lag i fokksnøen\n",
" 33247 Dårlig binding mellom lag i fokksnøen\n",
" 33248 Kantkornet snø over skarelag\n",
" 33249 Kantkornet snø under skarelag\n",
" 33250 Kantkornet snø under skarelag\n",
" 33251 Kantkornet snø under skarelag\n",
" 33252 Kantkornet snø under skarelag\n",
" 33253 Kantkornet snø under skarelag\n",
" 33254 Kantkornet snø under skarelag\n",
" 33255 Kantkornet snø under skarelag\n",
" 33256 Kantkornet snø under skarelag\n",
" 33257 Kantkornet snø under skarelag\n",
" 33258 Kantkornet snø under skarelag\n",
" 33259 Kantkornet snø under skarelag\n",
" 33260 Kantkornet snø under skarelag\n",
" 33261 Kantkornet snø under skarelag\n",
" 33262 Kantkornet snø under skarelag\n",
" 33263 Kantkornet snø under skarelag\n",
" Name: avalanche_problem_2_cause_name, Length: 33264, dtype: object, 0 Not given\n",
" 1 Not given\n",
" 2 Not given\n",
" 3 Vedvarende svakt lag (flakskred)\n",
" 4 Vedvarende svakt lag (flakskred)\n",
" 5 Vedvarende svakt lag (flakskred)\n",
" 6 Vedvarende svakt lag (flakskred)\n",
" 7 Vedvarende svakt lag (flakskred)\n",
" 8 Vedvarende svakt lag (flakskred)\n",
" 9 Vedvarende svakt lag (flakskred)\n",
" 10 Vedvarende svakt lag (flakskred)\n",
" 11 Not given\n",
" 12 Not given\n",
" 13 Dypt vedvarende svakt lag\n",
" 14 Dypt vedvarende svakt lag\n",
" 15 Dypt vedvarende svakt lag\n",
" 16 Dypt vedvarende svakt lag\n",
" 17 Dypt vedvarende svakt lag\n",
" 18 Dypt vedvarende svakt lag\n",
" 19 Dypt vedvarende svakt lag\n",
" 20 Dypt vedvarende svakt lag\n",
" 21 Dypt vedvarende svakt lag\n",
" 22 Dypt vedvarende svakt lag\n",
" 23 Dypt vedvarende svakt lag\n",
" 24 Dypt vedvarende svakt lag\n",
" 25 Fokksnø (flakskred)\n",
" 26 Fokksnø (flakskred)\n",
" 27 Fokksnø (flakskred)\n",
" 28 Fokksnø (flakskred)\n",
" 29 Fokksnø (flakskred)\n",
" ... \n",
" 33234 Not given\n",
" 33235 Not given\n",
" 33236 Not given\n",
" 33237 Not given\n",
" 33238 Not given\n",
" 33239 Not given\n",
" 33240 Not given\n",
" 33241 Not given\n",
" 33242 Not given\n",
" 33243 Not given\n",
" 33244 Not given\n",
" 33245 Fokksnø (flakskred)\n",
" 33246 Fokksnø (flakskred)\n",
" 33247 Fokksnø (flakskred)\n",
" 33248 Vedvarende svakt lag (flakskred)\n",
" 33249 Vedvarende svakt lag (flakskred)\n",
" 33250 Vedvarende svakt lag (flakskred)\n",
" 33251 Vedvarende svakt lag (flakskred)\n",
" 33252 Vedvarende svakt lag (flakskred)\n",
" 33253 Vedvarende svakt lag (flakskred)\n",
" 33254 Vedvarende svakt lag (flakskred)\n",
" 33255 Vedvarende svakt lag (flakskred)\n",
" 33256 Vedvarende svakt lag (flakskred)\n",
" 33257 Vedvarende svakt lag (flakskred)\n",
" 33258 Vedvarende svakt lag (flakskred)\n",
" 33259 Vedvarende svakt lag (flakskred)\n",
" 33260 Vedvarende svakt lag (flakskred)\n",
" 33261 Vedvarende svakt lag (flakskred)\n",
" 33262 Vedvarende svakt lag (flakskred)\n",
" 33263 Vedvarende svakt lag (flakskred)\n",
" Name: avalanche_problem_2_problem_type_name, Length: 33264, dtype: object, 0 Not given\n",
" 1 Not given\n",
" 2 Not given\n",
" 3 2 - Middels\n",
" 4 2 - Middels\n",
" 5 2 - Middels\n",
" 6 2 - Middels\n",
" 7 2 - Middels\n",
" 8 2 - Middels\n",
" 9 2 - Middels\n",
" 10 2 - Middels\n",
" 11 Not given\n",
" 12 Not given\n",
" 13 2 - Middels\n",
" 14 2 - Middels\n",
" 15 2 - Middels\n",
" 16 2 - Middels\n",
" 17 2 - Middels\n",
" 18 2 - Middels\n",
" 19 2 - Middels\n",
" 20 3 - Store\n",
" 21 2 - Middels\n",
" 22 2 - Middels\n",
" 23 2 - Middels\n",
" 24 3 - Store\n",
" 25 2 - Middels\n",
" 26 1 - Små\n",
" 27 2 - Middels\n",
" 28 2 - Middels\n",
" 29 2 - Middels\n",
" ... \n",
" 33234 Not given\n",
" 33235 Not given\n",
" 33236 Not given\n",
" 33237 Not given\n",
" 33238 Not given\n",
" 33239 Not given\n",
" 33240 Not given\n",
" 33241 Not given\n",
" 33242 Not given\n",
" 33243 Not given\n",
" 33244 Not given\n",
" 33245 1 - Små\n",
" 33246 2 - Middels\n",
" 33247 2 - Middels\n",
" 33248 2 - Middels\n",
" 33249 2 - Middels\n",
" 33250 2 - Middels\n",
" 33251 2 - Middels\n",
" 33252 2 - Middels\n",
" 33253 2 - Middels\n",
" 33254 2 - Middels\n",
" 33255 2 - Middels\n",
" 33256 2 - Middels\n",
" 33257 2 - Middels\n",
" 33258 2 - Middels\n",
" 33259 2 - Middels\n",
" 33260 2 - Middels\n",
" 33261 2 - Middels\n",
" 33262 2 - Middels\n",
" 33263 2 - Middels\n",
" Name: avalanche_problem_2_destructive_size_ext_name, Length: 33264, dtype: object, 0 Not given\n",
" 1 Not given\n",
" 2 Not given\n",
" 3 Noen bratte heng\n",
" 4 Noen bratte heng\n",
" 5 Noen bratte heng\n",
" 6 Få bratte heng\n",
" 7 Få bratte heng\n",
" 8 Få bratte heng\n",
" 9 Få bratte heng\n",
" 10 Få bratte heng\n",
" 11 Not given\n",
" 12 Not given\n",
" 13 Noen bratte heng\n",
" 14 Noen bratte heng\n",
" 15 Få bratte heng\n",
" 16 Få bratte heng\n",
" 17 Få bratte heng\n",
" 18 Få bratte heng\n",
" 19 Få bratte heng\n",
" 20 Få bratte heng\n",
" 21 Få bratte heng\n",
" 22 Få bratte heng\n",
" 23 Få bratte heng\n",
" 24 Mange bratte heng\n",
" 25 Få bratte heng\n",
" 26 Få bratte heng\n",
" 27 Mange bratte heng\n",
" 28 Mange bratte heng\n",
" 29 Mange bratte heng\n",
" ... \n",
" 33234 Not given\n",
" 33235 Not given\n",
" 33236 Not given\n",
" 33237 Not given\n",
" 33238 Not given\n",
" 33239 Not given\n",
" 33240 Not given\n",
" 33241 Not given\n",
" 33242 Not given\n",
" 33243 Not given\n",
" 33244 Not given\n",
" 33245 Noen bratte heng\n",
" 33246 Noen bratte heng\n",
" 33247 Noen bratte heng\n",
" 33248 Få bratte heng\n",
" 33249 Få bratte heng\n",
" 33250 Få bratte heng\n",
" 33251 Få bratte heng\n",
" 33252 Få bratte heng\n",
" 33253 Få bratte heng\n",
" 33254 Få bratte heng\n",
" 33255 Få bratte heng\n",
" 33256 Få bratte heng\n",
" 33257 Få bratte heng\n",
" 33258 Få bratte heng\n",
" 33259 Få bratte heng\n",
" 33260 Få bratte heng\n",
" 33261 Få bratte heng\n",
" 33262 Få bratte heng\n",
" 33263 Få bratte heng\n",
" Name: avalanche_problem_2_distribution_name, Length: 33264, dtype: object, 0 Not given\n",
" 1 Not given\n",
" 2 Not given\n",
" 3 Tørre flakskred\n",
" 4 Tørre flakskred\n",
" 5 Tørre flakskred\n",
" 6 Tørre flakskred\n",
" 7 Tørre flakskred\n",
" 8 Tørre flakskred\n",
" 9 Tørre flakskred\n",
" 10 Tørre flakskred\n",
" 11 Not given\n",
" 12 Not given\n",
" 13 Tørre flakskred\n",
" 14 Tørre flakskred\n",
" 15 Tørre flakskred\n",
" 16 Tørre flakskred\n",
" 17 Tørre flakskred\n",
" 18 Tørre flakskred\n",
" 19 Tørre flakskred\n",
" 20 Tørre flakskred\n",
" 21 Tørre flakskred\n",
" 22 Tørre flakskred\n",
" 23 Tørre flakskred\n",
" 24 Tørre flakskred\n",
" 25 Tørre flakskred\n",
" 26 Tørre flakskred\n",
" 27 Tørre flakskred\n",
" 28 Tørre flakskred\n",
" 29 Tørre flakskred\n",
" ... \n",
" 33234 Not given\n",
" 33235 Not given\n",
" 33236 Not given\n",
" 33237 Not given\n",
" 33238 Not given\n",
" 33239 Not given\n",
" 33240 Not given\n",
" 33241 Not given\n",
" 33242 Not given\n",
" 33243 Not given\n",
" 33244 Not given\n",
" 33245 Tørre flakskred\n",
" 33246 Tørre flakskred\n",
" 33247 Tørre flakskred\n",
" 33248 Tørre flakskred\n",
" 33249 Tørre flakskred\n",
" 33250 Tørre flakskred\n",
" 33251 Tørre flakskred\n",
" 33252 Tørre flakskred\n",
" 33253 Tørre flakskred\n",
" 33254 Tørre flakskred\n",
" 33255 Tørre flakskred\n",
" 33256 Tørre flakskred\n",
" 33257 Tørre flakskred\n",
" 33258 Tørre flakskred\n",
" 33259 Tørre flakskred\n",
" 33260 Tørre flakskred\n",
" 33261 Tørre flakskred\n",
" 33262 Tørre flakskred\n",
" 33263 Tørre flakskred\n",
" Name: avalanche_problem_2_ext_name, Length: 33264, dtype: object, 0 Not given\n",
" 1 Not given\n",
" 2 Not given\n",
" 3 Lite sannsynlig \n",
" 4 Lite sannsynlig \n",
" 5 Lite sannsynlig \n",
" 6 Lite sannsynlig \n",
" 7 Mulig \n",
" 8 Mulig \n",
" 9 Mulig \n",
" 10 Mulig \n",
" 11 Not given\n",
" 12 Not given\n",
" 13 Lite sannsynlig \n",
" 14 Lite sannsynlig \n",
" 15 Lite sannsynlig \n",
" 16 Lite sannsynlig \n",
" 17 Lite sannsynlig \n",
" 18 Mulig \n",
" 19 Mulig \n",
" 20 Mulig \n",
" 21 Mulig \n",
" 22 Mulig \n",
" 23 Mulig \n",
" 24 Lite sannsynlig \n",
" 25 Lite sannsynlig \n",
" 26 Lite sannsynlig \n",
" 27 Sannsynlig \n",
" 28 Sannsynlig \n",
" 29 Mulig \n",
" ... \n",
" 33234 Not given\n",
" 33235 Not given\n",
" 33236 Not given\n",
" 33237 Not given\n",
" 33238 Not given\n",
" 33239 Not given\n",
" 33240 Not given\n",
" 33241 Not given\n",
" 33242 Not given\n",
" 33243 Not given\n",
" 33244 Not given\n",
" 33245 Mulig \n",
" 33246 Mulig \n",
" 33247 Mulig \n",
" 33248 Mulig \n",
" 33249 Mulig \n",
" 33250 Mulig \n",
" 33251 Mulig \n",
" 33252 Mulig \n",
" 33253 Mulig \n",
" 33254 Mulig \n",
" 33255 Mulig \n",
" 33256 Mulig \n",
" 33257 Lite sannsynlig \n",
" 33258 Lite sannsynlig \n",
" 33259 Lite sannsynlig \n",
" 33260 Mulig \n",
" 33261 Mulig \n",
" 33262 Mulig \n",
" 33263 Mulig \n",
" Name: avalanche_problem_2_probability_name, Length: 33264, dtype: object, 0 Not given\n",
" 1 Not given\n",
" 2 Not given\n",
" 3 Stor tilleggsbelastning\n",
" 4 Stor tilleggsbelastning\n",
" 5 Stor tilleggsbelastning\n",
" 6 Stor tilleggsbelastning\n",
" 7 Liten tilleggsbelastning\n",
" 8 Liten tilleggsbelastning\n",
" 9 Liten tilleggsbelastning\n",
" 10 Stor tilleggsbelastning\n",
" 11 Not given\n",
" 12 Not given\n",
" 13 Stor tilleggsbelastning\n",
" 14 Stor tilleggsbelastning\n",
" 15 Stor tilleggsbelastning\n",
" 16 Stor tilleggsbelastning\n",
" 17 Stor tilleggsbelastning\n",
" 18 Stor tilleggsbelastning\n",
" 19 Stor tilleggsbelastning\n",
" 20 Stor tilleggsbelastning\n",
" 21 Stor tilleggsbelastning\n",
" 22 Stor tilleggsbelastning\n",
" 23 Stor tilleggsbelastning\n",
" 24 Stor tilleggsbelastning\n",
" 25 Stor tilleggsbelastning\n",
" 26 Stor tilleggsbelastning\n",
" 27 Naturlig utløst\n",
" 28 Naturlig utløst\n",
" 29 Naturlig utløst\n",
" ... \n",
" 33234 Not given\n",
" 33235 Not given\n",
" 33236 Not given\n",
" 33237 Not given\n",
" 33238 Not given\n",
" 33239 Not given\n",
" 33240 Not given\n",
" 33241 Not given\n",
" 33242 Not given\n",
" 33243 Not given\n",
" 33244 Not given\n",
" 33245 Liten tilleggsbelastning\n",
" 33246 Liten tilleggsbelastning\n",
" 33247 Liten tilleggsbelastning\n",
" 33248 Stor tilleggsbelastning\n",
" 33249 Stor tilleggsbelastning\n",
" 33250 Stor tilleggsbelastning\n",
" 33251 Stor tilleggsbelastning\n",
" 33252 Stor tilleggsbelastning\n",
" 33253 Stor tilleggsbelastning\n",
" 33254 Stor tilleggsbelastning\n",
" 33255 Stor tilleggsbelastning\n",
" 33256 Stor tilleggsbelastning\n",
" 33257 Stor tilleggsbelastning\n",
" 33258 Stor tilleggsbelastning\n",
" 33259 Stor tilleggsbelastning\n",
" 33260 Stor tilleggsbelastning\n",
" 33261 Stor tilleggsbelastning\n",
" 33262 Stor tilleggsbelastning\n",
" 33263 Stor tilleggsbelastning\n",
" Name: avalanche_problem_2_trigger_simple_name, Length: 33264, dtype: object, 0 Not given\n",
" 1 Not given\n",
" 2 Not given\n",
" 3 Flakskred\n",
" 4 Flakskred\n",
" 5 Flakskred\n",
" 6 Flakskred\n",
" 7 Flakskred\n",
" 8 Flakskred\n",
" 9 Flakskred\n",
" 10 Flakskred\n",
" 11 Not given\n",
" 12 Not given\n",
" 13 Flakskred\n",
" 14 Flakskred\n",
" 15 Flakskred\n",
" 16 Flakskred\n",
" 17 Flakskred\n",
" 18 Flakskred\n",
" 19 Flakskred\n",
" 20 Flakskred\n",
" 21 Flakskred\n",
" 22 Flakskred\n",
" 23 Flakskred\n",
" 24 Flakskred\n",
" 25 Flakskred\n",
" 26 Flakskred\n",
" 27 Flakskred\n",
" 28 Flakskred\n",
" 29 Flakskred\n",
" ... \n",
" 33234 Not given\n",
" 33235 Not given\n",
" 33236 Not given\n",
" 33237 Not given\n",
" 33238 Not given\n",
" 33239 Not given\n",
" 33240 Not given\n",
" 33241 Not given\n",
" 33242 Not given\n",
" 33243 Not given\n",
" 33244 Not given\n",
" 33245 Flakskred\n",
" 33246 Flakskred\n",
" 33247 Flakskred\n",
" 33248 Flakskred\n",
" 33249 Flakskred\n",
" 33250 Flakskred\n",
" 33251 Flakskred\n",
" 33252 Flakskred\n",
" 33253 Flakskred\n",
" 33254 Flakskred\n",
" 33255 Flakskred\n",
" 33256 Flakskred\n",
" 33257 Flakskred\n",
" 33258 Flakskred\n",
" 33259 Flakskred\n",
" 33260 Flakskred\n",
" 33261 Flakskred\n",
" 33262 Flakskred\n",
" 33263 Flakskred\n",
" Name: avalanche_problem_2_type_name, Length: 33264, dtype: object, 0 Not given\n",
" 1 Not given\n",
" 2 Not given\n",
" 3 Not given\n",
" 4 Not given\n",
" 5 Not given\n",
" 6 Not given\n",
" 7 Not given\n",
" 8 Not given\n",
" 9 Not given\n",
" 10 Not given\n",
" 11 Not given\n",
" 12 Not given\n",
" 13 Not given\n",
" 14 Not given\n",
" 15 Not given\n",
" 16 Not given\n",
" 17 Not given\n",
" 18 Not given\n",
" 19 Not given\n",
" 20 Not given\n",
" 21 Not given\n",
" 22 Not given\n",
" 23 Not given\n",
" 24 Not given\n",
" 25 Not given\n",
" 26 Not given\n",
" 27 Not given\n",
" 28 Not given\n",
" 29 Not given\n",
" ... \n",
" 33234 Not given\n",
" 33235 Not given\n",
" 33236 Not given\n",
" 33237 Not given\n",
" 33238 Not given\n",
" 33239 Not given\n",
" 33240 Not given\n",
" 33241 Not given\n",
" 33242 Not given\n",
" 33243 Not given\n",
" 33244 Not given\n",
" 33245 Not given\n",
" 33246 Not given\n",
" 33247 Not given\n",
" 33248 Not given\n",
" 33249 Not given\n",
" 33250 Not given\n",
" 33251 Not given\n",
" 33252 Not given\n",
" 33253 Not given\n",
" 33254 Not given\n",
" 33255 Not given\n",
" 33256 Not given\n",
" 33257 Not given\n",
" 33258 Not given\n",
" 33259 Not given\n",
" 33260 Not given\n",
" 33261 Not given\n",
" 33262 Not given\n",
" 33263 Not given\n",
" Name: avalanche_problem_3_cause_name, Length: 33264, dtype: object, 0 Not given\n",
" 1 Not given\n",
" 2 Not given\n",
" 3 Not given\n",
" 4 Not given\n",
" 5 Not given\n",
" 6 Not given\n",
" 7 Not given\n",
" 8 Not given\n",
" 9 Not given\n",
" 10 Not given\n",
" 11 Not given\n",
" 12 Not given\n",
" 13 Not given\n",
" 14 Not given\n",
" 15 Not given\n",
" 16 Not given\n",
" 17 Not given\n",
" 18 Not given\n",
" 19 Not given\n",
" 20 Not given\n",
" 21 Not given\n",
" 22 Not given\n",
" 23 Not given\n",
" 24 Not given\n",
" 25 Not given\n",
" 26 Not given\n",
" 27 Not given\n",
" 28 Not given\n",
" 29 Not given\n",
" ... \n",
" 33234 Not given\n",
" 33235 Not given\n",
" 33236 Not given\n",
" 33237 Not given\n",
" 33238 Not given\n",
" 33239 Not given\n",
" 33240 Not given\n",
" 33241 Not given\n",
" 33242 Not given\n",
" 33243 Not given\n",
" 33244 Not given\n",
" 33245 Not given\n",
" 33246 Not given\n",
" 33247 Not given\n",
" 33248 Not given\n",
" 33249 Not given\n",
" 33250 Not given\n",
" 33251 Not given\n",
" 33252 Not given\n",
" 33253 Not given\n",
" 33254 Not given\n",
" 33255 Not given\n",
" 33256 Not given\n",
" 33257 Not given\n",
" 33258 Not given\n",
" 33259 Not given\n",
" 33260 Not given\n",
" 33261 Not given\n",
" 33262 Not given\n",
" 33263 Not given\n",
" Name: avalanche_problem_3_problem_type_name, Length: 33264, dtype: object, 0 Not given\n",
" 1 Not given\n",
" 2 Not given\n",
" 3 Not given\n",
" 4 Not given\n",
" 5 Not given\n",
" 6 Not given\n",
" 7 Not given\n",
" 8 Not given\n",
" 9 Not given\n",
" 10 Not given\n",
" 11 Not given\n",
" 12 Not given\n",
" 13 Not given\n",
" 14 Not given\n",
" 15 Not given\n",
" 16 Not given\n",
" 17 Not given\n",
" 18 Not given\n",
" 19 Not given\n",
" 20 Not given\n",
" 21 Not given\n",
" 22 Not given\n",
" 23 Not given\n",
" 24 Not given\n",
" 25 Not given\n",
" 26 Not given\n",
" 27 Not given\n",
" 28 Not given\n",
" 29 Not given\n",
" ... \n",
" 33234 Not given\n",
" 33235 Not given\n",
" 33236 Not given\n",
" 33237 Not given\n",
" 33238 Not given\n",
" 33239 Not given\n",
" 33240 Not given\n",
" 33241 Not given\n",
" 33242 Not given\n",
" 33243 Not given\n",
" 33244 Not given\n",
" 33245 Not given\n",
" 33246 Not given\n",
" 33247 Not given\n",
" 33248 Not given\n",
" 33249 Not given\n",
" 33250 Not given\n",
" 33251 Not given\n",
" 33252 Not given\n",
" 33253 Not given\n",
" 33254 Not given\n",
" 33255 Not given\n",
" 33256 Not given\n",
" 33257 Not given\n",
" 33258 Not given\n",
" 33259 Not given\n",
" 33260 Not given\n",
" 33261 Not given\n",
" 33262 Not given\n",
" 33263 Not given\n",
" Name: avalanche_problem_3_destructive_size_ext_name, Length: 33264, dtype: object, 0 Not given\n",
" 1 Not given\n",
" 2 Not given\n",
" 3 Not given\n",
" 4 Not given\n",
" 5 Not given\n",
" 6 Not given\n",
" 7 Not given\n",
" 8 Not given\n",
" 9 Not given\n",
" 10 Not given\n",
" 11 Not given\n",
" 12 Not given\n",
" 13 Not given\n",
" 14 Not given\n",
" 15 Not given\n",
" 16 Not given\n",
" 17 Not given\n",
" 18 Not given\n",
" 19 Not given\n",
" 20 Not given\n",
" 21 Not given\n",
" 22 Not given\n",
" 23 Not given\n",
" 24 Not given\n",
" 25 Not given\n",
" 26 Not given\n",
" 27 Not given\n",
" 28 Not given\n",
" 29 Not given\n",
" ... \n",
" 33234 Not given\n",
" 33235 Not given\n",
" 33236 Not given\n",
" 33237 Not given\n",
" 33238 Not given\n",
" 33239 Not given\n",
" 33240 Not given\n",
" 33241 Not given\n",
" 33242 Not given\n",
" 33243 Not given\n",
" 33244 Not given\n",
" 33245 Not given\n",
" 33246 Not given\n",
" 33247 Not given\n",
" 33248 Not given\n",
" 33249 Not given\n",
" 33250 Not given\n",
" 33251 Not given\n",
" 33252 Not given\n",
" 33253 Not given\n",
" 33254 Not given\n",
" 33255 Not given\n",
" 33256 Not given\n",
" 33257 Not given\n",
" 33258 Not given\n",
" 33259 Not given\n",
" 33260 Not given\n",
" 33261 Not given\n",
" 33262 Not given\n",
" 33263 Not given\n",
" Name: avalanche_problem_3_distribution_name, Length: 33264, dtype: object, 0 Not given\n",
" 1 Not given\n",
" 2 Not given\n",
" 3 Not given\n",
" 4 Not given\n",
" 5 Not given\n",
" 6 Not given\n",
" 7 Not given\n",
" 8 Not given\n",
" 9 Not given\n",
" 10 Not given\n",
" 11 Not given\n",
" 12 Not given\n",
" 13 Not given\n",
" 14 Not given\n",
" 15 Not given\n",
" 16 Not given\n",
" 17 Not given\n",
" 18 Not given\n",
" 19 Not given\n",
" 20 Not given\n",
" 21 Not given\n",
" 22 Not given\n",
" 23 Not given\n",
" 24 Not given\n",
" 25 Not given\n",
" 26 Not given\n",
" 27 Not given\n",
" 28 Not given\n",
" 29 Not given\n",
" ... \n",
" 33234 Not given\n",
" 33235 Not given\n",
" 33236 Not given\n",
" 33237 Not given\n",
" 33238 Not given\n",
" 33239 Not given\n",
" 33240 Not given\n",
" 33241 Not given\n",
" 33242 Not given\n",
" 33243 Not given\n",
" 33244 Not given\n",
" 33245 Not given\n",
" 33246 Not given\n",
" 33247 Not given\n",
" 33248 Not given\n",
" 33249 Not given\n",
" 33250 Not given\n",
" 33251 Not given\n",
" 33252 Not given\n",
" 33253 Not given\n",
" 33254 Not given\n",
" 33255 Not given\n",
" 33256 Not given\n",
" 33257 Not given\n",
" 33258 Not given\n",
" 33259 Not given\n",
" 33260 Not given\n",
" 33261 Not given\n",
" 33262 Not given\n",
" 33263 Not given\n",
" Name: avalanche_problem_3_ext_name, Length: 33264, dtype: object, 0 Not given\n",
" 1 Not given\n",
" 2 Not given\n",
" 3 Not given\n",
" 4 Not given\n",
" 5 Not given\n",
" 6 Not given\n",
" 7 Not given\n",
" 8 Not given\n",
" 9 Not given\n",
" 10 Not given\n",
" 11 Not given\n",
" 12 Not given\n",
" 13 Not given\n",
" 14 Not given\n",
" 15 Not given\n",
" 16 Not given\n",
" 17 Not given\n",
" 18 Not given\n",
" 19 Not given\n",
" 20 Not given\n",
" 21 Not given\n",
" 22 Not given\n",
" 23 Not given\n",
" 24 Not given\n",
" 25 Not given\n",
" 26 Not given\n",
" 27 Not given\n",
" 28 Not given\n",
" 29 Not given\n",
" ... \n",
" 33234 Not given\n",
" 33235 Not given\n",
" 33236 Not given\n",
" 33237 Not given\n",
" 33238 Not given\n",
" 33239 Not given\n",
" 33240 Not given\n",
" 33241 Not given\n",
" 33242 Not given\n",
" 33243 Not given\n",
" 33244 Not given\n",
" 33245 Not given\n",
" 33246 Not given\n",
" 33247 Not given\n",
" 33248 Not given\n",
" 33249 Not given\n",
" 33250 Not given\n",
" 33251 Not given\n",
" 33252 Not given\n",
" 33253 Not given\n",
" 33254 Not given\n",
" 33255 Not given\n",
" 33256 Not given\n",
" 33257 Not given\n",
" 33258 Not given\n",
" 33259 Not given\n",
" 33260 Not given\n",
" 33261 Not given\n",
" 33262 Not given\n",
" 33263 Not given\n",
" Name: avalanche_problem_3_probability_name, Length: 33264, dtype: object, 0 Not given\n",
" 1 Not given\n",
" 2 Not given\n",
" 3 Not given\n",
" 4 Not given\n",
" 5 Not given\n",
" 6 Not given\n",
" 7 Not given\n",
" 8 Not given\n",
" 9 Not given\n",
" 10 Not given\n",
" 11 Not given\n",
" 12 Not given\n",
" 13 Not given\n",
" 14 Not given\n",
" 15 Not given\n",
" 16 Not given\n",
" 17 Not given\n",
" 18 Not given\n",
" 19 Not given\n",
" 20 Not given\n",
" 21 Not given\n",
" 22 Not given\n",
" 23 Not given\n",
" 24 Not given\n",
" 25 Not given\n",
" 26 Not given\n",
" 27 Not given\n",
" 28 Not given\n",
" 29 Not given\n",
" ... \n",
" 33234 Not given\n",
" 33235 Not given\n",
" 33236 Not given\n",
" 33237 Not given\n",
" 33238 Not given\n",
" 33239 Not given\n",
" 33240 Not given\n",
" 33241 Not given\n",
" 33242 Not given\n",
" 33243 Not given\n",
" 33244 Not given\n",
" 33245 Not given\n",
" 33246 Not given\n",
" 33247 Not given\n",
" 33248 Not given\n",
" 33249 Not given\n",
" 33250 Not given\n",
" 33251 Not given\n",
" 33252 Not given\n",
" 33253 Not given\n",
" 33254 Not given\n",
" 33255 Not given\n",
" 33256 Not given\n",
" 33257 Not given\n",
" 33258 Not given\n",
" 33259 Not given\n",
" 33260 Not given\n",
" 33261 Not given\n",
" 33262 Not given\n",
" 33263 Not given\n",
" Name: avalanche_problem_3_trigger_simple_name, Length: 33264, dtype: object, 0 Not given\n",
" 1 Not given\n",
" 2 Not given\n",
" 3 Not given\n",
" 4 Not given\n",
" 5 Not given\n",
" 6 Not given\n",
" 7 Not given\n",
" 8 Not given\n",
" 9 Not given\n",
" 10 Not given\n",
" 11 Not given\n",
" 12 Not given\n",
" 13 Not given\n",
" 14 Not given\n",
" 15 Not given\n",
" 16 Not given\n",
" 17 Not given\n",
" 18 Not given\n",
" 19 Not given\n",
" 20 Not given\n",
" 21 Not given\n",
" 22 Not given\n",
" 23 Not given\n",
" 24 Not given\n",
" 25 Not given\n",
" 26 Not given\n",
" 27 Not given\n",
" 28 Not given\n",
" 29 Not given\n",
" ... \n",
" 33234 Not given\n",
" 33235 Not given\n",
" 33236 Not given\n",
" 33237 Not given\n",
" 33238 Not given\n",
" 33239 Not given\n",
" 33240 Not given\n",
" 33241 Not given\n",
" 33242 Not given\n",
" 33243 Not given\n",
" 33244 Not given\n",
" 33245 Not given\n",
" 33246 Not given\n",
" 33247 Not given\n",
" 33248 Not given\n",
" 33249 Not given\n",
" 33250 Not given\n",
" 33251 Not given\n",
" 33252 Not given\n",
" 33253 Not given\n",
" 33254 Not given\n",
" 33255 Not given\n",
" 33256 Not given\n",
" 33257 Not given\n",
" 33258 Not given\n",
" 33259 Not given\n",
" 33260 Not given\n",
" 33261 Not given\n",
" 33262 Not given\n",
" 33263 Not given\n",
" Name: avalanche_problem_3_type_name, Length: 33264, dtype: object, 0 NaN\n",
" 1 NaN\n",
" 2 NaN\n",
" 3 NaN\n",
" 4 NaN\n",
" 5 NaN\n",
" 6 NaN\n",
" 7 NaN\n",
" 8 NaN\n",
" 9 NaN\n",
" 10 NaN\n",
" 11 NaN\n",
" 12 NaN\n",
" 13 NaN\n",
" 14 NaN\n",
" 15 NaN\n",
" 16 NaN\n",
" 17 NaN\n",
" 18 NaN\n",
" 19 NaN\n",
" 20 NaN\n",
" 21 NaN\n",
" 22 NaN\n",
" 23 NaN\n",
" 24 NaN\n",
" 25 NaN\n",
" 26 NaN\n",
" 27 NaN\n",
" 28 NaN\n",
" 29 NaN\n",
" ... \n",
" 33234 NaN\n",
" 33235 NaN\n",
" 33236 NaN\n",
" 33237 NaN\n",
" 33238 NaN\n",
" 33239 NaN\n",
" 33240 NaN\n",
" 33241 NaN\n",
" 33242 Tirsdag 08.01 ble det observert et str. 2 skre...\n",
" 33243 Tirsdag 08.01 ble det observert et str. 2 skre...\n",
" 33244 Tirsdag ble det observert et str. 2 skred i br...\n",
" 33245 Det er ikke meldt om ferske skred de siste 3 d...\n",
" 33246 Det er ikke meldt om ferske skred de siste 3 d...\n",
" 33247 Det er ikke meldt om ferske skred de siste 3 d...\n",
" 33248 Det er ikke meldt om ferske skred de siste tre...\n",
" 33249 Det er ikke meldt om ferske skred de siste tre...\n",
" 33250 Det er ikke meldt om ferske skred i regionen d...\n",
" 33251 Det er ikke meldt om ferske skred i regionen d...\n",
" 33252 NaN\n",
" 33253 NaN\n",
" 33254 NaN\n",
" 33255 NaN\n",
" 33256 NaN\n",
" 33257 NaN\n",
" 33258 NaN\n",
" 33259 Det ble rapportert om tre skred Haukelifjell s...\n",
" 33260 Det ble rapportert om tre skred Haukelifjell s...\n",
" 33261 Det ble rapportert om tre skred Haukelifjell s...\n",
" 33262 Det ble rapportert om tre skred Haukelifjell s...\n",
" 33263 NaN\n",
" Name: latest_avalanche_activity, Length: 33264, dtype: object, 0 Ikke vurdert\n",
" 1 Lokalt ustabile forhold i leområder. Lite snø ...\n",
" 2 Lokalt ustabile forhold i leområder. Lite snø ...\n",
" 3 Vær varsom i områder med ustabile fokksnøflak....\n",
" 4 Vær varsom i områder med ustabile fokksnøflak....\n",
" 5 Ustabile nysnøflak i leområder. Det finnes ogs...\n",
" 6 Ustabile nysnøflak i leområder. Det finnes ogs...\n",
" 7 Ustabile nysnøflak i leområder. Stedvis kan de...\n",
" 8 Ustabile flak av fokksnø i leområder. Stedvis ...\n",
" 9 Ustabile flak av fokksnø i leområder. Stedvis ...\n",
" 10 Vær varsom i områder med ustabile fokksnøflak....\n",
" 11 Vær forsiktig i terrengformasjoner som har sam...\n",
" 12 Det finnes svake lag i snødekket som kan trigg...\n",
" 13 Vær forsiktig i terrengformasjoner som har sam...\n",
" 14 Ver varsom i terrengformasjonar som har samla ...\n",
" 15 Ver varsom i terrengformasjonar som har samla ...\n",
" 16 Ver varsom i terrengformasjonar som har samla ...\n",
" 17 Ver varsom i terrengformasjonar som har samla ...\n",
" 18 Unngå leområder med fersk fokksnø. Unngå også ...\n",
" 19 Vinddreiing fører til omfordeling i snødekket....\n",
" 20 Ny vinddreiing fører til omfordeling i snødekk...\n",
" 21 Skredfaren avtar utover torsdagen pga. tilfrys...\n",
" 22 Vær forsiktig i leområder med fokksnø. Det fin...\n",
" 23 Vær forsiktig i leområder med fokksnø. Det fin...\n",
" 24 Vær forsiktig i leområder med fokksnø. Det fin...\n",
" 25 Det finnes svake lag som kan trigges av en ski...\n",
" 26 Det finnes svake lag som kan trigges av en ski...\n",
" 27 Kraftig vind og snøfokk kan gi stor skredfare ...\n",
" 28 Kraftig vind og snøfokk kan gi stor skredfare ...\n",
" 29 Utvis svært konservativ tilnærming til skredte...\n",
" ... \n",
" 33234 Det ligger fersk fokksnø i leformasjonar i ter...\n",
" 33235 Lokalt ustabile forhold. Vær forsiktig der det...\n",
" 33236 Vær varsom i bratt terreng med fersk fokksnø. ...\n",
" 33237 Lite snø og generelt stabile forhold, kun muli...\n",
" 33238 Lite snø og generelt stabile forhold, kun muli...\n",
" 33239 Lite snø og generelt stabile forhold, kun muli...\n",
" 33240 Litt nysnø fører til ustabile nysnøflak i enke...\n",
" 33241 Det finnes ustabile nysnøflak i noen leområder...\n",
" 33242 Det finnes ustabile nysnøflak i leområder, ell...\n",
" 33243 Det finnes ustabile fokksnøflak i leområder, e...\n",
" 33244 Det finnes ustabile fokksnøflak i leområder, e...\n",
" 33245 Det finnes ustabile lag i snødekket. Vær forsi...\n",
" 33246 Det finnes ustabile lag i snødekket. Vær forsi...\n",
" 33247 Det finnes vedvarende svake lag i snødekket. V...\n",
" 33248 Vær forsiktig i terrengformasjoner som har sam...\n",
" 33249 Vær forsiktig i terrengformasjoner som har sam...\n",
" 33250 Vær forsiktig i terrengformasjoner som har sam...\n",
" 33251 Vær forsiktig i terrengformasjoner som har sam...\n",
" 33252 Vær forsiktig i terrengformasjoner som har sam...\n",
" 33253 Vær forsiktig i terrengformasjoner som har sam...\n",
" 33254 Vær forsiktig i terrengformasjoner som har sam...\n",
" 33255 Generelt stabile forhold, men vær likevel fors...\n",
" 33256 Generelt stabile forhold, men vær likevel fors...\n",
" 33257 Vær varsom der det ligger fersk vindtransporte...\n",
" 33258 Vær varsom der det ligger fersk vindtransporte...\n",
" 33259 Vær forsiktig i bratt terreng med ferske nysnø...\n",
" 33260 Unngå leområder med fersk fokksnø. Det finnes ...\n",
" 33261 Unngå leområder med fersk fokksnø. Det finnes ...\n",
" 33262 Vær forsiktig i leområder med nysnøflak. Det f...\n",
" 33263 Vær forsiktig i leområder med nysnøflak. Det f...\n",
" Name: main_text, Length: 33264, dtype: object, 0 NaN\n",
" 1 NaN\n",
" 2 NaN\n",
" 3 NaN\n",
" 4 NaN\n",
" 5 NaN\n",
" 6 NaN\n",
" 7 NaN\n",
" 8 NaN\n",
" 9 NaN\n",
" 10 NaN\n",
" 11 NaN\n",
" 12 NaN\n",
" 13 NaN\n",
" 14 NaN\n",
" 15 NaN\n",
" 16 NaN\n",
" 17 NaN\n",
" 18 NaN\n",
" 19 NaN\n",
" 20 NaN\n",
" 21 NaN\n",
" 22 NaN\n",
" 23 NaN\n",
" 24 NaN\n",
" 25 NaN\n",
" 26 NaN\n",
" 27 NaN\n",
" 28 NaN\n",
" 29 NaN\n",
" ... \n",
" 33234 Snødekket er preget av uværet nyttårsaften. Mi...\n",
" 33235 Snødekket er preget av uværet nyttårsaften og ...\n",
" 33236 Vestvendt og vindeksponert terreng er for det ...\n",
" 33237 Snødekket er preget av mye vær og mildvær. Snø...\n",
" 33238 Snødekket er preget av mye vær og mildvær. Vin...\n",
" 33239 Snødekket er preget av mye vær og mildvær. Vin...\n",
" 33240 Snødekket er preget av mye vind og mildvær. Vi...\n",
" 33241 Snødekket består av en eldre del preget av vin...\n",
" 33242 Snødekket består av en eldre del preget av vin...\n",
" 33243 Det er lite snø regionen for årstiden og snøde...\n",
" 33244 Det er lite snø regionen for årstiden og snøde...\n",
" 33245 Det er lite snø regionen for årstiden og snøde...\n",
" 33246 Det er lite snø regionen for årstiden og snøde...\n",
" 33247 Det er lite snø regionen for årstiden og snøde...\n",
" 33248 Det er lite snø i regionen for årstiden og snø...\n",
" 33249 Det er lite snø i regionen for årstiden og snø...\n",
" 33250 Det finnes en del løssnø tilgjengelig for tran...\n",
" 33251 Stedvis finnes det fortsatt en del løssnø tilg...\n",
" 33252 Det finnes tørr snø tilgjengelig for vindtrans...\n",
" 33253 Det finnes tørr snø tilgjengelig for vindtrans...\n",
" 33254 Det finnes tørr snø tilgjengelig for vindtrans...\n",
" 33255 Det finnes tørr snø tilgjengelig for vindtrans...\n",
" 33256 Den dominerende vindretningen har vært V-NV, s...\n",
" 33257 Den dominerende vindretningen har vært V-NV, s...\n",
" 33258 Den dominerende vindretningen har vært V-NV, s...\n",
" 33259 Den dominerende vindretningen har vært V-NV, s...\n",
" 33260 Snøoverflaten består nå av løs nysnø eller myk...\n",
" 33261 Snøoverflaten består nå av løs nysnø eller myk...\n",
" 33262 Snøoverflaten består nå av løs nysnø eller myk...\n",
" 33263 Snøoverflaten består nå av løs nysnø eller myk...\n",
" Name: snow_surface, Length: 33264, dtype: object, 0 NaN\n",
" 1 NaN\n",
" 2 NaN\n",
" 3 NaN\n",
" 4 NaN\n",
" 5 NaN\n",
" 6 NaN\n",
" 7 NaN\n",
" 8 NaN\n",
" 9 NaN\n",
" 10 NaN\n",
" 11 NaN\n",
" 12 NaN\n",
" 13 NaN\n",
" 14 NaN\n",
" 15 NaN\n",
" 16 NaN\n",
" 17 NaN\n",
" 18 NaN\n",
" 19 NaN\n",
" 20 NaN\n",
" 21 NaN\n",
" 22 NaN\n",
" 23 NaN\n",
" 24 NaN\n",
" 25 NaN\n",
" 26 NaN\n",
" 27 NaN\n",
" 28 NaN\n",
" 29 NaN\n",
" ... \n",
" 33234 Det har vært observert et nedsnødd rimlag i sn...\n",
" 33235 Det har vært observert et nedsnødd rimlag i sn...\n",
" 33236 Det er usikkerhet knyttet til et nedføyket rim...\n",
" 33237 Det er usikkerhet knyttet til et nedføyket rim...\n",
" 33238 Det er usikkerhet knyttet til et nedføyket rim...\n",
" 33239 Det er tidligere observert kantkorn og rim i s...\n",
" 33240 Det er tidligere observert kantkorn og rim i s...\n",
" 33241 Det er tidligere observert kantkorn og rim i d...\n",
" 33242 Det er tidligere observert kantkorn og rim i d...\n",
" 33243 Det er tidligere observert kantkorn og rim i d...\n",
" 33244 Det er tidligere observert kantkorn og rim i d...\n",
" 33245 Det er tidligere observert kantkorn og rim i d...\n",
" 33246 Det er tidligere observert kantkorn og rim i d...\n",
" 33247 Det er tidligere observert kantkorn og rim i d...\n",
" 33248 Det er tidligere observert kantkorn og rim i d...\n",
" 33249 På 900moh i nærheten av Hovden er det før helg...\n",
" 33250 På 900moh i nærheten av Hovden er det før helg...\n",
" 33251 Torsdag ble det observert begynnende kantkorn ...\n",
" 33252 Siste dager er det observert kantkorn under sk...\n",
" 33253 Siste dager er det observert kantkorn under sk...\n",
" 33254 Siste dager er det observert kantkorn under sk...\n",
" 33255 Det finnes vedvarende svake lag med kantkorn i...\n",
" 33256 Det finnes vedvarende svake lag med kantkorn i...\n",
" 33257 Kantkorn er tidligere påvist i snødekket, men ...\n",
" 33258 Kantkorn er tidligere påvist i snødekket, men ...\n",
" 33259 Kantkorn er tidligere påvist i snødekket, men ...\n",
" 33260 Det finnes vedvarende svakt lag av kantkorn i ...\n",
" 33261 Det finnes vedvarende svakt lag av kantkorn i ...\n",
" 33262 Det finnes vedvarende svakt lag av kantkorn i ...\n",
" 33263 Det finnes vedvarende svakt lag av kantkorn i ...\n",
" Name: current_weak_layers, Length: 33264, dtype: object, 0 NaN\n",
" 1 Snøen i leområdene er generelt sett stabile og...\n",
" 2 Snøen i leområdene er generelt sett stabile og...\n",
" 3 Snøen i leområdene er generelt sett stabile og...\n",
" 4 Nedbør kombinert med vind fører til ustabile f...\n",
" 5 Det ligger ca. 30 cm løssnø i terrenget som er...\n",
" 6 En del av nysnøen har blitt flyttet på av vind...\n",
" 7 Vind fra N har flyttet mye av den siste snøen,...\n",
" 8 Ny vindøkning fra N vil føre til nye ustabile ...\n",
" 9 Fokksnø fra de siste dagene vil gradvis stabil...\n",
" 10 Fokksnøflakene fra siste snøfall virker å stab...\n",
" 11 Fokksnøflakene fra siste snøfall virker å stab...\n",
" 12 Det regnes nå at det generelt skal stor tilleg...\n",
" 13 Økende vind vil føre til transport av løs tilg...\n",
" 14 Litt nedbør kombinert med vind frå fleire retn...\n",
" 15 Litt nedbør kombinert med vind frå SØ torsdag ...\n",
" 16 Litt nedbør kombinert med vind frå S-SØ laurda...\n",
" 17 Dreiande vindretning vil føre til stadig pålag...\n",
" 18 I låglandet er øverste snødekke fuktig og nysn...\n",
" 19 OPPDATERT 22.12.2016 kl. 08:30 pga. observasjo...\n",
" 20 Nedbør i form av snø både tysdag og onsdag vil...\n",
" 21 Vinden vil framleis kunne flytte på laus snø f...\n",
" 22 Varsla vêr kan framleis gi noko pålagring av s...\n",
" 23 Relativt rolig vær gjør at tidligere ustabile ...\n",
" 24 Rolig vær gjør at tidligere fokksnøflak har få...\n",
" 25 Oppholdsvær gjør at fokksnøen stabiliseres.\\r\\...\n",
" 26 Oppholdsvær gjør at stabiliserer ytterligere f...\n",
" 27 Onsdag kveld ventes svært kraftig vind som vil...\n",
" 28 Torsdag ventes det at stormen fortsetter og gi...\n",
" 29 Vinden ventes å avtar utover fredagen og snøfo...\n",
" ... \n",
" 33234 Det vil mange steder ligge ferske fokksnøflak ...\n",
" 33235 Fokksnø etter det kraftige uværet nyttårsaften...\n",
" 33236 Mildvær høyt til fjells vil kanskje svekke bin...\n",
" 33237 Snødekket stabiliseres etter mildværet, og det...\n",
" 33238 Snødekket stabiliseres etter mildværet, og det...\n",
" 33239 Nysnø og vind av skiftende retning kan gi mjuk...\n",
" 33240 Noe nysnø og kraftig vind fra nord vil føre ny...\n",
" 33241 Vind fra nord vil føre til at flakdannelser i ...\n",
" 33242 Vind fra nordlig retning vil føre til at flakd...\n",
" 33243 Kraftig vind fra nordlig retning vil føre til ...\n",
" 33244 Kraftig vind fra nordvestlig retning har gjort...\n",
" 33245 Noe nysnø og vindøkning til nordvestlig kuling...\n",
" 33246 Kuling fra nordvest fra lørdag ettermiddag har...\n",
" 33247 Det ventes snøfall, forbigående minkende vesta...\n",
" 33248 Med vind fra vest og påfyll av snø kommende dø...\n",
" 33249 Med en del nysnø tilgjengelig for transport og...\n",
" 33250 Med en del nysnø tilgjengelig for transport og...\n",
" 33251 Med noe løssnø tilgjengelig for transport og v...\n",
" 33252 Det er fortsatt ustabile ferske fokksnøflak so...\n",
" 33253 Det er fortsatt ustabile ferske fokksnøflak so...\n",
" 33254 Det er fortsatt ustabile fokksnøflak som utgjø...\n",
" 33255 Det er fortsatt ustabile fokksnøflak som utgjø...\n",
" 33256 Fokksnøflak i Ø og S utgjør hovedskredprobleme...\n",
" 33257 Snø og vind fra SØ vil føre til pålagring av v...\n",
" 33258 Snø og vind fra SØ vil føre til noe pålagring ...\n",
" 33259 Snø og vind fra SØ vil føre til ustabile nysnø...\n",
" 33260 Oppdatert kveld søndag 27.1. på grunn av stor ...\n",
" 33261 Nysnø og vind fra SØ i helga har gitt flakdann...\n",
" 33262 Litt vind og nysnø vil danne nysnøflak i fjell...\n",
" 33263 Nysnø kan noen steder flyttes av vind og danne...\n",
" Name: avalanche_danger, Length: 33264, dtype: object, 0 Not given\n",
" 1 Vær forsiktig i områder brattere enn 30 grader...\n",
" 2 Vær forsiktig i områder brattere enn 30 grader...\n",
" 3 Vær forsiktig i områder brattere enn 30 grader...\n",
" 4 Vær forsiktig i bratte heng under og etter sn...\n",
" 5 Unngå bratte heng og terrengfeller under og et...\n",
" 6 Vær forsiktig i bratte heng under og etter sn...\n",
" 7 Vær forsiktig i bratte heng under og etter sn...\n",
" 8 Vær forsiktig i områder brattere enn 30 grader...\n",
" 9 Vær forsiktig i områder brattere enn 30 grader...\n",
" 10 Vær forsiktig i områder brattere enn 30 grader...\n",
" 11 Vær forsiktig i områder brattere enn 30 grader...\n",
" 12 Hold god avstand til hverandre og til løsneomr...\n",
" 13 Vær forsiktig i områder brattere enn 30 grader...\n",
" 14 Vær forsiktig i områder brattere enn 30 grader...\n",
" 15 Vær forsiktig i områder brattere enn 30 grader...\n",
" 16 Vær forsiktig i områder brattere enn 30 grader...\n",
" 17 Vær forsiktig i områder brattere enn 30 grader...\n",
" 18 Unngå terreng brattere enn 30 grader med fersk...\n",
" 19 Unngå bratte heng og terrengfeller under og et...\n",
" 20 Unngå terreng brattere enn 30 grader med fersk...\n",
" 21 Unngå terreng brattere enn 30 grader med fersk...\n",
" 22 Vær forsiktig i områder brattere enn 30 grader...\n",
" 23 Vær forsiktig i områder brattere enn 30 grader...\n",
" 24 Vær forsiktig i områder brattere enn 30 grader...\n",
" 25 Hold god avstand til hverandre og til løsneomr...\n",
" 26 Hold god avstand til hverandre og til løsneomr...\n",
" 27 For friluftsliv: Hold deg unna skredterreng (b...\n",
" 28 For friluftsliv: Hold deg unna skredterreng (b...\n",
" 29 Unngå ferdsel i skredterreng brattere enn 30 g...\n",
" ... \n",
" 33234 Vær forsiktig i områder brattere enn 30 grader...\n",
" 33235 Vær forsiktig i områder brattere enn 30 grader...\n",
" 33236 Unngå langvarige opphold i løsneområder og utl...\n",
" 33237 Vær varsom der skredproblemet er å finne i ko...\n",
" 33238 Vær varsom der skredproblemet er å finne i ko...\n",
" 33239 Kun enkelte spesielt utsatte områder er skredu...\n",
" 33240 Kun enkelte spesielt utsatte områder er skredu...\n",
" 33241 Kun enkelte spesielt utsatte områder er skredu...\n",
" 33242 Kun enkelte spesielt utsatte områder er skredu...\n",
" 33243 Vær varsom der skredproblemet er å finne i ko...\n",
" 33244 Vær varsom der skredproblemet er å finne i ko...\n",
" 33245 Kun enkelte spesielt utsatte områder er skredu...\n",
" 33246 Hold god avstand til hverandre ved ferdsel i s...\n",
" 33247 Hold god avstand til hverandre ved ferdsel i s...\n",
" 33248 Vær forsiktig i områder brattere enn 30 grader...\n",
" 33249 Vær forsiktig i områder brattere enn 30 grader...\n",
" 33250 Vær forsiktig i områder brattere enn 30 grader...\n",
" 33251 Vær forsiktig i områder brattere enn 30 grader...\n",
" 33252 Vær forsiktig i områder brattere enn 30 grader...\n",
" 33253 Vær forsiktig i områder brattere enn 30 grader...\n",
" 33254 Vær forsiktig i områder brattere enn 30 grader...\n",
" 33255 Vær varsom der skredproblemet er å finne i ko...\n",
" 33256 Vær varsom der skredproblemet er å finne i ko...\n",
" 33257 Vær forsiktig i bratte heng under og etter sn...\n",
" 33258 Vær forsiktig i bratte heng under og etter sn...\n",
" 33259 Vær forsiktig i bratte heng under og etter sn...\n",
" 33260 Unngå bratte heng og terrengfeller under og et...\n",
" 33261 Vær forsiktig i bratte heng under og etter sn...\n",
" 33262 Vær forsiktig i bratte heng under og etter sn...\n",
" 33263 Vær forsiktig i bratte heng under og etter sn...\n",
" Name: avalanche_problem_1_advice, Length: 33264, dtype: object, 0 Not given\n",
" 1 Not given\n",
" 2 Not given\n",
" 3 Hold god avstand til hverandre ved ferdsel i s...\n",
" 4 Hold god avstand til hverandre ved ferdsel i s...\n",
" 5 Unngå ferdsel i skredterreng og i utløpsområde...\n",
" 6 Hold god avstand til hverandre ved ferdsel i s...\n",
" 7 Hold god avstand til hverandre ved ferdsel i s...\n",
" 8 Hold god avstand til hverandre ved ferdsel i s...\n",
" 9 Hold god avstand til hverandre ved ferdsel i s...\n",
" 10 Hold god avstand til hverandre ved ferdsel i s...\n",
" 11 Not given\n",
" 12 Not given\n",
" 13 Hold god avstand til hverandre og til løsneomr...\n",
" 14 Hold god avstand til hverandre og til løsneomr...\n",
" 15 Hold god avstand til hverandre og til løsneomr...\n",
" 16 Hold god avstand til hverandre og til løsneomr...\n",
" 17 Hold god avstand til hverandre og til løsneomr...\n",
" 18 Unngå ferdsel i skredterreng brattere enn 30 g...\n",
" 19 Unngå ferdsel i skredterreng brattere enn 30 g...\n",
" 20 Unngå ferdsel i skredterreng brattere enn 30 g...\n",
" 21 Unngå ferdsel i skredterreng brattere enn 30 g...\n",
" 22 Hold god avstand til hverandre og til løsneomr...\n",
" 23 Hold god avstand til hverandre og til løsneomr...\n",
" 24 Hold god avstand til hverandre og til løsneomr...\n",
" 25 Vær forsiktig i områder brattere enn 30 grader...\n",
" 26 Vær forsiktig i områder brattere enn 30 grader...\n",
" 27 For friluftsliv: Hold deg unna skredterreng (b...\n",
" 28 For friluftsliv: Hold deg unna skredterreng (b...\n",
" 29 Unngå terreng brattere enn 30 grader med fersk...\n",
" ... \n",
" 33234 Not given\n",
" 33235 Not given\n",
" 33236 Not given\n",
" 33237 Not given\n",
" 33238 Not given\n",
" 33239 Not given\n",
" 33240 Not given\n",
" 33241 Not given\n",
" 33242 Not given\n",
" 33243 Not given\n",
" 33244 Not given\n",
" 33245 Vær varsom der skredproblemet er å finne i ko...\n",
" 33246 Vær forsiktig i områder brattere enn 30 grader...\n",
" 33247 Vær forsiktig i områder brattere enn 30 grader...\n",
" 33248 Hold god avstand til hverandre ved ferdsel i s...\n",
" 33249 Hold god avstand til hverandre ved ferdsel i s...\n",
" 33250 Hold god avstand til hverandre ved ferdsel i s...\n",
" 33251 Hold god avstand til hverandre ved ferdsel i s...\n",
" 33252 Hold god avstand til hverandre ved ferdsel i s...\n",
" 33253 Hold god avstand til hverandre ved ferdsel i s...\n",
" 33254 Hold god avstand til hverandre ved ferdsel i s...\n",
" 33255 Kun enkelte spesielt utsatte områder er skredu...\n",
" 33256 Kun enkelte spesielt utsatte områder er skredu...\n",
" 33257 Hold god avstand til hverandre ved ferdsel i s...\n",
" 33258 Hold god avstand til hverandre ved ferdsel i s...\n",
" 33259 Hold god avstand til hverandre ved ferdsel i s...\n",
" 33260 Unngå ferdsel i skredterreng (løsne og utløpso...\n",
" 33261 Hold god avstand til hverandre ved ferdsel i s...\n",
" 33262 Hold god avstand til hverandre ved ferdsel i s...\n",
" 33263 Hold god avstand til hverandre ved ferdsel i s...\n",
" Name: avalanche_problem_2_advice, Length: 33264, dtype: object, 0 Not given\n",
" 1 Not given\n",
" 2 Not given\n",
" 3 Not given\n",
" 4 Not given\n",
" 5 Not given\n",
" 6 Not given\n",
" 7 Not given\n",
" 8 Not given\n",
" 9 Not given\n",
" 10 Not given\n",
" 11 Not given\n",
" 12 Not given\n",
" 13 Not given\n",
" 14 Not given\n",
" 15 Not given\n",
" 16 Not given\n",
" 17 Not given\n",
" 18 Not given\n",
" 19 Not given\n",
" 20 Not given\n",
" 21 Not given\n",
" 22 Not given\n",
" 23 Not given\n",
" 24 Not given\n",
" 25 Not given\n",
" 26 Not given\n",
" 27 Not given\n",
" 28 Not given\n",
" 29 Not given\n",
" ... \n",
" 33234 Not given\n",
" 33235 Not given\n",
" 33236 Not given\n",
" 33237 Not given\n",
" 33238 Not given\n",
" 33239 Not given\n",
" 33240 Not given\n",
" 33241 Not given\n",
" 33242 Not given\n",
" 33243 Not given\n",
" 33244 Not given\n",
" 33245 Not given\n",
" 33246 Not given\n",
" 33247 Not given\n",
" 33248 Not given\n",
" 33249 Not given\n",
" 33250 Not given\n",
" 33251 Not given\n",
" 33252 Not given\n",
" 33253 Not given\n",
" 33254 Not given\n",
" 33255 Not given\n",
" 33256 Not given\n",
" 33257 Not given\n",
" 33258 Not given\n",
" 33259 Not given\n",
" 33260 Not given\n",
" 33261 Not given\n",
" 33262 Not given\n",
" 33263 Not given\n",
" Name: avalanche_problem_3_advice, Length: 33264, dtype: object, 0 None\n",
" 1 None\n",
" 2 None\n",
" 3 None\n",
" 4 None\n",
" 5 None\n",
" 6 None\n",
" 7 None\n",
" 8 None\n",
" 9 None\n",
" 10 None\n",
" 11 None\n",
" 12 None\n",
" 13 None\n",
" 14 None\n",
" 15 None\n",
" 16 None\n",
" 17 None\n",
" 18 None\n",
" 19 None\n",
" 20 None\n",
" 21 None\n",
" 22 None\n",
" 23 None\n",
" 24 None\n",
" 25 None\n",
" 26 None\n",
" 27 None\n",
" 28 None\n",
" 29 None\n",
" ... \n",
" 33234 Stiv kuling\n",
" 33235 Stiv kuling\n",
" 33236 Stiv kuling\n",
" 33237 Stiv kuling\n",
" 33238 Bris\n",
" 33239 Bris\n",
" 33240 Stiv kuling\n",
" 33241 Frisk bris\n",
" 33242 Bris\n",
" 33243 Liten storm\n",
" 33244 Liten kuling\n",
" 33245 Frisk bris\n",
" 33246 Sterk kuling\n",
" 33247 Frisk bris\n",
" 33248 Stiv kuling\n",
" 33249 Frisk bris\n",
" 33250 Frisk bris\n",
" 33251 Frisk bris\n",
" 33252 Bris\n",
" 33253 Frisk bris\n",
" 33254 Bris\n",
" 33255 Frisk bris\n",
" 33256 Bris\n",
" 33257 Bris\n",
" 33258 Bris\n",
" 33259 Frisk bris\n",
" 33260 Bris\n",
" 33261 Bris\n",
" 33262 Frisk bris\n",
" 33263 Frisk bris\n",
" Name: mountain_weather_wind_speed, Length: 33264, dtype: object, 0 A\n",
" 1 A\n",
" 2 A\n",
" 3 A\n",
" 4 A\n",
" 5 A\n",
" 6 A\n",
" 7 A\n",
" 8 A\n",
" 9 A\n",
" 10 A\n",
" 11 A\n",
" 12 A\n",
" 13 A\n",
" 14 A\n",
" 15 A\n",
" 16 A\n",
" 17 A\n",
" 18 A\n",
" 19 A\n",
" 20 A\n",
" 21 A\n",
" 22 A\n",
" 23 A\n",
" 24 A\n",
" 25 A\n",
" 26 A\n",
" 27 A\n",
" 28 A\n",
" 29 A\n",
" ..\n",
" 33234 A\n",
" 33235 A\n",
" 33236 A\n",
" 33237 A\n",
" 33238 A\n",
" 33239 A\n",
" 33240 A\n",
" 33241 A\n",
" 33242 A\n",
" 33243 A\n",
" 33244 A\n",
" 33245 A\n",
" 33246 A\n",
" 33247 A\n",
" 33248 A\n",
" 33249 A\n",
" 33250 A\n",
" 33251 A\n",
" 33252 A\n",
" 33253 A\n",
" 33254 A\n",
" 33255 A\n",
" 33256 A\n",
" 33257 A\n",
" 33258 A\n",
" 33259 A\n",
" 33260 A\n",
" 33261 A\n",
" 33262 A\n",
" 33263 A\n",
" Name: region_type_name, Length: 33264, dtype: object, 0 Nordenskiöld Land\n",
" 1 Nordenskiöld Land\n",
" 2 Nordenskiöld Land\n",
" 3 Nordenskiöld Land\n",
" 4 Nordenskiöld Land\n",
" 5 Nordenskiöld Land\n",
" 6 Nordenskiöld Land\n",
" 7 Nordenskiöld Land\n",
" 8 Nordenskiöld Land\n",
" 9 Nordenskiöld Land\n",
" 10 Nordenskiöld Land\n",
" 11 Nordenskiöld Land\n",
" 12 Nordenskiöld Land\n",
" 13 Nordenskiöld Land\n",
" 14 Nordenskiöld Land\n",
" 15 Nordenskiöld Land\n",
" 16 Nordenskiöld Land\n",
" 17 Nordenskiöld Land\n",
" 18 Nordenskiöld Land\n",
" 19 Nordenskiöld Land\n",
" 20 Nordenskiöld Land\n",
" 21 Nordenskiöld Land\n",
" 22 Nordenskiöld Land\n",
" 23 Nordenskiöld Land\n",
" 24 Nordenskiöld Land\n",
" 25 Nordenskiöld Land\n",
" 26 Nordenskiöld Land\n",
" 27 Nordenskiöld Land\n",
" 28 Nordenskiöld Land\n",
" 29 Nordenskiöld Land\n",
" ... \n",
" 33234 Vest-Telemark\n",
" 33235 Vest-Telemark\n",
" 33236 Vest-Telemark\n",
" 33237 Vest-Telemark\n",
" 33238 Vest-Telemark\n",
" 33239 Vest-Telemark\n",
" 33240 Vest-Telemark\n",
" 33241 Vest-Telemark\n",
" 33242 Vest-Telemark\n",
" 33243 Vest-Telemark\n",
" 33244 Vest-Telemark\n",
" 33245 Vest-Telemark\n",
" 33246 Vest-Telemark\n",
" 33247 Vest-Telemark\n",
" 33248 Vest-Telemark\n",
" 33249 Vest-Telemark\n",
" 33250 Vest-Telemark\n",
" 33251 Vest-Telemark\n",
" 33252 Vest-Telemark\n",
" 33253 Vest-Telemark\n",
" 33254 Vest-Telemark\n",
" 33255 Vest-Telemark\n",
" 33256 Vest-Telemark\n",
" 33257 Vest-Telemark\n",
" 33258 Vest-Telemark\n",
" 33259 Vest-Telemark\n",
" 33260 Vest-Telemark\n",
" 33261 Vest-Telemark\n",
" 33262 Vest-Telemark\n",
" 33263 Vest-Telemark\n",
" Name: region_name, Length: 33264, dtype: object, 0 0\n",
" 1 104166\n",
" 2 104482\n",
" 3 104622\n",
" 4 104723\n",
" 5 104843\n",
" 6 105009\n",
" 7 105202\n",
" 8 105355\n",
" 9 105498\n",
" 10 105663\n",
" 11 105746\n",
" 12 105852\n",
" 13 105955\n",
" 14 106090\n",
" 15 106228\n",
" 16 106340\n",
" 17 106488\n",
" 18 106566\n",
" 19 106810\n",
" 20 106812\n",
" 21 106895\n",
" 22 106995\n",
" 23 107133\n",
" 24 107251\n",
" 25 107348\n",
" 26 107448\n",
" 27 107559\n",
" 28 107728\n",
" 29 107876\n",
" ... \n",
" 33234 173413\n",
" 33235 173531\n",
" 33236 173707\n",
" 33237 173878\n",
" 33238 174065\n",
" 33239 174228\n",
" 33240 174369\n",
" 33241 174502\n",
" 33242 174670\n",
" 33243 174857\n",
" 33244 175025\n",
" 33245 175204\n",
" 33246 175357\n",
" 33247 175525\n",
" 33248 175783\n",
" 33249 175863\n",
" 33250 176059\n",
" 33251 176262\n",
" 33252 176464\n",
" 33253 176635\n",
" 33254 176786\n",
" 33255 176951\n",
" 33256 177125\n",
" 33257 177337\n",
" 33258 177521\n",
" 33259 177866\n",
" 33260 177982\n",
" 33261 178028\n",
" 33262 178172\n",
" 33263 178307\n",
" Name: reg_id, Length: 33264, dtype: int64, 0 2016-12-01 00:00:00.000\n",
" 1 2016-12-02 00:00:00.000\n",
" 2 2016-12-03 00:00:00.000\n",
" 3 2016-12-04 00:00:00.000\n",
" 4 2016-12-05 00:00:00.000\n",
" 5 2016-12-06 00:00:00.000\n",
" 6 2016-12-07 00:00:00.000\n",
" 7 2016-12-08 00:00:00.000\n",
" 8 2016-12-09 00:00:00.000\n",
" 9 2016-12-10 00:00:00.000\n",
" 10 2016-12-11 00:00:00.000\n",
" 11 2016-12-12 00:00:00.000\n",
" 12 2016-12-13 00:00:00.000\n",
" 13 2016-12-14 00:00:00.000\n",
" 14 2016-12-15 00:00:00.000\n",
" 15 2016-12-16 00:00:00.000\n",
" 16 2016-12-17 00:00:00.000\n",
" 17 2016-12-18 00:00:00.000\n",
" 18 2016-12-19 00:00:00.000\n",
" 19 2016-12-20 00:00:00.000\n",
" 20 2016-12-21 00:00:00.000\n",
" 21 2016-12-22 00:00:00.000\n",
" 22 2016-12-23 00:00:00.000\n",
" 23 2016-12-24 00:00:00.000\n",
" 24 2016-12-25 00:00:00.000\n",
" 25 2016-12-26 00:00:00.000\n",
" 26 2016-12-27 00:00:00.000\n",
" 27 2016-12-28 00:00:00.000\n",
" 28 2016-12-29 00:00:00.000\n",
" 29 2016-12-30 00:00:00.000\n",
" ... \n",
" 33234 2019-01-02 00:00:00.000\n",
" 33235 2019-01-03 00:00:00.000\n",
" 33236 2019-01-04 00:00:00.000\n",
" 33237 2019-01-05 00:00:00.000\n",
" 33238 2019-01-06 00:00:00.000\n",
" 33239 2019-01-07 00:00:00.000\n",
" 33240 2019-01-08 00:00:00.000\n",
" 33241 2019-01-09 00:00:00.000\n",
" 33242 2019-01-10 00:00:00.000\n",
" 33243 2019-01-11 00:00:00.000\n",
" 33244 2019-01-12 00:00:00.000\n",
" 33245 2019-01-13 00:00:00.000\n",
" 33246 2019-01-14 00:00:00.000\n",
" 33247 2019-01-15 00:00:00.000\n",
" 33248 2019-01-16 00:00:00.000\n",
" 33249 2019-01-17 00:00:00.000\n",
" 33250 2019-01-18 00:00:00.000\n",
" 33251 2019-01-19 00:00:00.000\n",
" 33252 2019-01-20 00:00:00.000\n",
" 33253 2019-01-21 00:00:00.000\n",
" 33254 2019-01-22 00:00:00.000\n",
" 33255 2019-01-23 00:00:00.000\n",
" 33256 2019-01-24 00:00:00.000\n",
" 33257 2019-01-25 00:00:00.000\n",
" 33258 2019-01-26 00:00:00.000\n",
" 33259 2019-01-27 00:00:00.000\n",
" 33260 2019-01-28 00:00:00.000\n",
" 33261 2019-01-29 00:00:00.000\n",
" 33262 2019-01-30 00:00:00.000\n",
" 33263 2019-01-31 00:00:00.000\n",
" Name: valid_from, Length: 33264, dtype: object, 0 2016-12-01 23:59:59.000\n",
" 1 2016-12-02 23:59:59.000\n",
" 2 2016-12-03 23:59:59.000\n",
" 3 2016-12-04 23:59:59.000\n",
" 4 2016-12-05 23:59:59.000\n",
" 5 2016-12-06 23:59:59.000\n",
" 6 2016-12-07 23:59:59.000\n",
" 7 2016-12-08 23:59:59.000\n",
" 8 2016-12-09 23:59:59.000\n",
" 9 2016-12-10 23:59:59.000\n",
" 10 2016-12-11 23:59:59.000\n",
" 11 2016-12-12 23:59:59.000\n",
" 12 2016-12-13 23:59:59.000\n",
" 13 2016-12-14 23:59:59.000\n",
" 14 2016-12-15 23:59:59.000\n",
" 15 2016-12-16 23:59:59.000\n",
" 16 2016-12-17 23:59:59.000\n",
" 17 2016-12-18 23:59:59.000\n",
" 18 2016-12-19 23:59:59.000\n",
" 19 2016-12-20 23:59:59.000\n",
" 20 2016-12-21 23:59:59.000\n",
" 21 2016-12-22 23:59:59.000\n",
" 22 2016-12-23 23:59:59.000\n",
" 23 2016-12-24 23:59:59.000\n",
" 24 2016-12-25 23:59:59.000\n",
" 25 2016-12-26 23:59:59.000\n",
" 26 2016-12-27 23:59:59.000\n",
" 27 2016-12-28 23:59:59.000\n",
" 28 2016-12-29 23:59:59.000\n",
" 29 2016-12-30 23:59:59.000\n",
" ... \n",
" 33234 2019-01-02 23:59:59.000\n",
" 33235 2019-01-03 23:59:59.000\n",
" 33236 2019-01-04 23:59:59.000\n",
" 33237 2019-01-05 23:59:59.000\n",
" 33238 2019-01-06 23:59:59.000\n",
" 33239 2019-01-07 23:59:59.000\n",
" 33240 2019-01-08 23:59:59.000\n",
" 33241 2019-01-09 23:59:59.000\n",
" 33242 2019-01-10 23:59:59.000\n",
" 33243 2019-01-11 23:59:59.000\n",
" 33244 2019-01-12 23:59:59.000\n",
" 33245 2019-01-13 23:59:59.000\n",
" 33246 2019-01-14 23:59:59.000\n",
" 33247 2019-01-15 23:59:59.000\n",
" 33248 2019-01-16 23:59:59.000\n",
" 33249 2019-01-17 23:59:59.000\n",
" 33250 2019-01-18 23:59:59.000\n",
" 33251 2019-01-19 23:59:59.000\n",
" 33252 2019-01-20 23:59:59.000\n",
" 33253 2019-01-21 23:59:59.000\n",
" 33254 2019-01-22 23:59:59.000\n",
" 33255 2019-01-23 23:59:59.000\n",
" 33256 2019-01-24 23:59:59.000\n",
" 33257 2019-01-25 23:59:59.000\n",
" 33258 2019-01-26 23:59:59.000\n",
" 33259 2019-01-27 23:59:59.000\n",
" 33260 2019-01-28 23:59:59.000\n",
" 33261 2019-01-29 23:59:59.000\n",
" 33262 2019-01-30 23:59:59.000\n",
" 33263 2019-01-31 23:59:59.000\n",
" Name: valid_to, Length: 33264, dtype: object]"
]
},
"execution_count": 256,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"del_list = [\n",
" 'utm_zone',\n",
" 'utm_east',\n",
" 'utm_north',\n",
" 'danger_level_name',\n",
" 'avalanche_problem_1_exposed_height_fill',\n",
" 'avalanche_problem_2_exposed_height_fill',\n",
" 'avalanche_problem_3_exposed_height_fill',\n",
" 'avalanche_problem_1_valid_expositions',\n",
" 'avalanche_problem_2_valid_expositions',\n",
" 'avalanche_problem_3_valid_expositions',\n",
" 'avalanche_problem_1_cause_name',\n",
" 'avalanche_problem_1_problem_type_name',\n",
" 'avalanche_problem_1_destructive_size_ext_name',\n",
" 'avalanche_problem_1_distribution_name',\n",
" 'avalanche_problem_1_ext_name',\n",
" 'avalanche_problem_1_probability_name',\n",
" 'avalanche_problem_1_trigger_simple_name',\n",
" 'avalanche_problem_1_type_name',\n",
" 'avalanche_problem_2_cause_name',\n",
" 'avalanche_problem_2_problem_type_name',\n",
" 'avalanche_problem_2_destructive_size_ext_name',\n",
" 'avalanche_problem_2_distribution_name',\n",
" 'avalanche_problem_2_ext_name',\n",
" 'avalanche_problem_2_probability_name',\n",
" 'avalanche_problem_2_trigger_simple_name',\n",
" 'avalanche_problem_2_type_name',\n",
" 'avalanche_problem_3_cause_name',\n",
" 'avalanche_problem_3_problem_type_name',\n",
" 'avalanche_problem_3_destructive_size_ext_name',\n",
" 'avalanche_problem_3_distribution_name',\n",
" 'avalanche_problem_3_ext_name',\n",
" 'avalanche_problem_3_probability_name',\n",
" 'avalanche_problem_3_trigger_simple_name',\n",
" 'avalanche_problem_3_type_name',\n",
" 'latest_avalanche_activity',\n",
" 'main_text',\n",
" 'snow_surface',\n",
" 'current_weak_layers',\n",
" 'avalanche_danger',\n",
" 'avalanche_problem_1_advice',\n",
" 'avalanche_problem_2_advice',\n",
" 'avalanche_problem_3_advice',\n",
" 'mountain_weather_wind_speed',\n",
" 'region_type_name',\n",
" 'region_name',\n",
" 'reg_id',\n",
" 'valid_from',\n",
" 'valid_to'\n",
"]\n",
"removed_ = [varsom_df.pop(v) for v in del_list]\n",
"removed_"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {}
},
"source": [
"Fill missing values where necessary"
]
},
{
"cell_type": "code",
"execution_count": 257,
"metadata": {
"collapsed": false,
"pycharm": {
"is_executing": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"[None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None,\n",
" None]"
]
},
"execution_count": 257,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fill_list = [\n",
" 'mountain_weather_freezing_level',\n",
" 'mountain_weather_precip_region',\n",
" 'mountain_weather_precip_region_prev1day',\n",
" 'mountain_weather_precip_region_prev3daysum',\n",
" 'mountain_weather_precip_most_exposed',\n",
" 'mountain_weather_precip_most_exposed_prev1day',\n",
" 'mountain_weather_temperature_min',\n",
" 'mountain_weather_temperature_max',\n",
" 'mountain_weather_temperature_elevation',\n",
" 'danger_level_prev3day',\n",
" 'avalanche_problem_1_problem_type_id_prev3day',\n",
" 'avalanche_problem_2_problem_type_id_prev3day',\n",
" 'avalanche_problem_2_cause_id_prev3day',\n",
" 'avalanche_problem_1_cause_id_prev3day',\n",
" 'danger_level_prev2day',\n",
" 'avalanche_problem_1_cause_id_prev2day',\n",
" 'avalanche_problem_1_problem_type_id_prev2day',\n",
" 'avalanche_problem_2_cause_id_prev2day',\n",
" 'avalanche_problem_2_problem_type_id_prev2day',\n",
" 'avalanche_problem_2_cause_id_prev1day',\n",
" 'avalanche_problem_2_problem_type_id_prev1day',\n",
" 'avalanche_problem_1_problem_type_id_prev1day',\n",
" 'avalanche_problem_1_cause_id_prev1day',\n",
" 'danger_level_prev1day'\n",
"]\n",
"filled_ = [varsom_df[v].fillna(0., inplace=True) for v in fill_list]\n",
"filled_"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {}
},
"source": [
"Eventually remove variables with many missing values."
]
},
{
"cell_type": "code",
"execution_count": 258,
"metadata": {
"collapsed": false,
"pycharm": {
"is_executing": false
}
},
"outputs": [],
"source": [
"del_list = [\n",
" 'danger_level_name_prev1day', 'danger_level_name_prev2day', 'danger_level_name_prev3day',\n",
" 'mountain_weather_change_wind_direction',\n",
" 'mountain_weather_change_hour_of_day_start',\n",
" 'mountain_weather_change_hour_of_day_stop',\n",
" 'mountain_weather_change_wind_speed',\n",
" 'mountain_weather_fl_hour_of_day_stop',\n",
" 'mountain_weather_fl_hour_of_day_start',\n",
" 'latest_observations', 'publish_time', 'date_valid',\n",
" 'mountain_weather_temperature_max_prev3day', 'mountain_weather_temperature_min_prev3day',\n",
" 'mountain_weather_temperature_max_prev2day',\n",
" 'mountain_weather_temperature_min_prev2day',\n",
" 'mountain_weather_temperature_max_prev1day',\n",
" 'mountain_weather_temperature_min_prev1day'\n",
"]\n",
"removed_ = [varsom_df.pop(v) for v in del_list]"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {}
},
"source": [
"Check again if there are still values missing...\n",
"\n",
"need to replace these Nans with meaningful values or remove the feature."
]
},
{
"cell_type": "code",
"execution_count": 259,
"metadata": {
"collapsed": false,
"pycharm": {
"is_executing": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mountain_weather_wind_direction_W 0\n",
"avalanche_problem_3_problem_type_id 0\n",
"avalanche_problem_3_type_id 0\n",
"danger_level 0\n",
"mountain_weather_freezing_level 0\n",
"mountain_weather_precip_most_exposed 0\n",
"mountain_weather_precip_region 0\n",
"mountain_weather_temperature_elevation 0\n",
"mountain_weather_temperature_max 0\n",
"mountain_weather_temperature_min 0\n",
"region_id 0\n",
"region_type_id 0\n",
"date 0\n",
"danger_level_prev1day 0\n",
"danger_level_prev2day 0\n",
"danger_level_prev3day 0\n",
"avalanche_problem_1_cause_id_prev1day 0\n",
"avalanche_problem_1_problem_type_id_prev1day 0\n",
"avalanche_problem_1_cause_id_prev2day 0\n",
"avalanche_problem_1_problem_type_id_prev2day 0\n",
"avalanche_problem_1_cause_id_prev3day 0\n",
"avalanche_problem_1_problem_type_id_prev3day 0\n",
"avalanche_problem_2_cause_id_prev1day 0\n",
"avalanche_problem_2_problem_type_id_prev1day 0\n",
"avalanche_problem_2_cause_id_prev2day 0\n",
"avalanche_problem_2_problem_type_id_prev2day 0\n",
"avalanche_problem_2_cause_id_prev3day 0\n",
"avalanche_problem_2_problem_type_id_prev3day 0\n",
"mountain_weather_precip_region_prev1day 0\n",
"avalanche_problem_3_trigger_simple_id 0\n",
" ..\n",
"mountain_weather_wind_direction_num 0\n",
"author_Matilda@MET 0\n",
"avalanche_problem_1_problem_type_id_class 0\n",
"avalanche_problem_1_sensitivity_id_class 0\n",
"avalanche_problem_1_trigger_simple_id_class 0\n",
"avalanche_problem_2_problem_type_id_class 0\n",
"avalanche_problem_2_sensitivity_id_class 0\n",
"avalanche_problem_2_trigger_simple_id_class 0\n",
"avalanche_problem_3_problem_type_id_class 0\n",
"avalanche_problem_3_sensitivity_id_class 0\n",
"avalanche_problem_3_trigger_simple_id_class 0\n",
"region_group_id 0\n",
"aval_problem_1_combined 0\n",
"emergency_warning_Ikke gitt 0\n",
"emergency_warning_Naturlig utløste skred 0\n",
"author_Andreas@nve 0\n",
"author_Eldbjorg@MET 0\n",
"author_Espen Granan 0\n",
"author_EspenN 0\n",
"author_Halvor@NVE 0\n",
"author_HåvardT@met 0\n",
"author_Ida@met 0\n",
"author_Ingrid@NVE 0\n",
"author_John Smits 0\n",
"author_JonasD@ObsKorps 0\n",
"author_Julie@SVV 0\n",
"author_Jørgen@obskorps 0\n",
"author_Karsten@NVE 0\n",
"author_MSA@nortind 0\n",
"avalanche_problem_1_cause_id 0\n",
"Length: 123, dtype: int64\n"
]
}
],
"source": [
"# Find the amount of NaN values in each column\n",
"print(varsom_df.isnull().sum().sort_values(ascending=False))"
]
},
{
"cell_type": "code",
"execution_count": 260,
"metadata": {
"collapsed": false,
"pycharm": {
"is_executing": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x2a725baac18>"
]
},
"execution_count": 260,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAzIAAALrCAYAAAAlc7J+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
"text/plain": [
"<Figure size 792x792 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Compute the correlation matrix - works only on numerical variables.\n",
"corr = varsom_df.corr()\n",
"\n",
"# Generate a mask for the upper triangle\n",
"mask = np.zeros_like(corr, dtype=np.bool)\n",
"mask[np.triu_indices_from(mask)] = True\n",
"\n",
"# Set up the matplotlib figure\n",
"f, ax = plt.subplots(figsize=(11, 11))\n",
"\n",
"# Generate a custom diverging colormap\n",
"cmap = sns.diverging_palette(1000, 15, as_cmap=True)\n",
"\n",
"# Draw the heatmap with the mask and correct aspect ratio\n",
"sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.8, center=0,\n",
" square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {}
},
"source": [
"We can see that some parameters are highly correlated. These are mainly the parameters belonging to the same avalanche problem. Depending on the ML algorithm we use we have to remove some of them."
]
},
{
"cell_type": "code",
"execution_count": 261,
"metadata": {
"collapsed": true,
"pycharm": {
"is_executing": false
}
},
"outputs": [],
"source": [
"#corr['avalanche_problem_1_cause_id'].sort_values(ascending=False)\n",
"#corr"
]
},
{
"cell_type": "code",
"execution_count": 262,
"metadata": {
"collapsed": true,
"pycharm": {
"is_executing": false
}
},
"outputs": [],
"source": [
"#sns.pairplot(varsom_df.drop(['date_valid'], axis=1))"
]
},
{
"cell_type": "code",
"execution_count": 263,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['avalanche_problem_1_cause_id',\n",
" 'avalanche_problem_1_destructive_size_ext_id',\n",
" 'avalanche_problem_1_distribution_id',\n",
" 'avalanche_problem_1_exposed_height_1',\n",
" 'avalanche_problem_1_exposed_height_2', 'avalanche_problem_1_ext_id',\n",
" 'avalanche_problem_1_probability_id', 'avalanche_problem_1_problem_id',\n",
" 'avalanche_problem_1_problem_type_id',\n",
" 'avalanche_problem_1_trigger_simple_id',\n",
" ...\n",
" 'mountain_weather_wind_direction_E',\n",
" 'mountain_weather_wind_direction_N',\n",
" 'mountain_weather_wind_direction_NE',\n",
" 'mountain_weather_wind_direction_NW',\n",
" 'mountain_weather_wind_direction_None',\n",
" 'mountain_weather_wind_direction_Not given',\n",
" 'mountain_weather_wind_direction_S',\n",
" 'mountain_weather_wind_direction_SE',\n",
" 'mountain_weather_wind_direction_SW',\n",
" 'mountain_weather_wind_direction_W'],\n",
" dtype='object', length=122)"
]
},
"execution_count": 263,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get all numerical features\n",
"\n",
"num_feat = varsom_df._get_numeric_data().columns\n",
"num_feat"
]
},
{
"cell_type": "code",
"execution_count": 264,
"metadata": {
"collapsed": false,
"pycharm": {
"is_executing": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>avalanche_problem_1_cause_id</th>\n",
" <th>avalanche_problem_1_destructive_size_ext_id</th>\n",
" <th>avalanche_problem_1_distribution_id</th>\n",
" <th>avalanche_problem_1_exposed_height_1</th>\n",
" <th>avalanche_problem_1_exposed_height_2</th>\n",
" <th>avalanche_problem_1_ext_id</th>\n",
" <th>avalanche_problem_1_probability_id</th>\n",
" <th>avalanche_problem_1_problem_id</th>\n",
" <th>avalanche_problem_1_problem_type_id</th>\n",
" <th>avalanche_problem_1_trigger_simple_id</th>\n",
" <th>...</th>\n",
" <th>mountain_weather_wind_direction_E</th>\n",
" <th>mountain_weather_wind_direction_N</th>\n",
" <th>mountain_weather_wind_direction_NE</th>\n",
" <th>mountain_weather_wind_direction_NW</th>\n",
" <th>mountain_weather_wind_direction_None</th>\n",
" <th>mountain_weather_wind_direction_Not given</th>\n",
" <th>mountain_weather_wind_direction_S</th>\n",
" <th>mountain_weather_wind_direction_SE</th>\n",
" <th>mountain_weather_wind_direction_SW</th>\n",
" <th>mountain_weather_wind_direction_W</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>33264.000000</td>\n",
" <td>33264.000000</td>\n",
" <td>33264.000000</td>\n",
" <td>33264.000000</td>\n",
" <td>33264.000000</td>\n",
" <td>33264.000000</td>\n",
" <td>33264.000000</td>\n",
" <td>33264.000000</td>\n",
" <td>33264.000000</td>\n",
" <td>33264.000000</td>\n",
" <td>...</td>\n",
" <td>33264.000000</td>\n",
" <td>33264.000000</td>\n",
" <td>33264.000000</td>\n",
" <td>33264.000000</td>\n",
" <td>33264.000000</td>\n",
" <td>33264.000000</td>\n",
" <td>33264.000000</td>\n",
" <td>33264.000000</td>\n",
" <td>33264.000000</td>\n",
" <td>33264.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>7.504990</td>\n",
" <td>1.160955</td>\n",
" <td>1.012145</td>\n",
" <td>335.918711</td>\n",
" <td>34.704185</td>\n",
" <td>10.563672</td>\n",
" <td>1.642496</td>\n",
" <td>0.536195</td>\n",
" <td>7.453523</td>\n",
" <td>10.168951</td>\n",
" <td>...</td>\n",
" <td>0.027718</td>\n",
" <td>0.012205</td>\n",
" <td>0.013949</td>\n",
" <td>0.027898</td>\n",
" <td>0.004630</td>\n",
" <td>0.690536</td>\n",
" <td>0.034392</td>\n",
" <td>0.097042</td>\n",
" <td>0.051166</td>\n",
" <td>0.040464</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>7.876866</td>\n",
" <td>1.170598</td>\n",
" <td>1.026995</td>\n",
" <td>401.052501</td>\n",
" <td>150.560137</td>\n",
" <td>9.951452</td>\n",
" <td>1.561384</td>\n",
" <td>0.498696</td>\n",
" <td>10.807930</td>\n",
" <td>10.027306</td>\n",
" <td>...</td>\n",
" <td>0.164165</td>\n",
" <td>0.109803</td>\n",
" <td>0.117281</td>\n",
" <td>0.164683</td>\n",
" <td>0.067885</td>\n",
" <td>0.462280</td>\n",
" <td>0.182235</td>\n",
" <td>0.296019</td>\n",
" <td>0.220340</td>\n",
" <td>0.197048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>10.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>200.000000</td>\n",
" <td>0.000000</td>\n",
" <td>15.000000</td>\n",
" <td>3.000000</td>\n",
" <td>1.000000</td>\n",
" <td>5.000000</td>\n",
" <td>10.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>13.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>600.000000</td>\n",
" <td>0.000000</td>\n",
" <td>20.000000</td>\n",
" <td>3.000000</td>\n",
" <td>1.000000</td>\n",
" <td>10.000000</td>\n",
" <td>21.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>24.000000</td>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
" <td>2100.000000</td>\n",
" <td>2000.000000</td>\n",
" <td>25.000000</td>\n",
" <td>5.000000</td>\n",
" <td>1.000000</td>\n",
" <td>50.000000</td>\n",
" <td>22.000000</td>\n",
" <td>...</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 122 columns</p>\n",
"</div>"
],
"text/plain": [
" avalanche_problem_1_cause_id \\\n",
"count 33264.000000 \n",
"mean 7.504990 \n",
"std 7.876866 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 10.000000 \n",
"75% 13.000000 \n",
"max 24.000000 \n",
"\n",
" avalanche_problem_1_destructive_size_ext_id \\\n",
"count 33264.000000 \n",
"mean 1.160955 \n",
"std 1.170598 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 1.000000 \n",
"75% 2.000000 \n",
"max 4.000000 \n",
"\n",
" avalanche_problem_1_distribution_id \\\n",
"count 33264.000000 \n",
"mean 1.012145 \n",
"std 1.026995 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 1.000000 \n",
"75% 2.000000 \n",
"max 4.000000 \n",
"\n",
" avalanche_problem_1_exposed_height_1 \\\n",
"count 33264.000000 \n",
"mean 335.918711 \n",
"std 401.052501 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 200.000000 \n",
"75% 600.000000 \n",
"max 2100.000000 \n",
"\n",
" avalanche_problem_1_exposed_height_2 avalanche_problem_1_ext_id \\\n",
"count 33264.000000 33264.000000 \n",
"mean 34.704185 10.563672 \n",
"std 150.560137 9.951452 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 0.000000 \n",
"50% 0.000000 15.000000 \n",
"75% 0.000000 20.000000 \n",
"max 2000.000000 25.000000 \n",
"\n",
" avalanche_problem_1_probability_id avalanche_problem_1_problem_id \\\n",
"count 33264.000000 33264.000000 \n",
"mean 1.642496 0.536195 \n",
"std 1.561384 0.498696 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 0.000000 \n",
"50% 3.000000 1.000000 \n",
"75% 3.000000 1.000000 \n",
"max 5.000000 1.000000 \n",
"\n",
" avalanche_problem_1_problem_type_id \\\n",
"count 33264.000000 \n",
"mean 7.453523 \n",
"std 10.807930 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 5.000000 \n",
"75% 10.000000 \n",
"max 50.000000 \n",
"\n",
" avalanche_problem_1_trigger_simple_id \\\n",
"count 33264.000000 \n",
"mean 10.168951 \n",
"std 10.027306 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 10.000000 \n",
"75% 21.000000 \n",
"max 22.000000 \n",
"\n",
" ... mountain_weather_wind_direction_E \\\n",
"count ... 33264.000000 \n",
"mean ... 0.027718 \n",
"std ... 0.164165 \n",
"min ... 0.000000 \n",
"25% ... 0.000000 \n",
"50% ... 0.000000 \n",
"75% ... 0.000000 \n",
"max ... 1.000000 \n",
"\n",
" mountain_weather_wind_direction_N mountain_weather_wind_direction_NE \\\n",
"count 33264.000000 33264.000000 \n",
"mean 0.012205 0.013949 \n",
"std 0.109803 0.117281 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 0.000000 \n",
"50% 0.000000 0.000000 \n",
"75% 0.000000 0.000000 \n",
"max 1.000000 1.000000 \n",
"\n",
" mountain_weather_wind_direction_NW \\\n",
"count 33264.000000 \n",
"mean 0.027898 \n",
"std 0.164683 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 0.000000 \n",
"75% 0.000000 \n",
"max 1.000000 \n",
"\n",
" mountain_weather_wind_direction_None \\\n",
"count 33264.000000 \n",
"mean 0.004630 \n",
"std 0.067885 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 0.000000 \n",
"75% 0.000000 \n",
"max 1.000000 \n",
"\n",
" mountain_weather_wind_direction_Not given \\\n",
"count 33264.000000 \n",
"mean 0.690536 \n",
"std 0.462280 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 1.000000 \n",
"75% 1.000000 \n",
"max 1.000000 \n",
"\n",
" mountain_weather_wind_direction_S mountain_weather_wind_direction_SE \\\n",
"count 33264.000000 33264.000000 \n",
"mean 0.034392 0.097042 \n",
"std 0.182235 0.296019 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 0.000000 \n",
"50% 0.000000 0.000000 \n",
"75% 0.000000 0.000000 \n",
"max 1.000000 1.000000 \n",
"\n",
" mountain_weather_wind_direction_SW mountain_weather_wind_direction_W \n",
"count 33264.000000 33264.000000 \n",
"mean 0.051166 0.040464 \n",
"std 0.220340 0.197048 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 0.000000 \n",
"50% 0.000000 0.000000 \n",
"75% 0.000000 0.000000 \n",
"max 1.000000 1.000000 \n",
"\n",
"[8 rows x 122 columns]"
]
},
"execution_count": 264,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# let's see the details about remainig variables \n",
"\n",
"varsom_df.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {}
},
"source": [
"## Save data for further analysis"
]
},
{
"cell_type": "code",
"execution_count": 265,
"metadata": {
"collapsed": true,
"pycharm": {
"is_executing": false
}
},
"outputs": [],
"source": [
"varsom_df.to_csv('varsom_ml_preproc_3y.csv', index_label='index')"
]
},
{
"cell_type": "markdown",
"metadata": {
"pycharm": {}
},
"source": [
"#### Now we have clean data and can build a model\n",
"\n",
"The library we'll use is called **sckit-learn**. \n",
"\n",
"http://scikit-learn.org\n",
"\n",
"- Python library\n",
"- Access to well known machine learning algorithms\n",
"- Built on NumPy, SciPy, and matplotlib\n",
"- Open Source\n",
"- Well documented with many good tutorials\n",
"\n",
"\n",
"## Worklflow of scikit-learn\n",
"\n",
"- Create model object\n",
"- .fit\n",
"- .predict\n",
"- evaluate"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true,
"pycharm": {
"is_executing": false
}
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
},
"pycharm": {
"stem_cell": {
"cell_type": "raw",
"source": [],
"metadata": {
"collapsed": false
}
}
}
},
"nbformat": 4,
"nbformat_minor": 2
} | mit |
elsonidoq/fito | examples/Why using fito?.ipynb | 1 | 7471 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Why using fito?"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Some printing boilerplate\n",
"\n",
"from IPython.display import Markdown, display\n",
"def printmd(string):\n",
" display(Markdown(string))\n",
" \n",
"def print_expr(expr, new_line=False):\n",
" print \"{:<20} {:<20}{}{}\".format(\n",
" expr, \n",
" \"evaluates to\", \n",
" \"\\n\" if new_line else \"\", \n",
" eval(expr)\n",
" )\n",
"\n",
"def print_title(title):\n",
" printmd(\"**{}**\".format(title))"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"f(1) evaluates to f(a=1, b=2)\n",
"f(1).execute() evaluates to 3\n"
]
}
],
"source": [
"from pprint import pprint\n",
"from fito import as_operation, Spec\n",
"\n",
"# Define the f operation\n",
"@as_operation()\n",
"def f(a, b=2): return a+b\n",
"\n",
"print_expr(\"f(1)\")\n",
"print_expr(\"f(1).execute()\")"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Define the g operation. \n",
"# This decorator call specifies that parameter a come from instances of f\n",
"@as_operation(a=f)\n",
"def g(a, c): return a*c\n",
"\n",
"# This call instances operation g\n",
"operation = g.auto_instance(\n",
" {\n",
" 'c': 1,\n",
" 'a': {\n",
" 'a': 2\n",
" }\n",
" }\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/markdown": [
"**It is human readable**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"operation evaluates to g(a=f(a=2, b=2), c=1)\n"
]
},
{
"data": {
"text/markdown": [
"**It works! (lot of testing)**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"operation.execute() evaluates to 4\n"
]
},
{
"data": {
"text/markdown": [
"**Wanna send it to a some API?**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"operation.json.dumps() evaluates to \n",
"{\n",
" \"a\": {\n",
" \"a\": 2, \n",
" \"b\": 2, \n",
" \"type\": \"__main__:f\"\n",
" }, \n",
" \"c\": 1, \n",
" \"type\": \"__main__:g\"\n",
"}\n"
]
},
{
"data": {
"text/markdown": [
"**Wanna write it on a config file?**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"operation.yaml.dumps() evaluates to \n",
"a:\n",
" a: 2\n",
" b: 2\n",
" type: __main__:f\n",
"c: 1\n",
"type: __main__:g\n",
"\n"
]
},
{
"data": {
"text/markdown": [
"**Wanna read it back? :P**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Spec.from_yaml().loads(operation.yaml.dumps()) evaluates to g(a=f(a=2, b=2), c=1)\n"
]
},
{
"data": {
"text/markdown": [
"**In case you were doubting...**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Spec.from_yaml().loads(operation.yaml.dumps()).execute() evaluates to 4\n"
]
},
{
"data": {
"text/markdown": [
"**Wanna hash it into a key value store?**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{operation: {'precision': 0.8, 'recall':0.1}} evaluates to \n",
"{g(a=f(a=2, b=2), c=1): {'recall': 0.1, 'precision': 0.8}}\n",
"d[operation] evaluates to {'recall': 0.1, 'precision': 0.8}\n"
]
},
{
"data": {
"text/markdown": [
"**Wanna get it back from the config file? ^j^**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"d[Spec.from_yaml().loads(operation.yaml.dumps())] evaluates to {'recall': 0.1, 'precision': 0.8}\n"
]
}
],
"source": [
"print_title(\"It is human readable\")\n",
"print_expr(\"operation\")\n",
"\n",
"print_title(\"It works! (lot of testing)\")\n",
"print_expr(\"operation.execute()\")\n",
"\n",
"print_title(\"Wanna send it to a some API?\")\n",
"print_expr(\"operation.json.dumps()\", new_line=True)\n",
"\n",
"print_title(\"Wanna write it on a config file?\")\n",
"print_expr(\"operation.yaml.dumps()\", new_line=True)\n",
"\n",
"print_title(\"Wanna read it back? :P\")\n",
"print_expr(\"Spec.from_yaml().loads(operation.yaml.dumps())\")\n",
"\n",
"print_title(\"In case you were doubting...\")\n",
"print_expr(\"Spec.from_yaml().loads(operation.yaml.dumps()).execute()\")\n",
"\n",
"print_title(\"Wanna hash it into a key value store?\")\n",
"print_expr(\"{operation: {'precision': 0.8, 'recall':0.1}}\", new_line=True)\n",
"d = {operation: {'precision': 0.8, 'recall':0.1}}\n",
"print_expr(\"d[operation]\")\n",
"\n",
"print_title(\"Wanna get it back from the config file? ^j^\")\n",
"print_expr(\"d[Spec.from_yaml().loads(operation.yaml.dumps())]\")\n",
"\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/sdk/SDK_Custom_Container_Prediction.ipynb | 1 | 34769 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ur8xi4C7S06n"
},
"outputs": [],
"source": [
"# Copyright 2021 Google LLC\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JAPoU8Sm5E6e"
},
"source": [
"<table align=\"left\">\n",
" <td>\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/sdk/SDK_Custom_Container_Prediction.ipynb\">\n",
" <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
" View on GitHub\n",
" </a>\n",
" </td>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tvgnzT1CKxrO"
},
"source": [
"## Overview\n",
"\n",
"This tutorial walks through building a custom container to serve a scikit-learn model on Vertex Predictions. You will use the FastAPI Python web server framework to create a prediction and health endpoint.\n",
"You will also cover incorporating a pre-processor from training into your online serving.\n",
"\n",
"\n",
"### Dataset\n",
"\n",
"This tutorial uses R.A. Fisher's Iris dataset, a small dataset that is popular for trying out machine learning techniques. Each instance has four numerical features, which are different measurements of a flower, and a target label that\n",
"marks it as one of three types of iris: Iris setosa, Iris versicolour, or Iris virginica.\n",
"\n",
"This tutorial uses [the copy of the Iris dataset included in the\n",
"scikit-learn library](https://scikit-learn.org/stable/datasets/index.html#iris-dataset).\n",
"\n",
"### Objective\n",
"\n",
"The goal is to:\n",
"- Train a model that uses a flower's measurements as input to predict what type of iris it is.\n",
"- Save the model and its serialized pre-processor\n",
"- Build a FastAPI server to handle predictions and health checks\n",
"- Build a custom container with model artifacts\n",
"- Upload and deploy custom container to Vertex Prediction\n",
"\n",
"This tutorial focuses more on deploying this model with Vertex AI than on\n",
"the design of the model itself.\n",
"\n",
"### Costs \n",
"\n",
"This tutorial uses billable components of Google Cloud:\n",
"\n",
"* Vertex AI\n",
"\n",
"Learn about [Vertex AI\n",
"pricing](https://cloud.google.com/vertex-ai/pricing), and use the [Pricing\n",
"Calculator](https://cloud.google.com/products/calculator/)\n",
"to generate a cost estimate based on your projected usage."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ze4-nDLfK4pw"
},
"source": [
"### Set up your local development environment\n",
"\n",
"**If you are using Colab or Google Cloud Notebooks**, your environment already meets\n",
"all the requirements to run this notebook. You can skip this step."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gCuSR8GkAgzl"
},
"source": [
"**Otherwise**, make sure your environment meets this notebook's requirements.\n",
"You need the following:\n",
"\n",
"* Docker\n",
"* Git\n",
"* Google Cloud SDK (gcloud)\n",
"* Python 3\n",
"* virtualenv\n",
"* Jupyter notebook running in a virtual environment with Python 3\n",
"\n",
"The Google Cloud guide to [Setting up a Python development\n",
"environment](https://cloud.google.com/python/setup) and the [Jupyter\n",
"installation guide](https://jupyter.org/install) provide detailed instructions\n",
"for meeting these requirements. The following steps provide a condensed set of\n",
"instructions:\n",
"\n",
"1. [Install and initialize the Cloud SDK.](https://cloud.google.com/sdk/docs/)\n",
"\n",
"1. [Install Python 3.](https://cloud.google.com/python/setup#installing_python)\n",
"\n",
"1. [Install\n",
" virtualenv](https://cloud.google.com/python/setup#installing_and_using_virtualenv)\n",
" and create a virtual environment that uses Python 3. Activate the virtual environment.\n",
"\n",
"1. To install Jupyter, run `pip install jupyter` on the\n",
"command-line in a terminal shell.\n",
"\n",
"1. To launch Jupyter, run `jupyter notebook` on the command-line in a terminal shell.\n",
"\n",
"1. Open this notebook in the Jupyter Notebook Dashboard."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "i7EUnXsZhAGF"
},
"source": [
"### Install additional packages\n",
"\n",
"Install additional package dependencies not installed in your notebook environment, such as NumPy, Scikit-learn, FastAPI, Uvicorn, and joblib. Use the latest major GA version of each package."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "747f59abb3a5"
},
"outputs": [],
"source": [
"%%writefile requirements.txt\n",
"joblib~=1.0\n",
"numpy~=1.20\n",
"scikit-learn~=0.24\n",
"google-cloud-storage>=1.26.0,<2.0.0dev"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wyy5Lbnzg5fi"
},
"outputs": [],
"source": [
"# Required in Docker serving container\n",
"%pip install -U --user -r requirements.txt\n",
"\n",
"# For local FastAPI development and running\n",
"%pip install -U --user \"uvicorn[standard]>=0.12.0,<0.14.0\" fastapi~=0.63\n",
"\n",
"# Vertex SDK for Python\n",
"%pip install -U --user google-cloud-aiplatform"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hhq5zEbGg0XX"
},
"source": [
"### Restart the kernel\n",
"\n",
"After you install the additional packages, you need to restart the notebook kernel so it can find the packages."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "EzrelQZ22IZj"
},
"outputs": [],
"source": [
"# Automatically restart kernel after installs\n",
"import os\n",
"\n",
"if not os.getenv(\"IS_TESTING\"):\n",
" # Automatically restart kernel after installs\n",
" import IPython\n",
"\n",
" app = IPython.Application.instance()\n",
" app.kernel.do_shutdown(True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lWEdiXsJg0XY"
},
"source": [
"## Before you begin"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BF1j6f9HApxa"
},
"source": [
"### Set up your Google Cloud project\n",
"\n",
"**The following steps are required, regardless of your notebook environment.**\n",
"\n",
"1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
"\n",
"1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
"\n",
"1. [Enable the Vertex AI API and Compute Engine API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,compute_component).\n",
"\n",
"1. If you are running this notebook locally, you will need to install the [Cloud SDK](https://cloud.google.com/sdk).\n",
"\n",
"1. Enter your project ID in the cell below. Then run the cell to make sure the\n",
"Cloud SDK uses the right project for all the commands in this notebook.\n",
"\n",
"**Note**: Jupyter runs lines prefixed with `!` or `%` as shell commands, and it interpolates Python variables with `$` or `{}` into these commands."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WReHDGG5g0XY"
},
"source": [
"#### Set your project ID\n",
"\n",
"**If you don't know your project ID**, you may be able to get your project ID using `gcloud`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "oM1iC_MfAts1"
},
"outputs": [],
"source": [
"# Get your Google Cloud project ID from gcloud\n",
"shell_output=!gcloud config list --format 'value(core.project)' 2>/dev/null\n",
"\n",
"try:\n",
" PROJECT_ID = shell_output[0]\n",
"except IndexError:\n",
" PROJECT_ID = None\n",
"\n",
"print(\"Project ID:\", PROJECT_ID)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qJYoRfYng0XZ"
},
"source": [
"Otherwise, set your project ID here."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "riG_qUokg0XZ"
},
"outputs": [],
"source": [
"if PROJECT_ID == \"\" or PROJECT_ID is None:\n",
" PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dr--iN2kAylZ"
},
"source": [
"### Authenticate your Google Cloud account\n",
"\n",
"**If you are using Google Cloud Notebooks**, your environment is already\n",
"authenticated. Skip this step."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sBCra4QMA2wR"
},
"source": [
"**If you are using Colab**, run the cell below and follow the instructions\n",
"when prompted to authenticate your account via oAuth.\n",
"\n",
"**Otherwise**, follow these steps:\n",
"\n",
"1. In the Cloud Console, go to the [**Create service account key**\n",
" page](https://console.cloud.google.com/apis/credentials/serviceaccountkey).\n",
"\n",
"2. Click **Create service account**.\n",
"\n",
"3. In the **Service account name** field, enter a name, and\n",
" click **Create**.\n",
"\n",
"4. In the **Grant this service account access to project** section, click the **Role** drop-down list. Type \"Vertex AI\"\n",
"into the filter box, and select\n",
" **Vertex AI Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n",
"\n",
"5. Click *Create*. A JSON file that contains your key downloads to your\n",
"local environment.\n",
"\n",
"6. Enter the path to your service account key as the\n",
"`GOOGLE_APPLICATION_CREDENTIALS` variable in the cell below and run the cell."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "PyQmSRbKA8r-"
},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"\n",
"# If you are running this notebook in Colab, run this cell and follow the\n",
"# instructions to authenticate your GCP account. This provides access to your\n",
"# Cloud Storage bucket and lets you submit training jobs and prediction\n",
"# requests.\n",
"\n",
"# If on Google Cloud Notebooks, then don't execute this code\n",
"if not os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n",
" if \"google.colab\" in sys.modules:\n",
" from google.colab import auth as google_auth\n",
"\n",
" google_auth.authenticate_user()\n",
"\n",
" # If you are running this notebook locally, replace the string below with the\n",
" # path to your service account key and run this cell to authenticate your GCP\n",
" # account.\n",
" elif not os.getenv(\"IS_TESTING\") and not os.getenv(\n",
" \"GOOGLE_APPLICATION_CREDENTIALS\"\n",
" ):\n",
" %env GOOGLE_APPLICATION_CREDENTIALS ''"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XoEqT2Y4DJmf"
},
"source": [
"### Configure project and resource names"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "MzGDU7TWdts_"
},
"outputs": [],
"source": [
"REGION = \"us-central1\" # @param {type:\"string\"}\n",
"MODEL_ARTIFACT_DIR = \"custom-container-prediction-model\" # @param {type:\"string\"}\n",
"REPOSITORY = \"custom-container-prediction\" # @param {type:\"string\"}\n",
"IMAGE = \"sklearn-fastapi-server\" # @param {type:\"string\"}\n",
"MODEL_DISPLAY_NAME = \"sklearn-custom-container\" # @param {type:\"string\"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ca1a915d641d"
},
"source": [
"`REGION` - Used for operations\n",
"throughout the rest of this notebook. Make sure to [choose a region where Cloud\n",
"Vertex AI services are\n",
"available](https://cloud.google.com/vertex-ai/docs/general/locations#feature-availability). You may\n",
"not use a Multi-Regional Storage bucket for training with Vertex AI.\n",
"\n",
"`MODEL_ARTIFACT_DIR` - Folder directory path to your model artifacts within a Cloud Storage bucket, for example: \"my-models/fraud-detection/trial-4\"\n",
"\n",
"`REPOSITORY` - Name of the Artifact Repository to create or use.\n",
"\n",
"`IMAGE` - Name of the container image that will be pushed.\n",
"\n",
"`MODEL_DISPLAY_NAME` - Display name of Vertex AI Model resource."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "62f861b68b50"
},
"source": [
"### Create a Cloud Storage bucket\n",
"\n",
"**The following steps are required, regardless of your notebook environment.**\n",
"\n",
"To update your model artifacts without re-building the container, you must upload your model\n",
"artifacts and any custom code to Cloud Storage.\n",
"\n",
"Set the name of your Cloud Storage bucket below. It must be unique across all\n",
"Cloud Storage buckets. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9724b00aeead"
},
"outputs": [],
"source": [
"BUCKET_NAME = \"gs://[your-bucket-name]\" # @param {type:\"string\"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "58cb4f5895f0"
},
"source": [
"**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2d2208676cee"
},
"outputs": [],
"source": [
"! gsutil mb -l $REGION $BUCKET_NAME"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "c664a5abc11a"
},
"source": [
"Finally, validate access to your Cloud Storage bucket by examining its contents:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2c1b1c29f5f6"
},
"outputs": [],
"source": [
"! gsutil ls -al $BUCKET_NAME"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3c2d091d9e73"
},
"source": [
"## Write your pre-processor\n",
"Scaling training data so each numerical feature column has a mean of 0 and a standard deviation of 1 [can improve your model](https://developers.google.com/machine-learning/crash-course/representation/cleaning-data).\n",
"\n",
"Create `preprocess.py`, which contains a class to do this scaling:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "6e74556ea0b4"
},
"outputs": [],
"source": [
"%mkdir app"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "58d843d21fa8"
},
"outputs": [],
"source": [
"%%writefile app/preprocess.py\n",
"import numpy as np\n",
"\n",
"class MySimpleScaler(object):\n",
" def __init__(self):\n",
" self._means = None\n",
" self._stds = None\n",
"\n",
" def preprocess(self, data):\n",
" if self._means is None: # during training only\n",
" self._means = np.mean(data, axis=0)\n",
"\n",
" if self._stds is None: # during training only\n",
" self._stds = np.std(data, axis=0)\n",
" if not self._stds.all():\n",
" raise ValueError(\"At least one column has standard deviation of 0.\")\n",
"\n",
" return (data - self._means) / self._stds\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4b816cd52f4b"
},
"source": [
"## Train and store model with pre-processor\n",
"Next, use `preprocess.MySimpleScaler` to preprocess the iris data, then train a model using scikit-learn.\n",
"\n",
"At the end, export your trained model as a joblib (`.joblib`) file and export your `MySimpleScaler` instance as a pickle (`.pkl`) file:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "43e47249f736"
},
"outputs": [],
"source": [
"%cd app/\n",
"\n",
"import pickle\n",
"\n",
"import joblib\n",
"from preprocess import MySimpleScaler\n",
"from sklearn.datasets import load_iris\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"\n",
"iris = load_iris()\n",
"scaler = MySimpleScaler()\n",
"\n",
"X = scaler.preprocess(iris.data)\n",
"y = iris.target\n",
"\n",
"model = RandomForestClassifier()\n",
"model.fit(X, y)\n",
"\n",
"joblib.dump(model, \"model.joblib\")\n",
"with open(\"preprocessor.pkl\", \"wb\") as f:\n",
" pickle.dump(scaler, f)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3849066a33bd"
},
"source": [
"### Upload model artifacts and custom code to Cloud Storage\n",
"\n",
"Before you can deploy your model for serving, Vertex AI needs access to the following files in Cloud Storage:\n",
"\n",
"* `model.joblib` (model artifact)\n",
"* `preprocessor.pkl` (model artifact)\n",
"\n",
"Run the following commands to upload your files:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ca67ee52d4d9"
},
"outputs": [],
"source": [
"!gsutil cp model.joblib preprocessor.pkl {BUCKET_NAME}/{MODEL_ARTIFACT_DIR}/\n",
"%cd .."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "480a1d88ecdb"
},
"source": [
"## Build a FastAPI server"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "94af0ba5eadd"
},
"outputs": [],
"source": [
"%%writefile app/main.py\n",
"from fastapi import FastAPI, Request\n",
"\n",
"import joblib\n",
"import json\n",
"import numpy as np\n",
"import pickle\n",
"import os\n",
"\n",
"from google.cloud import storage\n",
"from preprocess import MySimpleScaler\n",
"from sklearn.datasets import load_iris\n",
"\n",
"\n",
"app = FastAPI()\n",
"gcs_client = storage.Client()\n",
"\n",
"with open(\"preprocessor.pkl\", 'wb') as preprocessor_f, open(\"model.joblib\", 'wb') as model_f:\n",
" gcs_client.download_blob_to_file(\n",
" f\"{os.environ['AIP_STORAGE_URI']}/preprocessor.pkl\", preprocessor_f\n",
" )\n",
" gcs_client.download_blob_to_file(\n",
" f\"{os.environ['AIP_STORAGE_URI']}/model.joblib\", model_f\n",
" )\n",
"\n",
"with open(\"preprocessor.pkl\", \"rb\") as f:\n",
" preprocessor = pickle.load(f)\n",
"\n",
"_class_names = load_iris().target_names\n",
"_model = joblib.load(\"model.joblib\")\n",
"_preprocessor = preprocessor\n",
"\n",
"\n",
"@app.get(os.environ['AIP_HEALTH_ROUTE'], status_code=200)\n",
"def health():\n",
" return {}\n",
"\n",
"\n",
"@app.post(os.environ['AIP_PREDICT_ROUTE'])\n",
"async def predict(request: Request):\n",
" body = await request.json()\n",
"\n",
" instances = body[\"instances\"]\n",
" inputs = np.asarray(instances)\n",
" preprocessed_inputs = _preprocessor.preprocess(inputs)\n",
" outputs = _model.predict(preprocessed_inputs)\n",
"\n",
" return {\"predictions\": [_class_names[class_num] for class_num in outputs]}\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "469f55daf250"
},
"source": [
"### Add pre-start script\n",
"FastAPI will execute this script before starting up the server. The `PORT` environment variable is set to equal `AIP_HTTP_PORT` in order to run FastAPI on same the port expected by Vertex AI."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "69f438aca35b"
},
"outputs": [],
"source": [
"%%writefile app/prestart.sh\n",
"#!/bin/bash\n",
"export PORT=$AIP_HTTP_PORT"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8b62ddf1def3"
},
"source": [
"### Store test instances to use later\n",
"To learn more about formatting input instances in JSON, [read the documentation.](https://cloud.google.com/vertex-ai/docs/predictions/online-predictions-custom-models#request-body-details)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "b6605e9e6186"
},
"outputs": [],
"source": [
"%%writefile instances.json\n",
"{\n",
" \"instances\": [\n",
" [6.7, 3.1, 4.7, 1.5],\n",
" [4.6, 3.1, 1.5, 0.2]\n",
" ]\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "51e149fdec1b"
},
"source": [
"## Build and push container to Artifact Registry"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3bdb9a7768a5"
},
"source": [
"### Build your container\n",
"Optionally copy in your credentials to run the container locally."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "fbb77f4f56c7"
},
"outputs": [],
"source": [
"# NOTE: Copy in credentials to run locally, this step can be skipped for deployment\n",
"%cp $GOOGLE_APPLICATION_CREDENTIALS app/credentials.json"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "240578ec9efe"
},
"source": [
"Write the Dockerfile, using `tiangolo/uvicorn-gunicorn-fastapi` as a base image. This will automatically run FastAPI for you using Gunicorn and Uvicorn. Visit [the FastAPI docs to read more about deploying FastAPI with Docker](https://fastapi.tiangolo.com/deployment/docker/)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3d3a6b9ed22b"
},
"outputs": [],
"source": [
"%%writefile Dockerfile\n",
"\n",
"FROM tiangolo/uvicorn-gunicorn-fastapi:python3.7\n",
"\n",
"COPY ./app /app\n",
"COPY requirements.txt requirements.txt\n",
"\n",
"RUN pip install -r requirements.txt"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "04c988201499"
},
"source": [
"Build the image and tag the Artifact Registry path that you will push to."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "f1e7d639b9cc"
},
"outputs": [],
"source": [
"!docker build \\\n",
" --tag={REGION}-docker.pkg.dev/{PROJECT_ID}/{REPOSITORY}/{IMAGE} \\\n",
" ."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "147a555f6c93"
},
"source": [
"### Run and test the container locally (optional)\n",
"\n",
"Run the container locally in detached mode and provide the environment variables that the container requires. These env vars will be provided to the container by Vertex Prediction once deployed. Test the `/health` and `/predict` routes, then stop the running image."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "62ed2d334d0f"
},
"outputs": [],
"source": [
"!docker rm local-iris\n",
"!docker run -d -p 80:8080 \\\n",
" --name=local-iris \\\n",
" -e AIP_HTTP_PORT=8080 \\\n",
" -e AIP_HEALTH_ROUTE=/health \\\n",
" -e AIP_PREDICT_ROUTE=/predict \\\n",
" -e AIP_STORAGE_URI={BUCKET_NAME}/{MODEL_ARTIFACT_DIR} \\\n",
" -e GOOGLE_APPLICATION_CREDENTIALS=credentials.json \\\n",
" {REGION}-docker.pkg.dev/{PROJECT_ID}/{REPOSITORY}/{IMAGE}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ce629eea32fd"
},
"outputs": [],
"source": [
"!curl localhost/health"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "56986f93438e"
},
"outputs": [],
"source": [
"!curl -X POST \\\n",
" -d @instances.json \\\n",
" -H \"Content-Type: application/json; charset=utf-8\" \\\n",
" localhost/predict"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "a29fcbbe0188"
},
"outputs": [],
"source": [
"!docker stop local-iris"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "212b2935ea12"
},
"source": [
"### Push the container to artifact registry\n",
"\n",
"Configure Docker to access Artifact Registry. Then push your container image to your Artifact Registry repository."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "09ffe2434e3d"
},
"outputs": [],
"source": [
"!gcloud beta artifacts repositories create {REPOSITORY} \\\n",
" --repository-format=docker \\\n",
" --location=$REGION"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "293437024749"
},
"outputs": [],
"source": [
"!gcloud auth configure-docker {REGION}-docker.pkg.dev"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "1dd7448f4703"
},
"outputs": [],
"source": [
"!docker push {REGION}-docker.pkg.dev/{PROJECT_ID}/{REPOSITORY}/{IMAGE}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "b438bfa2129f"
},
"source": [
"## Deploy to Vertex AI\n",
"\n",
"Use the Python SDK to upload and deploy your model."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4ae19df6a33e"
},
"source": [
"### Upload the custom container model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8d682d8388ec"
},
"outputs": [],
"source": [
"from google.cloud import aiplatform"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "574fb82d3eed"
},
"outputs": [],
"source": [
"aiplatform.init(project=PROJECT, location=REGION)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2738154345d5"
},
"outputs": [],
"source": [
"model = aiplatform.Model.upload(\n",
" display_name=MODEL_DISPLAY_NAME,\n",
" artifact_uri=f\"{BUCKET_NAME}/{MODEL_ARTIFACT_DIR}\",\n",
" serving_container_image_uri=f\"{REGION}-docker.pkg.dev/{PROJECT_ID}/{REPOSITORY}/{IMAGE}\",\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bd1b85afc7df"
},
"source": [
"### Deploy the model on Vertex AI\n",
"After this step completes, the model is deployed and ready for online prediction."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "62cf66498a28"
},
"outputs": [],
"source": [
"endpoint = model.deploy(machine_type=\"n1-standard-4\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6883e7b07143"
},
"source": [
"## Send predictions\n",
"\n",
"### Using Python SDK"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "d69ed411c2d3"
},
"outputs": [],
"source": [
"endpoint.predict(instances=[[6.7, 3.1, 4.7, 1.5], [4.6, 3.1, 1.5, 0.2]])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "370d22f53427"
},
"source": [
"### Using REST"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ba55bc560d58"
},
"outputs": [],
"source": [
"ENDPOINT_ID = endpoint.name"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "95c562b4e98b"
},
"outputs": [],
"source": [
"! curl \\\n",
"-H \"Authorization: Bearer $(gcloud auth print-access-token)\" \\\n",
"-H \"Content-Type: application/json\" \\\n",
"-d @instances.json \\\n",
"https://{REGION}-aiplatform.googleapis.com/v1/projects/{PROJECT_ID}/locations/{REGION}/endpoints/{ENDPOINT_ID}:predict"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fa71174a7dd0"
},
"source": [
"### Using gcloud CLI"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "23b8e807b02c"
},
"outputs": [],
"source": [
"!gcloud beta ai endpoints predict $ENDPOINT_ID \\\n",
" --region=$REGION \\\n",
" --json-request=instances.json"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TpV-iwP9qw9c"
},
"source": [
"## Cleaning up\n",
"\n",
"To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n",
"project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
"\n",
"Otherwise, you can delete the individual resources you created in this tutorial:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "sx_vKniMq9ZX"
},
"outputs": [],
"source": [
"# Undeploy model and delete endpoint\n",
"endpoint.delete(force=True)\n",
"\n",
"# Delete the model resource\n",
"model.delete()\n",
"\n",
"# Delete the container image from Artifact Registry\n",
"!gcloud artifacts docker images delete \\\n",
" --quiet \\\n",
" --delete-tags \\\n",
" {REGION}-docker.pkg.dev/{PROJECT_ID}/{REPOSITORY}/{IMAGE}"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "AI_Platform_(Unified)_SDK_Custom_Container_Prediction.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
Upward-Spiral-Science/the-fat-boys | docs/Team Fatboys 5 Updates Report Part 1 (Updates 1,2,3,4).ipynb | 1 | 1474291 | null | apache-2.0 |
smcl/ProjectEulerJupyter | Problem 017 - Number letter counts.ipynb | 1 | 4631 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"If the numbers 1 to 5 are written out in words: one, two, three, four, five, then there are 3 + 3 + 5 + 4 + 4 = 19 letters used in total.\n",
"\n",
"If all the numbers from 1 to 1000 (one thousand) inclusive were written out in words, how many letters would be used?\n",
"\n",
"\n",
"NOTE: Do not count spaces or hyphens. For example, 342 (three hundred and forty-two) contains 23 letters and 115 (one hundred and fifteen) contains 20 letters. The use of \"and\" when writing out numbers is in compliance with British usage."
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"21124"
]
},
"execution_count": 138,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"let units = [\n",
" \"\"\n",
" \"one\"\n",
" \"two\"\n",
" \"three\"\n",
" \"four\"\n",
" \"five\"\n",
" \"six\"\n",
" \"seven\"\n",
" \"eight\"\n",
" \"nine\"\n",
"]\n",
"\n",
"let teens = [\n",
" \"ten\"\n",
" \"eleven\"\n",
" \"twelve\"\n",
" \"thirteen\"\n",
" \"fourteen\"\n",
" \"fifteen\"\n",
" \"sixteen\"\n",
" \"seventeen\"\n",
" \"eighteen\"\n",
" \"nineteen\"\n",
"]\n",
"\n",
"let tens = [\n",
" \"\"\n",
" \"ten\" // handled in \"teens\" case, probably not needed?\n",
" \"twenty\"\n",
" \"thirty\"\n",
" \"forty\"\n",
" \"fifty\"\n",
" \"sixty\"\n",
" \"seventy\"\n",
" \"eighty\"\n",
" \"ninety\"\n",
"]\n",
"\n",
"let hundred = \"hundred\"\n",
"\n",
"let thousand = \"thousand\"\n",
"\n",
"let breakNumberIntoPowerParts n =\n",
" let numberByPowerPosition =\n",
" n.ToString().ToCharArray() \n",
" |> Array.map (fun x -> int(string(x)))\n",
" |> Array.rev\n",
" \n",
" seq {\n",
" for i = 0 to numberByPowerPosition.Length - 1 do\n",
" yield (i, (numberByPowerPosition.[i]))\n",
" }\n",
" |> Seq.toList\n",
" |> List.rev\n",
"\n",
"let simpleStringify pow10 n = \n",
" match pow10 with\n",
" | 3 -> units.[n] + thousand\n",
" | 2 -> if n > 0 then units.[n] + hundred else \"\"\n",
" | 1 -> tens.[n]\n",
" | 0 -> units.[n] \n",
" | _ -> \"\"\n",
" \n",
"let rec stringifyPowerParts (digitPairs:(int * int) list) = \n",
" if digitPairs.IsEmpty then [] else\n",
" let (pow10, n) = List.head digitPairs\n",
" if pow10 = 1 && n = 1 then [teens.[snd(List.head(List.tail digitPairs))]; \"\"]\n",
" else [(simpleStringify pow10 n)] @ (stringifyPowerParts (List.tail digitPairs))\n",
"\n",
"let maybeInsertAnd (numList:string list) =\n",
" if numList.Length < 3 then numList // no \"and\" needed, number < 100\n",
" else\n",
" let revNumList = List.rev numList\n",
" let unitAndTen = revNumList.[0..1]\n",
" let allTheRest = revNumList.[2..(revNumList.Length-1)]\n",
" \n",
" if revNumList.[0] <> \"\" || revNumList.[1] <> \"\" then\n",
" (unitAndTen @ [\"and\"] @ allTheRest)\n",
" |> List.rev\n",
" else numList\n",
"\n",
"let countEnglishLongForm n =\n",
" breakNumberIntoPowerParts n\n",
" |> stringifyPowerParts \n",
" |> maybeInsertAnd\n",
" |> List.filter (fun s -> s <> \"\")\n",
" |> String.concat \"\"\n",
" |> String.length\n",
" \n",
"[1..1000]\n",
"|> List.map countEnglishLongForm\n",
"|> List.sum"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "F#",
"language": "fsharp",
"name": "ifsharp"
},
"language": "fsharp",
"language_info": {
"codemirror_mode": "",
"file_extension": ".fs",
"mimetype": "text/x-fsharp",
"name": "fsharp",
"nbconvert_exporter": "",
"pygments_lexer": "",
"version": "4.3.1.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
dataDogma/Computer-Science | Courses/DAT-208x/DAT208X - Week 4 - Section 1 - Numpy.ipynb | 1 | 22361 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"![Banner Here!]()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## List Recap\n",
"-- --\n",
"+ Powerful\n",
"\n",
"+ COllection of values\n",
"\n",
"+ Hold different types\n",
"\n",
"+ Change, add, remove\n",
"-- --\n",
"**The Problem:**\n",
"\n",
"But there's one feature is missing, when analyzing data, the need for Data Science is to:\n",
"\n",
"+ Perform mathematical operations over collections of values.\n",
"\n",
"\n",
"+ Speed\n",
"\n",
"Unfortunatly list don't support both of these issues and here's why:\n",
"\n",
"e.g:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "unsupported operand type(s) for ** or pow(): 'list' and 'int'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-2-ee8cd1551509>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[1;31m# Now if we go to calculate BMI\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mweight\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mheight\u001b[0m \u001b[1;33m**\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for ** or pow(): 'list' and 'int'"
]
}
],
"source": [
"# some random heights of the family\n",
"height = [1.75, 1.65, 1.71, 1.89, 1.79]\n",
"\n",
"# some random weights of the family\n",
"weight = [65.4, 59.2, 63.6, 88.4, 68.7]\n",
"\n",
"# Now if we go to calculate BMI\n",
"weight / height ** 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Solution : Numpy\n",
"-- --\n",
"\n",
"+ Numric Python or simply \"numpy\".\n",
"\n",
"\n",
"+ An alternative to python list: Numpy Array.\n",
"\n",
"\n",
"+ calculation is performed over entire arrays( element wise )\n",
"\n",
"\n",
"+ Easy and Fast.\n",
"-- --\n",
"\n",
"**Importing Numpy**\n",
"\n",
"Syntax: `import numpy`"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np # selective import"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"numpy.ndarray"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Convet the followoing list to numpy arrays\n",
"height = [1.75, 1.65, 1.71, 1.89, 1.79]\n",
"\n",
"weight = [65.4, 59.2, 63.6, 88.4, 68.7]\n",
"\n",
"np_height = np.array( height )\n",
"np_weight = np.array( weight )\n",
"\n",
"# Let's confirm this as numpy arrray\n",
"type(np_height)\n",
"type(np_weight)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 21.35510204, 21.74471993, 21.75028214, 24.7473475 , 21.44127836])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bmi = np_weight / np_height ** 2\n",
"bmi"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note:**\n",
"-- --\n",
"+ _Numpy assumes that your array contain elements of same type._\n",
"\n",
"\n",
"+ _If the arary contains elements of differnet types, then resulitng numpy array will converted to type `string`._\n",
"\n",
"\n",
"+ _Numpy array should'nt be missclassified as an array, technically it a \"new data type\", just like `int`, `string`, `float` or `boolean`, and:_\n",
"\n",
" - Comes packaged with it's own methods.\n",
" \n",
" - i.e. It can behave differently than you'd expect.\n",
"-- --"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['1', '2.5', 'are different', 'True'], \n",
" dtype='<U32')"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# A numpy arary with different types\n",
"np.array( [1, 2.5, \"are different\", True ] )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Numpy : remarks\n",
"-- --"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 3, 1, 2, 3]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# a simple python list\n",
"py_list = [ 1, 2, 3 ]\n",
"\n",
"# a numpy array\n",
"numpy_array = np.array([1, 2, 3])\n",
"\n",
"\"\"\" \n",
"remarks:\n",
"\n",
"+ If we add py_list with itself, it will generate a list of\n",
" new length.\n",
" \n",
"+ Whereas, if we add the numpy_array, it would perform,\n",
" \"element wise addition\"\n",
" \n",
"Warning: \n",
"\n",
"Again be careful while using different python types in a numpy arary.\n",
" \n",
"\"\"\"\n",
"py_list + py_list"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([2, 4, 6])"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numpy_array + numpy_array"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Numpy Subsetting\n",
"-- --\n",
"\n",
"All the subsetting operation on a list, also get's performed on\n",
"Numpy arrays, except for a few minor change, we look them now."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The bmi of the fourth element is: 24.7473474987\n",
"\n",
"The bmi's from 2nd to 3rd element is: [ 21.75028214 24.7473475 ]\n",
"\n",
"List of bmi have bmi larger than 23: [False False False True False]\n",
"\n",
"The element with the largest bmi is: [ 24.7473475]\n"
]
}
],
"source": [
"bmi\n",
"\n",
"# get the fourth elemnt from the numpy array \"bmi\"\n",
"print(\"The bmi of the fourth element is: \" + str( bmi[3] ) )\n",
"\n",
"# slice and dice\n",
"print(\"\\nThe bmi's from 2nd to 3rd element is: \" + str( bmi[2 : 4] ) )\n",
"\n",
"\"\"\" \n",
"\n",
" Specifically for Numpy, there's another way to do list\n",
" subsetting via \"booleans\", here's how.\n",
"\n",
"\"\"\"\n",
"\n",
"print(\"\\nList of bmi have bmi larger than 23: \" + str( bmi > 23 ) )\n",
"\n",
"# Next, use this boolean arary to do subsetting\n",
"\n",
"print(\"\\nThe element with the largest bmi is: \" + str(bmi[ bmi > 23 ]) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise :\n",
"-- --\n",
"\n",
"**RQ1: **_Which Numpy function do you use to create an array?_\n",
"\n",
"**Ans: **`array()`\n",
"-- --\n",
"\n",
"**RQ2: **_Which two statements describe the advantage of Numpy Package over regular Python Lists?_\n",
"\n",
"**Ans: **\n",
"\n",
"+ The Numpy Package provides the `array`, a data type that can be used to do element-wise calculations. \n",
"\n",
"\n",
"+ Because Numpy arrays can only hold element of a single type,\n",
"\n",
" - calculations on Numpy arrays can be carried out way faster than regular Python lists.\n",
"-- --\n",
"\n",
"**RQ3: **_What is the resulting Numpy array z after executing the following lines of code?_\n",
"\n",
"```\n",
" import numpy as np\n",
" x = np.array([1, 2, 3])\n",
" y = np.array([3, 2, 1])\n",
" z = x + y\n",
"```\n",
"\n",
"**Ans: **`array( [4, 4, 4] )`\n",
"-- --\n",
"\n",
"**RQ4: **_What happens when you put an integer, a Boolean, and a string in the same Numpy array using the `array()` function?_\n",
"\n",
"**Ans: ** An array element is converted to `string`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lab : Numpy\n",
"-- --\n",
"\n",
"**Objective:**\n",
"\n",
"+ Parctice with Numpy\n",
"\n",
"+ Perform Calculations with it.\n",
"\n",
"+ Understand subtle difference b/w Numpy arrays and Python list.\n",
"-- --"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**List of lab exercises:**\n",
"-- -- \n",
"+ Your first Numpy Arary -- 100xp, status : earned\n",
"\n",
"\n",
"+ Baseball's player's height -- 100xp, status : earned\n",
"\n",
"\n",
"+ Lightweight baseball players -- 100xp, status : earned\n",
"\n",
"\n",
"+ Numpy Side Effects -- 50xp, status : earned\n",
"\n",
"\n",
"+ Subsetting Numpy Arrays -- 100xp, status : earned\n",
"-- --"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**1. Your First Numpy array**\n",
"-- --"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[180 215 210 210 188 176 209 200]\n",
"<class 'numpy.ndarray'>\n"
]
}
],
"source": [
"\"\"\"\n",
"Instructions: \n",
"\n",
" + Import the \"numpy\" package as \"np\", so that you can refer to \"numpy\" with \"np\".\n",
" \n",
" + Use \"np.array()\" to create a Numpy array from \"baseball\". Name this array \"np_baseball\".\n",
" \n",
" + Print out the \"type of np_baseball\" to check that you got it right.\n",
"\n",
"\"\"\"\n",
"# Create list baseball \n",
"baseball = [180, 215, 210, 210, 188, 176, 209, 200]\n",
"\n",
"# Import the numpy package as np\n",
"import numpy as np\n",
"\n",
"# Create a Numpy array from baseball: np_baseball\n",
"np_baseball = np.array(baseball)\n",
"print(np_baseball)\n",
"\n",
"# Print out type of np_baseball\n",
"print(type( np_baseball) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**2. Baseball player's height**\n",
"-- --\n",
"\n",
"Preface:\n",
"\n",
"You are a huge baseball fan. You decide to call the MLB (Major League Baseball) and ask around for some more statistics on the height of the main players. They pass along data on more than a thousand players, which is stored as a regular Python list: height. The height is expressed in inches. Can you make a Numpy array out of it and convert the units to centimeters?\n",
"-- --"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Height of the baseball players are: [ 1.75 1.65 1.71 1.89 1.79]\n",
"\n",
"The Height of the baseball players in meters are: [ 0.04445 0.04191 0.043434 0.048006 0.045466]\n"
]
}
],
"source": [
"\"\"\"\n",
"Instructions:\n",
"\n",
" + Create a Numpy array from height. Name this new array np_height.\n",
"\n",
" + Print \"np_height\".\n",
" \n",
" + Multiply \"np_height\" with 0.0254 to convert all height measurements from inches to meters. \n",
" \n",
" - Store the new values in a new array, \"np_height_m\".\n",
" \n",
" + Print out np_height_m and check if the output makes sense.\n",
"\n",
"\"\"\"\n",
"\n",
"# height is available as a regular list\n",
"# http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights#References\n",
"\n",
"# Import numpy\n",
"import numpy as np\n",
"\n",
"# Create a Numpy array from height: np_height\n",
"np_height = np.array( height )\n",
"\n",
"# Print out np_height\n",
"print(\"The Height of the baseball players are: \" + str( np_height ) )\n",
"\n",
"# Convert np_height to m: np_height_m\n",
"np_height_m = np_height * 0.0254 # a inch is 0.0245 meters\n",
"\n",
"# Print np_height_m\n",
"print(\"\\nThe Height of the baseball players in meters are: \" + str( np_height_m ) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**3. Baseball player's BMI:**\n",
"-- --\n",
"\n",
"Preface: \n",
"\n",
"The MLB also offers to let you analyze their weight data. Again, both are available as regular Python lists: `height` and `weight`. `height` is in inches and `weight` is in pounds.\n",
"\n",
"It's now possible to calculate the BMI of each baseball player. Python code to convert `height` to a Numpy array with the correct units is already available in the workspace. Follow the instructions step by step and finish the game!\n",
"-- --"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"The Bmi of all the baseball players are: [ 15014.11036781 15288.0386275 15291.94924605 17399.09301044\n",
" 15074.69826837]\n"
]
}
],
"source": [
"\"\"\"\n",
"Instructions:\n",
"\n",
" + Create a Numpy array from the weight list with the correct units.\n",
" \n",
" - Multiply by 0.453592 to go from pounds to kilograms. \n",
" \n",
" - Store the resulting Numpy array as np_weight_kg.\n",
" \n",
" + Use np_height_m and np_weight_kg to calculate the BMI of each player. \n",
" \n",
" - Use the following equation: \n",
" \n",
" BMI = weight( kg ) / height( m )\n",
" \n",
" - Save the resulting numpy array as \"bmi\".\n",
" \n",
" + Print out \"bmi\".\n",
" \n",
"\"\"\"\n",
"# height and weight are available as a regular lists\n",
"\n",
"# Import numpy\n",
"import numpy as np\n",
"\n",
"# Create array from height with correct units: np_height_m\n",
"np_height_m = np.array(height) * 0.0254\n",
"\n",
"# Create array from weight with correct units: np_weight_kg \n",
"np_weight_kg = np.array( weight ) * 0.453592\n",
"\n",
"# Calculate the BMI: bmi\n",
"bmi = np_weight_kg / np_height_m ** 2\n",
"\n",
"# Print out bmi\n",
"print(\"\\nThe Bmi of all the baseball players are: \" + str( bmi ) )\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**4. Leightweight baseball players:**\n",
"-- --\n",
"\n",
"To subset both regular Python lists and Numpy arrays, you can use square brackets:\n",
"\n",
"```\n",
" x = [4 , 9 , 6, 3, 1]\n",
" x[1]\n",
" import numpy as np\n",
" y = np.array(x)\n",
" y[1]```\n",
" \n",
"For Numpy specifically, you can also use boolean Numpy arrays:\n",
"\n",
"```\n",
" high = y > 5\n",
" y[high]```\n",
"-- --"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Lightweight baseball players[False False False False False]\n",
"[ 15014.11036781 15288.0386275 15291.94924605 17399.09301044\n",
" 15074.69826837]\n"
]
}
],
"source": [
"\"\"\" \n",
"Instructions:\n",
"\n",
" + Create a boolean Numpy array:\n",
" \n",
" - the element of the array should be \"True\",\n",
" \n",
" - If the corresponding baseball player's BMI is below 21.\n",
" \n",
" - You can use the \"<\" operator for this\n",
" \n",
" - Name the array \"light\", Print the array \"light\".\n",
" \n",
" \n",
" + Print out a Numpy array with the BMIs of all baseball players whose BMI is below 21. \n",
" \n",
" - Use \"light\" inside square brackets to do a selection on the bmi array.\n",
"\"\"\"\n",
"# height and weight are available as a regular lists\n",
"\n",
"# Import numpy\n",
"import numpy as np\n",
"\n",
"# Calculate the BMI: bmi\n",
"np_height_m = np.array(height) * 0.0254\n",
"np_weight_kg = np.array(weight) * 0.453592\n",
"bmi = np_weight_kg / (np_height_m ** 2)\n",
"\n",
"# Create the light array\n",
"light = np.array( bmi < 21 )\n",
"\n",
"# Print out light\n",
"print(\"\\nLightweight baseball players\" + str( light ) )\n",
"\n",
"# Print out BMIs of all baseball players whose BMI is below 21\n",
"print(bmi[ light < 21 ])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"-- --\n",
"**5. Numpy Side Effect:**\n",
"-- --\n",
"\n",
"Preface:\n",
"\n",
"+ Numpy arrays cannot contain elements with different types.\n",
"\n",
"\n",
"+ If you try to build such a list, some of the elments' types are changed to end up with a homogenous list.\n",
" - This is known as _type coercion_. \n",
"\n",
"\n",
"+ Second, the typical arithmetic operators,\n",
"\n",
" such as +, -, * and / have a different meaning for regular Python lists and Numpy arrays.\n",
"-- --\n",
"\n",
"Have a look at this line:\n",
"\n",
"\n",
" ```In [1]: np.array([True, 1, 2]) + np.array([3, 4, False])\n",
" Out[1]: array([4, 5, 2])```\n",
" \n",
"Here, the `+` operator is summing Numpy arrays element wise, as a result, the `True` element ~ 1 as integer, get's added to 3, a `int` to give off `4`, only to be later converted to a string. Same happens with all the othere two numbers.\n",
" \n",
"_Which code chunk builds the exact same Python data structure?_\n",
"\n",
"**Ans: **`np.array([4, 3, 0]) + np.array([0, 2, 2])`.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"-- --\n",
"**6. Subsetting Numpy Arrays:**\n",
"-- --\n",
"\n",
"Luckily, subsetting the two, i.e. \"Python list\" and \"Numpy arrays\" behave similar while subsetting, wohoooo!\n"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "IndexError",
"evalue": "index 50 is out of bounds for axis 0 with size 5",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-72-2baeb4a975bb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[1;31m# Print out the weight at index 50\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp_weight\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[1;31m# Print out sub-array of np_height: index 100 up to and including index 110\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;31mIndexError\u001b[0m: index 50 is out of bounds for axis 0 with size 5"
]
}
],
"source": [
"\"\"\"\n",
"Instructions:\n",
"\n",
" + Subset np_weight: print out the element at index 50.\n",
" \n",
" + Print out a sub-array of np_height: It contains the elements at index 100 up to and including index 110\n",
"\"\"\"\n",
"\n",
"# height and weight are available as a regular lists\n",
"\n",
"# Import numpy\n",
"import numpy as np\n",
"\n",
"# Store weight and height lists as numpy arrays\n",
"np_weight = np.array(weight)\n",
"np_height = np.array(height)\n",
"\n",
"# Print out the weight at index 50\n",
"# Ans: print(np_weight[50])\n",
"\n",
"# Print out sub-array of np_height: index 100 up to and including index 110\n",
"# Ans: print(np_height[100 : 111])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
cuttlefishh/papers | red-sea-spatial-series/code/gene_count_normalization.ipynb | 1 | 5304 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## On the normalization of genomic gene count data to gene length\n",
"\n",
"by Luke Thompson - 2016/03/21"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A common method in metagenomics and transcriptomics is normalization of gene count data by gene size. \n",
"\n",
"In RPK, read counts (R) are divided directly by gene size (per kbp, or PK). In RPKM, they are further normalized by dividing by total counts and multiplying by one million (M).\n",
"\n",
"RPK and RPKM normalizations assume a purely linear relationship of sequencing probability to gene size. It has occurred to me that in cases of small genes and/or long reads, this purely linear relationship does not hold up."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given: A single bacterial genome containing many different sized genes is sampled randomly, generating a set of equally sized sequence reads.\n",
"\n",
"The probability, $P$, of counting a gene by random, assuming completely random DNA fragmentation and sequencing, is given by\n",
"\n",
"### $P = \\frac{g + 2 (r-o)}{d}$\n",
"\n",
"where $g$ = gene length, $r$ = read length, $o$ = overlap required for match, and $d$ = genome or database size.\n",
"\n",
"The \"flap term\" is given by $2 (r-o)$.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As gene size increases relative to the flap term, $P$ approaches $g/d$, which is the typical RPKM normalization.\n",
"\n",
"However, when gene size is similar to the flap term, the flap term is significant and increases the probability of counting the gene.\n",
"\n",
"Therefore, in circumstances where gene size is comparable to read length, with read length greater than ~200 bp, smaller genes will be observed more often than estimated by the $P = g/d$ formula."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Admittedly, given current short read technology, most genes are not short enough to make much difference. But it still is a small difference for all genes, and a big difference for the small ones. Further, as read lengths increase, the effect of the flap term will increase. Illumina is always increasing read lengths. We need to take the flap term into account!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When we do RPK normalization, we divide counts by gene length. That is, our correction factor is\n",
"\n",
"### $C = \\frac{1}{g}$\n",
"\n",
"whereas it really should be\n",
"\n",
"### $C = \\frac{1}{g + 2(r-o)}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bokeh slider graph"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from bokeh.models import ColumnDataSource\n",
"from bokeh.plotting import figure, show, output_notebook\n",
"\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"output_notebook()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"read = 100\n",
"overlap = 100\n",
"gene = np.arange(overlap, 5001, dtype=float)\n",
"prob = gene + 2*(read - overlap)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"source = ColumnDataSource(data=dict(x=gene, y=prob, gene=gene, prob=prob))\n",
"\n",
"p = figure(title=\"simple line example\", plot_height=300, plot_width=600)\n",
"p.line(gene, prob, color=\"#2222aa\", line_width=3, source=source, name=\"foo\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def update(read=100, overlap=100):\n",
" source.data['y'] = gene + 2*(read - overlap)\n",
" source.push_notebook()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"show(p)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from IPython.html.widgets import interact\n",
"interact(update, read=(0,1000), overlap=(0,200))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
QuantConnect/Tutorials | 05 Introduction to Financial Python[]/12 Modern Portfolio Theory/12 Modern Portfolio Theory.ipynb | 1 | 320210 | {
"cells": [
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import quandl\n",
"from cvxopt import solvers\n",
"from cvxopt import matrix\n",
"import matplotlib.pyplot as plt\n",
"from scipy.optimize import minimize\n",
"import random"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class stock(object):\n",
" def __init__(self,ticker):\n",
" self.ticker = ticker"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tickers = ['KO','JNJ','PFE','NKE','PG','WMT','MMM','IBM']\n",
"stocks = []\n",
"leng = len(tickers)\n",
"for i in tickers:\n",
" vars()[i] = stock(i)\n",
" stocks.append(vars()[i])"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.027400000000000001"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rf = quandl.get('USTREASURY/LONGTERMRATES')\n",
"rf = (rf.ix[-1][0]/100)\n",
"rf"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for i in stocks:\n",
" table = quandl.get('WIKI/%s'%i.ticker)\n",
" i.rate = np.log(table['Adj. Close']).diff()['2008':]\n",
" i.mean = np.mean(i.rate)*252\n",
" i.std = np.std(i.rate)*np.sqrt(252)"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" mean std\n",
"KO 0.072506 0.188207\n",
"JNJ 0.102055 0.165611\n",
"PFE 0.080836 0.227700\n",
"NKE 0.149412 0.286772\n",
"PG 0.052920 0.177927\n",
"WMT 0.078416 0.194245\n",
"MMM 0.116281 0.221956\n",
"IBM 0.053190 0.222997\n"
]
}
],
"source": [
"stock_list = [x.ticker for x in stocks]\n",
"rate_list = [x.rate for x in stocks]\n",
"mean_list = [x.mean for x in stocks]\n",
"std_list = [x.std for x in stocks]\n",
"cov_matrix = np.cov(rate_list)\n",
"df = pd.DataFrame({'mean':mean_list,'std':std_list},index = stock_list)\n",
"print df"
]
},
{
"cell_type": "code",
"execution_count": 201,
"metadata": {},
"outputs": [],
"source": [
"def min_var_generator(rate):\n",
" def target(x, sigma, mean,r):\n",
" sr_inv = (np.sqrt(np.dot(np.dot(x.T,sigma),x))*np.sqrt(252))/(x.dot(mean_list)-r)\n",
" return sr_inv\n",
"\n",
" x = np.ones(leng)/leng\n",
" mean = mean_list\n",
" sigma = cov_matrix\n",
" r = rf\n",
" c = ({'type':'eq','fun':lambda x: sum(x) - 1},\n",
" {'type':'eq','fun': lambda x: np.dot(x.T,mean) - rate})\n",
" bounds = [(-1,1) for i in range(leng)]\n",
" res = minimize(target, x, args = (sigma,mean,r),method = 'SLSQP',constraints = c,bounds = bounds)\n",
"# return res['x']\n",
" return (res['x'],(np.sqrt(np.dot(np.dot(res['x'].T,sigma),res['x']))*np.sqrt(252)))"
]
},
{
"cell_type": "code",
"execution_count": 203,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 0.20992676, 0.27623188, -0.00295919, -0.10248824, 0.31125431,\n",
" 0.23690646, -0.10606759, 0.1771956 ]), 0.1455789711354554)"
]
},
"execution_count": 203,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"min_var_generator(0.06)"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {},
"outputs": [],
"source": [
"simu_rate = [x for x in np.arange(rf,max(mean_list)*1.2,0.0001)]\n",
"simu_var = []\n",
"for i in simu_rate:\n",
" try:\n",
" res = min_var_generator(i)\n",
" simu_var.append(res[1])\n",
" except:\n",
" print i"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"rate 0.179200\n",
"std 0.240626\n",
"sharpe 0.630854\n",
"Name: 1518, dtype: float64"
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"port_df = pd.DataFrame({'rate':simu_rate,'std':simu_var})\n",
"port_df.head()\n",
"port_df['sharpe'] = (port_df['rate'] - rf)/port_df['std']\n",
"opt = port_df.ix[port_df['sharpe'].idxmax()]\n",
"opt"
]
},
{
"cell_type": "code",
"execution_count": 208,
"metadata": {},
"outputs": [],
"source": [
"#Simulation#\n",
"monte_rate, monte_std = [],[]\n",
"for i in range(100000):\n",
" w = np.random.dirichlet(np.ones(leng),size=1)\n",
" monte_rate.append(np.dot(w,mean_list))\n",
" monte_std.append(np.sqrt(np.dot(np.dot(w,cov_matrix),w.reshape(8,1)))*np.sqrt(252))"
]
},
{
"cell_type": "code",
"execution_count": 209,
"metadata": {},
"outputs": [],
"source": [
"simu_df = pd.DataFrame({'x':monte_std,'y':monte_rate})\n",
"simu_df['sharpe'] = (simu_df['y'] - rf)/simu_df['x']"
]
},
{
"cell_type": "code",
"execution_count": 225,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABKAAAAJQCAYAAAC5GxyfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nERERERGRZKUClIiIiIiIiIiIJKv/A907XhlB7lZHAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x118f8a990>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (20,10))\n",
"plt.scatter(simu_var,simu_rate,s=0.1,c = (pd.Series(simu_rate)-rf)/pd.Series(simu_var))\n",
"plt.scatter(simu_df.x,simu_df.y,s = 1,c =simu_df.sharpe)\n",
"plt.scatter(0,rf,color = 'r',s = 30)\n",
"plt.scatter(opt['std'],opt.rate,color = 'red',s = 100,marker = '*')\n",
"plt.annotate('risk-free',(0,rf),size = 15)\n",
"plt.annotate('Market Portfolio',(opt['std'],opt['rate']),size = 15,color = 'red')\n",
"for i in stocks:\n",
" plt.scatter(df.ix[i.ticker][1],df.ix[i.ticker][0],s = 25,c = (df.ix[i.ticker][0]-rf)/df.ix[i.ticker][1])\n",
" plt.annotate(i.ticker,(df.ix[i.ticker][1],df.ix[i.ticker][0]), size = 15)\n",
"plt.plot([0,max(std_list)],[rf,max(std_list)*opt['sharpe']+rf],color = 'black')\n",
"plt.xlim(0)\n",
"plt.xlabel('Standard Deviation')\n",
"plt.ylabel('Expected Return')\n",
"plt.axhline(rf,ls = '--')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"###three stocks version"
]
},
{
"cell_type": "code",
"execution_count": 234,
"metadata": {},
"outputs": [],
"source": [
"def efficient_frontier(tickers):\n",
" stocks = []\n",
" leng = len(tickers)\n",
" for i in tickers:\n",
" vars()[i] = stock(i)\n",
" stocks.append(vars()[i])\n",
" \n",
" rf = quandl.get('USTREASURY/LONGTERMRATES')\n",
" rf = (rf.ix[-1][0]/100)\n",
" for i in stocks:\n",
" table = quandl.get('WIKI/%s'%i.ticker)\n",
" i.rate = np.log(table['Adj. Close']).diff()['2008':]\n",
" i.mean = np.mean(i.rate)*252\n",
" i.std = np.std(i.rate)*np.sqrt(252)\n",
" \n",
" stock_list = [x.ticker for x in stocks]\n",
" rate_list = [x.rate for x in stocks]\n",
" mean_list = [x.mean for x in stocks]\n",
" std_list = [x.std for x in stocks]\n",
" cov_matrix = np.cov(rate_list)\n",
" df = pd.DataFrame({'mean':mean_list,'std':std_list},index = stock_list)\n",
" print df\n",
" \n",
" def min_var_generator(rate):\n",
" def target(x, sigma, mean,r):\n",
" sr_inv = (np.sqrt(np.dot(np.dot(x.T,sigma),x))*np.sqrt(252))/(x.dot(mean_list)-r)\n",
" return sr_inv\n",
"\n",
" x = np.ones(leng)/leng\n",
" mean = mean_list\n",
" sigma = cov_matrix\n",
" r = rf\n",
" c = ({'type':'eq','fun':lambda x: sum(x) - 1},\n",
" {'type':'eq','fun': lambda x: np.dot(x.T,mean) - rate})\n",
" bounds = [(-1,1) for i in range(leng)]\n",
" res = minimize(target, x, args = (sigma,mean,r),method = 'SLSQP',constraints = c,bounds = bounds)\n",
" return (res['x'],(np.sqrt(np.dot(np.dot(res['x'].T,sigma),res['x']))*np.sqrt(252)))\n",
" \n",
" simu_rate = [x for x in np.arange(rf,max(mean_list)*1.2,0.0001)]\n",
" simu_var = []\n",
" for i in simu_rate:\n",
" try:\n",
" res = min_var_generator(i)\n",
" simu_var.append(res[1])\n",
" except:\n",
" print i\n",
" \n",
" port_df = pd.DataFrame({'rate':simu_rate,'std':simu_var})\n",
" port_df.head()\n",
" port_df['sharpe'] = (port_df['rate'] - rf)/port_df['std']\n",
" opt = port_df.ix[port_df['sharpe'].idxmax()]\n",
" return port_df"
]
},
{
"cell_type": "code",
"execution_count": 235,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" mean std\n",
"PG 0.052920 0.177927\n",
"IBM 0.053190 0.222997\n",
"KO 0.072506 0.188207\n"
]
}
],
"source": [
"new = efficient_frontier(['PG','IBM','KO'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 238,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABKAAAAJQCAYAAAC5GxyfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115569390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (20,10))\n",
"plt.scatter(simu_var,simu_rate,s=0.1,c = (pd.Series(simu_rate)-rf)/pd.Series(simu_var))\n",
"plt.scatter(simu_df.x,simu_df.y,s = 1,c =simu_df.sharpe)\n",
"plt.scatter(new['std'],new['rate'],c = new['sharpe'],s = 0.5)\n",
"plt.scatter(0,rf,color = 'r',s = 30)\n",
"plt.scatter(opt['std'],opt.rate,color = 'red',s = 100,marker = '*')\n",
"plt.annotate('risk-free',(0,rf),size = 15)\n",
"plt.annotate('Market Portfolio',(opt['std'],opt['rate']),size = 15,color = 'red')\n",
"for i in stocks:\n",
" plt.scatter(df.ix[i.ticker][1],df.ix[i.ticker][0],s = 25,c = (df.ix[i.ticker][0]-rf)/df.ix[i.ticker][1])\n",
" plt.annotate(i.ticker,(df.ix[i.ticker][1],df.ix[i.ticker][0]), size = 15)\n",
"plt.plot([0,max(std_list)],[rf,max(std_list)*opt['sharpe']+rf],color = 'black')\n",
"plt.xlim(0)\n",
"plt.xlabel('Standard Deviation')\n",
"plt.ylabel('Expected Return')\n",
"plt.axhline(rf,ls = '--')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
AchyuthIIIT/mediacloud | python_scripts/notebook/media_import_through_api.ipynb | 1 | 133948 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import cPickle\n",
"import os.path\n",
"\n",
"api_key = cPickle.load( file( os.path.expanduser( '~/mediacloud_api_key.pickle' ), 'r' ) )"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import cPickle\n",
"import os.path\n",
"\n",
"cPickle.dump( api_key, file( os.path.expanduser( '~/mediacloud_api_key.pickle' ), 'wb' ) )"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import sys\n",
"#print (sys.path)\n",
"sys.path.append('../')\n",
"sys.path\n",
"import mc_database"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import psycopg2\n",
"import psycopg2.extras"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import mediacloud, json\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def cast_fields_to_bool( dict_obj, fields ):\n",
" for field in fields:\n",
" if dict_obj[ field ] is not None:\n",
" dict_obj[ field ] = bool( dict_obj[field])\n",
"\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def non_list_pairs( item ):\n",
" item = { k: item[k] for k in item.keys() if type(item[k]) != list }\n",
" return item\n",
"\n",
"def insert_into_table( cursor, table_name, item ):\n",
" item = { k: item[k] for k in item.keys() if type(item[k]) != list }\n",
" columns = ', '.join(item.keys())\n",
" \n",
" placeholders = ', '.join([ '%('+ c + ')s' for c in item.keys() ])\n",
" \n",
" query = \"insert into \" + table_name + \" (%s) Values (%s)\" %( columns, placeholders)\n",
" #print query\n",
" cursor.execute( query , item )"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def update_db_sequences( conn ):\n",
" cursor = conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\n",
" cursor.execute( \"select setval(pg_get_serial_sequence('tag_sets', 'tag_sets_id'), (select max(tag_sets_id)+1 from tag_sets))\" )\n",
" \n",
" cursor.execute( \"select setval(pg_get_serial_sequence('tags', 'tags_id'), (select max(tags_id)+1 from tags))\" )\n",
" cursor.execute( \"select setval(pg_get_serial_sequence('media', 'media_id'), (select max(media_id)+1 from media))\" )\n",
" cursor.execute( \"select setval(pg_get_serial_sequence('media_sets', 'media_id'), (select max(media_id)+1 from media))\" )\n",
" cursor.execute( \"select setval(pg_get_serial_sequence('media_sets_media_map', 'media_id'), (select max(media_id)+1 from media))\" )\n",
" cursor.execute( \"select setval(pg_get_serial_sequence('media_tags_map', 'media_tags_map_id'), (select max(media_tags_map_id)+1 from media_tags_map))\" )\n",
" cursor.execute( \"select setval(pg_get_serial_sequence('feeds', 'feeds_id'), (select max(feeds_id)+1 from feeds))\" )\n",
" \n",
" cursor.execute( \"select setval(pg_get_serial_sequence('feeds_tags_map', 'feeds_tags_map_id'), (select max(feeds_tags_map_id)+1 from feeds_tags_map))\" )\n",
" \n",
" cursor.close()\n",
" conn.commit()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def truncate_tables( conn ):\n",
" cursor = conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\n",
" cursor.execute( \"SELECT count(*) > 10000 as has_many_downloads from downloads\")\n",
" rec = cursor.fetchone()\n",
" assert ( not rec['has_many_downloads'])\n",
" \n",
" cursor.execute( \"TRUNCATE tag_sets CASCADE \" )\n",
" cursor.execute( \"TRUNCATE media CASCADE\" )\n",
" cursor.execute( \"TRUNCATE feeds CASCADE\" )\n",
" conn.commit()\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def get_tag_sets( mc ):\n",
" all_tag_sets = []\n",
"\n",
" last_tag_sets_id = 0\n",
" \n",
" while True:\n",
" tag_sets = mc.tagSetList( last_tag_sets_id=last_tag_sets_id, rows=20)\n",
" if len(tag_sets) == 0:\n",
" break\n",
" \n",
" #print tag_sets\n",
" last_tag_sets_id = tag_sets[-1]['tag_sets_id']\n",
" #print last_tag_sets_id\n",
" \n",
" \n",
" all_tag_sets.extend(tag_sets)\n",
" \n",
" return all_tag_sets "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def add_tag_sets_to_database( conn, all_tag_sets ):\n",
" cursor = conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\n",
" for tag_set in all_tag_sets:\n",
" cast_fields_to_bool( tag_set, [ 'show_on_media', 'show_on_stories' ] )\n",
" insert_into_table( cursor, 'tag_sets', tag_set )\n",
" print 'inserted ' + tag_set['name']\n",
" conn.commit() "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def add_media_to_database( conn, all_media ):\n",
" \n",
" cursor = conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\n",
" cursor.execute( \"SET CONSTRAINTS media_dup_media_id_fkey DEFERRED \") \n",
" \n",
" num_media_inserted = 0\n",
" \n",
" for medium in all_media:\n",
" medium = non_list_pairs( medium)\n",
" #del medium['dup_media_id']\n",
" cast_fields_to_bool( medium, [ 'extract_author', 'annotate_with_corenlp', \"full_text_rss\",\n",
" \"foreign_rss_links\", \"feeds_added\", \"moderated\", \"use_pager\", \"is_not_dup\"])\n",
" insert_into_table( cursor, 'media', medium )\n",
" \n",
" num_media_inserted += 1\n",
" \n",
" if num_media_inserted % 500 == 0:\n",
" print \"Inserted \" + str( num_media_inserted ) + \" out of \" + str(len(all_media) )\n",
" \n",
" #print 'inserted '\n",
" \n",
" conn.commit()\n",
" cursor.close()\n",
" conn.commit()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def get_media( mc ):\n",
" all_media = []\n",
"\n",
" last_media_id = 0\n",
" \n",
" while True:\n",
" media = mc.mediaList( last_media_id=last_media_id, rows=1000)\n",
" print last_media_id, len( media ), len( all_media )\n",
" \n",
" if len(media) == 0:\n",
" break\n",
" \n",
" last_media_id = media[-1]['media_id']\n",
" last_media_id\n",
" \n",
" all_media.extend(media)\n",
" \n",
" #if len( all_media ) > 10000:\n",
" # break\n",
" \n",
" return all_media"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"def add_feeds_from_media_to_database( conn, media ):\n",
" cursor = conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\n",
" \n",
" num_media_processed = 0\n",
" \n",
" for medium in media:\n",
" feeds_for_media = mc.feedList( media_id=medium['media_id'], rows=1000)\n",
" assert len( feeds_for_media ) < 1000\n",
" \n",
" for feed in feeds_for_media:\n",
" insert_into_table( cursor, 'feeds', feed )\n",
" \n",
" num_media_processed += 1\n",
" \n",
" if num_media_processed % 1000 == 0:\n",
" print \"inserted feeds for \" + str( num_media_processed ) + \" out of \" + str ( len( all_media ) )\n",
" \n",
" conn.commit()\n",
" cursor.close()\n",
" conn.commit()\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mc = mediacloud.api.MediaCloud(api_key, all_fields=True)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"conn = mc_database.connect_to_database()\n",
"truncate_tables( conn )\n",
"update_db_sequences(conn)\n",
"all_tag_sets = get_tag_sets( mc )\n",
"\n",
"add_tag_sets_to_database( conn, all_tag_sets )\n",
"all_media = get_media( mc )\n",
"add_media_to_database( conn, all_media )\n",
"add_feeds_from_media_to_database( conn, all_media[:100])\n",
"update_db_sequences(conn)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"inserted content_type\n",
"inserted usnewspapercirculation\n",
"inserted workflow\n",
"inserted collection\n",
"inserted manual_term\n",
"inserted term_study\n",
"inserted Calais (term_study)\n",
"inserted Yahoo (term_study)\n",
"inserted NYTTopics (term_study)\n",
"inserted NYTTopics\n",
"inserted tagged\n",
"inserted Calais\n",
"inserted source\n",
"inserted pklocation\n",
"inserted pkgeog-type\n",
"inserted word_cloud\n",
"inserted morning_analytics_russia_20100915_cluster\n",
"inserted morning_analytics_russia_full_20100911_cluster\n",
"inserted topic\n",
"inserted spidered\n",
"inserted sopa\n",
"inserted controversy_trayvon\n",
"inserted collection:_newyork_jessie_spidering_10242012\n",
"inserted date_guess_method\n",
"inserted controversy_prop 40\n",
"inserted controversy_prop 32\n",
"inserted controversy_prop 34\n",
"inserted controversy_prop 31\n",
"inserted controversy_prop 33\n",
"inserted controversy_prop 35\n",
"inserted controversy_prop 36\n",
"inserted controversy_prop 37\n",
"inserted controversy_prop 39\n",
"inserted controversy_prop 30 + 38\n",
"inserted controversy_sopa\n",
"inserted emm_type\n",
"inserted emm_subject\n",
"inserted emm_country\n",
"inserted emm_region\n",
"inserted emm_category\n",
"inserted emm_lang\n",
"inserted controversy_russia protests\n",
"inserted gv_country\n",
"inserted ca_ra_media_types\n",
"inserted date_invalid\n",
"inserted controversy_nsa / snowden\n",
"inserted portuguese_media_type\n",
"inserted portuguese_topic\n",
"inserted portuguese_state\n",
"inserted controversy_obama 2012-11\n",
"inserted controversy_obama-romney 2012-10\n",
"inserted controversy_tamarod\n",
"inserted partisan_coding_20140218\n",
"inserted [email protected]\n",
"inserted controversy_rolezinhos\n",
"inserted egypt_media_type\n",
"inserted egypt_valence\n",
"inserted controversy_tamarod - new\n",
"inserted foo\n",
"inserted [email protected]\n",
"inserted [email protected]\n",
"inserted controversy_network neutrality\n",
"inserted controversy_isla vista\n",
"inserted kenya_media_source\n",
"inserted controversy_gaza 2014-07\n",
"inserted media_type\n",
"inserted controversy_Chinese Bitcoin 2\n",
"inserted controversy_isla vista - simple\n",
"inserted controversy_Chinese Bitcoin 3\n",
"inserted controversy_Chinese Bitcoin\n",
"inserted controversy_sopa 20130507\n",
"inserted controversy_hobby lobby\n",
"inserted controversy_720_media_types\n",
"inserted controversy_gaza\n",
"inserted controversy_teen pregnancy - broken\n",
"inserted controversy_teen pregnancy\n",
"inserted controversy_ebola\n",
"inserted controversy_ferguson\n",
"inserted controversy_ferguson / mike brown\n",
"inserted controversy_791_media_types\n",
"inserted controversy_gamergate\n",
"inserted controversy_tobacco\n",
"inserted controversy_abortion\n",
"inserted controversy_contraception\n",
"inserted controversy_contraception / bc\n",
"inserted controversy_sex education\n",
"inserted controversy_reproductive rights\n",
"inserted controversy_mlk\n",
"inserted controversy_ferguson / garner\n",
"inserted fake_sources\n",
"inserted controversy_mlk simple\n",
"inserted extractor_version\n",
"inserted controversy_mtv the talk\n",
"inserted controversy_ap sentences\n",
"inserted controversy_867_media_types\n",
"inserted controversy_common core\n",
"inserted controversy_egypt spider 2015-03-01 - 2015-03-03\n",
"inserted controversy_climate change\n",
"inserted controversy_egypt spider\n",
"inserted controversy_vaccines\n",
"inserted mexico_state\n",
"inserted controversy_garissa\n",
"inserted controversy_charlie hebdo\n",
"inserted controversy_walter scott\n",
"inserted scraped\n",
"inserted controversy_climate change, Q3 2014\n",
"inserted controversy_gamergate2\n",
"inserted controversy_freddie gray\n",
"inserted controversy_nsa 2015-02-01 - 2015-04-28\n",
"0"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 0\n",
"1253"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 1000\n",
"2333"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 2000\n",
"3358"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 3000\n",
"4364"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 4000\n",
"5378"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 5000\n",
"6392"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 6000\n",
"7418"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 7000\n",
"8419"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 8000\n",
"9419"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 9000\n",
"10419"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 10000\n",
"11419"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 11000\n",
"12420"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 12000\n",
"13422"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 13000\n",
"14424"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 14000\n",
"15424"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 15000\n",
"16425"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 16000\n",
"17425"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 17000\n",
"18655"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 18000\n",
"19655"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 19000\n",
"20657"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 20000\n",
"21658"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 21000\n",
"22658"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 22000\n",
"23660"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 23000\n",
"24811"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 24000\n",
"25811"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 25000\n",
"26811"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 26000\n",
"27820"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 27000\n",
"29019"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 28000\n",
"30019"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 29000\n",
"31019"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 30000\n",
"32019"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 31000\n",
"33019"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 32000\n",
"34019"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 33000\n",
"35019"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 34000\n",
"39076"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 35000\n",
"40076"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 36000\n",
"41076"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 37000\n",
"42077"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 38000\n",
"43077"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 39000\n",
"44077"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 40000\n",
"45077"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 41000\n",
"46077"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 42000\n",
"47077"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 43000\n",
"48077"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 44000\n",
"49077"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 45000\n",
"50077"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 46000\n",
"51077"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 47000\n",
"52078"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 48000\n",
"53103"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 49000\n",
"54103"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 50000\n",
"55880"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 51000\n",
"56880"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 52000\n",
"57880"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 53000\n",
"58880"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 54000\n",
"59880"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 55000\n",
"60880"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 56000\n",
"61880"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 57000\n",
"62880"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 58000\n",
"63880"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 59000\n",
"64880"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 60000\n",
"65880"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 61000\n",
"67013"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 62000\n",
"68014"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 63000\n",
"69015"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 64000\n",
"70015"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 65000\n",
"71016"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 66000\n",
"72017"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 67000\n",
"73017"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 68000\n",
"74017"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 69000\n",
"75018"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 70000\n",
"76018"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 71000\n",
"77018"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 72000\n",
"78018"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 73000\n",
"79019"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 74000\n",
"80019"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 75000\n",
"81019"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 76000\n",
"82020"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 77000\n",
"83021"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 78000\n",
"84063"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 79000\n",
"85072"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 80000\n",
"86072"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 81000\n",
"87072"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 82000\n",
"88072"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 83000\n",
"89072"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 84000\n",
"90072"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 85000\n",
"91089"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 86000\n",
"92089"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 87000\n",
"93089"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 88000\n",
"94089"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 89000\n",
"95089"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 90000\n",
"96089"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 91000\n",
"97091"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 92000\n",
"98091"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 93000\n",
"99091"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 94000\n",
"100091"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 95000\n",
"101091"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 96000\n",
"102268"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 97000\n",
"103306"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 98000\n",
"104306"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 99000\n",
"105320"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 100000\n",
"106328"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 101000\n",
"107350"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 102000\n",
"108438"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 103000\n",
"109510"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 104000\n",
"110510"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 105000\n",
"111512"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 106000\n",
"112514"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 107000\n",
"113515"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 108000\n",
"114515"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 109000\n",
"115515"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 110000\n",
"116515"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 111000\n",
"117515"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 112000\n",
"118516"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 113000\n",
"119540"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 114000\n",
"120540"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 115000\n",
"121540"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 116000\n",
"122540"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 117000\n",
"123540"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 118000\n",
"124540"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 119000\n",
"125540"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 120000\n",
"126540"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 121000\n",
"127540"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 122000\n",
"128540"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 123000\n",
"129540"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 124000\n",
"130540"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 125000\n",
"131540"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 126000\n",
"132540"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 127000\n",
"133540"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 128000\n",
"134540"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 129000\n",
"135540"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 130000\n",
"136540"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 131000\n",
"137540"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 132000\n",
"138540"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 133000\n",
"139540"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 134000\n",
"140540"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 135000\n",
"141540"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 136000\n",
"142540"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 137000\n",
"143548"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 138000\n",
"144548"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 139000\n",
"145548"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 140000\n",
"146550"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 141000\n",
"147550"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 142000\n",
"148550"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 143000\n",
"149550"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 144000\n",
"150551"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 145000\n",
"151551"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 146000\n",
"152552"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 147000\n",
"153552"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 148000\n",
"154552"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 149000\n",
"155552"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 150000\n",
"156553"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 151000\n",
"157553"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 152000\n",
"158553"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 153000\n",
"159553"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 154000\n",
"160553"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 155000\n",
"161555"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 156000\n",
"162556"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 157000\n",
"163556"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 158000\n",
"164556"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 159000\n",
"165556"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 160000\n",
"166556"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 161000\n",
"167557"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 162000\n",
"168557"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 163000\n",
"169557"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 164000\n",
"170558"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 165000\n",
"171558"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 166000\n",
"172558"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 167000\n",
"173559"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 168000\n",
"174559"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 169000\n",
"175559"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 170000\n",
"176559"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 171000\n",
"177574"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 172000\n",
"178592"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 173000\n",
"179592"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 174000\n",
"180599"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 175000\n",
"181599"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 176000\n",
"182599"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 177000\n",
"183599"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 178000\n",
"184599"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 179000\n",
"185599"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 180000\n",
"186599"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 181000\n",
"187599"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 182000\n",
"188599"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 183000\n",
"189599"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 184000\n",
"190600"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 185000\n",
"191600"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 186000\n",
"192600"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 187000\n",
"193601"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 188000\n",
"194604"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 189000\n",
"195605"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 190000\n",
"196606"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 191000\n",
"197606"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 192000\n",
"198606"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 193000\n",
"199607"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 194000\n",
"200608"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 195000\n",
"201618"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 196000\n",
"202625"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 197000\n",
"203625"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 198000\n",
"204629"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 199000\n",
"205629"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 200000\n",
"206630"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 201000\n",
"207631"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 202000\n",
"208631"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 203000\n",
"209668"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 204000\n",
"210668"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 205000\n",
"211677"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 206000\n",
"212681"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 207000\n",
"213705"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 208000\n",
"214705"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 209000\n",
"215716"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 210000\n",
"216716"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 211000\n",
"217717"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 212000\n",
"218717"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 213000\n",
"219717"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 214000\n",
"220717"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 215000\n",
"221718"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 216000\n",
"222719"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 217000\n",
"223719"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 218000\n",
"224719"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 219000\n",
"225720"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 220000\n",
"226720"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 221000\n",
"227720"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 222000\n",
"228720"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 223000\n",
"229720"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 224000\n",
"230721"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 225000\n",
"231721"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 226000\n",
"232721"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 227000\n",
"233721"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 228000\n",
"234721"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 229000\n",
"235722"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 230000\n",
"236722"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 231000\n",
"237723"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 232000\n",
"238723"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 233000\n",
"239723"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 234000\n",
"240723"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 235000\n",
"241724"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 236000\n",
"242724"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 237000\n",
"243724"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 238000\n",
"244725"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 239000\n",
"245727"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 240000\n",
"246728"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 241000\n",
"247728"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 242000\n",
"248729"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 243000\n",
"249729"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 244000\n",
"250729"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 245000\n",
"251730"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 246000\n",
"252730"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 247000\n",
"253730"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 248000\n",
"254731"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 249000\n",
"255732"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 250000\n",
"256734"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 251000\n",
"257735"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 252000\n",
"258738"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 253000\n",
"259747"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 254000\n",
"260747"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 255000\n",
"261748"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 256000\n",
"262751"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 257000\n",
"263752"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1000 258000\n",
"264753"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 669 259000\n",
"265426"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 0 259669\n",
"Inserted 500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 1000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 1500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 2000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 2500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 3000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 3500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 4000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 4500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 5000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 5500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 6000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 6500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 7000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 7500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 8000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 8500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 9000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 9500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 10000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 10500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 11000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 11500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 12000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 12500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 13000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 13500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 14000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 14500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 15000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 15500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 16000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 16500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 17000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 17500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 18000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 18500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 19000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 19500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 20000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 20500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 21000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 21500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 22000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 22500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 23000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 23500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 24000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 24500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 25000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 25500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 26000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 26500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 27000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 27500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 28000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 28500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 29000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 29500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 30000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 30500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 31000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 31500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 32000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 32500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 33000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 33500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 34000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 34500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 35000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 35500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 36000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 36500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 37000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 37500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 38000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 38500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 39000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 39500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 40000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 40500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 41000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 41500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 42000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 42500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 43000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 43500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 44000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 44500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 45000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 45500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 46000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 46500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 47000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 47500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 48000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 48500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 49000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 49500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 50000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 50500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 51000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 51500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 52000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 52500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 53000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 53500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 54000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 54500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 55000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 55500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 56000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 56500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 57000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 57500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 58000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 58500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 59000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 59500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 60000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 60500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 61000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 61500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 62000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 62500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 63000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 63500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 64000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 64500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 65000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 65500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 66000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 66500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 67000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 67500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 68000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 68500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 69000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 69500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 70000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 70500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 71000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 71500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 72000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 72500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 73000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 73500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 74000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 74500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 75000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 75500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 76000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 76500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 77000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 77500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 78000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 78500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 79000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 79500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 80000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 80500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 81000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 81500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 82000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 82500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 83000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 83500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 84000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 84500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 85000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 85500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 86000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 86500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 87000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 87500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 88000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 88500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 89000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 89500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 90000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 90500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 91000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 91500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 92000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 92500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 93000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 93500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 94000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 94500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 95000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 95500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 96000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 96500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 97000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 97500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 98000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 98500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 99000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 99500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 100000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 100500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 101000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 101500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 102000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 102500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 103000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 103500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 104000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 104500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 105000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 105500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 106000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 106500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 107000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 107500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 108000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 108500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 109000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 109500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 110000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 110500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 111000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 111500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 112000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 112500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 113000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 113500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 114000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 114500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 115000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 115500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 116000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 116500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 117000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 117500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 118000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 118500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 119000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 119500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 120000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 120500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 121000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 121500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 122000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 122500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 123000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 123500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 124000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 124500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 125000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 125500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 126000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 126500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 127000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 127500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 128000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 128500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 129000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 129500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 130000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 130500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 131000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 131500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 132000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 132500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 133000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 133500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 134000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 134500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 135000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 135500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 136000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 136500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 137000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 137500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 138000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 138500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 139000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 139500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 140000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 140500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 141000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 141500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 142000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 142500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 143000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 143500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 144000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 144500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 145000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 145500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 146000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 146500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 147000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 147500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 148000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 148500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 149000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 149500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 150000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 150500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 151000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 151500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 152000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 152500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 153000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 153500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 154000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 154500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 155000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 155500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 156000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 156500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 157000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 157500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 158000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 158500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 159000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 159500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 160000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 160500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 161000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 161500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 162000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 162500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 163000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 163500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 164000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 164500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 165000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 165500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 166000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 166500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 167000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 167500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 168000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 168500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 169000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 169500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 170000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 170500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 171000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 171500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 172000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 172500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 173000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 173500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 174000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 174500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 175000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 175500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 176000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 176500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 177000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 177500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 178000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 178500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 179000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 179500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 180000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 180500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 181000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 181500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 182000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 182500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 183000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 183500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 184000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 184500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 185000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 185500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 186000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 186500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 187000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 187500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 188000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 188500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 189000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 189500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 190000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 190500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 191000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 191500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 192000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 192500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 193000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 193500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 194000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 194500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 195000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 195500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 196000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 196500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 197000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 197500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 198000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 198500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 199000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 199500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 200000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 200500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 201000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 201500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 202000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 202500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 203000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 203500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 204000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 204500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 205000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 205500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 206000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 206500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 207000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 207500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 208000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 208500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 209000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 209500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 210000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 210500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 211000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 211500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 212000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 212500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 213000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 213500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 214000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 214500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 215000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 215500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 216000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 216500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 217000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 217500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 218000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 218500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 219000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 219500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 220000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 220500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 221000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 221500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 222000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 222500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 223000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 223500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 224000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 224500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 225000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 225500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 226000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 226500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 227000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 227500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 228000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 228500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 229000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 229500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 230000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 230500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 231000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 231500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 232000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 232500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 233000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 233500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 234000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 234500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 235000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 235500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 236000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 236500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 237000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 237500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 238000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 238500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 239000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 239500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 240000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 240500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 241000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 241500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 242000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 242500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 243000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 243500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 244000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 244500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 245000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 245500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 246000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 246500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 247000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 247500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 248000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 248500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 249000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 249500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 250000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 250500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 251000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 251500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 252000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 252500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 253000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 253500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 254000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 254500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 255000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 255500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 256000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 256500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 257000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 257500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 258000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 258500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 259000 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Inserted 259500 out of 259669"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 43
}
],
"metadata": {}
}
]
} | agpl-3.0 |
suresh/notebooks | altair-sample.ipynb | 1 | 102079 | {
"cells": [
{
"cell_type": "code",
"source": [
"import altair as alt\n",
"from vega_datasets import data\n",
"\n",
"cars = data.cars()"
],
"outputs": [],
"execution_count": 3,
"metadata": {
"inputHidden": false,
"outputHidden": false,
"execution": {
"iopub.status.busy": "2020-05-17T10:12:03.744Z",
"iopub.execute_input": "2020-05-17T10:12:03.756Z",
"iopub.status.idle": "2020-05-17T10:12:05.661Z",
"shell.execute_reply": "2020-05-17T10:12:05.677Z"
},
"jupyter": {
"source_hidden": true
},
"collapsed": false,
"outputExpanded": false
}
},
{
"cell_type": "markdown",
"source": [
"# Faceted Scatter Plot with Linked Brushing\n",
"\n",
"This is an example of using an interval selection to control the color of points across multiple facets."
],
"metadata": {}
},
{
"cell_type": "code",
"source": [
"brush = alt.selection(type='interval', resolve='global')\n",
"\n",
"base = alt.Chart(cars).mark_point().encode(\n",
" y='Miles_per_Gallon',\n",
" color=alt.condition(brush, 'Origin', alt.ColorValue('gray'))\n",
").add_selection(\n",
" brush\n",
").properties(\n",
" width=250,\n",
" height=250\n",
")\n",
"\n",
"print(\"Select a region in the chart below to try this out!\")\n",
"\n",
"base.encode(x='Horsepower') | base.encode(x='Acceleration')"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Select a region in the chart below to try this out!\n"
]
},
{
"output_type": "execute_result",
"execution_count": 4,
"data": {
"text/html": [
"\n",
"<div id=\"altair-viz-c9cb2c7a41d44eeaa497f0ffc46218c6\"></div>\n",
"<script type=\"text/javascript\">\n",
" (function(spec, embedOpt){\n",
" let outputDiv = document.currentScript.previousElementSibling;\n",
" if (outputDiv.id !== \"altair-viz-c9cb2c7a41d44eeaa497f0ffc46218c6\") {\n",
" outputDiv = document.getElementById(\"altair-viz-c9cb2c7a41d44eeaa497f0ffc46218c6\");\n",
" }\n",
" const paths = {\n",
" \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
" \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
" \"vega-lite\": \"https://cdn.jsdelivr.net/npm//[email protected]?noext\",\n",
" \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
" };\n",
"\n",
" function loadScript(lib) {\n",
" return new Promise(function(resolve, reject) {\n",
" var s = document.createElement('script');\n",
" s.src = paths[lib];\n",
" s.async = true;\n",
" s.onload = () => resolve(paths[lib]);\n",
" s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" });\n",
" }\n",
"\n",
" function showError(err) {\n",
" outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
" throw err;\n",
" }\n",
"\n",
" function displayChart(vegaEmbed) {\n",
" vegaEmbed(outputDiv, spec, embedOpt)\n",
" .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
" }\n",
"\n",
" if(typeof define === \"function\" && define.amd) {\n",
" requirejs.config({paths});\n",
" require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
" } else if (typeof vegaEmbed === \"function\") {\n",
" displayChart(vegaEmbed);\n",
" } else {\n",
" loadScript(\"vega\")\n",
" .then(() => loadScript(\"vega-lite\"))\n",
" .then(() => loadScript(\"vega-embed\"))\n",
" .catch(showError)\n",
" .then(() => displayChart(vegaEmbed));\n",
" }\n",
" })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"hconcat\": [{\"mark\": \"point\", \"encoding\": {\"color\": {\"condition\": {\"type\": \"nominal\", \"field\": \"Origin\", \"selection\": \"selector001\"}, \"value\": \"gray\"}, \"x\": {\"type\": \"quantitative\", \"field\": \"Horsepower\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"Miles_per_Gallon\"}}, \"height\": 250, \"selection\": {\"selector001\": {\"type\": \"interval\", \"resolve\": \"global\"}}, \"width\": 250}, {\"mark\": \"point\", \"encoding\": {\"color\": {\"condition\": {\"type\": \"nominal\", \"field\": \"Origin\", \"selection\": \"selector001\"}, \"value\": \"gray\"}, \"x\": {\"type\": \"quantitative\", \"field\": \"Acceleration\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"Miles_per_Gallon\"}}, \"height\": 250, \"selection\": {\"selector001\": {\"type\": \"interval\", \"resolve\": \"global\"}}, \"width\": 250}], \"data\": {\"name\": \"data-f02450ab61490a1363517a0190416235\"}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-f02450ab61490a1363517a0190416235\": [{\"Name\": \"chevrolet chevelle malibu\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 8, \"Displacement\": 307.0, \"Horsepower\": 130.0, \"Weight_in_lbs\": 3504, \"Acceleration\": 12.0, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick skylark 320\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 165.0, \"Weight_in_lbs\": 3693, \"Acceleration\": 11.5, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth satellite\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 3436, \"Acceleration\": 11.0, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc rebel sst\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 8, \"Displacement\": 304.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 3433, \"Acceleration\": 12.0, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford torino\", \"Miles_per_Gallon\": 17.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 140.0, \"Weight_in_lbs\": 3449, \"Acceleration\": 10.5, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford galaxie 500\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 8, \"Displacement\": 429.0, \"Horsepower\": 198.0, \"Weight_in_lbs\": 4341, \"Acceleration\": 10.0, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet impala\", \"Miles_per_Gallon\": 14.0, \"Cylinders\": 8, \"Displacement\": 454.0, \"Horsepower\": 220.0, \"Weight_in_lbs\": 4354, \"Acceleration\": 9.0, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth fury iii\", \"Miles_per_Gallon\": 14.0, \"Cylinders\": 8, \"Displacement\": 440.0, \"Horsepower\": 215.0, \"Weight_in_lbs\": 4312, \"Acceleration\": 8.5, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac catalina\", \"Miles_per_Gallon\": 14.0, \"Cylinders\": 8, \"Displacement\": 455.0, \"Horsepower\": 225.0, \"Weight_in_lbs\": 4425, \"Acceleration\": 10.0, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc ambassador dpl\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 8, \"Displacement\": 390.0, \"Horsepower\": 190.0, \"Weight_in_lbs\": 3850, \"Acceleration\": 8.5, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"citroen ds-21 pallas\", \"Miles_per_Gallon\": null, \"Cylinders\": 4, \"Displacement\": 133.0, \"Horsepower\": 115.0, \"Weight_in_lbs\": 3090, \"Acceleration\": 17.5, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"chevrolet chevelle concours (sw)\", \"Miles_per_Gallon\": null, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 165.0, \"Weight_in_lbs\": 4142, \"Acceleration\": 11.5, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford torino (sw)\", \"Miles_per_Gallon\": null, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 153.0, \"Weight_in_lbs\": 4034, \"Acceleration\": 11.0, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth satellite (sw)\", \"Miles_per_Gallon\": null, \"Cylinders\": 8, \"Displacement\": 383.0, \"Horsepower\": 175.0, \"Weight_in_lbs\": 4166, \"Acceleration\": 10.5, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc rebel sst (sw)\", \"Miles_per_Gallon\": null, \"Cylinders\": 8, \"Displacement\": 360.0, \"Horsepower\": 175.0, \"Weight_in_lbs\": 3850, \"Acceleration\": 11.0, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge challenger se\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 8, \"Displacement\": 383.0, \"Horsepower\": 170.0, \"Weight_in_lbs\": 3563, \"Acceleration\": 10.0, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth 'cuda 340\", \"Miles_per_Gallon\": 14.0, \"Cylinders\": 8, \"Displacement\": 340.0, \"Horsepower\": 160.0, \"Weight_in_lbs\": 3609, \"Acceleration\": 8.0, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford mustang boss 302\", \"Miles_per_Gallon\": null, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 140.0, \"Weight_in_lbs\": 3353, \"Acceleration\": 8.0, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet monte carlo\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 3761, \"Acceleration\": 9.5, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick estate wagon (sw)\", \"Miles_per_Gallon\": 14.0, \"Cylinders\": 8, \"Displacement\": 455.0, \"Horsepower\": 225.0, \"Weight_in_lbs\": 3086, \"Acceleration\": 10.0, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota corona mark ii\", \"Miles_per_Gallon\": 24.0, \"Cylinders\": 4, \"Displacement\": 113.0, \"Horsepower\": 95.0, \"Weight_in_lbs\": 2372, \"Acceleration\": 15.0, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"plymouth duster\", \"Miles_per_Gallon\": 22.0, \"Cylinders\": 6, \"Displacement\": 198.0, \"Horsepower\": 95.0, \"Weight_in_lbs\": 2833, \"Acceleration\": 15.5, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc hornet\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 6, \"Displacement\": 199.0, \"Horsepower\": 97.0, \"Weight_in_lbs\": 2774, \"Acceleration\": 15.5, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford maverick\", \"Miles_per_Gallon\": 21.0, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Weight_in_lbs\": 2587, \"Acceleration\": 16.0, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"datsun pl510\", \"Miles_per_Gallon\": 27.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2130, \"Acceleration\": 14.5, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"volkswagen 1131 deluxe sedan\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 46.0, \"Weight_in_lbs\": 1835, \"Acceleration\": 20.5, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"peugeot 504\", \"Miles_per_Gallon\": 25.0, \"Cylinders\": 4, \"Displacement\": 110.0, \"Horsepower\": 87.0, \"Weight_in_lbs\": 2672, \"Acceleration\": 17.5, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"audi 100 ls\", \"Miles_per_Gallon\": 24.0, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2430, \"Acceleration\": 14.5, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"saab 99e\", \"Miles_per_Gallon\": 25.0, \"Cylinders\": 4, \"Displacement\": 104.0, \"Horsepower\": 95.0, \"Weight_in_lbs\": 2375, \"Acceleration\": 17.5, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"bmw 2002\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 113.0, \"Weight_in_lbs\": 2234, \"Acceleration\": 12.5, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"amc gremlin\", \"Miles_per_Gallon\": 21.0, \"Cylinders\": 6, \"Displacement\": 199.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2648, \"Acceleration\": 15.0, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford f250\", \"Miles_per_Gallon\": 10.0, \"Cylinders\": 8, \"Displacement\": 360.0, \"Horsepower\": 215.0, \"Weight_in_lbs\": 4615, \"Acceleration\": 14.0, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevy c20\", \"Miles_per_Gallon\": 10.0, \"Cylinders\": 8, \"Displacement\": 307.0, \"Horsepower\": 200.0, \"Weight_in_lbs\": 4376, \"Acceleration\": 15.0, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge d200\", \"Miles_per_Gallon\": 11.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 210.0, \"Weight_in_lbs\": 4382, \"Acceleration\": 13.5, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"hi 1200d\", \"Miles_per_Gallon\": 9.0, \"Cylinders\": 8, \"Displacement\": 304.0, \"Horsepower\": 193.0, \"Weight_in_lbs\": 4732, \"Acceleration\": 18.5, \"Year\": \"1970-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"datsun pl510\", \"Miles_per_Gallon\": 27.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2130, \"Acceleration\": 14.5, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"chevrolet vega 2300\", \"Miles_per_Gallon\": 28.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2264, \"Acceleration\": 15.5, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota corona\", \"Miles_per_Gallon\": 25.0, \"Cylinders\": 4, \"Displacement\": 113.0, \"Horsepower\": 95.0, \"Weight_in_lbs\": 2228, \"Acceleration\": 14.0, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"ford pinto\", \"Miles_per_Gallon\": 25.0, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": null, \"Weight_in_lbs\": 2046, \"Acceleration\": 19.0, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"volkswagen super beetle 117\", \"Miles_per_Gallon\": null, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 48.0, \"Weight_in_lbs\": 1978, \"Acceleration\": 20.0, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"amc gremlin\", \"Miles_per_Gallon\": 19.0, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 2634, \"Acceleration\": 13.0, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth satellite custom\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 3439, \"Acceleration\": 15.5, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet chevelle malibu\", \"Miles_per_Gallon\": 17.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 3329, \"Acceleration\": 15.5, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford torino 500\", \"Miles_per_Gallon\": 19.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 3302, \"Acceleration\": 15.5, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc matador\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 3288, \"Acceleration\": 15.5, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet impala\", \"Miles_per_Gallon\": 14.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 165.0, \"Weight_in_lbs\": 4209, \"Acceleration\": 12.0, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac catalina brougham\", \"Miles_per_Gallon\": 14.0, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 175.0, \"Weight_in_lbs\": 4464, \"Acceleration\": 11.5, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford galaxie 500\", \"Miles_per_Gallon\": 14.0, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 153.0, \"Weight_in_lbs\": 4154, \"Acceleration\": 13.5, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth fury iii\", \"Miles_per_Gallon\": 14.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 4096, \"Acceleration\": 13.0, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge monaco (sw)\", \"Miles_per_Gallon\": 12.0, \"Cylinders\": 8, \"Displacement\": 383.0, \"Horsepower\": 180.0, \"Weight_in_lbs\": 4955, \"Acceleration\": 11.5, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford country squire (sw)\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 170.0, \"Weight_in_lbs\": 4746, \"Acceleration\": 12.0, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac safari (sw)\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 175.0, \"Weight_in_lbs\": 5140, \"Acceleration\": 12.0, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc hornet sportabout (sw)\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 6, \"Displacement\": 258.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 2962, \"Acceleration\": 13.5, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet vega (sw)\", \"Miles_per_Gallon\": 22.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 72.0, \"Weight_in_lbs\": 2408, \"Acceleration\": 19.0, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac firebird\", \"Miles_per_Gallon\": 19.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 3282, \"Acceleration\": 15.0, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford mustang\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 3139, \"Acceleration\": 14.5, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercury capri 2000\", \"Miles_per_Gallon\": 23.0, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 86.0, \"Weight_in_lbs\": 2220, \"Acceleration\": 14.0, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"opel 1900\", \"Miles_per_Gallon\": 28.0, \"Cylinders\": 4, \"Displacement\": 116.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2123, \"Acceleration\": 14.0, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"peugeot 304\", \"Miles_per_Gallon\": 30.0, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 2074, \"Acceleration\": 19.5, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"fiat 124b\", \"Miles_per_Gallon\": 30.0, \"Cylinders\": 4, \"Displacement\": 88.0, \"Horsepower\": 76.0, \"Weight_in_lbs\": 2065, \"Acceleration\": 14.5, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"toyota corolla 1200\", \"Miles_per_Gallon\": 31.0, \"Cylinders\": 4, \"Displacement\": 71.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 1773, \"Acceleration\": 19.0, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"datsun 1200\", \"Miles_per_Gallon\": 35.0, \"Cylinders\": 4, \"Displacement\": 72.0, \"Horsepower\": 69.0, \"Weight_in_lbs\": 1613, \"Acceleration\": 18.0, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"volkswagen model 111\", \"Miles_per_Gallon\": 27.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 60.0, \"Weight_in_lbs\": 1834, \"Acceleration\": 19.0, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"plymouth cricket\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 1955, \"Acceleration\": 20.5, \"Year\": \"1971-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota corona hardtop\", \"Miles_per_Gallon\": 24.0, \"Cylinders\": 4, \"Displacement\": 113.0, \"Horsepower\": 95.0, \"Weight_in_lbs\": 2278, \"Acceleration\": 15.5, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"dodge colt hardtop\", \"Miles_per_Gallon\": 25.0, \"Cylinders\": 4, \"Displacement\": 97.5, \"Horsepower\": 80.0, \"Weight_in_lbs\": 2126, \"Acceleration\": 17.0, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"volkswagen type 3\", \"Miles_per_Gallon\": 23.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 54.0, \"Weight_in_lbs\": 2254, \"Acceleration\": 23.5, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"chevrolet vega\", \"Miles_per_Gallon\": 20.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2408, \"Acceleration\": 19.5, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford pinto runabout\", \"Miles_per_Gallon\": 21.0, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 86.0, \"Weight_in_lbs\": 2226, \"Acceleration\": 16.5, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet impala\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 165.0, \"Weight_in_lbs\": 4274, \"Acceleration\": 12.0, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac catalina\", \"Miles_per_Gallon\": 14.0, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 175.0, \"Weight_in_lbs\": 4385, \"Acceleration\": 12.0, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth fury iii\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 4135, \"Acceleration\": 13.5, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford galaxie 500\", \"Miles_per_Gallon\": 14.0, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 153.0, \"Weight_in_lbs\": 4129, \"Acceleration\": 13.0, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc ambassador sst\", \"Miles_per_Gallon\": 17.0, \"Cylinders\": 8, \"Displacement\": 304.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 3672, \"Acceleration\": 11.5, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercury marquis\", \"Miles_per_Gallon\": 11.0, \"Cylinders\": 8, \"Displacement\": 429.0, \"Horsepower\": 208.0, \"Weight_in_lbs\": 4633, \"Acceleration\": 11.0, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick lesabre custom\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 155.0, \"Weight_in_lbs\": 4502, \"Acceleration\": 13.5, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"oldsmobile delta 88 royale\", \"Miles_per_Gallon\": 12.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 160.0, \"Weight_in_lbs\": 4456, \"Acceleration\": 13.5, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chrysler newport royal\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 190.0, \"Weight_in_lbs\": 4422, \"Acceleration\": 12.5, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mazda rx2 coupe\", \"Miles_per_Gallon\": 19.0, \"Cylinders\": 3, \"Displacement\": 70.0, \"Horsepower\": 97.0, \"Weight_in_lbs\": 2330, \"Acceleration\": 13.5, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"amc matador (sw)\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 8, \"Displacement\": 304.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 3892, \"Acceleration\": 12.5, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet chevelle concours (sw)\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 307.0, \"Horsepower\": 130.0, \"Weight_in_lbs\": 4098, \"Acceleration\": 14.0, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford gran torino (sw)\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 140.0, \"Weight_in_lbs\": 4294, \"Acceleration\": 16.0, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth satellite custom (sw)\", \"Miles_per_Gallon\": 14.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 4077, \"Acceleration\": 14.0, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"volvo 145e (sw)\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 112.0, \"Weight_in_lbs\": 2933, \"Acceleration\": 14.5, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"volkswagen 411 (sw)\", \"Miles_per_Gallon\": 22.0, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 76.0, \"Weight_in_lbs\": 2511, \"Acceleration\": 18.0, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"peugeot 504 (sw)\", \"Miles_per_Gallon\": 21.0, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 87.0, \"Weight_in_lbs\": 2979, \"Acceleration\": 19.5, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"renault 12 (sw)\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 96.0, \"Horsepower\": 69.0, \"Weight_in_lbs\": 2189, \"Acceleration\": 18.0, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"ford pinto (sw)\", \"Miles_per_Gallon\": 22.0, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 86.0, \"Weight_in_lbs\": 2395, \"Acceleration\": 16.0, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"datsun 510 (sw)\", \"Miles_per_Gallon\": 28.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 92.0, \"Weight_in_lbs\": 2288, \"Acceleration\": 17.0, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"toyouta corona mark ii (sw)\", \"Miles_per_Gallon\": 23.0, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 97.0, \"Weight_in_lbs\": 2506, \"Acceleration\": 14.5, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"dodge colt (sw)\", \"Miles_per_Gallon\": 28.0, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 80.0, \"Weight_in_lbs\": 2164, \"Acceleration\": 15.0, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota corolla 1600 (sw)\", \"Miles_per_Gallon\": 27.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2100, \"Acceleration\": 16.5, \"Year\": \"1972-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"buick century 350\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 175.0, \"Weight_in_lbs\": 4100, \"Acceleration\": 13.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc matador\", \"Miles_per_Gallon\": 14.0, \"Cylinders\": 8, \"Displacement\": 304.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 3672, \"Acceleration\": 11.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet malibu\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 145.0, \"Weight_in_lbs\": 3988, \"Acceleration\": 13.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford gran torino\", \"Miles_per_Gallon\": 14.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 137.0, \"Weight_in_lbs\": 4042, \"Acceleration\": 14.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge coronet custom\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 3777, \"Acceleration\": 12.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercury marquis brougham\", \"Miles_per_Gallon\": 12.0, \"Cylinders\": 8, \"Displacement\": 429.0, \"Horsepower\": 198.0, \"Weight_in_lbs\": 4952, \"Acceleration\": 11.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet caprice classic\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 4464, \"Acceleration\": 12.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford ltd\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 158.0, \"Weight_in_lbs\": 4363, \"Acceleration\": 13.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth fury gran sedan\", \"Miles_per_Gallon\": 14.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 4237, \"Acceleration\": 14.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chrysler new yorker brougham\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 440.0, \"Horsepower\": 215.0, \"Weight_in_lbs\": 4735, \"Acceleration\": 11.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick electra 225 custom\", \"Miles_per_Gallon\": 12.0, \"Cylinders\": 8, \"Displacement\": 455.0, \"Horsepower\": 225.0, \"Weight_in_lbs\": 4951, \"Acceleration\": 11.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc ambassador brougham\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 360.0, \"Horsepower\": 175.0, \"Weight_in_lbs\": 3821, \"Acceleration\": 11.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth valiant\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 3121, \"Acceleration\": 16.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet nova custom\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 3278, \"Acceleration\": 18.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc hornet\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 2945, \"Acceleration\": 16.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford maverick\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 3021, \"Acceleration\": 16.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth duster\", \"Miles_per_Gallon\": 23.0, \"Cylinders\": 6, \"Displacement\": 198.0, \"Horsepower\": 95.0, \"Weight_in_lbs\": 2904, \"Acceleration\": 16.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"volkswagen super beetle\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 46.0, \"Weight_in_lbs\": 1950, \"Acceleration\": 21.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"chevrolet impala\", \"Miles_per_Gallon\": 11.0, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 4997, \"Acceleration\": 14.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford country\", \"Miles_per_Gallon\": 12.0, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 167.0, \"Weight_in_lbs\": 4906, \"Acceleration\": 12.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth custom suburb\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 360.0, \"Horsepower\": 170.0, \"Weight_in_lbs\": 4654, \"Acceleration\": 13.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"oldsmobile vista cruiser\", \"Miles_per_Gallon\": 12.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 180.0, \"Weight_in_lbs\": 4499, \"Acceleration\": 12.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc gremlin\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 2789, \"Acceleration\": 15.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota carina\", \"Miles_per_Gallon\": 20.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2279, \"Acceleration\": 19.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"chevrolet vega\", \"Miles_per_Gallon\": 21.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 72.0, \"Weight_in_lbs\": 2401, \"Acceleration\": 19.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"datsun 610\", \"Miles_per_Gallon\": 22.0, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 94.0, \"Weight_in_lbs\": 2379, \"Acceleration\": 16.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"maxda rx3\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 3, \"Displacement\": 70.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2124, \"Acceleration\": 13.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"ford pinto\", \"Miles_per_Gallon\": 19.0, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 85.0, \"Weight_in_lbs\": 2310, \"Acceleration\": 18.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercury capri v6\", \"Miles_per_Gallon\": 21.0, \"Cylinders\": 6, \"Displacement\": 155.0, \"Horsepower\": 107.0, \"Weight_in_lbs\": 2472, \"Acceleration\": 14.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"fiat 124 sport coupe\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2265, \"Acceleration\": 15.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"chevrolet monte carlo s\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 145.0, \"Weight_in_lbs\": 4082, \"Acceleration\": 13.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac grand prix\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 230.0, \"Weight_in_lbs\": 4278, \"Acceleration\": 9.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"fiat 128\", \"Miles_per_Gallon\": 29.0, \"Cylinders\": 4, \"Displacement\": 68.0, \"Horsepower\": 49.0, \"Weight_in_lbs\": 1867, \"Acceleration\": 19.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"opel manta\", \"Miles_per_Gallon\": 24.0, \"Cylinders\": 4, \"Displacement\": 116.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2158, \"Acceleration\": 15.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"audi 100ls\", \"Miles_per_Gallon\": 20.0, \"Cylinders\": 4, \"Displacement\": 114.0, \"Horsepower\": 91.0, \"Weight_in_lbs\": 2582, \"Acceleration\": 14.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"volvo 144ea\", \"Miles_per_Gallon\": 19.0, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 112.0, \"Weight_in_lbs\": 2868, \"Acceleration\": 15.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"dodge dart custom\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 3399, \"Acceleration\": 11.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"saab 99le\", \"Miles_per_Gallon\": 24.0, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 2660, \"Acceleration\": 14.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"toyota mark ii\", \"Miles_per_Gallon\": 20.0, \"Cylinders\": 6, \"Displacement\": 156.0, \"Horsepower\": 122.0, \"Weight_in_lbs\": 2807, \"Acceleration\": 13.5, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"oldsmobile omega\", \"Miles_per_Gallon\": 11.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 180.0, \"Weight_in_lbs\": 3664, \"Acceleration\": 11.0, \"Year\": \"1973-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth duster\", \"Miles_per_Gallon\": 20.0, \"Cylinders\": 6, \"Displacement\": 198.0, \"Horsepower\": 95.0, \"Weight_in_lbs\": 3102, \"Acceleration\": 16.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford maverick\", \"Miles_per_Gallon\": 21.0, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": null, \"Weight_in_lbs\": 2875, \"Acceleration\": 17.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc hornet\", \"Miles_per_Gallon\": 19.0, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 2901, \"Acceleration\": 16.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet nova\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 3336, \"Acceleration\": 17.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"datsun b210\", \"Miles_per_Gallon\": 31.0, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 1950, \"Acceleration\": 19.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"ford pinto\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 80.0, \"Weight_in_lbs\": 2451, \"Acceleration\": 16.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota corolla 1200\", \"Miles_per_Gallon\": 32.0, \"Cylinders\": 4, \"Displacement\": 71.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 1836, \"Acceleration\": 21.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"chevrolet vega\", \"Miles_per_Gallon\": 25.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2542, \"Acceleration\": 17.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet chevelle malibu classic\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 3781, \"Acceleration\": 17.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc matador\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 6, \"Displacement\": 258.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3632, \"Acceleration\": 18.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth satellite sebring\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 3613, \"Acceleration\": 16.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford gran torino\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 140.0, \"Weight_in_lbs\": 4141, \"Acceleration\": 14.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick century luxus (sw)\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 4699, \"Acceleration\": 14.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge coronet custom (sw)\", \"Miles_per_Gallon\": 14.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 4457, \"Acceleration\": 13.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford gran torino (sw)\", \"Miles_per_Gallon\": 14.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 140.0, \"Weight_in_lbs\": 4638, \"Acceleration\": 16.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc matador (sw)\", \"Miles_per_Gallon\": 14.0, \"Cylinders\": 8, \"Displacement\": 304.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 4257, \"Acceleration\": 15.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"audi fox\", \"Miles_per_Gallon\": 29.0, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 83.0, \"Weight_in_lbs\": 2219, \"Acceleration\": 16.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"volkswagen dasher\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 1963, \"Acceleration\": 15.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"opel manta\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Weight_in_lbs\": 2300, \"Acceleration\": 14.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"toyota corona\", \"Miles_per_Gallon\": 31.0, \"Cylinders\": 4, \"Displacement\": 76.0, \"Horsepower\": 52.0, \"Weight_in_lbs\": 1649, \"Acceleration\": 16.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"datsun 710\", \"Miles_per_Gallon\": 32.0, \"Cylinders\": 4, \"Displacement\": 83.0, \"Horsepower\": 61.0, \"Weight_in_lbs\": 2003, \"Acceleration\": 19.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"dodge colt\", \"Miles_per_Gallon\": 28.0, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2125, \"Acceleration\": 14.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"fiat 128\", \"Miles_per_Gallon\": 24.0, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2108, \"Acceleration\": 15.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"fiat 124 tc\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 116.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2246, \"Acceleration\": 14.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"honda civic\", \"Miles_per_Gallon\": 24.0, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 97.0, \"Weight_in_lbs\": 2489, \"Acceleration\": 15.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"subaru\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 93.0, \"Weight_in_lbs\": 2391, \"Acceleration\": 15.5, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"fiat x1.9\", \"Miles_per_Gallon\": 31.0, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 2000, \"Acceleration\": 16.0, \"Year\": \"1974-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"plymouth valiant custom\", \"Miles_per_Gallon\": 19.0, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 95.0, \"Weight_in_lbs\": 3264, \"Acceleration\": 16.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet nova\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 3459, \"Acceleration\": 16.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercury monarch\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 72.0, \"Weight_in_lbs\": 3432, \"Acceleration\": 21.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford maverick\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 72.0, \"Weight_in_lbs\": 3158, \"Acceleration\": 19.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac catalina\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 170.0, \"Weight_in_lbs\": 4668, \"Acceleration\": 11.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet bel air\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 145.0, \"Weight_in_lbs\": 4440, \"Acceleration\": 14.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth grand fury\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 4498, \"Acceleration\": 14.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford ltd\", \"Miles_per_Gallon\": 14.0, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 148.0, \"Weight_in_lbs\": 4657, \"Acceleration\": 13.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick century\", \"Miles_per_Gallon\": 17.0, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3907, \"Acceleration\": 21.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevroelt chevelle malibu\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 3897, \"Acceleration\": 18.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc matador\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 6, \"Displacement\": 258.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3730, \"Acceleration\": 19.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth fury\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 95.0, \"Weight_in_lbs\": 3785, \"Acceleration\": 19.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick skyhawk\", \"Miles_per_Gallon\": 21.0, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3039, \"Acceleration\": 15.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet monza 2+2\", \"Miles_per_Gallon\": 20.0, \"Cylinders\": 8, \"Displacement\": 262.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3221, \"Acceleration\": 13.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford mustang ii\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 129.0, \"Weight_in_lbs\": 3169, \"Acceleration\": 12.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota corolla\", \"Miles_per_Gallon\": 29.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2171, \"Acceleration\": 16.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"ford pinto\", \"Miles_per_Gallon\": 23.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 83.0, \"Weight_in_lbs\": 2639, \"Acceleration\": 17.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc gremlin\", \"Miles_per_Gallon\": 20.0, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 2914, \"Acceleration\": 16.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac astro\", \"Miles_per_Gallon\": 23.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 78.0, \"Weight_in_lbs\": 2592, \"Acceleration\": 18.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota corona\", \"Miles_per_Gallon\": 24.0, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 96.0, \"Weight_in_lbs\": 2702, \"Acceleration\": 13.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"volkswagen dasher\", \"Miles_per_Gallon\": 25.0, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 71.0, \"Weight_in_lbs\": 2223, \"Acceleration\": 16.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"datsun 710\", \"Miles_per_Gallon\": 24.0, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 97.0, \"Weight_in_lbs\": 2545, \"Acceleration\": 17.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"ford pinto\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 6, \"Displacement\": 171.0, \"Horsepower\": 97.0, \"Weight_in_lbs\": 2984, \"Acceleration\": 14.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"volkswagen rabbit\", \"Miles_per_Gallon\": 29.0, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 1937, \"Acceleration\": 14.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"amc pacer\", \"Miles_per_Gallon\": 19.0, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 3211, \"Acceleration\": 17.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"audi 100ls\", \"Miles_per_Gallon\": 23.0, \"Cylinders\": 4, \"Displacement\": 115.0, \"Horsepower\": 95.0, \"Weight_in_lbs\": 2694, \"Acceleration\": 15.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"peugeot 504\", \"Miles_per_Gallon\": 23.0, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2957, \"Acceleration\": 17.0, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"volvo 244dl\", \"Miles_per_Gallon\": 22.0, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 98.0, \"Weight_in_lbs\": 2945, \"Acceleration\": 14.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"saab 99le\", \"Miles_per_Gallon\": 25.0, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 115.0, \"Weight_in_lbs\": 2671, \"Acceleration\": 13.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"honda civic cvcc\", \"Miles_per_Gallon\": 33.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 53.0, \"Weight_in_lbs\": 1795, \"Acceleration\": 17.5, \"Year\": \"1975-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"fiat 131\", \"Miles_per_Gallon\": 28.0, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 86.0, \"Weight_in_lbs\": 2464, \"Acceleration\": 15.5, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"opel 1900\", \"Miles_per_Gallon\": 25.0, \"Cylinders\": 4, \"Displacement\": 116.0, \"Horsepower\": 81.0, \"Weight_in_lbs\": 2220, \"Acceleration\": 16.9, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"capri ii\", \"Miles_per_Gallon\": 25.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 92.0, \"Weight_in_lbs\": 2572, \"Acceleration\": 14.9, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge colt\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 79.0, \"Weight_in_lbs\": 2255, \"Acceleration\": 17.7, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"renault 12tl\", \"Miles_per_Gallon\": 27.0, \"Cylinders\": 4, \"Displacement\": 101.0, \"Horsepower\": 83.0, \"Weight_in_lbs\": 2202, \"Acceleration\": 15.3, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"chevrolet chevelle malibu classic\", \"Miles_per_Gallon\": 17.5, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 140.0, \"Weight_in_lbs\": 4215, \"Acceleration\": 13.0, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge coronet brougham\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 4190, \"Acceleration\": 13.0, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc matador\", \"Miles_per_Gallon\": 15.5, \"Cylinders\": 8, \"Displacement\": 304.0, \"Horsepower\": 120.0, \"Weight_in_lbs\": 3962, \"Acceleration\": 13.9, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford gran torino\", \"Miles_per_Gallon\": 14.5, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 152.0, \"Weight_in_lbs\": 4215, \"Acceleration\": 12.8, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth valiant\", \"Miles_per_Gallon\": 22.0, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 3233, \"Acceleration\": 15.4, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet nova\", \"Miles_per_Gallon\": 22.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 3353, \"Acceleration\": 14.5, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford maverick\", \"Miles_per_Gallon\": 24.0, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 81.0, \"Weight_in_lbs\": 3012, \"Acceleration\": 17.6, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc hornet\", \"Miles_per_Gallon\": 22.5, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 3085, \"Acceleration\": 17.6, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet chevette\", \"Miles_per_Gallon\": 29.0, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 52.0, \"Weight_in_lbs\": 2035, \"Acceleration\": 22.2, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet woody\", \"Miles_per_Gallon\": 24.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 60.0, \"Weight_in_lbs\": 2164, \"Acceleration\": 22.1, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"vw rabbit\", \"Miles_per_Gallon\": 29.0, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 1937, \"Acceleration\": 14.2, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"honda civic\", \"Miles_per_Gallon\": 33.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 53.0, \"Weight_in_lbs\": 1795, \"Acceleration\": 17.4, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"dodge aspen se\", \"Miles_per_Gallon\": 20.0, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 3651, \"Acceleration\": 17.7, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford granada ghia\", \"Miles_per_Gallon\": 18.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 78.0, \"Weight_in_lbs\": 3574, \"Acceleration\": 21.0, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac ventura sj\", \"Miles_per_Gallon\": 18.5, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3645, \"Acceleration\": 16.2, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc pacer d/l\", \"Miles_per_Gallon\": 17.5, \"Cylinders\": 6, \"Displacement\": 258.0, \"Horsepower\": 95.0, \"Weight_in_lbs\": 3193, \"Acceleration\": 17.8, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"volkswagen rabbit\", \"Miles_per_Gallon\": 29.5, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 71.0, \"Weight_in_lbs\": 1825, \"Acceleration\": 12.2, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"datsun b-210\", \"Miles_per_Gallon\": 32.0, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 1990, \"Acceleration\": 17.0, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"toyota corolla\", \"Miles_per_Gallon\": 28.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2155, \"Acceleration\": 16.4, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"ford pinto\", \"Miles_per_Gallon\": 26.5, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 72.0, \"Weight_in_lbs\": 2565, \"Acceleration\": 13.6, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"volvo 245\", \"Miles_per_Gallon\": 20.0, \"Cylinders\": 4, \"Displacement\": 130.0, \"Horsepower\": 102.0, \"Weight_in_lbs\": 3150, \"Acceleration\": 15.7, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"plymouth volare premier v8\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 3940, \"Acceleration\": 13.2, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"peugeot 504\", \"Miles_per_Gallon\": 19.0, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 3270, \"Acceleration\": 21.9, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"toyota mark ii\", \"Miles_per_Gallon\": 19.0, \"Cylinders\": 6, \"Displacement\": 156.0, \"Horsepower\": 108.0, \"Weight_in_lbs\": 2930, \"Acceleration\": 15.5, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"mercedes-benz 280s\", \"Miles_per_Gallon\": 16.5, \"Cylinders\": 6, \"Displacement\": 168.0, \"Horsepower\": 120.0, \"Weight_in_lbs\": 3820, \"Acceleration\": 16.7, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"cadillac seville\", \"Miles_per_Gallon\": 16.5, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 180.0, \"Weight_in_lbs\": 4380, \"Acceleration\": 12.1, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevy c10\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 145.0, \"Weight_in_lbs\": 4055, \"Acceleration\": 12.0, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford f108\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 130.0, \"Weight_in_lbs\": 3870, \"Acceleration\": 15.0, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge d100\", \"Miles_per_Gallon\": 13.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 3755, \"Acceleration\": 14.0, \"Year\": \"1976-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"honda Accelerationord cvcc\", \"Miles_per_Gallon\": 31.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 68.0, \"Weight_in_lbs\": 2045, \"Acceleration\": 18.5, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"buick opel isuzu deluxe\", \"Miles_per_Gallon\": 30.0, \"Cylinders\": 4, \"Displacement\": 111.0, \"Horsepower\": 80.0, \"Weight_in_lbs\": 2155, \"Acceleration\": 14.8, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"renault 5 gtl\", \"Miles_per_Gallon\": 36.0, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 58.0, \"Weight_in_lbs\": 1825, \"Acceleration\": 18.6, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"plymouth arrow gs\", \"Miles_per_Gallon\": 25.5, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 96.0, \"Weight_in_lbs\": 2300, \"Acceleration\": 15.5, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"datsun f-10 hatchback\", \"Miles_per_Gallon\": 33.5, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 1945, \"Acceleration\": 16.8, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"chevrolet caprice classic\", \"Miles_per_Gallon\": 17.5, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 145.0, \"Weight_in_lbs\": 3880, \"Acceleration\": 12.5, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"oldsmobile cutlass supreme\", \"Miles_per_Gallon\": 17.0, \"Cylinders\": 8, \"Displacement\": 260.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 4060, \"Acceleration\": 19.0, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge monaco brougham\", \"Miles_per_Gallon\": 15.5, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 145.0, \"Weight_in_lbs\": 4140, \"Acceleration\": 13.7, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercury cougar brougham\", \"Miles_per_Gallon\": 15.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 130.0, \"Weight_in_lbs\": 4295, \"Acceleration\": 14.9, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet concours\", \"Miles_per_Gallon\": 17.5, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3520, \"Acceleration\": 16.4, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick skylark\", \"Miles_per_Gallon\": 20.5, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 3425, \"Acceleration\": 16.9, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth volare custom\", \"Miles_per_Gallon\": 19.0, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 3630, \"Acceleration\": 17.7, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford granada\", \"Miles_per_Gallon\": 18.5, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 98.0, \"Weight_in_lbs\": 3525, \"Acceleration\": 19.0, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac grand prix lj\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 180.0, \"Weight_in_lbs\": 4220, \"Acceleration\": 11.1, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet monte carlo landau\", \"Miles_per_Gallon\": 15.5, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 170.0, \"Weight_in_lbs\": 4165, \"Acceleration\": 11.4, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chrysler cordoba\", \"Miles_per_Gallon\": 15.5, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 190.0, \"Weight_in_lbs\": 4325, \"Acceleration\": 12.2, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford thunderbird\", \"Miles_per_Gallon\": 16.0, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 149.0, \"Weight_in_lbs\": 4335, \"Acceleration\": 14.5, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"volkswagen rabbit custom\", \"Miles_per_Gallon\": 29.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Weight_in_lbs\": 1940, \"Acceleration\": 14.5, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"pontiac sunbird coupe\", \"Miles_per_Gallon\": 24.5, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2740, \"Acceleration\": 16.0, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota corolla liftback\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2265, \"Acceleration\": 18.2, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"ford mustang ii 2+2\", \"Miles_per_Gallon\": 25.5, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 89.0, \"Weight_in_lbs\": 2755, \"Acceleration\": 15.8, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet chevette\", \"Miles_per_Gallon\": 30.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 63.0, \"Weight_in_lbs\": 2051, \"Acceleration\": 17.0, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge colt m/m\", \"Miles_per_Gallon\": 33.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 83.0, \"Weight_in_lbs\": 2075, \"Acceleration\": 15.9, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"subaru dl\", \"Miles_per_Gallon\": 30.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 1985, \"Acceleration\": 16.4, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"volkswagen dasher\", \"Miles_per_Gallon\": 30.5, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Weight_in_lbs\": 2190, \"Acceleration\": 14.1, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"datsun 810\", \"Miles_per_Gallon\": 22.0, \"Cylinders\": 6, \"Displacement\": 146.0, \"Horsepower\": 97.0, \"Weight_in_lbs\": 2815, \"Acceleration\": 14.5, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"bmw 320i\", \"Miles_per_Gallon\": 21.5, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 2600, \"Acceleration\": 12.8, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"mazda rx-4\", \"Miles_per_Gallon\": 21.5, \"Cylinders\": 3, \"Displacement\": 80.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 2720, \"Acceleration\": 13.5, \"Year\": \"1977-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"volkswagen rabbit custom diesel\", \"Miles_per_Gallon\": 43.1, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 48.0, \"Weight_in_lbs\": 1985, \"Acceleration\": 21.5, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"ford fiesta\", \"Miles_per_Gallon\": 36.1, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 66.0, \"Weight_in_lbs\": 1800, \"Acceleration\": 14.4, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mazda glc deluxe\", \"Miles_per_Gallon\": 32.8, \"Cylinders\": 4, \"Displacement\": 78.0, \"Horsepower\": 52.0, \"Weight_in_lbs\": 1985, \"Acceleration\": 19.4, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"datsun b210 gx\", \"Miles_per_Gallon\": 39.4, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 2070, \"Acceleration\": 18.6, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"honda civic cvcc\", \"Miles_per_Gallon\": 36.1, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 60.0, \"Weight_in_lbs\": 1800, \"Acceleration\": 16.4, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"oldsmobile cutlass salon brougham\", \"Miles_per_Gallon\": 19.9, \"Cylinders\": 8, \"Displacement\": 260.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3365, \"Acceleration\": 15.5, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge diplomat\", \"Miles_per_Gallon\": 19.4, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 140.0, \"Weight_in_lbs\": 3735, \"Acceleration\": 13.2, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercury monarch ghia\", \"Miles_per_Gallon\": 20.2, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 139.0, \"Weight_in_lbs\": 3570, \"Acceleration\": 12.8, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac phoenix lj\", \"Miles_per_Gallon\": 19.2, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 3535, \"Acceleration\": 19.2, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet malibu\", \"Miles_per_Gallon\": 20.5, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 95.0, \"Weight_in_lbs\": 3155, \"Acceleration\": 18.2, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford fairmont (auto)\", \"Miles_per_Gallon\": 20.2, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Weight_in_lbs\": 2965, \"Acceleration\": 15.8, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford fairmont (man)\", \"Miles_per_Gallon\": 25.1, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2720, \"Acceleration\": 15.4, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth volare\", \"Miles_per_Gallon\": 20.5, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 3430, \"Acceleration\": 17.2, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc concord\", \"Miles_per_Gallon\": 19.4, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 3210, \"Acceleration\": 17.2, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick century special\", \"Miles_per_Gallon\": 20.6, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 3380, \"Acceleration\": 15.8, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercury zephyr\", \"Miles_per_Gallon\": 20.8, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Weight_in_lbs\": 3070, \"Acceleration\": 16.7, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge aspen\", \"Miles_per_Gallon\": 18.6, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3620, \"Acceleration\": 18.7, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc concord d/l\", \"Miles_per_Gallon\": 18.1, \"Cylinders\": 6, \"Displacement\": 258.0, \"Horsepower\": 120.0, \"Weight_in_lbs\": 3410, \"Acceleration\": 15.1, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet monte carlo landau\", \"Miles_per_Gallon\": 19.2, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 145.0, \"Weight_in_lbs\": 3425, \"Acceleration\": 13.2, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick regal sport coupe (turbo)\", \"Miles_per_Gallon\": 17.7, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 165.0, \"Weight_in_lbs\": 3445, \"Acceleration\": 13.4, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford futura\", \"Miles_per_Gallon\": 18.1, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 139.0, \"Weight_in_lbs\": 3205, \"Acceleration\": 11.2, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge magnum xe\", \"Miles_per_Gallon\": 17.5, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 140.0, \"Weight_in_lbs\": 4080, \"Acceleration\": 13.7, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet chevette\", \"Miles_per_Gallon\": 30.0, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 68.0, \"Weight_in_lbs\": 2155, \"Acceleration\": 16.5, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota corona\", \"Miles_per_Gallon\": 27.5, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 95.0, \"Weight_in_lbs\": 2560, \"Acceleration\": 14.2, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"datsun 510\", \"Miles_per_Gallon\": 27.2, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 97.0, \"Weight_in_lbs\": 2300, \"Acceleration\": 14.7, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"dodge omni\", \"Miles_per_Gallon\": 30.9, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2230, \"Acceleration\": 14.5, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota celica gt liftback\", \"Miles_per_Gallon\": 21.1, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 95.0, \"Weight_in_lbs\": 2515, \"Acceleration\": 14.8, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"plymouth sapporo\", \"Miles_per_Gallon\": 23.2, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 2745, \"Acceleration\": 16.7, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"oldsmobile starfire sx\", \"Miles_per_Gallon\": 23.8, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 85.0, \"Weight_in_lbs\": 2855, \"Acceleration\": 17.6, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"datsun 200-sx\", \"Miles_per_Gallon\": 23.9, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 97.0, \"Weight_in_lbs\": 2405, \"Acceleration\": 14.9, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"audi 5000\", \"Miles_per_Gallon\": 20.3, \"Cylinders\": 5, \"Displacement\": 131.0, \"Horsepower\": 103.0, \"Weight_in_lbs\": 2830, \"Acceleration\": 15.9, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"volvo 264gl\", \"Miles_per_Gallon\": 17.0, \"Cylinders\": 6, \"Displacement\": 163.0, \"Horsepower\": 125.0, \"Weight_in_lbs\": 3140, \"Acceleration\": 13.6, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"saab 99gle\", \"Miles_per_Gallon\": 21.6, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 115.0, \"Weight_in_lbs\": 2795, \"Acceleration\": 15.7, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"peugeot 604sl\", \"Miles_per_Gallon\": 16.2, \"Cylinders\": 6, \"Displacement\": 163.0, \"Horsepower\": 133.0, \"Weight_in_lbs\": 3410, \"Acceleration\": 15.8, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"volkswagen scirocco\", \"Miles_per_Gallon\": 31.5, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 71.0, \"Weight_in_lbs\": 1990, \"Acceleration\": 14.9, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"honda Accelerationord lx\", \"Miles_per_Gallon\": 29.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 68.0, \"Weight_in_lbs\": 2135, \"Acceleration\": 16.6, \"Year\": \"1978-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"pontiac lemans v6\", \"Miles_per_Gallon\": 21.5, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 115.0, \"Weight_in_lbs\": 3245, \"Acceleration\": 15.4, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercury zephyr 6\", \"Miles_per_Gallon\": 19.8, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Weight_in_lbs\": 2990, \"Acceleration\": 18.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford fairmont 4\", \"Miles_per_Gallon\": 22.3, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2890, \"Acceleration\": 17.3, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc concord dl 6\", \"Miles_per_Gallon\": 20.2, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 3265, \"Acceleration\": 18.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge aspen 6\", \"Miles_per_Gallon\": 20.6, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3360, \"Acceleration\": 16.6, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet caprice classic\", \"Miles_per_Gallon\": 17.0, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 130.0, \"Weight_in_lbs\": 3840, \"Acceleration\": 15.4, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford ltd landau\", \"Miles_per_Gallon\": 17.6, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 129.0, \"Weight_in_lbs\": 3725, \"Acceleration\": 13.4, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercury grand marquis\", \"Miles_per_Gallon\": 16.5, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 138.0, \"Weight_in_lbs\": 3955, \"Acceleration\": 13.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge st. regis\", \"Miles_per_Gallon\": 18.2, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 135.0, \"Weight_in_lbs\": 3830, \"Acceleration\": 15.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick estate wagon (sw)\", \"Miles_per_Gallon\": 16.9, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 155.0, \"Weight_in_lbs\": 4360, \"Acceleration\": 14.9, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford country squire (sw)\", \"Miles_per_Gallon\": 15.5, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 142.0, \"Weight_in_lbs\": 4054, \"Acceleration\": 14.3, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet malibu classic (sw)\", \"Miles_per_Gallon\": 19.2, \"Cylinders\": 8, \"Displacement\": 267.0, \"Horsepower\": 125.0, \"Weight_in_lbs\": 3605, \"Acceleration\": 15.0, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chrysler lebaron town @ country (sw)\", \"Miles_per_Gallon\": 18.5, \"Cylinders\": 8, \"Displacement\": 360.0, \"Horsepower\": 150.0, \"Weight_in_lbs\": 3940, \"Acceleration\": 13.0, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"vw rabbit custom\", \"Miles_per_Gallon\": 31.9, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 71.0, \"Weight_in_lbs\": 1925, \"Acceleration\": 14.0, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"maxda glc deluxe\", \"Miles_per_Gallon\": 34.1, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 1975, \"Acceleration\": 15.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"dodge colt hatchback custom\", \"Miles_per_Gallon\": 35.7, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 80.0, \"Weight_in_lbs\": 1915, \"Acceleration\": 14.4, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc spirit dl\", \"Miles_per_Gallon\": 27.4, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 80.0, \"Weight_in_lbs\": 2670, \"Acceleration\": 15.0, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercedes benz 300d\", \"Miles_per_Gallon\": 25.4, \"Cylinders\": 5, \"Displacement\": 183.0, \"Horsepower\": 77.0, \"Weight_in_lbs\": 3530, \"Acceleration\": 20.1, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"cadillac eldorado\", \"Miles_per_Gallon\": 23.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 125.0, \"Weight_in_lbs\": 3900, \"Acceleration\": 17.4, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"peugeot 504\", \"Miles_per_Gallon\": 27.2, \"Cylinders\": 4, \"Displacement\": 141.0, \"Horsepower\": 71.0, \"Weight_in_lbs\": 3190, \"Acceleration\": 24.8, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"oldsmobile cutlass salon brougham\", \"Miles_per_Gallon\": 23.9, \"Cylinders\": 8, \"Displacement\": 260.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 3420, \"Acceleration\": 22.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth horizon\", \"Miles_per_Gallon\": 34.2, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 2200, \"Acceleration\": 13.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth horizon tc3\", \"Miles_per_Gallon\": 34.5, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 2150, \"Acceleration\": 14.9, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"datsun 210\", \"Miles_per_Gallon\": 31.8, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 2020, \"Acceleration\": 19.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"fiat strada custom\", \"Miles_per_Gallon\": 37.3, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 69.0, \"Weight_in_lbs\": 2130, \"Acceleration\": 14.7, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"buick skylark limited\", \"Miles_per_Gallon\": 28.4, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2670, \"Acceleration\": 16.0, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet citation\", \"Miles_per_Gallon\": 28.8, \"Cylinders\": 6, \"Displacement\": 173.0, \"Horsepower\": 115.0, \"Weight_in_lbs\": 2595, \"Acceleration\": 11.3, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"oldsmobile omega brougham\", \"Miles_per_Gallon\": 26.8, \"Cylinders\": 6, \"Displacement\": 173.0, \"Horsepower\": 115.0, \"Weight_in_lbs\": 2700, \"Acceleration\": 12.9, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac phoenix\", \"Miles_per_Gallon\": 33.5, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2556, \"Acceleration\": 13.2, \"Year\": \"1979-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"vw rabbit\", \"Miles_per_Gallon\": 41.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 76.0, \"Weight_in_lbs\": 2144, \"Acceleration\": 14.7, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"toyota corolla tercel\", \"Miles_per_Gallon\": 38.1, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 60.0, \"Weight_in_lbs\": 1968, \"Acceleration\": 18.8, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"chevrolet chevette\", \"Miles_per_Gallon\": 32.1, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 2120, \"Acceleration\": 15.5, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"datsun 310\", \"Miles_per_Gallon\": 37.2, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 2019, \"Acceleration\": 16.4, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"chevrolet citation\", \"Miles_per_Gallon\": 28.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2678, \"Acceleration\": 16.5, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford fairmont\", \"Miles_per_Gallon\": 26.4, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2870, \"Acceleration\": 18.1, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc concord\", \"Miles_per_Gallon\": 24.3, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 3003, \"Acceleration\": 20.1, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge aspen\", \"Miles_per_Gallon\": 19.1, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 3381, \"Acceleration\": 18.7, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"audi 4000\", \"Miles_per_Gallon\": 34.3, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Weight_in_lbs\": 2188, \"Acceleration\": 15.8, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"toyota corona liftback\", \"Miles_per_Gallon\": 29.8, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2711, \"Acceleration\": 15.5, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"mazda 626\", \"Miles_per_Gallon\": 31.3, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2542, \"Acceleration\": 17.5, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"datsun 510 hatchback\", \"Miles_per_Gallon\": 37.0, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 92.0, \"Weight_in_lbs\": 2434, \"Acceleration\": 15.0, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"toyota corolla\", \"Miles_per_Gallon\": 32.2, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2265, \"Acceleration\": 15.2, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"mazda glc\", \"Miles_per_Gallon\": 46.6, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 2110, \"Acceleration\": 17.9, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"dodge colt\", \"Miles_per_Gallon\": 27.9, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 2800, \"Acceleration\": 14.4, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"datsun 210\", \"Miles_per_Gallon\": 40.8, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 2110, \"Acceleration\": 19.2, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"vw rabbit c (diesel)\", \"Miles_per_Gallon\": 44.3, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 48.0, \"Weight_in_lbs\": 2085, \"Acceleration\": 21.7, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"vw dasher (diesel)\", \"Miles_per_Gallon\": 43.4, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 48.0, \"Weight_in_lbs\": 2335, \"Acceleration\": 23.7, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"audi 5000s (diesel)\", \"Miles_per_Gallon\": 36.4, \"Cylinders\": 5, \"Displacement\": 121.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 2950, \"Acceleration\": 19.9, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"mercedes-benz 240d\", \"Miles_per_Gallon\": 30.0, \"Cylinders\": 4, \"Displacement\": 146.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 3250, \"Acceleration\": 21.8, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"honda civic 1500 gl\", \"Miles_per_Gallon\": 44.6, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 1850, \"Acceleration\": 13.8, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"renault lecar deluxe\", \"Miles_per_Gallon\": 40.9, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": null, \"Weight_in_lbs\": 1835, \"Acceleration\": 17.3, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"subaru dl\", \"Miles_per_Gallon\": 33.8, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 2145, \"Acceleration\": 18.0, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"vokswagen rabbit\", \"Miles_per_Gallon\": 29.8, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 62.0, \"Weight_in_lbs\": 1845, \"Acceleration\": 15.3, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"datsun 280-zx\", \"Miles_per_Gallon\": 32.7, \"Cylinders\": 6, \"Displacement\": 168.0, \"Horsepower\": 132.0, \"Weight_in_lbs\": 2910, \"Acceleration\": 11.4, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"mazda rx-7 gs\", \"Miles_per_Gallon\": 23.7, \"Cylinders\": 3, \"Displacement\": 70.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 2420, \"Acceleration\": 12.5, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"triumph tr7 coupe\", \"Miles_per_Gallon\": 35.0, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2500, \"Acceleration\": 15.1, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"ford mustang cobra\", \"Miles_per_Gallon\": 23.6, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": null, \"Weight_in_lbs\": 2905, \"Acceleration\": 14.3, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"honda Accelerationord\", \"Miles_per_Gallon\": 32.4, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 72.0, \"Weight_in_lbs\": 2290, \"Acceleration\": 17.0, \"Year\": \"1980-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"plymouth reliant\", \"Miles_per_Gallon\": 27.2, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Weight_in_lbs\": 2490, \"Acceleration\": 15.7, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"buick skylark\", \"Miles_per_Gallon\": 26.6, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 84.0, \"Weight_in_lbs\": 2635, \"Acceleration\": 16.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge aries wagon (sw)\", \"Miles_per_Gallon\": 25.8, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 92.0, \"Weight_in_lbs\": 2620, \"Acceleration\": 14.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet citation\", \"Miles_per_Gallon\": 23.5, \"Cylinders\": 6, \"Displacement\": 173.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 2725, \"Acceleration\": 12.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"plymouth reliant\", \"Miles_per_Gallon\": 30.0, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Weight_in_lbs\": 2385, \"Acceleration\": 12.9, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota starlet\", \"Miles_per_Gallon\": 39.1, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 58.0, \"Weight_in_lbs\": 1755, \"Acceleration\": 16.9, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"plymouth champ\", \"Miles_per_Gallon\": 39.0, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 64.0, \"Weight_in_lbs\": 1875, \"Acceleration\": 16.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"honda civic 1300\", \"Miles_per_Gallon\": 35.1, \"Cylinders\": 4, \"Displacement\": 81.0, \"Horsepower\": 60.0, \"Weight_in_lbs\": 1760, \"Acceleration\": 16.1, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"subaru\", \"Miles_per_Gallon\": 32.3, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 2065, \"Acceleration\": 17.8, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"datsun 210\", \"Miles_per_Gallon\": 37.0, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 1975, \"Acceleration\": 19.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"toyota tercel\", \"Miles_per_Gallon\": 37.7, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 62.0, \"Weight_in_lbs\": 2050, \"Acceleration\": 17.3, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"mazda glc 4\", \"Miles_per_Gallon\": 34.1, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 68.0, \"Weight_in_lbs\": 1985, \"Acceleration\": 16.0, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"plymouth horizon 4\", \"Miles_per_Gallon\": 34.7, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 63.0, \"Weight_in_lbs\": 2215, \"Acceleration\": 14.9, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford escort 4w\", \"Miles_per_Gallon\": 34.4, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 2045, \"Acceleration\": 16.2, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford escort 2h\", \"Miles_per_Gallon\": 29.9, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 65.0, \"Weight_in_lbs\": 2380, \"Acceleration\": 20.7, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"volkswagen jetta\", \"Miles_per_Gallon\": 33.0, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 74.0, \"Weight_in_lbs\": 2190, \"Acceleration\": 14.2, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"renault 18i\", \"Miles_per_Gallon\": 34.5, \"Cylinders\": 4, \"Displacement\": 100.0, \"Horsepower\": null, \"Weight_in_lbs\": 2320, \"Acceleration\": 15.8, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"honda prelude\", \"Miles_per_Gallon\": 33.7, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2210, \"Acceleration\": 14.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"toyota corolla\", \"Miles_per_Gallon\": 32.4, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2350, \"Acceleration\": 16.8, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"datsun 200sx\", \"Miles_per_Gallon\": 32.9, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 100.0, \"Weight_in_lbs\": 2615, \"Acceleration\": 14.8, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"mazda 626\", \"Miles_per_Gallon\": 31.6, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 74.0, \"Weight_in_lbs\": 2635, \"Acceleration\": 18.3, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"peugeot 505s turbo diesel\", \"Miles_per_Gallon\": 28.1, \"Cylinders\": 4, \"Displacement\": 141.0, \"Horsepower\": 80.0, \"Weight_in_lbs\": 3230, \"Acceleration\": 20.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"saab 900s\", \"Miles_per_Gallon\": null, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 2800, \"Acceleration\": 15.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"volvo diesel\", \"Miles_per_Gallon\": 30.7, \"Cylinders\": 6, \"Displacement\": 145.0, \"Horsepower\": 76.0, \"Weight_in_lbs\": 3160, \"Acceleration\": 19.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"toyota cressida\", \"Miles_per_Gallon\": 25.4, \"Cylinders\": 6, \"Displacement\": 168.0, \"Horsepower\": 116.0, \"Weight_in_lbs\": 2900, \"Acceleration\": 12.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"datsun 810 maxima\", \"Miles_per_Gallon\": 24.2, \"Cylinders\": 6, \"Displacement\": 146.0, \"Horsepower\": 120.0, \"Weight_in_lbs\": 2930, \"Acceleration\": 13.8, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"buick century\", \"Miles_per_Gallon\": 22.4, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 3415, \"Acceleration\": 15.8, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"oldsmobile cutlass ls\", \"Miles_per_Gallon\": 26.6, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 105.0, \"Weight_in_lbs\": 3725, \"Acceleration\": 19.0, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford granada gl\", \"Miles_per_Gallon\": 20.2, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 3060, \"Acceleration\": 17.1, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chrysler lebaron salon\", \"Miles_per_Gallon\": 17.6, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 85.0, \"Weight_in_lbs\": 3465, \"Acceleration\": 16.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet cavalier\", \"Miles_per_Gallon\": 28.0, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2605, \"Acceleration\": 19.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet cavalier wagon\", \"Miles_per_Gallon\": 27.0, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2640, \"Acceleration\": 18.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet cavalier 2-door\", \"Miles_per_Gallon\": 34.0, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2395, \"Acceleration\": 18.0, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac j2000 se hatchback\", \"Miles_per_Gallon\": 31.0, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 85.0, \"Weight_in_lbs\": 2575, \"Acceleration\": 16.2, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"dodge aries se\", \"Miles_per_Gallon\": 29.0, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Weight_in_lbs\": 2525, \"Acceleration\": 16.0, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"pontiac phoenix\", \"Miles_per_Gallon\": 27.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2735, \"Acceleration\": 18.0, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford fairmont futura\", \"Miles_per_Gallon\": 24.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 92.0, \"Weight_in_lbs\": 2865, \"Acceleration\": 16.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"amc concord dl\", \"Miles_per_Gallon\": 23.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": null, \"Weight_in_lbs\": 3035, \"Acceleration\": 20.5, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"volkswagen rabbit l\", \"Miles_per_Gallon\": 36.0, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 74.0, \"Weight_in_lbs\": 1980, \"Acceleration\": 15.3, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"mazda glc custom l\", \"Miles_per_Gallon\": 37.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 68.0, \"Weight_in_lbs\": 2025, \"Acceleration\": 18.2, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"mazda glc custom\", \"Miles_per_Gallon\": 31.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 68.0, \"Weight_in_lbs\": 1970, \"Acceleration\": 17.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"plymouth horizon miser\", \"Miles_per_Gallon\": 38.0, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 63.0, \"Weight_in_lbs\": 2125, \"Acceleration\": 14.7, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"mercury lynx l\", \"Miles_per_Gallon\": 36.0, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 2125, \"Acceleration\": 17.3, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"nissan stanza xe\", \"Miles_per_Gallon\": 36.0, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 88.0, \"Weight_in_lbs\": 2160, \"Acceleration\": 14.5, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"honda Accelerationord\", \"Miles_per_Gallon\": 36.0, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 75.0, \"Weight_in_lbs\": 2205, \"Acceleration\": 14.5, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"toyota corolla\", \"Miles_per_Gallon\": 34.0, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 70.0, \"Weight_in_lbs\": 2245, \"Acceleration\": 16.9, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"honda civic\", \"Miles_per_Gallon\": 38.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 1965, \"Acceleration\": 15.0, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"honda civic (auto)\", \"Miles_per_Gallon\": 32.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 1965, \"Acceleration\": 15.7, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"datsun 310 gx\", \"Miles_per_Gallon\": 38.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Weight_in_lbs\": 1995, \"Acceleration\": 16.2, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"buick century limited\", \"Miles_per_Gallon\": 25.0, \"Cylinders\": 6, \"Displacement\": 181.0, \"Horsepower\": 110.0, \"Weight_in_lbs\": 2945, \"Acceleration\": 16.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"oldsmobile cutlass ciera (diesel)\", \"Miles_per_Gallon\": 38.0, \"Cylinders\": 6, \"Displacement\": 262.0, \"Horsepower\": 85.0, \"Weight_in_lbs\": 3015, \"Acceleration\": 17.0, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chrysler lebaron medallion\", \"Miles_per_Gallon\": 26.0, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 92.0, \"Weight_in_lbs\": 2585, \"Acceleration\": 14.5, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford granada l\", \"Miles_per_Gallon\": 22.0, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 112.0, \"Weight_in_lbs\": 2835, \"Acceleration\": 14.7, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"toyota celica gt\", \"Miles_per_Gallon\": 32.0, \"Cylinders\": 4, \"Displacement\": 144.0, \"Horsepower\": 96.0, \"Weight_in_lbs\": 2665, \"Acceleration\": 13.9, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Japan\"}, {\"Name\": \"dodge charger 2.2\", \"Miles_per_Gallon\": 36.0, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Weight_in_lbs\": 2370, \"Acceleration\": 13.0, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevrolet camaro\", \"Miles_per_Gallon\": 27.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Weight_in_lbs\": 2950, \"Acceleration\": 17.3, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford mustang gl\", \"Miles_per_Gallon\": 27.0, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 86.0, \"Weight_in_lbs\": 2790, \"Acceleration\": 15.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"vw pickup\", \"Miles_per_Gallon\": 44.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 52.0, \"Weight_in_lbs\": 2130, \"Acceleration\": 24.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"Europe\"}, {\"Name\": \"dodge rampage\", \"Miles_per_Gallon\": 32.0, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Weight_in_lbs\": 2295, \"Acceleration\": 11.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"ford ranger\", \"Miles_per_Gallon\": 28.0, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 79.0, \"Weight_in_lbs\": 2625, \"Acceleration\": 18.6, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}, {\"Name\": \"chevy s-10\", \"Miles_per_Gallon\": 31.0, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 82.0, \"Weight_in_lbs\": 2720, \"Acceleration\": 19.4, \"Year\": \"1982-01-01T00:00:00\", \"Origin\": \"USA\"}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.HConcatChart(...)"
]
},
"metadata": {}
}
],
"execution_count": 4,
"metadata": {
"execution": {
"iopub.status.busy": "2020-05-17T10:12:10.908Z",
"iopub.execute_input": "2020-05-17T10:12:10.927Z",
"iopub.status.idle": "2020-05-17T10:12:11.049Z",
"shell.execute_reply": "2020-05-17T10:12:11.101Z"
},
"jupyter": {
"source_hidden": true
},
"collapsed": false,
"outputExpanded": false
}
}
],
"metadata": {
"kernel_info": {
"name": "python3"
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.7.4",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
},
"nteract": {
"version": "0.23.1"
},
"title": "Try out Vega through Altair"
},
"nbformat": 4,
"nbformat_minor": 1
} | mit |
manoharan-lab/structural-color | montecarlo_tutorial.ipynb | 1 | 990093 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tutorial for the montecarlo module of the structural-color python package\n",
"\n",
"Copyright 2016, Vinothan N. Manoharan, Victoria Hwang, Annie Stephenson\n",
"\n",
"This file is part of the structural-color python package.\n",
"\n",
"This package is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.\n",
"\n",
"This package is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.\n",
"\n",
"You should have received a copy of the GNU General Public License along with this package. If not, see http://www.gnu.org/licenses/."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading and using the package and module\n",
"\n",
"To load, make sure you are in the top directory and do"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import structcol as sc\n",
"import structcol.refractive_index as ri\n",
"from structcol import montecarlo as mc\n",
"from structcol import detector as det\n",
"from structcol import model\n",
"\n",
"# For Jupyter notebooks only:\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run photon packets in parallel plane (film) medium\n",
"\n",
"This is an example code to run a Monte Carlo calculation for photon packets travelling in a scattering medium."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set random number seed. This is so that the code produces the same trajectories each time (for testing purposes). Comment this out or set the seed to `None` for real calculations."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"seed = 1"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Properties of system\n",
"ntrajectories = 100 # number of trajectories\n",
"nevents = 100 # number of scattering events in each trajectory\n",
"wavelen = sc.Quantity('600 nm') # wavelength for scattering calculations\n",
"radius = sc.Quantity('0.125 um') # particle radius\n",
"volume_fraction = sc.Quantity(0.5, '') # volume fraction of particles\n",
"n_particle = sc.Quantity(1.54, '') # refractive indices can be specified as pint quantities or\n",
"n_matrix = ri.n('vacuum', wavelen) # called from the refractive_index module. n_matrix is the \n",
"n_medium = ri.n('vacuum', wavelen) # space within sample. n_medium is outside the sample. \n",
" # n_particle and n_matrix can have complex indices if absorption is desired\n",
"n_sample = ri.n_eff(n_particle, # refractive index of sample, calculated using Bruggeman approximation\n",
" n_matrix, \n",
" volume_fraction) \n",
"boundary = 'film' # geometry of sample, can be 'film' or 'sphere', see below for tutorial \n",
" # on sphere case\n",
"incidence_theta_min = sc.Quantity(0, 'rad') # min incidence angle of illumination (should be >=0 and < pi/2)\n",
"incidence_theta_max = sc.Quantity(0, 'rad') # max incidence angle of illumination (should be >=0 and < pi/2)\n",
" # (in this case, all trajectories hit the sample normally to the surface)\n",
"incidence_phi_min = sc.Quantity(0, 'rad') # min incidence angle of illumination (should be >=0 and <= pi/2)\n",
"incidence_phi_max = sc.Quantity(2*np.pi, 'rad') # max incidence angle of illumination (should be >=0 and <= pi/2)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/vhwang/anaconda/lib/python3.5/site-packages/pint/quantity.py:715: RuntimeWarning: divide by zero encountered in double_scalars\n",
" magnitude = magnitude_op(new_self._magnitude, other._magnitude)\n"
]
}
],
"source": [
"#%%timeit\n",
"# Calculate the phase function and scattering and absorption coefficients from the single scattering model\n",
"p, mu_scat, mu_abs = mc.calc_scat(radius, n_particle, n_sample, volume_fraction, wavelen, mie_theory=False)\n",
"\n",
"# Initialize the trajectories\n",
"r0, k0, W0 = mc.initialize(nevents, ntrajectories, n_medium, n_sample, boundary, seed=seed, \n",
" incidence_theta_min = incidence_theta_min, incidence_theta_max = incidence_theta_max, \n",
" incidence_phi_min = incidence_phi_min, incidence_phi_max = incidence_phi_max, \n",
" incidence_theta_data = None, incidence_phi_data = None)\n",
" # We can input specific incidence angles for each trajectory by setting \n",
" # incidence_theta_data or incidence_phi_data to not None. This can be useful if we \n",
" # have BRDF data on a specific material, and we want to model how light would reflect\n",
" # off said material into a structurally colored film. The incidence angle data can be \n",
" # Quantity arrays, but if they aren't, the values must be in radians. \n",
"r0 = sc.Quantity(r0, 'um')\n",
"k0 = sc.Quantity(k0, '')\n",
"W0 = sc.Quantity(W0, '')\n",
"\n",
"# Generate a matrix of all the randomly sampled angles first \n",
"sintheta, costheta, sinphi, cosphi, _, _ = mc.sample_angles(nevents, ntrajectories, p)\n",
"\n",
"# Create step size distribution\n",
"step = mc.sample_step(nevents, ntrajectories, mu_scat)\n",
" \n",
"# Create trajectories object\n",
"trajectories = mc.Trajectory(r0, k0, W0)\n",
"\n",
"# Run photons\n",
"trajectories.absorb(mu_abs, step) \n",
"trajectories.scatter(sintheta, costheta, sinphi, cosphi) \n",
"trajectories.move(step)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot trajectories"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfgAAAPWCAYAAAAMEYoQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0P8HZ3lMYzrU+0gAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 576x1224 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAFUCAYAAACp7gyoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzsnXmcXFWZv59z7lZLV69JCAmQhYQd\nhLDIoiCCKOKICqIwg4iAyMAPMCooIIwCKsKgooggIKAiOgriKAOCC/u+g2EJgZBA0tl6qa71Luf3\nx+1bS3dVd3V1VafTuc/ng3ZquefUrVvnve973vf7CqUUISEhISEhISMjN/YEQkJCQkJCNgVCgxkS\nEhISElIDocEMCQkJCQmpgdBghoSEhISE1EBoMENCQkJCQmogNJghISEhISE1oI/05Nq1ybDmJCQk\nJCRks2L69ISo9HjoYYaEhISEhNRAaDBDQkJCQkJqIDSYISEhISEhNRAazJCQkJCQkBoIDWZISEhI\nSEgNhAYzJCQkJCSkBkKDGRISEhISUgOhwQwJCQkJCamB0GCGhISEhITUQGgwQ0JCQkJCaiA0mCEh\nISEhITUQGsyQCWfVqnc58MB9+Pznjyv8d8IJx/LnP99Z9zG/972LefLJxwG47LJLeOWVJcMenyiu\nvPIybrjh2jG9Z926tXzpS19o2Bz+93//yO23/8+Y3/fVr57Jm28uq2vMd999h/PP/9qY3/fHP/6e\nX/7yprrGDAmZSEYUXw8JaRaWZXHTTbcW/r127RqOP/4z7LDDTixYsHDMx/v6179Z+PvJJx/nyCM/\nNezxycy0adP52c9ubNjxXnjhOebN23bM77viiqvqHnP16lW8/fbyMb/vE584uu4xQ0ImktBghkwK\npk+fwdZbb82KFctZsGAhN910Pffddw+aprH11tvw5S+fQ1fXNO6//+/cfPMNCCGRUnL66Wex++6L\nOOOML3LUUcfw2muvsm7dWr71rQu44IJvc801V3HUUcdw8MGH8sAD/+QXv7gOz1PEYjH+3//7Mjvt\ntAs33HAtq1evYv36daxevYrp02fwzW9ezLRp07jjjt/zxz/+AcMwME2Tr33tPObNm18291RqgO99\n7xKWLn2Nrq5paJrObru1A3D00f/GJZdcxg477FT277a2dk4//RTmzJnLqlWruOCC/+LLXz6de+99\ncMT5LFnyMldc8T0cx2bWrK3o7l7FGWd8mUWL9irM5/77/8FDDz3Ak08+jmVZ9Pb28PLLL7Ju3VoW\nLNiOM844m8sv/w4bNmxgw4b1bLHFTC6++Ht0dHSWzfehhx7g5ptvwHFsIpEIp59+NrvsshuO43DN\nNVfx8MMPoesau+yyG4sXn8tll13C2rVrWbz4DK688icjnu/S+cyevRV9fb0sXnwua9eu4corv093\n92pc1+GQQw7jc5/7Ao7j8MMfXs6LLz6PpunMmjWb8867iFgsNnEXachmTxiSDZkUvPTSC6xcuZKd\ndtqFv/zlTzz22CP8/Oe3cPPNtzFv3rZceum3ALj66h+xePHXueGGX3LKKafx7LNPlx3n1FNPZ9q0\n6Vx00SXsvPMuhceXL3+LK674Lpdc8n1uvvk3nHzyl/jGN75CKjUAwPPPP8vFF3+PW2/9A5FIhDvv\n/AOu63LVVf/Nf//3j7n++lv4+Mc/yQsvPDds7jfccC2WZXHrrX/g4osvY8WK2rysNWu6+fznT+a2\n226nq2ta2XOV5uM4Dueffw4nn/wlbr75Nj796c/y+uuvDTvuQQcdzPvedyDHHHMcRx11DOB7fzfe\n+GsuvPBi7rvvr+y8865ce+0v+N3v7iQSiXD33XeVHWPFire57rqrueKKH/GLX9zK1752Puef/zUy\nmQx33PF7Xn31FW6++VZuueW3pNNp/v73ezn33AuYPXs2V175k1HPd+l8Srn44gs54oiPc+ONv+K6\n627mqaee4G9/u5eXX36RZ599mptu+g033vgrZs2azRtvvF7TeQ4JaRShhxmyUcjlcnz+88cB4LoO\nbW3tXHjhxWyxxUwee+wRPvrRfyMajQLw6U8fyy23fAjbtjnkkMM4//yvst9+72Pvvd/Lccd9rqbx\nnn76Sfbcc29mz94KgD333Jv29k5effUVAPbYY0/i8RYAtttuB/r7+9A0jYMPPpTTTvsC++13APvs\nsx8f+tBHhh37qaee4Mwzv4IQgo6ODg488AM1zUnTNHbeedeKz1Waz7JlSwHYb78DAFi0aC/mz68t\n7Lrzzrui6/7P/ZhjjuX555/lttt+xcqVK1i27A122mmXstc/+eTjrF+/jrPO+s/CY0JIVq5cwVNP\nPc6HP/xRLCsCwLe//V0AnnnmqcJrRzvfpfMJyGQyPPfcM/T393P99T8bfCzN0qWvsc8++yKl5Itf\nPIF99tmPD3zgg8PmHBLSbEKDGbJRGLqHWYrnuQhR7N+qlIfruiilOPXU0/nYx47kyScf4667/sxt\nt/2Kn//8llHHG3rM4LiO4xTmU/6c3zv9wgsvZtmypTz11BP8+tc3cc89d3Hxxd8bdvzg9eAbwgAh\nRNlztm0X/jZNc5jRCKg0H03Ty44FIGVtQaLg5gPgpz+9iiVLXuaIIz7OokV74TjOsON6nsuee+5T\nMIYA3d2rmTZtOpqmU3oqN2xYj+cNf/9I57t0PqXvUUrxs5/dSCTiG+Pe3l5M0yQWi3HTTb/hxRef\n5+mnn+Sii87j2GOP51Of+nRNnz8kpBGEIdmQScd737s/f/nLn8hkMgD8/ve/ZffdFyGl5Oij/41s\nNssnPnE0X/nKubzxxlLy+XzZ+zVNKyzMAXvuuQ9PPPEo77yzEvA9oDVrukf0Unp7e/nUp46gtbWd\nY445jlNO+U9eeeVfw16377778+c/34nnefT39/Pgg/cXnmtvby9k7D7zzFOsX7+uvpMCzJkzF8Mw\neOyxRwD4179eYtmyN4YZJvDPges6wx4HeOKJxzjmmGP5yEeOoKOjkyeffBzP88pe45+vx1i+/C0A\nHn30IU444VhyuRx77bUP9957D/l8Hs/zuOKK73HffXejaXrhvNdzvuPxFnbeeVduu+1XACSTSU47\n7Qs89ND9PPzwg5x11mnssstunHTSqXzkI0dU/C5CQppJ6GGGTDo+9rEjWbOmm1NOOQGlPGbP3poL\nL7wYXdc588yv8K1vnY+u6wgh+frXL8Q0zbL3H3TQwXz729/kq1/9RuGxefPms3jx1zn//HNwXYdI\nJMJll/2AlpaWqvNob2/nc5/7AmeffRqmaaFpGuecc/6w133hC6dy+eXf4bjjjqajo4Ntt11QeO60\n087kiiu+y5133s722+/A9tvvWPd50XWdSy/9Ppdf/l2uvfYnbL31HDo7uwreWCn77rs/P/7xDyoe\n58QTT+bqq3/E9df/bDBBaXdWrlxR9pp58+Zzzjnnc9FF5w16txqXXXYlsViMI4/8FKtXv8tJJ/0H\nSvnh46OP/izpdArTtDjllM9x3XU3j/l8A1x00SX84Aff53Of+wy2bXPooR/msMMOx3VdHnvsET73\nuc8QjcZIJBKce+4FdZ/LkJB6EENDMaWsXZus/mRIyBTA8zyEEBW9tMnI1Vf/iGOP/Q86O7vo7l7N\n5z9/HL/73Z0kEolxH1spxRFHHMpPf3o9c+fOa8BsQ0I2TaZPT1RcEEIPM2SzRAiQUuG6Nv49Y2A0\nxeDfclIa0ZkzZ3LWWaeh6zpKwde/fkFDjOW6dWs55ZQT2H77HdhmmzkNmGlIyNQj9DBDNiuECIyl\n/+98Pl+W8KKUKjGUm4YRDQkJaSyhhxmy2RMkr45k88oNoioY06GGVEoxmOSiNqmQbkhISP2EWbIh\nUx5N8/8LvMt6GGpINU2SSERRysbz8rhuHs+z8TynUB4REhIytQg9zJApSyxm4HlqWIlJc6jujfp/\ny8LfoTcaErJpEhrMkCmHlL4nqWkSITyaYS+Vqu6tVgrrKuUOihhAuDcaErJpEhrMkClDYCgnxvYo\nfINXG7XujQohKRrS0BsNCZlMhAYzZJOnuqEc2agZho5p6ti2g+M4OI7bzGlWZLgh9fc/29sTZDI5\n8nk79EZDQiYJocEM2WQJykOq2Y9qYVNd14hGfa3WbDaHZZm0tEQHJfXcMgNq28N1VpuN71nKwbkH\nIV0VhnRDQjYyocEM2eQYWktZK5omiUYtpJRkMjls2xlWh2kYOrquoes6kYiFrmsopQaNaNGYTgxF\nI1h7SDcwpDIM6YaENJjQYIZsMpQaybHYASkF0ahv/DKZPPm8XfW1tu1g2w6QKzymaRJd1zEMnWg0\ngmFoBQ8wkYjjOA627U6gIS1SOcEo9EZDQppBaDBDNglqER0YilJgmjqWZZDL2aRS2QqvEvh7ndVx\nXQ/XzZPLFbui6LpGR0crrutimgaxWBRd13Bdd5g3OrT1VbOpxRttaYkNno+w3CUkpFZCgxkyqanH\nUAJEIiaRiInrevT3pxu+D+l7cpBOlxvhIJxrGDrxeBTD8DVffS/UKXijrjtaglHjjWypQYzFoqRS\n6ULfytAbDQkZndBghkxK6i0RsSyDSMTEtl2y2RxCyAlN2nEcF8fxxw6QUhb2RiMRi5YWHSklrlsM\n5QZe6UTNVYggKSosdwkJqZXQYIZMKuo1lKbpJ+m4rkcymcHzPCzLGPU4geEYKyMJFwzF8zxyuTy5\nog1FCFHmjfoJRjqe5+E4DpqmYRh+ycvQ5s4TQbVyl9AbDdmcCQ1myKSgXkNZWiKSTmfLainHYtQm\nmiDz1rYdMpni476h1GhpiWFZJtGohRCibE808EYnmrEkGEkpUSr0REOmFqHBDNmo1GsoiyUigkwm\nP5jZuunjuv7+ZiRikcnkyOXySCnQdR1d18sSjPzwr1OWZLQxakaLFEO606a10929PnhVWO4SMiUI\nDWbIRkFKiMcjZDKVMldHel/tJSJjla8bG808djmep8jn7WGfVde1wb3RoTWjblmSketOfEi3nLDc\nJWRqEBrMkAmlVJ3HsvSycOTI7xNEoyaGoZPNVisRqTzeVCVIMKpeM2qh63GkFCXe6MTUi1YyfqH4\nQsimTmgwQyaM8ZSIWJZJPm/T35+qOUmnmdHJybo/WqlmNEgwMgzfkMZiEYQQTJvWPixLd3IkGIXe\naMjkJDSYIU2nXkNZLBFxBg1l2JS5HkoTjAJmzpxGb29y0BvVCjWjQFm9aL2i9OP9rkbzRuPxKLmc\njesGofGw3CWk+YQGM6RpjL9ExC2UiNTHxO0zbmoopUpqRouPSykL3mg1UfqJrhktJTCIlmUW5hN6\noyETRWgwQxpOvYbSMPwSEaUUqVS2BjWckWlu2HRqGmPP88jnvbIEIyEoZOkahlaoGVXKGyYD6Lpe\n3bWtYyUw2KOHdBkivBAa0ZD6CA1mSMMYq6EMDJqUpSUiOWx74moM6104J+seZjNQiio1o5VE6f0E\nIyEgGrWalmQ0mlEe+r0q5Q3+f5hgFFI/ocEMGTf1epSgBmsK5WCz5ImtpQxKVAJjsDESXjZlqiUY\nmaZOW1uiSs1osC86XlH60UXzK74rFF8IGQehwQypm3r7UgYlIkIIXNcllaqxtqRBCAGRiIVp6uRy\ndqGWUYjGJLxszgQJRkpBX99A4fHSmlHLGo8ovU8jw76VjKhpGsTjUXp6+oNXhd5oSGgwQ8ZOvX0p\nfUNlYpomuVy+UJDfLMrDbz6WZRKJGOTzDv39aWzbLizSgaJOMeElhpSyYDiDhd1PNGnatKcAwy+K\nSjWj1UTph5/vSglG9XmYNX+Ckv1Pn7DcJSQ0mCFjpP4SEd9QlZaImKZOsxe+ANP0C/kdxyWZTFcM\nB1ZS1Blew+gnvLiuWyhv2Fh9L+tlYpJyRh9gJFH60vOtaUVR+iDJaGLs0vDPEHZ32bwJDWZITdRr\nKE3TIBo1Bw1VeYnIRCTOCAGJRAyAgYHMmGXiKtUwgh9i7OpqR0pZ0vdyMsrSVaK5J30832m18x2I\n0uu6L7wgpaSrq31Ylm6jQui+F1n7XUXY3WXzIDSYISNSf4mITjRq4nmqLkM1Xnxxdn9hTaWyDRdn\nDxbmZDJVNmapLJ1hxMfVaaS562lzXcxGe7CBKD34CUYzZnSyfn1voQ2aZRllNaPjFaVvhBc+WoKR\nZZkA5PMuoRHdNAgNZsgw/Lo7ied5dXQR0YjFgnZbuRqMQ2MXCD+hyMIwNHI5Gyn1CetkUi1rNEh2\nGZo1OpIQwKa9RzoxYXbPU7huJVF6veCN1itK7xuuxn+GUoNomgau66JUPix32UQIDWZIGVKCaWpY\nljmm7NWgllLTJNlsbSUi/iIxntkWGZpQ1NeXRUqBaRqNGaBOlKrWacRf1Cs1j7ZtByk3XW9jIqY9\nUsg0SMwaSZS+1Psf6o0OjtD0m5bSzxDq6W4ahAYzBBgeeq31t1jq0WWzeVKp5mW9VmNjac4Ge7D1\nDBcs6plM6aJe3KfTdY1EIk4iES/s6QUL+ngVkCaCyeYhj+z9axhG0fv3vT4/Kck0jaYldI22Txp2\nd5l8hAZzM6dSLWUtyTjFWkaj4NGNlfEm/QTegut6FTVnm59UFMjjNWYxLd2n0zSNXM731IOs0Uhk\naKmLUxZm3JxohFGo7v1rhfNcTOiizBNtRIJRvTdboTe68QgN5mbKyKIDI+uk+u22jAZ4dPXpseq6\nrzkLkE5np7S4QGVt1+HtugLPyPdGi0Z043R4af4eZjM/V1AzqpRT2Jbwa0a1QeEF/8ZF0+Swm5ax\niNKPNRN3tGMVKXqjiUQcx3EHG7WH5S7jJTSYmyGjlYhU88zKS0Qq1zKOlbH8Xkv3SX3N2dG8qqkp\nkD5SqUuwTxeJxKomu/geSfPm1+w6z4moIxVClEUs/JpRj1xudFH6oTWj1RKMGmkwqyFl6Rhhuct4\nCQ3mZkTttZTlhqZZJSK1rhWBlJ5h6GPaJ60tJDt1Fodiu65qyS5FgXQhxKD3MbZSl8lB8z3YWoxy\ndVH68ppRXS+K0pdmRk+U4R9qlMPuLvUTGszNgHq7iJSHPmspERkro3uAfvjXJJ+3B8O/DZ7COBhP\n0k9tjH+RqpTsomkanZ1tuK5X0EydTD0vR2NilIrqN8pDa0ahenlRe3sC27abFkav1YsNu7vURmgw\npzD1ig5I6f8QYrFIjaHP+hjJAwzCv7btkkymNhnZuU0BfwFVpNPlZUPBgl4tvBgY0dG6ujQ/1Dg5\nPMyxUCnBaNq0DpLJgcGm3eVh9PKbl9pF6Ycy3u+ilgQjXdeQUmLb3pT3RkODOQUZj6GMRPwSEYD+\n/tQo72g8Qear53kbRSFobDQ2S3b4sSeW0cKLlTNGx7eg18PGCmU2Ywy/rZyiUs1oZVH68hZpo02x\nGZ9jqBH164kNcrlkVW9UjrWl0SQlNJhTiHoNpRBisOi/WCLS3t7SnEmWUBru0TQ5qBAkpnzm66ZE\npfBiacZocUEvigBU6hLTWCbCg9l4XmwxjF762mpNALxhNy+lEYCJMfyywnde7o2C1dQ5TBShwZwC\nBOUh9axRQYmI3+5q4or+A/yOH35ixEQ3kQ7Czq5bmkE61mM0Z26TmcoZo8UF3bJMdF1jiy26hjWO\nbsQenW9oNq2QbOUxajdmo2VG67o2uC9a2tfVRUq/EXYzIzXlmbjlTLXwbGgwN2FGrqUcmWaUiIyF\nIAnCLxHJk0qNXfhgPATJRLlcHiFK9+zcssV9pAV+EubD1EQzjEHpgu66HtGoRW9vsqxxdHmpS3mX\nkckWep8Iz6wRFPuMFint6yqEoK2tZVhSV72i9JXwG8GP9P1NHaMZGsxNlHrbbdW6R1jc2G/8ohF4\nta7reymlGZzNprwvZop83sF1i3ftpbWMLS3lC3zpIj9a4kuIT6XG0dVKXWpt1TUxYcamHr6pn6G0\nr2tLS4x163qBIKmrkij9+G5e/M+yefweQoO5CSGlwDBkXQ10x6qO04ySiaFNnA1Db3oyQPA5NK34\n+Ue6URitltGvq9ML5z8ej5HP5wueVcjo102lUpdSr8hX0glKXYYq6UxUyL65RnliQr7l/y6GdEe/\neRmapTvSed9UvPFGEBrMTQRNA8OQRCImAwO1dxHx+0JaSFmrOk5A4zJA/f0Va5jwwUQ0kAZFPB6t\n4/MXqbzAS7q62lBKEYlYJBLj6305tRj7dVPqFRWOUkVJJ/Bm4vFo4Vw3ekuhtIi/GTSrfdjQMUYz\nZKOJ0pfWjAbSi6XXuOepEfcwg+NNFUKDOckZT4lINOqHXbLZfFmCRi00wpgVjbUYNFZDjUfzpOuC\nzN/AiGWztd9k1ILneXieIpMpeutDi9OLggD+XXqpId24bBoLWDUlnWjUIhaLIqUYodRlfB6/f+03\nt5Z0IspW6hljJFH6wBstPe9isBGDH1af2BKjiSY0mJOUSoaylnT9YomITi5nT3gyDZTXc2Yy+WE/\nvGZjWSaRiJ/5G3gu1Wjk3W+lhSbwkoYKpVfqwzixYa3mhhubied5gx1q0oXH/FKX0trFeKHUpT6P\nfyqEZEVDPe9K2xVSSjo72/A8VVYz6roOP/3pNcTjLcydO58FC7YnHh9/qVpPzwZOOul4fvCDq9E0\njUsv/S+EEMyfvy2LF5+LlJIbb7yORx99CE3TOfPMxey00y7jHjcgNJiTjJE8ytG8vnIZufS4fvD1\n1NIFd5q1tvxqdEjWNP1kBtctZv4axsa9xEu9pFLKs0f9SIDnqSnRQBqabQyGhzP9UpfhtYuVQou1\n3Kw026BNlDBCs8cIkt/S6eJWS1BiNGPGFrzwwvP85S9/ZtmyN+js7GLhwu046aRTmT9/wZjHchyH\n73//O5imn4vw4x9fySmnnMaiRXtx+eXf4cEH72fmzC157rlnuO66m+nu7uaCC87h+utvadjnDQ3m\nJKG2WsrKRqzYQHnjychtrCbOMFpCUzPVeOqncvbo8AbSLS2xIdJ0408uar5309zzXev8RwotDr1Z\nGZoJ3WxjM1UMJvgRpdI1JziXBx98CAcffAhCaCglWLlyBUuXvkYi0VrXOD/5yQ/5xCeO4pe//AUA\nr776CnvssScA++67P0888TjbbDOHvffeFyEEM2fOxHUdenp66OjoGP8HJTSYG52x1FIOvfabKSNX\nq4dZ7tUNb+LciDGqEezTaprWVM3b6jR2D7a8gbQkn7fJ5exCODcatTCM+LASDH+/bvLsG012gzxa\nqUssFgFgxozOYZmijUrimkoGs5ZxNE1jzpy5zJkzt64x7rrrf2lvb+e9792vYDBL149YLE4qNUAq\nNUBbW3vhfcHjocGcAoy1ljK4QCZDA+WNOYdi6Fcnmx15n7aZmbgTkeVbDDWOVIIRG6I1OnopwKZM\nMwzy0GzRLbboYt26nmGlLlJqg8pQpfWi9SlETYySUHPHGI1GyST+5S9/QgjBU089wdKlr3HJJRfS\n29tTeD6dTtHS0kI83kI6nRryeGLc4weEBnMjUK/ogKb5bmizu4hAdWMgpa/5Op4yjdHGGIkg9OtL\n+Y1vn3ZTpXIJRlGabuh+3dQrc2nuXUpgaEYqdRmu6VquEDVaqctEiS9MFS/26qt/Xvj7jDO+yNe+\ndh5XX/0jnnnmKRYt2ovHHnuERYv2Yvbsrbnmmqs49tjjWbNmDZ6naG9vH+HIYyM0mBNIvYaytEQE\nJqaLyNA7Q7+Js5/5Wk+ZSpVRqHXxC0KSfnbk2EK/I7EJ59WUUV1rVB/sJqGX1TH6Gr7RUeX/6mMi\n9jCbvUhXl0McSdO1vORCDTOipYkxU8GYDd2/rDKTpox9xhln8/3vX8q1117NnDlz+cAHDkHTNHbb\nbXdOPfVElFIsXnxuQ8cUI53QtWuTm9/texMYfxcRv0Qkm83T1hafEM8q2BvN5ewy3dVstnEydn6H\nkkhZeUDl1/hdTDKZsTexbmmJks3mq77P8zxsuz7j39nZRjKZaoqn39ragm3bZDK50V88RizLIJFo\nIZfLF7JIh0qkjUf+Lx6PIoRgYKD69zoeYrEImqaRTDbnxjEQpVi7tmf0F49ynMATDbz/oC7Y//n6\npTHN8vqb/T2Af6PQ1pZg/freqq+R0tzkMr6nT09UnHDoYTaReg0lDC0RSRX2IpohWVcJpdRgTZuf\nfduMzNeRQrKlXvVEdzGplea3sWoOruu3XSo1ONXk/0qzcydLUXrzM1gb8/uqtP8clLoERr+tLTGs\n1CW4cRl/V5ep4cVOJkKD2QTGYyhHLxFpfplEkOSglGpyE+fhIdmhtZwbQ3hh86D8+qmm71paflHa\n93KkfdFN8SainOb9voJSF9M0sG2HVMqXMApC58VSFx3P8xhaLzoWr98XLmiuvnFoMEPqprZaysoU\nyzNG3qNrZmZmaeZrkOjQTEHxoZ9lY9Zybk7U6kF5nhrsJlOeXFRZ/q8YyvWVXprniW4qHubIY5Qb\nsyDbtlpd7lCvv1Rusdq5nix7mJv+DVSR0GA2AE3zL8x6Gm+MvYtI48OAUkqiURNN08hm/fCnZRlN\n7yQSYBja4J5p4z3akW4wNE0Sj0dK9u/Gr0E61RlJ/q+0BCMIqZeWuEy8/F99TJZQZmldbkBpSVEk\nUqmkqHiuJ8vnmEqEBnOc6LqgrS1GX9/YEhDKhcnzG0WQ2898NTEMnWy2PPw5ETWGmiYK2bfp9NgT\nemqjUti3+LnT6Ry2nS+EwoKuI5NZGGCyMVQkvaUlhlKqJKmo2Dy6VP4vWODHGjZstEZqpeM3m3q9\n2NFKikr1ipXyf2NSak27YRFChgYzZHSCEpFqcnXVKE9mGbsweaM8zGpJRSUj0cxOIkGJCkB/f/Oy\n+IZSKsze35/CdRW2XTnsWH4XL8oMaFCsPpXCTY0iMGjVFHUCIxqLDe80Urv8X3MX6U3JM6tWUtTV\n1UY+76BpsnDDMlKpSz2M1tprU+mMUyuhwRwj9dZSlntz9XcRGa/nZ5oG0ejourPN8jBLS1T6+rK0\nt4+/g8FIBJ+jWMdZFGav/p7Kd/HBQl/a4FjXJZZllGU3hkA1gxYkF5WHGYvlF6Xyf0NvUIpt1EJh\n9FrxS6qKhrS8YfTwcz1WgQshxGa1hREazBqpv5bS92p8by5fxZsbC/V5fmPdJ2z0XmmpoZ7IhB4/\n69ZEqfFJ+FUyou3tiUKYq5IEvqLGAAAgAElEQVS6zuTpfzm5Gan8opL8n39dCmxbb4r830Ql/TSb\nSkZ55IbRo6tEDe/qIggaem8OhAZzFGoxlIFxGXoxNSPrc6yeX+leafP2Cauj6xqxWHMSekaiGPbV\nyecd0unmlKc4jjtk8ane/7J88Rm7/mijaPZi3QiDU83LD7q4+DWMLU2qYZyY7jaTxYut1tWlVHCh\ncqnL6IlFU23LIjSYVRiLRznUYI6ng0etY43Gxt4rbaTm7Fgp7s/6n9txxnf+V/cIvn9HjLV9kgUz\nXb5+dBrLqHzzUl06rShR538vgf5oY4vVa0UpBQ7EfmigrRWkTrPx5jZq7OYYnGCvznU9crlcQXWq\nNOGldGEfeoNSa6LQxAmjTw6DWY1iIle1UpcopmlgWQbRaHMFLlzX5bLLLmHFiuVIqXHeeRehlAob\nSG9s6gm9FvfJimHPVCrblMxKpUZuBTZUTm+iC/+HZt6OpjlbzTuvh/J9Sn9/Nhq1xr0X+63b4jyz\nzADg1Xd0TAO+cfTYEpWCtP/SxWd4X8biQq9pEs/zmrew5hVd74+hv+VfTNE/GGz4TQZn78kfXhtq\n0ILkouEL+8jartWEADYFY7axxhha6tLZ2UYq5f8WSgUuTjzx82SzObbddgELFmzPwoXbse22C7Cs\nSF3jPvzwgwBcc82NPPPMU/z4x1eilAobSG8sAiNZX+mhIhaLIASD3lQzw54KqDzJ8gzQ8enN1pP0\n43t2xQzUiQo5Bpq00JxWY2+vLaREIwSsWBec//FlEovUP8BbRlbbjqS5P1AU8Y7Ho0QiFrFYpKwU\nY6zeUjXk9RSMpT8XQeJSi54/ZsZ0HP0pQeI7FtpyiVgrEBq4H4T8r8c1vXETLOzZbOWel9FohNbW\noUIAzuBvd/KWlUw2gqQff2uieHP8rW9dyrJlb/Daa6/x8ssv8sc//p63317OJZd8n/32O2DM4xx4\n4AfYf//3AdDdvZqOji4effShsIH0RFNqJMdqIEoL/nO5PJlM44TJq1GplME0fc/KcUbPAB3DSNS6\ncIxn/PFo45aWpzRTb3arLo91Sf8iUQpmdY7fC4tkf0M0+zuE8lBIMt5/kI0cVfCWAum0TCZbKMUY\n7i3VL5tGJU33Md5n6A9pdB4fQWRLrhMb9Lsgcrwk+/Pq7x0P9XpO1eT/ikIAvncUePcAY80arZXm\nqxVt3ObRsViMXXbZlV133R0pfTPjOA5asR5vzOi6ziWXXMQDD/yTSy65jEceebCwFoYNpCeAxpSI\n5FGquRJypfghTP/vUpWgiVTICWjm+KMxtDylOuPP9r3gmAEuuz3Oun7JtjNdvvKJ8deNmtm/o7vL\nQGUAAyv3v2QjR5W8orgIBQt9aaeYoWLpleoZqxpRz0OsvhOlH4xw/Oa6yoKBc8bWGSV+q15uLAsI\nrD9riD5QbWM65IRTSQggqLt1XXeUrNH6E7emisGsrb2Xj66P39xccMG3WL9+HV/84ufJ5YrXa9hA\nuonUbygZ3B/0F+og7BiLSSauQNc3AH7392Ym1FT3MH3P2kLTGtFEOjBotf3oAm+21vKURqwZW09X\n/OTUgYrHruUaEiqFUCk8MQ2E76nq3jKESg8ewMZwXxvTnCp7S6X1jBFaW7WCalHpQm/+8mHk7/4B\n4m/AZ/BknP6LTez3bz2mOXjRkZ9PfNOk/6rGR10mwhi4rjcsszrYb66cuDW55P+CJtgTM061Wu7G\nlabdffdfWLt2DccffyKRSAQpJTvssGPYQLqZjK+LiL8/WKlEZCJk5MC/m7Mss9DyqjFNnCtT6TOV\nJhT5UnrNG38omqYN9sWceG92PESyvyOWuQGhUrhyOjnjI2Six+Jqc9DUOwjlooSGK7rGPValesah\nHUcSiThifRrRnwHhgvkLpFJYj7wHYeyIdf8rqIhB+uSDcHbeasTxUmfksf6uoa2usqd+n4H1J5fc\nxzctMYdqXT5K5f8CyhO3StV0xif/N17EhDTZnjgOOuiDfOc73+L000/BcRzOPHMxc+bMCxtIN4Px\nGMqg4N7PwstXvPAjEROgoc2VSxElLa9s2x5sntt8Obn29hZ6e33PqjShKJvNNezuNZGIkU5nqxrA\n0vIYX/d1bN5sLd9NPp+va3EZrcmz8JJ09B2FVOvRvG7AQaHhiS1IGccRt38LOHhiGnnzfQy0XFRy\n7Di27ZLJjDPL2YXEeSbmIzruFoq+azJ0HvsW+stRIA3cAeJN1NzpYGjgeqhUDhW36P3x8Th7zB3x\n8KIfYtfoWH81MF7VBvdBBegK1arIHOmQ/E5jm2B3dbXT1zfQFNECGP+5L5X/Czz+0nC547i0tsZZ\ns2ZDg2dexDB0Eok4Gzb0NW0MIQTTp3dU/Rx+Qwpzwpo4NJLNuoF0PcbSD7uYNRXc+xdGcy6KoeIH\nUgpisfo3zsfKWCTl6mGksE3pPmWt5TEKF1f0I1UMiTVqGU7tEx38fzH0wSHC7t4A7f3Ho7lvoUQC\n4fWg0Qs4g293kWo98fytKBFFCA9UjnTkMw2Y5HDa/p9F5A6/JEZfCtP3iCPcHUte0Qrq24jl6/Bi\nJpg6MpWDDSm6zv8DzvePxd5/YVXpP9UKqXMdUuc60AvTDo2hpTSUBrhgPvounUcswZ0dJfnNHfG2\njo1p/vqLvRhP9eDOj5M/aEbpyPWdkBoYyxZBJUaS/wv2/YUQzJjRWVX+b7xMVGuv0UQLQuGCKU55\nu63alHGaEZItGqry/pgTJfgdZLNFImZTSjVGol4ZPUf0sTZyCzm5DAV0ZT/NdA6iWhlOTSiQLwrE\naoHSwJvvwbzBpyp87+39x2M6jwEC1GoUGlB+syVwEaxCKoVQINFoHbiI3rbfFPY4G4X5QPnNlXB9\nWbmiQZgBJEAkkekcalDkQel+T0vnt4/i7DO/Num/dkhelKPtpzFUr0KpdYiBFxApD7k2R8v3XiV/\n0DSit76NcBS5Q7YgdfaCqj8e829rSHz3FUTGRQnIHJ8kfeq2E1CS0fj9v2K4HLLZHB0dbaxf3ztE\n/s/XJy4N5dYrrThRdZ7N7BozGdksDGYtBq00kSXoCVn78Ru3uT1af8xm75cGIVBN8/diBgYyTf3h\nlX6e4LMH4451n7LX+CsZfQmO6AGgO/YzEvkFxKiczOKt9RArQI9q2POcijlO4m0QKwUIf8tPe0Xi\nzvAgXnkOuruM4oEE4KEQJYcWgFNmwgUulnM/Hb2H09P6W9Aal6SgtNEulm4gCbpEaTpKl2DPw7N2\nRrw7DfEi5O/Pk9676OEHi3yZ9J/t4P7tHbz+HpwbZzMwwyJ24ovorxS/Q/3tFMbl69HW5RGuQlvS\njzMrQu4zlb+fyJ3vIFdlfNveamDd3U361G1ptnRds/f/AmM2kvxfJWnFscj/jd5FpHGfY3NiszCY\nI1Fay1d/Isv4CthhLJmnjW8gDaX7pEWFoNbW+ATczSuEkMTjBpqmjSvr1hMpHNFTNE/CoV97hJga\nHu703vHgXoUatAOxNRbRI6JDpOocyFD+1XrAAFUNpic6kWoNgbH056LhXyMe7qDPWQndfYXO/s+y\nof2PQLwhN0bJr2dpX1xMZ/VMhbuoD+P5JCrbh1C3oyISlYiS338h9ry9id3ai0zNREV0hJqDdZ+O\nmmbjzvMvhGHSf0rRctVSYtcuQ2Rc0F+m4+QFeLt2IV4dAAVKeXgJA/PFfoTjH0dmPWK3Lid3zFaY\n/1iD7M5h792B126ivZ3Curcb2Tc4Tr+NN83fj272NTnekGwtxx8ps7SytGJlVahqghYT4f3VYjDD\nkOwUoTSRZvRavpEZj9c3vInzaFJy9Y0zEuX7pKUKQcGNwAiDKoXwNqBEFGRxf0rmX0LLv45rLsQz\nK2s5ClFMkMhkat+nrEbE3R7U3SAECoWmEuhLZpN+OY1aB0SAWWC+38R9WeGm1eCnVOSXONiLBtDb\nyu/sc3ae9MoseP5i5loejFADvaH1F3T2fxrNW4dCIHHwM2F8T2uk3WcBSG81Zv4B4JP1n4gUxH6o\nI/sF2U84bLgtTes5EUQKnG091D7d6KlbcXM2oi+NN3NL8gfvROoLBxL/6VqUclFuDJIzMV5oQV8K\n2ipB/1WVk3fk2hyRXy2HjIMSEuEovBuX0nvfgSRe70V/cB0y52Jle8H2ipeVEIiUS9tRj2A904tQ\n4JmS7IFdyIyL7C0xGi6o3mD85oujT7b2YZV7iwaeqDZM/k8Iv8ZU02TTMsrHUoM5VdgsDeZwCbfx\nfen1hmRLRcInUkouINgn9Tyvokj8iDcCSqFl/k6k5zKksxqkRS7x79jxozAHfouefRykASlBruV4\nnPhHyt4e7FMq5YeeG6HS0+rsR9p+EWfNeuKv7IOe3ILo2oWoJL7NygA2iFZw+4csIh44Kxxss7xs\nwIjpyN0l4l2BZkii77FQCQp6r0O/M6XPZX3nk6DymNl76Eh9iVoldAR5pOpDeOv94yoFKgtiDPqb\nKej4dBR9hQQB1t8Nsp/I422l/LAyAuN/EhCPg+xFtcXANBg45whQCuP+F5DuHIS3BSIfB8MvezEf\n1dBeFrg7V7hIBShH+fuvjgeOQuQhcuFLqLU5SNsoD8S6knOu/P8x3khh5AYflwKZcok8vAF7p+HF\n5kLzA9kT4WE2NyTbmPkX5f+KjwWCFr4BlXR2thVqcYN9UV/Afvw5CWFIdooSfKelJSKNzPgcq8Es\nenQjN3GuPt74fnRByy8hRH0JPUph9f0IM/krNHclIFFuhGjv5ZjJXyLdHoSUuMaOKJnAyNxbMJhD\n231ZlkEjRR9mvHsS3t+ykAORM8EJdA8HX+BAbkMe5gBrSt6og6qwdWjbDkzD/w9I5TMY/UHJQIRo\nVCMej1RIhAEh/DISMYLB9DCQBFEFgSJOJHcf2uoHieSfIubm8GSCrPlx8tbhOMauI37+yJ80tHdl\nIc9J5gTmfQaUlnkqHfKdYPUg1w/Aql46D7oUZ9etkJk5eFYamckhZBwlFMoCHIG2UmD9n0b0DhMV\ng/7vZXD2VHjTLPL7dBC9pxthq8L5NJ/pQVtrI0a6TrOlRlT5SVYpB+uV8nIIBeT2adze7khMRFJR\nszzkIEPXNI1B8YXMEPm/8t6i9TSNLnyKUQ3m1ArHwmZiMA1Do6WleT0Zaw3Jlnp045tHDaHSCpS3\n/Bo9sanajYBw12Cm7kC6qyiWS/ihIt19y5+dBzK/FDeyBwhZtd1Xw5OYVgpkbtAjk/iR0GALMSDY\ng9wGWO0/7+3klRsVfNU6zwMtju8hDRqhwDD6HqYilcqU1d0VEjVS+0OqHdSa8gMjURi4tCKEjqfS\nJTubEtN5EOn0E3zHmteNnv0JtvM4/S2X4eoLqn58ZQx1ecFt89CkAE8gXPDmtSKNLGJJGjGQBV3D\neGsd+ooNKNkJ9IFQKNkCuoXsEaCg9csRtJ5iulLXx+J4HR59V2RJXrMIvvwckT+tQhoa3jQTbXWm\nzFiK8mmV/S1KHnQjEpErz20WBsQ+uQCzoxUhBKZp4DhOk8KNG28Ps3FjFBOXKsn/lSYX1Sv/J6UM\nPcypiOeppjZPHs3DbHQ3jXo8zGi0KOk33r1CmfsX0lkFQqs4iUKOqOoBoSHaP0kiEa2p3VcllIL0\n3zXcdyVi/gAc9Dy610HUWzj8xSVlftISeChoA1JA1IFcDnoE9Ft+of4B4EY9VHv558g/r+O8I1E5\n8Hok2tYeMqIw32MjW0sn54HKYuetYSowhjGbWMuXiCQv5ffPH8nTK/dibucKTjwsTrb9AtzUM0R7\nz8NQSxDkUbIT4W1AEsjwFeckyGM4L2LYT45oMPMf9vB+4PkhWQlupyL9DRuRFsSvMZArBe4CE7Xv\nf5Kf+yusB19Ge6fH30/MA2oGqK2BWSD1goeuEgq5YXjJi+yRtJ4fYd1H0ySv2B39jTTmgAeeh5IS\ncEf3MyS4MV9dxGvVsffqxHx0HcJVKEuCJvESGv1tINPZwUhRBMOoLP03XiM6FXReRxujWnJR8cZP\nIxLxG7D78n/lWbrBmrepKG41is3CYG6sL3WsHl2tjEV/dajwwVh+qNVuBKTqx9M6kU6QDaqq5Alr\n6Ft9i7y24wjtxkbPME7+xiD9Nx20HLmHehGZNciP/5UWe2/a7Q+Xj7hQInsE7psenvDQdtGI7Rch\nlexFW/E37PsPAKFgQPrG4B/+3qRqVXi7eqhtwV0ncFdLpAn2Sg2VEqiEghlgv6xj7Tf4PTqr0TP3\nEs0kUbKFnPkhlCyqjdu2Q595Gtc/OZvv/d/h5ByLZDbBBXdnOWD+s1x11MX8c9VhvPD2yey+5UMc\nscvDg+HZSt+tBuRQYmQ1c+sOHZkShV+2O9fF2cfDvF2ivSIRGujvClgi8ea8D2W9VjLcPFAJlJiG\nVFFQCuX40QL6RNXQqkgPfn+GJHXafPTfvIvnuIj+PHqqeHNY7ZsWHghX4WzfgrtNHDU7Su6wLbDu\nWYNQChXVSP37Ntg7tcCg7F9vbz8wvNtIIhFHCFEWIh/rnl3z90ibHfKt3yhXk/8LznFLS1H+L1Av\ncl2zotD/VMuQhc3EYE405ZqrE9/EGcqbWdcb/q32e3Os3fGMhSgRQ7PfxF8Ks8AQ71GfTtLeGi+f\nQ+SXYaT/gTJm48QOK8Q3K4ZkVR7h9qFkC8LrIff8VghNxxa9vjLOU3MRH3+RAeNpWu2DkZgoV2Gs\n1tENDXWAwp6ZhZfB63FxVnqI+NugBrNVcyY4vvqNQoGtECmBXCpx53ioHIWVXdm+Iz0YeUbli5PV\ns35TW6Tpe4D2Y+StDxc+AxjwhuCeBz+LnTNJOhJHCfoyER5/ayFHXf8d3unbCsezMLTDOWv97Zy5\n338R0dcP/y6QeKKDnHX4iN9Z5M86MiPAr8DAeE1Dvg5tF0hkvwIUXlJATOB9cBapsz9M7Ib70V9f\nDcmDEMxFEPNPgFII5QAGYoR7PW/L4oWSP2IWareZOFf/C+tfw2XZqi3hMqMwX07irsmR2ntbmB+n\n54yFaG+mkL02+Q/NqBi7rxZuLBcEGOue3VQIyTZujCBDd2hv0dZWPzHL34Io9ha9884/IaVg/vwF\nzJ49d1wqaI7j8N3vfotVq1Zh23lOOOEk5s6dz6WX/hdCCObP35bFi89FSsmNN17Ho48+hKbpnHnm\nYnbaqXJ2fnf3anp6NgCClpYWWltbaWlJ1DTPzcJgTkSYPfDGLMsoycAdXxPn0caqRJDQI6VoQBi6\n8jjKmEO27WyM9J9xje0QKgP2ALrzKJI8vhmyyEU/hacM9IG7iPZeiPByKGFiJ3+N7q0BlYPY3jgz\nrywcW9hLsQZ+C846NOdNPH0bDE4h724HxuCLzHLDbGga7n0e+bfy5AMHTQAuOAI2vJ6hd5tZzEpF\nQEmw/USj0q9GZW2k3Y+Xd9GnSWxtBiiBjCvcHoHeqVAeyC4P6S5HqByKAYRW/AkJcuBlMJ1H/e4k\n706Hx3cm6lrgCTxPDn53/sBvbpiHHPx77UAn5935Rb591wl8Ytfb+dVxJwICjzhC3xKhd6BmXker\nTAxX2CnFpMyNUxLiP+5BJjv8fyCQaYXbJ7Hf4+EuWETuyEVEbssRuyqK3p1HFLwLUX6wgh3xCk95\nbTbr/zjkRmmPLtSWEbAqF9EoneEGePDYcoNN/r2diKxLxzGPoQ8KF7hdJslv7ED+s3NG/T1XEwQI\nwo2maRCPR5FSG5SjKxcE2NSzcCdiDP8GXJHJZAvbLL78n0Y2m+GRRx5hyZIf09/fz8KF27Fw4fYc\nffRn2GqrsXXEueeeu2htbeeb37yYvr5eTjzx31m4cDtOOeU0Fi3ai8sv/w4PPng/M2duyXPPPcN1\n191Md3c3F1xwDtdff0vZsd5+ezlPPfUEq1e/y4oVK1i/fh0AM2duyfvedxAf/OCho7Yg2ywM5kTR\n2hpreAZuJSrJ45U3U86XLRbjG6fyc25kT9zIniVjm4jem3DX/Q+e6sWxPkiu83wArOS1CGWDkAg3\ni+Xei9KmAZL86y+Re/E3ePscg4yAmb4bcJFeNyrvkHp4DtYsnfgsASJOPi/p+eRfUXi0eO9Be6md\nnr84RGJ5hFaePKLwo69WHqYvjZLVZxJp6cGLp/B6OxHK8L1HJVCeQLjdRN66DzmrD2PRexh4++NI\nFNp8Gy0uIO4Rm3c/ur160Pi8xivpOaxzO3iP1Y/QZ2G4L6HcFdwSvZs3Etswe9clnJD9KG8+P5/+\npMRDETfTKAWtVj+9mQ5SdhzH0wFFzrH4n+c+zbGL7uHwnR4mbX2KVPzrAOhZHcNwhinAFPaVnnBR\nUeVXsbiABs6uLnLDBpQW80UUXD8DytuzD3eBv68uV67HeNpATWvFiVuIfhdthYtQvthCIWtKSlAe\nzk7deHP6Ue1Z0iethPaDhl0f+fd1Yd6+EjmoKVuGEKgoiEzJb2RQkUhFJNHfrST62xXIZPGNWnee\n9sUvkP/VCnjsY6Neu0MZTVWnNPEFIJGIj3xjMg4mi6hAI8Yo/Ry+/J/HYYcdzmGHHY4QGsnkAK+/\n/hqvv/4atj32Nenggw/l4IMPKfxb03ReffUV9tjDX3v23Xd/nnjicbbZZg57770vQghmzpyJ6zr0\n9PQUGkcnk0n+/vd7SafT7LHHnhx55FG0t7eTz9u88cbr/POff+NPf7qd008/ix133LnqfEKDOU6C\nMomgRKNR+5QjU/5DqL2Z8viRuZcx03eAl8M1d0N2HUskapHPO6S8+ZhaO0JpSPrB6wetDUHpOXEY\nTMVh7e0nsu73p+DlEujbRZh+RRbL8sM+Ki9Y/ZMvY2/YmvY95vk3CEIR0SNMW3Eo5i5p4gPv4e3r\nFEbEg6h/WpQo3ycrFanD03AyMXJdbyLebEPlTDTDf9YZMOHdLvTXDkfb4mmi+/0O9cR8VM82uK1t\naHuDtV0fen4lYIHq4afJ/XggtxDH0UBr5wtd7RyqruSHiVv4Q6tifcu79C14mK69/sAn/3Uo37nr\ni9wx0E1vbCkLWtfz2rt7cvsb2+Cock/MUzqPr3g/H9ltGZr3TvHMDXpD5ftLfujRfFPHus5ArB88\nDyjUVgraBfb8LOYT6xADCUAH+Q7aun8i1n6GxLf/SOQvz0H+EFR0Lu6sndG67UEXaylKxBFqO8Dw\na1Xn9JNe/CBqyyzoWdxpuw+7RoQAe+8ukhftRNvZzyF68mUepbAVSgiyR8wk+9GZJC5eguyzUaYk\n/dmtiN7VPWish6DAfK4H75alcPj426FVS3zZYosubHv4jclQVZ167dFEtN6aLF5sW1s7e+21D3vt\ntU9dY8RifhZfOp3iggvO5ZRTTuPqq39YcBhisTip1ACp1ABtbe0l7/MfDwwmwAEHHMjChdvR29tb\n6JEZj1OY3+rVq3jssUdCg9kMSkOfmUwOyzInTPUiCMnWK1I+1nEKeBms5PUIbDR3LZb9BC6vkWw9\nB8/TiSVvQpAHYSGdd7CSvyTXfgb52OFE+n+BvxEo8EQMLxuj+3cno/o6QWjkX9Lou9Egctb26Lkn\n6X/iQ9irtyC+w8ySOfhCc9b6LYm6EXpezOKmLLycTmyLXKEx+LDPge/kKOWBI1C9LfS/6mA/lwAp\naN3bV/JBzkZl4+TXHsiG/30vupdB61tBrP1Zssm9MHdRBDcr79pZHshuj0DxirclOUfj/G74vdyf\n5dP/h3f0NH3aAJqnk7RSPDLnWfbY7w9cstXWxNWVrPvXJ/ng89sxLaroz3mkXT9c6ilFRM9x5K73\no2QL3tBalyEERlQ9rKM/a6IvlzAoi6u6BeYuBtZB++O99nO4G5Rmo7Z8CzZYRK+/n8ifn0N4Hojn\nEQMW4jWFYGv8E9KCUFuhZA5v1gZUi4abiGHeuR9YNpljUri7zKs6t/wHZ9B/8c4kvrsEbXn5jZzX\nZdF37Z6gCZyd24j+ajn6v/qxnuhFJG1UVEek8uX3hoPqQKzO0GwymWzFvpdBclExe7QYyq21eXSz\npfeCMSaDwWwE3d2rOe+8r/HJTx7NYYd9hGuuuarwXDqdoqWlhXi8hXQ6NeTxovhFIpEgkUjwzDNP\n8eijD3P66WcNG2fmzC058shPjTiXzcZgNqrerzzztRj6tKwG1xOOQBACdd3x1nOOcVx3HVINoNOP\ncJfheUDyIQyvhVzr6QivZIURAum8iZG6EzvyITw5Gz33NK61I0okePeEnVE9WxZf78LA7w06Fh+B\ninbgWSauvg2ioHJTFDBXrYpsNo8xH3+PcZ1g7YMx2nbJYbS7fhhQghP1j6spcIVCUwo3J+h53cZ5\nrQOJX5uo8gKhK1ASZVs4yTZUJsmaTpevH7sdS2ebdOXznN3zPJ9O5BDuu7j9z5PMHsIStqWPFgSC\nnJvjUXcH1pkxcloGJcDWXFwjjR7vpneP+xHuUfQm/4+BiIatEugIOgywPYWjBJommNXeTzTehad3\nkDZOru3L6QFtpaDUmRd9YL/gkIxn0b+wFy3L/4DQJFJGwYNYNgi7CpBrgRcQHIqvIQiwBQLLz5Lt\n93AtFy0D+Wkz0ZLvEr0jj+z8Pbl5h6IipaICRYOQO3I2xhMbiN+4vGy6KqEXw7AtGtbjGxBpFxSI\nvMIzFF6XAUkbWdB1EHjtBuqQWbWdkzqoZgSCxJfS3qfV9F1LQ7mVjehEGbOmDjGiwHujGlJs2LCe\nxYvP4MtfPqfgpS5cuD3PPPMUixbtxWOPPcKiRXsxe/bWXHPNVRx77PGsWbMGz1MFLxJ8VSRN00il\nBliy5GXefvstpNSIxWIYhkk0Gh1MXBp5zpuNwRwvQ7Vnh2a+qmDhaSJB8b+mSWzbJZ1ubvi1tM+n\nEIJoYiv0bBdq4E2UBygPJRNIeykgcI2FaPl/+TV97mo0pxvprsZAkms9mWziEgAG/qSReT06fMAs\nrL/YYPqlB2AeBvrTFrl+G9lmo/m183gmaAf7P1TxpqJtzwzJf5pkVpmsv8MAXRHbMUd8exutHdbM\n9GjvB0N56C64jkHEmzstoq4AACAASURBVE427tDx/hQyAk6fwM0ozC1clCfJv5XDjSX57L5JVj5+\nFXgOq2fN55yWT7PVa8+wb+vTdKWnsX6Hdnr1Nmxp4CIwVA60NIZj4RgmSssjgIiQxE2XD/QKDO8Z\n8vHD6Yh4HDrd5v9Wmnj4twMz4x5mzMZVcW559kucv0M7Xs5Pd33gZZ23ujUO2yPPzI4Ki1SE4U6L\nANUJzo4KR81Df/92mP9YghICte8C+k/7IO13vIncENy4fARoKXl78XqW/W0orQ93fjf66vVIewAv\nbqEl3yGy5Pdk9iga9qFJM+bTvcOmO3Dq/MLf+itJxIAD0o+nezMi2Du0kN+3i9wRWyJXpGj5wesQ\n18mcsi2JRZ2wbvgxJ5qR9V3LSzBKS1wmwphNVNi32VG1W275Bclkkptuup6bbroegLPO+io/+tEV\nXHvt1cyZM5cPfOAQNE1jt91259RTT0QpxeLF55YdJ1jH2ts7yOWyfPe732bWrNkAJJMDHHjgQXzs\nY5/A87wRs2VDg1kDtdQyNrLF11CGCrQ7zsTWNxW0d3M2KbULEfcR8BSYW6P0mSBigCTb9iWs/psB\n0HOPghjsLoGHmbob2+1Bc5aRXH4IcMCQ7H0F0kOsWgJqPtLSmP1tuOvRCCvWCHZJO2R1wV93MLhA\n2Gj/dGEJGCjad89CBlJrLHAFfa8KbAXvHJCnc0DHcgE0XKnQTWid1UL7LIVyXfAEeqtCWgzmtgj0\nzhYe2/A6K195oNh9evkr9D/7Tx4d+CiP9b+fB+bM5E3RjlQOrrJASPpoQ7kuMXsaceHRbiTpkR47\n5iwu3jCPhW4LSm3Aiee4Y9f7WBjNsfDhw+hZP51fr7CQQiG8fgQuhvMSRv8LYJ7BJb9r5bq/RrFd\nuPYel+v+M8nOc8ozaZz5Ll4naGth0ALjzfBwdh98nRD0f+PjPHLcwbiaxiE7TMd9J03miFOw/rkE\nYbuIDdOQI0Q7va2XIhNrYSALno6a53uNMtsDTgb0CjdBgOizwZLgDJa1dJjkjtum8Ly9Wzteh4ns\n811JZUrSJ83DPsDXI/RmR+n9rf+3b4BGuFjHyXjDjEV91/ISjMATjcejmKaBaerk83ZZ0tbQOsZ6\nmUrlj2ef/VXOPvurwx7/yU+uG/bYSSedykknnVrxOMHavNVW2/C1r51HPp8nmUyi635i0jbbzCl7\nXTVCgzkCpumHWlzXrShOXkrDJd4GGS4U7xvw8dQ2jYjyMFJ3It130GPbY0S3xE2/ToptkdlnsLIP\noIx5SPt1PCVRmORbjkHP3I+Z+h8gj9K2wJPTkKq/cFhpv4H7Qisb7j0IL2uDlYFstCSD0kMYOdr3\n+yUReTJW647k8w6vzrd5cLrJPYOFhXmlWJe32WJlccrSgMg2DqkXLFzlkc87eC9ZvP1vgplpt1Ac\noZRgICnIZDTMdg8jCra0ialBL1qzAQ895pJxMmjrHFxrsKBRaogN3eDswj3bzkQ6IGyLjCUoirgJ\nBmin/e0vMWvO5az1Otgyu5BLNrSwm74cpTRy2lxObbuAh42nyXRBbNvbOeu2qzlabMntKwUom607\n1nDCPn9Gz68imfskP793BrbrX1zLujWu+2uEH51S3K8BcPZW5N/vYDyroa0ReLoiv6dL5j9sXAV/\n8wxucyxemtXBAJK2PsXNS/PsMbuT7L8fAED0tiS8URR6V2QRg+FZFX0W8cXvILt3RbxlojrSGNv8\nDX15FNdsJxWcAycHL/8Jq7eX3Kz3ouLTsXdKINMOIuWiIhqZD88sv+SmWyS/uSOxXy0HW5H70BYF\nYznRNKOkJNB3BV90obOzjVTKvzMJEov8TiMMSyyqZ7tlMmThCiEQDW6G3gg6OjpYvfpdnn32aQ44\n4EBisRhKKbbc0g/zhwZzkLEYtNGaOFc+fmM9TNP0dWcrlak0yzgDWMkbMLKPoBkGrP8zChtPzsZU\nILwkaF0gJJ65I64+l2zHNwBJdP1XBidlIbxelIyh3KQvPO5lyW3YhrU3HUP+3em4/a0IHOTMfoz4\nO7hpgRbJkjjkj9xxsM5Lq7Ymt9LhIwmbGZrEVYXtLto06DAo1mQG58QBT3g4yq8DzcsMba4g5vly\nCgawYZ0kldTYYEKiW5KY4ZLqMIhk/XPrKgPL8xOTdm3tZCuleMdVuBKE6/Jv6ztJaDqakcfLR335\nNzH4E1JAxz/wpt2NYBUviAhpDEjN5dj1X+DX0/+Pncx1PBp1+Lv1KHnPwzMhP3MZl+90M5dmv8ZP\nt7+O/1vZyQNv78sRP7mEAxa8yHbzWsk7pQuP4P4VBj9cF+XjrTnmm4MLqsH/Z++94/Ss6vT/9zl3\ne9rMM72nTHpCCCGE3gkdRVcRK7IgWABXXXfV/a3urvp1XV3BVewVu6j0XgJoCEhLQnpPJtP708td\nzvn98cwkM6kTkrC6eL1evMg8M885dzn3ue5Puz6k/7NI/h6LVwYtEiFJpF6zZLPkG+UOz5abvIpJ\nHoEBZJTgX6oiPOTlMazSxfXO2Ioq78VcH0Z4Q6B+hZYngFNEz/8DuncDQWgTzC2ds5EcQJg2MhSn\nfv2P8c/5FHrpNxCFAUKuh9X5ApmT/wH38qbSS4sCbQsK753M3vBOqyZ52qEzX/+vyNYFQcmdWxxR\nLoLROkZzRHP6tUv/vR7n8Ho0qD6aGHW1Pv30kzz//HL++MenaGhopL19FytWvMy///v/o7a27pDj\nvGEIcyLYk/l6qCbO+2JsvO9IMJasD5zQc2zipUIIbLUFw7IJggDh9yOkBNmMECCDPpQxuqkJtIyC\nMEvKPNodx+LKmo5Xdh2h5NdAGHjryijurMYfqisdu/QRXi/Nt/wr0ZkvI4TPQ94SXgzew/pMhH5f\n8HRa8v4qn7NjPmvyBoMBuL7mEx0O/zC3yKxXAkQB3IQkvcWhEMvyWP19xAYrmVaYSSFZSyEksSiJ\nqGeyEs+ArFEiDT8rCaoUO0OCmoImhEQj0crF2BThvxoX8rPuXnKpgFNyM7muchE77B7uslp4bz6g\nrE3zXNznB81hqL0LZnwBpEubLIzcHgFla9hZ8Rzf6f4kX4/NZG14Kb4ooPQI40sI6nexqvAnzq19\niJ8/cg95P4whA+5ZeS6n7ycjtF71k8pF+L12+FRNfvdl748I/vmdIeY8A/EEnPWgSaZbcI02MN+q\n6T1Xs711T5HPCy0mXzw5xMUvBJyRCPAnO3hXPYK1fA3WC3nkNg+q1qBnpOCCnUhRRGcAN4ooplHR\nWhASNPiblpKbdCGxRDuEI9iWiZACO70W77o3E8yrRO1MU5gbQ009QPftNwgOZMWW6hjdvUj08KX/\n/jdqMP/SMXo9li59nGuv/QBNTc34vs/113+Q7du3sWLFy1xyyeV/i2FOBGOL/o9EIPxIrD4pS2Rt\nGIcm62NhYY7WcgaJKJ5bkmYzEIxdIoE1a0QYwEMZcdzIW0u/EDaBfRyGuwaEgdYK3zkZ6W9HBGmk\n6kfGBH6qit3uy8BE+5Jta2p5LuFR0HH6ZpxHT8UF9BUFmaDUWnFZSvCPjT7nlfl8pD1MTgl2ePBu\nlWKB9w0ubD+OszZdgSMFwfkBj9Q+w6ynJjOtaxbRX/TTe1U1+TKJ6SikbbLOkmgpaNOwK2by8FRB\nW7nk+m6PJcMeocDjF7VFTjYNLho4nu/MuRrDXUnlGfcjT3yIf/vjNfxHj0Gjr4hqxTlJOGfY44b3\nfZ+CUWS3Ck7pTpX+V76Kp6yvscG9llmhp4ghSaLRCPBNrFWnUhPdys0PfZmUWwYIPGVR9DUv7ZI0\nBEl6jJKGbEVlCuuiPA+LCIbj0BQEXGO6PKtMfuSFeQmTSk9x3p8UzW0Q7xOUe/DeX0C8M+BLt2jS\n8VL9TYDg0bmSfK8gYcDFYg6yzSBY2IVaUMTOPo4xqwcR8xEadLeNNhz83hbMro0wUAODlaVzXuTi\nY+EHARZQKLqgNV7BB8/DOrUW56wmykwDP9uH2vFVcDsJZDWpmk+ANbHWXf9XLMyJzvFapP9KY//v\nn8NfopZsKBRmeHiY9vY2Zs+eA4DnuZSXlzoq/M0lewgcvaL/12b1jdeddclmD03WR9P9O971m0WH\n30XY+y6oYQJ7LqbwRlyPDsX4R1DmVETQgbJmgtxjKRTLP4yZexShEih7AYEzHzt5OzLoApVH+EXM\nyh78/kmAANMnMPM8ql6kvT9ASk3P1tX0zMnRTzmeUkgUq7NF1qVcsGLklKDgw0Dg4wUOSbOCV/2v\n8dMTfsQ7r/8FD7k15N3beWj609g/u58T+09k+Lkcw1U9FOqGuOptl2Eti7GjoGlIg4rCuWvh2RmC\n70wKcUejSUiXFBD+WNNCY3yQJbu+TXn8u1jl/fyi+510Ncdp6JfENEhdov+TMy5Vvkev1gT7vS2C\nkNPPg8Zd/J2U3KRq+ImbZygbI7ziDM5pr+cDJ3yBX65457g8KI3A1gqhAipUmkKzRe4qh7UzWhCm\nIGoK7gtCVKO5PQiTRiKBrgZBWS9E04qQq8maUJ6GqoTmvOcUA9WaiiTsmiKZ6oGQgm4tQCvk9gZ8\n9+2YzrNwio+QAdrRpYNq0DDsYE7bhsw1Q18FVAQgBGbewYs8R9eUJuqG2zEDg6C8HuGswXnxe+Ap\ncsYZuNMuJ5L6KbbfjZAGVpAknLwVf9Y38L0Af/0wPopiy+tYpzX2Tv2FEeb+cCjpP8exMAyTurrq\n/Ur/HQ38tTWPHt0vL7roUtauXc369etobZ3Oyy+/hGXZTJnSOu7vDoQ3LGGObeJ8NIr+X4vV5zg2\nodCx1Z09EMa2HMtkCntcOvZsctW3IlQGacWJ2AmKw6tLzaDNWgC0uZ+EDCHxo5eP+0jJOrTyMNQA\nkdYU4ckb8cJZ/FQNCMXQed+kL7YTQ1toilQNvwDFdjZbszCBMD4m0NbfzXDvenZFzyOI1gEWWBbJ\nee+nEGlkyI7wrcEwUZEimU1B9SyevrCNxa+UEyJOtVVNV6aLe194jOuuexPP3Gewo8qg3oSoVDR0\na9bNDngyGUKMXAbPClixZjtXZW/HqO2GIijt0WbWUDAE8ZG9ylRgSIu5vQtIhbIUrSyeViAVJbNM\n0KSrEUJjYSFVmDf7YT66phuRH8Y1HiC05A/ILbU40i9ZnXuuIMVZ68jMexkpPCx1BflIK1oLQkKh\nDVACvh+E6EeSQJJH8Nx8za6WHHN7M4QMg6zpUDTDOEW4/GmNrTRKCIYrfbpnQ9ERxKXGXJdCphVB\ncy06OxXRG4LZY4pLzAB0P8IJ0MnFEJFI4aO1SdI36FFfJTfj46SsRsp6ApoKK7EGViDcADCJ+Pdg\nbu/CjGxBCQFGOQGgMh1k+3uIfaOL8MY0UgrE2Q34H52NHwSve0nGscfRFy4YS6JBEBAOa5LJzEF7\nXh6J9J+Uf10u2VE364knnoQQgmKxyNq1q2ltnc7HP/5P1NTUTmicNwxhjj5kR6+J897jT9zqGz2G\nUvbt4evOHomFOaGWY8JAG/FSfMpqwg+X3GVBBtI/tQmSAmdOQPTt/kFfEgJnMdqsQruDmGVFGq/9\nd4aefB/atyhf/DiJxicwNoRKtKgDpGFylnqOjTnJEFEEEAuyvLTzMTa/9CuCdy1hnBUfqaY4+224\nyicvwjjZbgw3j+t7vL3reCqohXAVjrRoljWkO2fQu1QyS2jmVY9pO2Uo3q47WZGtIiNtMCRaCxr7\nehApCSdoUHAV9/BF/f/xw/o4n+70sRW4AroNeM/yT+N5PyMRStBenE18eDH9s7+NXbWCsOWwUM3j\nOnkhgbuOKZtvxXHyaAeCQDD0wtn4Wxcxycuxa+xGOm092ffdjte4C102REH+Etl2Lab6ELZt4ANd\nGhzZixIB2WAyckR48Bs3BVz+rS3kVlXj+oKt9a0MVBpUJaEYFkhg1jBkB3xSZobQum62J2HaCRGI\nKix3I2KwAsSO0jUCtOWgqyrQOIi4QGRLFvX2piG2tQ6hZIaY3oYZOg1l/Al7ZztGXoPQqEgSwj6G\nuxMVDWGqJEpGIXDRpon59Fb0RpdgoEjgadRDOyksjCAX1+5+XkaFAaDUIWN/snZHigNZTvaGP2H0\nbsObdBz+9Ncm87ZnjtdH3P3QPS9fu/TfoS3Mvyx37MaN67Fth7vv/j1XX/0ubrzxI0hZamz/85//\nhLe+9ardbtmD4Q1DmIYhKSs7ek2c98ZESMwwSrqzR3oMrzWGOdb9PJGWY3vPM/wVh+JqAyGg8FIp\nDha76iCxVns6fvhczCABQSfhaRto/uBn0JhoIpysInSkBMvaISDG8fUxToss508dKdaFzybvSxJd\nK/DW30Ny2hWU1BJ2j06pMl9hSIEGXCOC9AYI/DzhxACePRPLsJEaarMCW5XR8aqmIqxwasAdhtwO\nAcIg/LDJv1b+im9dcAUF0+acLWu44c+PErS3QiyFbM0Rzfpc13kn3zrlBp6tGGZJPsmCnim05myG\nVBlXLbuZ2uAFltaFSZo+rYl3cM65JyCD41gk5mNbAiP1FI4oJXUIwN25gORD/0x+61zqqmoINaQo\nuJHSWlqwnEjdZlQshY+FFnlU/aPUtM2jmD8Vy9Y4ZY/Qaa/DRYDfhM78PRaaHZNNfvvhBDPX/BJ/\n+xTub30HWaOGU1ealOc1J5thqhNp1Kou1k5tZyAxyJNdOd5UU0FDzXToF+gpFShtIYSPRhLQhB/M\nxVijMUQGkQ+zqyVJLlYkM62XQemSky8wP5OlatsWvG6JERYoqchX5vBCPsNiI1XO+YQLLirIMSxt\n1pVfzJTQJlqHBbK9oqQS1VuEDSnyx5WPU9eJRkM4jrM7Oc40zX02+yMh0f2RWWjZz4k89WOMfBJl\nGGSu/FeKpx5cQu1/E4ci5AP1vBwr/Vd6ORm1RIO94qN/fS7ZjRvXs3PnTu677y7y+SzhcBQpBZZl\ns2zZM7z1rVdNaJw3DGEKAcWie8zE0Q9GYke/kfThxUtH45SH734uzSP8HmRuFf7mixCipM8oBBTX\nG8Q48LlYlonT8jlUezsy1QdCokUcLatQsgLT7+UdJxi848QouaABMzyVdT0p/s56jkWZTTy8JSA5\nuBmtOlFlTTCwHkKVjCimg3KR2kNKE9fNQu8ags7nYNezdOQX0FRxAvVUoDMp1ha3k5YS3ZOkzgxx\n2YzjGXwZUn6aKlEBoompqWv5yeoHmP7iN4guz6AdBx22cX/aAMUssgAfnv1Tfv6ex9k0cxebZEDV\nQC2/uPl7vCkTJxcewpyxkrPSCbQysVoL6MKncVeYZFMuouE/KK+4g7HtO7Jrz8KMpqA8YKEeJJkN\n2FQ2zHAlBItdcnU+4WIRX/koLanoHmLS5rV44UnMdp5h5fHPI8QUlGmC0Yd2nscrnoFHmJXdJ3F8\nfg3P1J9HutJn2cIsdUMhzt/Qien2khJT2DFtiHRvG3+u8wgP9WI/P8iNf8ojIzMRTQq6ytFXLUNX\naBT18PtzYYON6NoAtf0UFq4lOXOQ/rAgpSvx+jvY0TXMlPUN9BfCNOUgO3M7eS3Jug5emaRfraO6\n+qP83Kpjm7TIIbCOV9y4YAsL2ypLyy4kkUl3nzUVBJogKK3jUYzd7I+cRPd1l4Zevh9zYCfCLyCB\n+B03kcwnKZ533QTHHDP6X2iM9NDSf5Fx0n8ll6z6qyHO0047i5aWyWQyaS666FJyuSyZTAYpJZde\nevmErEt4AxGm76uS+PYxwv4szD1yeibF4tFrJD1RC3NiJSqHmMfdQShxG2gPMzoLL9eMNirRGmRZ\nDiv9a4T28ULnoO3pwL49OYV9CY69k1L/LRO0RuAThI4DBJZpECp0oHxJi/wzG3M+040y7OJcKo2t\nyLAiRoJ0tgfaliLik1HZHozcAKL1AlwFdL4Aj94EXg6A+1lN0a+kQ7awtvehUqPoFDRRy3H2TF58\nZDUUbHztExcx3h66mEoRI9F1Pt1/t5ppK+9CpgJ0IYcXg+FLDfLT4NNLXLbO3rH7Gg3V9PNvn/oS\n57x0Br9/65240Qzn7Krj9j8voGLhGrrus/B2mFjB43i56fSFPoXywxjhNBXn3olWEqd1I+aWE3l/\nv2ahN8jgcCVff1cVbaG3U8xsJ1P+FLFcmlivwVBiJum73457SS1t1fMY9jpRlo3hBYRDHvkRgftZ\nHS6VbWV0td9Ac8GnqjPHxpkvcsc7p/Pi+unUFiJkFv6O7g2XsH3y+/BDFpGt/VT940M83ZXhYrsC\nhmej3AqUaMG7Mo+XOJvQikrYmUKsaQHdQGSnpO89q7HnWITJYNkSlXXItDViRXN4q2ezvqIAVRmw\nPRxZxLdNEtFT2ex3kgECISiYku++by6f3yyoS7kETWH0fvpp7s968v2Agh/QnfeoQFOGfs0kur/x\nZaoX4e95dgWa+B8+y+DMMwiaZx/WM/WXSpj7w8Gk/6LRMIZhUFtbOc71u3NnG6ZpUlV19IQn1q1b\ny3e/+02+9a0f0NHR/pqaRzc0NNDQ0MC8efPZtGkDkUiE88+/kMHBAaqrJ36sbxjCPPYvQeMJc7yc\n3tFN6DmU+3fUojUM47DrSfeaCdJPESRshu9fAoaPzvvoigBnZkD9276AmetACIVZXEGh8jM4ZdN2\nZ/yOlueI8LlY2XuRuqT/qYw4UI4gVZpDa1Aetvsk9ZGAsyYJ1ve7tIZfIUFA1BFEkj9nU/mHkGHw\nBzeQcQWyrJF48mm2JkOwY+lusgRQBDyS/gYMnTvujLoZoE5VM5QZ5gRjDrawyOPyrLuCN1tngTYo\n+JNJnqh5em0FWVNSV+kyM1rg/svg4UVj7uPIP7dP3cH26TsohvNIq8Ajc9v4TVU/Hxw6CX+7BaaL\nURyk2D2DwA0RmbqObKKa5MPXUL34ObznrqAsCBDEaLV68Gtj3HJ3hGXtEQT/ydYZqyhWfYv+rjLU\nYx9DuTGCNTC4+FSQq8FIEHgGflFiF2cRI0NNDqbtsAhlYkgzw1OnPInwsyTrdvB81U7IXQl8BrFI\noLVEFH3ys2t57PMLmHzjs1wclINroodbKdjnEbSXQ8FHmz3IoS6E0qBNWl6pYsPZ5WQqhigTYQIz\nBAhUKkZ6VwOTli4itngLlVPbKTM8cp7F9spmKqwm1okQU4vrMaWJQrCpagHPnTnAm1cPlOTzLhuv\nCFTCvhZgCsF/yTDt0sDRcLUqcLHvHdBiOhiJ7o9s/CknYPZsHt82TgWEVj5I9rAJ89jvRceSlEel\n/2zbGnHrFsZJ/91771088MAD2LbNzJlzmD17DrNmzWbx4lMJh/cvnXgw/OpXP+Oxxx4mFCp99/bb\nb3tNzaMBPM/jwQfv44knHqG3t5cf/vBn3HTTDXz+819m/vzjJ3Q8bxjCPNbYX1LRoeT0jgXC4aNn\n0fpJaP/qWQzf/37wRyTiQgVCcxR1//pHnOFHkcVhNDaYFYT619D7rZm4CR97riZ29cgGYVSRr/4y\nduZuANzYW5DBIE7q+6WemX4/huqmJIIKUyo0UyoU06oKPLJZkPUEi50uboh+ljSLSAQb+Xr5f6MM\njTBsrJoZeGUtUEwitj8+Rp7WYs/mOtIZA01b0ElAgG8XsVXpQfTxQUHBkKyNVPFAZhZ2KAcCNlU7\n5PKSdjsz3gE9sneHi2GGqodKNfzaRgifHruBdPxTIDWBTKBkQOCG+M8P/JblC9dgZW1uvu1N9JoJ\n3taUIhLLYMQ1gZxHfa9B4Ak8W6ANk9YdJ9GlP4//8kr6BpvQCNRmE1ISKq6Dac+BHRAUZ2NE06Sd\n1aye1sL7cjPwg0FWHbeCbChHICVggrUZRB+ohpLlLQQ6ZKCFS+K8Kn64eR4bHmrk9i9sIRI2UY0h\nzOWbcF7cDukIIpEnH3HY0RihaaiNc793Ls/c/BK5Sg83UmSur2mq7aNc+XD1amZTSc42AUUkgPn9\nCexnP0jzCR/hycYzqSBL1mlknpAU3+6QPTmGP68cwvtuT/sjnD9Ih15p4Izck7ulw/mBt7cY1EHd\njmNJtCRCskdhJ/OOL2KvfgIjP0bwXUhUKLLXBC6h536DRlI8/R1ghdgbr5+owLHdd0rnUZpjrPTf\njTd+hBtu+DC9vX1s3bqVTZs2ct99d2MYJmeccdZhz9Pc3MKXvvTffPGL/wbwmppHjyKZTLB06eN8\n4xvf5ZOf/Afq6xv4zGc+yze/eSs/+MEdE8pD+RthHiUYRiltORSyj0lS0aGwpzfm0bFotQsDnwlR\nWDabccWFhRDuGoVsux8Z7UAIiRBFCCSdt02l0AVgUFxnIGyI/V2JYrTZRLHilt3DBOYkcjW3I1QG\nO/1ZcLsoVTWOXjfNjLoGrot1058JaChThMwwD2duwbVXUJHLkbfL6YmdBEaMcLRA7PyPcelxaQY2\nv0rRXERbfxfbhgNKqup7EKAxsFnl7aKeClJBCss0ySiPTHWSFdkhXp60kPOHV2DZBVS1pCOwmdVg\nY2sXd69n6uxdC1nNFnbWdOArE8utZtPaD/H+Kd8k8/EhZm6axMcePZl//8y3eej01bt3/U/8z72U\n/fkO1naGeWtlioXtBRJlFlKYtDVDYBhIwJSCCk/TeOrTdHUfRzLVUpo4oyAdgo0XwAyFavwxKv5j\nMJN4yuQzX2li0arFDNYOkHNsRDBl5NIKEP7IcTDyLiHRGKRVKylm8evLBEtPmcfyh9ZSv2IHxh+X\nUQwX+PWis7n91ivpqSunKpnh+M1ncPMdq7jo1hg9M1yevj7CunqP7VGfU6vS1DZ2M9CUxDbLSndW\nK4reIDXJjfz0wWe5ZcnX2Vgzn9Z4BQtshzNrDfzaqoOszH03tOJen7oIigisCZRu7E2iJVej3N1A\nOhx2MKvi+N9cg/rYcchcSR9Zq4DYPV8i8sxPGP7Qz1B1U6n99HxksRRb1Xf+C4kbf4C7cHyp1V+T\nS/a1ziGEoLGxiebmyZx77gVHNM955y2hu7tr989jSe1wmkdDqUdmNBrD930sq/Q6VV1dg2ma+4x9\nIPyNMI8QUoqR1AeqfAAAIABJREFUhrKlNj6ZTP51CYKP3tzReOHo3EerTCa7zKCw3Bwhy73OR7lY\nkQGQTolZlY/nRXC7m0re1eGStmvuj8ZuwtwvhIk2KhBWbSkNXlYh1BAajTKmkKu5FaENpiRvBSzy\ntV/j8orZCPEOZm58gC+0h+kwy4jJIs3OIDJsMLX6RH606FXuH/xHHnjlSbzcn9nVFqK01CUGgkBK\nDGkw5A1SIENMRFgVbKKYu4urk28lqIuiqyy2nFlHdWMnGkHyHItFZc1cXajjV86zaFm6Js29TVz/\n3U8z0DjEby76PVnLI5K/hBcX/oRkfCNSFlhz5mqMOWt4omobuiSVBBLc2CCJKY+xPHseoZ46NjR4\nnNQuyEZLXBbNKAxT0TgU8EpNnHliO1df8QG2+E2s/fNH6es8iUBugfMeAGsYnA1gpkrlQNIjUbeD\nFYtsaruPp792A1k7BLoWVEvpPxgJhpeUYRQmu18uBPRVh/jE1VV87tGv0jVzOw+1vo9fH38ZWSsK\nUjJUEaG7Lk644HPBn3fyyKmnkqzroKFiOXmrwPJ5ZVzaLkmXO8SDUu2nEgZpM8a9x13Kjct/zvcf\n/Qhba46n65KvM71m5ojE/kGWzH7aVp2iPFYICy1KPoq5+ERH1qwGhihlUVfvVeW6//EFQaDI54vj\nLFG7ZwflNVOhezN4hRFtXA85uIuar12OXzttN1ky8rvyX36SoUnzUdX7auceS7weOq//W3WYY2Xr\nJto8ehSRSIwZM2bys5/9hGQyydatW7j33ruYPn3mhOd/QxHm0ZaUG+0kUix6JJMFysujr0uMAkpz\nRKOh16R7OxHknzLAG71Y40lTGBqDcrRRh/YzIEFHahFVEYJ1oFKlRV1cKcg+7hO9+ODWtq7/HF6u\nG8PdhLIXUIzfjB86E4RJEZ/flf0PSdun2bA5I9HH82tfIh6t4OcnL+Jdm9vJBArQKC2o0gN89qE5\nvNz3JIPpIYaGBTLSg8rVYVsmvm+RV1kiKj6iiwOOdJAIeoJ+ugY6mDGnFn9eN+0ZG9UaoiUmuKI/\nS+O/ZDlvuIpTTr6Z52e3UZ9t5F2PX0M2X0lLbxU3/O5z9ERdfnDt5+mufRHknnvy09ptY0549EIG\nBI0P01uznAcjn+CTvz+OjnrNmvnguIKWTsXq6n6eq8py1m+KzK1/M0/d/AlC5Ss5ZeFS1q+6nq2X\nrkFHiiDyIDxKygsSRBEEJKq7SEROAuYjvAqsxBw8edl+HoTxMoilw1SsjQzzzbdM5WXz/exQi3FV\nZMRCFSAgG3G456L53H5tO8kKi1woQ4uZpsUaIMImihVdLOcmTjeSRFSGFDFWhBfynHE6c1q2cnHb\nU8wcWk9tcjtuzcQ3rrE4iYCPBjlWSIsyFFcqd/eK/akM8YKwEGgWaZ8bVeGgpHmg51fuWAXu/kMc\nwitgDOzc9ztuDqt9LcUxhPm/bf39Nc2xP7yW5tGjqKmp4S1veRs/+9mPKS8v5wtf+CxnnnkOH/zg\nTQAT0gJ/QxHm0cJ4laA97s89Jv2x1LosZd5KKXBdRaFwkAaGRwBj3xe0kQOAYDhMz73vpu7dA0i6\nUEYVhcp/o/wjFv0fFqVkWFtjxDX55eYhCROznFzt9/b7RvMHu4NOuwBKs6m3ne/cdS8NeQOlNafs\nXMdbFtfw62yetJXleK+fyZ3Pc1/3pZiWpK68mp7hXrJFAdLCD0rfg4CwCGFikKeIRqG1QEhBnXqR\npi2bqJgR4qKYxWB0CqcNrKLx2TZiXR5Sr+XajtlcEruBfMs5CFvhhgo4dRrXifLgmd9n56RXSi7P\nA2H0FH0LMMDMsDjzNG9adiLaNGjtgvsv1AzJDMse/i7xfDlP+w5tF99FX0MNWaOOQlWS/LRfo61q\nMExKa250zsKeeYwhCD8C+UvQ2cvwvtMMHxZg6jHW5YEOM0CT5GHrerSKUaQS5IgFupv0Bb5l8ML5\nzYi8wrEcirYJ4Ty+EhT9gCWd9/K+U+9marAOrWFzMJlZfRsxUCAtdCiOObARd/olB18nHHijXkDA\nAjV+na3A5GVhYo8c7Cph8oIwOU0f7OVyT2xuLPxJC1CWgzRthLc3cQq8uhk47a+O/7hhOhUnnoNf\nVTFGUef/hjD6wQjzWPYGvuWWj/PVr37psJpHj0VZWTnXXHMdQghisTJM06RQKEw4IelvhHkYsCxj\nJKHnQO7PY7dQYHzmbRAoPO/wReInitg7PfLLTPwdBqO7o7BH3r41+PaZFCpLSTXKPgGEQeh4jT1X\nEfTvuQZy37yHfTDKk2MdZqPSfX26SOD5KAVtK1cy7GZpFHEMIfhR3ePIphgaj+l+iGkcx7PBDQyI\nHcSxyIkUYmoI2esSdErG7iFdupcYUYoUSQRpamUFp0Qmcbn3TcRAD2nPZMfMK/E7bWraMoSGPAR5\nEJIIW6kv/pqOwkyMkM0rhafZviWOYVYwdEYX2gaUBOMg7nENCAUyRcVQOZVDEivrUyg3aOyF1g0e\na/s2kXUqKZgSEcAu6ySkewZ+pINAD4OxHcwCYIIOgTagOA2cTSXrVhuAAJkAvxKKs2DIhY0BzDdH\nLvzIwYhBCD1Xsh69RRA0o02PTjGHUCDJmw743kgilbGH9LXGwkXMaqNMD/OexO/oDUmGMiaR7iRS\nxDhR9PCR7Uv59oLrSQlBQ36QOZmlnNW/liBaSxBrBuvwMygPhZQQjLUZJKWM2oPhQNJ7fstcspd9\ngtCff4fZsQ451IUkAARBWTWZa26juPoxoo9/Gxm4eJOOJ/umT+HqGGYyvTuxyLZtDENSXV1xWMo6\nh4PXQz7w9Wzv1djYxA9+cAcAkydPOezm0aMEvmXLJn7/+9/S19c78jkkEkNceOGlXHPN3xMEAYax\nbynTWPyNMCeAvesKD5TQc6zWj2mWFIKU0rszb2Ox8DElZ7tV0/CTAsFTYbQBifsVhdWlxWTNUsTe\n6aOcE/f5XuzdLqkf2qi0wGxRxN67b/H5vhjNPNHjOsfk80VMpVFjTtMaafa85pQ+tp2SxDZzaBQD\nMowdJFg5t5KeeSZ6SxJH9FNZYVBZF2Ggyx1nTPkEJEa2Tw+fMtPmWvEwjuzFJUdZImDRS3fSM3ke\noVwCuTsmptAyIBIapOX81fz4/sdYMZQhrl3ilk1oWTXl5wa4XpisMb7J87jT1QAKdJ7qgQqu/10z\nVmQp0aGZ4E2jdUXAlxs34xkOnvZBFaCmERIpsHMQTsCIpGDJDetDUAPpD4B1K4gBdrOatsFvQWzs\nAV2F/qMPTRLismRpOusg/lWQO0tj+Q2Q+mdQx+ERwpIFFGJ3DW0pMUuWxhcgrSI6FlBTGOJMsY5t\n26K80F1OQkXZ2VdPpizNpTOinBWkWIMBlsPpZT5yeh2kBxDZBMLLg18A8+BvWAeznjoQJBFUomlC\ns1h7PKZtsiPPSVRrTj6odTnq4Nj/+MUTr6B44hWlH9wc0ce/A8Us+fNvQFU140+aj6prxd72EipS\njjf9ZGB8YlE4XEo4yeXy+yjrlHpkHjmJ7i/Oe7Tx19TeSymFYRgsX76MbDbDV75yG4lEAt/3KRQK\nxOOlTkCHIksA4z/+4z8O+Mtczj3wL/8KIeXhxTCFEEQiIcJhm2LRI5crHnSRWJaJUvqopXRLKYlG\nQ9i2RT5fpFBwdz8Itm3h+8ExW7ShkE1ZfQiVh/47FEFGICKa6KUe1f+vgHmAXr/2NE3kQp/wWQFl\n7/IO+Hdj4Tg2nudh2xbRaIgg2KPzO1lF2CCTZPFpjFfjbO6iqynBq0v6UTYEUhEITUG4bJHdpI12\n9MIIVETwpw3Q8BZBviJLfqlkHPOOhdDkZIZ0qJMrjQ04BZdQsYDhe4SrynCrNVYyiwxKFpkyoujy\nBp7q7OfBXUmGlGRAmVh+jsmdwwjLQjonkvOSuGV52BqBthDYGiIKoShlp2Rtal6Ywz/dtoALujqp\nlFsoN3YgzCwd4cnUxMp50ekD14fAh6aZMKUbIr0QSoFXBlqWiMxvhuIZkHs76Dw4awANRUmFq7lq\nexfDjdPJbKtB91jQ7cG0FNT1QfmdYD0GMlNyJcsMiAQUl4CTJGTvwPCjeCpcatGCLI2NAlxMWaRa\ndvLF3v+mnCw5z+LhR+ci8g6pTJilm0/jghuvxwSa0EwdvptIfilGdhtS5aHMQQwXMdIdeC2nH3Kt\naK32idlvRLJRmGSFQZcoRacb0CzUPh6CFq14ry5Qc4hwSSjkjHT4OEQYwbDwZp2BN+88dLikEuO8\neBdlD34Vs3crVsd6rI51FBe9adzXbNtCCEGhUBxpIO2RzxfJZvO7PUajDaTLymIjL4/m7iz8iYiv\nRKNhcrnCMSXNWCxCNrv/cJAQAiGMv5j2XqXjEWQypWzaGTNmYds2ZWXlVFZW7jdBKBp1Pr+/sd5Q\nFubhJP28lrZfpfGPfJHs3fJrf/05j1WcYLwwfJbB30TQeYE0gRjovMCsPPgYRgUYFYf30hCLRUZq\nV8eL0TcS4R/9uSg0Rpmk76p5fMD4MtI0CcYk1ZTSFgMgBcZaOLX04TYt0Ys0XBqCByPsW5ZQsmAD\nUzHgSRwCtJQYvkLoAs7W1SjLYOC0WRRFiIrNg9jmdLzaU9n8wqNUaoOEjiCFpk87zNYpZv52PQ/8\n9lLexhzWtL7AI/N68ByNeiVO1Wn9eNM9cgVB/UNncuGzC2lNtzNQs4GZmV0QE3j+JiabG3lzvpkt\nTRaP+3X4Xie8koP6K2C4GeY/CMUw+BVQHgJmQDAHnGVQPB3aJ0PiCcy04J3OU9Tnetha+0dyn3QZ\nTFRBtQtlPiXSWwa4lFyzpXo67C4I90F0PeHicq7JP8FTzjWs4924IgaBAyIgLBPMsp/mdPcZ6vL9\npLVJKmfx/T+eTENzJTpQVIULdD9xJ3NOPo4gksPquwur73kEPpiabrOMO1svYHqgOO0Q6+RASTkd\nwsAcubcmgnYhma4VNWjerYv7fuEwx58I7G0vwmgcVQjMzvXg5sDeU695sNjfoepE99V43b8l+pfQ\nM/QvhSzHoqamlvvuu4uXX36ROXPmIoTE9z3OOed8pk+fMaEx3lCEORHsqWcsEcbhWHAlEjuy+Ufj\nlKWWX9kDPrxHO+N31O0shBhfR+qP30T0UQybjioSjbq6x/b3G0W/uZyE+SquGCTuL6A8ehGhUBRt\nCFheAS/EwdJwZR9MKVAigFLWLJhoRuowP2Fj5h38pWNdxKNqExo/qglbms8NzOcpr44ENieJYW7R\nW/CLmo8sncpgeTVN2uS3F59DMfNbsnaIqYU0PprNugwfyVStCOfn44kUm+ObYaPDTRsDtLAo2Jru\nLdXMmmGQXr8EV6c4Pr2KE3iFStqRDggklU4v+UCTiE3DuVhyQnULr3A2fPku+EMRJk+Hez8Gs3vA\nCKCjlYo5y0gsWV3anM01kD0Vbv0OTfWPkb/uLtbGBaJ/M/U9WbzZS8hGwiMVrxnAY08NrCi9eJg+\nlG8HtYym4h9JmUPU299kZWgW+HNAGWCmmJbbQIQUpyS3UAwknjL45v2nkcgIhjcmkAIijuKt/1Tk\nex97lCUXdmKm16JMjz9FPHaFomwoP5HbJt2MowSfwONmdbAX1P0nKkk0CkE78GsZJgNM0wHfURkO\n8X43fvQjIBsViY97MHW4fB8X8+ESyWsh0WOdePh6xi+PBkZdso8++iDhcIQ5c+aRy2XxfZ+BgX4K\nhdJ6+1sd5mFgVHf1SOoZj8TqKyUUhQ5DIWh3xfkRoRQztLEsk3ze3Ye0omcrirtGC9w14bOOjiDD\nWAteSrWnH+cYZOR2NpgvkTC3UUOGtLmZWjFIjBjB6jL4cRP026VjW1EOH2mDmXmo8nfHCQUGumjC\nFw38dXmwRIkfBCWitYGFLjT5RJ7x+b03hW5CKAQ9OsJOL8ogDkNY6HSWHcDlK37JVf+yjIUPfZI1\n+kUyIk8MnwXkeVlNw/Yv5Pz4dIpDBsv4A0mjmjgJQq7J6SsdCt3XU3PuCqpyHZzS300h5GMXCghL\nof0IUvhUWa/ym1MvR9hRTKlhSj3889vgy0/CYA6apsIJOyGchdoeEk02hEctGQnxzVjvPgXVsJP7\nWr9O+u5T0c81oJVEzkpTdt0OMtGAQNiQqgAzD+FkiYCJgZpB2N1Co7sMJVIsqxBsiTQDvwf/ajDD\nIBRZz+Sqtf08aryFX0cvY+cPhlnxTDn6J1dCSyVqSz/Bl35K5KJ1/EwlEX6KhX6Re2pctoUDstKj\nw9lFa+aHbCn/KPcIi09bcncsb+/n8EAW4FwdsFJIfi7D9IqS+3KlkFzjWfzpVzch8mm8aYuxtr+M\ntfMVgspmhj9xD1jOkS1kr4DMJlBlNeQu/ihm33asro2ocJzspR8rxYH2wpGSzaFIFKC2tvqQluhr\nxV9T/BL2vKSYpsU73vEujj/+hIP+3cHwhidMKSWRiHNU6hm13u/zcVCMTyiauELQ0bAwJ2LNVr5b\nQ51PYQtYMxTh04+MMMe6fEc7p4w+5Htjuexig5HCAzZiMFf341lPMM99Hw++2kbQEYJAQkFCtwOf\nng2zcvCBdjglDfhoIeDXDrzCSAboyEUbLTuMKs5csovjU0l2FlpJYRJQyq5UCJLYDOAghQYVlFqb\ntTcz+zNPINLHc7G4hwH7FeoxyOlackEzw9JAFHYxmMySN6byWCTKFMujwbJwI2+ixzqTswtbMDrT\ndKooYUtR6fZiS4/A0GgskpE807P3U7GhlvtPvBJDKoKmeig/C+obkO//EbVyKzKnSNo95GrzwJiA\nsQ4ROAb5FpvkL86FZ5ohElBV3UvZUIahP1UTXjJAYXMd/vor4LzfQt6GwIaK2Qg7ygmDjxLYO1lX\nVUveLEeTB68b8rsg9x4IDSPEs/x23mzyQZxQfxz7oybxD0yFWVlMK01kejezjl9FWf96iBRZJ7L0\nlGnaDEHON+m2a/DJU+m9DFqjKfVxDIUcysqiCCHGbfoHsp4a0VRql3/P+5hohNZo06LLLdDXvY1J\nqR7sNY8jg9ILoZHooeZfFzPw1TXjxjkcC9Ne/zRld38BmezBb5hF8v3fIHnDDxGFDNqJ7CnB2Wv8\nYyFbN0qihYJLKOTQ1zf0mty5E8Ffm0t29FgLhTzf//63Of/8JTQ3T6K8vJxIJEpLy6Tdaj+HwhuK\nMPf2849aVgeKE76GGYCJMeaRzn8k1uz+sm4PPA/EztHYpx2ZMMLoi8k+Ll8go12ek/34OmCBqiSO\nRRKXBBUokcKXOXboarbrOHUiS9FYR62upMczSl7E4sg1LxoYbWF4qA5OTpdEilwN94egsNe1UkAR\nIlUux3dm6Oiowy+qUm0gJbIE8BHU4tKvQ0gUOjBo0lUIz6QmfjcNuRVMlb0MESXlTaFdB2zWm5mW\n3kjRLGARJpJrpSM+DRG+mC4VcFzvMM+bz1M9EOVdubVIX+BhEAryZK0YQ04VpuVx8q6VDEUriLpZ\nYsUc9819M/rSqdDoM8VeweRsJ+WqJByxadVCNlfbEE2DKoPBywg9kyOz8jzYUgt5k3OmLue0E5aj\npKBYq/lD8VRaHp7O9s3vwl2/GE59CNwI4kSPUHMjLbnf0WaFyJkmkAIVBpkFezlkroF8HX3G6ZTF\nViPJ0xOCVKMNxTWwFuqDTUxvfZiQmcFsBm0qNklNFM1Q1sGzFEUvB1YYZVuE3SzvzXSQLdvTPaLU\ns9DEsizC4RCOY42ETcZ3HVFKEe3bTrWy6axoKq1dr0gslyDtREtjBeOfMSPdh0z2oeJ1uz87nJKM\n6MO3YQx1AGB1rqfsoa+R/PtvocMHKmJ+feOLRysm+nqfw9HGqCBBXV09fX29PP30UtLpFLlcjp6e\nbu644zdMnz7jby7ZA2FUoedQccLDxURJzHFsQqEjn/9w+bIUMwxhGJJ8voDnTcRaPDLX79gWZ/t7\nMSgScA/tFKSH0pqtMs07/MmlCKSOooI6ulEUMInjYomApJll3hU2yQcl+fYRH52dBKNAUJCIpEBL\nVcog/WQVJPa3zAUYgpOK9fTd0UhCe2ijmzLhU9SSNBYSzSAOJ5MgjqZgKepwmOcfxxND91LlbeUM\nilwmkzwgwvh4aL+C0+gF2UtaCyBDSCk2DUmGsisYCvp5VQ8T/HI2C+LdLLY95og8PV6cgVANPbEa\norE8ldk0KTOEZ4VoTnQxr2Mtz9aewsDUWspFitbhLDGZQAuDwIJLt+2g5XdvInWxSV9sCm+pupOz\nL3mavArzY/P9JMqaefOJD9KZacQo95niJLjplV10vno6baaF2TkT//5/BFuhz1hJmbeJGrOVXU5f\nqSxFuCD0SKZxCKy14J9Alipysp5K0UPKtkrKeiUdOnqHZpP/UgS5yiDW2MX8W74HtcN4UpNLazIh\nH2GmcTsV2zui3BZ8nHfNPpXCnL/bfZeU0hSL3u51U1FRTqFQ3O2ZiERCWFZJMN3f3skPfvMF3v2R\nOylYYWL5DFetfZiZg20HWJ0Hermd2AMp8+nxK6qQOeR3jrUS2KHI7NB9Lw9NooeWxfvLsS5hj1X/\n/vdfj1KKvr5eLMvap63X31yye8E0DcrLQ/h+sE825tHAodyk4zNQj2z+EjlP3P872sWkUHDJZidu\nzR6J63cigvDbRBqPPRauBjbLNPNVnI0izRYa6MHAIUczg2zSdeSJ0BqPI9/r86f/yeDKBNhjOkkM\nqpIF+UgI1h4oRqURruSF3ixKKKbrEM2qhlari2k6S9Gz2UIMhMlmXcVcM8w3r3iSl588h99l+ggL\nH4THCzrHjAA+aGTJizw9zvNMMnrY4Ie5s9hIn44REcMU8EgEu+gz+lDKhWzAQN7keWs6pt1Bi1XA\nViat0TYKZRZ5FUN6kA1FQGnsoEDRCVONQFcaXPZknI4ZimQ0T9XWOBf8OeCk+M85ecVK6sp7yJ4f\nxqqP0+OX8e0rP83q7uOYV7OZnIqyvfcM4h1xJj1Sx/CGCM9eMcTGnANhFy7pRkZ9hFnO+oqZTCoO\nsEKH8cVICYEqA3ek16AMAI02fYaMsj37pAAsIAaZt9UjH7DJtE0m293A6d/5N7rix7E1eyXih/cg\nqzvp7nCYbS7j2vPKyVa/76AvnkKUkjhc16NY3JPAZRgSc8piTlIFnvnvC3l+xukYlsMFs0/CaJ6J\nyqXwqltwtr4AI6UZxVmnj7MuS+NP3HryWheVLEwh0MLAnXmoHN/Xy8I8vO/sr+/lwUi0NM+xJ/+j\nCSklr766invv/cNIok+emppaPvShW5g6tXXC47yhCFMpRSZT2G+CydHAgR70A2agHtFcEyOyo93F\nZCIYVemBQzeuDmHga40cyX4M0IS0ZIUcokZkyWuFQYJ+bLqpwschQkATDrXzpxDMGeTFHb1kR/dO\nATqnYJsBPz54F3WNxhWl67FFFKgJKumqmIT2+skqi2LgYGhARFldMY+vKoe3bnwegaQ76iOtfiLC\npVdHmCazOMpkhjGAJzSL7RSO0Dzl1tImq0Bm6JFFlAqAPBiSrO/TZLdzibkDEAy5EYYHwjQ1Z1jV\nMBMjEWB7LgUzxGPzLiRSUaBKWfg6w+ZJJ3P9w50YQqBtn3jzFk5L9ZEdsgmyAU2PDBDMyOKk5hOU\nC2aV7UIHDlPCCSaLVxlcezn5VYswsgZvGw745Q1b6JqWpWCC0ha+suix6qkJTBanHP5c0UxJq3Y6\nSAvUTJAFiPSAOZpFNQYjP6qig5YhhAvp9klsv/stNL53FeVTM2yuuJmK5+7k1Hcv45ZZIV5teAsv\nOj9lKNmGLWKcH76JVvuUCa25IFAERpjh675PzeO38+agiLvgMvRpb8O98EYsy8I2DYK29egnfoTX\nPI/CaVcjikWid38Rq2MtKlqFuO6/0PbEGgqn3v0VgupJGIkevMkLKM44ndhvPo3fNI/Cudfu9zvH\nnjCPjmjBwUi01GvX3CexqL9/ACkl4XD0iOc/EJRS3Hrrf7F16xYsy+Izn/kcLS2TJvTdn/zkB5x3\n3gUsWXIxUkruv/9ubr/963zlK7f9LYa5PyiljxlZwr6EOV61Zt8M1KM5194wjFKcEg5NWkcyz1js\nrdLjuoeOe07TZXRSZB0JNAEtOspcHWeZ6CFvdOJQTq0qIybzuFg4aBrIEVK1xBuncMpx5Wwf3EF2\nIFfaLKTGiAfwJ5sgeQDljtEM2eKe81LAQFBLr9VHwphEKJfk/2fvvMPkuuq7/zm3Tp/Z3tRWvcuS\nbVlusmzJGBtjY8BAMDgBAqEYCAGS4JAXCC+hJtSEFnoxYIqxDTbuTW6SLVt91bXa3qffes77x+yu\nVs2SLMnhfcz3efZ5NFdz59w798z5nV/7fj1CNFXJY2rVHrsz1TzqTaYv3U9J03DDCFNEgK27lIN6\nJCZxOsmpEpaQ9IsYW6xa1of1OCgIJQi3EgmUiovsTrLKoF/ZtGolFGWKvknQLsnN6GNn+gLiRsht\n867kudhirr9vmGYzz0jEIexZzQbZwozgOVzpo7QCfqGMdAPIR+jP2cz8wRySNRdSDCyslgH2Letg\nqBwhL5P8bMM1THaaeQ0gTcHi59IMmBLnqQboi5CNN1F3/TD1yiRUgyzI19IWn46mTUUUErj1Gysh\nWqEmfKljFEYV1yPiCppqI5x7ZYZ1t5XpjubJ9U7DebKFwV/MRu9OkJhSi+XX8MOdGo2pH9OSqMHS\no7gqzwOlb/A284doEyIpxw05TppP9u3fPHjA8XCcCZ5oogXzDZ/GNA0SpoFx71cRT/z84PV/9/2I\nm35+3Hlb+TCT4pUfAsDc8hC1n7l0nGc2uv53DH/4tiOn3p9Bj+SLxZgR1TQNIQIKhdIhnuivfnUL\nv/zlL2loaGD27HnMmTOXOXPmMX/+QizreDo0J4ZHH30Iz/P49rd/wObNm/jGN77M5z73n8c9TylF\nT08X1133+vFjb37zjdx++20nbCzhZWYwz7RzNdHrG2ub8LwTJz44HaiwE9kYhk6pdPpVTI6FiRW3\n2ewxKOED01UyAAAgAElEQVSOgSvUJJapDHeqA2zTcrRpOQYo8pBQSBFSRRNT6ONy+tklG6gK5pLW\n5jIpluI971zCnKkZvnj7Lezp6kUqhcoJglvTB6U1EQgE8705NETqeOaap3GfUDg9h9L2HfAcSt0+\nWDaOp4M2SssnXeSBPrJ2L/c01jFd9tEdagh0hFbPf3rTeZdYwFSRxdQH8fUd9AVRfsYUngjr6DNs\nEHk0w0aWTcAFTbBZj7ONOHkV4d/CTViaQgsVfhBh5t5eqvr+yHPVs1m552esCYZQUxbRVF9m1o4k\n0++txtUvoZyM0K3upOQNsTxSICdNfKHjF20e3p/BDl2e6TcobqznmadzJKqS9KtpPF7tMbnZIxaY\nOJ0RMi0Okd9MJVe2IZDIzgTJb03nrXUriPhZ9OpnuSOzhc1VIRv/ZxG7rn8P/qsThzmWlRciAIQg\n6epMH4hQvtRnTTrKL+4rYEbzdH7uYsJsFFky6Nh3A/2bL2HeG7+IOZIlEtVpylR4ZR2VJx/2k9Tr\nDjGap4IwDAnDEMepeE7pPZuxYVQATEDvHmrTCQKhH0JTd8TvSClEKVuphjUsUr/95CGk7Nbup9GG\nOpHVLYecdqbDmC+NtJc2vgGf6Im+7W3v5K1vfRvt7e20te2grW07999/L9df/yYuv/yVp2XsjRuf\n47zzKmxQCxcuYvv2bSd0nu/7TJ3ayl133cnChYsxTYuOjnaqqo5UNXkhvKwM5plHxRtLpeLjTDln\nql/paJ7fxN7GYvH0GOmK+vyxF6uTqbh9gVHYRZFdWhFdCHaQZ4eWJYGOpyQeUerDOmb6C5nLbAqR\nDHEMlpSSeKHPpZfp/LZnJzt+ncTQwRs24bDLiMkoH87/Pclckuyvb+Bjcz6NK7oPUo0pKMnREJRf\nBk2Orv+i8lceZu+tLkZoY5s2cR0mqQAbSbfS6YrNoN4bYpv3GjqMX/NsmORO0YCniYqmqBCgQoSI\ngq6jtJA9Wpo4Pg9g8ZzKcF3YS40IaYxOplHfgxABkwfa2ZaZwTy3DdFcZsXmOzA643jiPPaL19Nb\nuI0t/n6GVA0pMUK97lJCZ1NQx5PDM/EGBFuygipT4oVpCn0pequWUvD3s7duMltLkil7LDytirKK\nABo4HlFD4nTMxWeEJdYQQpzDjau+y9ceNfj9s9PwHw/gs9Vw9xCMrTkh1A7qpEdMavpMWrqjjBXQ\nuK2SFct3sksPwdGQjgmaACVxi7V0PnEd1fO/jJNSkAFXlijJYe4ofIKkUc+q6HtI602VAg4VkLOe\nIdTy6DJJ0jsLwfF5QI+GsGYySFVpP0Ih6qfSO5Q/pJo0Go1gGDpBMBp+HBnE/tGH0Du3oaIpCq+4\naTwvenBKS/CPxjB0ZkkF/relvQzDYMaMmcyaNY+rr772tI9dLFZEoMegaZWe3eN5iZZl8fa3v5Nv\nfOMrbNu2hWKxyL59e3nvez9wUuP/xWCeJozl7YQQFIvl05KnfCFMNJiH09m9FE3FYyw9uq6flv7V\nQeGij3oorgiRgKUyJHHwRUg6nEK9tYQFZvUR4d5//+Wt/Ok3SUJfQwZqwg5+1ONRAk/4DGsjJMIE\nf8o/zdCQdtiPXgO7cIiG5UFUKmpdJ8qAqSG1GHF8fKlIBDZOopaFc/biNG9h+/3X862czg59N56d\nZVwNRClQBqmoTkiSQC/hBJBVGgVl0SmiKGKsMIvsL9p8aFYndZrJ1qpq+hva6ZVxXrf9ESJlhZIu\nIn4fhtvPUFikqIpIBN8qzaRzjmRnLEquXMNVpQwdfX1gSXpDg50dk9HsaoyaaGVfUF2irzWkYTBF\n26COFxOADipKMYiyk4B/4Rw+PNCLpeDsnav4v6/+HnrqGh7brXh8WxKxMs2lN97DRTMlr/7O+bQ+\nPo8w1PnyR9vZMa80qkCjyOQFzXUGXYZFoMxDeX0NBcqktKOBualmolqG7nArLfoiDN2irEZ4sPTf\nuKpArtel2pvJhTNmYmoaoZYnT0jKO7Fc5+EovOojiFIOs2MLStexl1yOKAwRJKqPUk06akDv/yZm\n19ZK03VpmPR938B5xbsxfvKPICtpl6BxJrL+yGKSlyIk+9JIe53+XtITQTwep1Qqjb9WSp1QSDWb\nHaGj4wBf//q3eeyxh7Esm8WLzzphWa8xvKzI1+HkiQWOh4kE7Y7jYZoGpdKJc1e+WIz1p1mWMcrS\n4+K6/mkP9+i6hq5rh7SgRCIWsVgE3w8pFp1TbsQ2TQOpFM+rITQh8AgpioCYMtGERVLEOc+azErZ\nQLnoHJKP7RnK8o9fvxMvCAjDijeoaQo1oWVAQ2NGMJ1Xl69ii9jH0/pWOlvLMNI/7kCO0gKN5uQm\negEaaAJr9iQMfwCvaFFUNgVlcUBG2ebX8dbLfkrveU/zracGuGPkaUJZwNFDfCVHycoFSA1NpkhG\nTQIkoeZXCoCEQAkNm4AUDk2iRDmaZ9akHL3zy/Rf4tA4Y5hd5w7yaF2R7hqfAi5hp05ZKbr9BLvp\n5yERYdPykM7zXNz5Hs1nZ4m2DBDbI+jTQqzGErZd4Pz5W3n1iqepbRxi+5zJFJ/qYmtyBz11aeqD\nGDk5plAgCNDpCBM4pQR5mWDryCxKVf1szM6jNuVS8kqsueSPnJ++nzf3mLS07CXsbkQN1jF9R4Rt\n80rkEwE1fRbnfzfD99sd5sULbM3PgsEJE0DpGGIEtesc2p9YwMLEO6idswehVZ6BJ8tsc+/j0Xuq\nue87q3j43gzrNiguvdDEMkEJn2gw48VNPk3HW7QGrXMr9oa7EM/fQ/S+bxNG04Styw55q5SSIAjQ\nN9yF1rcXcv1QGERk+xGtS1BXvQ9RyiPnX4z3kd+gjxJyTDRg8XiMUql8xsKylmUCnNZ6icMRjUbw\nff+YdRFCaCdVwX8yKJdLPPHEY6xcuYrNmzexf/8+rrjiyuOed+DAfn7wg+9y9dXXMnXqNCZNmoxp\nmsd8/1/I10dxOjlYj0bQHosd56TTgDFydl3XjsnBerowkVD+UJae01lxq5hNmivDZjZpw0wiyiuC\nJvoMl2HT5yIaWF6qwg+PvM9sMY/nB5hGZecehoJUnYW25q8o7NqIn80ydaSFv9/yBgxMSsIhV+eQ\nTvXhJj2cvEXN5BzxjEPXjiYCR4HQEHYEzRAQFkEvEbNCmpoL7BzWCJwIAQECybm1O8gMCH70nRr2\nZ+NIBTpDCD2DgSAIIpX2C2mgvCqEUGjmMKlIhIERDx0wCYgSIFFY0iE15PFM0EDhFV3cM5Bm59Z6\ncpqGlRomPxilVK9RlZGsPJBje66ZgXiMQV0hWz2oCUlIxYUh+JkeltWPcJfTSNXkOCoosHRFSKJG\n8dZrnkDbVOTBBQvJ72nk7GSZPn0IbbCGkIOtOErXuCeZJJ3Pk4tJ9LbVkPYQKBqrisyc0klEl+xz\nS6T9JNqSjQQ75+D3ayz+1you1HWGZMA/mF0YtWnWdjWTbtlLtjQDBiSEIcLyyHXNJ4fBHmD9x4dJ\nf+0GqlsGueKdT1KzdC25ks/uey8DIdEsl13tGj+8ReO977DQ5ClqaZayxB/8PmI06a2pgPSt/8JQ\n/QyChZce8XZ/xnIiz/0R4VY8HRVNILc8RnHyOQTv/nElnIvAsgzi8SiadjAfKkQljCjlmYlAvRQh\n2eP3YZ45rFx5KevWPcW73/12lFLcfPMnXvD9Y5G4MAwZHh7iP/7j88ycOYtkMoVt2zQ1NTN9+olv\ntl52BvN0wLIqhiMIDtK7jWHMwJypSTuxuEYpdUaN5UQkEtGTpu87UYxtYpbJapbJaoRgXNboeNW2\n9ZNzLF0U55mNeWxTkK7TmPOWNzLQeB77z7oIJVyWtnyP4QNfwr3rMvbOLDOv5Ucsj/fh5T1KWYtY\nykcJgVGts/G3syn1udQmA7bv6aBcHgAUpcGAxDKNc4XFMxsCQHF2ZpglmT5+sr2ZQcdCSQMvFEhL\nw8AhRshcw2dn0EDot+IZJslME1ee28eGti40w6Y3rzPD3U4ItKQdzBbJgkgW8zmduzZVs3HbFBxd\nJ+dAWExAwQVDMJDW+G17SNQYwtHEmMQ3SNAFTKvuZ2m8xILVitb7otxV28gVVdVoJdhudPLEQIxU\nVZ5XrHqW65b00xD9Mb/eez7fv//DhNJiYjWPqwueThhcEikx3exlcPcFDDkZTEcQSfyeWDjIpngC\nw/SIMpkdYpgH9Tw/0gYJRSXQrushqiSI7jdIT99NoBkUE1MxnBDhKXxMQDBb2iyQMdq7bXYryf3f\nWc37P5NhR3gA6RtoZjAaDJCs732Y72z9HbOtVVw0YxDX3I2QUWqLryGqmk94/pm71zOhQmzsmyTz\n45sY+MKWI97vLn0V5p51RJ69szJ5vTLm/ueJPP0b8rPPP6JISAgxXkkqhCCTSaFpYjQn6o+//8VW\nsh9y3WeIeu/wMY61vp0OAYoXgqZpfPSjN5/w+8c2+7FYjHPOOQ+lFA89dD9SKgYHBzj77HP40If+\n8YTEo+EvBvOkcCKtGmMT5nTby0pxzUFydqXkePjlTOLwkO+ZH6/SN3qiLEiGZvKZj0/mlt/1Mez2\ns+j8kD2Z7ezono8KavjbuW9mcvwZrFqD2Nl/YAVlsm6JA5tthFDMOq+Hrh1VSF+nLuFz+XvmUSy9\njkLN93l+/XYuTPVRFy3hTI9woGcJl3/lPcwN95GTBwjFo4wU0wwWUjihBsokiOqgV7QzTBFSKwax\nbZcNhsI0bYp6A+u7ZlGsuZbBSDfC3Morux/klUu7GFwew7YrBShhjcR6MIrM6JQiFqHnQ58FZR0y\nAfRFQFOUrcGDrR0PxOFvXBY0Frmg1aFGweRXe0y/ej0X7xxk40/O4rZH59A/K0H5wRwtZ6f42Nzb\nETUjeJ7Om2s20B/L8Yc7P0mITiAtlNJRmqI/7pGZsoHN5S0kC/vY172ItLqUkfumos47QNIYYpOC\nH7fBvurnaanO05hN0pdLE6IRIhGBiQx16pZuwBmuolyoRRc6dqSI78W4QCb4u6AOQ2gQKG4fMNlY\n1cHkB1uYe4nGxmm7yB2YiiYAPaT67PWMWA7ruJsw38fS+gqhQrf+HSbl34sl6489cSZApeqOelwE\nx57vxVfchN63D2v7I2iBC5pG5Pk/4rcuxbnwhkM/f3Rj63k+yWSMgYHhcSM6RlF3NN7cIDh5I/pi\niAtOFsd3CP68mH4ApkyZxpvedAOaptHU1Ew+X2FpSiYrNIYnYizhZZrDfDGUcrFYhEjEolz2KJfd\nY06YCuVeeNo8TE0TxONRLMukVHIOEZEey5ueCViWQSIRRcrKD35i8cPphq7r6LogErFHw8zOcXs4\nFYoO6w56zUcpm/uYtTjL/LNCtuuzcUKTTLSTyalfMLPq90jh4eISKpeIBo4mKGY1vDKkassURyLI\nQENSIh1byr3V97HV2sPMmt1Mbcqj1WtYumLZ/loSD1+HqWv0DnfieCnKuVYCL0ZJ6XiaJLAr4sUx\nQjQBQyJCvXDp06OEwkcT23ByzzOz5j7mLGtChMv5Ve9nmHlDiehkyUjJRBewcEYW50nYU0xSKlo4\nwxrkjIrsVrcJ/WaFTD40wJCVSV3U4fko7zh/gAW1HrPqPIyowjAhqeVpmLWHUk89bbkM8bjDTXMe\nojnRg2eECA0cIShG91FnPMKSzO9Y2HA7XraNoe6duN4ehidvpsAwSbfEwI4F7C+so7+tid88m+H5\ntiY6tia4am4XM+ocolZIQyxgwDEpC0arjiWR2n7m/tXPaVn5OOHMJFqNQ2beZoY3z+QdQT11jPbr\nCWgIbbbJp/hg+ltcvm4/3upn6E8I4nW9TH3lH6mdubNCeCHAKDmc2x7HT9jIKNjBZOzwxLxMma7H\n2L0eY2DfIce91rNxzn/T0U+yY+C7RLbeD7qBsmKgaQjfwT3nNUc9ZazeYUx4OQwrQtiO41EqOZTL\nlfy8pmnYtkUiESeRqPz2DUMfN1QvtLZEItaEVo8zg0TihfOwFfHoM5PDPFlIKRFCsGHDM/zXf30N\nXdeZN28BP//5j/ne977N4sVnkckc2VrylxzmKE42h3myrRqnK0d6PA7WM4XDWXqA8ddnAkIILEtH\n1/UXNJQFbS9Z43kUinjYyuboFyno+0BoxMMWJBLlLcINEwgBMb1MJvkI45WygEOZjKhG4ZJpkuT6\nBEOdCQKvsrvU8NgUKbE3+jze3pBZPlCjQ3WlIMgwymCXqU1OosVqpdu1sHWdGuZQUBtQEYmnhRiy\n0pLiIjDQyWLhayANl1wREBobtwScr/0PP3hXgeHtKWrcLK11JVrrSigBOIp3J0q05evYm01C2cAY\nFshSBDlWYRpEiMYgWR0wlDMr91GCVhHQ+5TFnsEkS+cMMWu6hz4MSRlw9evupXn/NHzhkUoNkAoD\nEkXo1YFQQ8cjZvQQi0Wpqykwb1obX7/lEgZUNft7+igt0tg9pBGLbOesyzYxs0nQmtd4aO1ULrpa\n8aY5I8jIMzys97Fl+wx6b38t2a2LUaEO8RyZWW3suPWNdD9xIUrqpKfvYer7fgWyjPrJByeEZhQC\nxeXpdbSUN6HQmJp1WXbRnbg25OKMyndWqqJbt+ep37+Tmqfb6XztuYj4SSzYQpD9wC8IfvVx4o//\nDBD4zXMZefcPDnmbvf42zJ1PYAy0E0xagDNvFdJOINSocZIKGa9+oWFQCsr6PorWFoTSSbsXY6g4\nIQ69kdtxzP1EgsnUj7yekrkTzzyAqcWpYyWxWHyUN5dRL9Qfz42O5RRfqraSY+UwhRivovuzwNh3\n8fjjj3HOOct57WuvJwgC3va2d6KU4t577+Zv//bdfwnJniomCkkfnqd8IZyKisiRY790dHaH6mIe\nzBvq+pnbKY7dZxhKXNc/prH0yTFgrkUb7bXbEv1P8voeEKMSyPpeIkEzKRHBEuAiyTuTScUtPGkT\n0Ud5UDHoD2agG0ViVVliWsCvPjmP5jmD1E0qs2KN4u74btQmC24TDMzKMHlJHuUpaArJCZt4KYHv\nCJpiFxCPPEwxb5NHI70ih5zSRrglg9udQg8kk1SJahVQiJjYVplC2UBKDQR4vs7TW6qRThvmx0PE\nl3VoDlEtApICHPD/SeM9u/by5GMBN7+pl5Sjs6Etwx131fHEukaaaxVTplS4cOtqi2zvTDGlKs9v\nfltDbWASseHRTfX806pdTJ3jIjQwNMnipg56sjbxqE/BFCR8RWMY8rzSieYFLWFAXesIfgiWIbni\nws388tHpOPsESnOY11hg8Xv3UDc5hxKKBSNprnnTLraU5nNnUz3N8SFw99LV1UJx8T7M6BLCUgZl\npSjVzSd7RwOaCgmcCP3PLWbTVz7Eyq9+kO2+xoxffwBL6ggUz5k7uaHmKUToM5AWbJgtiYWCWLGS\n9nCNCmFTa7fGm+7XCBMjOC11ND4dwEVzjzqXQsqUzO0gQmy/FUuNSqIJgXvD54m8978YHBw54rzI\n47cQv++/0UZ6UZqG0bMTfbCD8tJXE33uToQMCOpbKVxbya+J/CDxe/8LLddPWDOZ4hXvR0TjFNhH\nd/J/8PVhpCgyEL+DmuJrKJnbKNmbAEXJ3EbR3IYdtqChUUZRDnupG34tUMnjjYVzY7HoOPm87wcY\nho5h6Hje/56iyJ+TtNcY4vE4vb095PN5TNNE0zSGh4fHeWRP9Jr/YjAPw6kKSZ+q7NbJjD021qn+\nMF6Iped0VhWPYcyLHbtPXdcxjGMbZlfrQyDwyFPUO3BEP4cyEyg8bYTmcAlLjDJ35GppG7wMXesg\nbn8dSR6UIifPphydRq0YJM9s5q3azSdm7KZ9V4S5Z5XxEvW4gcRYHyeQJXbsaMAsKxoas7i+5I4Z\nDhdcfBd1v6xmTrqNf7noTu5/+Gvcf8V3SUefpbAlzqt6utlfKDCCSQofE8VVf+WwayDFQxtqGclF\n0HWJbVW0NXcfiLFkSY6gJ439qQLhDRryQg2pBwymFPbygHcv7eZVqcpzWTy1wAULhnnFjWcza85u\nSoAR9elvjzNveR91epmGWo+5LQNEcxpuUWcwF2GKcEEq4skSsahDVVSj5Cn6HQssn4GIxoP70hhb\n4pw1YwhLg4gRUPBNHnfraKx1sMyA9y3ch26GnDV1iHJKsDHMYDdL6ic5PF+opmB7DOqzqd44F7t3\nEoMHmtDK/VjpDmoWdWLE95ANryFwo6iwUhCUb5/C9p++hQXv/zzfGMowadMK2s0sF8fu5qKqjQS6\nQaCZrPj131PbMRc3lmPd1d9jumzn8p5m4ru6K/MqKzG1BF5NKyWOrJxVhIxEH0KJSrTGNTpJly/B\nVJWQ3DF5WL0SkSd/hSjlEKGHCEEKDa04RLj4FQxcdzNaKYdM1YFeWVLjd38Vs3Nr5bqGu0A3cV79\nEfrUQ4R6HimKICShyJKLrMXTOwGF1MooAkrmNgSCSDgJgcDXB5C4aNhIKXFd7wjyedM0SCbjRCIW\n8XgUKdURbEWnulb8/ybtNeY1rly5il/84md885tfY+bM2ezcuQPXdViyZCnwF4N50jhdjfgvxsCM\njX2ydHZjY73Y+XtiLD2nJu81ES/sxR57DFvW44phes21SOERUJzAYTp6L8Swdrbx2t11XBEvsHWR\nw13p17LTtnDkbgaoYZ7ZhcSghxaEkOzyo7Q0rqO+UZIjSUpdyEzVyl4eRgt1nAGTti1T2I3EMcrE\nE4J1/XeAATfOjRBGFds+8lZyoUGzCGloLvFk2yTOzxdoV/EK3wGKm67fTaZGsvb5Kt79hXMoOAa6\npmipK9HaXEKWJFLEEX4aWXBwZQ3ldDe5aBEhFFUTHnA6ETB3WoGqTEjd8hGGlMPISIBR0EmlPWJR\nl+WL+mmJ+sSKCqukERZ1wkBgmApNhpW0Z0KSLiniMmTvsM1+N8J5/ZJoQ5ZJWglPSnJejJF9BhfF\nh7nbn89bLtpHNKYjNY/AhgYjJD0loHZ6DktJqs082mAGu1iLe8/FnHfrGjrqdrG2DKg0CWMv8276\nCe0/upRyOTY6uxSRqiEK7VPRNWj95MdwR9JMRRJ3Ctzyxyq6+lKs3Ho9c8JzySUkphNn5S/fy/45\nX6BcJbHTcayeIYyyB9lNGIUc3qIrKLRmKJv7EEpU+jQVSFFGYOBpffj6EFJK6pzXVMgThUCqkFCU\n0ZQ9KiMOWrb3sOKHSp+usqIoOwp2HGkfSjiuj3RNnPhoI92MaV8rQAk5nizQ0EAopHCQuONz2zE6\nMWUtuooglIng2EV+YSgJQ494PEY2WyAIwlEjalZ4cxOxUcUROcGA+gTBydVbvBTUe2cC06fP5F3v\neh8PPHAvnZ0dzJo1myuuuHKcNehEDebLruhnTJZm4uto1CYWs/G8kFLp1BrxDaOyozlRz7RCAhDF\n94OTJgGwLPOkJzwcLGKy7UoR08RCoiMhiETMU86h2rZJPB4hDOUR3rOmaRiGdsyNgo5Nr7GWkt6O\nEBq2qsUjB0JSWbwMap8rMPmuA8QGQmJdu2l1+nhwwT5q1BepZz317CWkejzHkg43UMNaBBWdvxCX\nvfkpFAZu5j27vkxhq4HXbRIJFHOtImG2heqRet4dvIc3indgD69hoFFn3bT1OGElxyk0yJZstmyv\noYxBneYxVXdoWeDSNM1napNDxA4pOzpNtQ6vv6yTq1b0kv2ejz9Sxi5HGclX4axyKKWHCcyQDaHF\nXSrCasPBHJ23rqcze+oIGSlZsbyDSKbA0N4UesajuqHI2dVFJvmKpgBiAmJmiF8riNSFiAD0cJR4\nXorKhsuQlPtMlK+jlEkSj7qoT0SZTM2EHNhUR5H5LLOgPlEmEtPp1wKmZRwmzSlRLTz2UEVK8xAD\nKfbsXkT42Wuoc6KszDYzw08gHYON+xvpfHQVMz/yHfrW6KhXtBMxc0REgJUaJihHGNy0GDeXJjNz\nN9mkZGedwfbO6azpvBBXnMNwvJrdrQVqhiBLnPT+JaSSKexCG0Iq0PSKTqU3SP+KWhAhSvh4ejdm\nWIdrHqBgPU/J2oavD+NrPQTkSQQLCI0hBuy7GdE24Bh7MIN6dGIo08ba8wwIDc0tIsKAoHoS/uwL\nKF/01sqDPwzmnvXo+f7KC6UIJi1Azr2QjDWTfrWWQOQRQqLLBJGwFcubimPuAc2v6LiGCTQRYsoa\ndBkn7VyMJWuO+zuLx6OUy854cVAQhHieT7nsUiyW8Tx/lB1HJxqNkEzGx1u4xtIvL7QG6bo2um4c\nu55D0/58/LCf/exHTJkyjfvuu5tnn11PfX0DU6dOo7q6mq6uLqqrq49KDP+Xop9RTLQLZyJXeKIh\n2Ur+4ei9nGcSJ1/EdGoe5sTw64vnmgVbVWOr2nECnrRagKO68LUcrvQoFkfonKRR113pdevZ8Sy2\negBEJyCJMkgot4E0R8l9/HFyH6kkEhP0dvRnH+aajTn+ytzPB/Ql5E2TUK+hQxpkZA3zjCVELIvQ\nT1Peuxp5/vcADyTYdsg1Fw6TmFtiy+Y4991Tg4nk3k9W0dpSpG4uvP3q/fz1lfsr1ceAFwjsv7Io\nT/V59pEs37kkSmnzZObVJxiZ18Hvgjh5XedGP8/5RoCpQzoecN2aXfQORulUGqtai8x09nPf3ipk\nFEZ0SbVbeW66phCmQoSC0ASrF0QgECgkAg8YKETJ59Pk3RpqYgViqRIyVFR15enSqrjsnP0syv2O\n/twMWstT6M84lOtdNjXESQiHQAriEjYUGvHXnU3tLQuZOn0n0/bPpbdgURVaXKvq6ESyK9/ApqVL\n0GbvIPRsnEsjZL5ZR0LvpNg9Bac/g5NLsP3W15Ns6Cda38clF7Tzk2nnsvzxNF/9+7vZN3Mntlng\n9btLXPeHgODBT+L6S4hO/uT4fBHCxTQs7I0bsTs6KMyfD/XzENLA1XsIpM+BQhdK6EyLloiEU9HE\nICp00JSFEgEFez3VzhVgxSiuegfJ334KL1NNecocBq99I5H4ZZjH4LAtvvKDlZxnto+wZgrFNe/G\nEoO3KkcAACAASURBVAJTpZk08g/0RX5HMfosAguFQ3PxHSAgG30ERYiugSZTNGffg0503Ns9Hk5U\nQHqMfB44jDe3on05zps72t5yUAfz/y8Ps7GxGdu2KZVKdHV10tl5gHw+j+t69PZ28/nPf5l4PHHC\n6/bLzmDCoaHIU5G+ejGoGJCKPkKxeGokACeTLz0VvtkXk8OcKPV1vDDzidxHk3c5I/pWfC0HKJr8\nVRS0drrEUwyxA2dKSF9tB7mddzFrSz17olUgOphIdK0DCn/c/I+LUQkQyqezOJ8Fe9p4Uizm8uIj\n/H1sB1/zZ/FQyUZHY7lajK6NKXdCuH8+9esupnDW/UihWJAKWWD6pKc6LF+UQ5QVuad0XjPXIil1\nQj9EN0HXK39SQjnQKCcM8qst/q4pwnkFj+vrNlJbE7B+v03Z1fnkrGGWmQHaYY8sEfVJ5WMIobNs\nfpHqdEi7kvQO6XhGAJZEhBoiIugvR0hvAxHISq2UACElBTTKrs323lZm1Q3QnMkSj3iEvkYpHaEx\nXURKDdMcxjS6KOcW0d41i5GGegqRdhaonSAUlgvndm7iQM0BqmpuIBNMIlLfg7Z8O43DGXqeX8Y0\nZbPr+h3ImTmULjDiHmZMsub9D3LxY8P88/yzGa5yUTkBn5pDcf1M4s0d3BvOZcH73sD+ha9k96wI\ncT0ETfLrWRGun17grOzP8X7+QcrOEGbtI2iTD+BfeD01d91J5ne30ZF26NgXpfpCl9iicyGI82jv\nU3SVB0Aptlj7+OA9u2hc3wGWzsClF9F/1aWE2sENZezxn2N0bEJpPrFyP8UtDzF8qaK2+Bq0o4RK\nVbKGwnX/esixgrGJXm0D5XiRQO8mGSwenYeSorURV9+FEpVcdah0zHASBienLfliDNqYET2UN1cf\nD+fGYgfJ58Ow0qYx9vrPHatXXw7AG97wZorFAq7rYts2pmlhGMa4sMRfcpjHgGVVPLtTJQw/Fo6l\n7nFo/u70aGOeSL5U0yoG+kyx9BwNpyL1dSzEVDMLy//MkPEsEVlLVbiEHuNB2qy70YVJudYn1unS\nPmmAlu1lhtb0czRViImpz0N0I5Tgj3v/m//ufC8zetsZKk9jslPi38Mc99nP8XDqeuKFijKCaego\nBQEeXVGDXft1Ou6cygc+08aC+Tl0rfJs3Bt6+PRgPX/3qZ18uh4u1g71RoQA0wiJC4UlFavbk6ye\nO8LchgAjpmitcUnulSz2vaNqXDiejpHXiG8waOjV8aMmbovO/9GqaDJ6OcsLyMV08prBU1aUD+cd\npKshlGTsUsQA9NmCx8/vp5V+ghzkHRtLhCTTLiEarle532RihFw+pLB3Jc/EQ+r2bGN7dYr584cp\nt4TcsaCWnz+TwQxuRcfGtlOYvRpT7RRXt4ZsDzJw0xawQQkdH4VSIUapju8vbaA0SaJcBTEHPrcO\n94JrEUNNaAe6sRhhx6Jn8GNngVcJoSkEWQswy6hCFd7wR3CMGyj/dTfBzKk03vIZNk12uOu8AN8o\nMKB9jlj3JEItR2e5d/x7LBVdflkzwEdyFcNXd8fd9C7TcFtmkreeIVlcQOTp36A5OQD0skt86w4G\nL1uOr/Vjy+P3e3paP0PWQ+jCINQLuEY3IohgqhQCjay5gbK15+BTFiEKicQ/qkE+Fk4XacpBI3rw\n2EEPVCedTmIY+rgHunfvPvL5PJMmTeUEOQBOGA8//CAPPngfn/zkZwDYvHkTX/3qlzAMnXPPXcHb\n3/6uYwpLj7WL3HrrL9iyZdN4hEsIKJcdPvGJTx+ifnI8vOwMpucFhOGZ04g8mrd0JgzI6Gi8ULg0\nGn1p+zjHmJBONfx6LNiqiiZ/NVDZgEy3rmSHdjs94SY83aAwBcqex4Z5NTh2iPAFaqKZOcpCMmaI\nNKF4n/sWjDBOjacTL5eIl0PKWhVrgnlc3/sNvnLWjXQXbSJDU9GFySOLf8YDq39PbbxAJB9h3owS\nxpghEnDhOQVu/2EBYQg2BRIhDj6vsUXN0MHQJbaCT712ENcXlEo6ytVQSmNRxsUrargxHV2rkCEI\nAUEIhJL0fVmiawPiQyaNooh/dh3izXFuKjaxRs8yRUi2DtfQW4pwietySVBGCI0gAKcI/XnwJ5X5\nB/YzOfTIxgx272qiLlKgOl5kKJtEtyQKRSmQPNQueGxTP7f+aZja1FUsmn+AO2/z2ZBPMLJmHzya\nJ6PZOF5Iv19EMwT7/AJrF38P/4ZFUFfioGwaBEJwb8ysFN2MufwAKR9V6+OU66hK7kRTZSbldtAV\nb0XKRoQmWdwfckGPT7hnHtFPvxFt1iaUF0H99CMMfvpseme8kvVv+BZSFwymFSXDp+T3UJaFQ+aA\nErB9GpRNiPoCveyS6jIoNk6hZO4kvXXbKG9sZbaIMMQazgECXaUo6btxzQNoKkLSPfuoBs7Te0at\nGWjKQlc2SpRApVAqxDd6Km1SYzs6pZAijziJJfpMa22Ota2AIJcrIMRBBZcNG57lZz/7Kd3d3cyY\nMYu5c+cxZ848zj//oqMSA5wovvKVL/H0008wa9bs8WNf+tJn+cxnvkBzcwsf/egHaWvbTk9P11GF\npceqZH/60x/yT//0caqra3BdlyDwcRyHaPTkyL9fdgbzpRSRHqOzC0NJPl867YTFxwpljnHdnq7c\n7PGqcU8m/Hr0zz+5VpyJ9Hlzi28nZ/wHoR7gasMk7DSzftpDw7MlnlslWPcqhTq8CHesVmj02Fi1\nYmLuOqq9xRQjs0jkenA0g1CkCLQUQ4k3ck13HQ9ds44n79nEnc7tuC3t2JqD61VR8xqHiWkmRSXs\namuA0DjbFORDiIoK16tQMDYdBOB4lZNjlkRTkC9qhGXB8N4I3/ltPV/913aiSQcpFaEEx9fRHB9r\nk09uWEOYFlrZYtL2QV7fV+bhpYpd+2x2F+qgkIJA8M+98/hhTxstjUWcMhzYDNWXwLnKxQISSOKE\nRKZ0E4Y6/dhE8BF+SHc+wo+3Tufbj0zCVNshaGBgSOfBxxoAD7QCem83H7tkIwuaSgRF+N3WGn67\noxYQ+H0+NHeDdZj7IaCvyiRRkoRSq1gvgGEbulOoZpP5b/keGiGpoMiK7nvJBcu5dOdC3vtQGm3L\nOejn3Idx1trKefEs6b/9FN47rybc8DnM8/ay9LGFxHoX4yRd1l77fXYvfPyIOaUB7Q2KOR0Cv6UJ\nseRCEokYoQwxMg7YMZRbBBWiNEF28TySzrl4Wg/Z6GMIdBQKXxugpvwqxGEb2UgwhSJPMbbJtYMp\n40VFEX86w5EHD52kAgQmrt5FJDxUhPpYeKm1MA8SKASsWXMFa9ZcQbFYYteu3Wzfvo0nnliLYRin\nJB69aNFiVq5cxe9//xsAisUCvu/R0jIJgOXLz+eZZ55mcHDgBYWlV626jDlz5lJTU/uirwVehgbz\nTGNs8T+TZOUHxzo0JHs4S8/py82OeUZH/hjPnPd8JI5WQFTPIi7zv0hf8Dw7ol9nwTc3MO3uPLof\ncvnzMOthjZ9/VkNNWKetUTk9bzQSI2Tl1gxf4MyMEt3QQdvkBIVInB0NF7DqqQPYpSkUGw4QmfYs\n9TcJVu2AP2zSSHg2IlLxqnNSUKONMa5MuPBRQn5PSHKBRjUCU0j8gIo8FaBGDUXfkI2hS0ZyOt19\nFn96uI4VCz2SEReBIggrXqlth7hphYlk3RsUAy1FRFigZiNYPZDybLyWALkji+zJIATs21nH8ttW\n8A/xP9BiD3LBdR6xpE+NlJgc7GzNWC7CN+mXEUKlky/afHltK79afyE6BkWp4JBiFwOkwbXTssxN\nlQmcyr285awevFDxUHeGQjoGE9f9CV6mZ+gYey287kaYnYeiAf+6goRV5KoFjzOjK0ZfC5R0mOLl\nuTx8iFk5h/jUOrRkH8bixysyarKyCxLxPHrDAYKeWbQ+8kXiWjuuFaJnJSt/9V72zVlHaE6IuAhB\nnT6D1PQ5eFMS7HvlLFxdh0IJXTNg7uuQKzagP/MHkBI193zSV/+AuNLp1e4eF6+u9Ev2o/AQExRf\nAAyVpt57FTnraVTgEfcXEPfnj/9/2dxBQcaQeml0TkYwVTWe3vlnazCPhkQiwbJl57Bs2Tkn9bl3\n3nkbv/zlzw85dvPNn2D16lfw7LPrx48Vi0VisYN53VgsRldX5wsKS0spqaur5+abP8oFF1xENBrF\ntiPE43HWrLnipK7zLwbzNEIIxnkfXxqy8oohO1Z/42kb5Sge5kSShVMNvx4vFzuRJvBo9xejjmlq\nDV3iF9RuKqOHCi2stExMe1ay8GGNPcsUvi0ILIgXNAglgQFSr3h7RllnxpYqGhdt4L3vD5jrFgik\nyZ4OkwfP8/nMF2rZ8M4HKDZnibk2DdU+swYFxdvn07RKYNoht2w6nxsvfoBIpEwowbYOdhxICX96\nLokVmKRNxbmtWVKJg+19wgrpH7Y40G3z5Poq0omQtt0x5kwr87Y3HmCs8n00qocJGDFB2/UaeamI\nyBAVwPBiuPj3Lqk+j6euTLC+3sRN91L803LKDy4Fpfi8+yamND7KF/QttASA4Y5vhzygIuWtEQQa\nCJ2oKQjdOpSfJEBRYT+fmA7QQfgkhImSEdDLLJ2UZ3rGZeW0PN1li39sWMJdGZ3GzgQ9zYVD7a2A\nJRsu4ombZ1EIFRiSq264k5vecRfNQYmukSoGnl/KQ2dtYHFYkc4qtLbRVe3RlO/BcCMIs4zyYpU+\nx0KaoG8anu1iFUMy/WcRlVHcZJaexm2ki/VkM72EBOjoTDYWc2HynSTfdiO+EESL+1FBGyCIe/Px\nlM7A6/8d86xrEaEH81diaiYR0yBlViG0AWSokFIiQwNds1ASSsYOCtbzICDmzaOBc0n6MyhMEEAe\nQ3XpaorWVspiD0IZaDKBHqYwwxP3iF4qaa8wPP1jXH31a7j66qNz8E5EPB6nXD74/ZVKJRKJJK7r\nHFNYulgs8uijD7Nw4WIGBwdwHIdSqYht238xmP9bGAsTjlWSvRQ5Q6UqhisSMc+wh3fQwzzV8OsL\nf/6RGAsvn4h6iUmyohZC5bsvxxVr3wh2QbHkLnCqDfaereNGQ0rpgwbeHDGZe+d5RFpq+eHK5zjH\n6EAfTUNZM39Hqa2ZnpU76Vu6D2VIlAZVElZNyfDH3JX0xKZQ/cAQO59/nK31acqhyX/8qIXrrxrk\ndasHEMCDz8X4wE+nE4/rXDs9ywWzcwitwoOqgEDCd3/VREONx9JFeWRosuqCLiY1FbEPdVYORpMF\nhBfrqL4Q9igIIbAFCaGzbJ1Ohx3j2YszBK6OY8ZAeBgRiZIa2exUotWb6XXSoA+RMgMcBCWhM6B0\nbDm6NHg6bqCzdncTlcBleTSWHAUCwADhgD7Ak/uruGyOxcy6YWZUu2gCbEvSGPf55+Qe7hIzcOpy\nfOLeav7vpVlCu/Iwp7U38tE7r2Vomc+/rf4jbe98jHviPk8Gi3hX237e1bmLfUPVzPM1Bu0ET9Yu\n5MmZTQwYMSaVff7tjgRrciMoI0TPpcj+6F/JR0J6pj/G5N0LiOWihIYikZ9Ek29TK6ZhGRaaMHhF\n5q20aqtJytnjBsdSdVjuYQomQuDPWlH5twRvlEBdMg8vvh9pDmFoERq0S6iuqcGhn0H1FJqsGNK8\n9hRpmojISUeduwZRpo7czED0dsrmLgyZIektJRpOP/aEPwwvjYepodSZqwE5HuLxBIZh0tnZQXNz\nC08//QRve9u76O/vZe3aR1m9+nI2b97E9Okzx88pFotUV1fz/vd/6JTHf1kazNNJ93Y4nZ1SikTi\nzKtIG4aObZsoxRkpsJmIse/rYPjVHxfMPlMYq+4VQlTCy8OD6O1tyLpmVMOUo56zsPRPbHv7Uyz7\n9zZ85fP9b+iU05VsUnwYrvvuZDIePPDqvYec56Z9nnzLLMryRlztbw5xfpKGos4bZP2bu1D6aD6U\niucYS4+wyr6Fe5x3kXfb+MHHHyBhBhCGfP+zO3ntx2bzvi9Ph0kB9Aguu3KYD7yyl8WNDvHYQRk4\nJSvcsrNbS1xy7ghBqLF7f5zqquBoPfGHoAXYXytgGGQI8WGNaGcc9BBvMMWB56dARwwyLkhB4OjU\n1peZvaLE9oZzKA1AUNjOSHKIUCjagyj35ZPU9qY5O1LALVn8530L6c4mgE6gGrQ86IVRq22CXgQF\nO4bSfPGphXzl1f2EUhCOWnZLC4mGToWe0BRMN3r4xC3n82STTTIfJdQUN33hP8klSvRXjxCaEk0I\nsrrJz2Y38/bcdiarER7JLOSzU87FFZXCk6gM6YhpfOWaflY9E9Kx5RpyFz/H3qW1iPSzLHxuMrFC\nFYZvY5ctFBDtq+O6j93Cg3/zXepLJv5ZMeIzZ4KAtfkf8UzhdpCCFZG/Zr69+rjzVMempngNUjho\nyiTEoJ8hSlYbQSxE13RMw8C2LZQaIhJOr1RYB0dS1WmY1JdfB+UXGPAF8OcQkj2Vnu0TxUc+8jE+\n9amPI6Xk3HPPY8GChUg5/whh6YPi0QG9vT1897vfZO7cecTjCWKxGNXVNdTXN5zU2C9Lg3k6cCwq\nPSHEGRVQrUgEVcb1vIqC+5kWjAVGuSnlGTHOh29gDq/uFV17idz5PxV9Qhnin38VwdlHLmZR1cii\n1ntp//qtPME3KFftqXw+UKiBta/q59V3X84D2gSDKSp1Jrb6EYG2CCVakGp/JeoIaDmgrFU80kCg\n9INLQohA2GVS9h6um7eFhBVUPtCAqB7yjjd28+HfKlRc4VfVcON5OVrSIRFbEozmLwH8EIZGDJrr\nfUBgxzz01oA9SjA3EFhGxbh2SUm7lBSBWk1jDlCtaawAOnIh/j5F5o9xcl1p3Poi69RU2Dm58g30\n1oLfCHofViD5P+fmeOuO7/O11e8ipo/QMZjiN/viPLC2haEna8mXLCJ2NfliRRcT8iC6QZUrcWx7\nGKx+UGnwKl6TSLvsqM/ymJukVSvRqAUIvZJafDKdhJxCJuHzS0y+c0+cC3/9Or5345384YrHKcUc\nlFCVmp/xBlko6gYlw8TPJ/8fe+cdZ1dVr/3vWrucfs70kpn0TDrpIRAQkI5UG3oBRUS9Iiq+VxF9\nr6h4vdZrQb1iQfS+FkRBBQQVASEhhU56nWRSZybT5/Sz917r/WOfMyWZSYGEy73wfD75JJlzZq1d\n1t6/9WvPww9qllAYsp3JSYOIdkkaBjrehXXZH6n5yS1MSj+NmrqHUKKC3P0fRORDCATK0Lh2gcot\nIS785rvpGtOC92eH3157M7VLF/G33LfwdB5DQ2d6J/XmdMqNI+cPBQJDD+euNZ1aPM9DeaoY71BU\ny2oc7VPWBYMlqjpvGNfrK4nYvHoG89XrWwcOyYnOnn0SP/nJL4Z953DC0kIIxo4dxwsvPMtLL72A\n4zj09HQzc+ZsbrvtKyilRmwFHAlvGMyXgcOx5RwPtZLDz+vT1KXTOWzbHIjTnwiUwq+GIcnlCidM\ne7P0diyxH/kKMYPVvdZzf0d4rm9VDRPzucdxF5w9YpjAIsK44Pv4W+A/DvmsbZpFX/y9wB8HrZ4u\n/VMRVt/jo8m/8IvYmwnKVqL9msqnFKaTRDVDYa6Bwk+/efhFMjmiIAV7QrVotN8eAWih6dBglDlo\nBI7pEK7PYgZchKXIFiySaUk2B8ueSxCPeMRjHns7TfaOz5GKuhQcgwMKzvM0LWhe0opODUhoVYoO\nIXiz1lRKSUsTrKoyyJ/sYLQmufcTH2TNWCCRhc4YPD61eOQ2NQue4J6l21nIedRXvcBUCVMnw6lz\nJd/tg3s3VtGaieFEOzAjBdw2G0pN/KLb/9vJgyXA6gErCYSwp/Rg1nj88rRaGgK9nNajsVx4UMa4\n5fYmaG+FBpONt5Rz86X7mKeX0VZxAMcsGgktBtoqlBZIBbM60tR12hghBwvXr0ZV/jXQQK+0yJgm\nPWU2huES87qInXkvwrNgkoVs3EHmmz/EcC1cy0UZaQLpwGD21bEY+49Z3DPv60jhIhC4QtOhetjl\nPD/MYCo0+6XC0lCj5SGVsENh60rKsmeQtouSdM4MQuFG8gVnFJYdi1BokCDAcZwhTDtHVzT4auUw\nj3e1/4lCZ2cHqVSKCRMmctttXx322bEYyaF4w2AeA/676OyGs/QMtqecCCWREkrh13zewXFOrCBt\nySsPhewR2Y/EwQ/oUexwY7qRNEUC7OI18myPAxPWY5OgoPuGzwEERRdTdRn1+Sv5efC7mCHFNVmY\n2Qnz74DWeR7bToO26QLXFnSZFfRSxRZ7ARumLuLy7DbOjDdjCEV/HnZ0lrwhjRQFNsSSNEbAsTSW\n4XCgq4xNW8J88mtT+NT1O1l8Uj+7LUVXTlIomJgmtBQET7RGyFal6DFdcmgsU5MxFWmt6VSaSMFk\nlQ2tZhRtGDBBMfnWZaz5l5vwzbqGaAZm78c0ssx4/x4kEDH3wNYATl5ghT2Ckx3+5b2biDe/lUeX\nTyCZ2EDFko3s2hvixRYPZ7cFXuklY4ECWduLsApcfuUGFs7roqJ3Ol+YNZ2b9tdhfr+DZIsJWekL\nSOcVtLnw8Q5W322zdWoHda0JpCvQtr9pkp6BShuwJ0z5s2P4nfsw5VVBhIbzWpP8eoJv7EqMjRbQ\nboT4fNVcPuXuoLxxg/9MmA7SkQQattMxto1Cez1auCihyEVS9Fe2D7v7wuvFMyOYxQyxklHGmnMH\nvuGh+UUoR4vh0yTNdUzenrcPazTDbhNht2lwlhEM2kgsOyWaOtu2iERCSGkcojrieYc+j/74ox7O\nccHhjPKJdBZeDvL5PI888hfGjGngpJPmUl8/ZoA3Npfzebu3bt1Mf38fZ5115PA7vGEwjwrD2zVy\nIy7WoXilKiIlHIml50Qs0OEKJr5xjkSCJ9xrBujvP7R6EMCZcxqB1h2IzlZk+25UzViMNcvw5p05\n6rjnObfzS+PUYSkVKTQZcy8Jt4YO+g75nYiqA+B5awUScC341WXwwy+Ca8CU1dC0GvbUapZdGGbF\ntDN5kXkIurEnlPO70BnE3F6k2UYGyTsXpvjHlhhdOYOyS7dxVzBHmyGZpATlwBYjz28emEGhzORb\n94zn+v79VM7vIekKwgFJRZlJ2stDPkbEyBEL5lBa09MTJhjJ064Fz3SWoTvLyE9rYUAOQ2rKJrXR\ndPIL7Ng4Ec+WmO96AaQmuHQT99nV6LvmsHzKY8yozKGFgi4DtddCWxYz3EmMPa+CMe9bxw+/XM/O\njQo3N6iigdSQyBO+djPRq5q5vEfy9m6b+k6TCmc3M9d0c/2jU2neXhQezym/TLh0L9pd9J096Gui\naKk5/6+zeOKczeRDDgUnAh8+BR4Zy01OA4F/3k2rCNAWk3z3j2FqFpk8ERzPrgkd9FTnscmAEDxq\nz+amc+7FWPJDRNkudLCADiXpsTS/+eif2Vtfw7l/t2mrfAEtepm37CKEkjiBHM+f/WcKdg17yt5J\nY2oFpjapiFxFRAzmyldbDvsNj2DxJNZZLvMcgynqWF6hI7dmHYyDQ7NCiAEjGgzaxGJhhBCHhHJf\nGznM1w5qa+tYuHAxq1atYNeuFkKhEPF4nEAgSH9/H2vWvEhFRSXXXffBox7zdWkwj9YzG1oRemzt\nGqP3LR4tXm2WnqG50YNpA0+EJztI6uCHXxOJ0Tkz1aTZ5E+/nOA930HXjUeXV2Mvf4BcZT1IE3vV\nw6Bc3Fmn4s5aTFa0YmESF1VkSaJQSARBIqTdfpK0EdUNpEU7Gv88TR3h/PSdALhCoYo6TNEsREqc\nBMWWjrIkzFlnsaLgMS60nht+8jcuTgmeum0G4SZNb41A2IrQ+Azjrm7BjToYCRdXwAMoP54LUNVD\nskES2j2OsnCeu5dD4tGFnPfP22mc3YsU3Uzwqjiz2qQn4LBaQUYKgvEsOJKAa9FmesjaLnLZAGbA\nwc2biFiaZE0H+ldfo+6hU+h46hRUKI+9ZB/ZCV14SrD0otVMaetDOgaWWewd1QKzoFhw3s3cnPoK\nf1v+HXr3/Rwz1+6zFAUUJBx0PI8RK5D5XROF5WOYd8MLzNQe1QUPLfMEU4ov1DRzPZMpIAdj2MXH\nobEyh9UP/QqimQA/v+6D5AyHXScd4AOLt7DhkXLAr3X+VV0jX3lXP+0RwZwDip/dNp8b/vxernjo\np/TU+XqTnSQ478mZjNseI7/n45g1e/CmrWfV7Ajfrr+av8aXYhUcnlyS5Npt20hFdvPovL3EW+po\nnrWW9nFZdld8nJbyi9hfcT0TPEmFa/AtneGMgsWbHJuCgHQxuRotdl5mpR4uy3oEvNxNtNaaQsEZ\nRqc51IiGQkHicV90QClVFJP2Q7rHO3z6Wij6OVqYpsnChYuZO3c+y5Y9wfr1a9ixoxnTNJk8uYlP\nfvIz1NbWHduYJ+hY/8cjELBfdrvGKxF2HmTp8Y7I0nO8PMyh4deRFUxGb/s4Vgw1zMdC6iCcPLpu\nHLp0vlJgtGzCWrsC4fkvEtG6g7b4WlKTY2RlGxWihg7t4OEi0JRTR6rfoCsTxHMVhlFNorJAo5zP\neamfUhBdrAveT4WqHmDtSYahJwbhDMTS/qVIh6CyRvOec17k6Vie3144jfs7J3P6tj6qw0m09hCi\nm73CY39DgaDj9zeWrmQJXdd/FmflXFDQH8gxedHzXH3lTJo2v59AawtVMx9jku3AhNXU5uAiG9ql\nyUuOorsnRsAQtKUDtHdFadtfzoQlW9FK0bbX5ummDI4ALl5N/JIXcfKV5Iw4nukrq9R0a1xDE9Oq\nlHVF4Ot3Rq0MV8fuoDuT4A+6Amn0I718cWuhYV8Mr+CHm922MLVryimb2emH1jWEPZepZQ5TE3k2\n9Zp+tSwS0Pzw8h1cO/8AhgWPbO6g484fUECRKc8jA4Jk42Ce/AdGG/GLejgQNhBo1lWbfPNNcPvv\n4JoNNfxyzlo2UQtU8LnbrvCXaCFE+rvfJ13Vx8Vb9nFydi3/cuDXoDXNxkRWjf8IP85Uo5fUuoF+\nuwAAIABJREFUwhK4287wZDhHp6GpxKQfl5xQxLUfuvuLncdBcW8gx/KAhwZqPMkleYvprv/6dEWa\n/sBqlMgTdMcSdU4ade0fL+9suBH1y2pjsQiyWK0WDoewLLNoPI+fiPThcph+auW1YzDBz1WapsnZ\nZ5/L2Wef+4rHe8NgHgTL8nXiXgmd3cvxyF4OS88r9fxGCr+eiHlKKG1CRjfMo0M1NjHUaGsN2jAQ\nheyAyr0j+7B2b8NoOoWYXUOdmE7UrSOrU4SMAAlvMv/5jYUUJvfSdPZzBFUWd2OCc+s/T2vZg+wK\n/5a8TDLZyfGsCmDlPMy8yw/fCR/6zGT6+8GVgi2zJGPeVuBP8W5WYZCnBVV1gOVV72eOXsIZejOu\nKnB/vh9H/h1pKBBqgPVNa8j8ZTHOU3MpWebJTg/1myNEzl1Ld0Ul4X1T8NZchLsjwph3fZbYmX+n\ntyZDwkgRknmygSxJU5ATDoFglqpQAbegETKNkYkwVcBmfMfOVAVSAQeXAqDBM1hb6bG13WBJzuXQ\nSIgiktjLRanHWLnoMtpWZQilOvxQrKVx1ZDFoCRrW8o4Y2YflnZBgEQxsayX71y7nofag9yxrIGL\nwzl+9pYdJIJ6YC1dktrKE9F76Z4SJFTdQ3JWK99ZfhEttS6f69zLVaqCP0SLub3ixcuEHAIFwbkP\nTSB5tcVW3oZHCNcOM7ixExyIhaj1Ojg1vRYXEwTMdJppDzagrFkDK+lCJ8iPRZ5w8ScSQVLAGtNh\ng+HSY8C9oQIKqPMESgoyUlPrSSx8ruLu8MO4MolAUDBaERjDWHxKeDXCmY7jkskMPlsji0gfv8rc\n1zqklGit8TxvmDE3XiZD/BsGs4jjqepxLJ7fK2PpeXme3+HCrycCR2OYj5T31TWNFM55N9YLj6PR\nuHPP8I3oM38f/I7nQW0lwYCF4wpi3nSCpAnIGLUsoDxzJrpjDSsfqOKcF1upr2hj4jNQW/PPtF6l\n6V0aZ8fGOuqaDtBUbrPDypHbWsfab1/Nh7bNIOo5fqHJyjw33fITtur9RPbUMD7tsHt8mmz4GZ43\n3s3zTEG6HqbtYVOLzTPoPOR/NRvdEcR5YRrutkZKfH2nWpuYZLYjPYdIdjXd8TI8cwzx5A5a9tWx\n7T8/QmT2bsLxdqaZ++gVAdxwL54LntSYQZdE3QGCiTxIiNX0U4fE1ooXAUNLYlkb142SjKXwAC9e\n4J5IgrGqm3rHxUQPUOEqYKyXprbxr5gTUrRelmBRYj2PJvbym59NY9+9UxjsXRc8vrOaxYkUU3Ip\nGtMOhnRolRFETT+X1KV5Sgp+UdtL1NLDNl5CwJZ/epL//NhPeMdPbuSCu25Den4++9KqMhJ9Bj1P\nTOfuq1cjEIRTNhf8bTZCC1zbwXAdKmU/rdLmYz9s40cfqKdhv0VHtcc1d++jyu3FHdIHZEmLBZ5D\nIPsP7NxTaBFExD7ARNdgj6kxpSShNLuExxrTpa/IwpQrhuL7tKZRSTTQXxxTiRyu7EEUCdeFkOTN\n/f8tBlNKgesOH9/zFJ6XP4z+5dDKXHcglDvS++9/Uv5yKHw5suGm7o0q2WPA0Hs+lHbteOULj9Zg\nDuVhPRKLzcjzHLvn93K8vJcb+j02VqAj5329GYvwZgznqMyf/U6sVQ9jKI/Q3MvIzQ1SyBVQWhGl\nCXBxRZIN6kFy1p+Y954EXc+NYdEzGRrIMd7sxEoLZv2H4DHvQr7f/3YCQQenYJBO2ngFu+jdCPqF\nR52Vp7fT4O49DuZNH+LS9gMoCafO28WfvpJjazElog2BEIo0F5DiIviugD8DvQpy0ifKKWKScQCl\nDcxAjhf+NI9cqIfec1ew9RqPee0G4/Obydw9np2TTsN7c4Az635OwC0j1R2mwmzHSaTJWvlBkgMD\nLC1pQPCS1kQyCeZsX0hnNMSBQgs5O8c4ZRLPNnBXtcHcXB/n9CcJuH5gVplQ7Sq0cYArah8gHQuQ\n8eJ8vsLl/Mv3csPT9WzbmfCJfmx46qw4lePjnN4lObvfJFooELGzVKchpgV3T+4imvcPbuiaVcA8\nr50fl03irPYqQtd8Dt1bS+GR66npTOAB3/voNUzbUk/rmB6Wrmjisgfmo/HY+E9Pk09HWJJdz5MV\n89l4kuTM1S2EcoJMCAw8ou44zhTPM2tTgPpeybgINEzpJZq8Hald0BqzsI0b7dv5bsTBMQXdAkwB\n/RI8Aab2U7CuGEw9WwguLkqMSW0jdACfkBg0GkONnI8/0Woifg7zyBMcqTI3HA5hGIdW5oI+osF8\nLYVkS0bxvvvuoaFhLKecsnTgs6efXklT03Sqqo6NjF0c7gJ0dCT/520njgJC+EoSJTo7x3HJZgvH\nbfcUDgdxXXdUb3Gox5XJ5F8REUBZWZTe3tQRvzd8ztwxhZqDwVIp9tH3YQ7dDAx9MEdDPB4hmTx2\nZZWDc6I5N0m/sQ2BpN1aTtrYzU69mm52I7SBK/Jklk3jyi8nmZZtxpb+azCrJM1eDW/2vkh3d7zY\nuVJ6+AdfAlI6WIs34aQCfHTnVsxS5ahQ7L84yL2Rs/HCkuB1WSJleXpFzA8JXiDggIAcfkLTAIJA\nSvOu+BMEA3kilkPBDNOlytlYtYCZ53q87b6XqE/CugWd1GS72D19DDtm7EaF85S5FlMn7yJZ1UE6\nkMMLpIu9jAJXQyYbpXfduczdPYdFraexPn6ApyesRUmHqU/NoPWcOzDsDAVDcFm2nSUiRcgDO28g\nlV8qpULgJTSp+hCRRIZ+Q/Pw7jF8+m+L6eyz4YICgXkdCCGo7g7xnicSfMh6grFWb9Ewaoptk8Ou\npQYyCFoDIcrcLFVOsVXKNVC7ZpP69/vR6XJG8gEKk1p4aNWn2XRgMvm2CI2TdvLdmvfRZSRQCCLa\n4Uu9FSxNLmfS5vsxWhOY+y9CtS3CfsvPMcbcj9a6+Melp/pu+u0xZCojfMk5wGbDY79UpIR/7EEg\nomF2wWSCMrgmYzND2QPHkzVa6A8+hZIFbLeOiuwFA4TsQ1FTU0FHR88J89LKy+Ok09njorVbEoku\nhXMtqyS27L8/SjqYB6eODCMw8oDHiFQqxZe+dCuZTBrHcfjYx/4Ps2fPeVlamNdccyWBQIDrr/9n\nli49HYCPfOQDfPSjn2DmzNkjOgTV1bERLf/r0sM0DEksFhygszt+qh4+RvPIXu1Q6KFz5nCcYw81\njyaKPRJGUhQ5+mM9th34SMVKJmEqvLl45Glt/wGT/r6NHefuwKh0KUR9JpnEmVuxX7Kw/qhRCjwp\nyOkAQmsUAj00R3dQyFuZisAVT5D//IcxrC2DH2iJvD+Eq01EUDF12T7e96sHeCh4Oo+aS4ZcoOLf\nHuCCYSnCuo1EMoJrJyBsEfQE7+p4gqblARb3dNOiaxm3MUU+1EnlHouNf5hG/Mrn2V4raOuawHXj\nJ7F35graLMhLDfkQ0e5qTln2XmY8fz7N5y6n4LYxq7+cUx++ikBkK2sLq7m6p4VKO4uHZHs+Smuk\nihpXU04vIBESDBdEGuy9FeTMIB1lBR6Y0MSiD2d4TgTo1Y6/xhzBgj0RZq6bSfms5YgBSUgxotHL\nA2nLIqpcKr0B9giE5SHHbSD58c+T/+m3qe4cri3ZV+aS+u7XGOu20b65EWNON3dVvYNuI4HWggZV\nYInXz5S+PzGv8/uIMhcS4AR7yG6+FL0rQahOIYTw17RRQXn1OKIqTEDYhIRBuVJ4SAyp8LRmjmvw\nrmyAtxZCh54IEPImEEpPwF89oz8nJzqkebyLig7Ob9q2RSwWQWtdjBxFEELwj388wQsvvMDUqdOY\nNm32K5bQArjnnl+zaNFirrzyKnbvbuGLX/xX7rrr18ekhVl6B9fV1TF37nwefPBPdHd3csklV2Db\nNrFYHDg2r/h1aTDB95ZOrMEafhMOZul5NXA4RqLjDZ94IDBqLnabfJAOuZa4Hs8s7+oRGr6PvhVn\nNKPskmezcS+gmJa7hPG/24gs5In0QTpUwDAkyhZUNefpmWjQMr2Kip29KClxC/BbtRTHM/1+Q12k\n1BmWJ1YY49tRyShIzRp3PAvNHXjCwMDjRWcieAKtDLY1jyf5jE3+FMt/ys7V8HvhD1W6NIbAy2X4\nRXAOAcNlSnIyEUdxZeghKtO9eJs0e4wuXnRmo3rzxPuDtHmSlGtSuGs+y/RsUl4YY3o3/3Fdhp6Z\nj7JTmFCAuo5Gxhe2kp+7ic5zNuA4EWqXv5Vo5GmqrRWcH3mJmJUFDQrFDDPFr3svplGlWBx+gTKr\nj6JDQaYQpqdrBrnpW8msm0H5znPYOLmT6Q27CKRDLK5fTSRZwfjN41Dj9xFUQ4xc6XYK/7RdCbaC\npClpCUVozGUQJbqlYmGPslw2XLCZ6z6xll9dPJM5a4Kg4KUFOd776FoesJ5n3K67yUz6f9xetZQO\nsxYLhScknhDUO3Us6LsHUbrQAsyxy8BKkdt7HfJN67EKa9EESYeuQ6UlluUSDNpca5bzLdmL1B7j\nlOCmXJjFOeOoNtWHM5avBl6NHKNSinR6kOxWSkl5eSVKKe677142bvwSoVCI6dNncsUVb+fkk095\nWfNceeVV2EXuSNf1sO3AMWthlgxhR0cHV199LZs2beR73/sWwWCIZDJJJDJ6K9toeF0aTNdVHFMD\n1THC9zD9f4/G0nN85xr+oAwXrk6/4jmPlMM8kqLIWvPnvGjeUQzOQb/Yw1L3swfNcXT52FJ/6sFG\n2SXPw/b1tMsXAWhWf+CyVIK83cnMZ+N0X5BH9Ts0rlec+2NFJJljw5R6Vp86j4Jl8vBpC3jAW8qE\n9V0s6F/JYw/OpaOzslgK4x+3QBKvCyJPaiFT2cfj7bNp88qoNJLsdGvoURE+Gv4zEZmnJxbh12PO\nZVu4qBT/L0CVhh3AcuHnMPs8yCl0rp5cYB+eEWZB4QC1VpI0Pg1fhxchKjrp0HEyKsh6d6xPy6cN\n0k4YpGBZSyWt955B0xlbqIl3sHNPgsppa4ie8ySGhIXJIMvH1LH38juw9uWJ7lWMb88PBpw1GFpT\nLjP8qPdDSPFj5ou1RHQWF4O/dp/H6orFbPzLx9i+6XTqezVn3N/NyZWPcdbSL1MW7QP6SNbtoTVz\nPr2ykSrRNsBzoF2TlBskEEohFLwYlXRb5ZS7in2WpK5Q6nP1NyeOgKbAXn4Y/ww3//WTLPzVPBwD\nfveeHq401lPbeSd2qJzZZQshYCGEwEDgIGkTUX4YhhsEVFCUTys96okk1qV/wjUnkg2eixM+G4QF\nRU8qGg1zUleBr4sAzUGYIwI0WEGssHkQUcCJ6XF8pTjROVIpDzXISinGjGng2mvfDwik9JVENm3a\nMEy38nAYTQtzxoxZdHV18m//disf//gnX5YWJsC4cePJ5bLMmjWbz3/+3/jyl7/Avn17jvr4huJ1\naTBPNLTWGIZ8VUSkYfBBOR7h15EwmjHzPb0AIA4b2t4jnxowlgD7jdWDXtZRYpBndmSjvN14kHb5\n4oDneiCwla1LJjJxyxSePX816XIQeU3Ffk2iSyAdxdyX9hFu0KycM4PnmqZjj8mx9E0TuGVTnmmP\nreNnzKWdMgqYWNKmMbCA8q1BUmUF8p94nvwD80htD1OZTjFb7OLK4CrqjR4UBuvnNCA3wJbp04sX\nS8B1xYP9lIZNElIKvDAoiRQhtllJFnhx8ggMHDQGvUT4r+wZrPEmM93cyzjZSVRkeMEbh2sqQtEM\nuUgBU3Tw3NaJ/HHNWbhKEl+dZYmzjYuXvshEJ8fGdJaOSIicCeH+MI7nYJpqgEvXUxId28eDey9C\nCI8L3UeoNTr5e/osfpG6CjoNcm4YEYf9EegNVvGZ6d+mLNjqGyQBZTmXhPEw7fOj7AlEqdieQzSP\nJfP1W+mb1MHE93weaeWZn1K4dGFIf20V0AMMOuC3C7U99kEWf+x8Hizv5RcfXc6yJZP5nm7kyuy7\nySg/WhLMn8ZpgafZENB4AgpYA8x936n9J76593YC2kFLSV/oFMwPfI+AtxsKAqvwIhkZwgmdccha\nq1GSmgyAQ2+ROl1KOZDLK/U4KqWHGVHXdUc1WP8bWHiOpqhICEFj41gaG8ce9bijaWE2N2/nC1/4\nv9x4403Mn7+QdDp1zFqYADfd9EnCYT+U3NDQyLe//QN+97vfEDhYN+8o8IbBPAEwTXOAHehEs/To\nIqlmMGi9qiHfQU+vMGqRwX7xNDuNR+gR24pivf5ys/Sh8mejebFSCsLh4KDM16ihsUN/1517Js/W\nPUJnZQbXMNGWw4p3SfY3Ka6+xac3bejo5vSX1nPfLc2snDOHUM27yL74YZZ7z3JAHKCg/Yb+gKH5\n1L9YXHqJZqd9OR+/ogl3STPnPvc8WcfklF9uYlLvAVJmCCZomhJt9KRjIx4XNwK3CVgfAMPFCubw\nXBM3F2BVsJ1zyVFv9OBisCw3nY3eOECz26tggtnGwvLNnGuuZo07id+YS5l80QqeaFjByjsvQWRM\nhNZ05BPseqyGi5e+iACqkyY7LYst5UkmhkzImAjHw5QKtGDV/kZ+NmMJIHggfSkPpC8duKwChXYk\nJWWXvKn5xMxPUVbe7DMADb0LGqrXpVgfOpttzYupiDQzc0sr8X/9dwwrXxwPbM1A2amBHPAuAUzX\n4KTWXfRvn0ok5/GxVSk++oG1GJ+bgR4SshcIbug/hd5ImrvCuSJBgo876q5ifbCJ93U9SEtwJuOD\nV/Hm9pupEQaW6kHqPHZuNa4xHiv/LMqsR4jL0EoN3x2qAuG+b2M6+3CtcaQS/wekv46H9jgGgxFM\n08TzvIOMqH+S/1sM5uHGP54Vsjt37uDWW2/httu+SlOTH6V5OVqYAFVV1cOOLxQKce2117+s43rD\nYB5HlEKTnqdwHPdVobQDiEZDJyzkC8ON2WiKIgejQ6xnhf0lXHIo4eKQAkKEdR3z3A8d1bylvO/R\ntPs0eZex3XiAVvkcALVqHk1lH+Mrdc0E5WaCKj1gu3acLPn59zXXfVgQyXlM2X8AgWb2zv0U5Hoe\nsht4fF/nwKtZA2nXoTu9n0BgHCvtXjqlw9JdbWRNA8/QiJMVxl6FDHkoUyKVpr0sjlAKQyvcl0y/\nSnYpMAasziCeyKMQuDmfsEIAZ4Y2sduZQJcbBxS9hJkc2MOF1c8zNbif2eYemmr3kXEEZ/et5U1z\nH+EHxpv5xh03Mq+vjYidRxgGT/bOwF6j+MavL+f6RTnKCtMQTTvIVj3Pw+MM5ukwuR21dO5s5KEN\nixCTQ3RkP4RlZ3HUQcUtHkPLXDk9uIJ3lN2Hh4FZChUMWQZGAbJ/fTP71ryXfUqz8uwObgjeOrh3\nOGjJCEDrQVk8Q2mENsjbAsc0iWbiBFr0APUbADkQWwyo0Wyc7pEWHLI3eSqxmJWJxcxyDD6atnFk\nCM9pJuQeAK0JeA9hZVf6a9tLQ+rfKdP1eGYDqcQn0EYF0e7PE8g9idQ5Anm/DaW/9if+ZRm1x9Eq\nPifBgfYM33AKDEMe9yLDgev4P6io6Ej48Y9/QKFQ4PbbfdWhaDTK17727aPWwjxReN0azOPJj3ow\nS48QYqAV40ShpMcppXxVPFnwDbPv6Q0noM/SzSrrKyTFfgwdYII6hyT7KZChn90oCkgdYKlzK5PV\nRdjEDjtPqQXG89QR6QFLMLC4qHAn24z70Xg0eVewSqZYLU7hfO/e4gt/cJyOCcL3jrTjl/YIAwNN\nQLXT2X0HmncOG19r+FtnFx+W4/kvaw/091JQDih/Ie2rqiSRytCZiFGeTLNxQgP3nHMq1V29nHHV\nFh5sX0zesJGTNZVvC9G/ycVA4BoermcAmkA4RzTah9kVptesJm3nCRlZrqh4ktmXrmJyJsnEZyHS\nD4WYhoocVRsTpDctYFd+MruZRDDr4BrgeDYyD5/7yVX89N4sf7mhE2f2KkQ+TqqsndWBKNGIjbvt\nEt5yQRvVdUGead6HZSZYLwPUVK4nl6ugrWs+EdcjZUqQMN7cxe1VnyEh+yld0YMfIycfpr35NJRn\nkPcCBBwPT0vkQXUDWgk0BkK4KC0wDP/+5FSC/JPvISIyZESYlvEONUsjVD+7D+PLHYQ3TUZnKgCJ\nrlSc/wmPRz/SPXwNKajUgpgSvD1ro4XB5vg7Gdt2Mx4aJUxsrx3NPrQI+Qo4eYUwg5hOgXDyLtJl\nn8IurEHqbDHvoQjkV2CnHqQQuWTEF8hgj6P//xLnayBgIQRUVCRGIE53XnP50JEgpcDzXp3j/NrX\nvj3iz49VC/N443VrMI8HhrP0DIYmTfPl0S4dLYZW3ArhnbAdawm+7uboIeZV1lfYa6wkTy9Z0cMO\n/QgCD49CMQQn0SJDs/EQM9SVI87ht6744dejIzo4FAYW0713DI5Jkgx1yAFF4iIEWHmNFGrIJxqk\nAAXOsMs5WOaZbHB9Ls32fWAKlp00jctWPEckl2dvbSVPz2oib5l4CJ6fOYVIpsA5973EYzsXEcEh\noh28lyTTAl1siNRipAWelqRDWRI1XVi2S2uhignl+3hPspzHTllPS4VkRvUm7IRDsVceqcBSkLeh\ns6MWW/lJRI0gSwC8QTOmtKClM8p3lgWYO2cs6ZoW3JhExbtJyQJLTunEdk1E6ACnzPsNNbF2VoR7\nENLDcwNsar6MsWtP4XFjEnkMPpD4L8ZYrXjawBD+pkkp35gKFK4X4Onln6Gnayae4eFhkPfirM+f\nzuzwcgwUWkuSnRMIRbsBQWf7DHbWb2VCeT/5Gos9DQblW18isUyx26jhV+/P854JVdTf8Az5TReC\nE0UoiRYC0SW55uvl/ObUFC8tGOwTPqdgcWdfjGctl5cMh62my/7wXBqCM3HopbGwjwqtCerSlcsB\nNkJn0TKK9Hw1G60U6CzoAhIXDSS6P4Ho/gQai1zwLLLxD+AGhxNqDF3XhYKD1hrLsuju7kPKEnG6\nVVzv0VfM+fraEI9+7ZAWnCi8YTBfJg7H0nOidOGGKny8GtJbJV5dpRSep0b1YpNiHwJBXvSjcEH4\n4acSNBqDAN1y66hz+R5zkELBOWay+4NhP3gn1qq/cF4swa9vfBu60kCIIfJSCi64vVj5WvyZJzR5\nC9KRSpI9NwMtDDOypqbrkgxLwk+RTEEyFiIXtPnVhWdQ0ZckGY2Qsy2EUixcu4NTn91CZU+Sm77z\nENWBPFoIfpdbiittCuF+vCtTJP9UT7QnwITaHq676R5S+6rJ9JYxNdtP8I9zGRvopPuMbdBqkEFj\nCIE3xUFuVyBMMhGXdUYj+7yKYecvUGgM/+iLoc4tbSFObZ0FnkVPVQumJwmbceLGQur31XOg9S+k\npmRwJjxOnVNGd7IB08pw9twfkarcwbTeiaAliVQ7pvDI6hC2LiAQ7Nu3kD0xk2BnHduf+ASFtiZQ\nJgYeluWwf4Lkhqn/jxuT36RW98CuU9m3YiaTylbgKsk/Jm5h9/THOL+mgSkxE0Pa7Pv072m++SEe\nVG8jU1jKxF/uQffH0d6QyI0GsoJw1uDhd4zl5u+18fBFaWa4Jl9M+tqWCSUwDIgpQUZ6bLGjzM/u\nJuxlKQgDocEv/ZAgggjtYuQ3IOVuYp23oLWNJDtsLcgilb6gQDj3EFZ+J5n4VeQj56PN+hHX5FCD\nppQmn3eGPU9+PtQ3oiXOV8/zBghQSkQBo+HVMZgSrf/38s4eDd4wmMcI0zQIhQKHbcw/3gazFH4d\nSeHjREhvHTyfUppoNDjq92N6LH3sKv7v0MpcAwsDG5NDG79L4WwpBbncKw8t2/f/lPAdnwblYQHf\nX/dnfv0NSd9kQIOZ17z3E4pxGwd/x5WCVCjIjy8/j6+87+382wcaqbH2c2BlwQ+5BoH3a/Y2uqBd\nqC1HOi6hTA7DVfRGw0xs7aArEWHiznZuuvMveKbkLY88T0U4y/Tgb3AwuTjwPB93rke/dR/6gm5C\nF2zioj/EOX3aJgoVfVQZAj2uA2flPP6SWYCzfCLnrhLU7K6mfeKzdC/cTMviPeyYYdG8Ic7u+Rvo\nO2M/ez9bPtAlJQyPieN62bW7HKUkUoCJYKFVT8XeubihFG5uHHaon2gUti/4Fl2RJURbDHIN27Ci\nPUwS3Uwt30mpaXRNahKBjF84sdWUpNIhqowCSlu4+QjNmYUsL7cY/9f3I7vHFH9PACamp8mGW+h9\n2uHHicv4we8TTPxbEyhBumYsqWgXl4dO57lnLqatuovEm59m8pu3YhoRtuuxjMnN4KJ8iHB9Ahnc\njhfIgmMih5JOSQh3GXzvR9V8c3FgWI/vPsOjGgM09Oe383zkJPqsSuam1xFz+wiKIBMLXbjmSdix\nsdD3TLEdVBDIr0DoDn+C4roe6VGz9EYSfbei+r5KJvIe0hX/95CH8kgtH34+tDCMTWuQ89Ualg8d\nSUj6teFh/u/H65IaD3xqvGMxNMfC0iOEIB4Pv2JPCYaTD4xETRcOB3BddVzosGA412xpPikF0WiY\n/v6RzydHH6utr7JHLqNH7ESJPH4No0TjYeogIV3NBYU7aNSDfI5DK21NUx6X84hevxBzzzYAPAPu\n/ppkxwKBsorVmUk470eKRQ8OLm1XSlqrylnw86/TlYjzrh9PBkNy37RmvF0KPRVYMnQWjVSa8p4k\nVz66iq/ceTexjJ+0cl0w2gAPRAREBbiGwNEWCrh36Rl8/gsTaI8m0cCFL2S58QdzuDuQpjsVYppj\ns7K5kX095SAEFxTK+I9UjHTYoaMmy399+FGy7/wRIXKE2+uY+Pt3s9b0eGizINWVYMysHbz/TbDj\niYXc91g15EKcXh/nU3ObQBtkE/vpHbeVAxf9J864loEzEgomaOjWadpVBilAaE0Eg9j2Rgq769is\nx/GQuYD5e0xunvB1jL5ydqlJrJ5sEfn2/yW2aSb6IEo4jSKb6KP9Z+9n3iOLifzHZ0nbgqADc/YL\ngh6sGA/JkIdjuwjXIuxpJsVSNH6vHeoqB8Za//BGVnRGWPpII3UpTWNzOTJj+S07gHc1EYPIAAAg\nAElEQVR6gcJdyWHzP2EX6JH+vY5n17FBvYiQvvVqym7nJFXHBPMUVORkyuMR9IZTECUPUmcQqtsn\nVjgqSDxRTm/1nYeEaAMBm1AoQG9vcpTfPTKEKFXgD6WrE0XDqbBtP+T7Sqg2D4eKigTJZHrEd5+f\nUrGQ8sSmo14tvEGNdxCOxTM7Vpae4+FhDsqMeYclHzheHuZgoc2hiiJHmiNIgrOcrwGwXTzEU9Zt\npGUbBjaT3UtY4N1AQo/HJFg8N7+a2HX9StuUbmO9eScFkaFBnskkdf7LOgelNGLIy6KrEfbMFGgT\nKEoxeQHYuVAMM5ig2VdZTm1nD13xGAcaspz0fDXWleAtBUMNSwv6YwnN9NYkX/zlH0hkBplPLAN0\nGagOv1NCaDCVRlkuTsCkpncBH/rCJWyfvJf7rn+IJxbE2GMHaF8xFSEUj2mN9gxCCGwl+LtI84xd\nwywjTyHaR/DiOzDtXqSSeHW76bjwd9SpKO+4cR1aQmXLImpfeiczyqZxzYIZmNomX96L8ATKyBPI\nlVHWM4bg3z9E+1l3k5q8jihQIaFMQ4wwJoIsLhXSZMZ2hzEtezHUXrR+jvfkVvLwvY+zLH4W3m8v\nBv0Sib1jsQ7UwEH8Tbr4k2AmSMMTp+D97XI6IwJPQH8AHm0auJygDYQrQUNBSHb0Jqj/cpy6ewZz\ne398zxT6Cg7ejY9zkvUo9ndPo/Zb70TkguiExH3noc/mfMfkCdshi6Y/MI1/6mnmWTONKzXjzCU0\n2FfgCIkpDXR+K1qYSK8HhERr4xiMpX/GQmeRuv+QT46HB6g1h9DVlYqK/AJAQVVV2XHXwBw61/9E\ntZLjidetwTwaDDdaJ6Zl42CUcnmGIY+K8OCVGueSokipqGe03ePRzjFFX8yUwsW45DAYHh4bDPUO\nnpuHw+P2J+nXLWgJu62nMByb8eqsoz4H/75olNKk3/uvxL7+AYRShPshkBUUIgwYOwHY2eGvQVNp\nTtm0nXXX3swfzjqVny34Fo9esQeBIJARGNogq12UPWQcIemZMYVAfjixvBCgQqAkvq6vA9IGy1F0\nGtOJvXgLiwKwYPlELnwsza433cHX9i4lX+kSyhYQXWFyXhAXPw8cF9BvhciG8hyYvw4VLnn5GiTk\nqrtIrJtA/zQDtGL8mrcxZus52O2TkIaB0mB4IZTwhaKFFggE2vCI7J1JduJ6okJR2elQltKoEMjy\nAPlAGIGmdk8OUxU3TcAEo43r33sqPV0zeLTbIhMXGPFepJEH6aHVofRw0rGxf/hJ0kqCoQco8Ib2\nXvp0hMU7pMGT0N3mYXcnB8KSniGwwxYn6SeYzIvkPrWW/pPWYWyYg1jQhJw5/ZC5E1pyad4mKyCg\nA5jht3Opsw20xLObGJR4EZDbhhJVCOkgVBI/DGsV/z4ar02jCOHYCw/55EQZm1JRkRB+tWhPT/+Q\nfOjx1cA88jm8UfTzukSpWvPVYOkZisG+Q2cYX+OJwlDy8v7+40t2UPImR5prqJeeFHvpltswi3qC\nSrjsl6uP2mAqpYt5FZ+O0DnjCpJaEfrjHZimSSA/hbT3Z5RII7RBbVsFS5dX4JjbMF1nGPunAK54\ncjVbpj7Ej8pOI5YKkAkVsB2DUMbELAi6q3KYUmAHBbtlgcIIFdGGAFkJIlQc1APdC7vCX8UNKcL5\nAtpVGF1TcFOS2lSSZoJU9nlklRzkVpUKK9rPlLf8kt0rz6e5IkTWFPhlL34Lf2R/PU33v5X+aZtQ\nIUGkdyw6U4YHGEIhJMh8CB3Mg2ehhULJPOmxm9Dat1+zNqSp35/HdkBLyIQFm0+Ksz5uMK9kJ0pR\nBgHBSB/14dW89XnJXxZU0RkpYC5ZhvrHWxCpMgabNv37AoafCy5d5WHvVT3sb0MJTA1BBypmO8Ny\ne/Ntkx79B8YZa7FlDguJc+EKvAvXUe5+EqsQHiiQGRqWlAgiA9ObePaMQ++ZswPym/DMWoTbjyxG\nJKSXBkZPExSPmlLONht5F1oc2lL2akh7lYzZ4fOhZnFDbuB5fkFRqbBoaKvYkeYY6bPXkrTXicIb\nBvMghEI2tn10zfKHw0gcr6NhqCd7tH2HQ+c5ViHUV6IocqwYPtehXnpIV2LrGErkEPhE4CGOrHYw\n1Ks8mG3GOfNtOGe+DYB3A/2FL1BwujAJEKtsIHDqfdgrPzLiuEJrpHkAITV2TlK7L0E4bbLwqVq2\nz+rj2dPb0DaUW5LCgXbMgyoXNaAIYURyDBgDE4wwlPd10u314gQFNgY1hSTTvcm8aeYyete+HeWU\nERKSuNGPCppIy2Ne0w5mf+QrtL3/u/x786eJJOcyPraGaE4S2zOGMz71OQLZSvjpB0nPKKfywBz6\nOiqoCBjI4ntcKIlMmTg4qKpectV7QCgydTsI9MWoae/B8krfBTunKWsWyLljWDPW4eTm5AAR+8B5\nCrAdqG4uY/P0EHU3fxMZzyGXnY3OWZAu80OyroUsFFmCtG/mlaEwPGOY3ZTxPprelSbzxzpkVjB2\nRgHj1gxDcY1SrNV7MCjDEv0YFNC4eJ4gm8ljSF9FIx73eUVLnK9HCkvamccIOquRag+G6kOJSrRV\ni1YCw9t/WL/Jox5lj0GoNFL1EnCew+r+JKn4x/HsQY/3v5u27kgamJFIeCAfOvTPsI2HPDq9zf/N\neN0azIPXboml50gMNkc//pEN5vHwZI8lhzlUUeTl9DkeC452rgBxFjofY13gZzhkGePNZ477vsOO\nrZRGKYUQ+rDnnsHlm4GdNBsZYsrgukIdi7RFYOWDo/5OR1Ulf535JsJ9FhpoaIkSS9pcfN9ErD9J\nfnbjepoX9xAPwlf//RskMsM9c61Ba+nLUg79wIBx7m04hW+Ss6cRCXcyYdETZGINNDkd/PPsSUzP\ndLLNE/wWEyeaIhQscM0FyxBAfbCXm53H+UzLeXz41ruo6Q0R6qghH+kkX3eAcP4sZvUvomBDzlY+\nZbwGWYp0SpAZC29rJYQaULk8Od2HUbETF1mUpiq2PSDIA21ugu5JYzhQkFywdws2atg5ZXWEnFdO\nHgttt8FN30CduhzvxUUo4eDNfgnz4bcSePQyP0YtNG5NJ73nr6Tm7kvB9b1zq9xh3i0elbNDcHnf\nqPdGCoNGJXG8WhRpMHpAB5HeJApyA6n0IkQxsiGlxLYPDUuWPNAB2jrtYedXg2mhrYngbMTwevBE\nFZa7AcnoWq7ZwIWkym8mmF9JIP0nPKMcLUIIlSaUvpeU/bmB774WWXhGy4eWvNB43Cx+zxn43us9\nj/m6NZglHC2B+LHiiIUyQ8Kv+fzRCzMfOs/R5RdLYtmFgvuyqndL53M0z8qR1EsOxlR1ObPV29HS\nwXFGPxd/d6uGqcEcDr+29rPVSCEQ9EmXn9l7WJiLo2rGjfo7lb19fPauF1CrvsbWGT04Qc2cZyuJ\nVQp0WnDTfbN4vrWdul3/xbxtuw6JLgpAigzD3THAgZDcw6zox1Hv/zgsWErfk4uxIx5OTCFyBr0z\n4yzdmWJ6eYbUJfcxf1oL86e2DAwzN+3wg09/jMqeSqQWIDQiWYNXKancG8DJSszZHtUXdbP//ihR\n18YKQigkcTzfK7SETe36k6lddTKF5DX8+kPPMH7irSwWa7CVRgnImgZba2N0OxE29FzG7xOV5NN3\nc2rvRupVGxJNliBtoXI2VDXQXxiDEewE0Y1a9CyFuc9jWXksAWrxszjRFOZzS5FlacI3/ozAf34A\n7Q6+eipmaipnH/mGGiQwnBm4EpBdCCWQ7iSk2wQ4aJFFaN9gKqXI5UYKSx5EW+fkIGPi7ygkrj0D\nzzQJOM8hVfvw+zhwOwWp8IfJVPnsMhl7KmZ+DVINsg0JPUJu+wR2ZByfoiI/H+pXqvspoRLpfElq\nq7q6vEg67xTpPwtkMlkCgcBxC8lms1luu+1f6e/vJxgMceutX6K8vPyYxKNPFF63BlMIvyXjYJae\n44XRDNlQua/j4ckeCQfT9r38DUGpQONwHrO/+TgyUfqhEAgMgjgj7OgPF349HPqlO6zoKCVd8igi\nH/sGesNKxI6Ng9UsxbeZ6bqct/4uni7cyoJna/2fVSgm/aofoeHAT4Oc/GIdda19CBVACw8xRHpF\ngH+MAhQmCIGTq6Al/6+oSB2J0E4iiy5l9+8n4XSeDAac2riO1myafIVFd8Ji/rkPM2nBCgxz+JrM\nr59PeU8ZQgvfg1USQwXIa4NYfRSFwtubxyrrZuxVq/CWzYF0BUgDvDBeGkLlxcFMsLXHP/1wIb3V\nt/PMFz9IuddHd9ylpaqCRyJL2bTzDF5SbwMEq0Nv51Iepko1Y8tuzjCStNa49CXKOMXyKFPzaM13\nsrdLEo/tIBDI+oUodgF1y5dI5UIkk9MIl0O8r2xY84nTL3ACT6KN/UhnOqYzf+Q1IgRW4QK0nojn\nTEWZ25GlXLlKIHT8sOthNNq6UGAetrMWKQ3CYjdKuAi3DTFCT7F/jzXRzI/JejegDf+COoEFBLKP\n8P/ZO/M4x8oq/X/f9y7Zq6qr927olQZ6AZp9EUFtRRxxxwXRwRX1547byKjDjOMGCC7grriMzujo\niIiouCDIJioC3bLTdNt001ut2ZN77/v74+YmN6kklVTdJEV3PZ8PHyCV5L25ubnnPec853k8D9Vi\nqHqspNMzjJ3K/BzHIZ8vYFmuX+i+fSNV/dBbbrmdf/3Xf2XFihUcccQ61q5dz7p161m+fCWaNrUR\nk5///KccccRaXv/6N3PDDT/nO9/5Ju95z/vbMo/uFA7agKnrGkrRUgY0FdRmmJ0iEjXLMFtxFGl9\nneYZZjtC6e2gltTTDtbZcf6qjZUJHKuIsXjOHIpFi5HP/x7t3lvBtuj75OsR2VT5ddLJsvT/bWfv\nTXFEGBa9N4Ness7Txnayat8HMazHsIiga25GKer4leVXreayq/aTk/tAvYfEzuUc8ft3cdiPV2CP\nCmQhDZbFwNblJE/8LcZjEjuawzhyM3usCHvCRbIC5juwYqgf65FjEVKhLIVQgqH1d/PkSTdTCBkM\njrwBG4HKO+RG0ozNfQzteZsZePxURHIuxv6lkJ8DRUmJEQQDEN4j6X9kNXvuPJdtZ95BwbD4R7RI\n33icFE/HKywrIflD9EgO1bajWMpWUeSZsb+jCXjE0jlExUlJgSbGEULWXPsOmWyc7TsWsyAygly7\nlf7tXpaviK+/h3zfJ0EUQGnoyQuQoh/IouXPQHNc9RyLneTDv8SxQc+dhbJW4eiPAgZa4RQE7d2g\nyxmVdhZRYwWmGkaNPYDU4+7NvslPVOCQGP4Y4/O/CEA2/hocfTHSegJLX00x8vQJr2i22ZwuqoTp\nO/b+7vH7+6EnnHAK1113A1u3PsYDDzzE3/72F77//e/wtKedwTve8Z4prfWKV7y6TEDas2c3g4OD\nbZtHdwoHbcAsFm0cp5Ps10og61QwAS+QVUeSau/IoLLY+hmmX65vOmvVbjCmmlX68TxrAQr4u5Zi\nrhHhLdoqUqmM24+RGtaxZwJQ2HgGoTtucF8kJNaKdcRevoSVr66UrveQ52vFLXzk4fMYtPYjdPeY\nx61jEX0GcftuRKE6aN7xzN3k9EpPLrnsce575YfY99gvMHeuZsUfXsDg9qPRcg4nnbiA/37Vv5Az\n9jIk9jImc6RL93+pYK7IseiMOzjmz2ej5cLs23Afd33oQ4xsuBfbKGBcByvveSlgk8sp9IeO44HX\nvJ3VG3/OMmsQ5/aXYlx/Lo7qL7lTKdeTNCkIWzbLrngD+rZ5pJbtwAoNkX3Om8nUkK/my7vo1x4h\nG5rDcD7LtXvnEelfTb+WYrOzjpXFtSyb/1UKdvX36TiQTvfzvz+6mGPO+S3iXX/j2MQgR+4YIb7c\n5tB3fhxkGpd1VMRKfB2cRUgExcj1aIWNoGLY2k6K5ji22IcVuRaZeRmh3Itrpj+nACFQ4WNwZBb2\n/wDHdlBqIRrVUo7eKt7VGCr+jXBIUrRcZmo+8uwmS8y8HmZQ7x8KhVi7dj0bNtSvDDRDM/Pod73r\nrWzd+ihXXnn1lM2jg8ZBGzA73bd2TaQ1+vo6XX6tvOd0SqKTrlIT0PzKR8FkzJWZvFZJPa3gJdoh\nvCYWIZcrkEnVH9VJXvw9Rt/0H0R33U6WNewz/x3x1T5QMPCCArkVOf45eg/H/uE3zBvZX75xCgF9\nxj0M97+C1CvOJ/61ixE5N8juO3ION790oqpLIVJg5/rfMdC/g/HFD3DS1Vcwr08ixreRDe8iy35S\nMl/FGHIE7I/lsV/wX+jZ7Zzw+Q+z+2k3MrL+HqxIBnCdV5Lzt6EVoiAE0tFx7Bz7ixkO7Q9jrHmc\n/KpHcR49CqdoIh0QaYFVMAgpB1sqEn/aSHYsRu7wf/DH4gLGHXeEpQBoZImGtvPYwrNRGmgqz+Lh\nO9g1spCdzj8TB14UK5DZ/yzmDTxMzBlFChvHkTiOyejoQpKpheyWS5lnwj1vvpPB/V9llCgnyKIv\n6Nkg88AQjpKgpbH0J0BJHGGiZALkEA4KJ/ojhMwQypw/vYuk9F0qEcM21mMU7kcZCyg6KzDsbROf\nS+lqtXYRKf6ZxNxnVzmQFArFCazczo+VdG9sJUg0Mo8G+MIXvsL27dv4wAfezTXXfH9K5tFB46AN\nmJ2ElAJd1xEC0unOznF6JdlOZrGllfDu4o1mKqf17qrS51HKmXaglFISj0cRQjA2lmo6Yzb2a5Pd\n/7iiTMxQf1QsufsqIsY/UL+LEFl+Pf+dH+bRpfNxoKrwJ1AM7v4huYcMCsdvInTbdSDAHEmyYJti\n55qJ61lCMbb0UWJmkcff9Elscw6j+gBxhumjyB4mjskrAVo8xfi6PzG04XrSh/wDJ5IpfSWKPStv\nIz60isTYXHKRJCMLHmHUEQyoKBFtEOOIRxl9/o/IXwu5vavZbw8Qzxqk18DSvYKIlsZJFBgeyPHX\nlSvZbYfpk4qkA/0SpPMkTw6cjpQ5NHIgJDv7n8Hc5OOE2Y8QEWLiWnKFX/P3J08gr0IcktiKnQ8x\nNjqfrY8+nVUbM8w9OoVScHjq19iigK3CjGcX0B/f737nqiSSL3Oubq+gLHTgMFYSO6BEsDJw9Aen\neIVUQwiXI5zpezNm7iaEPYbV/y5sYxUL/nEEgsyE1ygiWHu/x4hzfMmBxCUUxWIRDEOvIsdIKZ/S\nGaaU3WPHfu971zB//gLOPvv5hMNhpNSmbB4dNGYDZsDwApdtO+VafyehaRpSusa0nSQRKQWaJohG\no4HPb7o3FgvT1BkY6EMpp+zOUCzakw5U1yISCROJhMhkclXmvg3XT1eis8rA6si7WGh+DwTIXAbn\nUY1EWGPR3n3sWDiP5Xv2TygCGvffhbZ3Rzkz7N9rccKvdTdg+gRtPBSlzeiCrViJYfYU+0hEJLoq\nkGlSfh7TILVWsufKH2AO7K96zyeOvpb+vasxhwZIhv/B1mOuZY46nkOkYuv4EClGUMJk30k7GR/V\nGbOGid9zJH+PCmwR5rBdcRLGAP/39A3cvvI4NFsQl4oTQkWeFbZJqAhf0ASj9KGUjgLsks9HmDGO\n0y8l4uzEZjthsY27d53CQ5GjWBpN8/LQM7jwac+gKLL8LrmT4fzvSFoPI5As107DsYdRKuqSbESB\n8kxOWQQo4qujuKmUcPoRKoogOun32xaERqGmtDo29wr6h94DuJtDl6s9ADICJRkJ14GkUMV41zSt\nPNqi6zpz5vRVuY/4xdOnfdg9nvMMUrTg+c9/If/5n5dw/fU/w3EcLr74YwA9N4+G2YAZGCp9Q7f8\napp624IC7aAioSdQisAyvcbrSSKRMNlsjkIhuPlNj9Rj24rxcZd4o2myJDKtl3aYsuRa7zrXNxpC\n13WNeDyG49iMjiZbDuiJTUVGf2GS2+oSPQbMm9w/KIUQDlIJNHRsAb8/eSOrd+7ljL/eV9XRFf2D\nMLwbCq5ggQJsaaMpjZASZLAmKocJSEVHSTHKPgXrimFG9Bwm3q25GgUJzBlCFA0Mo0i/gnzpAOJY\nHHrPEtRgGnH0MKsXPo1l+ikkCw+B8Tcc+TD3DZhE9ubQYkVsx2LzfIlKuYzbrQv7+fOGfu5fWfpe\ngHFHsEp3eGtfHltF+Ft+Hj8zCmgih6VCRDMpwmI+R2hPcLy5m0ENthQWo9Q/SMhtDGXPxig8nz3x\nOBsiFgaS53AONxcfJC92EFaD5OUojrYDZQ8gRQRH2wnYYM8HighloGQe4RgMiLeSGVtOIfJDhJZE\nOAMY6Ze19B1PhmYBJx97Pnujm9CK24gPfxqjeB8IiaPNJd3/jobvads22axLjjEMg7GxpM9Q2iQe\nrycWMDUz6ad6j9SPwcG5XHHFFyc83mvzaDjIA2YQwuVe31BKUVV+7YTtlodqMfgCAwPxyV80RXgb\nAaUgk8kFJnbQjNTjSXt5u3UhRJnKHg6HiMejZYFpN4u3CIVMTNMknc60zQg25ioOvTTNtnfGKT6u\nYSuPXCBAuCo1YSWxpGTJYafxxNvPY/+3v8bgr3+AyKTIHTKfv164giU32ay8/l5EscDuVYI7zpVo\ngI5GWEEeqyyjOgECntRsFjpRiqLIEEWy9UifUqFMi7xSJICT83BGHlBwZ7KffYc9yjEbXkjMnIOj\nHO7P7yKX30gOm6KdZv+q+4lKwZ3pp3PrUslxmyGegZ0L4aHVE5f7kxVGj+tEHYsrzUM5qWBxp1jM\nErmb03XQ5pyAKe5iW1FiK5t5WoRt1mrGnDOJaK8loZWGUEvYbW9mjraMEWc7u+x7GSo+wOLiofSF\nixiEKdoGSljYdoaIvYFw8kMoOYpQ/cyZtw6rOIReOA5EElQCEdAtbLIMChHGNo9kbOE16Lk7kM4+\niqFNKL21357XY/TsubwtUSMzab/AQiu/uQMpYM5kHNQBc7poJqPXCRPpegbSnYJ/DCaVyhEKGYFt\nANol9fjdFzx4WWgoZBKLuT6blmWXG/7tODQoBSPXmqisK/z9ROb9rIxfjCH3kJ97JGLNMpSUWEed\nxgnPeRs4IC74GNb/+xSPJX/EH7VvotjDliNiHHbKMzh036Fcf+r15GMZoqqIhmSBnWCbMYTVZLTA\nEX2ssg/nIf0fWKLkETYBJf26bJSomWFd3m37pfJ93Pf6G3Hm5FgljiXGHELWr1hjX0PSGeIRcRQr\nMRkTRfrl08kbS/mrprhrY+MvQKDI2Iqd6TwrwhqxSIi39cV4G1AszqFQKGJZFtnC0aTsP7PH2sJq\nPY1SSxjVzkEIRb9UnBaqfG8WeUacf5BT48TEXJSweXxsLgO6Im+MkLR1to8eScHRiao5nBnpx1AL\nCYdDgCqNzeooZ6AD2qUtXC9CYEVOm/x5E15Wf6yksZm0Udogxspm0n6pv9pWTzd6mLbd7P0PfB1Z\nmA2YU0Jt+bX+hVqncTVFVHtx5igWO9sXra9CpDPdz9OuUk8zKKUwTQMpZZnUo+saut44C23UTx67\nwWDkp2GXSGLC/uIrGCs8i8FTtzL3kkMhVG187QXpXK7AY/J+VElsXCB4dGOBw5z3sUTY7OcepNrO\nXCfMC3Pr+B+xnW36Lqw6BBKABerpHKN9k4T6Db/nAxTFjrrPM3MDbHz0fRxz6HY055c8IbP8PryR\nFeEHieOwovgZzMJ+NPU4JgYJkWUOd/AX8VoO0ZawNh/hlNtszCHFL5co7juy/pdholit28x1ijiO\nKDna5MjlCui6xn71AJaWYc7AoTyX97EzswXLsnmZWsOf8gaWKnByyCbs60wMait5kBuQQpTFKhQa\nT44dw7i9iwIVa6ws46TEHpb3b0DT3O9Z17VydQI8vQlVviamGkQ7n0G1/v5uhSVf1X+fTPe106Sc\nWfNoF7MBsw34xzYmY78GVZJthZHajtB7M0zHE7MZgpip9CMcDhGNhsnl8iSTlVlJ7+bhKblU90Jd\nWzGPSOSVxpRSFHZo5c2/TCiUDYsvCxE/9vCqdT3mrZQV5m2EuSjhIEpBM0wfMebxDPVxdvBHhEqz\n2oaiOZdNHMH31CYsURswBcvsZ/ACvolAsJqzsFSeG9QFOL5zriuQMkFfeAnbjr6ehLWYE7NLsdU2\nXuXcghQOugDl7AAiSMbQiOFgEqfAYpln2JGonyXoy0vebOU5/m6Nb8sRNh/+JE9yGBYxPLeRow2L\nywZzzOmLoWlaFdv4odQtjDo7kEJjm9rMHP0Q4uYg0dAA86IDnCIcipbFQFHHLm1YlIJBbQWHG8/h\nQedGRtlGVMxBCI2F2loEsMcaRZYst0w9xJI5y7GLdtX3LGWFESSle/27135lLAnaC6AzfexjMt1X\nIQRz5w50xAfTXU+iVOe0p58qOKgDZjtBwFPNaXVsY7olWU3TSr3D+i4f1WtN7wfpeWIahjaJKHv7\nn2c6Sj210DRJPO72F8fGkpPOmU7shbqO9bVZqHWcw9j1oIpu3DQXOUTWVG+GvCCdzebJZisbl6N4\nLSm1i/3iQUwSbHTegETHRGc1Z7ukTsPVCIgDa9TzuVd8s+q9n2F/muN4Y3kWMc84W+Uv6WMNo+qR\n8mm3BEiVJWXvYfndp7Mn08f2+KEcftiDaNLC/TnbCEawiWFl+xndfAG2kESO+Qmp8FyWFk5EjOkU\ncSiMwFory7/9KcuN877O5sUP8Lj1Evar5zBPW8O3thWJ3anz2AMFnIz7HaqIw5xXw9iiJ5DCbbKm\n7f1sL97OYGEVD5ur+IUeY9joZ0xAOARHhDUukVGOs3Usy+Ko0Dn0ZRazv/gYWUZYpp3KQuNIltjH\ncIf6KmPODuLmHE7tex1W2sCymhPaaq2lqoOoJ4jfPIg2KpkGhaAz2Frd14UL5zI8PFbuh3qC824p\nt9ILnSprv9nxd6L9NFMhmn2J+/YlD+gur3T1lpvCX37NZvMtX/RSCuLxKOPj7Qmdu8HLbMtRJJGI\nksnkpiRU4Bdl91v/1CIcdunzfjHrZgg6q4xGw4TDITKZbMvH0Aq8LHT4JybJ26Iq9j4AACAASURB\nVDRkSLDoApvwepeV6zgOsVgUUKRSmYbnWKEmVZwZYwc/la9iSPjluwwGnGUYIkKIAU523gso/ig/\nzj65GeVNZPpO4Yab38/A3iMAh6VbTyeUi2H0b2fFa85CM9zvMJ87hV3/9wny+1cDEn1xlr7zB5FC\nI39NiPR+BzuvsFQeR7cYWnYvv3/hxxlShyLYyEs2n86iu1chR6JA9Y9EhQrse9//oaR7bA/nf4et\nslihjfzXwCZ2m0sYkYNkNQgpWKhgoQ03pHVChlHetNRjP3tM50KhEKgnrBtAKyfSX8oFGBhIkM3m\np2WE0AwLF85lz56hjry3EIL58wfZu3fi+3uC8954i3vO3X5oPe/QRhgc7CeZTDc0mJfSQMqpacfO\nRMyfn6j7Yz6oM8xmqFbNybU9LzWVEuZ0HUXagZSSWCx4lxYIVqnHs2eyLJvR0fHAiU5eFho7p0Ds\nHPc703QdpXSi0QiaJvmZtYMfOzuxQw7PsubyksLCCe9TN1gqhWH/AaGexBareMJ4nJwYLT3X/RxC\nFUnKnUSZR45RbpefYROfISl3usFywscVxIaXo4Rg4cPPwEgtxgHyewe5/0sP8fgHV7LWUvTf/3Jy\nQ2tdzVgEhScSDF8lEQbIeBGr6JahlebgGEU0K0y/BodoeWLmgwzuORuV16gNlgAib7B476nsWPgH\nHGwslaVfLuZ+YxBH6qSkQckCE0tAUoEpYMi2GbQVrnZQhf3sZfyJhFs9cOcTHXRdC2yO2c1C3fMH\n1Vmox8D2AnTQGVPnGaw0fP9GgvOeCUSr3qGzLFkXswGzDtotv9ZDOz+66TqKtPsD99i97Yiyt2JU\nHSSpRwhRUkwxpjQqMlV45btQyMRxbP6U3MVV5iMYmobQBD8x9rI2OpeT1ZxyH9TNRCfeTEzrRxjW\nrQihodQdzBWHETIMckJVKfmYPu2gnBxiWXw9idQScmoYVaP5E1ULCIsB4qMrCacqgVsBMjeHx0YO\n58GFj3G2HUfPuxsihAWWwLEVKIm9txSwJSjTQQnF0LJ7EVKxMHQ4Y/k92KECoazR6CwRt5ZydOjl\nFJw0hoqw29nCgsIe+qwUmDoSl+Mr3SOgHxiYcK5V6eZMqU9fIJvNlTJQjVAoWg6a/iw0KMEMl0wX\nIRIJkU5nSnKWsq1SbmvrzBzZuupSrouKhZfnHarhOE5J5s8t5zZbw92MdG7mfCZhNmD60Br7NTgI\n4fbGXEeR/JQFAVrNZg1DIxIJY1ntf75mawRdfjVNtweTzxcYHR3ruO6vH7FYhFDIJJVyg/SDWgpb\ngHQUCgdlw18KuzlKhMtjLW4vlKoAalk2hr0ZUerzCaGx0spxLKfxgPYEI8Ih7ggOtQ0eM013PlMI\nFsi1FEe2cayzhL9pexmR4xRFDk0ZHKrO5OQnLiY6uhF7VNTYObtw9AJ5Afmth6ErAY5GWcxPWGCX\nbmylf0XmhRk57u9ENo5znHMeCXUoUbmdzAkPEfvrEXXPkUCS+bVg4HyTaL/JKvMMtKLBXDVOKJsn\nG9J5EkjiJrhzHLgkCbJOthqNuufbvyly+86V53gZoGkaRKMRhKCKvOURitqBlJJEIoZSqqpyUdsP\nbVTKbY9QNLNnJD0LL385umLhZZS8QyX9/YmqfmiQValabN++jQsvvIDrrruRUCg0I7ww4SAPmN41\n1knR8kbwTJZdR5HpWow1H2EJRii9/hpBknr8LNTx8VTHZQX98Eq/xaLFyMh4+Qa0XIUpKpsRWcRB\noTmCVU6UolPNWvR26bquEQ5H0YSDs283yk7hiMV4CejJ9jGcmf0dghQSC4XiTnk8O2NL0KwYJ+Ze\nQSz/UU5T4ywUUVJyhEMsnQUqQlY/mv0PHEPWyOIstiETh1wlCKUST5CZvxuRH8QcORTMnBswlQLd\ngqJvPEYAEuJHmKze9AxSqRPL53uRPsz+O/J43/eEr1UHa68g+1dJ/FkOCW0h6+QLsClwHCHOTMF3\nw4ohAQsdeF1eMKiqg6WmaSQSMWzbLbU3u+FXJCbzpXMtyn3QaDRcIrc4vg1LczlFbyyoFenEZqVc\naJ1QNJMDZj34LbzA7cGmUpky4zyRiLF58xa+9KUvccQRR3L44etYt249fX390147nU5x1VVXYhhm\n+bGZ4IUJB3nAFCKY8msj1Bv36ERwbpb9BSWUXrtGp0ZFstlcU/JR0KiUfvWK9ZcPxzj9rLMT3CvG\nAckGJ064ju9iZZcOODkSuXPRnUeQJJEkwDgSoTaj7FHAwM36BBLJqdl7sLIZbLkQxfeQagyQHG4P\nI+087s80Q9T6KmnnFeRVAoFALChiDA8gQpJsbCd3v+D9SAZYapxEaE4eZ08CNNdry9h4OyqzEOvR\nwyCbQEiBPiiIbYTR0fGqzxLKDBJ6qHbuts537IuBUmhI3IC8SMEHs42JUJ7WbzqdmdJvznEmlhU1\nTcMwtPKYRYVQVBkhAojHo6WZzslZ1o1Qm4V6x1SZB60OonTBC7Mb/cXacz5v3kLOPvt53H///Xz/\n+9/hoYceZHBwLi94wYs4//wLprSGUopLL/0EF174dj784fcBzBgvTDjIA6ZhuI4inSq/1o57NFMG\nmt46E3e2Xl+0lbGUdhEkqcfNNKI4jmpL/zUIeEPghUKhlOVMfI6N4nhngNMKg+XHcqL5MYaLX0B3\ntiPI4UaVUZziZpCHIRhA8KgbRkQEVBahMujchW6Dgyv35sjDcCXlKjOHigKpFT9BDL8SQ0VR8Tz2\nuh3MXXgIcxcuYmni6xTtLBEGKfxTmvRNI6isjjxkK9GX/BIpBSLzXNTt5yAciTyygNU3MRNzsni8\nnGp437UC4xBF5OTm56E2WHpjQbVl0CBg215WWZ9QFI/HEMIleeVy+cDHIPyzoVBdyo1EzCnNhraK\nXmWwuq5z2mmnc9ppp6Npro3h9u2Pt7wRqeeFuWjRYjZtOos1ayoz0DPFCxMO8oBZKFjYdueGcb0f\nh9s7DE2pd9jiSpTLZ6WsuZ2xlJZWKJN+grHfArd/FQ6bpNPZjtH560FKQSwWRdM0ksnmpV8dSZ/S\nyJey6CIO85XZ8PkAQmVKwdLBq31KcthOAbQQUluJUiM4KoFkO4KMV/BDI4MSA0gximIAV3NUoFAU\nibJr2ZOo+HUM7DuC6JwY0aVRQpq789aIYpTcO0KL44Re7d5MFHMhux4R+QPawN+wn7sZJ/kSpLOy\n/meeC8ZKRXFr6UvWFfp8hb5IYa5WoCC8TiGan4YqRCIhIpFw175rv5yilFE0zSGVypYDaTcIRZom\nyoE6mcx0hFDkrTUTrL00TWvLXqueF+YrX/lirr/+Z1x//c8YHh7ioovewaWXXjkjvDDhIA+Yna5i\nKKWIRsMIQUAmy43WcQNlsH3RChxHYVkWtm3Q359wh/1LN5hmknON0Khf2A1UZO2qVYKa4Qx7kHu0\ncQooFiiTNU6s6fPz+usJWT9C4jmXRAC3TCg1DcuZQ0Z7C6Z9O6a9G8gjKAkDoOGoOFbso2ix50Dq\nM6jsL8k4Bf5hnotOiMy8/QzNG8eUxzJXHDPp8UshMeIPkjN/StEqsZhjOxDJzyCYGPWEhDnn26Ru\nVqgChNc5mPVj6+RrS0ki4QbxblcQdN3tkxaLVlUFodOEIqgQ1/xCF60SitpFr629gtSR/eEPry3/\n97nnvoArrriKUCg0I7ww4SAPmJ1EOGyi6xqFQpFMpvM9OdM0UEp1ZKbSs99KJl37rQrBpVZyrnKj\nqfcDnqxf2ElUG0q3178KoXGyPafl5zv6cpLmNcQLFwEWjjgSPX4sytHIFnPkjedhaxso8iryhV+T\nKLwfGMa9WeoU5VGk7edAUgMuRo9/lG3qJhytwBw1hLAtQqqfDeY/gW1gWY3Po6f6MmY/gOXTIHbE\nXpTcjXCW1X2dMCHxnPavo+HvSoo7BNKE+RdKBlZGu96XhorQhcd2boSgCUXg9kkNQ5+UuDYZoQha\nk/lzA2bTQ5oWZsIM5kzwwoSDXOkHIOgM3u8oApTVNDqFcNgkHDaxLJtUKjhllHZIPX77Le9m4zj+\nm4zbV/AEyzOZ4I6zFXjlwFYNpYOCcEaJGfdihuaQKh5HocF1YBZ/imn9HKn2UBAnkgt/FEQ1sSjt\n7Gez9UPG1A4MGWGevobDw09jYXhD3dKiUqpqc5IVP8cO/zfCIyypMEbycgTNs2U/lILsXwWF7QJ9\noSL2tEoPO/ewIHm9wBnxmEACDFj0MXvCLGkn4e+TJpPpQG70HqFookJRtSaxP6N15zoD+EBMzsqN\nxSIlEmF9Uf/pwu31RxgZGW/wDIGmtVGffwqgkdLPbMAMKGDWG92IRkNYltORoXt/YLYsu7xuEPCT\neqaKCmvRwDTdAXjLssuWUFMtdbV7DB6hKJXK9KQc6G5kgulbP2D9DJsCYW0fCwZ+jCay6MxHG/83\nwvKk8obFJbMJHMcluFiWTaFYxA5/C2VsBhVCy52LtE5sa/3Rn0ryf5MVofq4ou81NtY/JOlbJWoc\napm1g++yMOZP+6O3BI9p3emNkZ9Q5I0SeY/n84XyOe8Uaku5fX0JCoUi6XSmI5quoZBJJBJidDRZ\n9+9CSKRsJHLx1MSsNF4DTEXCrhaNRjeCeO9a1LP6cn+0039vL6sMgtRj2zamaZQlx/L5QvmGXil1\n2VXOIUEGNK8k121CEbjiB1M1s24GiY4Qkrl9P0eT6RILdRQ78RmssR9h2zaxmFYqzbvZhivGYdKn\nx7Ht95TLikXHwmkj87OzkL9XVrXYnJQg+RMNGQFhg/LTaL1j7py3eRlCCBKJ2JTK7VOBn1CUz4uS\nAAIUCgU0TSMW6zyhSAiQUqOvL4ZtO6W1Zen4giMUgVum7qT37lMJB33AnA7cH0cIx6k/uhE0hbyi\nNVsbmKe3TtAzlZ6AtuM4VUSP2jkub5fuJ1x4JdypOivouksosm2764QiP5lpsmH8qWCBXM8u526k\nyAIKSWlXL1KltWMUCsUqgktr57t6VrEerB2CevHVzgiEUerF6YBV+czG4TZaZOJrgoRHrsnl8oFV\nWNpdu1GPtlOEIqgvvlArXdlsNrSd+8VM6GHOFMwGzCmgVTsspbz5rOmhojWrSCazgWZiQSr1COGO\nirSaXXmEi8oPXpTLXLW79GaarZW1o5hmd7Vna9dOpeo7OgSBOXIFMeZRdH6Frt0PSBAOulpJJF5k\nyPowltyOiM5Dy74JYS9jn3qAokoTF4vptw4hP2xT2JMndCjo0cr5jkYjpfNtkRu1cLCxNbt8rWlz\nFSIGKuU7IAlGn2DwTJ39vyqC729igaL/pZ1kbkIs1hq5plNr67pe5RFai8kIRZpW2+ufnFBUvXbz\nbLrZbCi0LvM3ecA8OKy9YLaHiaa1VzZt1Q7Le66uy2mxZD0lomZC6V5ATSZbb/p3Qv81FotSLBZJ\np7OB7UjdG3qlX1RvpMVjgga9divwMjt37eCIHs3gUMCKfQxh7EIXS4kVP82483ls4y8IBLbKM1y0\n+fPwPAxCLNOfBkKx8KazULfOczNFDfpeaRFZC9YQJH8rKWwTkBagBGjQf5JkyWvC5axo9DaL5G0K\ne0SAhNAhgv7TNOQROcZ+Jcne6VqUCOluFvteaBM5JvgTous6iUSUQsEl13QTXm86qLVbJRR1Ym1o\nTeYvkYhi26ohWU8IDSkPrNxrtoc5TVQr57Sa5TXXeG2GdoTg2+2VBqnUUy0CkG464jAVeKWryi59\n4kgLQD7fuvNKEKgekelcVlkPmjBJ8AUMoTGeuZuk/Vec0BACgVI2SbWLgsjyOJsBxR5rMxu1C7Bv\nn4P0Llsbxn+gMW4K11bELTKUoMCGsT/ZaBvHia1wz/eCZ0ZYvEmiHIXQJLlcjny+gG2DMV+R1SpX\nu9BAGwg+WHpi7e5YUve+b6hI+k02qtIOJlcoipaDmpQy8BGdVmZDNU3DsoodUSl6quGgD5iTBZvp\nOIpMhfQjpSASCaNpgnS6VbGD1gJzkKQeqLAS2xEBmC48zVZwyVbZbJ5isVgWhfZ6p/4ybtAkEL+k\nXmOqfXCwjOuwIz8ETMLZTzEQP5xCscjewttR0dtASVD9QAJL7iHMKEpYbExINichxzhPOHewxH59\nzTuL+hJ4Hhw3+ywscXvPXm/adhysQh5d1+nriyOEoHiGhbOrQObvCjSIbHQwlwd3DjRNlsTanY70\nh5uhkbNJJ+AnFGWzFUKTlIJCoYhpmkSjkY4TisANpP39UaSUFIvp8ubUPxvqveZgwUEfMJvBU84p\nFKamnNPujiwUMgmHjRLbtnVm52SBOejyqzfrBnSFleiH39HE3z/yZ3h+IW5XaUnUlHGnRrYQQpT9\nAjuRTdeDZdyInfg315oLRd54Kfuzb8XmcVT4t24eIBRCZaA4B03PYSHJ2oLFJgyFHPbnNRwcRERB\npo2bWxjMw9wTVV8IIE/6j5LcFoGQkr6TJAtfpKOHdDBVYJsWb2PWC8ZzO84mQcMwdBKJWJVakIf6\nhKLqVsV0NhWV8m+xtBkWZT7GRMeWA75zV8ZswKyDoBxFWg2Y7kxlY7btdNYJktQDlRtnJpMll+vu\nzaviaDLxBuJHvTLXdEdaKmzMQteyaQA7/KNSsHShGMUKfxaEg1I2Dt4MoA3mX5BCYSqTARPGijBH\nDqA4guO01zP4FofhrwDZBma/EQUWIASyT9F3ro0Zd7Mrx3EmsI5zDwvSt8sSe1Yx8lsbZ34e8xDQ\nC5qPUDS1TYuUnhar6Lqsnrc50jStKbGnU3BHkwzGx+tvzNohFHk96FY/gyf00az07N5LXMedg8U8\nGmYD5gS0QrJpFZOXewWRiBm4UDoEn1V6IxOWZXe8LFWLSkarppTR1nOZb3WkxX/j7DYbE8DQ+imW\nS+6lzy1Kn0OAxMYpX2fudyJFgQgaUosz13o5643nsEg/CgZhwQcd9l8tcPb7LkwBsbNs4qdXf6eT\nCabb+6tHTZwiWHsE5iHKd0MvLVF309K4dB4KuWXvyTZHnUCFVFTs6uYIakvPyZazt2aWZ24/1NUy\ntm2raqPof3//PGuz37j7Eln65+Apx8JswCzvctsh2bT+3o0zP3+5d2ws2B9lkKSeCrml++MaUCFa\nBJ3RtjLS4jgOUkoKhQLj48mubhK8m3a+eDkj3Afak3Wfp5Al4fYaCJsQMTbqF1U/rMOc19iMX6th\n7xeIPkXfC23MpZXnVPfsGmd2xnIHEZKoUqVSiynMlY1uso1u6BOzUCllyXav+xuUaDRMKBTqOpEL\n/OXfYK71VglFluWOD5mmST7ffJ61OlgefJgdK9EEiUQYKQWZTLCSVkJAf3+c0dHKgJq/3JvJ5ALr\n/3nrKOWUSrDTh1eGzOdd/ddutioq4gd2SdaumyQPtxToEi0sNE02HGnpBGqVghwsHP1e7MhVYPwZ\nSn6cCrCdMJZjEtJd8lHV/sjpwxy5pa4jCYCyJ0jWti0vl3tAkLtXgIDICQ6h1VP4wCV4ZCrHccpW\nTZ0mcHnwbxKC0qBtFUJAPB4r2811mxPgMo8NbNspGSlUMtC9e/fR39+P6+vrZZQHfrCc1ZJtANPU\nMAwZqKGzHwMDlYAZDpuEQsEbSDuOor8/VrqhW2WW3VR/9BVijSSVSnd9lx+LVUYHup3Rerv8eqVA\n/0iLYei+Ele16PlUUS3ePXGe1JaPYA28AkQlkDlOgmJxDbpxL5r0f08CnHmYoz9HqL5J1/b3C1Op\ndNObtirNoQiC0w/1Ngm1mZ2/rOhq5EpfX2765BboLbHHT65Jp7trSuB95wCpVLq8KfUIRfl8npe/\n/FxSqRRr165n/foNrFt3FGvXrieRSHT1WLuN2YDZAEK44gWdwsBAnFQqWxZKz2Tyge5e/aQe/81c\n17Uqinqru/NWiTWdgF9artsCBH77r8kChgch8J3v+i4trWYLrejP2toDbsBEUW4e2oeCSIMYrkkv\nBeRPw0x9raQ52xjNNgm1sEI/wgn/GCWKyMLT0DPvm/T9m8EVyI9h262J1LtlRb1q2H+q5JZKf1qS\nTLb2nQeJTsx1topmDFw/lIKhoSG2bPk7f//7Fu6/fwvJ5Djf/vZ/H9DjJLMBswE6GTDdOaYYjqMC\nN5BuhdTjlRL9DhZeJuTd0CvP7Z2zRy99MsG/SZj+UPjEjKg5O7QdVxNFlkL/q0F/BE+uR1jrUNqD\nFSKQB8dAH7kZjYGG7+cnNLkBo/n16cjHKfa9vWotLf1u9MI5TV/XCF7AmO64SGO1nMZZaEV7t9Cz\nzE4ISCbTXW03gF/8oVmfVh1UJdhazCr9NECnkhjPwUQpAu/BtUrqsW0H2y5QO17hEls87VD3Jqlp\nsiclKb8IgF80vBtw+1ZRIDiXi3ZGWqQU5U1CKxmGIIKR+jR29Cs44glQMaS9HlvsAm2o+slqMZL+\nhu81lTEZpe0olYN9N1C5t6XX+hG0EEAjcoth6EQi7jlXyilvWjRN77jubyNUBNu7X8GRUpBIxMvn\nvdHm7GBmwU6Ggz5gBo1aCb1YLFwqXUw/EkxXqaeWqeiVQB1Hlfw73R2/f7yicyQLv6Re99mQ3TKV\nrscODYUMotEo7ndJuQzdikuLtI9AJq+sfrB4NHb8X0CWPoeKI3OvqFsq9Wfz7bJQhbURYS9GaXtK\n6xgI6/CWXw/d6Rf6WxEeNE2WR4i8IBCJhDGMYPrPraAyW9n9630yZxUPBzsLdjIc9CVZCMZEupGE\nXiIRIZstTOsHEvRMZbMSqKbJcjmxXkkxiB25d9PshSWTN9PpeUZ2s/QMbjksHK4mNPlHWtyyYusu\nLR4csljGj0HfjVY8Fc06fcJzgihDOnIrduT7OMbNoO12H7SWYIz/EKnmNHxdr/uF4bArKeeVf/1Z\nqHfOvUDrXe9BiRX4ZyuDMhNvB16gdtWpGn2mg7sEW4vZHmYTTDdgVmY4XQcT/ymNxyPkclMPmH5S\nTxCoBKtCQ/cBP/wlRY9M1O7N3MNUiDVBolMzna2grMPaIrlloksLVee83eupEQt1KrDC38SOXUHV\nNVk8jND4dXWf7wVqbzypm2gnUPs3i14vdLosaO/31gtZP6/07TjNA7X7sBcoZ0uwMNvDbIqpiKSD\nmxl4A9eNJPSmqvAfdFbpD1btSH3VV8lxb+bhsImuR30788YsxW6VQOvBT2jqtsQaNNJhbY5WXFr8\nJdxGN3P/qMpURcsVRZQYQag+BGGUTDFhA6ftrPvaWCyKaepd0971wx+oW+nTej1/L7D5s1D/oH8r\nWag/UHdbbxkqJdjJfm+zJdj2MJth0r4nJrQ+UxmNhrEsuy3aeNBZZSWz6kywalbGdRyHSCTUE/Yt\nVIJVL3b47WaV7cA/0qLpkrz+LYo8CHY/eu6tOFaMUCg0ofzbLhyxH8e8ESUyCHRk4ekg0hQHXk21\nLt5cQiN/LP9v9Uxpd3xC/Qgyo/ajNgutbFwqVRevBNuL2UqobFLGx5sxn2dLsM0wm2EGBFco3Z2p\nbEVCz80wW3tvP6knCFTUcpyOZlb1duYuQzGEXq5321WEok5T6f1KQd3WvgV/oM50RBRDKSrjQeHv\nYsufINBAgoqOMVd+DqB0A9cwDDUllxZl/AWEjSAEgGPchZ4/D5F5MyryDRA2qAha6l/Lr5lKRh0U\nOm0DNvFapzzOEg6HSCRcIQBvBtfPRO80Jn72+s+bZcFOHbMBs0W4QukhdF0reTC2tmtttSTrOE6A\npB6XXDLZIHynoGka0WgE27YZHh4ryZy5pa1QyPSVtiq78iDdIDxiTaeCVTN45V/bnuju0SkobSsC\nCSgEEkduI5lKUyxY03Zpce1LJv6/mX03Tu7VKO0xpL0WofqrCFW92KT0wgbM27jYto1p6hSLFplM\nFk3zNFvNkgFzY9HzINBqr3S2BDs9zAZMJu9hmqZBJGKWfTHbfW/Z5Np0bypOW5loM3hzjcVisetG\nu26gjmKaE4Xaa+2I/KWtcDhUHjafjmelvwTarWDlR8/Kv85cFKBrGkqBXZhDobRRaM+lxZ7QZxT2\napT8E66Vk41wKmKxUs0Haz5QYaH2okdd7bLR/R51vdlKy7IbZqF+0fMgdIldj1Z9kl7pbAk2CMz2\nMHEDWr2g5s1UAlMWSg+FDKSUE2afgif1VOYaU6lMDwgWRmmesDglWbt6bFw3G2qNjdtL/dkKqcjp\nulA8gBm2sKKfpeA8jLIG0LJvRdprWnptKyMtFltR2pMIpx9pr696vT9Y9YL57Em89WJECVod2ZiI\nal1irZSFVitCTXYdebKClmWRSmUaPm+WBds+ZsdKmqBewAzKF9M03R+F/8ccNKnH29334qbhsQF1\nXQtc1s4/J1c7WuGVwXqpPwvBybtNBX7msyuxNv1g5VBEhv8AxuNoop+YfhaaWlF3pKWiFtTrYNX9\nDaLbL4yXCF3paZOa/FmoN07UbJSodq60EWZLsFPDLOmnRQTti+kv9wZN6qkYK9MT6rp/prMTRrsT\nXeVlmYnrjVaAW3Z0bxrdNbV2Z9x606+rCKZPX/8WwCGFE/kOtvFbd0REKFAGmcLvMPNvIyxPrurJ\neb+LTCbX9Y2CX7C9HZPloNBqsGoHfhJXtkSsrWShGuFwtJyFCiFa8AudLcF2ArMBswQpXVKPpknS\n6eCE0j3ST5CkHqj0y3oxhF+x/2pvpnO6cByHfL6A4zgl+yE3+/eYyxVSS6Un14l+Vi+zyk7N9znh\nn2FrO0DbA6L0mYQEbSu2fifZQgxL+xHSThIRxxKyXodjU+6Felqtnfat7AWxx0O3Zyu96z1f2g95\nozqO42Dbir6+eDkLHR4e4bHHHmPlylWYZgi35yyYLcEGi9mAiTfgHCKfL5JOB1tasiwHTdPo749X\nyctN9Uau624J0t1d946J2Avx6EaSfrWkFsPQMU2TWGz6Cjl+9JoF6i+BBp7RizQCDeVl6UKVyiMO\nCodi7N9AfxRbOKTUrWTyAqPwyvLLPWcct0ITntSlpV34/Tp7QezRdb00ymhOAwAAIABJREFUW9mZ\naspk8H53tT16Lwt94okdXH75pWzbto2VK1ezYcNRrF9/FMceezyDg3O7cowjI8O88Y2v5corr0bT\nND7xiUsQQrBq1WouuuhDSCn51re+xh133Iqm6bzrXRexbt2GrhxbUJgNmLilv2QyeEcRj9QzPDxa\nmoXTSyzWSN1+XDNUgsVEBmo3UCn/qp4pl7TiajJZGXei9VNrN/JeZ5WxmNsn7pRwt7CXg/Y42PNB\nfwKUA0oHexHSXosTvcZ9ogLI4YT/D3wBs5EzzvRGWlz00uEDKtWcZLL77iZeViulrLtR8LLQlStX\n8fWvf5tsNseDDz7I3/++md/+9tfcd989vPe9H+z4cVqWxaWXfrKU3cIXv3gFb37z2zjuuBO47LJP\n8sc/3syiRYu55567+drXvsOePXv4yEc+yDe+8d2OH1uQmA2YUCqVBvt+HqnHe1/Pgsij3De6kdfz\nqvRuGPl8gdHRsa6rpvRSg7U6q2z/hlUpa02UO2vlRt7rrNIvmD462rnMRhbOAiSOXIVjbwUVQhAl\nql5LOG4ypKDSI1agmpvI1pdUbG+kBfyqNd13+HDtsGIoRU/GlLwSbKFQbJrVuofllmAjkRjHHns8\nxx57fLcOE4CrrvocL37xy/je99yN1UMPPVg+hlNOOY277voTy5Yt58QTT0EIwaJFi7Bti5GREebM\naSzaP9MwGzADRDujIvVu5N6O3POqtG27LHrQi91tr9Vy/BuFkZHxQN6znvWT/0buz/7BDVi92Sh4\nM61T2yi0vR4CrfAcvDDoL4GOj45BfAVoO9xSraMj8y9oew0v+69sGisjLbGYWTXSYtsO4XAIy7K6\n7pMKrdthdQqe9nLzMSmFUh6pp3fEnhtu+DkDAwOcfPKp5YDpF2yJRmOk0ynS6RT9/RVDc+/x2YD5\nFEMQP0Y/qScIr0qvZ+HtqhOJOI7jVGWhnezj9HKusUKu6FwJ0o/aG7lhaMRiMYRwN0GxWJRQyPRl\n/3ZHsw23Xxb1CaZ3bKm6mFgClejJL2PHPgsqjbROQy+cN+11HKe+sH80GiIcDqGUN98bC6QH3Sp6\nmdVWizA026TOHBbsL35xHUII/vKXu3j00Yf5z//8GKOjI+W/ZzJp4vE4sVicTCZd83iiF4c8ZcwG\nzGkiaKUeTdPKSiC1PQt/H9Q1wm2vD9oK/HONvShD+b0ye0GuqOeq4i/jRiJuGddxnEBIXLWIRt2N\nSi/61P6NSi37WVPz0VKf7uj6FfcfGBkZw3FUjUuLX2YueONnb7ayV1ltxV2l+VyrvwQ7E1iwV1/9\n9fJ/v+MdF/KBD1zM1Vd/nrvv/gvHHXcCd955O8cddwJLlx7Kl7/8Bc4777Xs3bsXx1EMDAw0eeeZ\nh9mAOUUErdQDfg3U+sSSRn1QT3KrVl6unTJeM1PpbsAtAbrkht70q2RJOHviRmWyMq6/HzfVTMgv\n69dtSUOo7pX2YqPSiNgzsXVRcWlxZeZigYy09HJcBVxiUSgUmqT8PjNKsK3gHe94D5de+gm++tWr\nWb58Bc94xiY0TePoozfylre8HqUUF130oV4fZtuYVfopoR0T6aCVeoJSq5nM7LnRbtzPQM1ksl3f\nWfuzyl4oxng3y+nooPqHzA1Dr8qEPEJLo++1l+4e0DkrrFYghFsCNQy9bXk5D95Ii3fdtzPS4p+t\nHB8PRi2pHXi9YnB5CpObPPe+BHswYFYabxK04okZdFbZjazO71Op6xqOo6ocQiKRcEl/dmo3q+nA\nL4CQTGa6JoDgXz+RiAKUxoqCu1n6MyFP8qy2By0EZQZuKpXuOqnKr5gTtF9nK/B7ZjbTQm0X1RtH\nrSET2m8wncl037fS08GdbFymIm83M0qwBwNmA+YkmCxgBq3U45eV6+aP1euDhkIuKxHcwf8g+6Ct\noJcCCP71u+mu4Z37WqPtQqFQ7sl1C16vtlclSG9UqVtZtVdC9wKplO6P3TWAL3Rdi9brVbtZ9VO/\nBHugYTZgToJGAdNP6gkCfsHsVKo3WVVl/XSJiVgpZ02nD9rq+m5W1xt3C//6QQmWT219SKezVfO4\n3fBN9Hq1vcpqvc+vFD1c3/38+XyhHEjrubR04tj868+WYGcuZgPmJKgNmJ0g9VQEALrvGeiuP5EB\nWguXEVpdxm2lD9oKepHVzaT1JxNMb6WMO50AH7Rge/vru73yXq0/2Wylv4Rb644TxEhLq7OdsyXY\n3mM2YE4Cf8AMmtRTEQDw/BK7m9VU/BrVlNb3iCzejdzPGm3lJu6X1Qu6V9gKarPq7me1LrHD7dW2\nt76/jKvreo06Tms3cT+xpd31g4BH7NF1vbR+d6sq4Jost0ssqu9ZObUKQGu+mbMl2JmC2YA5CVw/\nzKBJPW6vwjR7M1cHFQZmkL2qeqzERiMVrWS1nYRnxdTrrCYoBrBfHadSxrWrgqj/N91rYotf3i2d\n7v76fpPldDozLQZ4tWelt3msnset3YxIKenri2HbTlNi1WwJdmZhNmBOAiEclHJKgXL6ZRBvVKNY\nLPbE2NjvatJpBmTtTVxKV9ZPSonjOD3rFfYyq/SLAHSagVw9SlQp40opS1ll9w2WoffjMt2YrWw2\n0gK01IKZLcHOPMwGzEmhqBKXLv13uwFUStdZQtM00unuCwBUNEh742oClV5tsWiVg2lQfdBW0Ote\nnSvn5s619iKr8rJa7xx7FYBmIudBolViS6fgd/jo9mbNG2lxFaFcFnpjcf/ZEuxMRaOAOav0U0a9\n3Z1T2v21FkC98l+vZN28G3WxWOyJWoy/V1qrg+n1QcNhs3wzD9pw2C8Y3gsLsolD+DMjq2skct6o\njDsdeJuVXpXg/SXoXvwGXXm/CLZtMzycRKlqVagnntjBhRe+mXXr1nHkkes56qijOfLI9YRC4a4d\no2VZfOpT/86TTz5JsVjgggveyIoVqw5I/8qgMZthtgVFbSYqhGL79m184xvf4JJLLsE0Qz0r/+m6\n1hNZO2i/V9qoD9oOmcWPSlbZm7lOT62pULDIZKbXK5sK2h0XqVWEmu4Gxl+CTiZTXf8NQKvycp2D\ndw1O9hvYu3cv999/P/fccw9bttzH449v5cQTT+ZTn/psV47zF7+4jkcffYR3v/t9jI2N8vrXn8+a\nNYfzyleeX/avPOmkU1m0aDFXX/05Pv/5Lz9l/SunitkMMxBUZ6H5fI7vfvdb/Oxn/8db3vI2hNCx\nLCdQb83J4BdA6MWO2q+B2o5Ye63hsD8LcstpWktGz34Gai+ySuittBxUKhvtZHVecMyWKsbeBsbz\nCXV7cW4Jt9aftRa91qH1bxZ6UVkBShtWfZJr0C3Bzp+/iDPPXMKZZz4bgFwux/DwUNeO9ZnPfDbP\nfOam8v9rmn7A+lcGjdmAOUXYts2b3vTPLFu2nGuu+QHz5y/Ayz7bKeNOFX5ZuVpniW4hSAuwWqsn\n/0xitdFzJYAahtHTrNLbLFhWbwTT/VZQ090s1G5gvF6cW8aNNJzH7XVW5/Vre1UC9rNwR0cbe7ZW\nE3uq+5XhcJglS5Z28jCrEI26whmZTJqPfORDvPnNb+Pqqz93QPpXBo3ZgDlFaJrGpz71WQ455FDf\no7V90M4E0F7LylX8GtvLKtuBUpUsqLKu1wcNYRiuYHWhUMRxHDRNdjW77DUDNOhxlVrU+rNCpYzr\nuYS4fqEO2Wyu6yxoqMxW9mrD6GX2k5VgZyILds+e3Vx88Qd4yUvO5ayzzubLX/5C+W8Hkn9l0Jil\nZk0D1cGyHvwMOK30j0QpDaUESuELppND0yT9/QlCIYOxsWTXg6VLaomQSMRIp7Mlab3uZVWWZeM4\nCl3XyGRyjI6OUygUMQydvr44g4P9JBIxIpFQmaEYNDRNMjCQQNc1RkbGe+BZ6QaKWCzC+Hiqq+4u\nbgk3VwoOimw2SyaTQ9e18vnv64sTiYQxjM7txTVNY2CgD4CRkfGuB0shIJGIEQ6HGB1NNgmWyhcs\nvYDZewwPD3HRRe/gbW97J+ec8yIA1qw5grvv/gsAd955O8cccyxHHXUMd911J47jsHv37qekf2XQ\nmCX99BwVirn379oMtFgsopTDokULyGSy5HLdF8sOyoJsqqhWq6mvwVt/qN+qKuNO57B7LVjuZfaF\ngjuE321MphjkL+PW02cNgo3rVVd6ldm3KsQwk4UIPve5y/n973/DsmXLy4+9+93v5/Ofv5xiscjy\n5Sv40Ic+gqZpfPObX+XOO29HKcU733kRxxyzsYdH3j3MzmE+ZVDNxL3//i1cdtmnedaznsXrXveG\nrotV+0clesXAnapfZq02q2FM7IO2cj6rGajdl/aD3peAp6oYVNnAaFWyih6hqNXs0N+v7YUQBrQe\nrGdiCXa6cByHdDpNInFwlGRnA+ZTDNlslm9848v85je/4t3vvohnPes5Jfat1wft/DFU2I/FnoxK\ntJJVtguvD1oR2FZV4yy1WVOvBds1TZY8K5tLq3USQbKANU1WVQFqzZ7rvX8lWPfGYLx1IYQDU4jA\nsix+/vNr2bZtKytXruLMMzcd8MSf2YD5FMOvf30Df/7zn3jHO95b0zcIRpGoGbphbD0ZOk1q8VD/\nBu7OgRqGAdATaT3ojrRbM3QjWFebPU8s4xqG3tORHbcEG59UtWkml2CnA8uy0HW3H71//36uueZr\n6LrOiSeewumnn9Hjo+scZgPmAYtgA6ingdsrWbduarA2Wt8rfzqOQkpR0we1O57l+RWLel1+7EWw\n1nUN0zQIh0MIIXAcRbFYLGei3SL5eBKPyWSGYvHgKsGCy5T2Rk1uvPFXHH30RkIhk9tvv5X//d//\n4WMf+w9WrTqsx0fZGcwKFxywmL6kn/v3aqWWbgcqqM4qezEA7z8Ho6NJbNuu6oO6+qA6jtN+H7RV\nVDwTezMy5PYKowghGR1N9iRYa5pGOFwRLfdXAcLh0AST8+mSuWpR6ZcyQeKxGgdmCdaDV2354hev\nYN++fZxyymn09fXx3Of+E/v27eWqqz7PFVd8sdeH2VXMBswDErU/XlXKiiYycZVS3HHHbaxbt5ZI\n5JCeB6rx8V4H62q1mkbzoLquEwpV6+I26oO2ArcM7sob9mqusLpX2IvroOKb6T8HnqiCl+n6y7jR\nqCuqMBUyVz202i+dieMiQcC2bTStMpJ12223cNddd/K6172Jvr4+bNtG13XOOedFPPnkLm699WZO\nP/3MHh5xdzEbMA8KTBRUUEqxf/9errjiMnbt2smnP31ZT/wSGwWqbsHfr201WLs9NrtMAvKMhr0s\ntCIr15ourl9abnS0++cAKsSeXonGa5pGX1+MYrG5Yg7UF1XwyFyVTQxV30ErG5BWVYsO1BKsP1g+\n8sjDzJ+/gOOPP4nnPvefuPnmmzj66I0sXrwEgEQiweLFS8qlan/59kDGbA/zIMXNN/+eyy77FC9+\n8cv4539+PaZplP7SWUk/D73uVUJ1oAqyX9toHrF6HtQ9x73WoXWJPfGu+KY2gjffGuTIjH8To+t6\nuYxbT5tYSlEaG2ISO7IDuwQLLjv/kksuJhyOsHfvbs4++xyWLVvO7bffipSSt73tneXn3nLLH/jd\n727k3//9kz084s5gtoc5iyrk83muvPIq1qw5os5fO6uJ6xGLemXBBJ0NVM1k5VwVoljJrFxg2w7j\n470Rje81C9c/Wxl0v9RxHPL5RmXcijaxbTsYhk4u13y+9EAtwW7ZsplQKMSaNYejlOKrX72KjRuP\n57zzXsMb3vAatm9/nLPP/ieUUtxww8/5n//5L171qtcAcOihy9i48bgqJu2BjtkMcxYtIBgmrr/8\n6Zb+up9VekotvVIsgkpGVSgUyzfyqZQQp4qZIALQ69lKcCUGTdPAsuySxVnlOxgaGiYUCpXEFuBA\nzCrT6RS/+c2vGB8fZ9my5RxzzLH88pfXA3D77beybt16XvvaN/C///vfnHvuK7n55psIhcKcddbZ\nAIyPj5dMIOK9/BgdwWyGOYtpYDImrvvvZgHUP64yMtK8R9UpRKMRwuFg3FWmAlcxyO2v1bIv/SVE\nlwkqJpRxg0C35lubIRr1XG56U4b2Kzf5zQP838Hll3+GW291g8aGDUdz1FEbWb/+qBkRHBzH4bOf\n/TSPPvoIhmHwL//y0RZ0rSvw+o2xWJzBwXl85StXsWrVYZxyytMYHR3lt7/9NR/4wIc59dTTAbj5\n5ps477zX8rznnVOVSfb19QX+2WY6tEsuuaThHzOZQuM/zuIgh39Iu5YA4f5bCEUymeRLX7qKBQvm\nEY/39WgAX6O/35X0Gh/vDQM1FDLp63Ol5dzMtvrvSils26ZQKJLL5cnlCijlCs277iBuNqRpWnk2\nsV3EYtHSXGGafL43G4a+vjhCeN9D9zNb0zTo64uTz+cn9K3938GZZz6TF7/4pSxYsJDdu3fzm9/8\niquv/hyrVh1WpcHaC9xyy008/vhWLr30cyxbtoJvfOPLPPvZz23ptbZtI2UlU5ZSliy7BlmwYAEn\nn3wqN998E/PmzSeVSvK1r32J+fPnc+aZzyoHy4OB4BOLhf693uOzJdlZdAiK2267hcsv/wybNm3i\nTW96C+FwuKvm2lAZPu9ln66ZYHk7qFbE0XEcp2qcpVFp1e/ZmE53X+IQKnrAvZIYBK9vbUzaDmhU\ngrUsq7xh6SW++MUrWLt2fTlIvvjFz+Paa3/Z1nt8+9vfwDRNIpEoL3nJuXz3u99i7969vOlNb2V4\neD833fQ7Hn30EQ47bA1vfONbOvExZjRmS7Kz6Cq+/vWvcOONv+KjH/0PjjvuBLpprg0u+zMed8tu\nzYfPOwe/YHkQ5CavPJstJUaappWk41wj7Xp90IrDSqYnWWWj2cpuws1sXYm/0dHkJCzY+ibPwIwh\ntqTTaWKxSmnYYwC3cnz5fJ6Pf/yjRKMxXvWq87nggvMYGBjg9NPP4LrrfsrPfvYTli49hJe97JVE\nIhFCoRAwcT7zYMXMuAJmELZv38aFF17AddfdSCgUYsuWzXz+85ej6xonnngKb3jDhdPuIRwM2LTp\nLM4//4Kyu3u3zLWh94Lp4AYJ09Q72qdzWZ6N5kFdRRylFLlcoScbhooV1uSzlZ2C17Od7Fp4KrFg\nY7EYmUzF3s0t3de/ldcGukwmzWGHHc7551/AVVddyemnn8natRvo7+/n+c9/ET/+8f9w//1bOO20\np5eDpVJqNliWMBswfUinU1x11ZUYhll+7PLLP8UnPnEpS5Ys5QMfeDcPPfQgu3fvolAo8NWvXsOW\nLZu56qor+fSnr+jhkc88rFq1epJnBCPp54fL2It2ZEyhVfhZuKOj410tf3qjFEopTNMgk8mVlVk8\nRZxaZ5BOsYQ7MVvZLuJx15Zussz2qcaCPeqoY7jttj+yadNz2LJlc0M9V3+g2759G/PnL2B0dJTr\nrvspt956C09/+pm8970f5Je/vJ5bb72ZT3ziMt7zng8QiUSq3qfXJeiZhNmAWYJSiksv/QQXXvh2\nPvzh9wFuAC0WCyxdeggAJ510Kn/9610MDe3n5JNPBWDDhqN48MEHenbcBxZal/Srhdcjy2ZzZLO9\nySq9fulMCBJ+1aJqRZwKEzcej+E4TlUZd7qbjNZ1WDsHT4zBsibbtDQvwc5UnHHGM/nzn//EW9/6\nBpRSXHzxv1X93SPlCCHI5XJ86EPvJZ1Os3TpIbz3vR/kmc/cxL333sMFF7wRgC1b7uPEE08GKAfL\n2RJsfRyUAfP666/lhz/8QdVjixYtZtOms1iz5vDyY+l0mmg0Vv7/aDTKrl07p9VDmEU7qC/p5w+g\nQ0NDXPn/2zvzsCir9o9/ZoZlGIZdEBfABUEFRE3LzDXfzAxzybC0xSUVF8wss1Rel1xKc8stl9Q0\n/b1qZRqGS1mpIRKhucW4oLgkoCiCLMLMPL8/xhkGAkRk93yui+uCZ+CZ8wwz537Ofb739140n5Yt\nW/LqqwMrRXlpXqZQWUGipCtbo9tNYfugKpWN6XdKUw9qaWmBnZ1tpZasGG+cHiTyqk4p2ILI5XIm\nTpxc6GN6vd6kgr18+RL//PMPHTt2pkePIDZu/JKlSxcwYcIkQkNHEhb2Iampt7GxUTFixOh85xHB\nsnAeyxk+KKgPQUF98h0bMKAP4eE7CQ/fya1bKUyYMJZ58xaRlZW3V5CZmYlabce9e9kl3kMQlCX5\nA+jPP+9j8eLP6Nu3H71790erNQTL8rT0K4hSaYVKVbnqz0dZ2Ra1D2phUXhnkKL2Y43OSWlpleNF\nm19cVLxzUnVLwT4MxmC5ZcsmfvxxFwqFBd2790CtVtO798t88cUy9uzZzZo1Gzl3TsO1a9fo1u05\nIH+wFRSOmOXvs3Xr96bv+/fvxcKFy+47fVhy7dpV6tatR3T0EYYMGcGNG0kl2kMQlB8REeFs2rSe\nTz5ZiJ+f//2jFafENXfLedAEXV6Ye6CW1cq2eEu5vH1Q884sdnaq+wrUtHLbEy0O87KZ4sVF1TMF\nW1IM/7t7fPbZHFxcarFq1Xo2bdpASspN4uPP06iRN/37D2Dhwk+pXbsOHTp0omnT5oBIwZYUETAf\nwPvvf8SMGVPR6/W0bfsUfn7+6PXNi91DKIysrCxmzJhCWloaSqUNYWEzcXJyEircUtKtW3eee65H\ngZV9xShxq4JbTl7fzPLdsy28M4hhH1SlUqJQKJAkCa1Wh5WVZZnsgz4MxhV+TU7BFod5oJPL5djY\n2HDsWCw+Pr7Y2qp56aW+fPXVl/z++2EcHBwJDGzF6NHvEBDQIt95RLAsGcK4oILYtm0LGRkZDBky\nnB9//IGzZzWMH/8+gwcPzKfCHT58NImJ/3D48EGmTJnOqVMn+frr9UKFW2YYJ/OSWfqZY0z7VaYX\nrnnq0WCEUFljsMXCwtBsHGT5TBVkMvJZ+pXH62RuCJGWVrwfbk1NwZo77uzevQsbGxV+fv5IEgwY\n0Jvly9fi7x/An3/+wfbt/8czz3SiZ89epuAoUrBFI4wLKpng4IGmyS0pKRFnZ2ehwq0USqfEzczM\noF69OuTmaivNC7cq1DXmjSE3X+9Ow3v70fZBSzOG4g0hamYK1lwFm56ezkcfvYednT1OTk589902\nhg4dQVjYTCZNGs/u3T/zxBNtSUpKpHlz/3wrSREsHx4RMMuBwlS4kydPo1kzP8aNCyE+/jyLFi0X\nKtwqQVFKXMP3ubk5bNy4joiICHbs+J5KKO0EqkbJSknHUNg+qKHBs2Wh+6APUw9qNKV40Bhqagq2\n4KowNvYPGjZszHvvTUKr1XLsWAyLF89nzZqNHDjwE0OGDGT9+i307NmrEkddcxAzcDlQmArXyOef\nf0FCwiUmTnyH9es3CxVulSMvgF68GM/HH4fh6urGmjUb0OlkVERzbXOqQsmKXC5DrS59baUkSabA\naMQQQC1QKq1Qq1X5fqewfVCDyEqFTCZ/oClFTU3BQt6qcPnyJTRq1Jh797K5evWy6TE/vwA8PDz5\n++/TzJkzn23bDDfuj4NhekUgZuEKYtOm9bi6utGjx4solUrkcgW2tuoyU+HevXuXmTPDyMzMIDc3\nl9DQd/H3byFERaVEkiT++98PeeWV1+jVq4/ZZFNxStyqYFhuaWmJnZ2KrKx7ZGWVncDJsMLUmQRL\nCkWerZ9KpUQmk5mtPvWoVIaG45mZj28KFgxbA3PnfoylpSVDhgwnMfE68fEXOHz4Nzp06IxKZaiD\nlckM1x8cPBAQbj1lhQiYFcSLL77ErFnTCQ/fiV6vZ/Lk/wJlp8LdunUzbdq0JTh4IJcvX2L69Cms\nW7dZWPuVEplMxqZN2wp7hLK29CvsuQ2CFkWlGZZDXncPc9eg8kKn06PT5QCGNK5cLsPCwgIbGyUW\nFoZ9NwsLBSqV0rQXap7FrakpWHMVrE6nQ6WyJTk5CVdXN1QqFc7OLnh4eLF580Y0mjhOnz6FnZ0d\nTZs2q+SR10yESraGkJ6ejpWVJdbWSuLjLzBv3mwWLPicESMGs3nzNwBs2/Z/aLW5pKTcfOT2QILi\nkDAPng+jxLWwsMDOTkVOTu6/+jVWFAZrOUN3j7t3MyulttLcYi89PQNJykvjWlpacO/ePUaNGkXj\nxt40b+5PQEBLatVyq/BxmlOeWZ7Fi+dz584dPD296NWrD6NHv01o6Lt07NiFnJwc4uLOoNHEYWlp\nSZ8+LwMiDfsoCJVsDaI4UVFKyk0+/jiMcePeE6KiSuPBln5Avt6gWq2Wu3fv0KhRI9LTy6/DyYMo\nqbVceZLXFi1/nWvBfdCQkNEcP36MPXsimD//U9RqNYGBrRg7djwODo4VPu6yyvIcOfI73t5NcHV1\nIzc3lwULPiErK4tRo8YRExNFrVquvPHGENasWYm3tw916tSlRYuWtGjR0jQWYURQPogZshpSlKjo\nwoXzTJs2mTFj3qFVqyfIyLgrREVVguINFa5cSWD27Jn4+fnxzjsTKm1FZ6xrrCznIgCVSom1tfUD\n26JJEvj4NMPHx4/g4DfQ6/UkJFwiLu5Mpb2Xg4MHYmVlCXDfyMG6VKVjV64k0KyZH2AwFEhPT2PQ\noLdwd3cnKKgPt26l0KFDJy5dimfmzKksXbr6X9csgmX5IGbJGsLFi/GEhU1ixoy5JgP5shQVCcoS\nQwCVJIndu3excuVShg0bQd++/fOVrVSUErfkdY3lR0Gbv+JuGgpTwcrlcho2bETDho3KfaxQPlme\n7OxslEolwcEDiYgI5+xZDUOHjsDaWsmNG8ncu5eNtbWSo0ePmNpx7dsXIW52KxDxStcQVq1aRk5O\nDkuWfAaAWq3mk08WlpmoCOC3337hl19+Yvr02QBCgfuIXLmSQHj4LpYuXVXgpqXilLhVob7T2OXk\nwUrcqqOCLY8sj1KpJCsrCxsbG5o39+fLL1fRqVMXmjZtxqFDv6FQKOjQoTMXL17Ax6cpAN27v1D+\nFyswIUQ/ghKxePFnREcfoUkTH2bMmAsgbP0qlNJb+hWGsbYS4O600U1ZAAAS0ElEQVTdjEqp7wRQ\nqWywtra6bzVYfAo2TwFbNYUsFy/GM2XKxHxZHvj358Q8y/PRR//lzJnTrF+/hp49g9iwYS0dO3Yh\nKKg3Fy6cY9261cydu4ADB/Zz9qyGlJSbODg4EBY2E2trZSVebc1GiH4Ej0RAQAs6derCzp3fAqK5\ndsVT+ubaBckzbi/b2sqHIc+Q4cFdTqqLEcHDZnmio6MYPfptJEli0KA3iY2N4e23Qzh37izr16/h\ngw+mcPx4LF98sYwPPpiCQqHg8uVLNGniCwhhT2UgAqYgH0XtzXTr1p3Y2BjTMaHArWyKt/QrTIl7\n71426elpNGniXSG1lUVhDNgPNmSoOinYklBUJsXfP4DVqzfkOyaXy/nggykkJl5n6dKFbN/+P5o1\na07nzs/i4eHJrl072Lz5K0JDJ/D666+wY8d2Bg16yxQs9Xq9CJaVgJjFHjMe1KGgOFs/c2xtbYUC\nt0pRvBI3Pv4CM2dOo337pwkJGVMpSlwouRmCYXgKqnIKtjRERh6mfv36eHo2IDo6ioiIcDp3fpYr\nVy5z8uRfXL/+D40aedOhQ2d27vyO/fv3sGLFWmxsVPnOI4zTKwfxqj9mmH/Q9Hp9qSdOcwWuJElE\nRx8hMLAVAQGBREX9DiAUuJWKYVUmSTJ27tzJuHGjeeWVAQwfPga93hCQ8kRF5Y9cLsfR0Q653OAF\nW3SwlAqkYGtOsMzMzOTYsT+pXdsdgL17d3PmzCm6d3+BYcNG4uDgyI4d28nIuEuLFi15+ulnsLOz\nx97eAUtLy0pzfBLkIQLmY8Thwwc5fPg3089yubxQJxCtVktOzoOL1o17M8OHG1JFfn7+dOrUFSsr\nK0JChrJ06ULGjZvw0OPU6/XMnz+HkSOHMHbsCK5evfLQ5xAYuHr1Cnv3/sjy5Wt58cU+yGQKDCs3\nCwwBVYEkye5/QXkEUGtrSxwd7cjOzrnv2lP4cxiCuDFQ1rypyeCHm01ERDgAI0eORafT8e23BgvG\n4cNHodHEsXv3D1hZWfH88z1p16696e9FCrbyESrZx4iff97P5s0bWLduM0ePHiE8fCczZ879V9Dc\nunUzkiTRu/fL2NjYVHij2d9+OyCUtpVC6S39ikKtNjTcTksrvtl1dVDBPgpGm7o9e3aTknKTvn37\no1LZEhUVyYIFnzBnznyaNPFlz57dJCZeZ/Dgtyt7yI81Ralka95tnKBI2rZ9kmbN/Jg1axpbtmzi\n2Wf/g0wmM01kWq2WyMjDREcf5dVXX8fGxgbIn8aVJKnY1kplwYkTx4XStlIwimsUpq+CK9CSZvAV\nCjmOjvaAwYig6GBZc1Ow5osR401p7druHDnyO9euXQOgXbv2BAX1ZurUSQD06PGiCJZVGBEwHxMk\nScLe3oFr164SF3eGMWPG0bXrf/L9zp9//sHRo5EMGGBoCRQZeZj//e9rTpw4TkbGXcDwwTcPoNnZ\n2ezataPYGrqHpSilraCi+XcANaRx5QUCaF5gkCSJpKREHBzsyMrKvm/eXvjZa3oKViaTkZqaSnp6\nuulYq1ZP4O3tw7p1q7l1KwWAN98cSps2T5KYmGi6GS3vm1JB6ah571JBochkMhYtmodabQhE7u51\ngfx3wQcO7MfJydlk4vzdd9vYty+CffsiGD9+DHv27Obnn/exb1+EKYDqdFqsra1NSli9Xo9Op3sk\nFaatra1Q2lZJjAHUPIjm7YNmZGQwe/bHhIVNITU1rVjz9vyrypozDRnf95IkkZ2dzbRpH3Hq1AkA\n003f+PHvo1DI2bp1C1FRkchkMiZOnIy7u7vpPEIFWzUR/5XHAJ1Ox6efzubChfNMmhRGvXr1+fvv\n04AhkBrFBBpNHC1atESpVJKbm0tCQgIjRozh/fc/om7deuzd+yM5OTls3vwV27f/D4A//jhqsukC\nwwddoVDk2xd92AAqlLbVhbwAevbsOYYOHYKFhSVLlqxEpytKiVtzU7CQl3rNzc1FqVTy1FPt+emn\nvYChdZsxNf3uux/g4eHB1q2b+fbbbZw8+RcgAmVVR9y2PwYoFAr69x+AlZUVdnZ2dOzYmbVrV/LU\nU0+bBD03b95ArVbj7l4HgGvXriKTYVLp3bmTSvfuL/DCC0GoVLYcPPgLAGvXfsHAgW/SsGEjwsO/\n58SJv/Dw8CIoqDdOTk6m5zfHmG4qanLo1Klrqbxui+P06VOsXPk5y5at5urVK8yePR2ZTEajRo2Z\nMGEScrmcdetWc+TIYRQKC8aNm0Dz5v6P/LyPA5IkMX/+HIYOHUH37j0KPKovEDSNop6aGRiysrJY\nt241jo6ODBr0Fk8+2Y7U1NtotVoUCgUKhQJJknBxqUVQUB+efPJp/v77tMigVBPEf+kxoXFjwypN\np9MRFNSHVq3aAHlBKzMzA2/vJty5k0rduvU4c+YUderUA+Dy5Uvo9Xr8/QPIyckhJeUmKpUter0e\nrVZL69Zt2LRpPSdPnqBLl2c5efIEX3+9gdDQd1m6dBG+vk2pU6cuAQGB+Z7THPNmt3K5nIkTJ5fZ\ntW/e/BV79/6IUmkQMS1dupDhw0fRunUb5s+fw6FDv+HuXofjx2NZvforkpKSmDr1A9au3VhmY6jJ\nyGQy1qwp6rWqmoExIeESI0a8xa5d+7C2ti6zRgI2NjYEBrZi//49TJ8+hZde6ktU1O8EBfXG09Mr\n3/tckiTc3Grj5la7Ii5ZUAZUzXezoMwxpkSNqz2jBywYVnyeng3IzMwkNfU2AEePRuLp6QUYVKvO\nzi44O9ciKyuTxMR/aNzYmwsXzuHk5ERmZiZ//XWMAQMG0rNnL8aPf4/4+PPk5uaye/dO/vzzD1as\n+JwbN5KJizvDhg1riY6OMtV66vX6f6Vwy5J69eoze/Z8088aTRytWj0BGFbQMTHRnDhxnLZt2yGT\nyXB3d0en03L79u0yHYegapCRcZdlyxZhaWllOvbZZ3OZPn02K1Z8yZkzp9Bo4jh06FdTk+eQkFCW\nLVtUovN36NCJGTPmYGdnz4kTx0lOTubIkcMA+d7nhdVAC6o2ImA+JhT34TSu+Fxd3UyKxtDQ9xg8\neBgASUlJuLvXQa1Wk5yczPXr12nWzI/Y2BiaN/cnJeUmTk7OeHp6odVqsbZWsmjRck6dOkHt2nUI\nCRnLypVfotH8zYIFn6DVatm2bQuLFs03Pf8ffxw1CX3KukC7S5du+VJe5nf5KpUtGRl3yci4axJE\nmR8X1CwkSWLevNmMGDEGpdLQ7cO8kYBMJjM1EnjU8qaxY98hKKg3LVu2Ijk5GRDq1+qOSMkKTPTp\n8zLHjsUCUKtWLdPxYcNGmhR+lpaWuLm50aBBAxYtmkf37i9Qu7Y7WVmZZGVl4urqxqFDv/LUU+2J\nioqkeXM/nJycOX/+HDEx0Tz3XA+CgweSlpbGvHmz7isIZUyYMJbevV/m3DkNvXr15umnO6DT6QpN\nVxkt/UobWM1TwpmZGajVamxt1WRmZhQ4bleq8wuqBoU1EnB3r0O3bt3ztd8qr0YCVlbW1Kql5L33\nPiQ0NIQ33xyCg4PjI16VoDIRAVNgwtXVzSTaMF+FAaZJokGDhoSGGuzuxo4dT7169XFycqZePQ8W\nLPgUX9+mREYexsurIRcvxtO1azcA7t5Nv7/f2RYAe3t7rK2VpKamkpBwkaZNm/H++x8SERHOd99t\nIy0tjV27vsfb25spU2ZgY2NjmqwKGin89dcx9Ho9rVu3KdF1NmniS2xsDK1btyEqKpLWrdtQr54H\nK1d+zmuvvUFycjJ6vYSjo5jcqjOFNRIYMKAP4eE7CQ/fya1bKUyYMJZ58xaVSyMB4+cnJycHtVqN\nTCYSetUdETAFhVJUCtc8kPr7tzAdDwkZy6lTJzh9+iSffLKQ+vU9OHHiGCEhYwHw9PQiJuYPhg8f\nBRhUuAkJl/Dx8eXrrzfQr18wYFAZOjg40rnzs/Tt+wqzZ08nIeESzs7OHDiwn3379lCrVi369Qum\nXbv2JCUlEhMTjZdXg3xjNO6LFiYwGjt2PPPmzWbVquV4eTWgS5duKBQKWrRoyciRQ5AkiQkTJpX4\ntdJqtcydO4Pr16+Tm5vDW28No0GDRkKJWwXZuvV70/f9+/di4cJl9+uIDY0E6tatR3T0kXxNnrt1\ne+6Rypvi48/Tq1cf7O3ty+oyBJWECJiCh8I8kJoHT5lMRkBAoEkJC7BmzUaTuMjBwZGuXbvx+ecL\naNnyCY4d+xMfH1/c3Gpz4cI52rV7BoDLlxN46qn2uLjU4vbtW6Sl3cHe3p4VKz7Hw8OTVavWc/as\nhl9//Rlv7yZs3/5/7NjxLa+99jqQ176sYLrW3b0OK1d+CRiC97Jlq/91bcOGjWTYsJEP/Zrs3fsj\n9vaOhIV9zJ07qQwZMogmTXyEErcaUVST57Iob+rQobMQ+NQQRMAUlJqCk4AkGZoYG1d1Hh6epscU\nCgUDB77JTz/t5cyZ0zRt2ozg4IGcOXOK7OxsnJycSEtL4+7ddJydXVAqlVy//g+SJOHg4EB0dBQJ\nCRc5duxPGjf25rvvtvPSS33x9W1Oq1aX8PT0Ijk5icjIQ0RG/o6bmxsvvzyAhg0bmcZqDKIXLpwn\nIeESzzzTEWtr60d+Hbp2/Y8p9Wy4Vot/KXGjo4/i6elVqBLXWK8qqFi++eYH0/dFNXkui/ImESxr\nDiJgCsoMmUxW7ORgZ2dH37798x1TKpWmPdH4+POkpd3BxcXF9LOnpxfp6elYW1uzfv0Wrl69wtmz\nGuztHXB0dOKff64SGNiS55/vyeLFn+HiUouxY8cTF/c3P/ywg9Gj3+Hs2TguXoxHrbbDzy+Axo29\n+f77b6ld2x0/v0dPiapUhua+mZkZTJ06ieHDR7F8+eJClbjmog/jcREwBYLqgdiFFlQoOp0un7S+\nUSNvgoNfQ5Ik/P1bMGrUOBo3boJWq+XSpYu4urqhVtvRuLE3cXFnqF/fA1dXV554oi1arZbbt2/h\n4lKLlJSb/P77Ifbvj+DrrzeQknKTvXt/JDk5iYSES6xd+wV79uxmy5avANBqc8nMLLuykaSkREJD\nQ3j++Z50795DKHEFghqIWGEKKpSCe4s6nc7kPWthYWFKoQKMHz+R7Oxs1Go17dt3ZO7cmVhYWOLo\n6MTzz79A3br1yM7OpkWLlly5chlvb28mTQrjzJlTnD59khde6MWNGze4cSOZli1bM23aLMCwz+nj\n05TTp0/Rtm27R74mo9ry3Xc/oE2bJwGhxBUIaiIiYAoqleJqKS0sLExmAv36vUK/fq+g0cTh6uqK\ns7MLt2/fIj7+AhER4XTq1BVJkrh16ybt23fA2dkFV1dXsrOzuXXrlqnkxChUuncvm6SkxDK5ho0b\n15Oens6GDWvZsGEtAO+88z5LlnxWZkpcg4H+LK5cSUAuVzB58jQkSRJKXIGgApEV10Xixo300vdo\nEgjKEKP6tSBXr14hJeUmgYGt2LBhLQcP/kqdOnVISLjE22+H4OZWmx07vqFfv1do1szP9HezZk2j\nQYOGvP764Aq8itJz8OCvHD78G5MnTyM2NoZt27YgSRIDBgwyKXGffPJp3N3rsHz5YpYsWSmUuAJB\nKXF1tStUjCFWmIJqgTFYFjRUqF/fw2SIPXjw2/ToEcSJE8fx8fGlQYOG7Nu3h9TU23h4eJn+5vLl\nBG7cuEH//q9W7EU8Ap06daF9+w6AYb/UycmFI0cOCyWuQFCBiIApqFYUVspifszd3R1397wWU507\nd8Xb29uU2tVqtRw8+Au+vr74+jalOmFhYcGsWdM4ePBXZs36lMjIQ0KJKxBUICJgCqo1hZWxmKdv\nra2t8zm0/PXXMY4fj2Xq1JnVsj5u6tQZpKTcZMSIwdy7d890XChxBYLyR5SVCGocBfc6jfv0sbEx\nnD59kkGD3sLR0bFadY7Ys2c3mzatBwy1q3K5nKZNmxEbGwNAVFQkgYGtCAgIJDo6Cr1eT2JiolDi\nCgRliBD9CB4rtFqtqYylOpGVlcWcOTO4dSsFrVbL66+/hZdXQ+bNm01ubi5eXg2YNGkqCoWCL79c\nRVRUJJIkERo6gcDAlg/1XLdv32LYsDdYtGg5CoVCKHEFjx1FiX5EwBQIBCa0Wi1hYR9y8WI8n366\nkBUrlgglruCxo6iAKVKyAoHAxLJli+nT52VTP9SCnrgxMYbGyoUpcQWCmk6xK0yBQPD44OvrOxio\nr9FoZvn6+v4KhAAHNBpN3fuPPwsMBeKAFI1Gs/L+8YPAUI1Gc75SBi4QVBBCJSsQCIwMBSRfX9//\nAC2BjYCb2eN2QCqQdv/7gscFghqNWGEKBIJ/YbbCnA8s0Gg0v/r6+n4B/AKcB+YBzwH1gR80Gk1g\nUecSCGoKYoUpEAiK4z1gja+vrxXwN/CNRqPR+fr6HgKOYNBBjKnMAQoEFYVYYQoEAoFAUAKESlYg\nEAgEghIgAqZAIBAIBCXg/wGiME3TI4PtcwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"trajectories.plot_coord(ntrajectories, three_dim=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Calculate the fraction of trajectories that are reflected and transmitted"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reflectance = 0.7013787589631549\n",
"Transmittance = 0.29862124103682736\n",
"Absorption coefficient = 1.1116844352234298e-16 1 / micrometer\n"
]
}
],
"source": [
"thickness = sc.Quantity('50 um') # thickness of the sample film\n",
"\n",
"reflectance, transmittance = det.calc_refl_trans(trajectories, thickness, n_medium, n_sample, boundary)\n",
"\n",
"print('Reflectance = '+ str(reflectance))\n",
"print('Transmittance = '+ str(transmittance))\n",
"print('Absorption coefficient = ' + str(mu_abs))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Add absorption to the system (in the particle and/or in the matrix)\n",
"Having absorption the particle or in the matrix implies that their refractive indices are complex (have a non-zero imaginary component). To include the effect of this absorption into the calculations, we just need to specify the complex refractive index in n_particle and/or n_matrix. Everything else remains the same as for the non-absorbing case."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reflectance = 0.29437815489599634\n",
"Transmittance = 0.07463595358754266\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/vhwang/anaconda/lib/python3.5/site-packages/pint/quantity.py:715: RuntimeWarning: divide by zero encountered in double_scalars\n",
" magnitude = magnitude_op(new_self._magnitude, other._magnitude)\n"
]
}
],
"source": [
"# Properties of system\n",
"n_particle = sc.Quantity(1.54 + 0.001j, '') \n",
"n_matrix = ri.n('vacuum', wavelen) + 0.0001j \n",
"n_sample = ri.n_eff(n_particle, n_matrix, volume_fraction) \n",
"\n",
"# Calculate the phase function and scattering and absorption coefficients from the single scattering model\n",
"p, mu_scat, mu_abs = mc.calc_scat(radius, n_particle, n_sample, volume_fraction, wavelen)\n",
"\n",
"# Initialize the trajectories\n",
"r0, k0, W0 = mc.initialize(nevents, ntrajectories, n_medium, n_sample, boundary, seed=seed, \n",
" incidence_theta_min = incidence_theta_min, incidence_theta_max = incidence_theta_max, \n",
" incidence_phi_min = incidence_phi_min, incidence_phi_max = incidence_phi_max)\n",
"r0 = sc.Quantity(r0, 'um')\n",
"k0 = sc.Quantity(k0, '')\n",
"W0 = sc.Quantity(W0, '')\n",
"\n",
"# Generate a matrix of all the randomly sampled angles first \n",
"sintheta, costheta, sinphi, cosphi, _, _ = mc.sample_angles(nevents, ntrajectories, p)\n",
"\n",
"# Create step size distribution\n",
"step = mc.sample_step(nevents, ntrajectories, mu_scat)\n",
" \n",
"# Create trajectories object\n",
"trajectories = mc.Trajectory(r0, k0, W0)\n",
"\n",
"# Run photons\n",
"trajectories.absorb(mu_abs, step) \n",
"trajectories.scatter(sintheta, costheta, sinphi, cosphi) \n",
"trajectories.move(step)\n",
"\n",
"# Calculate the fraction of reflected and transmitted trajectories\n",
"thickness = sc.Quantity('50 um')\n",
"\n",
"reflectance, transmittance = det.calc_refl_trans(trajectories, thickness, n_medium, n_sample, boundary)\n",
"\n",
"print('Reflectance = '+ str(reflectance))\n",
"print('Transmittance = '+ str(transmittance))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As expected, the reflected fraction decreases if the system is absorbing. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculate the reflectance for a system of core-shell particles\n",
"\n",
"When the system is made of core-shell particles, we must specify the refractive index, radius, and volume fraction of each layer, from innermost to outermost. \n",
"\n",
"The reflectance is normalized, so it goes from 0 to 1. "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Properties of system\n",
"ntrajectories = 100 # number of trajectories\n",
"nevents = 100 # number of scattering events in each trajectory\n",
"wavelen = sc.Quantity('600 nm')\n",
"radius = sc.Quantity(np.array([0.125, 0.13]), 'um') # specify the radii from innermost to outermost layer\n",
"n_particle = sc.Quantity(np.array([1.54,1.33]), '') # specify the index from innermost to outermost layer \n",
"n_matrix = ri.n('vacuum', wavelen) \n",
"n_medium = ri.n('vacuum', wavelen) \n",
"volume_fraction = sc.Quantity(0.5, '') # this is the volume fraction of the core-shell particle as a whole\n",
"boundary = 'film' # geometry of sample\n",
"\n",
"# Calculate the volume fractions of each layer\n",
"vf_array = np.empty(len(radius))\n",
"r_array = np.array([0] + radius.magnitude.tolist()) \n",
"for r in np.arange(len(r_array)-1):\n",
" vf_array[r] = (r_array[r+1]**3-r_array[r]**3) / (r_array[-1:]**3) * volume_fraction.magnitude\n",
"\n",
"n_sample = ri.n_eff(n_particle, n_matrix, vf_array) "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reflectance = 0.7699860578331252\n",
"Transmittance = 0.23001394216696266\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/vhwang/anaconda/lib/python3.5/site-packages/pint/quantity.py:715: RuntimeWarning: divide by zero encountered in double_scalars\n",
" magnitude = magnitude_op(new_self._magnitude, other._magnitude)\n"
]
}
],
"source": [
"#%%timeit\n",
"# Calculate the phase function and scattering and absorption coefficients from the single scattering model\n",
"# (this absorption coefficient is of the scatterer, not of an absorber added to the system)\n",
"p, mu_scat, mu_abs = mc.calc_scat(radius, n_particle, n_sample, volume_fraction, wavelen)\n",
"\n",
"# Initialize the trajectories\n",
"r0, k0, W0 = mc.initialize(nevents, ntrajectories, n_medium, n_sample, boundary, seed=seed, \n",
" incidence_theta_min = incidence_theta_min, incidence_theta_max = incidence_theta_max, \n",
" incidence_phi_min = incidence_phi_min, incidence_phi_max = incidence_phi_max)\n",
"r0 = sc.Quantity(r0, 'um')\n",
"k0 = sc.Quantity(k0, '')\n",
"W0 = sc.Quantity(W0, '')\n",
"\n",
"# Generate a matrix of all the randomly sampled angles first \n",
"sintheta, costheta, sinphi, cosphi, _, _ = mc.sample_angles(nevents, ntrajectories, p)\n",
"\n",
"# Create step size distribution\n",
"step = mc.sample_step(nevents, ntrajectories, mu_scat)\n",
" \n",
"# Create trajectories object\n",
"trajectories = mc.Trajectory(r0, k0, W0)\n",
"\n",
"# Run photons\n",
"trajectories.absorb(mu_abs, step) \n",
"trajectories.scatter(sintheta, costheta, sinphi, cosphi) \n",
"trajectories.move(step)\n",
"\n",
"# Calculate the reflection and transmission fractions\n",
"thickness = sc.Quantity('50 um')\n",
"\n",
"reflectance, transmittance = det.calc_refl_trans(trajectories, thickness, n_medium, n_sample, boundary)\n",
"\n",
"print('Reflectance = '+ str(reflectance))\n",
"print('Transmittance = '+ str(transmittance))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculate the reflectance for a polydisperse system \n",
"\n",
"We can calculate the reflectance of a polydisperse system with either one or two species of particles, meaning that there are one or two mean radii, and each species has its own size distribution. We then need to specify the mean radius, the polydispersity index (pdi), and the concentration of each species. For example, consider a bispecies system of 90$\\%$ of 200 nm polystyrene particles and 10$\\%$ of 300 nm particles, with each species having a polydispersity index of 1$\\%$. In this case, the mean radii are [200, 300] nm, the pdi are [0.01, 0.01], and the concentrations are [0.9, 0.1]. \n",
"\n",
"If the system is monospecies, we still need to specify the polydispersity parameters in 2-element arrays. For example, the mean radii become [200, 200] nm, the pdi become [0.01, 0.01], and the concentrations become [1.0, 0.0].\n",
"\n",
"To run the code for polydisperse systems, we just need to specify the parameters accounting for polydispersity when calling 'mc.calc_scat()'. \n",
"\n",
"To include absorption into the polydisperse system calculation, we just need to use the complex refractive index of the particle and/or the matrix. \n",
"\n",
"The reflectance is normalized, so it goes from 0 to 1. \n",
"\n",
"Note: the code currently does not handle polydispersity for systems of core-shell particles. "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reflectance = 0.7387919731435268\n",
"Transmittance = 0.26120802685645683\n"
]
}
],
"source": [
"# Properties of system\n",
"n_particle = sc.Quantity(1.54, '') \n",
"n_matrix = ri.n('vacuum', wavelen) \n",
"n_sample = ri.n_eff(n_particle, n_matrix, volume_fraction) \n",
"\n",
"# define the parameters for polydispersity\n",
"radius = sc.Quantity('125 nm')\n",
"radius2 = sc.Quantity('150 nm')\n",
"concentration = sc.Quantity(np.array([0.9,0.1]), '')\n",
"pdi = sc.Quantity(np.array([0.01, 0.01]), '')\n",
"\n",
"# Calculate the phase function and scattering and absorption coefficients from the single scattering model\n",
"# Need to specify extra parameters for the polydisperse (and bispecies) case\n",
"p, mu_scat, mu_abs = mc.calc_scat(radius, n_particle, n_sample, volume_fraction, wavelen, \n",
" radius2=radius2, concentration=concentration, pdi=pdi, polydisperse=True)\n",
"\n",
"# Initialize the trajectories\n",
"r0, k0, W0 = mc.initialize(nevents, ntrajectories, n_medium, n_sample, boundary, seed=seed, \n",
" incidence_theta_min = incidence_theta_min, incidence_theta_max = incidence_theta_max, \n",
" incidence_phi_min = incidence_phi_min, incidence_phi_max = incidence_phi_max)\n",
"r0 = sc.Quantity(r0, 'um')\n",
"k0 = sc.Quantity(k0, '')\n",
"W0 = sc.Quantity(W0, '')\n",
"\n",
"# Generate a matrix of all the randomly sampled angles first \n",
"sintheta, costheta, sinphi, cosphi, _, _ = mc.sample_angles(nevents, ntrajectories, p)\n",
"\n",
"# Create step size distribution\n",
"step = mc.sample_step(nevents, ntrajectories, mu_scat)\n",
" \n",
"# Create trajectories object\n",
"trajectories = mc.Trajectory(r0, k0, W0)\n",
"\n",
"# Run photons\n",
"trajectories.absorb(mu_abs, step) \n",
"trajectories.scatter(sintheta, costheta, sinphi, cosphi) \n",
"trajectories.move(step)\n",
"\n",
"# Calculate the reflection and transmission fractions\n",
"thickness = sc.Quantity('50 um')\n",
"\n",
"reflectance, transmittance = det.calc_refl_trans(trajectories, thickness, n_medium, n_sample, boundary)\n",
"\n",
"print('Reflectance = '+ str(reflectance))\n",
"print('Transmittance = '+ str(transmittance))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculate the reflectance for a sample with surface roughness\n",
"\n",
"Two classes of surface roughnesses are implemented in the model:\n",
"\n",
"1) When the surface roughness is high compared to the wavelength of light, we assume that light “sees” a nanoparticle before “seeing” the sample as an effective medium. The photons take a step based on the scattering length determined by the nanoparticle Mie resonances, without including the structure factor. After this first step, the photons are inside the sample and proceed to get scattered by the sample as an effective medium. We call this type of roughness \"fine\", and we input a fine_roughness parameter that is the fraction of the surface covered by \"fine\" roughness. For example, a fine_roughness of 0.3 means that 30% of incident light will hit fine surface roughness (e.g. will \"see\" a Mie scatterer first). The rest of the light will see a smooth surface, which could be flat or have coarse roughness. The fine_roughness parameter must be between 0 and 1. \n",
"\n",
"2) When the surface roughness is low relative to the wavelength, we can assume that light encounters a locally smooth surface with a slope relative to the z=0 plane. The model corrects the Fresnel reflection and refraction to account for the different angles of incidence due to the roughness. The coarse_roughness parameter is the rms slope of the surface and should be larger than 0. There is no upper bound, but when the coarse roughness tends to infinity, the surface becomes too \"spiky\" and light can no longer hit it, which reduces the reflectance down to 0. \n",
"\n",
"To run the code with either type of surface roughness, the following functions are called differently:\n",
"\n",
"- calc_scat(): to include fine roughness, need to input fine_roughness > 0. In this case, it returns a 2-element mu_scat, with the first element being the scattering coefficient of the sample as a whole, and the second being the scattering coefficient from Mie theory. If fine_roughness=0, the function returns only the first scattering coefficient in a calculation without roughness.\n",
"\n",
"- initialize(): to include coarse roughness, need to input coarse_roughness > 0, in which case the function returns kz0_rot and kz0_refl that are needed for calc_refl_trans(). \n",
"\n",
"- sample_step(): to include fine roughness, need to input fine_roughness > 0.\n",
"\n",
"- calc_refl_trans(): to include coarse roughness, need to input kz0_rot and kz0_refl from initialize(). To include fine roughness, need to input fine_roughness and n_matrix.\n",
"\n",
"$\\textbf{Note 1:}$ to reiterate, fine_roughness + coarse_roughness can add up to more than 1. Coarse roughness is how much coarse roughness there is on the surface, and it can be larger than 1. The larger the value, the larger the slopes on the surface. Fine roughness is what fraction of the surface is covered by fine surface roughness so it must be between 0 and 1. Both types of roughnesses can be included together or separately into the calculation. \n",
"\n",
"$\\textbf{Note 2:}$ Surface roughness has not yet been implemented to work with spherical boundary conditions. "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R = 0.764572202901259\n",
"T = 0.19707275383599052\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/vhwang/anaconda/lib/python3.5/site-packages/pint/quantity.py:715: RuntimeWarning: divide by zero encountered in double_scalars\n",
" magnitude = magnitude_op(new_self._magnitude, other._magnitude)\n"
]
}
],
"source": [
"# Properties of system\n",
"ntrajectories = 100 # number of trajectories\n",
"nevents = 100 # number of scattering events in each trajectory\n",
"wavelen = sc.Quantity('600 nm') \n",
"radius = sc.Quantity('0.125 um')\n",
"volume_fraction = sc.Quantity(0.5, '')\n",
"n_particle = sc.Quantity(1.54, '') # refractive indices can be specified as pint quantities or\n",
"n_matrix = ri.n('vacuum', wavelen) # called from the refractive_index module. n_matrix is the \n",
"n_medium = ri.n('vacuum', wavelen) # space within sample. n_medium is outside the sample. \n",
" # n_particle and n_matrix can have complex indices if absorption is desired\n",
"boundary = 'film' # geometry of sample, can be 'film' or 'sphere'\n",
"n_sample = ri.n_eff(n_particle, n_matrix, volume_fraction) \n",
"\n",
"# Need to specify fine_roughness and coarse_roughness \n",
"fine_roughness = sc.Quantity(0.6, '')\n",
"coarse_roughness = sc.Quantity(1.1, '')\n",
"\n",
"# Need to specify fine roughness parameter in this function\n",
"p, mu_scat, mu_abs = mc.calc_scat(radius, n_particle, n_sample, volume_fraction, wavelen, \n",
" fine_roughness=fine_roughness, n_matrix=n_matrix)\n",
"\n",
"# The output of mc.initialize() depends on whether there is coarse roughness or not\n",
"if coarse_roughness > 0.:\n",
" r0, k0, W0, kz0_rotated, kz0_reflected = mc.initialize(nevents, ntrajectories, n_medium, n_sample, boundary,\n",
" seed=seed, incidence_theta_min = incidence_theta_min, \n",
" incidence_theta_max = incidence_theta_max, \n",
" incidence_phi_min = incidence_phi_min, \n",
" incidence_phi_max = incidence_phi_max,\n",
" coarse_roughness=coarse_roughness)\n",
"else: \n",
" r0, k0, W0 = mc.initialize(nevents, ntrajectories, n_medium, n_sample, boundary, seed=seed, \n",
" incidence_theta_min = incidence_theta_min, incidence_theta_max = incidence_theta_max, \n",
" incidence_phi_min = incidence_phi_min, incidence_phi_max = incidence_phi_max,\n",
" coarse_roughness=coarse_roughness)\n",
" kz0_rotated = None\n",
" kz0_reflected = None\n",
" \n",
"r0 = sc.Quantity(r0, 'um')\n",
"k0 = sc.Quantity(k0, '')\n",
"W0 = sc.Quantity(W0, '')\n",
"\n",
"sintheta, costheta, sinphi, cosphi, _, _ = mc.sample_angles(nevents, ntrajectories, p)\n",
"\n",
"# Need to specify the fine roughness parameter in this function\n",
"step = mc.sample_step(nevents, ntrajectories, mu_scat, fine_roughness=fine_roughness)\n",
" \n",
"trajectories = mc.Trajectory(r0, k0, W0)\n",
"trajectories.absorb(mu_abs, step) \n",
"trajectories.scatter(sintheta, costheta, sinphi, cosphi) \n",
"trajectories.move(step)\n",
"\n",
"z_low = sc.Quantity('0.0 um')\n",
"cutoff = sc.Quantity('50 um')\n",
"\n",
"# If there is coarse roughness, need to specify kz0_rotated and kz0_reflected. \n",
"reflectance, transmittance = det.calc_refl_trans(trajectories, cutoff, n_medium, n_sample, boundary,\n",
" kz0_rot=kz0_rotated, \n",
" kz0_refl=kz0_reflected)\n",
"print('R = '+ str(reflectance))\n",
"print('T = '+ str(transmittance))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Run photon packets in a medium with a spherical boundary\n",
"\n",
"Example code to run a Monte Carlo calculation for photon packets travelling in a sample with a spherical boundary\n",
"\n",
"There are only a few subtle differences between running the basic Monte Carlo calculation for a sphere and a film:\n",
"1. Set boundary='sphere' instead of 'film'\n",
"2. After initialization, multiply r0 by assembly_diameter/2. This corresponds to a spot size that is equal to the size of the sphere. \n",
"3. Assembly_diameter is passed for sphere where thickness is passed for film\n",
"\n",
"The sphere also has a few extra options for more complex Monte Carlo simulations, and more plotting options that \n",
"allow you to visually check the results.\n",
"\n",
"1. initialize():\n",
" When the argument boundary='sphere', you can set plot_initial=True to see the initial positions on of the trajectories on the sphere. The blue arrows show the original directions of the incident light, and the green arrows show the directions after correction for refraction. **For sphere boundary, incidence angle currently must be 0**.\n",
" \n",
"2. calc_refl_trans():\n",
" when argument plot_exits=True, the function plots the reflected and transmitted trajectory exits from the sphere. Blue dots mark the last trajectory position inside the sphere, before exiting. The red dots mark the intersection of the trajectory with the sphere surface. The green dots mark the trajectory position outside the sphere, just after exiting."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Calculate reflectance for a sphere sample"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/vhwang/anaconda/lib/python3.5/site-packages/pint/quantity.py:715: RuntimeWarning: divide by zero encountered in double_scalars\n",
" magnitude = magnitude_op(new_self._magnitude, other._magnitude)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reflectance = 0.26284944672915533\n",
"Transmittance = 0.7371505532708418\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADnCAYAAAC9roUQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADnCAYAAAC9roUQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzsvXmcJHV9//+ss+9jzt3Z2ZNjEUEE\nFrkk0aAgCog3URPwxBuveITIFwnRKBgVxYgXikENXviLt/n5TQgIGBRUQFhg7925Z/o+6v7+UV3V\n3TM9M927PTuzQ70ej35Md01V9aeqq171/ryP11twHIcAAQIECHB4IC73AAIECBDgqYSAdAMECBDg\nMCIg3QABAgQ4jAhIN0CAAAEOIwLSDRAgQIDDCHmhf05OFoLUhgABAgToEAMDCWG+/wWWboAAAQIc\nRixo6QYIcKThwfEHeDzzGKqk8vxNLyChJpZ7SAECNCGwdAOsGjwy9RC/2vMz9hf3sTO3g289+k1s\nx17uYQUI0ISAdAOsGuzM7kAWFf/zdGWKol5YxhEFCDAXAekGWBDnnHMal112Ka973Wt4/etfw6tf\n/TLe9KbLeOyxPy+67RNPbOdVr7qEN7zhb/jZz37MBz/4noMex9e//hXuuuu/F1wnpsabLFtVCvGy\niy5idHTkoL/Xw9TUJG996xsAGBk5wD/8wwcOeZ8BWsOyLXRLX+5hLBkCn26ARfG5z32JdDrtf/72\nt/+Nz3zmBr70pa8vuN3dd/8Pp556Gh/+8NX87Gc/PqQx/P7397N585YF13nO+r9ivDzG3vweVFHl\ngi0v5Ff89JC+10N//wA333wLAGNjo+zdu6cr+w3QjF/t/gX/seMOTNvgGf3P5G0nvwtRWF22YUC6\nATqCaZpMTIyRTCb9Zbfe+jXuvPP/YtsOQ0NDvP/9H+aBB37HHXd8H9u2uPZajWc96wx//WKxyI03\nfoodO57Esky2bXsWb3/7u5FlmUceeZjPfvYGqtUKiqLwjne8h927d7F9+6P8679+DlGUOPvsc/ji\nFz/HH/7wAJZls3XrcbznPX9HLBbn6doJ/PJff4ogCOhP07Ht1j7dyckJPv3p6xkfH8OyTJ73vPO5\n7LI38MADv+Pqqz/EN77xHfr6+nn3u9/Gtm3P4gUveBGXXXYpv/jFf/PJT/4Tk5OTvO997+T66z/L\nZz97Aw899EckSWbdumGuuuoaotHokv8Wqw0zlWm+v/12RNHNtvrDxAP8cvfPeeGWC5d5ZN3F6nqE\nBFgSXHnlW7j88r/mkksu4NWvfjkAV111DQA///lP2LnzSb785Vv5xje+zZlnPptPfOI6zj//hVxy\nycs499zzuOaaf2ra3+c+9y8cd9zTuOWW27jllm+Ry2W5/fZvYZomV131fl7/+jfzb//2XT74wY9w\n442f4qUvfQXHHXc8b3/7lTznOX/Fbbd9A0mS+drXbuPWW79Df/8AX/ziTRiGwdVXf5h3vvO9fP3r\n3+bUU7ehaVrLY7ruuv/DhRe+mFtuuY0vf/lWfve7/+XXv/5PTj31NF784pfxyU/+E7fe+jUUReWy\ny97gbydJEh/60EcYHh7m05++iUceeYgHH/w93/jGd7jllttYt26YHTueWKJfYnVjqjKF6Rj+Z0mU\nyFRnlnFES4PA0g2wKDz3wvbtj/GBD7ybU045jZ6eXgDuueduHn30Ed70pssAsG2LarW64P68bX7y\nk/8AQNPc9XfseNK3ZAGe9rTj+eY3b2+x/V0UCkXuv/+3AJimQTrdw44dTyLLMqeddjoA5513ATfc\n8M9ztq9UKvzhDw+Qz+f56ldvri0r8+STj/O8553HG9/4Ft7+9jdxxx3f55vfvB1RnN82OeqoYxBF\nkSuuuJzTTz+L5z73XJ7+9BMXPP4ArbEptZmB6CA5LesucAROHjhleQe1BAhIN0DbOO64p/Gud72X\nj3/8o2zdehxDQ+uwbYvXvvZyXvrSVwCg6zqFQn7B/di2zXXXfdL30RYKBQRBYGxsFEFoLuTZufNJ\nNm7c3LTMsmze/e73c9ZZzwagXC6j6zpjY6PM1oeWJKnF91s4jsPNN99COBwGIJvNoqoqAKVSkenp\nKQRBYP/+vU3+7NlIJBJ84xvf4aGH/sjvf38/11xzFa9+9d/yspe9csFzEGAuQlKI92/7MD968vsY\ntsmZ687m6f2r7wEWuBcCdITzzruA448/gc997tMAnH76Wfz4xz+iVCoC8NWv3sx11/2fBfdx+uln\ncvvt38ZxHHRd58Mffh8/+MHtbNy4CYD7778PgO3bH+PKK9+G4zhIkoxpmgCcccZZ/PCH38UwDGzb\n5pOf/Ce+9KWbOOaYY3Ech3vvvRuAu+++s+UDIBaLc8IJz+Df//02wCX9t73tDdx9950AfOIT1/GC\nF7yIq666hmuvvZpisdi0feNYfvObu3j3u9/GiSeexBvf+BYuuODCtjI7ArTGYGyQK575dt5xypVs\nW3Pacg9nSRBYugE6xvve90Euv/zV/Pa393LxxS9hamqSt7zl9YDAmjVr+Yd/+OiC27/nPR/gxhs/\nxWWXXYppmpx22hm89rWXI8syH//4Ddx447/whS98DkWR+djHbkBRFM455y/50pe+gGmavO51b+Sm\nm27k9a9/LbZtceyxW3nnO9+DLMv88z9/ihtu+Ge+9KV/5dhjt/pukNm45pp/4jOfuZ7LLrsUwzB4\n/vNfwPnnv5Af/OC7jI+Pcd11n0SWZc4440yuv/5jvO1t7/K33bJlC6oa4s1vvoybb/469913D5dd\ndimRSJREIsGHPvSRLp7tAKsNwkLtegLBmwABAgToHIHgTYAAy4jx0hj3HribvfkgtzdA4F4IEGBJ\n8eeph/nBE98FR8DG4rxNF3D28DnLPawAy4jA0g0QYAnxm5G7ERARBAFJkLl39DfLPaQAy4yAdAME\nWELYs2MmQZTkKY+AdAMEWEKcNXQ2To1pTdvk9KEzl3lEAZYbgU83QIAlxEmDz6Q30svO3A7WxYY5\npufY5R5SgGVGkDIWIECAAF1GkDIWIECAACsEAekGCBAgwGFEQLoBAgQIcBgRkG6AAAECHEYEpBsg\nQIAAhxEB6QYIECDAYURAugECBAhwGBGQboAAywDbtud0uQjw1EBQkRYgwEHC6ywkzJsG3/r/ggC6\nbtU6FQsIggiIC/ZiC7B6EJBugKc0OiHOeDxMpaJjWfai6y/+vQKuoetg2yaCIGBZ+AQsCOKcfnEB\nVgcC0g1wRKORlxrfq6qMZdlYlj3nf60+twNRFBGEg9t2ITSSq+PYOI6FIAiEwyEMw8a2CazgVYSA\ndAMsOyRJwLadg5qmzwdVdZtHOo596AM8zPBIOBoNUyiUsCwzsIJXEQLSDXDImM/aXGxd73M8HqZa\n1TFNq/uDW0VotIIbfcGCIAQkfAQhIN0AQPu+zWQyQqFQmbNdt74/wMKok2vdFyyKIqqqUq2agBC4\nIlY4AtJdRWgkTklybzzPp9lqvfk+LwRFkbtOkEHm1MHBI2BJkgiHVcrlSi0g51nBQuCKWIEISHcF\nod1pejukGQq5P221qndhZHUsZ27pdHma343fD8Bpa55FX7Rv2cZyOFDPcGh/fRcOjuO6ahwHn4CD\ntLSVgYB0u4xOczdlWSIcViiVqks7sBWPhU9YQS9w+/bv+D3HdmSf5G9PuJyEmjgcg1tGHPpDzvUF\nO25zTEkBBAzDDqzgZULw2GsBLy1IFOsvWRaIROSmZZLU/JLl+vvG9Vq9vO9wX0/1C39xYnl8ZntT\nk0fbcXh8ZvtSDuqIgWsRL3wOvWtMVRUURcJxTGxbx7J0TEtnf34fk+XJwzHcpzxWtaW7WE5lJ75N\nWRYJhRQMw+zO4AJ0hLgax7RMZMm9ZE3LJK7GD+sYDvezURC67+8WBLAb3PymbXDnvv9GszQcHNbE\nhnjW2jMCK3gJsaot3c6szYX35frGun8RLtV+YXVlBBzX+zS29m1FM6toZpWtfVs5rvdph30c3SPB\ndnbU3g/YGTkLTd+9I/skpm0iizKKqDBWGmGqPI5t64iijSg6tXLlAN3CqrZ0u4ulCiAtzX5dH97q\neqa+6KgLee6GvwIgqkSXeTTtw8ujddO7qPlWBapVDdte7Pdvl5wP7jpysJse+gICdq2gJBxWsSwL\nw6gGxRldREC6ATqCa5kvX5rXcpGtxzGSJCKKjSQqNJAqc5aD+wB0X+5723aQJIl4PIplWWiaUSsO\nMVt+Z7ePo/G325jYzO78HkREbMcmoSboi7TOCpmvOCPIiOgMAem2CY9sjpT9BmiNuURZJ8u5y+v/\n936ncFhtIFGXQF2ZRuYsXyi4ZRgGtm2jqgqhkEI6HUcQRDRNR9N0dN1N9Wvn4XYo7oWoEuUvh5/D\n3sIeJEHm6PTRiLUZUqv9tirOsCwQRQlRlLDtIDC8GALSnQcVo8L/7L8TyzZ55uApbEitp10fW2dw\nlmi/SwVvvMuXr9sOWcqyhCyLhMNqU5nsbFJsJEvLshuWO00WKkAyGaNUqrThEmgfum6g6waFQhlJ\nEgmFVKLRMKlUHEEQiERCVKtayyKXg0ErIo0qUZ7We3yrtVnod24kV0WRiERCZLMFghLlhRGQbguY\ntsnXH/4aRb0IwMNTD3PZia/juMTRyzyy9rHSLejGqbhHks0EOtsXOps4WxGo44uDi6KAaVrouun/\nb6XDsmzK5SrlchVBEOjv70GWJXp709i27VvBczNoOnkItn9RHLwbqW4F14sz3AKNwBWxykn3YInn\nQOEAM5UZVEn1lz00+UeOW9d90l1aclx61p3r22xNlrOn64BPlo3bzibR2VP2diHLMrZtH3Lk/c9T\nj/DLXT9Hs3SetfE0nj98/iHtr124gVDI50s4ThFFkQmFVJLJOJLkuSEMNE3viBy9c9zm2m2Pd3au\n8HxylUGJ8ion3YNFXI03XUC2YxOWwkfYBdI+QXVCloIgkExG55muz/Zr2pjm3Kn67Js+Fguj6+aK\ny4GumBW+8+i3cWrn8s49dxIXkpwxdOZhH4thuOenWCwjim7OeDiskkzGfL+yJElYVveU2joh6MVu\njdklyr29KfL5IpbluaueOgG5gHRboC/SxxnDZ/HbA/fgABuTGzl7+Jwl+rbu+XQbidKLsodCqp+m\nNPslih5x0tKqbJyue+vEYmEKhUrXcze7/Tzrxv4mSxNUzAphOQyALMqMlkYOfcdto/VB2LZNpaJR\nqWgAJJNxFEWitzeJ49AQjDPm2edSuFoOTieisUTZtYJdAl7NVnBAuvPg/M3nc9a6s9BNnd5I75Jd\nAF7LlpJRIiJHkESpgRTnJ8vZ1ifQNC2vr2v7QaJWU/ZOfXbeeLt/DrqPQ93vYGwNMSWGVROPsW2L\njYlNXRhZJ1kJi69oWW6/tWKxjCxLhEIq8XgUWZbQdcMnYU8ovn1XRPsE3Znbotkd0WwFO4BdG+Pq\nTEsLSHcBJNQEqIuvNxuzp+oLpSlNV6a44d5/Zbo8TUJN8OoTX82W9BYchzm+Tc/qbF7e+mJXFBlV\nlX1rKEDnCMthLj/x9fx8588wbJ0zN53OtsHTVnRQzjQtTLNCqeTKPIZCKqGQSiIRw7Ks2ixIAlpZ\nwXOxVIfaHvnXA3LxeIJ8vsRqsIID0l0As8kSIBRSFvB51nM65wsEzZ6uf++hH1DUijimQN4s8u0H\nb+dd265c5iMP4OGo9FG849R3ApBKxcjny8s8ormYj4Acx6Fa1ahW3QevqiqkUnHi8QjxeLQpJ7gV\nAXZCbLMDaW1sQTtWtHdPhUIqjlNYFcUZq550202Ad5fTRLCzrUpwGwQ6joNpdme6XtKbb+KqWZln\nzU6xVPm/y5+n+1RBJ6TXDuHpuoFl2RQKJWzbbsoJNgzTJ+HGnOClsuo7S0erX2/zFWccSSXKq5p0\nIxGVcFhpsixb5XS675ut01ZIpWJUq3pXL8Rj0kfzwPTvALBsi619W7uy35WepxtgcSwF4XlkNzsn\n2K2MU4nFon5OsBdobW+/S2Pp1vfdermHxrQ0QZAQxZVLbSt3ZF1ApeI+uVcyzt30fAbSfTw2+gS9\nkR7O3fT85R5SgCMMwiy5xkXWZjbZOY7jW7mAnxMsSRI9PUn/f5pmdO1BsBS5xY1W8ErGqibdbsOz\nHrtpgAiCwF9s+kue2XPqkgUtuonAgl6JEIDupfB5OcHhsEo+X6z1YAuRTMYxTQtN06lW9aacYEEQ\nllACcnW5swLS7QhL5c8M/KRHhv5E6zG2U1gye5nj2D55tcqn7Xy63uYRdOhLtSwbXTf9LBhVdYsy\nZucEd9sYOfgxw0q/lgLS7QBHkpXn5eoeGVg5D5vZ5Dg7JzoWC88JzrZTXDI7/c80zaZ82kOZwh9s\nUKqd/c6GJ9ADpaacYEWRURQTcPyc4Pn329kDZakeQMuFgHRXAJbCbRHAJVBPSKeVtdkqY2VubnQj\neeITykI50u3AsmxKJTef1q0cDBGJhEkm65kE7aPTIob297zQuo05walUHNt2UNV6TnC16j5ITLO5\nNPmpfq0HpNsBjizrcXVgfrKsW5uz/+9BkqRZGSqdZ6w0IhIJYRjd0zbwYNsOlUqVSqVaK2hQCIdD\nCIJAb2+qpQ/14NHJ9duZeplhuOMEGnSCEwiC4IvzuDrBnbnSAks3wBLgSPPpdv7gWcjXKcsSkiSh\nqvKcYpRWU/NWRSaz86lXqojOYnALGnR03aSvT6ZYLDf4UB3femw8rqXIBDi4/dY/L6QT7HbIcPVB\n2tEJni9l7EhFQLorAEvhK14q/7PjOEiSmwvZTJ7zlz57281PoK42hGGYc8jzqY5GH2qjvKMoCr4F\nvJQiNt3Q6Z2dExwOh0gk5DZ0ghvR3jiOhNnoqibdbt+3R8IP2oz5xzp3qt5q2VzyBLcqr3Ga7vg6\nEfYcQm2HPCUpVJOB7P7U/UhFq8usUd7RtR5DfhBLktxS2MUDcUvj/23XgnYcB8NwK+Omp7Pz6AQ3\nBxQD90KAFYX5rE1RdMshm6PtjXKOs8mxvmx2iXMjecbjkVoTxe4R5Cq6n7qKhc6Laz1WKJcrpNMJ\nLMsmHK4H4jw3xOzc2c6IdGkMjEYSnasTrPrHYZpuMG61Bd4C0u0ASzllF0UBx2k/z3MueTJrau6+\nGtvVeAR6xMJxoFwGRQH1IOTfjih0NrV3JRxLeCW94bBKIhGdt5ihHRzutC5XJ9gNKEI9JzgcDtXG\nE1tAJ/jIQUC6HWFxEZnFEuXn68ggSZGW1qWXrjS7A8Ni5OndfEdaIMmH40Ch4BJsJAKWRegLn0d6\n6E+gKOgXvhjzwguXe5RLhk4e7o1T+9klvZ6mgheIEwQBRZG7fl10akG3K9Cj6wamafntl+bTCZ71\nDZ0fwGFEQLrzYDZ5elN2RXGjrrMJ9I/jf2RXdieD0UHOWn820Fpgx7LmBpNise5P2Y9oWBbSbd9E\nKxaxwiHMp5+A49iUYlGM88+nvH7YvcN370AIhfxpsGWaWOUSoUoFac1a7JrvGdxZgKIohMMRZFlG\nlmUkSa7LAi7VNOYQ0I0pdT2LoIQsy/T1pWqBOBFN07pYEbd0ucJeibGX1+ym1TXrBLfK6lipWNWk\n6/2w7VibswNKjT5O71VPoLeaCPWe/ffwy10/RxIkDNtk5+QeXnzMJR2Nc4Xd7wui22MtlYrMzMyg\n6zqmaWLpGs45Z8/9orVrmj/bFlRm6dvKMmYiAeXSnO+pVqFQyM1ZLugG4bFR4gcOIG07ndCxxyJJ\nq+/WsCyXkKans02BOLcirm45HoyboDMi7TxPtxFu6lyzTnAopJLNTnPFFVdwyimn8bznnc+znnVG\n299xOLH6rqwGCAKk0/FFqoxmT+fn92VFIiq27UaIG/Gn8T8hCRIAiijz6PSjHZHu0mjfLo2ebjes\nL8dxKBbzZDIzmGYL/9zBilIf5NPLURUqGzdS2bjRXbBnJ5Ikk0ymSKV6fGv48GaudCp92M669fUa\nA3FeLz1X1CaGYZjzCpt3Z7ydX0cLHZ9nzcfjaW644TPce++9/OY3dwWkuxxwHMhmi13dX6v7TpWU\nps/KCtDyXDrr+eDI3HEcKpUy2ewM1Wq3hNqbvqB+N3fhwC3LJJOZJpOZrpW2pkkmo10YaHs4nBF7\ntyLObXQpCKCqKpFICFFsvyJuqS3ddhXMhoeHedWrXrOiUzuXnx1WAZ6/+Xy+9ed/o6DnUaUQ528+\nv6PtjzT3QrtwHIdyuUwul0HTKgcX3W735HjrHMyJXOQ7dF1nenqC6ekJFCVEKpUkHk8t2CZmoYCq\nKAooikS1qh1WP347xOjU9CVM00SW3Yq4eiAO3w8813e6lD7dlZ0yZlkWn//8Z9i+/c/ousEb3nAF\nL3nJi+Zdf9WTbjcJzXFoqaY/nBjmPae9j4nSOL2RPqLK4bOIVho8izaXy1CZ7W89GLT48VzikmqW\nl4MkySiKgmkYmDW/pSyKmO3qu3ZwgRiGxtTUJFNTk6hqiP7+PtLpHhRFbiLZ2TGBRheXadoIgkBP\nT72091AIuBP3QmcVZs6sQFxdV9cNxOlomoamGUuSvXBw4z78+OUvf4Zpmnzxi7cwOTnBf/3X/7/g\n+quedLuL+afWqqSyPrmh6/s9EuBatCUymWl0vf3uw26/OXCcueQoihJehZuHUChMPJ5AVcNkMlNY\nlkkslqCnpw9RFDBNk5GRfQD0PvggicefYPTCCylHI/4+IpEYjmOj61pbU1axIQNiNnRdY2RkhJGR\nESRJJpFIkUqla91254fjOOi67mcUhMMq6XQSoBYg0pekCqvzCrPmZaZpUSyWGyri3PY+qZRbEh4O\nq222s+rsWj+YbIfDid/+9l6OPvoYPvCBd+M4Du997wcXXD8g3Q6wdMURS7Pfpbz43BSeIvl8Fk2r\nzrueLMuA0BQwSybTpNO9lEoFZmamAFBKJZRIDOIxTNOoqVE1Q9Oqc76rVCpQKhXmrDvxzGcy8cxn\nzlleqTRnNTQqkQH09Q2QTvfUMirqD5FIJEoiEWdqaqolCVuWSTY7TTY7jSRJ9PevIRqNtfwNGgnE\nNE2KRbciy7Mk0+k4guA2QG0np3Y5XFONegqiKDAw0OuncHnSlNXq3Io4b7xHqp7uT37yI26//dtN\ny9LpHkKhENdf/1n+8IcH+PjHr+W73/33efcRkG7HOHIs0vku1EPRkLBth5GR/RQK+XnXiUSiKIpK\ntVqZY/muXbsOQRAZHd2PYehI5TJDt91G/69/jdPTS+XmLzPd18P0tEvGfX19JBJJbNvCsmxs22Ji\nYgLLsujp6cGbenpT9+rkFJqqIOVy9N59N2K1in3CidjPOInKgX2UBwZAEFizZphoNOrf0BMTo5RK\nRaanJ9F1nb6+AYaHY1QqJTKZGSqVMpVKmUjE3aY8KyWtkRgsy2J8fARBEIjHE/T09NcePguj0ZKM\nRELEYlFf3MZzQcxHwO1wkvubdzsjwr0mBEEgmy34gbhw2BU3b6Wr2/m1t3LcCxdd9BIuuuglTcuu\nuebvOfvscxAEgVNO2ca+fXsX3EdAuh1hKX/4pSfzkeIIdzz+Q/JajoHoAJce/2oSaqLt7S3L4okn\nHm/pQhAEgVQqhSBIFAo5358bjUbRNA3LshgaGqoF1rIA9Gka697xDpSSS2BOZoaZvbuZxr05ZVlm\nYGBNk3+0UChgWRbxeJKenoE549ByBQ5gI5om67/2NRwHqm99O+qNn0Pas4sdH/0o+dNPZ2JihA0b\ntiDLri92cHCIkZF9aFrVH//g4Fqi0TiRSIxKpUyxmKNYLPpjM00TUZQIhcIt/dfuePMUCnn6+gZJ\npdJtn2u3kMYik8nXepSpCxQ1tE9KnRqMVbNKUS+QCqVRZmXpNO/Xq4ijZUWc6792A3GCIHZUkrzS\nA2knnXQy9977G5773OfxxBOPs2bNmgXXP8iEyKcmls694ByWKeJPnvwPSkYRSZSYqc7wsx0/XXB9\nrwrPLbvU2Lt3Z0vCjcXi9PX1k8/nyWSmsSyLvr5+jj76WCzLqpFkgvHxcXK5LKFQmPXrN9FrCUiF\nghtoEgR2v/WtTG3cgCi6PtFkssfXxDVN19LN5TIA8xKYuuUocBzMRAInkUS/6GIErYK0dzcIAluu\nvRb1wAEcx+HAgb3+9FcQBAYG1vrvTdNgZGSf7/6IRmNs3bqVoaH1hMORmiasWyijaRWGhobp6xtA\nUVprQkxPTzA5Od5B88Zmy7lUqjA9nWVmJotl2SQSUQYGekkm4x2QUvsXmSAIPD61ne889i3u2HEH\nt2//DlOVqXnWnf/7vSDc5GSGbDaP4+D7gpPJOKHQ4hoaK8m90AoXX/xSHMfhiitex/XXf4y/+7ur\nFlw/sHRXMZpTVwVKZqmmPuZ+1pyKn485n5iObduMjo4yMTHh71eSJN9SUVWVUqlIqVSstezuJ5lM\nIQgCBw7sR9M0RFGkWCwgiiL9/YMkEu7/rRNPRH/lpSj/3x3sfte7yJ11FqqsYNUCa4lEohZM84JP\nGpVKmVAohCiKNb+vU7vhG2QkBQFHVZn+1X/iSDLqt29D6ulBMAzEapWt77qSR77/PSxMxsYOsG7d\nBv9Y0ulestkZYrE4mlYlm52hXC4xOLgWiBOJRIlEolSrFTKZaSqVcu0c7ae/f5Dh4Y3oukahkKNY\nLDSRRaGQQ9Mq9PcPHfRDtrHNj1dVFomE6O9P+y6I+QRhOvWl/mb/b5BEGQkZG4f/HfstL9rSSu+i\nPUvbc594ebdeCXwqFUfXDd8NMXuMnZ2rw+/+U1WVq666pu31A9LtACtFT3d2zuf85c2QSsUBd+xb\n+jaxK7MbQQDTstiQ2thSD8ITELEsk7GxET94JcsyjlMvJwU3h1VVQ6RSPcTjCd8qGRs74BdB2LZN\nLBYnkUgBbjWaZVmukM8b30jl0ldi4T4IDMvysxn27NnZ8vg1TWPfvt2Lnqd9owfcN3/5F+7Lg20j\nSBI4DtVqhd27n0SWlaZzVyoViUbjiKKBrmvs378HTSsTjSaQJJlwOMLQ0Hqq1SqTk2MYhs7U1AS5\nXJbe3n6SyR5M05oTuNN1ndHRvaRSvcRi8UWPYSF4VWWxWIRMJoeqKk2NLj0SOxgIgoBuNfuPTXsh\nMu9s366imNYgbK7WXCitAnGel4QcAAAgAElEQVQr29LtFKuedFdy4UGj1oMoCqiq3JI8Z7ewmU2Q\n3ns30FT/HI9HyOfL/gV74cZL+KX9CzKVDOt6h3juunPnlDR7KJdLjI+P+gSYSKRqGQvNmQJipUJo\nfAIjGiOXy9Si8fk502jPGl7sfID7m6mqiihKtWWuJVWpuFZSNBpverBA/dwIgkA+n8O2LT+g5zg2\ntmnC5CSOLGOnUtiOU3N9mNi2jWHMtbDK5ebxTk5OApMIguAL5siyTDQao1oV0bQqhqEzPj7ibxMO\nR0ine5mensAwjNrxOWSz02halXS6t2WRRafpXc3ZBCLhcL09jqYZVKuaf120i6PTR/HH0kNIooRp\nGxyVOma+EdBZhVnzsbm53c0VcfVAnI0oCjWX0+oQhFr1pNtNLETg7ViesxsqztZ5aAwYWVYrXYhD\ne9orksJFR1+8yDE6ZDLTZLMz/nH19Q2Sz2eb/LnhvXsxkkmsZJLCmgjU1m8+JyKKoiBJEpIk1/5K\nNTLF116IxxOsX78BXTcZHT1ApVJiaGhDU75rNutmEPT09JFO9y54DJpWpVIpI8sy/f2D9X8MrZ+z\nbiYzRSYzg+M4Tf7afft2oSgKvb0DWJaJYRhUKqWaJoHb/cAj0YUgywqO47B27TAzM1MNDx6BSqWE\nYWj09AygztEHPviIvW03p3N5/ckUxR1LOKzWpvHz70MQ4K82n4tqhclpOdbF17ElddS863Z2ac5/\nbLMDcYoi09OTJJWK4enpLpTFcSQgIN0WmI88RVFEFEXi8UhL/+dsgmwW1Znb3ttDKKQgiiKVSvuF\nBe2g0afbDkzTZHy87k5QCgUiVY2pFjuobtwIpol64ADKzAzWwADVdetmfb9bhOBquKooiuJbpLlc\nBsuyiMUSDA+v95cbhuaTc30/Dvl8FkEQai6KheGlZ1Wr8+cPe+jp6UfTNMrlEmNjB1i/fnNNwlPF\nMHQMQ/ctYkVRME1zbjDMcVAEASkc8aviPDdLsZinWHTT60RR8vfrkY5pmkxOjvruhoNzX81PYo26\nCuGwG8DyWr0v5Ef19nli/zMO6ftbrt3BNWkYJo7jKqN5nSW81j7e2A/WhbJcWPWkKwhCrZHiwpbn\n7CaKrZXJbBxH8qtu6q9DG+NKcIG4pDOCd/MImoaRSGAk6illoijS29tHZGKC6Ic+RGT7YwiWhZFI\n8PjNNwP4pNLT01ezCPWaVai3zHwolQps3/6o/1CzLAtBEBkbO4AoSrVlJqZpEgqFKRYLDeeq0b1Q\nX+ZlFhiGTj6fA+oPP9u2a+/thmXutNVxHPbt29U0Pi97wYMkyYRCCka5jC1J9E7N0JfJIs7MQDRC\nVVHY87SthENhItFYbXuBcDiMaRo1wp2LXG4GTavWquvEjt0L7azr+uOtWj6tpyzm+lF13fRT0bxs\nmm5/f8MWHAxJm6aFaTYGEesuFF032LFjJ4oSpqenv5PBHHZ0lXQLWoGHp/6I5dgc07OVtbG13dz9\nQSESUVAUeY4lejDdGFwLSF4CkZLlKwO2bZvx8dE5AR9R14k9+ij5k04CUSQajTE4OEQsFsHuH8B5\nzl/hHDiAZdvsuOF69HSaVCpNPp9DURTS6d4mq82p+VCz2Rny+SyKonLffXGmpx0SCYvNm03Wr9dr\n69pzig+gdUXaYpiaGm9rvcZy31AohCBIVKtlYrE4qVQPiqLQ05Mkn3fzce3/+2v2blhHpjdNIp8j\numsn9smnEAbC5QoVx6F/YC2yrDAxMYqmVRkcHCIcjlCtVimXixSL+SYLs1otMz5epa9vkEgk1NFx\ndopGTdpGAvYqytzW6J3pNLSLgwm8zbbEZ3cYDoVU/vM/f8U3v/lNtm49jne+870cd9zT2v+Sw4iu\nka5hGfx636+wa4GX0dII5248j75IX7e+4qBQLuuIYremH0eaRoI33vkr0w4c2NPkm0wkkvR/9asI\ne/ey68p3gSgS+/Of2XLbt3AG18A/Xgtr1qJf+W60172esdwMFUOnUkmRy0VIpbLEYok502RvRlEs\n5hEEkfvv38Rb3pLAO5+C4PC9742zefMBensHalVoNpZlMjq6H0EQG/yz9TQx9zia35fLRb9YIZFI\nEYlEEATXNfTLXyp85SshJicljj8evvAFnXjcHcP09CS5XIZq1eaxxzZy/PFPMDXl0N8fQZabOyJL\nRx3N2l07GNm4ngPrh9k0PuHfTOlcnrFohEIhR29vP6IoMj4+wvj4CGvXDpFKpQmHQ0SjccbHDzSd\nJ9u2mZwco1Ips379cFu/crsuifnIrpmA3UBWPB6pBQul2jRem9co6QaJHgq88f/1X7+Wl73slTzw\nwB9IpRZ3Qy0Xuka64+UxDEtHqmnJSqLEvsLeZSfdbmLpiiOWzr0w33694gCPcCORKIODQ0iSROmS\nlzAuiyBJCJrGUdddh2TbsONJnI98BP0rX8NxHCYqRSqGTiaTYMeOdRx99P7ad7aucpuZcXUL+voG\nGB1VaXyAOY6A47juh1Ao5Pt1vS4dsViMeLy96jnHsX3SdQnEFZMpFuHqq6OMjbnZAjt3wubNAtde\n6z6Ue3r6atVyBrGYhq4rqGqF7dsFTjih8ZwKCEcdRXxwgIHJCSYFgdFTTmbLyAiSINBfKjIhrKVU\nKrBlyybS6TjJZIwdO3YwNjYK4FctaVqZbDaDq7VgN/mCx8dlotHFj7l9AlvcIvUCWZIkIkkmum40\nNbn0BHka/dorqXghFArz7Gf/xeIrLiO6VpEWV+K+lQtg2SYRObLAFgGWGrPvA8dxmKlMk6tm2bdv\nl+9jjcXiDA2tRxRFMplpxsMqyDIIAgO//V+XcGsQ9u4FHKanJyiVijhOhB07hpEkm1SqSLkc4sCB\nub97tVqhUMihKCrJZJqXv9xk69a6m+bpT7c59thaAK+hqsvL9Y1E2r+WGi0/z78LkMsJZDJiw3ru\nMlEUaj5ChWg0jSDA2rXj6HocSbJRFEgkXM0Fl0CjxGMhot+/nXVf/QqpmRnK4RAjGzZg9PWjn3wq\niWQKwzAYGRknmy1imjA0tB5JkhgbG2XHjp1kMnn6+vpqcQeRRKLWPLL2w2WnJpHuuaft4178vHQu\nv6hpOrlckYmJGUqlCooi09+fprc3RTQaXlBTeLF9t79ux19x2LFnz25e8ILnoGmLB8O7Zummwz0c\n33cij04/jO04rE+sZ2vPcd3a/QrBUhVHLL3bwnZsvrf9u+zf9SCVbJZNW87k2YPPRhQlBgbW4jgO\nk5PjFIt5P3PAtm3SI6NNprizcQNTU5Pk87lamtMwpinS15dFFB2mp5MMD7t3ifzTHyM9+ijGWWcz\nvdmt+urvH0QQBNaudfjOdyp89asKsizx9rdb6LpbvdaYueBZrOFwlD/9SeC++2QSCYdXvMJEaSEF\n4BJYfXvbtgiHVURR4JhjBM44w+HOO93AWzwOF1+sEI/Lvk9/48ZhHn00g6JoFIuulZlI5CiVBOLx\nCLmc62sOX3sN4a99FXDY9L3vsv07/04mlUQaOppkMkVcq5LLZSkUcn4RhKqGWLduI6Oj+/1c5nS6\nl2g0TqlUIBKJkIxGODDqWsOOJFHa+QT9P/w+9ubNVC95KfaGDbOO9/DpEszWVAiHQ/T3R/04iSSJ\nNV/wwuh8zCubdUulIjfd9Jl5S8Bno6uBtJMGnskJfSdiO/aC4hhHKpbq4j4c2QsPjj/A+O6HiN11\nD+Vzns327GMckziG4fgwlmUyOTlOtVohFAoTi8WZmZkimUxjvvf9GKUi4s6d2INrmP77DzM5OYEs\nK6xdux5ZFtm82UZV3bQoSUpw9NEOoRs+Sfjzn0MwdIqvfCXae95DPJ4gEqkLvG/Z4vCxj+mEwyq2\nbfHYYwaqGvJdCo7jkq4kyTz6qMittzqAATjk8zZXXNH4gzSKhNd91JpWrWU8uEUWX/uawBe/qJDJ\nSJxzjsW559rk880nPxZLUC4XSCa9VK8ytt3sI5TvvguPDORymc033cQTV3+EqanxWnJ/BFUNUS6X\n/C4MAIqiMDQ0zNjYAcrlIo5jk0z2UC4XmZqaYsiySe3cSe7oowGYPufZDNx3H/LIASLf/Dqlv//I\nrB5ynQuTt7XmItaoJ26ez0MsFiESCdPbm8a2bd8/PD8BL43aWX3fhw+O43D99R/jiivewd///fvb\n2qbrKWOSKCGxsIhzgMOFumVeNauou3ZT3LIFR1GRHAs9P4UZWcP+/XtwHIdoNMbAwFpGRlxpunS6\nB2SFysc+AUCxWGBiYhRJkhgaGvaJ5IwzDPbsKSLLIc45p0YuP/0pZiKONjzMgTe9CaFWLpzNzmDb\nVk2qsf7y3AC6rs0p/7Usk3h8B+94R/PR7WrO7moJXdfZu3dP07JLL23eRz2NUKzlXbvnTJZdq65U\nKjA9LVOtRjAMG1mWUYeHkZ58AsHToNA0BgeHGBs7wPj4CMPDm0gmU0xNTVAo5Ojpqcc2JEmmv38t\n09MTvnZDJBKjXC4yIgo4xxyDpGlYqgqKwtiFL+Ko++/HKhWxLYOqFD4oH+pSWcWWZWEYJrlcwVcV\n6+1NYduO7wNuVBVb2nS0pUMrLd21a4d43vPO59hjt7a9H2GhH29ysrBCDvfgIYoH31y2FdLpeFeb\nXQI18WqVYrG7DRvj8Qj37L6Xe3/5FcoHnmRvdYKeMy8Ex0GVI7zvV9OMvfXtmLVrQBRFVDVEtVoh\nHk8wODjk78srHhBFkU2btuA4Aqapo+s6xWKecrmMLCt+B4d21bQEwVUx8yQLFUVBVUO4+bZu1Dwc\njrB9e4Q9e0RsW8CyXGK85BITVa2X/3r5urqu1yroJFTV4vHHBxAEh4EBmw0bPCu6nrfrfa7nYjvt\njd+2kTMZlEIBcXgYqa+/VrlWRlEU+vvXMDq6H0mS2bhxS0MZt41hGDiOzfT0RE3usG7RxQ2L9ff8\nhvHeXjLPOBGATd/9HrFKBeETn0ANh9B1s1aZZdDbm2Zycm5F4GxEoxEkSaRQmJuONxuJRBTLciiX\nF78mw+EQoZBCLtd8XyiKTDgcIhxWaxkGbjVZT0+K6enMoimaQE1EKcHUVHbRdQEEQUI8jI1hL730\nJQwMuFk1f/7zwxx//Al84QtfYWAgMa/JveqLI44ELJWQzlR5iv+56xuEH/oDCUEkdsJJhOUo66Pr\n+YvxEPJZT8d0HBRFIRKJUSoV/cBVqVRkcnK05o90GB93811DoRD79u1pClB5ME2jpkegIE9OIu7c\nQfHkk5HyeXotG+FpxyOKUlM5sFdlpGkVdu7cQTQap6/P1cl1izU0BgbWEIup3HFHiF27RFTV4bWv\nNRkcbF0KOjNTBLKMjqps2lThT39KE4+rzMw4nHpqe+Wjpmmyd2/d4h4e3oAsSxQKJb9Yw9I1rN4+\nqn39ODiQz/nrG4bB6KibzWFZJvv37yEcjqAoMrZto2kauq75FqB3DTiOg9jXj/3mK+g1TbJPPI4j\niYyfey4D6R7sfAmhUG4qbPDyVBerzBI6UhkTgHYfnK2tUcNwZTkLhVKNgN22RKIoEItFqFQW7wu3\nkizdVrj99h/571/xiov59KdvWnSbgHQ7RKeltcsBr7pusjiBMpNFECW0nh5CazdzVGQjpw48i6NP\nXMNItQr5PJs2bSIWizExMcHo6Gitd5krGF4oNAvcVCoVJMkV7g6FQoRCYUqlEuVykXXrNhIOhwFw\nhjcykkiAILCmb4Dwxs2LjLr5oeNaRmWfxFMp+Jd/0dizR6Cvz6Gnp75uqQT33SfR0+Nwyik2Dz4o\nsmUL6Lrr5pqctInFIJVq/0eTZZlQKOwXY0QiUeLxKJI0N1jiWcYeGRuGQTbr6gp7RRdeSfFsuE01\nVarVujBRsZjDcQaRZJl4uodCIUe1v49Sbz8R/9y4flNJkujrSzWI2xyaupiHTjMdFvPR1gm4zJo1\nfTgOfl+4+TsMr6x0tG4hIN2OsXDBQbexkIjOfGXMXjT5KPUo5HQvsIP8scdg2RbrkhtJxRMUlDD5\niQlUVWViYopSabcv2ehNrWVZqZXNNlsjnh9W06qIYtEPArqSjSaqqlIul9BEt11NuMFNsTjc82oY\nbi5oPF7vM6YocMwxzed9ZgauuSZEJiNgWfAXf2GxebP7Py+LIZXSGRxUOe+8zioJBwbWsn//bgBs\nu5kQvAo70zQxTaP2Mv2/HlE0uik84R9wrV9P06FRKlMQXJKemBhjcHAt8XiSUqmAbdtksxnC4eis\nWZGbdZHJ5GviNiFfo3a2vm5ns6nOrvFOedFrS+T1hUulXN2J2W2JOhnzckuvfv/7P25rvYB0DyME\nwQvaiE2EKUnu53aEdOYrY/aWNyIWC3PJhR/m1/bn0foGOD12DEPhNajRGGNjbiWUrrt+WbdDhNuC\nZmhomHDYzTLYt28XjiOwYcOW2jHY6LpBuVxG113rzcsWyOez5PPNvjdN0xgZ2ee7E9xjlXw3g2tV\nWw3TbHe7xlSxhfAf/6GQybi+XFmGO++UOPlkTw/YJd0zzqhy3nnz5/k2ihM16zLUCXP//v3E43Eq\nlcocYp0NV/pRQZLwrdu+vgFSKdc893y67l+t9htoVKsV/ztzuRzVapVIJE4ikSaXcwOQxWJ+luhP\nnRxdcZsqlUpd3tG1zqVa/qjQdpucTrjrUKzR5g7DjW2JBD8AF1i6T3E0uhc6sUC9JO+56mNe0MYV\nnm4k125gMLqGM09/Xf1mEwQmJ+t6BIlEiljM7Yjg5enKsoon5G2aJolEys9UCIUU4nGRSCTm72Nq\naoJ8PlsTGnF1b6vVCqIo1qbb7U918/lsrejCJZ9CIVsrHa6zQOP744+X6O11z61tC5imwLp1VWwb\n1q1zifu447KMj1cWFL1ZDLquMTPjJr57amGyrCDLMori/nU/KzXBGleoe8+eHa6fVpyb0eP6syOE\nQhH/2AuFnH+Mmqb5nTe8irV8Pks0Gvet+PncAI3yjpIk1sqOIwiC+73tyCMuBdct5Lbw2hK5gjZS\nTRPYDf4lk7FZfeGOXKx60u3ELzW/pGPzsmTSJZz5xMRNszMtXFfAXMEwui/SPDMz1WTduDe/OzVe\nt24D4XArC9Adq2e1LtZQ0SPBSCTi69FWqxXWrh0mHI7gKXl53XzdrhH1z+Dq05ZKRV9r18vxXKzC\nZ2DAfTXCm9HHYq4/1pv+18cr1oSxRSRJ9iU7G5d7Wg35fNYPGh577LEYhtN2FZaXDeKJmy+GeDzp\ni+DIpsnGfQeY7OulkEw0XT/Z7DR9fYML7KkZXosfd1bhdmJwO4oIvm94dkCrHT9t47rdLEV2x+wS\nsGVZhMMhLMv2u2Is1pZopWPVk64r4CHPmdLPFRSfS5K27QVI6suj0TDlcrWtypt2sSTFEbaNPjPD\nzMy0v8gNugwwMeFmJcwm3MYx6LpOpVL2k/wXQuN2juNQqZRqVly49n+hJmLeevtQSEHXNXbseJJ4\nPEFf3wC7dz+JoqgMD29s2vfc9w6Tk3D//RLxuMOzn21Rqbit1Ht6+shkplHVEENDwwiC6P/m7aJU\nKvikWy6XCYVii2wx+9jcYJyXFeKOvfW6rlZzkkIhh21ZhEZHGNy1i/SGjeS2bSOfz9SCaBUMw0Bp\nVZK3CGzbTQNr9Ke6nXrdlK5KRZvj6ukmOg9Cu4TuWcCNbpN6WyKtoQPKyhekegqQrlDr7eURpzWH\nXFeZywhh1y5C37iFXS84D9auRQCEmvWWybgk3Fpz1PMj163cZLKdtuH1C90wXH/nwQty41ekee3R\n/W+ZZ39r18LFF3vjEDEMr/pL9q1SL4DVKRqvjXw+z8BAZ6TrfW+1WsW2rdoxSViW0fK6i8cTFPJZ\nLEXBFgUQRZSxMeLxBKqqMjk5BkClUkJR0h3qGDQfT6M/1cup7e1NYttOzQ3R3jF6rpQ21+ZQZCCb\nu2KIvjB7uTzB5z9/E9u2ncFZZz3bf+CvRKz6Fuzuk71KpaKhaXqtpbc3te2ccJcmQtpd7YXQD75L\nsa+Xytq14DhsvPtePypuGDrxeLLeHsYwoFwGp94G3rYtCoU8kiTPaZ64kFXuWblAk8+3PXg7dXzL\nUpYPvZTcPe7uuG0qleZCAZeYXK0HRZFregQqkUiIaDRMPB4hHPZSzBxs2yAejxKPRxgY6CWViqOq\nzccoipJ/kjMDrhKZE3VnJKoa8n3DrfSGF8f8hOfl005OZsjni7hdOuKHJGzTcgRdlIH0CHhmJocg\nyBx77FbuuOP7vO1tb+rKWJcKq97SPRLQdfdCqYwTqT3pBQE9pNT8pC75eCWp4hPbkR54ACwbp7cH\nTj3V3bxUwtME6PQB45FBNNoZ6XrpwI6D7389VNL9/e9FYjGJWMwkl4PFJFbd7JK5bigv19a14MPI\nsuSfl+askuYqN9t2mvyO4+MTyHK49sA3CYdDJBJRRFGiWtWoVKqub1UUwXGYHh4iOj6BcfY5/j5k\nWUbXLT8nWFHav4Xb/Sk9EfNi0csgcYVt3O7Gmt/ksr7fTlXDuq+lIIoSF154MRdf/PIO9r08WPWk\nu9pcB+3AOvZYxFr7ccEwGT1tG0LNB51MplxfoGm6hKuooIBQKiNMTUI4RKnkCr20048MaCAgm2q1\ngqqqfrbDQvAI6vbb4Y9/1Pjbv4VHHjHZutUtJzUMvaZv2xyMbP2+LmzuaQTbdhZZtlAUi1/9KsMF\nF9T73Lk5s6JfHSdJkp/O19zxwv3rpdNNT2eIRKJtE4e3XmNATRRlfwbmZhdIRCKhWrGAw8jIXiRZ\nxhQEZi64wNcRnh2Qc1sbdUZindwPjuNgGKYvbONWwbkPCsMwfV2FpUyN7ayKzttmZft1Vz3pdhvL\nnYDdDoy/fg3Wnf8FQKpaJZdM0NhK3V3JgMa0NEHwxVtM0yQeT8xDnPO7QjStiuM4qGqYSqXckKXQ\n+DKblgGcdJL7Ati4sYTXT9JLnzpYbNpUdwds2zbJ5OQh7Q6AyclxEomknyLmtWGf75rwCCMSiaLr\nGoVCnlSquZuxZTX7Vt1zqNYi+HkikSj5fGaOS0GSOp3yd5KRMHeZJ+0oCI0EHPOtekHQFiX1Th8S\nK7kirVgs8o//eDXlcgnDMHjXu97LiSeetOh2AemuRogi9llnw8g+pM2b6UVkenoCgAMH9hIOR9ze\nX/19KPmCe4cZBiSSUKu8mi+A5kknVipldN1N7PfIwGvb3tgBt/Xw3AIJN2tB5q67QmiaxPOeN8Ho\naAgQGRqqoOvD5PMK3/qWiGW5QjeiKHDNNTZr1jRbbb3X9YAMz7k1xa3/+yempib54Q+H2Lq1wIkn\nFvn2tzfxvveZzBa3mesWaF7mZR14gSLD0Oc0q4R6tZlLxnVC9qxut++aSKGQJ5nsmbO9By931nEc\nEokE+XyeiYkRbNtGVVUch47ynhvRmT91frJzHGppWzqCINDTk0RVZQYGetE0o5ZN0HqMS22wHE6D\n6Pbbv8Vppz2LV73qNezdu5uPfvQfuOWWby263VOCdLvpM12S9K4lhdPUdSEUClOtVqhWK0wfvYWw\nYRKvVon29mNHQpDPIcsKoVAYy7J8YvUqp7yW5AshmUyjKHUC8ooHVFWpkZGbizsy4rB9u8NddwmM\njAhs25ZBFB0sC0xT4Mtf7iUaddPBvPvftuGhhzQGB+tj6P1UEmpdbe68IscF7zmO2z5yN+m0TKHg\nBrJe9CKxYz+zruvs37+7pri2lp07nwBgzZp1ruCN5ZX+mn4RSKuOxwCTkxN+Z+NcboZIJDanzbxl\nmX7b+Gq16gfubNumv3+ANWvWsGfP7gbSFTq0BLt/4XoPq0pF81v7NOpAeMtnb9MuOsuMOLx41ate\n4wdCTdNaNLXSw1OCdLuLpe/y0A3U28k3Lx8e3ohpmpRKRUqlAlUqVJU4GFUwXMKwbYs9e3a0vNhV\nVSUWixMKhYhEXPFqRVF4+OGHAddnvHnzpqa0PO+vaTrouobj2Dz8sMCPfuROyzdsEBkaEtB1hYGB\nMtWqSKGg1Ep7bWRZwuud2d/vcPLJDYT7sSQ0cqkIjx3vEtff/I1NseiQz8PTn24BnQXmdN3dTygU\n9kuWbduu6RvE56xfL892CdkwTDIZty+cJIl+VkYulyGXy/il0EDN7WI37SsWi1EqlVDVEKFQlGy2\n0NREtL8/jUe87aAT/2gnbXI84nezVzQqFQ1RFAiHQ3MKGjptv9N5Xu/SoJWW7lVXXcPxx5/A9PQU\n1113NVdeuUwi5qsdS2XpNpYXdxtNXWwlEVl2U5r6+/soFvPMzGRqWgetRVpUVSUUChOJRIlGXaI1\nDHcablk2pqkjywqmaSDLSlu6wPfdJ/n6EuGwQDwOmzbJVCoQjdpMT6vYNpx8ssOpp2r84hcyigKX\nX67T19jrdAG9fE/XAqBRR6FdeApjXs5nOBymXC5TKOSbRMmbv88jUjdwZts24XCU3t4+NK3KzMyU\nT3yemHsjPGJX1RD9/WsolXa2DOwB5HJFUqkEqqrQ15f2Mwu6YRm6X3nwF2NjoNDNp3X9v5IkYtsO\niiIvWobsjuPwiUsthIsuegkXXfSSOct37HiSa665ine8492ccsq2tvYVkO6KQefqZXPLlOvpTl4M\nTBRFv2wZIBxWKRQKZLNZ8vlcg6KYjGW5Pk1RFInF4r47oVKpUKlUyGYz/n4kSUZVVRRFrfka64Td\nTrDRceb+PxxW8VJh83mFLVtsLrzQ7YX24he3dmls+yn8/mU0Tz5q8SavlNcdV+e5ul4JsjdtjMcT\nlMtlKpWyT7qeW8CTdGxUHPNIu1otMzJSbtq3ILgVe4riWvSmaTXJP+q6xsjI/jljarRULcuqpW9B\nuVwhEgnT359uyiyYndrVCTq1dOeDm09boVyuEI2GiURCDaI2GpWK3lKfubb3FRtI27VrJ1df/SGu\nvfafO+ocEZDuQWFp3AteqxiXOMWW+g+tJBznTuUtdN29iC3LJJ+v3/CPPPKwT7SSJJNKpWrugjAj\nI/t86ywWizMwEMfTTQPp7cAAACAASURBVKgripmYpl5Tsyr7amAestkZstmMryZWVxeTmpadfbbM\nXXepGIaMriuceabYVNZ6xhkiF1+8eG39f96V55TLk+w5sbagBE9euZNsdqZ2vjzSXdj6aw6k2ViW\nm/4mSTKlUhE3Jc3dR7VaYXR0v0+yiyEWi9f82gqVSplSqUhvb39L3YupqXE0rUo0GvMDlJpWrTW4\nnN2O3Wst5NT0aot+alck4lqWum74hUHecbaHpbEw3TQ0i3y+6Jchp9PucbXSgVgp7oVW+NKXbkLX\ndW688VMAxONxPvGJTy+6XUC6HcJpqNxaCEILGceFxHTAba/j6j00kqndRKrtKpB52hCOQ1NwRxAE\nUqk0sViCUCg8r/WTy7lqVp5uQiQiE4lEa5VXMqVS1SdjXdeZmqorl6mq6ot6zxdpD4fhvPOal01O\n1lOgqtV8k3XojbNVWfAvrt9e+03czy5JQiYz4wf98vkc5XKpY6Uxt2nn2JzlbsNMV8y9WW1Mqbla\ndMbGRkgkUgzUKsug3hK+1Xk3DB1Nq6IoKlu2bCGTyfrWrqdANnu7VqRUT+0SamWyrr6uW1ottTmt\n756lO2ttPDJvLEOWZZlIRKWnxy1D9gi4M/fC4Y21tEOwrRCQbptorFYCVxVsITKFudVKnoBOqyBT\nMhmjWCx3TdLRuwAty2Riok4aiqLOq1DljTscjlCplNE0jVBo/oisKEqEw66y2NTUOJIkY1kmvb19\nRKNuoKmVwlhj/q6ns2DbVs1ac0l6tjLYwaBYrHe90LQqjYJljYpibp6t2KQ25qXFRaMxotE4iiIT\nCqns3r0LgM2bj1mwNNZLn/MKGzx45NSKdL285GQy5T/sAF90qFgsNLlJFnfh1ANbkiTS25siFosS\ni0Vr03ptgUyUpSGw+cjcNE0KBbezRF0HIu3nBBuGtWKzGDrFU5Z025FybKWFW0drFbJWYuLtobsd\nKbz7sVgs+uNxy00rlMulBdOnUqk01WqFXC7D4ODaecY5F5IkYlluGbFHuospjAG1VDKJcrnKrl1P\n4DgOa9asIxaLN5zLRp2MevVZq2UzM5OUSiWGhzdQrWpMT0+QTPaQTvf4WreLEdbUlNupN53uJRyO\n+M1DPSxEuLZtUyoVkSR5HunMuag3tFR9fV0Prg5CCsdx9YWb0a5UovvQz+XcB5HbMj2FbVsNAbhG\n/2+nmQ7dc1s09lbr60sjiiL9/WlM0/It4O4ZJ4cfTwnSjUTUWsfZ2SRqz5m2L9aRIRRyRaorlYV1\nXjtFt7MivCE7jkNvbz8zM1OoqkK1ajEzM0UkMrvtS6MubhRFUSgWC/T29jdVpi08TgFRlCiXizjO\nYAeBm/r5lSSpqStDfR9C2+dHEFxCDIVCvptFkqS2SpM9eC6ZdnMvG1Euu9oViUSq7XNQLLpWrreN\ne416lp1Qq1rLMZu0OnMDuOtalkWhUKJQKKGqCpGIm9plGGbN/6t18Nsd3Bjah0OxWME0C3PG2ipY\neCTgKUG6um5hmlZbguLt4EgojnDTltwb1Jumi6JEPJ6gWCxQLBZIJJLzbC2QTPYwPe12hOjtbSUD\nOXcbQRCIRmMUi/la6/TO5fVcFS2z7bYyHjTNfSUS+FaQl5IGnf1mjuP4vtXF1LVa+e4rFdenPDAw\nQCQSaQiMQqHgZoC4XXElKpUqmqZRLpeQZaXJMvYuU0FwfbquGypFPl/vLtEZ5lqZum74xQvhsEo4\nHCaZdEt7VVVpUyi8k/LigxG8ceaMtbEM2TBMdu/ei6KESCTmNg5daXhKkK5pWm1rgy6GI+WhKooi\na9cOMTY24t+kjuPQ09NPsVgkk5kiFovPIpX6TZxIJMlkpsjnc6TTvW2RD0As5pJuuVzsmHRt2/aJ\nRNMq7NgR4sEH3bzOdessTj7Zpi5+g/9++3aBP/3J7WA8MGBzyimulTozk/GLCTStSqlU9DMa6n/F\npmXg+pMdx63k8wTwZVls0jpIpeoNMxv71hmGQaFQJBQKI4oy1are9LD3xLaLxRKpVIq+vjR79uwB\n6r7cOuppeF4mRbncnC1yqHoKjWgs7R0c7PX7q3lT+vkCcEuZYTAfSTcGC0MhlV/84mfceuutbNv2\nLN785rexZctRSzOgLuApQbrdxdJUpC2FkE463UM+n/VvVMsykWWZVCpNLpchn8+STtfFV+pf7/b0\nSiRS5HIZisUCyWQ7imNOTUdXoFwu0dPT1yR0Uw+imTSK4NSDavVASbFYQBAKntokABMTrb+1vx/O\nPbf+2TOSGzUS3Aq84oKjb0wxA7cUd2xsFEVRUBSlKaiYz5dbkoEr/u4Qi8XnISmnNkabQqFENptj\nZmYGVVXZuHG9n+LVSGRevm9j12BPNKdTwmt3XcdxmJnJ+f3VUik3IFipVKlWtYPunNJpWe9ix+fq\nY2hceulruPDCF3PPPfeueHdDQLod4sjSXnBYv34jjz/+GOBqCezdu9NPBctkpkkkUg0aAM2lwx45\n53IZEonkvA8Fr2OrK304gSAI6LrGrl1PtDFGAVmWUNUQgiA0NLKU+OMfe5EkAdsWcByBwUGHZzzD\nfeh5mQfFosDNN6uIoruebQu8/OV7iccNjjrqaEqlImNjY6RSKaLRWEMmhe0TfePLcWzfOnZ1J1r7\n7nfufKJJ3MZ770pRzs1a8H8Rnw/cc1kouMJA0WiC6emsP2V2z4lc28Zzl9Sr1VqVIbdzrjsN1Hr9\n1UqlSlNWgWXVA3CH0r2im2NOJJK88IUXdbLzZUFAuqscqqr6vcLAnao2SiaOju6nv39NTQXLW+pe\n5LKs+D5gr19atapTLhcpFIo+KXn+VzdAU1cXE0WRSCRaa/4o8atfqUxOKlQqMsWizHnnOWzd+v/a\n+/JwSery3Lequpau3s9+ZmUZVEQQvLggEkXcSLhXIETFEEUNYC4qVwNBRgENBmGiSOKooLJEEr0u\nRB803GDEFaOgogLKNsPAbOf0WbtP77XeP371q67qru6u6lN9lpl6n2eeOUud6qruqre+3/e93/sB\n1SqPsbEYEgkFhcKiNa3YwP7949C0GBgG0HUGL3whsGWLe0k/PGzCMBg880yzdVWSTJsMaZ8wx/GI\nx3sTlaoq2LfvWUhSHBMTG+1uMzpAM5+ftt6bWFcd8sGD+yFJEkQxDkmS7IeKE8S6sQyO4yDLCei6\ngaWlEmZnZ2GaOkqlpuSNdMORZolsdshTt9wLfgmv03ZOVQEtaqVSMgCSD67X/bifBSP+tdgcYRgG\nPv3pG7Br19PgeR4f/vDV2LRps++/j0g3IAblpzuICJruM5PJoVRasnOVIyPjVmdUyWo33YtYjLfP\nS9cNcBw5V1GUUC6XkM8f9IxmYrGY3T3FcRwmJzchFuORzx9ErVZFOp1BOp1BqcTgl7+MQRCaZPGb\n35io11kIArBnD/CSl7CQJBp1mzj33BLuvz8NRQGOOUbHscfqKBTaj+GKK4Cvf51HtQq87GXEtJzj\nBDvvGwQ0/51OZyzdrmCNWyeSMUK6DDZvPgIALLWLCkVRMDubh2kaEEUJqqrYBUt6vqIo2Tpb09RR\nLpNON1GUUCjMo9GoexYQKXGXyyUMDw8jkUi4ClzhL6d7EyMtatH8bzwuIZ1O9pzUG5RE16Kf7s9+\n9mMoioJbb70Djz32KHbu/EygRomIdNcMBudeRnSOo5iePgiA5DcnJzdiaUnG3Fzejtroxb1//7Ng\nGDqu2zo6k4weT6UySKUSYFmiQeV54hT2+ON/AM/zGB0dslQMAh5//HHMzuaRy2WRTrNIJABFIUUw\nwzCwuGhY4+rJBIff/U7BSSc1b1ZRLOId72jmUTulAhMJ4N3vpikBE/v3Gy7bRL+gqwCW5dqW750e\niCzLgucFy8jGQC43jFxu2Gp3VVCv19Fo1FCvuycCk2iegEawdGUgihJEkUc+T7ZJp9OYnc2D4zhk\ns0NIJGSwLGPlfsMflROEGOn+FheXbGcxcn0wdgOGe7x70Eh37eXyHnnkd3j5y08BALzoRcfjiSce\nD/T3h/xgyggEspy0GyJqtQqq1QrS6QxEUYSmaRgfn3CpDbxcuQzDQLm8hFKpZHsIVKtVKIpi/35p\nqYpCoYx6XUM2OwRN07B//34oSgNve1sd8bgOwMC2bTpe+lLd0ku3Hy/DMG2TEjQN+PGPOfzXf3FY\navFIn5sD3v52CW9+MyHpajU46VYqZRiGYeWv228Np4TLiWKxgGq1AkmKI5lMo16voVwuoVIp2+PS\nexWP4nEZo6MTGBoaRTyewIxVNUwm03b0ncnkUK8rmJ8vYHFxyXq4SYjHiYlMeATVnwSMOovNzxew\nsLAE0wRyuTSGh7NIJOKWQiRo4S+ovGzwqFQqrocyy7JdDHvaEUW6AbHW0wvNbjoicyLRkwiWZbBl\nyxY8+eQTVmV6FuPjI9iwYQP27NmD6ekp137GxiYhywkoSsNqoSWSK13XUSx6j9ExDAP5/EGH0Q0L\njothaakIUZRw7LEJfPzjBmijw+7dDP7wBxYcRxQHz3++DnrjCIKARqMBVVXA88Tm8Qtf4PHkkyz2\n72fxpS+ZuP76OrZtI6/9gQ9IeOwxDmNj5OL/7W85HHNMsPeu29h5Gp2Tr01UKsTbtl6vo1olqoh6\nvY59+/Z47pvjYhBF0XYuAwihchyHUmnJNg+S5aTdKk6j6HJ5CaIoQZJk+281jTQ4ALBbZVOpREfj\ncLKdvwssjGaH1hFE8bho+f/CMsdXexLqWkwtAEQW6ZTuEU8L/1Qake6aQvtN0bk9ud2FDHD7PdC/\nb3bZkUnACwtzlpJhvy1HAkhjQjabw8LCHCqVEpLJlO2tABD96oEDe6HrujUdgrfdx+r1GkzTdPkd\nOOFcTjMMSSdIEosTT+Sg6zySSR6xmGkXY7g9zwIbJlFYXEAimcKBAwyqVR61GodMhhTWPv95Ex/9\nqIJEgtz8mzez2LCBnE+lwkBVVVeRjzqotZrdUH1to1EHy3KYm8t7buNEPu9+SDEMYw3kJIY31PyG\nTs1gGBb5/EGbdDOZHJJJ0pySSKSs4uSSTeAAKZhRO01n8awVmqajWq2AYRi7uEW7Jp3+CoMhMP9t\nvUtLFQwPZ6w0lNTmgNa257WXWQAAHH/8i/Hzn/8MZ5zxejz22KM46qhtgf6e6fZBzM6W1t5jpg+w\nLEJrjmBZFsmk5LJLDALaweT0AGBZBoIQs/0dnB1MXuY4rSY6nZzHaPHHaSpuGAb273/WtRyKx2UI\ngohicdFqBOCgqio2btzaZnij6yoOHNgHTdMwNjZhE0ehsICFhTnkcsNWJb6pvy2VilAUBYIgWKY4\nbrMbL/yh8AccrB7EkDiElwy9ZFVye04DHI7jYJqG1SLLIpcbQqVSRqNRRyaTw/DwaNd9VSpl5PMk\np57NDntKvhSl4XI0SybTKJeXkEplOs6sS6cTUFUdtVrd9fNYjEwYliTJlnel0wnk8/M9z5vnY0il\nElhY6D0YlONY5HIZzM0t9twWAIaGMiiVKtA03eqAE8HzpImkVqu7tM0sy2J4OIPZWX/7ZhgOLDv4\nOJKqF3bv3gXTNLF9+7XYuvUI1zajo6mOF2wU6QZGe8Gr3TSn3c6R/k+JtZUwqe+Domg2oQ4iMCFF\ntTG7qMayLMbHN4BlWQiCiLm5vK1TXViYw+TkRtffC4KIo446Ck8/vQszM9NgWRaynEQymcLCwpzL\n4JsimUxj375noaoqxsc32r659AFCH4rlcg3a//0q7js+iYd/+y2YwyN4WhShxDScufVP8aMfcdi9\nmwHHAYJgYGjIxEkn6RgbMzE7C/z61xzS6QaOP74Mlo0jkRDs9AhZnktwuok1O9KA6emD4DgOGzdu\nActy9udJwfMx7N//HBqNBlKpNFiWRaNRhyTFe7ZJ12pVm3CTyRRSqUybqXq1WrZlfRTl8hI4jrMf\nbN7wjjRJ+qGKUqkKUeQRj5N8fSaTQq1W99ne6wf9ScDco31Yl7E51f82Ow/XFliWxRVXbO/77yPS\n9UA31zFyszJIpxP2kt4rGu1k4dgJ8bhopQGCTzgICllOIhaLWfpTw2r1zSGVSoPneUxPH7Tcpyqo\n12ttfgCJhIyJiQ2Ynj6AfH4KExMbEY/Llo63Zo/toeA4DsPDo5idncbc3AwmJjbY7zHDMLZHr2Gw\niP32YezeuhG8ooF7ehcqL34xnqs8i6GhYZxzDvD3fy/gkUcY5HLAqacaOPZYFQxDutKOPBKYm1uC\nopSxYcMIZDmJ+fk5Oxrt1LBAGxrS6azruFtRrZIVQzwuY2ZmCizLYmxsomsUXqtVMTVFPHEZhsHI\nyLirm8s0TRSLi6hUnJrctD1NOZPp3YLdC42GCkXRMDqag6KoVntve/qBYqWbHYgrG2nAoMbmuVwa\n1LuaNoV0w6BqLYPAYUO6zgJTbxtHr2kMJjTNAKCD49iQvW8pwr1oSHHOe58bN27Fvn17YBgGFhZm\nYRg6crlhSFIcGzduwdQUSSFMTx/A5s1Htkmw4nEZ4+OEeKenD2LDhk2uyr2zvRiA1WSxhFqtgkql\n3JEAjXe+C/zi96Fls4jv2UMm72ikFdY0gSuvrGFpyQTDmEgmTVSrNFdJoiKeL0NRgFqtZplhk2U3\n8etV7EjX+VnTAloq1bnVudEgAzUZhsHCwiyI/eRkD5Iuu3K/2WwOsRgxIqKpl4WFWShKAyzLwTB0\ny92NEC7HcYjH5Q57Jwja8EAmftStfYsYGkpD1w1XdBkc4TU7OI3N6fSLbiOI1iMOC9LleQ6JhNSW\nC3XaOPp1IGMY4kUaNuH6nUgRcK8df8NxHDZt2oq9e0m1vVBYQK1Wxfj4JHied5Hy/v3PYXJyEwRB\ngDO9IssJjI1NYmZmClNTBzAxsQEAg1JpCZlMzkX4JMobw759z2JuLg+WJTpg2kJM86X6hkmcFn8t\n7tFLmD79NAgw8bLcS3HgwN62cyh3sVKYn59zfT83127cQI6PWCiyLIv5+VlrdDzvau9lWc42JWdZ\nFqqqIpPJWe3U8EwnlctLyOen7Mq2ruvYtGmjZUxuolw2sWfPAei6BkmK2wU2mtqJx+PI5fy5u/kj\nPfd2TnUB6S6TkErJaDRUaJoeQL0QbLpvkHSEphF3wIWFomsEUaOhWvrnsFIkK4vDgnTJTKZK7w19\nYH15L3RHLMZjYmIjpqcPACDR5N69e5DJ5JDLDWNsbBLT04QYDh7caxmLu83Pk8kUDEPH3NwM8vkp\nxONk6oSiNMDzgt0qTHKrDZAoz7Bf0wsj8VG8Y9s70WAUxNk4WJOxi33kvW+uSgAGi4vEm2F4mPjS\nNhp1jI9PwDAMFIsFKIpi50VJrrw5rod6K5Alrrfywrm81XUdgiBg48ZJSJJk5Sfd+flCYREHDuwH\nwzB2C3Y6nUGl0oBp1rG4uIBCgeRvx8fHLaP55vI5k8nh6KOP9FX08otu16yzu0ySRCQSkpVLlttm\nloVxHMEInWzsHkEkIpGQkclwdoQeXo568DgsSHedr0YGCllOYGJiI2ZmpmxioSY3iUQKPC9AVRUY\nhoGpqf0YHR1HMule8qbTWStNMYd6ndygU1P7PfNwRHNLltaxGI9MJod4XIIkiVAUHRzHYXFxHoXC\nAjZNbIUgCNi7dw80TcXExEZX9GyawHe/y2FqihTDhoYMvPKVhDhHR0egKLTVVsHo6DhiMc5V7Jya\nOgBFaWB0dAyTk5OWl4KKRqMBRSE3uaoq1tdNfa2iKPjjH/+IWCxmS+pEMQ5BIJra2dk8GIbFxMQG\nzM+TCDuTGYJhGJidzdvkznGc3XUGEG3ypk1bApFcsO6xXr83UavVYZqG3ShDZpYZdv63dSUYVEu7\nXO0tPUaSImGt66aG9773vXjFK16FN73pzwL5IKwGDgvSXQ8wTbfpdlj79BOVy3ICW7cehYWFeRSL\nTXmOM/Ij6QATs7N56LqKdJoUeHRdc3kMNLuTDKudVYIoihAE0TYFN00TMzNTqFTKKJWKyGaziMfj\nMAxSqCJpDGI+I8sJpFJplEpLbbng3btZ5PMcJIlEvOUyh1qNEDfVyVKJXjabdEWjxWIBhcIiRFFC\nNjuEWq1h/R7gOBHxuIh4nGqT99mvOTo6AV3XUK/X7Px1U5tMls4Mw2DDhk1WE4ACSZJRLC6iVCq6\nCMdZwJLlJEZGRiEIAjIZItPzbyLjB0FUBiTl0pp+SCZlS1vbXNoP0pCmF0FTBzSG4XH55R/Gfff9\nJ2666UbcdNPOwRxQSIhId81gcN4LfsAwLIaHRyHLCeTzU22SJmfUurCwgIWFhbaqMvFiEGwXM01T\nkc0OQZYTaM3vjo1NYm5uBqVSEXv37sFRRx0NgDx4RJFEWSTfKWBiYgKl0hKWlhYxOTlut5NKkglJ\nAljWtJf5AFFOGIaJRkOBoqhgWRbFYjO9pGmavfwfHXWrCZyoViuuFcDw8Ihr2gYxJW+gWFywx7TT\nn09PH7D/rl6vol6vWudHuvSoOxnDEAmfIIi2x26pVMHExIhtIkOizLpnBDwIP4XWbd3pBwGJhIx0\nmkO9XreKy4OJdIMc8zHHHIPnP/+4daFgiEi3D9AIcu2nLYITeTwuY/PmIzA3l3eZftPqOtC8cVrT\nB9RnQBBE2/Ixnz8IUZQwNDQKWSYGOVR2l0hswczMDGZm8njmmd3Ytm2bpacVsG8fbGKKxXh7TE0+\nP4tEIgHTBCYmSHttrUbaigVBB8MYViMD1T7rLTPeTMzOTsMwDAwPj3rOQDNNE4XCgqWbbb5/mUwG\ntVrVdhWr12uujj4nvNzCMpkhVKslqKqCqeoU5rR5HL/pxI7HsLi4BI5jEY9L9jK/Wl2OysAvvK8Z\np7aWqh+otaMsS57ph7Y9ByLF4P6/6wER6fYFSmbhXRBrqUDHcRzGxiZRLpes9l1CXjSypZpIkl4g\n5MLzvDVUUm8z/m406pia2ufaP/1Hx7jX6zU88cQTyGRykKS4NT+shlKpDICBLCextFTE/Pys7U3L\nsgz+/M8VPPooC9Nk8IIX1DE7S+wmy2Xge99j8MIXGjAM1i6cLS0VUKtVIQgiOC6GUmnJ1e6r6xqq\n1Qp0Xbdeg7MnNjzzzG7P94s2ltC0SKlUchXGGIZBJpOzFRCPLDyC/178BQRBxEPFh/Dmo8/Fi0df\n7LlvXXcv82WZqgwUVKvehO+F4BFm922p+kHTdMTjpKusmX7o3NobPP/re/MVgaZp+OQnP46pqSmo\nqoJ3vvM9eNWrXh1oHxHprhmEn14IQuReDSGyPIJcLot9+/aiVqu6IlvTNCFJEnK5ISwuLqBSqUBV\nVfC8YLfEzs/PAgBEUYKiOKMgxiJn941JI0wn9u171vW9qpLpF04MWZLgWfJyKJWWUCg8YhveqGod\nzz67y/U3itLAzIzbP6EVpmnahMtxHERRtMZ/kwdNMplCLjcCnueh6zrm52dtjS35m5hN5vS8eF7A\nk8ouO7plwOKBAz/rSLruY1atdAkDSZKQySTtBx1pdFl5hmIYWObrZUf6IW5567rTIsGLboPyi+gf\n9913L9LpLK6++joUiwW8611/GZGuF8L+3NZLeoEu5Zzj5zs1h3hJn0iLLofNm7dgfn6uTftaqVRQ\nrVYxPDyKTGYYS0sFlMslzM/PgmVZJBJJVCpkwsTY2CQajToKhUVbmzo8PGoVu0xwHIu5uWYhj+d5\nO1VBI0jiIVAFy7J2l5zzpqSjflSVx/w8D0HQMTTUQLkcA8eJSCZrMAwDspyAIIhgWc5+P+r1mp2L\nTqezyGaHcPDgPmiaahfmqLMUIdthxGI8Go26bRBEIYoS0uksarWqi4Sz2RxyuRGwB7o/CXtFeMRC\nsYZqtYbh4Sw4jsXICOk2q1a9W3yDTZgIQo7NFZ87/eBMi5jWbLVgRUFyzGvLe/f001+H008/w/6e\n6K6D4bAg3fCxeukFL7cx0p7s3VlHow83mept5NoLmcwQJEm2dLvNfKVpmpibm7ENzicmNtgERnPC\npmkin5/Chg2bkEqlMTc3i1qtggMH9iKTyWFkZBSJRBymyUKWE5iePmg3Cei6hmx20paZ6boGRSGd\nZbFYzNGNZtrHZRgCDhxIYHi4iqGhBvbsSeGYY1SbcMfGJu3WWqeMi2U5jI1NQJYTmJ2dtkfXU4Oe\nVCqFTGYIsVgMpVIJpVLBFa0zDINEIoVGo+4yrgGAiYlNkGWS/3z55Cn4z2f+AyxLTHROmXyF1xXR\n+2IAtZmsoVgsQ5KElhbfelvLcdjoFHy0pkXicRHJpAwyQUPomH5oxVoLbOhnWK1W8NGPXomLLvqb\nwPs4LFzGACCA3WVPpFIyqtV6x6p3ULAs8R/g+RgUReti5RjM54FhgHQ6iWKx+xTcIDAMA/Pzedf8\nrlbwvGCPdy+XS64c79DQCDKZHKrVCubnZ+1IcnJyEobBQFUVVKvVjo0KYYFGuaQjzgDPC5iY2ACe\nF7C4uIDFxWZUL0lxjI0Rne/s7CzK5ZJNYDTP3Sk6TCSSGBubdEVhqqpi18Iu7C/vwxHpI7AlvdX1\nN0RVkcPMzELr7towPJxFsVhyKRuIwxgxNldVDbVaHYZhIpGIY3FxqcveCKgOu1zu7aQny3FwHINS\nqfe2sRiLXI4MtaSj3TupMuhx0IeKH3Bce0FyEMjnp7F9+xU455zzcNZZb/bcppvL2GFDuhwXXqHK\nD+l2W847I1Sg6bbFMAxUVe9Iov1EKtlsEoVCeKRL97l3734sLRXa8rKtEARiL1ir1Ww1AsdxSKdz\nSAoiCgf2oRTrbeiiKDwMI4nNm0XU61WUSkvguBjGxiYsXwgG+fxBqKqCjRu3QpIE7Nr1DAAdDENe\nM5FIQdM0KEq9g9M/+Wxo7pplOYyMjNlaZKpUIEVAHorSuZBFmyO8vBN6TZJgWQbDwznMzvZHuk5I\nkoB4XALPx6xuuaWezRdByC6RiFttz71Jl+M45HIpzM0V7PQDMXqi6YeGKy+dSsnQddM2Guq9/8GT\n7sLCPN7//kvwUb7e5QAAIABJREFUwQ/+HU4++WUdt4usHUMAFdlTjagg8Gh637rJFYBnFOr2ejDg\ntK6j3rdBKtLLgWma+M89/w9PLTwJgRPw+iPeiG05/2bM6XQW6XQWxDCmiKWlgudkXOcYc47j7KX6\n4uIcFk0TsAiXMQyYLW5aoihZInwdgqACWMRjjyVx4olZMAyLpaUCFhfnbftJVVXsZgxNU8AwhFzI\nqCIZ5XLJnlNGTMdF6xgVS23g9iUWBAGzs9P2wy4W46Hrmn0OrSBLZwnJZAbJZLLNHYzu2/mQHTTI\noEgF8biIRCKOXI4a3NQ7mscwTOd5dO3b9jeNuFP6wa1+YACEs5oMC1/5yh0olUq4884v4847vwwA\n+PSn/9nWlvvBYR3pdjIr8cqVApRIiTmKpunQ9dbCk9F3BdnLcDwMdIp0f3nwl/j+nv8Ex1ojyhkO\nH3rp5RA4oe99Ggap4NNZY13RJYlN32/ntUmcw2LIZEiEKoqSXQBLJFLIZLI4eHAfkkliT0m9aSnR\nU7Dwvo1bB3H6RSwWQzyeQCqVdRgCuU7UJhpyypTYnWmgVn9m/+bdIyNZLC6WPB8CTkiSAFEUUSyW\nbOmZIPCe5uGpFDGh9xMABImKeT6GdDqB+Xlvc3Raf4jHJcRiHHTdQL2uoFLxNzBgpdILfhBFugBk\nWQTHuZf6nSr2ZEKt5vq5E8lk3HZjWq/IV6ZswgWAilpBoVHAmDzW9z5ZlsPo6ARGRwlhVqsVFIuL\nrim4NrpESF6BAM8DgtAkBmdTQqXSjGArFbd5jK7rLoLvRKtBCJfnBSSTKSSTaSvXTiPY7tcDPWWy\nGmLAMKa92nFGv8EC4OAuY17SMwBW40U90OsHiYqB7oWxVvVDNpuCLJNmGWJsU+8S1KwRkbsPHDak\nq6o6VFULVLHvhEEsDQe13Oy0zw3Jjfj9zO9t4k0KSWRF75Ew/b5uIpG0x9I0Gg1b0UC1r8H21/33\ndJ+e5LnM95VMNogjkxlCLpeBIPCWvWC7AXgQOKdTtEa//bbsdtuuFU7pGc/HIMsSksmcdQyN9j/w\n3jP8pgCCSNF03YCmGWg0atB1A/G4ZMniNMv7ISxPipXHYUS62prp+FpJdLrIT554KZYaRTy+8ARE\nTsAbjniTr9QC0J8eUhRFiOIYRkbGYBg6SqUlFGfy0FgmtApnmF19RGecQjqdcbURl8tVUHvBdJpM\n7q3XO3dgtYLm/5tTSJxfN9NadBUV5sO4G9+pqoZikTQ45HJpiKIISRI9pWfu8xmk4Q3ZNxlsWUap\nRK4jWSbROY2Kg4w/Xws4bEg3TKyllt1+wTAMzjji9TjjiNcH/tvl6j1ZlkMmk0Mmk7OWlFWUSgVr\n2oP/taqzSUXXyZy1fj8XWlhLJtNIJBJdic5pLygIvOXvGoeqkrE47cTaLLQ2vXyb/+u64ViFNdNd\npBDXOfdrHTn6MTHvdm40n6tpGuJxCcPDWVt61t7g4F+vvlwbSNOENT2iYds6ZrMp3H333ZiZmcHr\nXvenGBnpPiB0LSAi3b6wui276xHtS+bmN7IsY3JyzJ5qW6s1oCh1VKtlu0mCkg/1SQCa7xfD+NBh\nWy/OsCwkSYIkxRGL8eC4GGKxWNs4IgAdiLP9Z6ZJ9MuxWAyCIICMdCdjx50EG/Rh1Sn360SQ9IL/\nlAUhUjLckrie0QJXq+tZsP2GFxVTW8dKpYajjtqGX/3qV7jggrfg7/7uI3jta18XzosMCIeNeiHM\nMeyyLELTjFDd6lmWQTIphzbhgmJQOl26z/bChun6urVy3w1kJLcEwOxpmkJagoEbbpBw4AApZp18\nsoa//us6ABMcF3PNQmu+Blk2E020Cl3XXct8p5qlNSKlPgrOn3lF5qJIRouzLBP6XK/W3O/4+DBm\nZxd77l+WyTSIUqn39ZXNpjq+/059LU05VKs1Xy2+kiRCFHnfzTp0XLtTWdEJDMNCVXWwLGdPm15N\nROqFkLGeotJ+fCJ6EamqakinZVSrDddEhW7w835RTSnPxxCPS0gk4lbXktsykGEYxGIxpFLANddo\nePBB8vXJJ3PguGTHnGnr8p7nYxBFwYpKFWia4Vre9ws6WsY52VZR1MCjb5qSxvboutml6NdPtz8/\nhVY49bWiyCOdTiKdTkEQGm3SM6/zCfK2Bk1HBNHKriYi0u0b6yW9QFIh7RGZt57UT0S6tFS2W00T\nCcnOs4XlckULJ/Q1hoYy0DTNIiymjYDOPhueUahzed9JQ01kUyLi8Tg0TbcUCeGsYOhkW+o3kEol\nQIzPFbvdu72Y1vwagG1J2em8DMPwmfv1D7/k2GioVkqogliMQyaTAmCiWq13HO1zKPjjLi4u4D3v\n+St85jOfw9atRwT++4h0+wD1NVhtdMuT0u8bDRXptOx7xLbf86L5PiKnEpHNpu1uom4yqs5FptYo\njrFJRVVVMAwLURRs0qJ+AkEKb14gsqk6qtW6NXG2GWEHSQt0j0qdDTgsZDkOWYbVoUiNfAyLTJt5\na79Bnp/cbxAE7zIz7fxqU3ome7qeDTLSXQlomoYdO673NJ73i4h01wxM+yJrv876z5NWKlXU602b\nPSdhhQEyzYDk9CRJRCZDZpFRGU9rrtTZEu2O4HTXzzrdbHRelygKVt6xEVpxplNaQFFUW02w3Ki0\naZTDWDlOkvslxcPlRdjddL9BCcz/w8a9X6f0TJLIZAmWJa5nQfZL9r32XMZ27rwZZ5/957jrrjv6\n3kdEun3ANIMX5fwUnBRFs/SHxPyjF/wGJDQPRzuQmlFpb9MeSi7d9KXk/Jok0/SmIE0RjYa6rBZp\nJygJUmKU5Yz1IGn4jnq9o1JviZcg8HaETc3CW6PSfqJtZ4RNZWd05PlyUjX0c+J596pBEHib+FYC\nTlkdTRPJsmS/d350zWstHXHvvd9FNpvFy19+yrJIN1Iv9AFR5MFxLMrl1t70/vOkFPQC5fmYTb5h\nPu0ZhgHHEfIVBN7SiWrWg8S9FHYudVsr9t2q9xS0IEZt/AYx24tGjJIkQtN0NBqKnecMGpW2/sx5\nrNSUhWUHdy5Ue0qLe/V6wy5MdX5AeK0kvM/H+fDrlvvt5VzW77bU65dliRdyL2vHsbEhX8oMAGAY\nDiw72Bjy0ksvst+zXbuewubNW3DDDTdheHikbdvI2hH+Sbd7REq+Z1kO6TQ1BakNxIOBSnOIKUl7\nBb8VvbucvJf3DMOC58kgx3pdgaIoy67et58LGWJI2mcHF5U2l9GkLdgdjfYflXY6l34UCe5zYzqS\nZyzGubTDnR4QTZL1v5Kg+WLyVrT/zaBI12nV2Co98wowxseHkc/P+zqnlSBdJ973votxxRXbOxbS\nIskYAMCAYdD3wTsiBfxFpbquYXGxaFekdd2wO3jCAtlnDYqiWNN0JXt5221577wB3V1O3W9Kd640\n3N52OsSQRqXZLPG1rddJ5NUtKiUrBa9cqY5OUSmJsEn0W683oCjhzQ+j50IdsVKpBAzDdLUCu13q\nup+bO49tWIVD0xWxi6IAQSC2kssheQpqiM+yztwveZ+bU0f87itI3rXp0+CWngnWZOGEy/VsPYxT\n7weHTaRLPmxygYX9WYqiAFmWrKJSL61i5+jGa3nvXNpzHFmW0aWnpumBqtx+4E4JBEtvkBu5VYnQ\nKypt5kqDpC78wLlcX25UCnReTRBTc9YmiW5L/H7PjZK8JIltJB8G6IqCTC5OYn6+4GsyyshIDouL\nRV/bptMJu+vQCyzLWNGvBNM0EYtxmJlZWDPphSCI0gsuDIZ8aUQiSSJoAwH5ebflfecup06fC8Mw\ndhTnpxjWL1pTAo2GapNq0Ki0d65UAq3g+ykgBkUrYdVqTRmT10Ow/YFIIsNeRMow5AFMc7Ld8pXL\nAc/HIElk7HmjoVjaYu9roNvn5ZW/J2PoSTTvR/c7OprD/HzR10Mkk0nassJeEAQeuVzakgiqrs/M\nC+uJdNfOUa4YaGKX3vydybf7MrGpL3XekJrmrN6TPKmq1gPl3LqB+NSSqjCRaKX6usGDRKXUcYqe\nn1tXuryo1KlGoBXuMJotWpUXAANF0RCLsUgmE/Zn3knC5o68/ee4NY1YJYqiiGQygV4tzf1A03Qr\nL8pBFAVks2mLLA1XDrzTg6Jbaqb5/rFgGAOmyXTM/QLBtbR+N6Xv/9zcoiU9S4BlGbvxwnm9rdQU\njrBwGJIuBYuHHvolnnnmaSwszKNYLKBWq2LHjh22y5TXMjGIiJ049MchCDyq1RoMI7ycr2nCtrYj\nF2USuq7bUU83kX4/uVJnhA3AmjMWXhTnt9mie+TWfFB4rSJ0Xbfmk9WsZgsegkBGvXeLFoPCNJtu\nWDS/TBouFOth0skmsZvtY+dccL3esFujWZYJXKzsBBpQkKYL8rPldL3104rsJT0bGSGuZ9Xq+vTV\nPYxJF/jtb3+DRqOOoaFhbNu2DRMTk1AUA4qyFEr1nkZxgsAjkZDtCyioCN65rG/emK0KBdgThQHY\nnU7uB0X/USmNsKvVuh15+Mlh+zs/N9kQeRPxYMhmU65t+43c2kEImBb3MpkUNE3v6R8QFKpKVgax\nGAdRFO0ZZfRB4vwsvaLuoJ+d0/KwVXbWL5xNF63Rb3Ai9fua7VGx2/WMPMhiMRZ33HEHXvGK03Dk\nkUf73v9q4jDM6fbC4ApudDYVAFvt0L7E9xuVds4Dt75OmG5oTtCqMwDPJbSfXGn3LjVqTEOW0QAz\n0KkBzfPx9zp+olKvqJtGpQBpHgnTt4KC5JebKxNaeAsSTPhZVQCwi129ot9cLo1KpebreqReDvPz\nha7bmaaBf/mXO3DPPffgqKOOxk037VwTqYaokNYX+iNfP1FpM88IOxINq8rtBOmDJyOywyIrr/OL\nxUiEzbKMXVBqLc54Fw2D5blXotmCrBh4+3VoDrv1s2y3fuxdNPQ6H2dBLIyUgBfI8EzyOoqiWuTb\n66HodX7t50ja1qnut/tnMTSUQblc9Um6MWQynYdYOkEe2iz27duLo4/2P9F6kIhId1kwoGkqFhbm\nsbS0CEEQ8bznPa9nvs3dxeUdlfI8iUgZhrE0uYOJSJtkxXZUCATJJ3YiT1JAFBCLcb4aOvpFP80W\nvVQK9GugmS+lfguEfHU0Gg07nx/mebV21fWT4uikLGEYFhznVmEA1JOYFgy9yTQo6HBNwDv3G8Qf\nl+djSKUSWFjoTboAwLLCmohwKSL1Qp+YmcnjoovegUKhgGw2h7GxUbz0pS/Dcce9MJRcqaqqKBZV\nOyKV5Xjo5EuIkhRbYrGYnQsjRSOza/toPx4D9bpidxs1DXbCjeDczRYSstkUdF2HojRleu0Pi275\n0u5FUWrNSJpH+svLd4PTh4FovuNwmuD0kuq1Pizc56mj0WhGqvT8qN+DKAqWwiaMwpvTML5puOP8\nfb9GOmsFd911Bx544KdQVRXnnnsezjrr7MD7iEi3C8bGxnHXXd9EIpGwWjLJhVQq1RBmzpc4M5Vc\n5NstHRCswcIdZZN9mhAEHhzHodFQUK3WQ73AabcRUTwQUqRTXP1Oz+2dK3U/LBiGtUX1dAkdRooG\ngP3QqtcbtgdDIiFbzSP9TYXoFnlTJBJxJBJx0EkZJA1F7S41O1rtp0GmOYaddXQJ6lYH3/IfKOR8\nOPs8aaQdxCpzreHhh3+NRx99BF/4wm2o1+v42tfu6ms/UXqhL5iuf2FdIPTipLk+lmWgaU4zGr8p\nDH9LRJZlIcuSY5kenuWjEwxDx+RIVkSqwjA6t/8uJx+8Es0WQGuKQ0W9Xrd0sq1k2ilN0714SL+m\nFomSJNqSwEEWRptjhpSOOfPu59fpMzTs1nY/1xgtahYKJV/HvhLphVtuIUW6PXt2o1Kp4NJLL8ML\nXvBCz22j9ELoYNAcaULJrVuThb8qt3OJT30cqPaSmmqHuUw3jKblI4lI03b3T5DX6RS1eeUTGYYD\nz8dgmqRrT9P0PiVf3hhUs4VXzpueA1mqk/H1Yci+nHDqVOkDpR+TdT+gM+NisZirtZ3m651qjNbi\nmt8hnGQfbrN1L+VDEP+HlUKxWMD09BR27LgZU1MHcOWVH8JXv3p3YLKPSHdZIGT4k5/cj7m5WSws\nzKFYLGDLli247LL/YxONt7bU/6TYWIy48WcywkAE4YZBnP+r1brVmJCyvQp6Vbm7R97OfKI78qYE\nIklcKFrfVvhttvAuILafZ7ecN10h0EYI0v2m+BrWGAT0gUKjbGqy3mtaR+fPr13aRs9N03TrtVjr\nIQnr2lu+1Wgns3X3NkAQL92VKKKl0xls2XIEeJ7Hli1HQBBEFAqLyOWGAu0nIt1lYn5+Dg899CBy\nuSEMD4/huOOOxzHHbEOpVIJhLG9sCoWmafZcMpLzlSwrvP5u6tYWWS8SpR4C1CaR5hPDMPAGnBFp\nDLJMint+Rwr5PUcSabPQNB2GQfKx2WzKjrI6k6kOVXVHcn5AJ0/Q9JAs0y60cNM2zUIia0/roMZB\nRHHh/nxbneZoTth5zr3e8+Y5ST39HoKAZTuPGlprhbQTTjgR3/zm1/C2t/0l5ufnUK/XkE5nAu8n\nyukOBDTfa4DYRIb7FKbky3Gca8pEP2mMXvpgMrRRsryDw7WvdMLtH9yZqPzIvjqfI/meehYAGGiz\nBY2ygxrg9BN9A7CdzmghkXQkhnsLs6zTi0Pve8xQr9w3x7GoVusolSq+oliO639mWRB8/vP/hIcf\n/g0Mw8All1yKl7/8FM/tIp3uqmH55Os0MGm96YilIGfnkpcr1u+GIPaV/YCeGyVEno/ZTSNeS2DT\nNKDry2u4WIlmC6DZHRaPizBNQFEUhz+Gv1SG18PDC6QNWIIo8lAUDfX6YJzOgKbsjOqy6fvXq8jm\nXUhsP0eizOie+22e98qQrl9EpLvqcJOvaZKUAakU96u/dFa4SUQVi8Vcke8gELTF2G/07XVusRgp\nug3CE8GJ5Uy2APw0ljTJlBIHwzBQVc1WcgR9YPg5JqoAIMW45TuddSPRWIxzdVkS0uxMqkHhzP16\nkW9EuhFcKJVK+Md/vB75/DTm5+cwPz+HI488Et/+9nc8L0hnDi5I4MVxHGS5OV9tkIMI6Q3NMKzl\n3GW03YydKt1Bo2+S4hCtKLsBVR2MZMrZGUbNYrzOK3jU1q5Y6CQ5GwRoRO+MSJ3k7l8C5h1xO78X\nBHpdMANRWNAHV2v0G5HuCkPXdXz2s5/Bk0/+EYqi4t3vvhinnnraah+WDU3T8OCDv0AqlcLw8AhG\nRoYgihIGlfNdDvn2irxbb0DTNNtyiWFIv7zQjLKX5yXRvSW4+T1F07HNm3CWA6rDjcf7bwHutm/n\n+XAcB0GIgeM4+7PptMpYblqKTmsOY5acN0wYltMZKYoKIe57+Tjkdbr33XcvNE3DF75wO2ZnZ/Cj\nH/1gtQ/JhVgs1vEhQK7lcMlX14lciuNYyHIcQ0OSNXRS7Uo23vI2MrPLeRN63YBUF5tMJuwxP2GD\nKh5o5Nbs3CMyJne7bLd8ItqIpZMqg0rbyAMs/GYLpw5XFAUkEnFQKWKn1+rdgNE5N0yj3GYx0bQ7\n68KEprXPkvOX5qDKBee90HpfUJtJ8vVa7F7rhkMi0r322u04+uhtePTR38M0TXzwg3+HjRs3rfZh\nBYBh/x+EfDv35NOItEmmdBlG5FDhmZy0whllh2V604lMOY5z5RK9yDSMfCLQfKjQ8xqEHSNAzlMQ\neIgiMQ4icjfD9fn2k87ohJVyOqOvxfMxvOUtf4GTTnoJ3vzmc7B165GOLbzIdZ0xqoVDKr3wve99\nB1//+lddP8tmc5ic3ICrrroGv/vdw/jyl2/B5z73pVU6wuWAVOXL5RIWF+dQKBRx3HHHIZVKepJr\nN8cv5w1JP2KnLCtMTawXWLbZYkwnvHYaC9NUY9CHBOlk611MpA8LQJKEvgthQc+LysD8NCa4z7N7\ndNpcabhTNxwXs6YnD64FmGVZK58tQNM01GpBzM97Radu/snn8/jOd/4dDz743/jiF//F9hY+lHBI\nka4Xrr32Kpx++uvwmtecAQD4X//rjbjnnvtW+aiC47rrrsaPfvRDxGIxjI6OYmxsFJdffgWe97zn\ne+TY+p8C7CbEwVgwOvWW7RIwb5Lp9dDoBedIITrOxa/BTlA4c7G6bkBR1LbGBLc6o3cRqtN5OiVn\nhhH+JOBW0CLp7bffDtME3vCGM5FMJunRtB6dx9frMzoNE4c86d5999fx1FNP4qqrrsHTTz+FHTs+\ngS996Stt2z333LO4+OJ34p57vg9RXFvVTgCoVisAGMiybP3EaaQefsGtH/LtHbF1rnQTs3Myk2xQ\nk3IBJ0mRpo7+/Gm9GxScEXhzKgQhWwAtMrDwC4rEYF0MQVtstkSmQCuB7t79NL7ylTvx0EMP4rOf\nvQXbtj1vGUd+eOGQJ11FUfCpT30Szz67B6Zp4vLLr8Lzn/8C1zaVShkf+9hH8Pjjf8Tdd39vTZJu\nZ6wc+aqqBlXVApFpkNywM0IcdDQKwPbBBUy7qaPfIpTXuTqxUs0WgDtV9Itf/AKCIOHII49C0KW+\nn7zp0lIRspw4JNMAg8IhT7q9YJomPvax7bjggnfhqqv+Fv/2b99aZ6RLEXyEkB/RvpNM6e9JxKY4\nRgmFOy0BCL/FmKYzupEqAEutoHcl1eXASYjh5Zi9yZRhGNx///fxz/98M7ZuPQLbt1+L8fFxREv9\n1cUhLxlzwqvQNjExiTPOeAOOOWa9L48IaWiagvn5OczNzaBYXAQAvP71r++QSwxuw0etHmU5PhCT\ncwoaDYqigFRKBml+aE8F9FOEol+TGWfN750qhFpNG0g0Sk3cabMFncw7MzOLeFz2+IveS/323zUV\nG6997Ztw2mln4IEHfgKeF0CvkwhrE4dFpPvWt56N0dExAMAf//gYjj32uHWqbgD279+HCy88H8lk\nCiMjI5iYGMfRR2/DpZe+z6PYtryPj5KvKAoDMTlvjcB5nrdGyDMwTcNul+1VgOonCm+PRgdh4N6M\nTiVJxCWX/DUaDQXnn38BXv3q1+BQkkhFcOOwTy84cd55/9MzvVAul/H3f381qtUKVFXF+9//Qbzo\nRSes0lF2R3vv+eDGxgMkDyvL/sk3uEuWu0+fZdkVcQEDaOuvBEmiEjA/7bj+BfzOr3XdwAMP/AQ/\n+MH3cc0114Hn+eUefoQ1ioh0HehEurfdditSqRTe8pa3Y+/eZ/Gxj30Et9/+b6t0lP1isOTbLLgR\nLaemaR38ePt3yXLCPcU4/K4pJ2iB74kn/ojbbrsN5577FzjhhBdbD7flFaIiHH44rHK6vfCtb33X\n8+dvecvbIQgk8tA0HYKwHgttNJdHUwtBC27uJgWvZgyaJ6WpByLaV1zeBGGBKCmoebuEeDyOer1f\nI5/u0alpMqhWFUxObsXJJ78c11//CZx99nk4//wLlncSESK04LCLdAHvYtv27dfi2GOPw/z8HC6/\n/AP4wAf+Fied9D9W6QjDgao2MD8/i7m5WRSLi0in03jFK07pIgXzjk47TZx1NiMoiopqNdhstaCg\nLcYsy+Ab3/gWTjnlFKTTaQDBl/q9olNdJ1MYIplUhH4QpRd8YvfuXbj22u249NLLcMopp9o/NwwD\nn/70Ddi162nwPI8Pf/hqbNq0eRWPtDd++tMf45prPoyhoWGMjIxicnIcJ598Mt761vOXXYRqxeDI\n17uqb5oGbrrpH3H//T/ABRdciLe//QL7d9FSP8JaQES6PrBnzzP4yEeuwMc//sk2adlPfvJDPPDA\nT/GRj3wMjz32KP71X+/ADTfctEpH6h+0KOX4CQZdcKOND4qiYnGx2KFYFI6Af3p6Cs88sxuvfOWr\nlnnkESKEiyin6wO33roTiqLgn/7pUwCAZDJpE+sjj/zOnoX0ohcdjyeeeHzVjjMI3IQLLCfn6wem\naaBWq9njws8//y04/vgTcOGF78GWLVutrcLr1Z+YmMTExORyDjlChBVHRLoWukWulUoFiUTS/p5l\nWWiato7zff2QbzABf6Nh4M47v4Z///dv4hvf+Douv/yqZR1xhAiHCtYra6woEokEqtWq/f2hU2Bh\nQdMNlHwJwpFIJRJJ/NVfvWu5BxkhwiGFQ4E5Bo7jj38xfv7zn+GMM16Pxx57FEcdta1tG03T8MlP\nfhxTU1NQVQXvfOd78KpXvXoVjjYoKIl6EW6ECBHCRkS6PvAnf3I6fvWrB/He974bpmli+/Zr27a5\n7757kU5ncfXV16FYLOBd7/rLdUK6FBHZRlhbWOuzD/vFYUe63/zm/8VPfvJDfPazt+KRR36PT37y\n47j99n9zeNi2g2VZXHHF9q77Pf301+H008+wv+e4w+6tjRAhVKz12Yf94rCzIzrvvLeCYRh8+9vf\nwo03Xoft26/tSrh+IcsyZDmBarWCj370Slx00d+EcLQRIqwfPPfcs3jjG1+NRiOc4Z0PPvgLjI2N\n4YorLsONN34Cp576J6Hsd7Vx2IVjDMPgqquuwTve8Vacc85f4IQTTgxt3/n8NLZvvwLnnHMe3vCG\nN4W23wgR1joqlTJ27vyMZS0ZHJ1mH4qiiB07bsbvfvcwrr/+4+vWHdCJwy7SBYioXpYTeOqpJ0Lz\nUl1YmMeHPvQ+/M3fvB9nnfXmrtsuLi7g3HP/DM8992worx0hwmrCNE3s2PEPuPjiSyFJUl/7OOus\ns3HXXd9w/RsaGsIrX/kqMAyDk076H9i3b2/IR746OOwi3Wq1ih07/gE33ngT7rjjS/j2t7+Fc8/9\ni2Xv9ytfuQOlUgl33vll3HnnlwEAn/70P0MU3RehpmnYseP6dWqoE2E9Iwz70pUcEnDCCSfiF7/4\nOV7zmjPw9NNPWRMx1j8OuzbgT33qBvA8j8su+1tMT0/h4osvxC233I4NGzauyOvffPOncMopp+Ku\nu+7AFVdsx9atR6zI60aIMCj70kENCfAz+3CtIvJeWCO4997vYmYmjwsv/Gu8730XR6QbwYVBGyuV\nSiUIAg9cBa+QAAADkElEQVRRlPDMM7uxY8c/4JZbbg9t/0Bnv+rDDZH3whrBf/zHPWAYBr/+9UPY\ntespfOIT1+CGG27C8PDIah9ahDWAn/3sx1AUBbfeegcee+xR7Nz5mb6NlXrZl1533dX4wAf+NozD\njhAQUaS7Sogi3Qit+Oxnb8Kxxx6H173ujQCAs88+E9/5zv8L9TU62ZdGCBdRpLsOcdddd+CBB34K\nVVVx7rnn4ayzzl7tQ4qAwbZ7D9pYac+eZ3D11Vd62pdGWDlEpLtK2Lnzix1/9/DDv8ajjz6CL3zh\nNtTrdXzta3et4JFF6IZBtnsP2lipm31phJVDRLprEA899EscffQ2bN9+OSqVCi699LLVPqR1icXF\nBbznPX+Fz3zmc6GlcQbZ7u3HWGk5iAh2bSAi3TWIYrGA6ekp7NhxM6amDuDKKz+Er3717pax6xG6\nYVB6aNoyPoh2bz/GShHWPyLSXYNIpzPYsuUI8DyPLVuOgCCIKBQWkcsNrfahhYpB5q137rwZZ5/9\n57jrrjtC2yfFoNq9/RgrRVj/OCzbgNc6TjjhRDz44H/DNE3Mzc2iXq8hnc6s9mGFCmfeeufOLyKf\nz4e273vv/S6y2aw9YilMBGn3DgvXXXc17rnn2/b373vfxfjDHx5bkdeOED4iydgaxec//094+OHf\nwDAMXHLJpQMhkNXELbfsBMMw2LNnt523fsELXhjKvi+99CI7FbNr11PYvHlLaHrom2/+FH74w/9y\nzHzzbvcOEw8//Gvcdtut+NznvoTp6Slcfvll+Nd//cbAXi/C8hF1pEVYc7jxxk+sSN76UNBDm6aJ\nt73tHNx88+dx3333IhaL4YILLlztw4rQBd1IN0ovRFgVpNMZvOxlp7TlrSO0g2EYnHnmWfjBD+7D\n/fd/H29605+t9iFFWAYi0o2wKlipvPXOnV9c11EuxZlnnoXvfOdujI9PYGRkdLUPJ8IyEKkXIqwK\nTj31NPz+9w/jooveCcMw8KEPXQmO41b7sNYsxscnMD4+gTPP/J+rfSgRlomIdCOsGv73/46aPvzA\nNE3Mz89hYWEep522noadRvBClF6IEGGN48c/vh8XXng+LrnkUghCf+NwIqwdROqFCBEiRAgZkXoh\nQoQIEdYIuka6ESJEiBAhXESRboQIESKsICLSjRAhQoQVRES6ESJEiLCCiEg3QoQIEVYQEelGiBAh\nwgoiIt0IESJEWEH8f4jPTJryleTGAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADnCAYAAAC9roUQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJ8rPxZQceNx3AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Properties of system\n",
"ntrajectories = 100 # number of trajectories\n",
"nevents = 100 # number of scattering events in each trajectory\n",
"wavelen = sc.Quantity('600 nm') # wavelength for scattering calculations\n",
"radius = sc.Quantity('0.125 um') # particle radius\n",
"assembly_diameter = sc.Quantity('10 um')# diameter of sphere assembly \n",
"volume_fraction = sc.Quantity(0.5, '') # volume fraction of particles\n",
"n_particle = sc.Quantity(1.54, '') # refractive indices can be specified as pint quantities or\n",
"n_matrix = ri.n('vacuum', wavelen) # called from the refractive_index module. n_matrix is the \n",
"n_medium = ri.n('vacuum', wavelen) # space within sample. n_medium is outside the sample. \n",
" # n_particle and n_matrix can have complex indices if absorption is desired\n",
"n_sample = ri.n_eff(n_particle, # refractive index of sample, calculated using Bruggeman approximation\n",
" n_matrix, \n",
" volume_fraction)\n",
"boundary = 'sphere' # geometry of sample, can be 'film' or 'sphere'\n",
"\n",
"# Calculate the phase function and scattering and absorption coefficients from the single scattering model\n",
"# (this absorption coefficient is of the scatterer, not of an absorber added to the system)\n",
"p, mu_scat, mu_abs = mc.calc_scat(radius, n_particle, n_sample, volume_fraction, wavelen)\n",
"\n",
"# Initialize the trajectories for a sphere\n",
"# set plot_initial to True to see the initial positions of trajectories. The default value of plot_initial is False\n",
"r0, k0, W0 = mc.initialize(nevents, ntrajectories, n_medium, n_sample, boundary, \n",
" plot_initial = True, \n",
" sample_diameter = assembly_diameter, \n",
" spot_size = assembly_diameter)\n",
"\n",
"# make positions, directions, and weights into quantities with units\n",
"r0 = sc.Quantity(r0, 'um')\n",
"k0 = sc.Quantity(k0, '')\n",
"W0 = sc.Quantity(W0, '')\n",
"\n",
"# Generate a matrix of all the randomly sampled angles first \n",
"sintheta, costheta, sinphi, cosphi, _, _ = mc.sample_angles(nevents, ntrajectories, p)\n",
"\n",
"# Create step size distribution\n",
"step = mc.sample_step(nevents, ntrajectories, mu_scat)\n",
"#print(step)\n",
"# Create trajectories object\n",
"trajectories = mc.Trajectory(r0, k0, W0)\n",
"\n",
"# Run photons\n",
"trajectories.absorb(mu_abs, step) \n",
"trajectories.scatter(sintheta, costheta, sinphi, cosphi) \n",
"trajectories.move(step)\n",
"\n",
"# Calculate reflectance and transmittance\n",
"# Set plot_exits to true to plot positions of trajectories just before (red) and after (green) exit.\n",
"# The default value of plot_exits is False.\n",
"# The default value of run_tir is True, so you must set it to False to exclude the fresnel reflected trajectories. \n",
"reflectance, transmittance = det.calc_refl_trans(trajectories, assembly_diameter, n_medium, n_sample, boundary, \n",
" plot_exits = True)\n",
"\n",
"print('Reflectance = '+ str(reflectance))\n",
"print('Transmittance = '+ str(transmittance))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For spherical boundaries, there tends to be more light reflected back into the film upon an attempted exit, due to Fresnel reflection (this includes both total internal reflection and partial reflections). We've addressed this problem by including the option to re-run these Fresnel reflected trajectories as new Monte Carlo trajectories. \n",
"\n",
"To re-run these trajectory components as new Monte Carlo trajectories, there are a few extra arguments that you must include in calc_refl_trans()\n",
"\n",
"1. run_fresnel_traj = True\n",
"<br> This boolean tells calc_refl_trans() that we want to re-run the Fresenl reflected trajectories\n",
"\n",
"2. mu_abs = mu_abs, mu_scat=mu_scat, p=p\n",
"<br> These values are needed because when run_fresnel_traj=True, a new Monte Carlo simulation is calculated, which requires scattering calculations\n",
"\n",
"\n",
"##### Calculate reflectance for a sphere sample, re-running the Fresnel reflected components of trajectories"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reflectance = 0.26996170258165064\n",
"Transmittance = 0.730038297418345\n"
]
}
],
"source": [
"# Calculate reflectance and transmittance\n",
"# The default value of plot_exits is False, so you need not set it to avoid plotting trajectories.\n",
"# The default value of run_tir is True, so you need not set it to include fresnel reflected trajectories.\n",
"reflectance, transmittance = det.calc_refl_trans(trajectories, assembly_diameter, n_medium, n_sample, boundary, \n",
" run_fresnel_traj = True, \n",
" mu_abs=mu_abs, mu_scat=mu_scat, p=p)\n",
"\n",
"print('Reflectance = '+ str(reflectance))\n",
"print('Transmittance = '+ str(transmittance))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
iemejia/incubator-beam | examples/notebooks/get-started/try-apache-beam-java.ipynb | 1 | 53106 | {
"license": [
"Licensed to the Apache Software Foundation (ASF) under one",
"or more contributor license agreements. See the NOTICE file",
"distributed with this work for additional information",
"regarding copyright ownership. The ASF licenses this file",
"to you under the Apache License, Version 2.0 (the",
"\"License\"); you may not use this file except in compliance",
"with the License. You may obtain a copy of the License at",
"",
" http://www.apache.org/licenses/LICENSE-2.0",
"",
"Unless required by applicable law or agreed to in writing,",
"software distributed under the License is distributed on an",
"\"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY",
"KIND, either express or implied. See the License for the",
"specific language governing permissions and limitations",
"under the License."
],
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Try Apache Beam - Java",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true,
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/get-started/try-apache-beam-java.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"metadata": {
"id": "lNKIMlEDZ_Vw",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Try Apache Beam - Java\n",
"\n",
"In this notebook, we set up a Java development environment and work through a simple example using the [DirectRunner](https://beam.apache.org/documentation/runners/direct/). You can explore other runners with the [Beam Capatibility Matrix](https://beam.apache.org/documentation/runners/capability-matrix/).\n",
"\n",
"To navigate through different sections, use the table of contents. From **View** drop-down list, select **Table of contents**.\n",
"\n",
"To run a code cell, you can click the **Run cell** button at the top left of the cell, or by select it and press **`Shift+Enter`**. Try modifying a code cell and re-running it to see what happens.\n",
"\n",
"To learn more about Colab, see [Welcome to Colaboratory!](https://colab.sandbox.google.com/notebooks/welcome.ipynb)."
]
},
{
"metadata": {
"id": "Fz6KSQ13_3Rr",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Setup\n",
"\n",
"First, you need to set up your environment."
]
},
{
"metadata": {
"id": "GOOk81Jj_yUy",
"colab_type": "code",
"outputId": "68240031-2990-41fa-a327-38e15dc9fdf9",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 136
}
},
"cell_type": "code",
"source": [
"# Run and print a shell command.\n",
"def run(cmd):\n",
" print('>> {}'.format(cmd))\n",
" !{cmd} # This is magic to run 'cmd' in the shell.\n",
" print('')\n",
"\n",
"# Copy the input file into the local filesystem.\n",
"run('mkdir -p data')\n",
"run('gsutil cp gs://dataflow-samples/shakespeare/kinglear.txt data/')"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
">> mkdir -p data\n",
"\n",
">> gsutil cp gs://dataflow-samples/shakespeare/kinglear.txt data/\n",
"Copying gs://dataflow-samples/shakespeare/kinglear.txt...\n",
"/ [1 files][153.6 KiB/153.6 KiB] \n",
"Operation completed over 1 objects/153.6 KiB. \n",
"\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "Hmto8JTSWwUK",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Installing development tools\n",
"\n",
"Let's start by installing Java. We'll use the `default-jdk`, which uses [OpenJDK](https://openjdk.java.net/). This will take a while, so feel free to go for a walk or do some stretching.\n",
"\n",
"**Note:** Alternatively, you could install the propietary [Oracle JDK](https://www.oracle.com/technetwork/java/javase/downloads/index.html) instead."
]
},
{
"metadata": {
"id": "ONYtX0doWpFz",
"colab_type": "code",
"outputId": "04bfa861-0bf8-4352-e878-0f24c6c7b61e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 187
}
},
"cell_type": "code",
"source": [
"# Update and upgrade the system before installing anything else.\n",
"run('apt-get update > /dev/null')\n",
"run('apt-get upgrade > /dev/null')\n",
"\n",
"# Install the Java JDK.\n",
"run('apt-get install default-jdk > /dev/null')\n",
"\n",
"# Check the Java version to see if everything is working well.\n",
"run('javac -version')"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
">> apt-get update > /dev/null\n",
"\n",
">> apt-get upgrade > /dev/null\n",
"Extracting templates from packages: 100%\n",
"\n",
">> apt-get install default-jdk > /dev/null\n",
"\n",
">> javac -version\n",
"javac 10.0.2\n",
"\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "Wab7H4IZW9xZ",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Now, let's install [Gradle](https://gradle.org/), which we'll need to automate the build and running processes for our application. \n",
"\n",
"**Note:** Alternatively, you could install and configure [Maven](https://maven.apache.org/) instead."
]
},
{
"metadata": {
"id": "xS3Oeu3DW7vy",
"colab_type": "code",
"outputId": "1b2c1f11-5e35-4d22-8002-814ea61224c9",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 595
}
},
"cell_type": "code",
"source": [
"import os\n",
"\n",
"# Download the gradle source.\n",
"gradle_version = 'gradle-5.0'\n",
"gradle_path = f\"/opt/{gradle_version}\"\n",
"if not os.path.exists(gradle_path):\n",
" run(f\"wget -q -nc -O gradle.zip https://services.gradle.org/distributions/{gradle_version}-bin.zip\")\n",
" run('unzip -q -d /opt gradle.zip')\n",
" run('rm -f gradle.zip')\n",
"\n",
"# We're choosing to use the absolute path instead of adding it to the $PATH environment variable.\n",
"def gradle(args):\n",
" run(f\"{gradle_path}/bin/gradle --console=plain {args}\")\n",
"\n",
"gradle('-v')"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
">> wget -q -nc -O gradle.zip https://services.gradle.org/distributions/gradle-5.0-bin.zip\n",
"\n",
">> unzip -q -d /opt gradle.zip\n",
"\n",
">> rm -f gradle.zip\n",
"\n",
">> /opt/gradle-5.0/bin/gradle --console=plain -v\n",
"\u001b[m\n",
"Welcome to Gradle 5.0!\n",
"\n",
"Here are the highlights of this release:\n",
" - Kotlin DSL 1.0\n",
" - Task timeouts\n",
" - Dependency alignment aka BOM support\n",
" - Interactive `gradle init`\n",
"\n",
"For more details see https://docs.gradle.org/5.0/release-notes.html\n",
"\n",
"\n",
"------------------------------------------------------------\n",
"Gradle 5.0\n",
"------------------------------------------------------------\n",
"\n",
"Build time: 2018-11-26 11:48:43 UTC\n",
"Revision: 7fc6e5abf2fc5fe0824aec8a0f5462664dbcd987\n",
"\n",
"Kotlin DSL: 1.0.4\n",
"Kotlin: 1.3.10\n",
"Groovy: 2.5.4\n",
"Ant: Apache Ant(TM) version 1.9.13 compiled on July 10 2018\n",
"JVM: 10.0.2 (Oracle Corporation 10.0.2+13-Ubuntu-1ubuntu0.18.04.4)\n",
"OS: Linux 4.14.79+ amd64\n",
"\n",
"\u001b[m\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "YTkkapX9KVhA",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## build.gradle\n",
"\n",
"We'll also need a [`build.gradle`](https://guides.gradle.org/creating-new-gradle-builds/) file which will allow us to invoke some useful commands."
]
},
{
"metadata": {
"id": "oUqfqWyMuIfR",
"colab_type": "code",
"outputId": "292a06b2-ce06-46b6-8598-480d83974bbb",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"cell_type": "code",
"source": [
"%%writefile build.gradle\n",
"\n",
"plugins {\n",
" // id 'idea' // Uncomment for IntelliJ IDE\n",
" // id 'eclipse' // Uncomment for Eclipse IDE\n",
"\n",
" // Apply java plugin and make it a runnable application.\n",
" id 'java'\n",
" id 'application'\n",
"\n",
" // 'shadow' allows us to embed all the dependencies into a fat jar.\n",
" id 'com.github.johnrengelman.shadow' version '4.0.3'\n",
"}\n",
"\n",
"// This is the path of the main class, stored within ./src/main/java/\n",
"mainClassName = 'samples.quickstart.WordCount'\n",
"\n",
"// Declare the sources from which to fetch dependencies.\n",
"repositories {\n",
" mavenCentral()\n",
"}\n",
"\n",
"// Java version compatibility.\n",
"sourceCompatibility = 1.8\n",
"targetCompatibility = 1.8\n",
"\n",
"// Use the latest Apache Beam major version 2.\n",
"// You can also lock into a minor version like '2.9.+'.\n",
"ext.apacheBeamVersion = '2.+'\n",
"\n",
"// Declare the dependencies of the project.\n",
"dependencies {\n",
" shadow \"org.apache.beam:beam-sdks-java-core:$apacheBeamVersion\"\n",
"\n",
" runtime \"org.apache.beam:beam-runners-direct-java:$apacheBeamVersion\"\n",
" runtime \"org.slf4j:slf4j-api:1.+\"\n",
" runtime \"org.slf4j:slf4j-jdk14:1.+\"\n",
"\n",
" testCompile \"junit:junit:4.+\"\n",
"}\n",
"\n",
"// Configure 'shadowJar' instead of 'jar' to set up the fat jar.\n",
"shadowJar {\n",
" baseName = 'WordCount' // Name of the fat jar file.\n",
" classifier = null // Set to null, otherwise 'shadow' appends a '-all' to the jar file name.\n",
" manifest {\n",
" attributes('Main-Class': mainClassName) // Specify where the main class resides.\n",
" }\n",
"}"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"Writing build.gradle\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "cwZcqmFgoLJ9",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Creating the directory structure\n",
"\n",
"Java and Gradle expect a specific [directory structure](https://docs.gradle.org/current/userguide/organizing_gradle_projects.html). This helps organize large projects into a standard structure.\n",
"\n",
"For now, we only need a place where our quickstart code will reside. That has to go within `./src/main/java/`."
]
},
{
"metadata": {
"id": "Mr1KTQznbd9F",
"colab_type": "code",
"outputId": "2e4635b9-0577-4399-b8d6-078183ff9da2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
}
},
"cell_type": "code",
"source": [
"run('mkdir -p src/main/java/samples/quickstart')"
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": [
">> mkdir -p src/main/java/samples/quickstart\n",
"\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "cPvvFB19uXNw",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Minimal word count\n",
"\n",
"The following example is the \"Hello, World!\" of data processing, a basic implementation of word count. We're creating a simple data processing pipeline that reads a text file and counts the number of occurrences of every word.\n",
"\n",
"There are many scenarios where all the data does not fit in memory. Notice that the outputs of the pipeline go to the file system, which allows for large processing jobs in distributed environments."
]
},
{
"metadata": {
"id": "Fl3iUat7KYIE",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## WordCount.java"
]
},
{
"metadata": {
"id": "5l3S2mjMBKhT",
"colab_type": "code",
"outputId": "6e55ec70-e727-44c9-a425-4afed97188fe",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"cell_type": "code",
"source": [
"%%writefile src/main/java/samples/quickstart/WordCount.java\n",
"\n",
"package samples.quickstart;\n",
"\n",
"import org.apache.beam.sdk.Pipeline;\n",
"import org.apache.beam.sdk.io.TextIO;\n",
"import org.apache.beam.sdk.options.PipelineOptions;\n",
"import org.apache.beam.sdk.options.PipelineOptionsFactory;\n",
"import org.apache.beam.sdk.transforms.Count;\n",
"import org.apache.beam.sdk.transforms.Filter;\n",
"import org.apache.beam.sdk.transforms.FlatMapElements;\n",
"import org.apache.beam.sdk.transforms.MapElements;\n",
"import org.apache.beam.sdk.values.KV;\n",
"import org.apache.beam.sdk.values.TypeDescriptors;\n",
"\n",
"import java.util.Arrays;\n",
"\n",
"public class WordCount {\n",
" public static void main(String[] args) {\n",
" String inputsDir = \"data/*\";\n",
" String outputsPrefix = \"outputs/part\";\n",
"\n",
" PipelineOptions options = PipelineOptionsFactory.fromArgs(args).create();\n",
" Pipeline pipeline = Pipeline.create(options);\n",
" pipeline\n",
" .apply(\"Read lines\", TextIO.read().from(inputsDir))\n",
" .apply(\"Find words\", FlatMapElements.into(TypeDescriptors.strings())\n",
" .via((String line) -> Arrays.asList(line.split(\"[^\\\\p{L}]+\"))))\n",
" .apply(\"Filter empty words\", Filter.by((String word) -> !word.isEmpty()))\n",
" .apply(\"Count words\", Count.perElement())\n",
" .apply(\"Write results\", MapElements.into(TypeDescriptors.strings())\n",
" .via((KV<String, Long> wordCount) ->\n",
" wordCount.getKey() + \": \" + wordCount.getValue()))\n",
" .apply(TextIO.write().to(outputsPrefix));\n",
" pipeline.run();\n",
" }\n",
"}"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"Writing src/main/java/samples/quickstart/WordCount.java\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "yoO4xHnaKiz9",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Build and run"
]
},
{
"metadata": {
"id": "giJMbbcq2OPu",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"Let's first check how the final file system structure looks like. These are all the files required to build and run our application.\n",
"\n",
"* `build.gradle` - build configuration for Gradle\n",
"* `src/main/java/samples/quickstart/WordCount.java` - application source code\n",
"* `data/kinglear.txt` - input data, this could be any file or files\n",
"\n",
"We are now ready to build the application using `gradle build`."
]
},
{
"metadata": {
"id": "urmCmtG08F-0",
"colab_type": "code",
"outputId": "a2b65437-4244-4844-82d2-1789d5cfd7ca",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 510
}
},
"cell_type": "code",
"source": [
"# Build the project.\n",
"gradle('build')\n",
"\n",
"# Check the generated build files.\n",
"run('ls -lh build/libs/')"
],
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"text": [
">> /opt/gradle-5.0/bin/gradle --console=plain build\n",
"\u001b[mStarting a Gradle Daemon (subsequent builds will be faster)\n",
"> Task :compileJava\n",
"> Task :processResources NO-SOURCE\n",
"> Task :classes\n",
"> Task :jar\n",
"> Task :startScripts\n",
"> Task :distTar\n",
"> Task :distZip\n",
"> Task :shadowJar\n",
"> Task :startShadowScripts\n",
"> Task :shadowDistTar\n",
"> Task :shadowDistZip\n",
"> Task :assemble\n",
"> Task :compileTestJava NO-SOURCE\n",
"> Task :processTestResources NO-SOURCE\n",
"> Task :testClasses UP-TO-DATE\n",
"> Task :test NO-SOURCE\n",
"> Task :check UP-TO-DATE\n",
"> Task :build\n",
"\n",
"BUILD SUCCESSFUL in 56s\n",
"9 actionable tasks: 9 executed\n",
"\u001b[m\n",
">> ls -lh build/libs/\n",
"total 40M\n",
"-rw-r--r-- 1 root root 2.9K Mar 4 22:59 content.jar\n",
"-rw-r--r-- 1 root root 40M Mar 4 23:00 WordCount.jar\n",
"\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "LrRFNZHD8dtu",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"There are two files generated:\n",
"* The `content.jar` file, the application generated from the regular `build` command. It's only a few kilobytes in size.\n",
"* The `WordCount.jar` file, with the `baseName` we specified in the `shadowJar` section of the `gradle.build` file. It's a several megabytes in size, with all the required libraries it needs to run embedded in it.\n",
"\n",
"The file we're actually interested in is the fat JAR file `WordCount.jar`. To run the fat JAR, we'll use the `gradle runShadow` command."
]
},
{
"metadata": {
"id": "CgTXBdTsBn1F",
"colab_type": "code",
"outputId": "5e447cf9-a01a-4a82-9237-676f0091d4bd",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1822
}
},
"cell_type": "code",
"source": [
"# Run the shadow (fat jar) build.\n",
"gradle('runShadow')\n",
"\n",
"# Sample the first 20 results, remember there are no ordering guarantees.\n",
"run('head -n 20 outputs/part-00000-of-*')"
],
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"text": [
">> /opt/gradle-5.0/bin/gradle --console=plain runShadow\n",
"\u001b[m> Task :compileJava UP-TO-DATE\n",
"> Task :processResources NO-SOURCE\n",
"> Task :classes UP-TO-DATE\n",
"> Task :shadowJar UP-TO-DATE\n",
"> Task :startShadowScripts UP-TO-DATE\n",
"> Task :installShadowDist\n",
"\n",
"> Task :runShadow\n",
"WARNING: An illegal reflective access operation has occurred\n",
"WARNING: Illegal reflective access by org.apache.beam.vendor.grpc.v1p26p0.com.google.protobuf.UnsafeUtil (file:/content/build/install/content-shadow/lib/WordCount.jar) to field java.nio.Buffer.address\n",
"WARNING: Please consider reporting this to the maintainers of org.apache.beam.vendor.grpc.v1p26p0.com.google.protobuf.UnsafeUtil\n",
"WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations\n",
"WARNING: All illegal access operations will be denied in a future release\n",
"Mar 04, 2019 11:00:24 PM org.apache.beam.sdk.io.FileBasedSource getEstimatedSizeBytes\n",
"INFO: Filepattern data/* matched 1 files with total size 157283\n",
"Mar 04, 2019 11:00:24 PM org.apache.beam.sdk.io.FileBasedSource split\n",
"INFO: Splitting filepattern data/* into bundles of size 52427 took 1 ms and produced 1 files and 3 bundles\n",
"Mar 04, 2019 11:00:43 PM org.apache.beam.sdk.io.WriteFiles$WriteShardsIntoTempFilesFn processElement\n",
"INFO: Opening writer 7d12bbc4-9165-4493-8563-fb710b827daa for window org.apache.beam.sdk.transforms.windowing.GlobalWindow@3ad2e17 pane PaneInfo{isFirst=true, isLast=true, timing=ON_TIME, index=0, onTimeIndex=0} destination null\n",
"Mar 04, 2019 11:00:43 PM org.apache.beam.sdk.io.WriteFiles$WriteShardsIntoTempFilesFn processElement\n",
"INFO: Opening writer 5b1bdb18-9f9a-47cd-80d2-a65baa31aa60 for window org.apache.beam.sdk.transforms.windowing.GlobalWindow@3ad2e17 pane PaneInfo{isFirst=true, isLast=true, timing=ON_TIME, index=0, onTimeIndex=0} destination null\n",
"Mar 04, 2019 11:00:43 PM org.apache.beam.sdk.io.WriteFiles$WriteShardsIntoTempFilesFn processElement\n",
"INFO: Opening writer 6302e6b5-5428-48e6-b571-76a9282d7f45 for window org.apache.beam.sdk.transforms.windowing.GlobalWindow@3ad2e17 pane PaneInfo{isFirst=true, isLast=true, timing=ON_TIME, index=0, onTimeIndex=0} destination null\n",
"Mar 04, 2019 11:00:43 PM org.apache.beam.sdk.io.FileBasedSink$Writer close\n",
"INFO: Successfully wrote temporary file /content/outputs/.temp-beam-2019-03-04_23-00-23-1/7d12bbc4-9165-4493-8563-fb710b827daa\n",
"Mar 04, 2019 11:00:43 PM org.apache.beam.sdk.io.FileBasedSink$Writer close\n",
"INFO: Successfully wrote temporary file /content/outputs/.temp-beam-2019-03-04_23-00-23-1/5b1bdb18-9f9a-47cd-80d2-a65baa31aa60\n",
"Mar 04, 2019 11:00:43 PM org.apache.beam.sdk.io.WriteFiles$WriteShardsIntoTempFilesFn processElement\n",
"INFO: Opening writer 493e3ec4-f0f7-4d10-8209-079f7ac4db16 for window org.apache.beam.sdk.transforms.windowing.GlobalWindow@3ad2e17 pane PaneInfo{isFirst=true, isLast=true, timing=ON_TIME, index=0, onTimeIndex=0} destination null\n",
"Mar 04, 2019 11:00:43 PM org.apache.beam.sdk.io.FileBasedSink$Writer close\n",
"INFO: Successfully wrote temporary file /content/outputs/.temp-beam-2019-03-04_23-00-23-1/6302e6b5-5428-48e6-b571-76a9282d7f45\n",
"Mar 04, 2019 11:00:43 PM org.apache.beam.sdk.io.FileBasedSink$Writer close\n",
"INFO: Successfully wrote temporary file /content/outputs/.temp-beam-2019-03-04_23-00-23-1/493e3ec4-f0f7-4d10-8209-079f7ac4db16\n",
"Mar 04, 2019 11:00:43 PM org.apache.beam.sdk.io.WriteFiles$FinalizeTempFileBundles$FinalizeFn process\n",
"INFO: Finalizing 4 file results\n",
"Mar 04, 2019 11:00:43 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation createMissingEmptyShards\n",
"INFO: Finalizing for destination null num shards 4.\n",
"Mar 04, 2019 11:00:43 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation moveToOutputFiles\n",
"INFO: Will copy temporary file FileResult{tempFilename=/content/outputs/.temp-beam-2019-03-04_23-00-23-1/7d12bbc4-9165-4493-8563-fb710b827daa, shard=3, window=org.apache.beam.sdk.transforms.windowing.GlobalWindow@3ad2e17, paneInfo=PaneInfo{isFirst=true, isLast=true, timing=ON_TIME, index=0, onTimeIndex=0}} to final location /content/outputs/part-00003-of-00004\n",
"Mar 04, 2019 11:00:43 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation moveToOutputFiles\n",
"INFO: Will copy temporary file FileResult{tempFilename=/content/outputs/.temp-beam-2019-03-04_23-00-23-1/5b1bdb18-9f9a-47cd-80d2-a65baa31aa60, shard=2, window=org.apache.beam.sdk.transforms.windowing.GlobalWindow@3ad2e17, paneInfo=PaneInfo{isFirst=true, isLast=true, timing=ON_TIME, index=0, onTimeIndex=0}} to final location /content/outputs/part-00002-of-00004\n",
"Mar 04, 2019 11:00:43 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation moveToOutputFiles\n",
"INFO: Will copy temporary file FileResult{tempFilename=/content/outputs/.temp-beam-2019-03-04_23-00-23-1/6302e6b5-5428-48e6-b571-76a9282d7f45, shard=1, window=org.apache.beam.sdk.transforms.windowing.GlobalWindow@3ad2e17, paneInfo=PaneInfo{isFirst=true, isLast=true, timing=ON_TIME, index=0, onTimeIndex=0}} to final location /content/outputs/part-00001-of-00004\n",
"Mar 04, 2019 11:00:43 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation moveToOutputFiles\n",
"INFO: Will copy temporary file FileResult{tempFilename=/content/outputs/.temp-beam-2019-03-04_23-00-23-1/493e3ec4-f0f7-4d10-8209-079f7ac4db16, shard=0, window=org.apache.beam.sdk.transforms.windowing.GlobalWindow@3ad2e17, paneInfo=PaneInfo{isFirst=true, isLast=true, timing=ON_TIME, index=0, onTimeIndex=0}} to final location /content/outputs/part-00000-of-00004\n",
"Mar 04, 2019 11:00:43 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation removeTemporaryFiles\n",
"INFO: Will remove known temporary file /content/outputs/.temp-beam-2019-03-04_23-00-23-1/6302e6b5-5428-48e6-b571-76a9282d7f45\n",
"Mar 04, 2019 11:00:43 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation removeTemporaryFiles\n",
"INFO: Will remove known temporary file /content/outputs/.temp-beam-2019-03-04_23-00-23-1/5b1bdb18-9f9a-47cd-80d2-a65baa31aa60\n",
"Mar 04, 2019 11:00:43 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation removeTemporaryFiles\n",
"INFO: Will remove known temporary file /content/outputs/.temp-beam-2019-03-04_23-00-23-1/7d12bbc4-9165-4493-8563-fb710b827daa\n",
"Mar 04, 2019 11:00:43 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation removeTemporaryFiles\n",
"INFO: Will remove known temporary file /content/outputs/.temp-beam-2019-03-04_23-00-23-1/493e3ec4-f0f7-4d10-8209-079f7ac4db16\n",
"Mar 04, 2019 11:00:43 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation removeTemporaryFiles\n",
"WARNING: Failed to match temporary files under: [/content/outputs/.temp-beam-2019-03-04_23-00-23-1/].\n",
"\n",
"BUILD SUCCESSFUL in 24s\n",
"5 actionable tasks: 2 executed, 3 up-to-date\n",
"\u001b[m\n",
">> head -n 20 outputs/part-00000-of-*\n",
"==> outputs/part-00000-of-00001 <==\n",
"(u'canker', 1)\n",
"(u'bounty', 2)\n",
"(u'provision', 3)\n",
"(u'to', 438)\n",
"(u'terms', 2)\n",
"(u'unnecessary', 2)\n",
"(u'tongue', 5)\n",
"(u'knives', 1)\n",
"(u'Commend', 1)\n",
"(u'Hum', 2)\n",
"(u'Set', 2)\n",
"(u'smell', 6)\n",
"(u'dreadful', 3)\n",
"(u'frowning', 1)\n",
"(u'World', 1)\n",
"(u'tike', 1)\n",
"(u'yes', 3)\n",
"(u'oldness', 1)\n",
"(u'boat', 1)\n",
"(u\"in's\", 1)\n",
"\n",
"==> outputs/part-00000-of-00004 <==\n",
"retinue: 1\n",
"stink: 1\n",
"beaks: 1\n",
"Ten: 1\n",
"riots: 2\n",
"Service: 1\n",
"dealing: 1\n",
"stop: 2\n",
"detain: 1\n",
"beware: 1\n",
"pilferings: 1\n",
"swimming: 1\n",
"The: 124\n",
"Been: 1\n",
"behavior: 1\n",
"impetuous: 1\n",
"Thy: 20\n",
"Tis: 24\n",
"Soldiers: 7\n",
"Juno: 1\n",
"\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "T_oqlIM55MzM",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Distributing your application\n",
"\n",
"We can run our fat JAR file as long as we have a Java Runtime Environment installed.\n",
"\n",
"To distribute, we copy the fat JAR file and run it with `java -jar`."
]
},
{
"metadata": {
"id": "b3YSRjYnavpd",
"colab_type": "code",
"outputId": "ef88153a-f75f-4e80-8434-ac452a77a199",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1907
}
},
"cell_type": "code",
"source": [
"# You can now distribute and run your Java application as a standalone jar file.\n",
"run('cp build/libs/WordCount.jar .')\n",
"run('java -jar WordCount.jar')\n",
"\n",
"# Sample the first 20 results, remember there are no ordering guarantees.\n",
"run('head -n 20 outputs/part-00000-of-*')"
],
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"text": [
">> cp build/libs/WordCount.jar .\n",
"\n",
">> java -jar WordCount.jar\n",
"WARNING: An illegal reflective access operation has occurred\n",
"WARNING: Illegal reflective access by org.apache.beam.vendor.grpc.v1p26p0.com.google.protobuf.UnsafeUtil (file:/content/WordCount.jar) to field java.nio.Buffer.address\n",
"WARNING: Please consider reporting this to the maintainers of org.apache.beam.vendor.grpc.v1p26p0.com.google.protobuf.UnsafeUtil\n",
"WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations\n",
"WARNING: All illegal access operations will be denied in a future release\n",
"Mar 04, 2019 11:00:49 PM org.apache.beam.sdk.io.FileBasedSource getEstimatedSizeBytes\n",
"INFO: Filepattern data/* matched 1 files with total size 157283\n",
"Mar 04, 2019 11:00:49 PM org.apache.beam.sdk.io.FileBasedSource split\n",
"INFO: Splitting filepattern data/* into bundles of size 52427 took 1 ms and produced 1 files and 3 bundles\n",
"Mar 04, 2019 11:01:10 PM org.apache.beam.sdk.io.WriteFiles$WriteShardsIntoTempFilesFn processElement\n",
"INFO: Opening writer 273fc5ad-09b8-4e87-95c9-5d9ec72ed294 for window org.apache.beam.sdk.transforms.windowing.GlobalWindow@7a362b6b pane PaneInfo{isFirst=true, isLast=true, timing=ON_TIME, index=0, onTimeIndex=0} destination null\n",
"Mar 04, 2019 11:01:10 PM org.apache.beam.sdk.io.WriteFiles$WriteShardsIntoTempFilesFn processElement\n",
"INFO: Opening writer c224db69-5869-4259-bd43-ca0431ec77fe for window org.apache.beam.sdk.transforms.windowing.GlobalWindow@7a362b6b pane PaneInfo{isFirst=true, isLast=true, timing=ON_TIME, index=0, onTimeIndex=0} destination null\n",
"Mar 04, 2019 11:01:10 PM org.apache.beam.sdk.io.WriteFiles$WriteShardsIntoTempFilesFn processElement\n",
"INFO: Opening writer f45d6e07-d37a-4af3-ad8b-bc316cef7d99 for window org.apache.beam.sdk.transforms.windowing.GlobalWindow@7a362b6b pane PaneInfo{isFirst=true, isLast=true, timing=ON_TIME, index=0, onTimeIndex=0} destination null\n",
"Mar 04, 2019 11:01:10 PM org.apache.beam.sdk.io.FileBasedSink$Writer close\n",
"INFO: Successfully wrote temporary file /content/outputs/.temp-beam-2019-03-04_23-00-49-1/f45d6e07-d37a-4af3-ad8b-bc316cef7d99\n",
"Mar 04, 2019 11:01:10 PM org.apache.beam.sdk.io.FileBasedSink$Writer close\n",
"INFO: Successfully wrote temporary file /content/outputs/.temp-beam-2019-03-04_23-00-49-1/c224db69-5869-4259-bd43-ca0431ec77fe\n",
"Mar 04, 2019 11:01:10 PM org.apache.beam.sdk.io.FileBasedSink$Writer close\n",
"INFO: Successfully wrote temporary file /content/outputs/.temp-beam-2019-03-04_23-00-49-1/273fc5ad-09b8-4e87-95c9-5d9ec72ed294\n",
"Mar 04, 2019 11:01:10 PM org.apache.beam.sdk.io.WriteFiles$FinalizeTempFileBundles$FinalizeFn process\n",
"INFO: Finalizing 3 file results\n",
"Mar 04, 2019 11:01:10 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation createMissingEmptyShards\n",
"INFO: Finalizing for destination null num shards 3.\n",
"Mar 04, 2019 11:01:10 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation moveToOutputFiles\n",
"INFO: Will copy temporary file FileResult{tempFilename=/content/outputs/.temp-beam-2019-03-04_23-00-49-1/273fc5ad-09b8-4e87-95c9-5d9ec72ed294, shard=2, window=org.apache.beam.sdk.transforms.windowing.GlobalWindow@7a362b6b, paneInfo=PaneInfo{isFirst=true, isLast=true, timing=ON_TIME, index=0, onTimeIndex=0}} to final location /content/outputs/part-00002-of-00003\n",
"Mar 04, 2019 11:01:10 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation moveToOutputFiles\n",
"INFO: Will copy temporary file FileResult{tempFilename=/content/outputs/.temp-beam-2019-03-04_23-00-49-1/c224db69-5869-4259-bd43-ca0431ec77fe, shard=0, window=org.apache.beam.sdk.transforms.windowing.GlobalWindow@7a362b6b, paneInfo=PaneInfo{isFirst=true, isLast=true, timing=ON_TIME, index=0, onTimeIndex=0}} to final location /content/outputs/part-00000-of-00003\n",
"Mar 04, 2019 11:01:10 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation moveToOutputFiles\n",
"INFO: Will copy temporary file FileResult{tempFilename=/content/outputs/.temp-beam-2019-03-04_23-00-49-1/f45d6e07-d37a-4af3-ad8b-bc316cef7d99, shard=1, window=org.apache.beam.sdk.transforms.windowing.GlobalWindow@7a362b6b, paneInfo=PaneInfo{isFirst=true, isLast=true, timing=ON_TIME, index=0, onTimeIndex=0}} to final location /content/outputs/part-00001-of-00003\n",
"Mar 04, 2019 11:01:10 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation removeTemporaryFiles\n",
"INFO: Will remove known temporary file /content/outputs/.temp-beam-2019-03-04_23-00-49-1/273fc5ad-09b8-4e87-95c9-5d9ec72ed294\n",
"Mar 04, 2019 11:01:10 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation removeTemporaryFiles\n",
"INFO: Will remove known temporary file /content/outputs/.temp-beam-2019-03-04_23-00-49-1/f45d6e07-d37a-4af3-ad8b-bc316cef7d99\n",
"Mar 04, 2019 11:01:10 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation removeTemporaryFiles\n",
"INFO: Will remove known temporary file /content/outputs/.temp-beam-2019-03-04_23-00-49-1/c224db69-5869-4259-bd43-ca0431ec77fe\n",
"Mar 04, 2019 11:01:10 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation removeTemporaryFiles\n",
"WARNING: Failed to match temporary files under: [/content/outputs/.temp-beam-2019-03-04_23-00-49-1/].\n",
"\n",
">> head -n 20 outputs/part-00000-of-*\n",
"==> outputs/part-00000-of-00001 <==\n",
"(u'canker', 1)\n",
"(u'bounty', 2)\n",
"(u'provision', 3)\n",
"(u'to', 438)\n",
"(u'terms', 2)\n",
"(u'unnecessary', 2)\n",
"(u'tongue', 5)\n",
"(u'knives', 1)\n",
"(u'Commend', 1)\n",
"(u'Hum', 2)\n",
"(u'Set', 2)\n",
"(u'smell', 6)\n",
"(u'dreadful', 3)\n",
"(u'frowning', 1)\n",
"(u'World', 1)\n",
"(u'tike', 1)\n",
"(u'yes', 3)\n",
"(u'oldness', 1)\n",
"(u'boat', 1)\n",
"(u\"in's\", 1)\n",
"\n",
"==> outputs/part-00000-of-00003 <==\n",
"With: 31\n",
"justification: 1\n",
"hither: 15\n",
"make: 46\n",
"opposed: 2\n",
"prince: 5\n",
"Burn: 1\n",
"waking: 1\n",
"waked: 3\n",
"inform: 6\n",
"mercy: 5\n",
"about: 11\n",
"danger: 6\n",
"Croak: 1\n",
"happier: 1\n",
"stick: 2\n",
"oppressed: 1\n",
"erlook: 1\n",
"untented: 1\n",
"myself: 10\n",
"\n",
"==> outputs/part-00000-of-00004 <==\n",
"retinue: 1\n",
"stink: 1\n",
"beaks: 1\n",
"Ten: 1\n",
"riots: 2\n",
"Service: 1\n",
"dealing: 1\n",
"stop: 2\n",
"detain: 1\n",
"beware: 1\n",
"pilferings: 1\n",
"swimming: 1\n",
"The: 124\n",
"Been: 1\n",
"behavior: 1\n",
"impetuous: 1\n",
"Thy: 20\n",
"Tis: 24\n",
"Soldiers: 7\n",
"Juno: 1\n",
"\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "k-HubCrk-h_G",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# Word count with comments\n",
"\n",
"Below is mostly the same code as above, but with comments explaining every line in more detail."
]
},
{
"metadata": {
"id": "wvnWyYklCXer",
"colab_type": "code",
"outputId": "275507a3-05e9-44ca-8625-d745154d5720",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"cell_type": "code",
"source": [
"%%writefile src/main/java/samples/quickstart/WordCount.java\n",
"\n",
"package samples.quickstart;\n",
"\n",
"import org.apache.beam.sdk.Pipeline;\n",
"import org.apache.beam.sdk.io.TextIO;\n",
"import org.apache.beam.sdk.options.PipelineOptions;\n",
"import org.apache.beam.sdk.options.PipelineOptionsFactory;\n",
"import org.apache.beam.sdk.transforms.Count;\n",
"import org.apache.beam.sdk.transforms.Filter;\n",
"import org.apache.beam.sdk.transforms.FlatMapElements;\n",
"import org.apache.beam.sdk.transforms.MapElements;\n",
"import org.apache.beam.sdk.values.KV;\n",
"import org.apache.beam.sdk.values.PCollection;\n",
"import org.apache.beam.sdk.values.TypeDescriptors;\n",
"\n",
"import java.util.Arrays;\n",
"\n",
"public class WordCount {\n",
" public static void main(String[] args) {\n",
" String inputsDir = \"data/*\";\n",
" String outputsPrefix = \"outputs/part\";\n",
"\n",
" PipelineOptions options = PipelineOptionsFactory.fromArgs(args).create();\n",
" Pipeline pipeline = Pipeline.create(options);\n",
"\n",
" // Store the word counts in a PCollection.\n",
" // Each element is a KeyValue of (word, count) of types KV<String, Long>.\n",
" PCollection<KV<String, Long>> wordCounts =\n",
" // The input PCollection is an empty pipeline.\n",
" pipeline\n",
"\n",
" // Read lines from a text file.\n",
" .apply(\"Read lines\", TextIO.read().from(inputsDir))\n",
" // Element type: String - text line\n",
"\n",
" // Use a regular expression to iterate over all words in the line.\n",
" // FlatMapElements will yield an element for every element in an iterable.\n",
" .apply(\"Find words\", FlatMapElements.into(TypeDescriptors.strings())\n",
" .via((String line) -> Arrays.asList(line.split(\"[^\\\\p{L}]+\"))))\n",
" // Element type: String - word\n",
"\n",
" // Keep only non-empty words.\n",
" .apply(\"Filter empty words\", Filter.by((String word) -> !word.isEmpty()))\n",
" // Element type: String - word\n",
"\n",
" // Count each unique word.\n",
" .apply(\"Count words\", Count.perElement());\n",
" // Element type: KV<String, Long> - key: word, value: counts\n",
"\n",
" // We can process a PCollection through other pipelines, too.\n",
" // The input PCollection are the wordCounts from the previous step.\n",
" wordCounts\n",
" // Format the results into a string so we can write them to a file.\n",
" .apply(\"Write results\", MapElements.into(TypeDescriptors.strings())\n",
" .via((KV<String, Long> wordCount) ->\n",
" wordCount.getKey() + \": \" + wordCount.getValue()))\n",
" // Element type: str - text line\n",
"\n",
" // Finally, write the results to a file.\n",
" .apply(TextIO.write().to(outputsPrefix));\n",
"\n",
" // We have to explicitly run the pipeline, otherwise it's only a definition.\n",
" pipeline.run();\n",
" }\n",
"}"
],
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"text": [
"Overwriting src/main/java/samples/quickstart/WordCount.java\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "wKAJp7ON4Vpp",
"colab_type": "code",
"outputId": "9a4c7a72-70a1-4d31-89c1-cf7fb8fcdf53",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 2060
}
},
"cell_type": "code",
"source": [
"# Build and run the project. The 'runShadow' task implicitly does a 'build'.\n",
"gradle('runShadow')\n",
"\n",
"# Sample the first 20 results, remember there are no ordering guarantees.\n",
"run('head -n 20 outputs/part-00000-of-*')"
],
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"text": [
">> /opt/gradle-5.0/bin/gradle --console=plain runShadow\n",
"\u001b[m> Task :compileJava\n",
"> Task :processResources NO-SOURCE\n",
"> Task :classes\n",
"> Task :shadowJar\n",
"> Task :startShadowScripts\n",
"> Task :installShadowDist\n",
"\n",
"> Task :runShadow\n",
"WARNING: An illegal reflective access operation has occurred\n",
"WARNING: Illegal reflective access by org.apache.beam.vendor.grpc.v1p26p0.com.google.protobuf.UnsafeUtil (file:/content/build/install/content-shadow/lib/WordCount.jar) to field java.nio.Buffer.address\n",
"WARNING: Please consider reporting this to the maintainers of org.apache.beam.vendor.grpc.v1p26p0.com.google.protobuf.UnsafeUtil\n",
"WARNING: Use --illegal-access=warn to enable warnings of further illegal reflective access operations\n",
"WARNING: All illegal access operations will be denied in a future release\n",
"Mar 04, 2019 11:01:26 PM org.apache.beam.sdk.io.FileBasedSource getEstimatedSizeBytes\n",
"INFO: Filepattern data/* matched 1 files with total size 157283\n",
"Mar 04, 2019 11:01:26 PM org.apache.beam.sdk.io.FileBasedSource split\n",
"INFO: Splitting filepattern data/* into bundles of size 52427 took 1 ms and produced 1 files and 3 bundles\n",
"Mar 04, 2019 11:01:46 PM org.apache.beam.sdk.io.WriteFiles$WriteShardsIntoTempFilesFn processElement\n",
"INFO: Opening writer e2eeada2-5a8b-4493-acc5-c706204d9669 for window org.apache.beam.sdk.transforms.windowing.GlobalWindow@8c3619e pane PaneInfo{isFirst=true, isLast=true, timing=ON_TIME, index=0, onTimeIndex=0} destination null\n",
"Mar 04, 2019 11:01:46 PM org.apache.beam.sdk.io.WriteFiles$WriteShardsIntoTempFilesFn processElement\n",
"INFO: Opening writer 7acdc85e-ff7d-42d0-9b2f-9ce385956c0e for window org.apache.beam.sdk.transforms.windowing.GlobalWindow@8c3619e pane PaneInfo{isFirst=true, isLast=true, timing=ON_TIME, index=0, onTimeIndex=0} destination null\n",
"Mar 04, 2019 11:01:46 PM org.apache.beam.sdk.io.WriteFiles$WriteShardsIntoTempFilesFn processElement\n",
"INFO: Opening writer d1a6a591-77f0-4994-affc-d83378e7b7c0 for window org.apache.beam.sdk.transforms.windowing.GlobalWindow@8c3619e pane PaneInfo{isFirst=true, isLast=true, timing=ON_TIME, index=0, onTimeIndex=0} destination null\n",
"Mar 04, 2019 11:01:46 PM org.apache.beam.sdk.io.FileBasedSink$Writer close\n",
"INFO: Successfully wrote temporary file /content/outputs/.temp-beam-2019-03-04_23-01-25-1/e2eeada2-5a8b-4493-acc5-c706204d9669\n",
"Mar 04, 2019 11:01:46 PM org.apache.beam.sdk.io.FileBasedSink$Writer close\n",
"INFO: Successfully wrote temporary file /content/outputs/.temp-beam-2019-03-04_23-01-25-1/7acdc85e-ff7d-42d0-9b2f-9ce385956c0e\n",
"Mar 04, 2019 11:01:46 PM org.apache.beam.sdk.io.FileBasedSink$Writer close\n",
"INFO: Successfully wrote temporary file /content/outputs/.temp-beam-2019-03-04_23-01-25-1/d1a6a591-77f0-4994-affc-d83378e7b7c0\n",
"Mar 04, 2019 11:01:46 PM org.apache.beam.sdk.io.WriteFiles$FinalizeTempFileBundles$FinalizeFn process\n",
"INFO: Finalizing 3 file results\n",
"Mar 04, 2019 11:01:46 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation createMissingEmptyShards\n",
"INFO: Finalizing for destination null num shards 3.\n",
"Mar 04, 2019 11:01:46 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation moveToOutputFiles\n",
"INFO: Will copy temporary file FileResult{tempFilename=/content/outputs/.temp-beam-2019-03-04_23-01-25-1/7acdc85e-ff7d-42d0-9b2f-9ce385956c0e, shard=2, window=org.apache.beam.sdk.transforms.windowing.GlobalWindow@8c3619e, paneInfo=PaneInfo{isFirst=true, isLast=true, timing=ON_TIME, index=0, onTimeIndex=0}} to final location /content/outputs/part-00002-of-00003\n",
"Mar 04, 2019 11:01:46 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation moveToOutputFiles\n",
"INFO: Will copy temporary file FileResult{tempFilename=/content/outputs/.temp-beam-2019-03-04_23-01-25-1/e2eeada2-5a8b-4493-acc5-c706204d9669, shard=0, window=org.apache.beam.sdk.transforms.windowing.GlobalWindow@8c3619e, paneInfo=PaneInfo{isFirst=true, isLast=true, timing=ON_TIME, index=0, onTimeIndex=0}} to final location /content/outputs/part-00000-of-00003\n",
"Mar 04, 2019 11:01:46 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation moveToOutputFiles\n",
"INFO: Will copy temporary file FileResult{tempFilename=/content/outputs/.temp-beam-2019-03-04_23-01-25-1/d1a6a591-77f0-4994-affc-d83378e7b7c0, shard=1, window=org.apache.beam.sdk.transforms.windowing.GlobalWindow@8c3619e, paneInfo=PaneInfo{isFirst=true, isLast=true, timing=ON_TIME, index=0, onTimeIndex=0}} to final location /content/outputs/part-00001-of-00003\n",
"Mar 04, 2019 11:01:46 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation removeTemporaryFiles\n",
"INFO: Will remove known temporary file /content/outputs/.temp-beam-2019-03-04_23-01-25-1/e2eeada2-5a8b-4493-acc5-c706204d9669\n",
"Mar 04, 2019 11:01:46 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation removeTemporaryFiles\n",
"INFO: Will remove known temporary file /content/outputs/.temp-beam-2019-03-04_23-01-25-1/7acdc85e-ff7d-42d0-9b2f-9ce385956c0e\n",
"Mar 04, 2019 11:01:46 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation removeTemporaryFiles\n",
"INFO: Will remove known temporary file /content/outputs/.temp-beam-2019-03-04_23-01-25-1/d1a6a591-77f0-4994-affc-d83378e7b7c0\n",
"Mar 04, 2019 11:01:46 PM org.apache.beam.sdk.io.FileBasedSink$WriteOperation removeTemporaryFiles\n",
"WARNING: Failed to match temporary files under: [/content/outputs/.temp-beam-2019-03-04_23-01-25-1/].\n",
"\n",
"BUILD SUCCESSFUL in 33s\n",
"5 actionable tasks: 5 executed\n",
"\u001b[m\n",
">> head -n 20 outputs/part-00000-of-*\n",
"==> outputs/part-00000-of-00001 <==\n",
"(u'canker', 1)\n",
"(u'bounty', 2)\n",
"(u'provision', 3)\n",
"(u'to', 438)\n",
"(u'terms', 2)\n",
"(u'unnecessary', 2)\n",
"(u'tongue', 5)\n",
"(u'knives', 1)\n",
"(u'Commend', 1)\n",
"(u'Hum', 2)\n",
"(u'Set', 2)\n",
"(u'smell', 6)\n",
"(u'dreadful', 3)\n",
"(u'frowning', 1)\n",
"(u'World', 1)\n",
"(u'tike', 1)\n",
"(u'yes', 3)\n",
"(u'oldness', 1)\n",
"(u'boat', 1)\n",
"(u\"in's\", 1)\n",
"\n",
"==> outputs/part-00000-of-00003 <==\n",
"wrath: 3\n",
"nicely: 2\n",
"hall: 1\n",
"Sure: 2\n",
"legs: 4\n",
"ten: 1\n",
"yourselves: 1\n",
"embossed: 1\n",
"poorly: 1\n",
"temper: 2\n",
"Dismissing: 1\n",
"Legitimate: 1\n",
"tyrannous: 1\n",
"turn: 13\n",
"gold: 2\n",
"minds: 1\n",
"dowers: 2\n",
"policy: 1\n",
"I: 708\n",
"V: 6\n",
"\n",
"==> outputs/part-00000-of-00004 <==\n",
"retinue: 1\n",
"stink: 1\n",
"beaks: 1\n",
"Ten: 1\n",
"riots: 2\n",
"Service: 1\n",
"dealing: 1\n",
"stop: 2\n",
"detain: 1\n",
"beware: 1\n",
"pilferings: 1\n",
"swimming: 1\n",
"The: 124\n",
"Been: 1\n",
"behavior: 1\n",
"impetuous: 1\n",
"Thy: 20\n",
"Tis: 24\n",
"Soldiers: 7\n",
"Juno: 1\n",
"\n"
],
"name": "stdout"
}
]
}
]
}
| apache-2.0 |
GoogleCloudPlatform/mlops-with-vertex-ai | 06-model-deployment.ipynb | 1 | 7687 | {
"cells": [
{
"cell_type": "markdown",
"id": "ee01c81b",
"metadata": {},
"source": [
"# 06 - Model Deployment\n",
"\n",
"The purpose of this notebook is to execute a CI/CD routine to test and deploy the trained model to `Vertex AI` as an `Endpoint` for online prediction serving. The notebook covers the following steps:\n",
"1. Run the test steps locally.\n",
"2. Execute the model deployment `CI/CD` steps using `Cloud Build`.\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "0da8290c",
"metadata": {},
"source": [
"## Setup"
]
},
{
"cell_type": "markdown",
"id": "4873f8cf",
"metadata": {},
"source": [
"### Import libraries"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "59085129",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import logging\n",
"\n",
"logging.getLogger().setLevel(logging.INFO)"
]
},
{
"cell_type": "markdown",
"id": "e37fb189",
"metadata": {},
"source": [
"### Setup Google Cloud project"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e45be804",
"metadata": {},
"outputs": [],
"source": [
"PROJECT = '[your-project-id]' # Change to your project id.\n",
"REGION = 'us-central1' # Change to your region.\n",
"\n",
"if PROJECT == \"\" or PROJECT is None or PROJECT == \"[your-project-id]\":\n",
" # Get your GCP project id from gcloud\n",
" shell_output = !gcloud config list --format 'value(core.project)' 2>/dev/null\n",
" PROJECT = shell_output[0]\n",
"\n",
"print(\"Project ID:\", PROJECT)\n",
"print(\"Region:\", REGION)"
]
},
{
"cell_type": "markdown",
"id": "1574964f",
"metadata": {},
"source": [
"### Set configurations"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4a01278c",
"metadata": {},
"outputs": [],
"source": [
"VERSION = 'v01'\n",
"DATASET_DISPLAY_NAME = 'chicago-taxi-tips'\n",
"MODEL_DISPLAY_NAME = f'{DATASET_DISPLAY_NAME}-classifier-{VERSION}'\n",
"ENDPOINT_DISPLAY_NAME = f'{DATASET_DISPLAY_NAME}-classifier'\n",
"\n",
"CICD_IMAGE_NAME = 'cicd:latest'\n",
"CICD_IMAGE_URI = f\"gcr.io/{PROJECT}/{CICD_IMAGE_NAME}\""
]
},
{
"cell_type": "markdown",
"id": "87f6f1e0",
"metadata": {},
"source": [
"## 1. Run CI/CD steps locally"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a223cdf6",
"metadata": {},
"outputs": [],
"source": [
"os.environ['PROJECT'] = PROJECT\n",
"os.environ['REGION'] = REGION\n",
"os.environ['MODEL_DISPLAY_NAME'] = MODEL_DISPLAY_NAME\n",
"os.environ['ENDPOINT_DISPLAY_NAME'] = ENDPOINT_DISPLAY_NAME"
]
},
{
"cell_type": "markdown",
"id": "b6546ac1",
"metadata": {},
"source": [
"### Run the model artifact testing"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "74c0f8a8",
"metadata": {},
"outputs": [],
"source": [
"!py.test src/tests/model_deployment_tests.py::test_model_artifact -s"
]
},
{
"cell_type": "markdown",
"id": "77885b24",
"metadata": {},
"source": [
"### Run create endpoint"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0efe73b5",
"metadata": {},
"outputs": [],
"source": [
"!python build/utils.py \\\n",
" --mode=create-endpoint\\\n",
" --project={PROJECT}\\\n",
" --region={REGION}\\\n",
" --endpoint-display-name={ENDPOINT_DISPLAY_NAME}"
]
},
{
"cell_type": "markdown",
"id": "3eb28c6f",
"metadata": {},
"source": [
"### Run deploy model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9cb3f19d",
"metadata": {},
"outputs": [],
"source": [
"!python build/utils.py \\\n",
" --mode=deploy-model\\\n",
" --project={PROJECT}\\\n",
" --region={REGION}\\\n",
" --endpoint-display-name={ENDPOINT_DISPLAY_NAME}\\\n",
" --model-display-name={MODEL_DISPLAY_NAME}"
]
},
{
"cell_type": "markdown",
"id": "ee492355",
"metadata": {},
"source": [
"### Test deployed model endpoint"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3d4bce50",
"metadata": {},
"outputs": [],
"source": [
"!py.test src/tests/model_deployment_tests.py::test_model_endpoint"
]
},
{
"cell_type": "markdown",
"id": "37b150c9",
"metadata": {},
"source": [
"## 2. Execute the Model Deployment CI/CD routine in Cloud Build\n",
"\n",
"The CI/CD routine is defined in the [model-deployment.yaml](model-deployment.yaml) file, and consists of the following steps:\n",
"1. Load and test the the trained model interface.\n",
"2. Create and endpoint in Vertex AI if it doesn't exists.\n",
"3. Deploy the model to the endpoint.\n",
"4. Test the endpoint."
]
},
{
"cell_type": "markdown",
"id": "839e540c",
"metadata": {},
"source": [
"### Build CI/CD container Image for Cloud Build\n",
"\n",
"This is the runtime environment where the steps of testing and deploying model will be executed."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a7f9bf4e",
"metadata": {},
"outputs": [],
"source": [
"!echo $CICD_IMAGE_URI"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3855daae",
"metadata": {},
"outputs": [],
"source": [
"!gcloud builds submit --tag $CICD_IMAGE_URI build/. --timeout=15m"
]
},
{
"cell_type": "markdown",
"id": "90fbd4b9",
"metadata": {},
"source": [
"### Run CI/CD from model deployment using Cloud Build"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e1aec70c",
"metadata": {},
"outputs": [],
"source": [
"REPO_URL = \"https://github.com/GoogleCloudPlatform/mlops-with-vertex-ai.git\" # Change to your github repo.\n",
"BRANCH = \"main\" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "01995fa5",
"metadata": {},
"outputs": [],
"source": [
"SUBSTITUTIONS=f\"\"\"\\\n",
"_REPO_URL='{REPO_URL}',\\\n",
"_BRANCH={BRANCH},\\\n",
"_CICD_IMAGE_URI={CICD_IMAGE_URI},\\\n",
"_PROJECT={PROJECT},\\\n",
"_REGION={REGION},\\\n",
"_MODEL_DISPLAY_NAME={MODEL_DISPLAY_NAME},\\\n",
"_ENDPOINT_DISPLAY_NAME={ENDPOINT_DISPLAY_NAME},\\\n",
"\"\"\"\n",
"\n",
"!echo $SUBSTITUTIONS"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8849d3e4",
"metadata": {},
"outputs": [],
"source": [
"!gcloud builds submit --no-source --config build/model-deployment.yaml --substitutions {SUBSTITUTIONS} --timeout=30m"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "01831724",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "4418b01e",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"environment": {
"name": "common-cpu.m79",
"type": "gcloud",
"uri": "gcr.io/deeplearning-platform-release/base-cpu:m79"
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| apache-2.0 |
FcoManueel/sherlock | Commit-Message-Classifier.ipynb | 1 | 4210 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# First we define the model."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Unknown\n"
]
},
{
"data": {
"text/plain": [
"0.6608478802992519"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from textblob.classifiers import NaiveBayesClassifier\n",
"\n",
"\n",
"def correct_format_training(line):\n",
" string = line.replace(\"\\n\", \"\").replace(\"- \", \"\").replace(\"the \", \" \").replace(\" and\", \" \").replace(\" from\", \" \")\n",
" label = string.split(\", \")[1]\n",
" words = string.split(\", \")[0].split(\" \")\n",
" return (words, label)\n",
"\n",
"def correct_format_production(line):\n",
" string = line.replace(\"\\n\", \"\").replace(\"- \", \"\").replace(\"the \", \" \").replace(\" and\", \" \").replace(\" from\", \" \")\n",
" words = string.split(\" \")\n",
" return words\n",
"\n",
"training_set = []\n",
"with open(\"training_data/training_shuffled_data.txt\", \"r\") as ins:\n",
" for line in ins:\n",
" training_set.append(correct_format(line))\n",
" \n",
"\n",
"NBC = NaiveBayesClassifier(training_set)\n",
"\n",
"print NBC.classify(\"Refactoring something or other\")\n",
"\n",
"validation_set = []\n",
"with open(\"training_data/validation_shuffled_data.txt\", \"r\") as ins:\n",
" for line in ins:\n",
" validation_set.append(correct_format(line))\n",
" \n",
"NBC.accuracy(validation_set)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pickle\n",
"f = open('my_classifier.pickle', 'wb')\n",
"pickle.dump(NBC, f)\n",
"f.close()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"f = open('my_classifier.pickle', 'rb')\n",
"pickled_NBC = pickle.load(f)\n",
"f.close()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pickled_NBC.classify(\"Refactoring\") == \"Unknown\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Then we use that model to get our analysis output, based on the user input."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from github import Github\n",
"from random import randint\n",
"\n",
"\n",
"g = Github(\"username\", \"password\")\n",
"input_string = \"facebook/react\" #Replaced with user given string\n",
"repo = g.get_repo(input_string, False)\n",
"root_dir = repo.get_git_tree(sha=\"master\", recursive=True)\n",
"\n",
"\n",
"fileHash = {}\n",
"\n",
"for file in root_dir.tree:\n",
" fileHash[file.path] = [0,0,0,0]\n",
" \n",
"for key in magicHash:\n",
" commits = repo.get_commits(path=key)\n",
" for commit in commits:\n",
" fileHash[key][randint(0,3)]+= 1 #Replace with model results.\n",
"\n",
"print g.rate_limiting\n",
"\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| apache-2.0 |
CCI-Tools/sandbox | notebooks/Janis/test.ipynb | 1 | 2662 | {
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div id=\"2d86bd60-feba-49e7-acbc-1509fa7bd033\" style=\"height: 525; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"2d86bd60-feba-49e7-acbc-1509fa7bd033\", [{\"type\": \"scatter\", \"y\": [10, 15, 13, 17], \"x\": [1, 2, 3, 4]}, {\"type\": \"scatter\", \"y\": [16, 5, 11, 9], \"x\": [1, 2, 3, 4]}], {}, {\"linkText\": \"Export to plot.ly\", \"showLink\": true})</script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import plotly.offline as py\n",
"from plotly.graph_objs import *\n",
"\n",
"py.init_notebook_mode()\n",
"trace0 = Scatter(\n",
" x=[1, 2, 3, 4],\n",
" y=[10, 15, 13, 17]\n",
")\n",
"trace1 = Scatter(\n",
" x=[1, 2, 3, 4],\n",
" y=[16, 5, 11, 9]\n",
" \n",
")\n",
"data = Data([trace0, trace1])\n",
"\n",
"py.iplot(data)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div id=\"1b8edbc7-242a-49fc-ad2f-77921c112b48\" style=\"height: 525; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"1b8edbc7-242a-49fc-ad2f-77921c112b48\", [{\"y\": [3, 1, 6], \"x\": [1, 2, 3]}], {}, {\"linkText\": \"Export to plot.ly\", \"showLink\": true})</script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"py.iplot([{\"x\": [1, 2, 3], \"y\": [3, 1, 6]}])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
""
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3.0
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.0rc4"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | gpl-3.0 |
takanory/pymook-samplecode | 4_scraping/4_2_scraping.ipynb | 2 | 16545 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 4.2 サードパーティ製パッケージを使ってスクレイピングに挑戦\n",
"\n",
"* Requests http://docs.python-requests.org/\n",
"* Beautiful Soup http://www.crummy.com/software/BeautifulSoup/"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import requests\n",
"import bs4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## RequestsでWebページを取得"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"200"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Requestsでgihyo.jpのページのデータを取得\n",
"import requests\n",
"r = requests.get('http://gihyo.jp/lifestyle/clip/01/everyday-cat')\n",
"r.status_code # ステータスコードを取得"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'<!DOCTYPE html>\\r\\n<html xmlns=\"http://www.w3.org/19'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.text[:50] # 先頭50文字を取得"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Requestsを使いこなす\n",
"\n",
"* connpass APIリファレンス https://connpass.com/about/api/"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"人工知能のコードをハックする会 #1\n",
"[秋葉原] 詳解ディープラーニング 輪読&勉強会(1章+2章+keras超入門)\n",
"Pythonで作る初心者のためのニューラルネットワーク実装\n",
"Pythonで作る初心者のためのニューラルネットワーク実装\n",
"Python札幌 プログラム初学者向けハンズオン 2017年 #2 懇親会\n",
"BPStudy#120〜小さなチーム、大きな仕事。開発・運営が一体となったチーム運営とは\n",
"[秋葉原] 自然言語処理と深層学習の勉強会 (第五回 分散表現/系列変換モデル)\n",
"Excel ベイズ入門 #Last：ベイズ決定・線形回帰モデル\n",
"データビジュアライゼーション講習 - P/L（損益計算書）の可視化・分析 8/22\n",
"機械学習 名古屋 分科会 #5\n"
]
}
],
"source": [
"# JSON形式のAPIレスポンスを取得\n",
"r = requests.get('https://connpass.com/api/v1/event/?keyword=python')\n",
"data = r.json() # JSONをデコードしたデータを取得\n",
"for event in data['events']:\n",
" print(event['title'])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# 各種HTTPメソッドに対応\n",
"payload = {'key1': 'value1', 'key2': 'value2'}\n",
"r = requests.post('http://httpbin.org/post', data=payload)\n",
"r = requests.put('http://httpbin.org/put', data=payload)\n",
"r = requests.delete('http://httpbin.org/delete')\n",
"r = requests.head('http://httpbin.org/get')\n",
"r = requests.options('http://httpbin.org/get')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'http://httpbin.org/get?key1=value1&key2=value2'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Requestsの便利な使い方\n",
"r = requests.get('http://httpbin.org/get', params=payload)\n",
"r.url"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"200"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r = requests.get('https://httpbin.org/basic-auth/user/passwd', auth=('user', 'passwd'))\n",
"r.status_code"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* httpbin(1): HTTP Client Testing Service https://httpbin.org/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Beautiful Soup 4でWebページを解析"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"bs4.element.Tag"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Beautiful Soup 4で「技評ねこ部通信」を取得\n",
"import requests\n",
"from bs4 import BeautifulSoup\n",
"r = requests.get('http://gihyo.jp/lifestyle/clip/01/everyday-cat')\n",
"soup = BeautifulSoup(r.content, 'html.parser')\n",
"title = soup.title # titleタグの情報を取得\n",
"type(title) # オブジェクトの型は Tag 型"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<title>技評ねこ部通信｜gihyo.jp … 技術評論社</title>\n",
"技評ねこ部通信｜gihyo.jp … 技術評論社\n"
]
}
],
"source": [
"print(title) # タイトルの中身を確認\n",
"print(title.text) # タイトルの中のテキストを取得"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://gihyo.jp/lifestyle/clip/01/everyday-cat/201708/04\n",
"2017年8月4日 甘えん坊なはちべい\n"
]
},
{
"data": {
"text/plain": [
"['2017年8月4日', '甘えん坊なはちべい']"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 技評ねこ部通信の1件分のデータを取得\n",
"div = soup.find('div', class_='readingContent01')\n",
"li = div.find('li') # divタグの中の最初のliタグを取得\n",
"print(li.a['href']) # liタグの中のaタグのhref属性の値を取得\n",
"print(li.a.text) # aタグの中の文字列を取得\n",
"li.a.text.split(maxsplit=1) # 文字列のsplit()で日付とタイトルに分割"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2017年8月4日,甘えん坊なはちべい,http://gihyo.jp/lifestyle/clip/01/everyday-cat/201708/04\n",
"2017年8月3日,ケーブルカー駅のしろくろこ,http://gihyo.jp/lifestyle/clip/01/everyday-cat/201708/03\n",
"2017年8月2日,ビビりな丸子,http://gihyo.jp/lifestyle/clip/01/everyday-cat/201708/02\n",
"2017年8月1日,河原の公園のしろくろこ,http://gihyo.jp/lifestyle/clip/01/everyday-cat/201708/01\n",
"2017年7月31日,技評ねこ部の投稿コーナー！,http://gihyo.jp/lifestyle/clip/01/everyday-cat/201707/31\n",
"2017年7月28日,しろこの青春,http://gihyo.jp/lifestyle/clip/01/everyday-cat/201707/28\n",
"2017年7月27日,クリーニング屋のしろこ,http://gihyo.jp/lifestyle/clip/01/everyday-cat/201707/27\n",
"2017年7月26日,見返りしろこ,http://gihyo.jp/lifestyle/clip/01/everyday-cat/201707/26\n",
"2017年7月25日,2匹のしろこ,http://gihyo.jp/lifestyle/clip/01/everyday-cat/201707/25\n",
"2017年7月24日,風通る公園のしろこ 三たび,http://gihyo.jp/lifestyle/clip/01/everyday-cat/201707/24\n",
"2017年7月21日,塀の上のしろこ,http://gihyo.jp/lifestyle/clip/01/everyday-cat/201707/21\n",
"2017年7月20日,マダムしろこのご近所しろこ,http://gihyo.jp/lifestyle/clip/01/everyday-cat/201707/20\n",
"2017年7月19日,風通る公園のしろこ 再び,http://gihyo.jp/lifestyle/clip/01/everyday-cat/201707/19\n",
"2017年7月18日,マダムしろこ,http://gihyo.jp/lifestyle/clip/01/everyday-cat/201707/18\n",
"2017年7月14日,ドヤ顔のしろこ,http://gihyo.jp/lifestyle/clip/01/everyday-cat/201707/14\n",
"2017年7月13日,風通る公園のしろこ,http://gihyo.jp/lifestyle/clip/01/everyday-cat/201707/13\n",
"2017年7月12日,学校のしろこ,http://gihyo.jp/lifestyle/clip/01/everyday-cat/201707/12\n",
"2017年7月11日,住宅街のしろこ 夜の部,http://gihyo.jp/lifestyle/clip/01/everyday-cat/201707/11\n",
"2017年7月10日,住宅街のしろこ,http://gihyo.jp/lifestyle/clip/01/everyday-cat/201707/10\n",
"2017年7月7日,植え込みのしろこ２,http://gihyo.jp/lifestyle/clip/01/everyday-cat/201707/07\n"
]
}
],
"source": [
"# 技評ねこ部通信の全データを取得\n",
"div = soup.find('div', class_='readingContent01')\n",
"for li in div.find_all('li'): # divタグの中の全liタグを取得\n",
" url = li.a['href']\n",
" date, text = li.a.text.split(maxsplit=1)\n",
" print('{},{},{}'.format(date, text, url))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Beautiful Soup 4を使いこなす"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"bs4.element.Tag"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# タグの情報を取得する\n",
"div = soup.find('div', class_='readingContent01')\n",
"type(div) # データの型はTag型"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'div'"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"div.name"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['readingContent01']"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"div['class']"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'class': ['readingContent01']}"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"div.attrs # 全属性を取得"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"131"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# さまざまな検索方法\n",
"a_tags = soup.find_all('a') # タグ名を指定\n",
"len(a_tags)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"base\n",
"body\n",
"br\n",
"br\n",
"br\n",
"br\n",
"br\n"
]
}
],
"source": [
"import re\n",
"for tag in soup.find_all(re.compile('^b')): # 正規表現で指定\n",
" print(tag.name)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"html\n",
"title\n"
]
}
],
"source": [
"for tag in soup.find_all(['html', 'title']): # リストで指定\n",
" print(tag.name)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('div', {'class': ['headCategoryNavigation'], 'id': 'categoryNavigation'})"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# キーワード引数での属性指定\n",
"tag = soup.find(id='categoryNavigation') # id属性を指定して検索\n",
"tag.name, tag.attrs"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"57"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tags = soup.find_all(id=True) # id属性があるタグを全て検索\n",
"len(tags)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'class': ['readingContent01']}"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"div = soup.find('div', class_='readingContent01') # class属性はclass_と指定する\n",
"div.attrs"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'class': ['readingContent01']}"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"div = soup.find('div', {'class': 'readingContent01'}) # 辞書形式でも指定できる\n",
"div.attrs"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<title>技評ねこ部通信｜gihyo.jp … 技術評論社</title>]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# CSSセレクターを使用した検索\n",
"soup.select('title') # タグ名を指定"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"131"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tags = soup.select('body a') # body タグの下のaタグ\n",
"len(a_tags)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a_tags = soup.select('p > a') # pタグの直下のaタグ\n",
"len(a_tags)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"soup.select('body > a') # bodyタグの直下のaタグは存在しない"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"div = soup.select('.readingContent01') # classを指定\n",
"div = soup.select('div.readingContent01')\n",
"div = soup.select('#categoryNavigation') # idを指定\n",
"div = soup.select('div#categoryNavigation')\n",
"a_tag = soup.select_one('div > a') # 最初のdivタグ直下のaタグを返す"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
crdguez/mat4ac | notebooks/19-estadistica.ipynb | 2 | 263065 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "XLjKRF_kknDB",
"outputId": "bf368ad3-b06b-4d98-c873-15e25c6e4091"
},
"outputs": [],
"source": [
"# Ejecutar para que funcione el parse_latex en google colab\n",
" \n",
"# !pip install sympy==1.5 antlr4-python3-runtime==4.7.1"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tVIMjcDSknDa",
"outputId": "8a4c95fe-51d7-4ded-d63f-41e146567df4",
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"IPython console for SymPy 1.6.2 (Python 3.6.12-64-bit) (ground types: gmpy)\n",
"\n",
"These commands were executed:\n",
">>> from __future__ import division\n",
">>> from sympy import *\n",
">>> x, y, z, t = symbols('x y z t')\n",
">>> k, m, n = symbols('k m n', integer=True)\n",
">>> f, g, h = symbols('f g h', cls=Function)\n",
">>> init_printing()\n",
"\n",
"Documentation can be found at https://docs.sympy.org/1.6.2/\n",
"\n"
]
}
],
"source": [
"from sympy import init_session\n",
"from sympy.parsing.latex import parse_latex\n",
"from sympy.parsing.sympy_parser import parse_expr\n",
"from IPython.display import Markdown as md\n",
"from IPython.display import display\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import scipy as sc\n",
"from tabulate import tabulate\n",
"from scipy.stats import cumfreq, relfreq, stats\n",
"\n",
"init_session()\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def texto_a_datos(str_datos) :\n",
" datos = np.loadtxt(str_datos.split())\n",
" datos = datos.astype(int)\n",
" return datos\n",
"\n",
"\n",
"def analisis_discreto_ant(texto_ejercicio, str_datos, n_ejercicio='_'):\n",
" texto_ejercicio = texto_ejercicio + str_datos\n",
" enunciado_latex = [r'Realiza una tabla de frecuencias',\n",
" r'Realiza un diagrama de barras',\n",
" r'Calcular los parámetros de centralización',\n",
" r'Calcular los parámetros de posición P70, Q1, Q3, D4',\n",
" r'Calcular los parámetros de dispersión'\n",
" ]\n",
" enunciado, solucion = [], []\n",
" enunciado = enunciado_latex\n",
" datos = np.loadtxt(str_datos.split())\n",
" datos = datos.astype(int) \n",
"\n",
" #x_i, f_i, F_i, r_i = np.unique(datos),np.bincount(datos), cumfreq(datos, numbins=len(np.unique(datos)))[0].astype(int), np.multiply(relfreq(datos, numbins=len(np.unique(datos)))[0],100)\n",
" tabla = pd.DataFrame({'x_i':np.unique(datos), 'f_i':np.unique(datos, return_counts=True)[1], 'F_i':np.unique(datos, return_counts=True)[1].cumsum(), 'h_i':np.unique(datos, return_counts=True)[1]/len(datos), 'H_i':(np.unique(datos, return_counts=True)[1]/len(datos)).cumsum(), '%_i':np.unique(datos, return_counts=True)[1]*100/len(datos), '%A_i':(np.unique(datos, return_counts=True)[1]*100/len(datos)).cumsum()}).set_index('x_i')\n",
" solucion.append(tabulate(tabla, headers=\"keys\", tablefmt=\"latex\"))\n",
"\n",
" d = np.diff(np.unique(datos)).min()\n",
" left_of_first_bin = datos.min() - float(d)/2\n",
" right_of_last_bin = datos.max() + float(d)/2\n",
" plt.clf()\n",
" plt.hist(datos, np.arange(left_of_first_bin, right_of_last_bin + d, d), rwidth=0.9, cumulative = False)\n",
" plt.title(\"Diagrama \"+n_ejercicio)\n",
" plt.savefig(\"../img/\"+n_ejercicio)\n",
"\n",
" solucion.append(r\"\\\\ \\includegraphics[width=1\\columnwidth]{%s}\" % n_ejercicio)\n",
"\n",
" solucion.append({\"media\":datos.mean(), \"mediana\":np.percentile(datos,50), \"moda\":stats.mode(datos)})\n",
" solucion.append({\"P70\":np.percentile(datos,70), \"Q1\":np.percentile(datos,25),\"Q3\":np.percentile(datos,75),\"D4\":np.percentile(datos,40),})\n",
" solucion.append({\"rango\":np.amax(datos)-np.amin(datos), \"varianza\": np.var(datos), \"desviación típica\":sqrt(np.var(datos)), \"coeficiente variación\": sqrt(np.var(datos))/abs(np.mean(datos))})\n",
" display(tabla, solucion[2] , solucion[3], solucion[4])\n",
" return texto_ejercicio, enunciado_latex, enunciado, solucion, tabla, n_ejercicio"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "U_EQ3XUBknDf"
},
"outputs": [],
"source": [
"def latex_exam(question, parts):\n",
" tex=r\"\\question \"+question+r\"\\begin{parts} \"\n",
" for p in parts :\n",
" tex+=r\"\\part[1] \"+p[0]+r\"\\begin{solution} \"+p[1]+r\"\\end{solution} \" \n",
" tex+=r\"\\end{parts} \"\n",
" \n",
" return tex\n",
"\n",
"def mostrar_ejercicio(ejercicio,solucion,tipo=0) :\n",
" #tipo=0 se pasa el ejercicio y la solucion en formato latex\n",
" if tipo == 0 :\n",
" display(md(\"#### Ejercicio:\"))\n",
" display(md(r\"{} $\\to$ {}\".format(ejercicio, solucion)))\n",
" print(\"enunciado_latex: \" + ejercicio)\n",
" print(\"solucion_latex: \" + solucion)\n",
" return ejercicio, solucion\n",
" elif tipo == 1:\n",
" # falta desarrollar ...\n",
" display(md(\"#### Ejercicio:\"))\n",
" display(md(r\"{} $\\to$ {}\".format(ejercicio, solucion)))\n",
" print(\"enunciado_latex: \" + ejercicio)\n",
" print(\"solucion_latex: \" + solucion)\n",
" return ejercicio, solucion\n",
"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def texto_a_datos(str_datos, tipo=int) :\n",
" datos = np.loadtxt(str_datos.split())\n",
" datos = datos.astype(tipo)\n",
" return datos\n",
"\n",
"def datos_a_tabla(datos) :\n",
" tabla = pd.DataFrame({'x_i':np.unique(datos), \n",
" 'f_i':np.unique(datos, return_counts=True)[1],\n",
" 'F_i':np.unique(datos, return_counts=True)[1].cumsum(),\n",
" 'h_i':np.unique(datos, return_counts=True)[1]/len(datos), \n",
" 'H_i':(np.unique(datos, return_counts=True)[1]/len(datos)).cumsum(), \n",
" '%_i':np.unique(datos, return_counts=True)[1]*100/len(datos), \n",
" '%A_i':(np.unique(datos, return_counts=True)[1]*100/len(datos)).cumsum(),\n",
" })\n",
" tabla['x_if_i']=tabla['x_i']*tabla['f_i']\n",
" tabla['x^2_if_i']=tabla['x_i']**2*tabla['f_i']\n",
" \n",
" totales = pd.DataFrame([[np.nan,tabla.f_i.sum(),\n",
" np.nan, tabla.h_i.sum(), np.nan, tabla['%_i'].sum(), np.nan,\n",
" tabla.x_if_i.sum(),tabla['x^2_if_i'].sum()]],columns=list(tabla.columns))\n",
"\n",
" tabla=tabla.append(totales,ignore_index=True)\n",
" return tabla\n",
"\n",
"def diagrama_barras(datos) :\n",
" d = np.diff(np.unique(datos)).min()\n",
" left_of_first_bin = datos.min() - float(d)/2\n",
" right_of_last_bin = datos.max() + float(d)/2\n",
" plt.clf()\n",
" plt.hist(datos, np.arange(left_of_first_bin, right_of_last_bin + d, d), rwidth=0.9, cumulative = False)\n",
" plt.title(\"Diagrama \")\n",
"# plt.savefig(\"../img/diagrama\"+n_ejercicio)\n",
" \n",
" return plt.figure\n",
"\n",
"def analisis_discreto(texto_ejercicio, datos, n_ejercicio='_'):\n",
" texto_ejercicio = texto_ejercicio + \" \". join(map(str,datos))\n",
" enunciado_latex = [r'Realiza una tabla de frecuencias',\n",
" r'Realiza un diagrama de barras',\n",
" r'Calcular los parámetros de centralización',\n",
" r'Calcular los parámetros de posición P70, Q1, Q3, D4',\n",
" r'Calcular los parámetros de dispersión'\n",
" ]\n",
"\n",
" enunciado = texto_ejercicio+r\". \"+r\". \".join(enunciado_latex)\n",
" \n",
" tabla = datos_a_tabla(datos)\n",
"\n",
" solucion=\"$\"+tabulate(tabla, headers=\"keys\", tablefmt=\"latex\", showindex=False)+r\"$\"\n",
"\n",
"\n",
"# d = np.diff(np.unique(datos)).min()\n",
"# left_of_first_bin = datos.min() - float(d)/2\n",
"# right_of_last_bin = datos.max() + float(d)/2\n",
"# plt.clf()\n",
"# plt.hist(datos, np.arange(left_of_first_bin, right_of_last_bin + d, d), rwidth=0.9, cumulative = False)\n",
"# plt.title(\"Diagrama \")\n",
"# plt.savefig(\"../img/diagrama\"+n_ejercicio)\n",
"\n",
"# plt.show()\n",
" fig=diagrama_barras(datos)\n",
" plt.savefig(\"../img/diagrama\"+n_ejercicio)\n",
" plt.show()\n",
" \n",
" plt.boxplot([datos,datos], vert=False)\n",
" plt.savefig(\"../img/cajas\"+n_ejercicio)\n",
" plt.show()\n",
"\n",
"\n",
"\n",
" solucion += r\"\\\\ \\includegraphics[width=1\\columnwidth]{%s}\" % (\"diagrama\"+n_ejercicio)\n",
" solucion += r\" \\\\ $\"+latex({\"media\":datos.mean().round(2), \"Me\":np.percentile(datos,50), \"Mo\":stats.mode(datos)})+r\"$ \\\\\"\n",
" solucion += \"$\"+latex({\"P70\":np.percentile(datos,70), \"Q1\":np.percentile(datos,25),\"Q3\":np.percentile(datos,75),\"D4\":np.percentile(datos,40),})+r\"$ \\\\\"\n",
" solucion += \"$\"+latex({\"rango\":np.amax(datos)-np.amin(datos), \"var\": np.var(datos).round(2), \"desv.tip\":sqrt(np.var(datos)).round(2), \"C.V\": (sqrt(np.var(datos))/abs(np.mean(datos))).round(2)})+r\"$\"\n",
"\n",
" display(tabla, solucion[2] , solucion[3], solucion[4])\n",
" return texto_ejercicio, enunciado_latex, enunciado, solucion, tabla, n_ejercicio\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAEICAYAAAB25L6yAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAOrElEQVR4nO3df6zddX3H8eeLFiMgitgrIuV6cTNMx8IwF9SRuQ3JhivKlrhNN5kjLDdZxOFi4up+gYlb+sdmMJlzdvwOCCjCxmAwnUoIRnAtsAkUo7IilV9FBhRUGPDeH+cUrtdT7ik933M+9/b5SJqeH99+v+9vuTz77fd8v72pKiRJ7dpj0gNIkp6foZakxhlqSWqcoZakxhlqSWqcoZakxhlqNSnJPyb5y0nPIbUgXketcUuyGTgAeAp4GrgdOB9YX1XPTHA0qUkeUWtS3lFV+wKvAdYBfwqc1fVGk6zsehvSqBlqTVRVPVJVVwC/A7wvyWEASc5N8rH+45cnuTLJ1iT/23+8evs6khyS5Lok25L8R5JPJrmg/95MkkpycpLvAl/uv/65JPcleaT/a3923vrOTfIPSa5O8liSryZ5VZIz+tu/I8kR85Zfm+Q7/e3fnuQ3x/Kbp92GoVYTqurrwBbgFwe8vQdwDr2j72ngh8Dfz3v/M8DXgVcApwMnDljHLwGvB36t//xq4HXAK4GbgAsXLP/bwF8Aq4AngK/1l1sFXAp8fN6y3+nP/TLgo8AFSQ58/j2Whmeo1ZJ7gP0XvlhV36+qz1fVD6pqG/DX9MJLkmngSOCvqurJqroeuGLAuk+vqser6of9dZ5dVduq6gl6cT88ycvmLX95VW2sqh8BlwM/qqrzq+pp4BLg2SPqqvpcVd1TVc9U1SXAt4Cjdvl3Q+oz1GrJQcBDC19MsneSTye5K8mjwHXAfklWAK8GHqqqH8z7JXcPWPezryVZkWRd/3TFo8Dm/lur5i1//7zHPxzw/CXz1vf7SW5J8nCSh4HDFqxL2iWGWk1IciS9UF8/4O0PAYcCb6qqlwJv3f7LgHuB/ZPsPW/5gwesY/7lTb8LnAAcS+90xcy89e3s3K8B/gk4BXhFVe0H3PpC1iXtiKHWRCV5aZLjgYuBC6rqGwMW25feUezDSfYHTtv+RlXdBWwATk/yoiRvAd6xyGb3pXfe+fvA3sDf7MIu7EPvD4Gt/f05id4RtTQyhlqT8q9JttE7JfHn9D6cO2kHy54B7AU8CNwAXLPg/d8D3kIvvB+jdw75iefZ9vnAXcD36F3DfcML2gOgqm4H/o7eh433Az8HfPWFrk8axBtetOwkuQS4o6pOW3RhaQnwiFpLXpIjk/xUkj2SHEfv/PM/T3gsaWS8S0vLwauAy+hdR70F+KOqunmyI0mj46kPSWqcpz4kqXGdnPpYtWpVzczMdLFqSVqWNm7c+GBVTQ16r5NQz8zMsGHDhi5WLUnLUpK7dvSepz4kqXGGWpIaZ6glqXGGWpIaZ6glqXGGWpIaN1Sok+yX5NL+94rb1P+nJCVJYzDsddSfAK6pqncleRG9f8NXkjQGi4Y6yfbvqPEHAFX1JPBkt2NJkrYb5oj6tfS+e8U5SQ4HNgKnVtXj8xdKMgfMAUxPT496TuknzKy9aizb2bxuzVi2s9A49m9S+6adM8w56pXAG4FPVdURwOPA2oULVdX6qpqtqtmpqYG3q0uSXoBhQr0F2FJVN/afX0ov3JKkMVg01FV1H3B3kkP7L72N3veZkySNwbBXfXwAuLB/xced7PibkEqSRmyoUFfVLcBst6NIkgbxzkRJapyhlqTGGWpJapyhlqTGGWpJapyhlqTGGWpJapyhlqTGGWpJapyhlqTGGWpJapyhlqTGGWpJapyhlqTGGWpJapyhlqTGGWpJapyhlqTGGWpJapyhlqTGGWpJapyhlqTGGWpJapyhlqTGGWpJatzKYRZKshnYBjwNPFVVs10OJUl6zlCh7vuVqnqws0kkSQN56kOSGjfsEXUBX0hSwKerav3CBZLMAXMA09PTo5tQasTM2qs638bmdWs634aWnmGPqI+uqjcCbwfen+StCxeoqvVVNVtVs1NTUyMdUpJ2Z0OFuqru6f/8AHA5cFSXQ0mSnrNoqJPsk2Tf7Y+BXwVu7XowSVLPMOeoDwAuT7J9+c9U1TWdTiVJetaioa6qO4HDxzCLJGkAL8+TpMYZaklqnKGWpMYZaklqnKGWpMYZaklqnKGWpMYZaklqnKGWpMYZaklqnKGWpMYZaklqnKGWpMYZaklqnKGWpMYZaklqnKGWpMYZaklqnKGWpMYZaklqnKGWpMYZaklqnKGWpMYZaklqnKGWpMYZaklq3NChTrIiyc1JruxyIEnSj9uZI+pTgU1dDSJJGmyoUCdZDawBzux2HEnSQsMeUZ8BfBh4ZkcLJJlLsiHJhq1bt45iNkkSQ4Q6yfHAA1W18fmWq6r1VTVbVbNTU1MjG1CSdnfDHFEfDbwzyWbgYuCYJBd0OpUk6VmLhrqqPlJVq6tqBng38OWqem/nk0mSAK+jlqTmrdyZhavqWuDaTiaRJA3kEbUkNc5QS1LjDLUkNc5QS1LjDLUkNc5QS1LjDLUkNc5QS1LjDLUkNc5QS1LjDLUkNc5QS1LjDLUkNc5QS1LjDLUkNc5QS1LjDLUkNc5QS1LjDLUkNc5QS1LjDLUkNc5QS1LjDLUkNc5QS1LjDLUkNW7RUCd5cZKvJ/mvJLcl+eg4BpMk9awcYpkngGOq6rEkewLXJ7m6qm7oeDZJEkOEuqoKeKz/dM/+j+pyKEnSc4Y5oibJCmAj8NPAJ6vqxgHLzAFzANPT06OccVmZWXtV59vYvG5N59sYZBz7BpPbv+VuOX9tLnVDfZhYVU9X1c8Dq4Gjkhw2YJn1VTVbVbNTU1MjHlOSdl87ddVHVT0MXAsc18UwkqSfNMxVH1NJ9us/3gs4Frij47kkSX3DnKM+EDivf556D+CzVXVlt2NJkrYb5qqP/waOGMMskqQBvDNRkhpnqCWpcYZakhpnqCWpcYZakhpnqCWpcYZakhpnqCWpcYZakhpnqCWpcYZakhpnqCWpcYZakhpnqCWpcYZakhpnqCWpcYZakhpnqCWpcYZakhpnqCWpcYZakhpnqCWpcYZakhpnqCWpcYZakhpnqCWpcYuGOsnBSb6SZFOS25KcOo7BJEk9K4dY5ingQ1V1U5J9gY1JvlhVt3c8mySJIY6oq+reqrqp/3gbsAk4qOvBJEk9wxxRPyvJDHAEcOOA9+aAOYDp6elRzDYWM2uv6nwbm9et6XwbO7Lc909Ll1+bwxv6w8QkLwE+D3ywqh5d+H5Vra+q2aqanZqaGuWMkrRbGyrUSfakF+kLq+qybkeSJM03zFUfAc4CNlXVx7sfSZI03zBH1EcDJwLHJLml/+PXO55LktS36IeJVXU9kDHMIkkawDsTJalxhlqSGmeoJalxhlqSGmeoJalxhlqSGmeoJalxhlqSGmeoJalxhlqSGmeoJalxhlqSGmeoJalxhlqSGmeoJalxhlqSGmeoJalxhlqSGmeoJalxhlqSGmeoJalxhlqSGmeoJalxhlqSGmeoJalxi4Y6ydlJHkhy6zgGkiT9uGGOqM8Fjut4DknSDiwa6qq6DnhoDLNIkgZYOaoVJZkD5gCmp6df8Hpm1l41qpF2aPO6NZ1vQ1JblnJbRvZhYlWtr6rZqpqdmpoa1WolabfnVR+S1DhDLUmNG+byvIuArwGHJtmS5OTux5Ikbbfoh4lV9Z5xDCJJGsxTH5LUOEMtSY0z1JLUOEMtSY0z1JLUOEMtSY0z1JLUOEMtSY0z1JLUOEMtSY0z1JLUOEMtSY0z1JLUOEMtSY0z1JLUOEMtSY0z1JLUOEMtSY0z1JLUOEMtSY0z1JLUOEMtSY0z1JLUOEMtSY0z1JLUOEMtSY0bKtRJjkvyzSTfTrK266EkSc9ZNNRJVgCfBN4OvAF4T5I3dD2YJKlnmCPqo4BvV9WdVfUkcDFwQrdjSZK2S1U9/wLJu4DjquoP+89PBN5UVacsWG4OmOs/PRT45ujHHWgV8OCYtjUJ7t/S5v4tbePcv9dU1dSgN1YO8Ysz4LWfqHtVrQfW7+RguyzJhqqaHfd2x8X9W9rcv6Wtlf0b5tTHFuDgec9XA/d0M44kaaFhQv2fwOuSHJLkRcC7gSu6HUuStN2ipz6q6qkkpwD/DqwAzq6q2zqfbHhjP90yZu7f0ub+LW1N7N+iHyZKkibLOxMlqXGGWpIat6RDvZxvbU9ycJKvJNmU5LYkp056plFLsiLJzUmunPQso5ZkvySXJrmj/9/wLZOeaZSS/En/6/LWJBclefGkZ9oVSc5O8kCSW+e9tn+SLyb5Vv/nl09qviUb6t3g1vangA9V1euBNwPvX2b7B3AqsGnSQ3TkE8A1VfUzwOEso/1MchDwx8BsVR1G7yKDd092ql12LnDcgtfWAl+qqtcBX+o/n4glG2qW+a3tVXVvVd3Uf7yN3v/oB012qtFJshpYA5w56VlGLclLgbcCZwFU1ZNV9fBEhxq9lcBeSVYCe7PE762oquuAhxa8fAJwXv/xecBvjHOm+ZZyqA8C7p73fAvLKGTzJZkBjgBunPAoo3QG8GHgmQnP0YXXAluBc/qnds5Mss+khxqVqvoe8LfAd4F7gUeq6guTnaoTB1TVvdA7cAJeOalBlnKoh7q1falL8hLg88AHq+rRSc8zCkmOBx6oqo2TnqUjK4E3Ap+qqiOAx5ngX5tHrX+u9gTgEODVwD5J3jvZqZa3pRzqZX9re5I96UX6wqq6bNLzjNDRwDuTbKZ3yuqYJBdMdqSR2gJsqartfwO6lF64l4tjgf+pqq1V9X/AZcAvTHimLtyf5ECA/s8PTGqQpRzqZX1re5LQO8e5qao+Pul5RqmqPlJVq6tqht5/ty9X1bI5Iquq+4C7kxzaf+ltwO0THGnUvgu8Ocne/a/Tt7GMPiyd5wrgff3H7wP+ZVKDDPOv5zVpCdzavquOBk4EvpHklv5rf1ZV/za5kbQTPgBc2D+IuBM4acLzjExV3ZjkUuAmelcn3Uwjt1q/UEkuAn4ZWJVkC3AasA74bJKT6f3h9FsTm89byCWpbUv51Ick7RYMtSQ1zlBLUuMMtSQ1zlBLUuMMtSQ1zlBLUuP+Hw5oiXfC+zDQAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>x_i</th>\n",
" <th>f_i</th>\n",
" <th>F_i</th>\n",
" <th>h_i</th>\n",
" <th>H_i</th>\n",
" <th>%_i</th>\n",
" <th>%A_i</th>\n",
" <th>x_if_i</th>\n",
" <th>x^2_if_i</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>1.0</td>\n",
" <td>0.029412</td>\n",
" <td>0.029412</td>\n",
" <td>2.941176</td>\n",
" <td>2.941176</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.0</td>\n",
" <td>2</td>\n",
" <td>3.0</td>\n",
" <td>0.058824</td>\n",
" <td>0.088235</td>\n",
" <td>5.882353</td>\n",
" <td>8.823529</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2.0</td>\n",
" <td>3</td>\n",
" <td>6.0</td>\n",
" <td>0.088235</td>\n",
" <td>0.176471</td>\n",
" <td>8.823529</td>\n",
" <td>17.647059</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3.0</td>\n",
" <td>2</td>\n",
" <td>8.0</td>\n",
" <td>0.058824</td>\n",
" <td>0.235294</td>\n",
" <td>5.882353</td>\n",
" <td>23.529412</td>\n",
" <td>6</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4.0</td>\n",
" <td>3</td>\n",
" <td>11.0</td>\n",
" <td>0.088235</td>\n",
" <td>0.323529</td>\n",
" <td>8.823529</td>\n",
" <td>32.352941</td>\n",
" <td>12</td>\n",
" <td>48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5.0</td>\n",
" <td>6</td>\n",
" <td>17.0</td>\n",
" <td>0.176471</td>\n",
" <td>0.500000</td>\n",
" <td>17.647059</td>\n",
" <td>50.000000</td>\n",
" <td>30</td>\n",
" <td>150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>6.0</td>\n",
" <td>5</td>\n",
" <td>22.0</td>\n",
" <td>0.147059</td>\n",
" <td>0.647059</td>\n",
" <td>14.705882</td>\n",
" <td>64.705882</td>\n",
" <td>30</td>\n",
" <td>180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7.0</td>\n",
" <td>6</td>\n",
" <td>28.0</td>\n",
" <td>0.176471</td>\n",
" <td>0.823529</td>\n",
" <td>17.647059</td>\n",
" <td>82.352941</td>\n",
" <td>42</td>\n",
" <td>294</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>8.0</td>\n",
" <td>3</td>\n",
" <td>31.0</td>\n",
" <td>0.088235</td>\n",
" <td>0.911765</td>\n",
" <td>8.823529</td>\n",
" <td>91.176471</td>\n",
" <td>24</td>\n",
" <td>192</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>9.0</td>\n",
" <td>2</td>\n",
" <td>33.0</td>\n",
" <td>0.058824</td>\n",
" <td>0.970588</td>\n",
" <td>5.882353</td>\n",
" <td>97.058824</td>\n",
" <td>18</td>\n",
" <td>162</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>10.0</td>\n",
" <td>1</td>\n",
" <td>34.0</td>\n",
" <td>0.029412</td>\n",
" <td>1.000000</td>\n",
" <td>2.941176</td>\n",
" <td>100.000000</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>NaN</td>\n",
" <td>34</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" <td>100.000000</td>\n",
" <td>NaN</td>\n",
" <td>180</td>\n",
" <td>1158</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" x_i f_i F_i h_i H_i %_i %A_i x_if_i \\\n",
"0 0.0 1 1.0 0.029412 0.029412 2.941176 2.941176 0 \n",
"1 1.0 2 3.0 0.058824 0.088235 5.882353 8.823529 2 \n",
"2 2.0 3 6.0 0.088235 0.176471 8.823529 17.647059 6 \n",
"3 3.0 2 8.0 0.058824 0.235294 5.882353 23.529412 6 \n",
"4 4.0 3 11.0 0.088235 0.323529 8.823529 32.352941 12 \n",
"5 5.0 6 17.0 0.176471 0.500000 17.647059 50.000000 30 \n",
"6 6.0 5 22.0 0.147059 0.647059 14.705882 64.705882 30 \n",
"7 7.0 6 28.0 0.176471 0.823529 17.647059 82.352941 42 \n",
"8 8.0 3 31.0 0.088235 0.911765 8.823529 91.176471 24 \n",
"9 9.0 2 33.0 0.058824 0.970588 5.882353 97.058824 18 \n",
"10 10.0 1 34.0 0.029412 1.000000 2.941176 100.000000 10 \n",
"11 NaN 34 NaN 1.000000 NaN 100.000000 NaN 180 \n",
"\n",
" x^2_if_i \n",
"0 0 \n",
"1 2 \n",
"2 12 \n",
"3 18 \n",
"4 48 \n",
"5 150 \n",
"6 180 \n",
"7 294 \n",
"8 192 \n",
"9 162 \n",
"10 100 \n",
"11 1158 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'b'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'e'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'g'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"Las calificaciones de un grupo de 34 alumnos han sido: 9 6 5 0 1 5 7 9 10 7 5 1 2 5 7 6 3 4 6 8 8 6 4 4 6 5 3 5 7 7 8 7 2 2. Realiza una tabla de frecuencias. Realiza un diagrama de barras. Calcular los parámetros de centralización. Calcular los parámetros de posición P70, Q1, Q3, D4. Calcular los parámetros de dispersión $\\to$ $\\begin{tabular}{rrrrrrrrr}\n",
"\\hline\n",
" x\\_i & f\\_i & F\\_i & h\\_i & H\\_i & \\%\\_i & \\%A\\_i & x\\_if\\_i & x\\^{}2\\_if\\_i \\\\\n",
"\\hline\n",
" 0 & 1 & 1 & 0.0294118 & 0.0294118 & 2.94118 & 2.94118 & 0 & 0 \\\\\n",
" 1 & 2 & 3 & 0.0588235 & 0.0882353 & 5.88235 & 8.82353 & 2 & 2 \\\\\n",
" 2 & 3 & 6 & 0.0882353 & 0.176471 & 8.82353 & 17.6471 & 6 & 12 \\\\\n",
" 3 & 2 & 8 & 0.0588235 & 0.235294 & 5.88235 & 23.5294 & 6 & 18 \\\\\n",
" 4 & 3 & 11 & 0.0882353 & 0.323529 & 8.82353 & 32.3529 & 12 & 48 \\\\\n",
" 5 & 6 & 17 & 0.176471 & 0.5 & 17.6471 & 50 & 30 & 150 \\\\\n",
" 6 & 5 & 22 & 0.147059 & 0.647059 & 14.7059 & 64.7059 & 30 & 180 \\\\\n",
" 7 & 6 & 28 & 0.176471 & 0.823529 & 17.6471 & 82.3529 & 42 & 294 \\\\\n",
" 8 & 3 & 31 & 0.0882353 & 0.911765 & 8.82353 & 91.1765 & 24 & 192 \\\\\n",
" 9 & 2 & 33 & 0.0588235 & 0.970588 & 5.88235 & 97.0588 & 18 & 162 \\\\\n",
" 10 & 1 & 34 & 0.0294118 & 1 & 2.94118 & 100 & 10 & 100 \\\\\n",
" nan & 34 & nan & 1 & nan & 100 & nan & 180 & 1158 \\\\\n",
"\\hline\n",
"\\end{tabular}$\\\\ \\includegraphics[width=1\\columnwidth]{diagrama0} \\\\ $\\left\\{ Me : 5.5, \\ Mo : \\left( [5], \\ [6]\\right), \\ media : 5.29\\right\\}$ \\\\$\\left\\{ D4 : 5.0, \\ P70 : 7.0, \\ Q1 : 4.0, \\ Q3 : 7.0\\right\\}$ \\\\$\\left\\{ C.V : 0.46, \\ desv.tip : 2.46, \\ rango : 10, \\ var : 6.03\\right\\}$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: Las calificaciones de un grupo de 34 alumnos han sido: 9 6 5 0 1 5 7 9 10 7 5 1 2 5 7 6 3 4 6 8 8 6 4 4 6 5 3 5 7 7 8 7 2 2. Realiza una tabla de frecuencias. Realiza un diagrama de barras. Calcular los parámetros de centralización. Calcular los parámetros de posición P70, Q1, Q3, D4. Calcular los parámetros de dispersión\n",
"solucion_latex: $\\begin{tabular}{rrrrrrrrr}\n",
"\\hline\n",
" x\\_i & f\\_i & F\\_i & h\\_i & H\\_i & \\%\\_i & \\%A\\_i & x\\_if\\_i & x\\^{}2\\_if\\_i \\\\\n",
"\\hline\n",
" 0 & 1 & 1 & 0.0294118 & 0.0294118 & 2.94118 & 2.94118 & 0 & 0 \\\\\n",
" 1 & 2 & 3 & 0.0588235 & 0.0882353 & 5.88235 & 8.82353 & 2 & 2 \\\\\n",
" 2 & 3 & 6 & 0.0882353 & 0.176471 & 8.82353 & 17.6471 & 6 & 12 \\\\\n",
" 3 & 2 & 8 & 0.0588235 & 0.235294 & 5.88235 & 23.5294 & 6 & 18 \\\\\n",
" 4 & 3 & 11 & 0.0882353 & 0.323529 & 8.82353 & 32.3529 & 12 & 48 \\\\\n",
" 5 & 6 & 17 & 0.176471 & 0.5 & 17.6471 & 50 & 30 & 150 \\\\\n",
" 6 & 5 & 22 & 0.147059 & 0.647059 & 14.7059 & 64.7059 & 30 & 180 \\\\\n",
" 7 & 6 & 28 & 0.176471 & 0.823529 & 17.6471 & 82.3529 & 42 & 294 \\\\\n",
" 8 & 3 & 31 & 0.0882353 & 0.911765 & 8.82353 & 91.1765 & 24 & 192 \\\\\n",
" 9 & 2 & 33 & 0.0588235 & 0.970588 & 5.88235 & 97.0588 & 18 & 162 \\\\\n",
" 10 & 1 & 34 & 0.0294118 & 1 & 2.94118 & 100 & 10 & 100 \\\\\n",
" nan & 34 & nan & 1 & nan & 100 & nan & 180 & 1158 \\\\\n",
"\\hline\n",
"\\end{tabular}$\\\\ \\includegraphics[width=1\\columnwidth]{diagrama0} \\\\ $\\left\\{ Me : 5.5, \\ Mo : \\left( [5], \\ [6]\\right), \\ media : 5.29\\right\\}$ \\\\$\\left\\{ D4 : 5.0, \\ P70 : 7.0, \\ Q1 : 4.0, \\ Q3 : 7.0\\right\\}$ \\\\$\\left\\{ C.V : 0.46, \\ desv.tip : 2.46, \\ rango : 10, \\ var : 6.03\\right\\}$\n"
]
}
],
"source": [
"lista_problemas = [\n",
" ['p089e01','Las calificaciones de un grupo de 34 alumnos han sido: ',r\"9 6 5 0 1 5 7 9 10 7 5 1 2 5 7 6 3 4 6 8 8 6 4 4 6 5 3 5 7 7 8 7 2 2\"], \n",
" ]\n",
"for i,p in enumerate(lista_problemas):\n",
" texto_ejercicio, enunciado_latex, enunciado, solucion, tabla, n_ejercicio = analisis_discreto(texto_ejercicio=p[1], datos=texto_a_datos(p[2]), n_ejercicio=str(i))\n",
" mostrar_ejercicio(enunciado,solucion)\n",
"# datos = np.loadtxt(p[2].split())\n",
"# datos = datos.astype(int)\n",
"# display(datos)\n",
"# plt.boxplot(datos, vert=False)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 9, 6, 5, 0, 1, 5, 7, 9, 10, 7, 5, 1, 2, 5, 7, 6, 3,\n",
" 4, 6, 8, 8, 6, 4, 4, 6, 5, 3, 5, 7, 7, 8, 7, 2, 2])"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"array([10, 15, 11, 11, 14, 14, 11, 14, 17, 11, 17, 15, 10, 16, 12, 12, 13,\n",
" 16, 13, 16, 18, 12, 18, 16])"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"array([174, 157, 168, 166, 169, 168, 173, 184, 176, 171, 172, 168, 167,\n",
" 162, 162, 163, 166, 166, 167, 167, 174, 159, 170, 172, 173, 164,\n",
" 161, 163, 176, 177])"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"array([16, 11, 17, 12, 10, 5, 1, 8, 10, 14, 15, 20, 10, 3, 8, 10, 2,\n",
" 5, 12, 6, 16, 7, 6, 16, 10, 3, 3, 9, 4, 12])"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"array([4, 2, 6, 8, 3, 4, 3, 5, 7, 1, 3, 4, 5, 7, 2, 2, 1, 3, 4, 5])"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"lista_problemas = [\n",
" ['p089e01','Las calificaciones de un grupo de 34 alumnos han sido: ',r\"9 6 5 0 1 5 7 9 10 7 5 1 2 5 7 6 3 4 6 8 8 6 4 4 6 5 3 5 7 7 8 7 2 2\"],\n",
" ['p089e03','Estos datos reflejan el tiempo, en minutos, que tardan en llegar a su centro escolar varios alumnos. ',r\"10 15 11 11 14 14 11 14 17 11 17 15 10 16 12 12 13 16 13 16 18 12 18 16\"],\n",
" ['p089e04','La altura en cm de 30 alumnos de un curso son:',r\"\"\"174 157 168 166 169 168 173 184 176 171 172 168 \n",
" 167 162 162 163 166 166 167 167 \n",
" 174 159 170 172 173 164 161 163 176 177\"\"\"],\n",
" ['p089e02', \"\"\"En un grupo de personas de 1º de Bachillerato hemos preguntado por el número medio de días que\n",
" practican deporte al mes. Las respuestas han sido las siguientes:\"\"\",\n",
" \"\"\"16 11 17 12 10 5 1 8 10 14 15 20 10 3 8 10 2 5 12 6 16 7 6 16 10 3 3 9 4 12\"\"\"],\n",
" ['autoevaluacion1',\"\"\"Se realiza una encuesta a un grupo de 20 personas acerca del número \n",
" de veces que acuden al cine a lo largo de un año, obteniéndose los siguientes resultados:\"\"\",\n",
" r\"\"\"4 2 6 8 3 4 3 5 7 1 3 4 5 7 2 2 1 3 4 5\"\"\"] \n",
" ]\n",
"for i,p in enumerate(lista_problemas):\n",
" texto_ejercicio, enunciado_latex, enunciado, solucion, tabla, n_ejercicio = analisis_discreto(texto_ejercicio=p[1], str_datos=p[2], n_ejercicio=p[0])\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Funciones\n",
"\n",
"def dibujar_ejes(size=10) :\n",
"# xs = [0, 2, -3, -1.5]\n",
"# ys = [0, 3, 1, -2.5]\n",
"# colors = ['m', 'g', 'r', 'b']\n",
"\n",
" # Select length of axes and the space between tick labels\n",
" xmin, xmax, ymin, ymax = -size, size , -size, size\n",
" ticks_frequency = 1\n",
" \n",
"\n",
" # Plot points\n",
" fig, ax = plt.subplots(figsize=(10, 10))\n",
"# ax.scatter(xs, ys, c=colors)\n",
"# # Draw lines connecting points to axes\n",
"# for x, y, c in zip(xs, ys, colors):\n",
"# ax.plot([x, x], [0, y], c=c, ls='--', lw=1.5, alpha=0.5)\n",
"# ax.plot([0, x], [y, y], c=c, ls='--', lw=1.5, alpha=0.5)\n",
"\n",
" # Set identical scales for both axes\n",
" ax.set(xlim=(xmin-1, xmax+1), ylim=(ymin-1, ymax+1), aspect='equal')\n",
"\n",
" # Set bottom and left spines as x and y axes of coordinate system\n",
" ax.spines['bottom'].set_position('zero')\n",
" ax.spines['left'].set_position('zero')\n",
"\n",
" # Remove top and right spines\n",
" ax.spines['top'].set_visible(False)\n",
" ax.spines['right'].set_visible(False)\n",
"\n",
" # Create 'x' and 'y' labels placed at the end of the axes\n",
" ax.set_xlabel('x', size=14, labelpad=-24, x=1.03)\n",
" ax.set_ylabel('y', size=14, labelpad=-21, y=1.02, rotation=0)\n",
"\n",
" # Create custom major ticks to determine position of tick labels\n",
" x_ticks = np.arange(xmin, xmax+1, ticks_frequency)\n",
" y_ticks = np.arange(ymin, ymax+1, ticks_frequency)\n",
" ax.set_xticks(x_ticks[x_ticks != 0])\n",
" ax.set_yticks(y_ticks[y_ticks != 0])\n",
"\n",
" # Create minor ticks placed at each integer to enable drawing of minor grid\n",
" # lines: note that this has no effect in this example with ticks_frequency=1\n",
" ax.set_xticks(np.arange(xmin, xmax+1), minor=True)\n",
" ax.set_yticks(np.arange(ymin, ymax+1), minor=True)\n",
"\n",
" # Draw major and minor grid lines\n",
" ax.grid(which='both', color='grey', linewidth=1, linestyle='-', alpha=0.2)\n",
"\n",
" return fig, ax\n",
" \n",
"\n",
"\n",
"def añadir_vectores(X,Y,U,V,T,fig,ax, clr='red') :\n",
"# puedes pasar listas o enteros. (X,Y) punto de origen, (u,v) vector\n",
" ax.quiver(X,Y,U,V, angles='xy', scale_units='xy', scale=1, width=0.004, headwidth=3., headlength=4., color=clr)\n",
" if type(X) == list :\n",
" for i in range(len(X)):\n",
" plt.text(X[i]+U[i]/2+0.75,Y[i]+V[i]/2-0.75,r\"$\\overrightarrow{\"+T[i]+r\"}$\",c=clr)\n",
" else :\n",
" plt.text(X+U/2+0.75,Y+V/2-0.75,r\"$\\overrightarrow{\"+T+r\"}$\",c=clr)\n",
"# plt.show()\n",
"# fig.savefig(\"a.png\")\n",
" \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Ejemplo de uso\n",
"\n",
"fig, ax =dibujar_ejes(20) \n",
"\n",
"# datos en listas\n",
"X,Y,U,V,T = [-1,-5],[6,1],[1,8],[-4,2],['u','v'] \n",
"añadir_vectores(X,Y,U,V,T,fig,ax)\n",
"añadir_vectores([10],[10],[-5],[8],['s'],fig,ax)\n",
"# datos en enteros\n",
"añadir_vectores(-10,10,3,2,'t',fig,ax,'b')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"**Representa y calcula las coordenadas de $\\overrightarrow{u}+\\overrightarrow{v}$**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'u'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/latex": [
"$\\displaystyle Point2D\\left(9, -4\\right)$"
],
"text/plain": [
"Point2D(9, -4)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Representar y calcular vectores suma\n",
"\n",
"texto_ejercicio = r'Representa y calcula las coordenadas de $\\overrightarrow{u}+\\overrightarrow{v}$'\n",
"\n",
"display(md(\"**\"+texto_ejercicio+\"**\"))\n",
"\n",
"datos = [ [[1,2,3,4, 'u'],[-10,-5,6,-8, 'v']], \n",
" \n",
" ]\n",
"\n",
"\n",
"for d in datos :\n",
" display(d[0][4])\n",
" fig, ax =dibujar_ejes(20)\n",
" for v in d:\n",
" X, Y, U, V, T = v\n",
" añadir_vectores(X, Y, U, V, T,fig,ax,'b')\n",
" sol=[x + y for x, y in zip(d[0],d[1])][-3:-1]\n",
" U,V=sol\n",
" añadir_vectores(0, 0, U, V, 'u+v',fig,ax,'r')\n",
" display(Point(sol))\n",
" \n",
" \n",
"# p1.save('sistema_ine_ex{}.png'.format(j))\n",
"# p1.show()\n",
"# mostrar_ejercicio(r\"$\"+sist_latex+r\"$\",r\"\"\"\\scalebox{.99}{\\includegraphics[width=1\\columnwidth]{sistema_ine_ex\"\"\"+latex(j)+r\"\"\".png}}\"\"\")\n",
"# lista.append([r\"$\"+sist_latex+r\"$\",r\"\"\"\\scalebox{.99}{\\includegraphics[width=1\\columnwidth]{sistema_ine_ex\"\"\"+latex(j)+r\"\"\".png}}\"\"\"])\n",
" \n",
"# print(latex_exam('Resuelve el siguiente sistema de inecuaciones con dos incógnitas:',lista))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"**Representa y calcula las coordenadas de las siguientes combinaciones de $\\overrightarrow{u}$ y $\\overrightarrow{v}$: **"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'$\\\\overrightarrow{u} + \\\\overrightarrow{v}$, $\\\\overrightarrow{u} + 2 \\\\overrightarrow{v}$, $- 2 \\\\overrightarrow{u}$. Siendo $\\\\overrightarrow{u}$ y $\\\\overrightarrow{v}$: \\\\\\\\\\\\scalebox{.65}{\\\\includegraphics[width=1\\\\columnwidth]{comb_lineal_0.png}} \\\\\\\\ '"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/latex": [
"$\\displaystyle Point2D\\left(9, -4\\right)$"
],
"text/plain": [
"Point2D(9, -4)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/latex": [
"$\\displaystyle Point2D\\left(15, -12\\right)$"
],
"text/plain": [
"Point2D(15, -12)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/latex": [
"$\\displaystyle Point2D\\left(-6, -8\\right)$"
],
"text/plain": [
"Point2D(-6, -8)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"$\\overrightarrow{u} + \\overrightarrow{v}$, $\\overrightarrow{u} + 2 \\overrightarrow{v}$, $- 2 \\overrightarrow{u}$. Siendo $\\overrightarrow{u}$ y $\\overrightarrow{v}$: \\\\\\scalebox{.65}{\\includegraphics[width=1\\columnwidth]{comb_lineal_0.png}} \\\\ $\\to$ \\scalebox{.65}{\\includegraphics[width=1\\columnwidth]{comb_lineal_sol_0.png}}\\\\ $Point2D\\left(9, -4\\right)$, $Point2D\\left(15, -12\\right)$, $Point2D\\left(-6, -8\\right)$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: $\\overrightarrow{u} + \\overrightarrow{v}$, $\\overrightarrow{u} + 2 \\overrightarrow{v}$, $- 2 \\overrightarrow{u}$. Siendo $\\overrightarrow{u}$ y $\\overrightarrow{v}$: \\\\\\scalebox{.65}{\\includegraphics[width=1\\columnwidth]{comb_lineal_0.png}} \\\\ \n",
"solucion_latex: \\scalebox{.65}{\\includegraphics[width=1\\columnwidth]{comb_lineal_sol_0.png}}\\\\ $Point2D\\left(9, -4\\right)$, $Point2D\\left(15, -12\\right)$, $Point2D\\left(-6, -8\\right)$\n"
]
},
{
"data": {
"text/plain": [
"'$\\\\overrightarrow{u} + \\\\overrightarrow{v}$, $\\\\overrightarrow{u} - \\\\overrightarrow{v}$, $\\\\overrightarrow{u} + 2 \\\\overrightarrow{v}$, $2 \\\\overrightarrow{u} - \\\\overrightarrow{v}$, $- 2 \\\\overrightarrow{u}$. Siendo $\\\\overrightarrow{u}$ y $\\\\overrightarrow{v}$: \\\\\\\\\\\\scalebox{.65}{\\\\includegraphics[width=1\\\\columnwidth]{comb_lineal_1.png}} \\\\\\\\ '"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/latex": [
"$\\displaystyle Point2D\\left(1, 7\\right)$"
],
"text/plain": [
"Point2D(1, 7)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/latex": [
"$\\displaystyle Point2D\\left(-7, 1\\right)$"
],
"text/plain": [
"Point2D(-7, 1)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/latex": [
"$\\displaystyle Point2D\\left(5, 10\\right)$"
],
"text/plain": [
"Point2D(5, 10)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAKAAAAAVCAYAAADByOXrAAAHm0lEQVR4nO3af6yXVR0H8NcFGxE/jZa0MgkDLGXCxZWsiSJCP7fMspZDxWqt1bSfK5NKQEv7paY1rLkgTNOMlQlmNmOokD8iKUsoIa/UwEwlBEM0pT8+5/R97nOf53u/3Hvxsnbf23ef537Oec7zOed8zufXuW0LFiwwgAH0Fwb1wzc/hb14fz98e39jGR7FsP4W5ADDNLHnHyw3VCngr1Ln4u8J3JsGaOulMMckuq6X42RFPq3EH4MP4afYhN3YgTuF/M0OXXnuz2MnHsR1eKf6+R+DubgYT+3zbLrHe3AF7sCTSb4fdvPOq/B9bMUedOAyHNxLWTp01ZH8e6Si/zr8DBdieLHhoIrO7WLhL0wDDsJrxQJchcPwpV4Ify4WiU3tDdoTLSvyqViMbViFLTgEpwj535r67K0Zcy8uSLQNI3EETsb78Ov0vLP07leEYizu8Yya4ws4Grvw9yRTMxyOtXg5bsRGvAEfx1vwJjzeC3l2CGUuY1dN/4twN84RawXaSjHg4cJqPIAjSwOchmuEizmkBwL3NTaIEz5KHJiME4ULXFnij8U9OFQcpuWl8fLc/6x6c8fiRzgBN+C9hbaJYoOvwod7MpkWMFMo3iYcLw7XNcLqVuGXmCM2/IoC/xJ8Et/FR3ooS0ei4/bxvQ14CcbjObq6o+we7654eXWiY0r8NsxL7Y8Ll3cfzqwYY6awLF8r8GYn3kU4CtfiH+IkrcUbS2N8NfU/Qpjz5zTM/1xhoW7SWfkI13Blej6hQrbuQoNHhNL9W1jQ1xTaPiDW4fqad/sCq4TXqLLcZYwXyteB75Tazhchwule+Fj1OrwaJ2VGnQLeU/HypEQfLvCG4hYswWj8ID2PxVKcVxoju837CrypiU4UcebwNM5qTMfPMaLQf11qJxR0YeG3WnM8m+h/Ktry3H/b5P1/4jfp+dgC/yRxEO7q5vsvFE5M9FZdD+JOrBGW6Fg9xxBx4M8Tbn0mBnfzzppEZ2dGOQasU8DRwvIQmV7GteKkzVfw6+KUbRSx4mJsT/ysgL8r9M2840RcUmxbLmK3KSL4hh8nec4Uivg9reEgnJGeb6lobzU5ynHT6ESHJfk22D/JR0+QjcVfatofFPs2Ebf18BtjcXWJ9xDOUm8I7k10RmYULWCbhjU6BQtEIrIMm8UGrRRZHrxDBOM36Kx8hKVYIU5Je4HfLlxrMQHJ3zxLZ+UjNhVeXOJXKXJ3uFi4+JtFfFREnvvzOlvnKoxKNCviK8XJ37YPsuxvZBl31LRn/ugejr8Es4QSDsNkEVOOwy9EslT33aeFG0ZnCzhRQ/D5iT4rFvouoe3Xa8QgOdjOlrGMvEHZLA9L31ir4RaGiwx7i1CMMsYnurnEb0+y3V/z7TLOwaeFVT69oj3PfaOu2W1VXyJZoRETb6/om9Ehqgetolly0RfIpaRW4skqLCz9/UeR0OwS67wA76p59wmFJLaogNkFXSaypO5wvCg71LmsVyS6JdGjhcUtWq0piXer+rLIDmHaizJPFpn6nhbk/Bi+lfrPEgtQRqvud4JIPrbi94m3O9GylS5iszj5rWLrPvStQrZwo2raR5b69RWuFAo4o0mfoRprVqmA3bkgIikYiT/UtA8WCvqohqWoSkDqann5GxNwu87K+Xqx2a2430/gUnFCZyV5qtCqAp6baDGzzGOWqwNFzOpm3L5GXvOJNe0TEq2LEXuKvBZ12fUg4fYfKjIy9kUB9wg3WldRnycs4BIN5amK23L8V5V5Tk3ylZViSotyfk4o33qRodUpH60p4FxRbunQua62TcS8kyre6S+sSnSOrpWOESLZ263vs/bpif61pn2ScP/rM2NQgU4RirWh/FYFnhEZzaEKNZ2EWcLldYjaXka7cEMPlHjPqLak0xItW7psaZ5sIt8XRdKxLsnzWJO+ee51CcgYfF0kY9vFdVwxTtwrrPTLRDx7IGCzCGvGiRCkiIXCQi3TNWtfKuYzr8nYR+KlFfzD8O30XHdFmMs++YD8zwW/TiQE61TXyKowX2STK0RpZKuI894saoWzNWKMIcJ1ri+MPyR9936hhGVkBSxbpfz3l0VW+xT+JLJxojyzSNTl7hAJSBkdYrFpzH2niF8IpTxYLPZ04fLXiASmGI9mLMe7xdw3VbT3BU5OPyL7lGRbmp4fw2cK/T8qEr7LxSHcIIr6M4Xrna8rskFqpgOnilBklViLneIW6e1inW7GN2renSP25cbMyAqYN3t9kw+XcZuY2PmibCMJdEESoGgljsKLdLZmmVdX+J2ma8mGsDZnF35DRBkoK2C+oRgsYsAqrNbYuDz3EWkuhHv6V5rPYvxEbGYdlovbmzN0vXnoK0zR9XZpvEal4GGdFTCXzhaJu9+3iXDhcmEFq5KxyWLfVjaRY5VwpVPFARgm1upOUSm5WnVCOUocoBX4W2aW74IH0HN8XhyEdq3F0QcaRovS2Tfx2f0w/tlC+WdoXCr0y/8D/r/iUlFyWtTfgvQQx4na6iX7Yeyh4oAuV1A+qv8dawA9w9MiRpwp3NKBci3XKm7SvJbZG4wTV6ZLyw0DCti3uD39BtAZG8TtSBcMuOAB9Cv+C+wWyAtdhIhwAAAAAElFTkSuQmCC\n",
"text/latex": [
"$\\displaystyle Point2D\\left(-10, 5\\right)$"
],
"text/plain": [
"Point2D(-10, 5)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/latex": [
"$\\displaystyle Point2D\\left(6, -8\\right)$"
],
"text/plain": [
"Point2D(6, -8)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"$\\overrightarrow{u} + \\overrightarrow{v}$, $\\overrightarrow{u} - \\overrightarrow{v}$, $\\overrightarrow{u} + 2 \\overrightarrow{v}$, $2 \\overrightarrow{u} - \\overrightarrow{v}$, $- 2 \\overrightarrow{u}$. Siendo $\\overrightarrow{u}$ y $\\overrightarrow{v}$: \\\\\\scalebox{.65}{\\includegraphics[width=1\\columnwidth]{comb_lineal_1.png}} \\\\ $\\to$ \\scalebox{.65}{\\includegraphics[width=1\\columnwidth]{comb_lineal_sol_1.png}}\\\\ $Point2D\\left(1, 7\\right)$, $Point2D\\left(-7, 1\\right)$, $Point2D\\left(5, 10\\right)$, $Point2D\\left(-10, 5\\right)$, $Point2D\\left(6, -8\\right)$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: $\\overrightarrow{u} + \\overrightarrow{v}$, $\\overrightarrow{u} - \\overrightarrow{v}$, $\\overrightarrow{u} + 2 \\overrightarrow{v}$, $2 \\overrightarrow{u} - \\overrightarrow{v}$, $- 2 \\overrightarrow{u}$. Siendo $\\overrightarrow{u}$ y $\\overrightarrow{v}$: \\\\\\scalebox{.65}{\\includegraphics[width=1\\columnwidth]{comb_lineal_1.png}} \\\\ \n",
"solucion_latex: \\scalebox{.65}{\\includegraphics[width=1\\columnwidth]{comb_lineal_sol_1.png}}\\\\ $Point2D\\left(1, 7\\right)$, $Point2D\\left(-7, 1\\right)$, $Point2D\\left(5, 10\\right)$, $Point2D\\left(-10, 5\\right)$, $Point2D\\left(6, -8\\right)$\n",
"\\question Representa y calcula las coordenadas de las siguientes combinaciones de $\\overrightarrow{u}$ y $\\overrightarrow{v}$: \\begin{parts} \\part[1] $\\overrightarrow{u} + \\overrightarrow{v}$, $\\overrightarrow{u} + 2 \\overrightarrow{v}$, $- 2 \\overrightarrow{u}$. Siendo $\\overrightarrow{u}$ y $\\overrightarrow{v}$: \\\\\\scalebox{.65}{\\includegraphics[width=1\\columnwidth]{comb_lineal_0.png}} \\\\ \\begin{solution} \\scalebox{.65}{\\includegraphics[width=1\\columnwidth]{comb_lineal_sol_0.png}}\\\\ $Point2D\\left(9, -4\\right)$, $Point2D\\left(15, -12\\right)$, $Point2D\\left(-6, -8\\right)$\\end{solution} \\part[1] $\\overrightarrow{u} + \\overrightarrow{v}$, $\\overrightarrow{u} - \\overrightarrow{v}$, $\\overrightarrow{u} + 2 \\overrightarrow{v}$, $2 \\overrightarrow{u} - \\overrightarrow{v}$, $- 2 \\overrightarrow{u}$. Siendo $\\overrightarrow{u}$ y $\\overrightarrow{v}$: \\\\\\scalebox{.65}{\\includegraphics[width=1\\columnwidth]{comb_lineal_1.png}} \\\\ \\begin{solution} \\scalebox{.65}{\\includegraphics[width=1\\columnwidth]{comb_lineal_sol_1.png}}\\\\ $Point2D\\left(1, 7\\right)$, $Point2D\\left(-7, 1\\right)$, $Point2D\\left(5, 10\\right)$, $Point2D\\left(-10, 5\\right)$, $Point2D\\left(6, -8\\right)$\\end{solution} \\end{parts} \n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Representar y calcular vectores combinaciones lineales\n",
"\n",
"texto_ejercicio = r'Representa y calcula las coordenadas de las siguientes combinaciones de $\\overrightarrow{u}$ y $\\overrightarrow{v}$: '\n",
"\n",
"display(md(\"**\"+texto_ejercicio+\"**\"))\n",
"\n",
"datos = [ [[1,2,3,4, 'u'],[-10,-5,6,-8, 'v'],[(1,1),(1,2),(-2,0)]], \n",
" [[1,2,-3,4, 'u'],[-10,-5,4,3, 'v'],[(1,1),(1,-1),(1,2),(2,-1),(-2,0)]], \n",
" \n",
" ]\n",
"question=texto_ejercicio\n",
"parts=[]\n",
"\n",
"for n, d in enumerate(datos) :\n",
"# display(d[0][4])\n",
"\n",
" fig, ax =dibujar_ejes(20)\n",
" for v in d[:-1]:\n",
" X, Y, U, V, T = v\n",
" añadir_vectores(X, Y, U, V, T,fig,ax,'b')\n",
" plt.savefig('../img/comb_lineal_'+latex(n)+'.png')\n",
"# sol=[x + y for x, y in zip(d[0],d[1])][-3:-1]\n",
"# U,V=sol\n",
"# añadir_vectores(0, 0, U, V, 'u+v',fig,ax,'r')\n",
"\n",
" enun = \"$\"+r\"$, $\". join([latex(c[0]*x+c[1]*y).replace('x',r'\\overrightarrow{u}').replace('y',r'\\overrightarrow{v}') for c in d[-1]])+\"$\"\n",
" enun += r\". Siendo $\\overrightarrow{u}$ y $\\overrightarrow{v}$: \\\\\"\n",
" enun += r\"\\scalebox{.65}{\\includegraphics[width=1\\columnwidth]{comb_lineal_\"+latex(n)+r\".png}} \\\\ \"\n",
"\n",
" display(enun)\n",
" puntos_solu=[]\n",
" for c in d[-1]:\n",
"# display([c[0]*x + c[1]*y for x, y in zip(d[0],d[1])][-3:-1])\n",
" U,V = [c[0]*x + c[1]*y for x, y in zip(d[0],d[1])][-3:-1]\n",
" añadir_vectores(0,0,U,V,latex(c[0]*x+c[1]*y).replace('x','u').replace('y','v'),fig,ax,'g')\n",
" display(Point(U,V))\n",
" puntos_solu.append(Point(U,V))\n",
" plt.savefig('../img/comb_lineal_sol_'+latex(n)+'.png')\n",
" sol = r\"\\scalebox{.65}{\\includegraphics[width=1\\columnwidth]{comb_lineal_sol_\"+latex(n)+r\".png}}\"\n",
" sol += r\"\\\\ $\"+\"$, $\".join(map(latex,puntos_solu))+\"$\"\n",
" mostrar_ejercicio(enun, sol) \n",
" parts.append([enun, sol]) \n",
"\n",
"print(latex_exam(question, parts))\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"**Calcula el punto medio del segmento que une los puntos:**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"$A\\left( -5, \\ 1\\right) y \\ B\\left( 3, \\ 7\\right)$ $\\to$ $M\\left( -1, \\ 4\\right)$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: $A\\left( -5, \\ 1\\right) y \\ B\\left( 3, \\ 7\\right)$ \n",
"solucion_latex: $M\\left( -1, \\ 4\\right)$\n"
]
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"$A\\left( 4, \\ -1\\right) y \\ B\\left( -2, \\ -4\\right)$ $\\to$ $M\\left( 1, \\ - \\frac{5}{2}\\right)$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: $A\\left( 4, \\ -1\\right) y \\ B\\left( -2, \\ -4\\right)$ \n",
"solucion_latex: $M\\left( 1, \\ - \\frac{5}{2}\\right)$\n"
]
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"$A\\left( 1, \\ -5\\right) y \\ B\\left( 5, \\ -3\\right)$ $\\to$ $M\\left( 3, \\ -4\\right)$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: $A\\left( 1, \\ -5\\right) y \\ B\\left( 5, \\ -3\\right)$ \n",
"solucion_latex: $M\\left( 3, \\ -4\\right)$\n",
"\\question Calcula el punto medio del segmento que une los puntos:\\begin{parts} \\part[1] $A\\left( -5, \\ 1\\right) y \\ B\\left( 3, \\ 7\\right)$ \\begin{solution} $M\\left( -1, \\ 4\\right)$\\end{solution} \\part[1] $A\\left( 4, \\ -1\\right) y \\ B\\left( -2, \\ -4\\right)$ \\begin{solution} $M\\left( 1, \\ - \\dfrac{5}{2}\\right)$\\end{solution} \\part[1] $A\\left( 1, \\ -5\\right) y \\ B\\left( 5, \\ -3\\right)$ \\begin{solution} $M\\left( 3, \\ -4\\right)$\\end{solution} \\end{parts} \n"
]
}
],
"source": [
"# Calcular el punto medio\n",
"\n",
"texto_ejercicio = 'Calcula el punto medio del segmento que une los puntos:'\n",
"display(md(\"**\"+texto_ejercicio+\"**\"))\n",
"\n",
"pre_enunciado_latex = [\n",
"# [(-5,0),(3,7)],\n",
" [(-5,1),(3,7)],\n",
" [(4,-1),(-2,-4)],\n",
" [(1,-5),(5,-3)],\n",
"\n",
" \n",
" ]\n",
"question=texto_ejercicio\n",
"parts=[]\n",
"for i in pre_enunciado_latex :\n",
" enun = \"$A\"+latex(i[0])+\" y \\ B\"+latex(i[1])+\"$ \"\n",
"# enun = enun.replace(r'[',r'(').replace(r']',r')')\n",
" sol = \"$M\"+latex(list(Point(i[0]).midpoint(Point(i[1]))))+\"$\"\n",
" sol=sol.replace(r'[',r'(').replace(r']',r')')\n",
" mostrar_ejercicio(enun, sol) \n",
" parts.append([enun, sol]) \n",
"\n",
"print(latex_exam(question, parts).replace(r'\\frac',r'\\dfrac'))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"**Halla el valor de z para que los puntos A , B y C estén alineados. Siendo:**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"$A\\left( 1, \\ -2\\right)$, $B \\left( 3, \\ 1\\right)$ y $C\\left( 4, \\ z\\right)$ $\\to$ $Point2D\\left(2, 3\\right)\\parallel Point2D\\left(3, z + 2\\right) \\to z=\\left[ \\frac{5}{2}\\right]$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: $A\\left( 1, \\ -2\\right)$, $B \\left( 3, \\ 1\\right)$ y $C\\left( 4, \\ z\\right)$ \n",
"solucion_latex: $Point2D\\left(2, 3\\right)\\parallel Point2D\\left(3, z + 2\\right) \\to z=\\left[ \\frac{5}{2}\\right]$\n"
]
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"$A\\left( 2, \\ -4\\right)$, $B \\left( 5, \\ 3\\right)$ y $C\\left( 6, \\ z\\right)$ $\\to$ $Point2D\\left(3, 7\\right)\\parallel Point2D\\left(4, z + 4\\right) \\to z=\\left[ \\frac{16}{3}\\right]$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: $A\\left( 2, \\ -4\\right)$, $B \\left( 5, \\ 3\\right)$ y $C\\left( 6, \\ z\\right)$ \n",
"solucion_latex: $Point2D\\left(3, 7\\right)\\parallel Point2D\\left(4, z + 4\\right) \\to z=\\left[ \\frac{16}{3}\\right]$\n"
]
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"$A\\left( 5, \\ 4\\right)$, $B \\left( -5, \\ -2\\right)$ y $C\\left( 1, \\ z\\right)$ $\\to$ $Point2D\\left(-10, -6\\right)\\parallel Point2D\\left(-4, z - 4\\right) \\to z=\\left[ \\frac{8}{5}\\right]$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: $A\\left( 5, \\ 4\\right)$, $B \\left( -5, \\ -2\\right)$ y $C\\left( 1, \\ z\\right)$ \n",
"solucion_latex: $Point2D\\left(-10, -6\\right)\\parallel Point2D\\left(-4, z - 4\\right) \\to z=\\left[ \\frac{8}{5}\\right]$\n",
"\\question Halla el valor de z para que los puntos A , B y C estén alineados. Siendo:\\begin{parts} \\part[1] $A\\left( 1, \\ -2\\right)$, $B \\left( 3, \\ 1\\right)$ y $C\\left( 4, \\ z\\right)$ \\begin{solution} $Point2D\\left(2, 3\\right)\\parallel Point2D\\left(3, z + 2\\right) \\to z=\\left[ \\frac{5}{2}\\right]$\\end{solution} \\part[1] $A\\left( 2, \\ -4\\right)$, $B \\left( 5, \\ 3\\right)$ y $C\\left( 6, \\ z\\right)$ \\begin{solution} $Point2D\\left(3, 7\\right)\\parallel Point2D\\left(4, z + 4\\right) \\to z=\\left[ \\frac{16}{3}\\right]$\\end{solution} \\part[1] $A\\left( 5, \\ 4\\right)$, $B \\left( -5, \\ -2\\right)$ y $C\\left( 1, \\ z\\right)$ \\begin{solution} $Point2D\\left(-10, -6\\right)\\parallel Point2D\\left(-4, z - 4\\right) \\to z=\\left[ \\frac{8}{5}\\right]$\\end{solution} \\end{parts} \n"
]
}
],
"source": [
"# Calcular puntos alineados\n",
"\n",
"texto_ejercicio = 'Halla el valor de z para que los puntos A , B y C estén alineados. Siendo:'\n",
"display(md(\"**\"+texto_ejercicio+\"**\"))\n",
"\n",
"pre_enunciado_latex = [\n",
"# [(1, -5), (3, 0) , (6,z)],\n",
" [(1, -2), (3, 1) , (4,z)],\n",
" [(2, -4), (5, 3) , (6,z)],\n",
" [(5, 4), (-5, -2) , (1,z)],\n",
" ]\n",
"question=texto_ejercicio\n",
"parts=[]\n",
"for i in pre_enunciado_latex :\n",
" enun = \"$A\"+latex(i[0])+\"$, $B \"+latex(i[1])+\"$ y $C\"+latex(i[2])+\"$ \"\n",
" A,B,C = map(Point,i)\n",
" u=B-A\n",
" v=C-A\n",
" s=solve(u[0]*v[1]-u[1]*v[0],z)\n",
" sol = \"$\"+latex(u)+r\"\\parallel \"+latex(v)+r\" \\to z=\"+latex(s)+\"$\"\n",
"# sol=enun\n",
" mostrar_ejercicio(enun, sol) \n",
" parts.append([enun, sol]) \n",
"\n",
"print(latex_exam(question, parts).replace(r'\\frac',r'\\frac'))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"**Calcula el punto simétrico:**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"De $A\\left( 7, \\ 6\\right)$ respecto de $M\\left( 2, \\ 1\\right)$ $\\to$ $Point2D\\left(\\frac{x}{2} + \\frac{7}{2}, \\frac{y}{2} + 3\\right) = Point2D\\left(2, 1\\right)\\to A'\\left(-3,-4\\right)$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: De $A\\left( 7, \\ 6\\right)$ respecto de $M\\left( 2, \\ 1\\right)$ \n",
"solucion_latex: $Point2D\\left(\\frac{x}{2} + \\frac{7}{2}, \\frac{y}{2} + 3\\right) = Point2D\\left(2, 1\\right)\\to A'\\left(-3,-4\\right)$\n"
]
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"De $A\\left( 5, \\ -3\\right)$ respecto de $M\\left( 1, \\ 3\\right)$ $\\to$ $Point2D\\left(\\frac{x}{2} + \\frac{5}{2}, \\frac{y}{2} - \\frac{3}{2}\\right) = Point2D\\left(1, 3\\right)\\to A'\\left(-3,9\\right)$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: De $A\\left( 5, \\ -3\\right)$ respecto de $M\\left( 1, \\ 3\\right)$ \n",
"solucion_latex: $Point2D\\left(\\frac{x}{2} + \\frac{5}{2}, \\frac{y}{2} - \\frac{3}{2}\\right) = Point2D\\left(1, 3\\right)\\to A'\\left(-3,9\\right)$\n"
]
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"De $A\\left( 6, \\ -5\\right)$ respecto de $M\\left( -3, \\ 2\\right)$ $\\to$ $Point2D\\left(\\frac{x}{2} + 3, \\frac{y}{2} - \\frac{5}{2}\\right) = Point2D\\left(-3, 2\\right)\\to A'\\left(-12,9\\right)$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: De $A\\left( 6, \\ -5\\right)$ respecto de $M\\left( -3, \\ 2\\right)$ \n",
"solucion_latex: $Point2D\\left(\\frac{x}{2} + 3, \\frac{y}{2} - \\frac{5}{2}\\right) = Point2D\\left(-3, 2\\right)\\to A'\\left(-12,9\\right)$\n"
]
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"De $A\\left( -6, \\ -2\\right)$ respecto de $M\\left( 4, \\ 1\\right)$ $\\to$ $Point2D\\left(\\frac{x}{2} - 3, \\frac{y}{2} - 1\\right) = Point2D\\left(4, 1\\right)\\to A'\\left(14,4\\right)$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: De $A\\left( -6, \\ -2\\right)$ respecto de $M\\left( 4, \\ 1\\right)$ \n",
"solucion_latex: $Point2D\\left(\\frac{x}{2} - 3, \\frac{y}{2} - 1\\right) = Point2D\\left(4, 1\\right)\\to A'\\left(14,4\\right)$\n",
"\\question Calcula el punto simétrico:\\begin{parts} \\part[1] De $A\\left( 7, \\ 6\\right)$ respecto de $M\\left( 2, \\ 1\\right)$ \\begin{solution} $Point2D\\left(\\dfrac{x}{2} + \\dfrac{7}{2}, \\dfrac{y}{2} + 3\\right) = Point2D\\left(2, 1\\right)\\to A'\\left(-3,-4\\right)$\\end{solution} \\part[1] De $A\\left( 5, \\ -3\\right)$ respecto de $M\\left( 1, \\ 3\\right)$ \\begin{solution} $Point2D\\left(\\dfrac{x}{2} + \\dfrac{5}{2}, \\dfrac{y}{2} - \\dfrac{3}{2}\\right) = Point2D\\left(1, 3\\right)\\to A'\\left(-3,9\\right)$\\end{solution} \\part[1] De $A\\left( 6, \\ -5\\right)$ respecto de $M\\left( -3, \\ 2\\right)$ \\begin{solution} $Point2D\\left(\\dfrac{x}{2} + 3, \\dfrac{y}{2} - \\dfrac{5}{2}\\right) = Point2D\\left(-3, 2\\right)\\to A'\\left(-12,9\\right)$\\end{solution} \\part[1] De $A\\left( -6, \\ -2\\right)$ respecto de $M\\left( 4, \\ 1\\right)$ \\begin{solution} $Point2D\\left(\\dfrac{x}{2} - 3, \\dfrac{y}{2} - 1\\right) = Point2D\\left(4, 1\\right)\\to A'\\left(14,4\\right)$\\end{solution} \\end{parts} \n"
]
}
],
"source": [
"# Calcular el punto simétrico\n",
"\n",
"texto_ejercicio = 'Calcula el punto simétrico:'\n",
"display(md(\"**\"+texto_ejercicio+\"**\"))\n",
"\n",
"pre_enunciado_latex = [\n",
"# [(-7,-15),(2,0)],\n",
" [(7,6),(2,1)],\n",
" [(5,-3),(1,3)],\n",
" [(6,-5),(-3,2)],\n",
" [(-6,-2),(4,1)],\n",
" \n",
" ]\n",
"question=texto_ejercicio\n",
"parts=[]\n",
"for i in pre_enunciado_latex :\n",
" enun = \"De $A\"+latex(i[0])+\"$ respecto de $M\"+latex(i[1])+\"$ \"\n",
"# enun = enun.replace(r'[',r'(').replace(r']',r')')\n",
" A,M = map(Point, i)\n",
" B=(x,y)\n",
" sol = \"$\"+latex(Eq(A.midpoint(B),M))+r\"\\to A'\\left(\"+\",\".join([latex(i[0]) for i in list(map(solve,list(A.midpoint(B)-M)))])+r\"\\right)$\"\n",
" sol=sol.replace(r'[',r'(').replace(r']',r')')\n",
" mostrar_ejercicio(enun, sol) \n",
" parts.append([enun, sol]) \n",
"\n",
"print(latex_exam(question, parts).replace(r'\\frac',r'\\dfrac'))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"**Halla las coordenadas del punto D, de modo que ABCD sea un paralelogramo siendo**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"Siendo $A$, $B$ y $C$ respectivamente: $\\left( 2, \\ -3\\right) $, $\\left( 0, \\ 1\\right) $, $\\left( 4, \\ 3\\right)$ $\\to$ $\\overrightarrow{AB} = \\overrightarrow{DC} \\to Point2D\\left(-2, 4\\right) = Point2D\\left(4 - x, 3 - y\\right) \\to D\\left( 6, \\ -1\\right)$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: Siendo $A$, $B$ y $C$ respectivamente: $\\left( 2, \\ -3\\right) $, $\\left( 0, \\ 1\\right) $, $\\left( 4, \\ 3\\right)$\n",
"solucion_latex: $\\overrightarrow{AB} = \\overrightarrow{DC} \\to Point2D\\left(-2, 4\\right) = Point2D\\left(4 - x, 3 - y\\right) \\to D\\left( 6, \\ -1\\right)$\n"
]
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"Siendo $A$, $B$ y $C$ respectivamente: $\\left( 1, \\ -1\\right) $, $\\left( 1, \\ 1\\right) $, $\\left( 2, \\ 3\\right)$ $\\to$ $\\overrightarrow{AB} = \\overrightarrow{DC} \\to Point2D\\left(0, 2\\right) = Point2D\\left(2 - x, 3 - y\\right) \\to D\\left( 2, \\ 1\\right)$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: Siendo $A$, $B$ y $C$ respectivamente: $\\left( 1, \\ -1\\right) $, $\\left( 1, \\ 1\\right) $, $\\left( 2, \\ 3\\right)$\n",
"solucion_latex: $\\overrightarrow{AB} = \\overrightarrow{DC} \\to Point2D\\left(0, 2\\right) = Point2D\\left(2 - x, 3 - y\\right) \\to D\\left( 2, \\ 1\\right)$\n"
]
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"Siendo $A$, $B$ y $C$ respectivamente: $\\left( -2, \\ -3\\right) $, $\\left( -2, \\ 2\\right) $, $\\left( 5, \\ 4\\right)$ $\\to$ $\\overrightarrow{AB} = \\overrightarrow{DC} \\to Point2D\\left(0, 5\\right) = Point2D\\left(5 - x, 4 - y\\right) \\to D\\left( 5, \\ -1\\right)$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: Siendo $A$, $B$ y $C$ respectivamente: $\\left( -2, \\ -3\\right) $, $\\left( -2, \\ 2\\right) $, $\\left( 5, \\ 4\\right)$\n",
"solucion_latex: $\\overrightarrow{AB} = \\overrightarrow{DC} \\to Point2D\\left(0, 5\\right) = Point2D\\left(5 - x, 4 - y\\right) \\to D\\left( 5, \\ -1\\right)$\n",
"\\question Halla las coordenadas del punto D, de modo que ABCD sea un paralelogramo siendo\\begin{parts} \\part[1] Siendo $A$, $B$ y $C$ respectivamente: $\\left( 2, \\ -3\\right) $, $\\left( 0, \\ 1\\right) $, $\\left( 4, \\ 3\\right)$\\begin{solution} $\\overrightarrow{AB} = \\overrightarrow{DC} \\to Point2D\\left(-2, 4\\right) = Point2D\\left(4 - x, 3 - y\\right) \\to D\\left( 6, \\ -1\\right)$\\end{solution} \\part[1] Siendo $A$, $B$ y $C$ respectivamente: $\\left( 1, \\ -1\\right) $, $\\left( 1, \\ 1\\right) $, $\\left( 2, \\ 3\\right)$\\begin{solution} $\\overrightarrow{AB} = \\overrightarrow{DC} \\to Point2D\\left(0, 2\\right) = Point2D\\left(2 - x, 3 - y\\right) \\to D\\left( 2, \\ 1\\right)$\\end{solution} \\part[1] Siendo $A$, $B$ y $C$ respectivamente: $\\left( -2, \\ -3\\right) $, $\\left( -2, \\ 2\\right) $, $\\left( 5, \\ 4\\right)$\\begin{solution} $\\overrightarrow{AB} = \\overrightarrow{DC} \\to Point2D\\left(0, 5\\right) = Point2D\\left(5 - x, 4 - y\\right) \\to D\\left( 5, \\ -1\\right)$\\end{solution} \\end{parts} \n"
]
}
],
"source": [
"# Calcular punto de paralelogramo\n",
"\n",
"texto_ejercicio = 'Halla las coordenadas del punto D, de modo que ABCD sea un paralelogramo siendo'\n",
"display(md(\"**\"+texto_ejercicio+\"**\"))\n",
"\n",
"pre_enunciado_latex = [\n",
"# [(1, -1), (0, 2), (6, 5)],\n",
" [(2, -3), (0, 1), (4, 3)],\n",
" [(1, -1), (1, 1), (2, 3)],\n",
" [(-2, -3), (-2, 2), (5,4)],\n",
" ]\n",
"question=texto_ejercicio\n",
"parts=[]\n",
"for i in pre_enunciado_latex :\n",
" enun = \"Siendo $A$, $B$ y $C$ respectivamente: $\"+\" $, $\".join(map(latex,i))+\"$\"\n",
" A, B, C = map(Point,i)\n",
" D = Point(x,y)\n",
" sol=r\"$\\overrightarrow{AB} = \\overrightarrow{DC} \\to \"+latex(Eq(B-A, C-D))+r\" \\to D\"\n",
" sol += latex([i[0] for i in list(map(solve,list(B-A-C+D)))])+\"$\"\n",
" sol=sol.replace(r'[',r'(').replace(r']',r')')\n",
" mostrar_ejercicio(enun, sol) \n",
" parts.append([enun, sol]) \n",
"\n",
"print(latex_exam(question, parts).replace(r'\\frac',r'\\dfrac'))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"**Escribe las ecuaciones vectorial, paramétricas, en forma continua y explícita de la recta que:**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"Pasa por el punto $P$ y tiene por vector dirección $\\overrightarrow{d}$ respectivamente: $\\left( 3, \\ -1\\right) $, $\\left( -2, \\ 5\\right)$ $\\to$ Solución orientativa: $Point2D\\left(x, y\\right) = Point2D\\left(3 - 2 t, 5 t - 1\\right) \\to - 5 x - 2 y + 13 = 0 \\to y = \\frac{13}{2} - \\frac{5 x}{2}$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: Pasa por el punto $P$ y tiene por vector dirección $\\overrightarrow{d}$ respectivamente: $\\left( 3, \\ -1\\right) $, $\\left( -2, \\ 5\\right)$\n",
"solucion_latex: Solución orientativa: $Point2D\\left(x, y\\right) = Point2D\\left(3 - 2 t, 5 t - 1\\right) \\to - 5 x - 2 y + 13 = 0 \\to y = \\frac{13}{2} - \\frac{5 x}{2}$\n"
]
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"Pasa por el punto $P$ y tiene por vector dirección $\\overrightarrow{d}$ respectivamente: $\\left( 1, \\ -3\\right) $, $\\left( 3, \\ -2\\right)$ $\\to$ Solución orientativa: $Point2D\\left(x, y\\right) = Point2D\\left(3 t + 1, - 2 t - 3\\right) \\to 2 x + 3 y + 7 = 0 \\to y = - \\frac{2 x}{3} - \\frac{7}{3}$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: Pasa por el punto $P$ y tiene por vector dirección $\\overrightarrow{d}$ respectivamente: $\\left( 1, \\ -3\\right) $, $\\left( 3, \\ -2\\right)$\n",
"solucion_latex: Solución orientativa: $Point2D\\left(x, y\\right) = Point2D\\left(3 t + 1, - 2 t - 3\\right) \\to 2 x + 3 y + 7 = 0 \\to y = - \\frac{2 x}{3} - \\frac{7}{3}$\n"
]
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"Pasa por el punto $P$ y tiene por vector dirección $\\overrightarrow{d}$ respectivamente: $\\left( 2, \\ 3\\right) $, $\\left( -3, \\ 5\\right)$ $\\to$ Solución orientativa: $Point2D\\left(x, y\\right) = Point2D\\left(2 - 3 t, 5 t + 3\\right) \\to - 5 x - 3 y + 19 = 0 \\to y = \\frac{19}{3} - \\frac{5 x}{3}$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: Pasa por el punto $P$ y tiene por vector dirección $\\overrightarrow{d}$ respectivamente: $\\left( 2, \\ 3\\right) $, $\\left( -3, \\ 5\\right)$\n",
"solucion_latex: Solución orientativa: $Point2D\\left(x, y\\right) = Point2D\\left(2 - 3 t, 5 t + 3\\right) \\to - 5 x - 3 y + 19 = 0 \\to y = \\frac{19}{3} - \\frac{5 x}{3}$\n",
"\\question Escribe las ecuaciones vectorial, paramétricas, en forma continua y explícita de la recta que:\\begin{parts} \\part[1] Pasa por el punto $P$ y tiene por vector dirección $\\overrightarrow{d}$ respectivamente: $\\left( 3, \\ -1\\right) $, $\\left( -2, \\ 5\\right)$\\begin{solution} Solución orientativa: $Point2D\\left(x, y\\right) = Point2D\\left(3 - 2 t, 5 t - 1\\right) \\to - 5 x - 2 y + 13 = 0 \\to y = \\dfrac{13}{2} - \\dfrac{5 x}{2}$\\end{solution} \\part[1] Pasa por el punto $P$ y tiene por vector dirección $\\overrightarrow{d}$ respectivamente: $\\left( 1, \\ -3\\right) $, $\\left( 3, \\ -2\\right)$\\begin{solution} Solución orientativa: $Point2D\\left(x, y\\right) = Point2D\\left(3 t + 1, - 2 t - 3\\right) \\to 2 x + 3 y + 7 = 0 \\to y = - \\dfrac{2 x}{3} - \\dfrac{7}{3}$\\end{solution} \\part[1] Pasa por el punto $P$ y tiene por vector dirección $\\overrightarrow{d}$ respectivamente: $\\left( 2, \\ 3\\right) $, $\\left( -3, \\ 5\\right)$\\begin{solution} Solución orientativa: $Point2D\\left(x, y\\right) = Point2D\\left(2 - 3 t, 5 t + 3\\right) \\to - 5 x - 3 y + 19 = 0 \\to y = \\dfrac{19}{3} - \\dfrac{5 x}{3}$\\end{solution} \\end{parts} \n"
]
}
],
"source": [
"# Calcular rectas\n",
"\n",
"texto_ejercicio = 'Escribe las ecuaciones vectorial, paramétricas, en forma continua y explícita de la recta que:'\n",
"display(md(\"**\"+texto_ejercicio+\"**\"))\n",
"\n",
"pre_enunciado_latex = [\n",
"# [(3, -2), (-1, 5)],\n",
" [(3, -1), (-2, 5)],\n",
" [(1, -3), (3, -2)],\n",
" [(2, 3), (-3, 5)],\n",
" ]\n",
"question=texto_ejercicio\n",
"parts=[]\n",
"for i in pre_enunciado_latex :\n",
" enun = r\"Pasa por el punto $P$ y tiene por vector dirección $\\overrightarrow{d}$ respectivamente: $\"+\" $, $\".join(map(latex,i))+\"$\"\n",
" P, d = map(Point,i)\n",
" r=Line(P, P+d)\n",
" sol=r\"Solución orientativa: $\"+latex(Eq(Point(x,y),(r.arbitrary_point())))+r\" \\to \"\n",
" sol += latex(Eq(r.equation(),0))+r\" \\to \"+latex(Eq(y,solve(r.equation(y=y),y)[0]))+\"$\"\n",
" mostrar_ejercicio(enun, sol) \n",
" parts.append([enun, sol]) \n",
"\n",
"print(latex_exam(question, parts).replace(r'\\frac',r'\\dfrac'))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"**Escribe las ecuaciones vectorial, paramétricas, en forma continua y explícita de la recta que:**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"Pasa por los puntos $P$ y $Q$ respectivamente: $\\left( 2, \\ -1\\right) $, $\\left( -2, \\ 5\\right)$ $\\to$ Solución orientativa: $Point2D\\left(x, y\\right) = Point2D\\left(2 - 4 t, 6 t - 1\\right) \\to - 6 x - 4 y + 8 = 0 \\to y = 2 - \\frac{3 x}{2}$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: Pasa por los puntos $P$ y $Q$ respectivamente: $\\left( 2, \\ -1\\right) $, $\\left( -2, \\ 5\\right)$\n",
"solucion_latex: Solución orientativa: $Point2D\\left(x, y\\right) = Point2D\\left(2 - 4 t, 6 t - 1\\right) \\to - 6 x - 4 y + 8 = 0 \\to y = 2 - \\frac{3 x}{2}$\n"
]
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"Pasa por los puntos $P$ y $Q$ respectivamente: $\\left( 2, \\ -3\\right) $, $\\left( 3, \\ -2\\right)$ $\\to$ Solución orientativa: $Point2D\\left(x, y\\right) = Point2D\\left(t + 2, t - 3\\right) \\to - x + y + 5 = 0 \\to y = x - 5$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: Pasa por los puntos $P$ y $Q$ respectivamente: $\\left( 2, \\ -3\\right) $, $\\left( 3, \\ -2\\right)$\n",
"solucion_latex: Solución orientativa: $Point2D\\left(x, y\\right) = Point2D\\left(t + 2, t - 3\\right) \\to - x + y + 5 = 0 \\to y = x - 5$\n"
]
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"Pasa por los puntos $P$ y $Q$ respectivamente: $\\left( -4, \\ 3\\right) $, $\\left( -3, \\ 5\\right)$ $\\to$ Solución orientativa: $Point2D\\left(x, y\\right) = Point2D\\left(t - 4, 2 t + 3\\right) \\to - 2 x + y - 11 = 0 \\to y = 2 x + 11$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: Pasa por los puntos $P$ y $Q$ respectivamente: $\\left( -4, \\ 3\\right) $, $\\left( -3, \\ 5\\right)$\n",
"solucion_latex: Solución orientativa: $Point2D\\left(x, y\\right) = Point2D\\left(t - 4, 2 t + 3\\right) \\to - 2 x + y - 11 = 0 \\to y = 2 x + 11$\n",
"\\question Escribe las ecuaciones vectorial, paramétricas, en forma continua y explícita de la recta que:\\begin{parts} \\part[1] Pasa por los puntos $P$ y $Q$ respectivamente: $\\left( 2, \\ -1\\right) $, $\\left( -2, \\ 5\\right)$\\begin{solution} Solución orientativa: $Point2D\\left(x, y\\right) = Point2D\\left(2 - 4 t, 6 t - 1\\right) \\to - 6 x - 4 y + 8 = 0 \\to y = 2 - \\dfrac{3 x}{2}$\\end{solution} \\part[1] Pasa por los puntos $P$ y $Q$ respectivamente: $\\left( 2, \\ -3\\right) $, $\\left( 3, \\ -2\\right)$\\begin{solution} Solución orientativa: $Point2D\\left(x, y\\right) = Point2D\\left(t + 2, t - 3\\right) \\to - x + y + 5 = 0 \\to y = x - 5$\\end{solution} \\part[1] Pasa por los puntos $P$ y $Q$ respectivamente: $\\left( -4, \\ 3\\right) $, $\\left( -3, \\ 5\\right)$\\begin{solution} Solución orientativa: $Point2D\\left(x, y\\right) = Point2D\\left(t - 4, 2 t + 3\\right) \\to - 2 x + y - 11 = 0 \\to y = 2 x + 11$\\end{solution} \\end{parts} \n"
]
}
],
"source": [
"# Calcular rectas\n",
"\n",
"texto_ejercicio = 'Escribe las ecuaciones vectorial, paramétricas, en forma continua y explícita de la recta que:'\n",
"display(md(\"**\"+texto_ejercicio+\"**\"))\n",
"\n",
"pre_enunciado_latex = [\n",
"# [(3, -2), (-1, 5)],\n",
" [(2, -1), (-2, 5)],\n",
" [(2, -3), (3, -2)],\n",
" [(-4, 3), (-3, 5)],\n",
" ]\n",
"question=texto_ejercicio\n",
"parts=[]\n",
"for i in pre_enunciado_latex :\n",
" enun = r\"Pasa por los puntos $P$ y $Q$ respectivamente: $\"+\" $, $\".join(map(latex,i))+\"$\"\n",
" P, Q = map(Point,i)\n",
" r=Line(P, Q)\n",
" sol=r\"Solución orientativa: $\"+latex(Eq(Point(x,y),(r.arbitrary_point())))+r\" \\to \"\n",
" sol += latex(Eq(r.equation(),0))+r\" \\to \"+latex(Eq(y,solve(r.equation(y=y),y)[0]))+\"$\"\n",
" mostrar_ejercicio(enun, sol) \n",
" parts.append([enun, sol]) \n",
"\n",
"print(latex_exam(question, parts).replace(r'\\frac',r'\\dfrac'))"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"**Calcula la recta $s$ que:**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"pasa por P$\\left( 3, \\ 1\\right)$ y es paralela a $r \\equiv 4 x - 2 y + 1 = 0$ $\\to$ $s\\equiv y = 2 x - 5$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: pasa por P$\\left( 3, \\ 1\\right)$ y es paralela a $r \\equiv 4 x - 2 y + 1 = 0$\n",
"solucion_latex: $s\\equiv y = 2 x - 5$\n"
]
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"pasa por P$\\left( -1, \\ 2\\right)$ y es paralela a $r \\equiv 2 x - 3 y + 1 = 0$ $\\to$ $s\\equiv y = \\frac{2 x}{3} + \\frac{8}{3}$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: pasa por P$\\left( -1, \\ 2\\right)$ y es paralela a $r \\equiv 2 x - 3 y + 1 = 0$\n",
"solucion_latex: $s\\equiv y = \\frac{2 x}{3} + \\frac{8}{3}$\n",
"\\question Calcula la recta $s$ que:\\begin{parts} \\part[1] pasa por P$\\left( 3, \\ 1\\right)$ y es paralela a $r \\equiv 4 x - 2 y + 1 = 0$\\begin{solution} $s\\equiv y = 2 x - 5$\\end{solution} \\part[1] pasa por P$\\left( -1, \\ 2\\right)$ y es paralela a $r \\equiv 2 x - 3 y + 1 = 0$\\begin{solution} $s\\equiv y = \\dfrac{2 x}{3} + \\dfrac{8}{3}$\\end{solution} \\end{parts} \n"
]
}
],
"source": [
"# Calcular rectas paralelas\n",
"\n",
"texto_ejercicio = 'Calcula la recta $s$ que:'\n",
"display(md(\"**\"+texto_ejercicio+\"**\"))\n",
"\n",
"pre_enunciado_latex = [\n",
"# [(-3, 2), 8*x-3*y+6],\n",
" [(3, 1), 4*x-2*y+1],\n",
" [(-1, 2), 2*x-3*y+1],\n",
"\n",
" \n",
" ]\n",
"question=texto_ejercicio\n",
"parts=[]\n",
"for i in pre_enunciado_latex :\n",
" enun = r\"pasa por P${}$ y es paralela a $r \\equiv {} = 0$\".format(latex(i[0]),latex(i[1]))\n",
" s=Line(i[1]).parallel_line(Point(i[0]))\n",
" sol=r\"$s\\equiv \"+latex(Eq(y,solve(s.equation(y=y),y)[0]))+r\"$\"\n",
" mostrar_ejercicio(enun, sol) \n",
" parts.append([enun, sol]) \n",
"\n",
"print(latex_exam(question, parts).replace(r'\\frac',r'\\dfrac'))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"**Calcula la recta $s$ que:**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"pasa por P$\\left( -1, \\ 2\\right)$ y es perpendicular a $\\overrightarrow{v}\\left( -2, \\ 1\\right)$ $\\to$ $s\\equiv 2 x - y + 4 = 0$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: pasa por P$\\left( -1, \\ 2\\right)$ y es perpendicular a $\\overrightarrow{v}\\left( -2, \\ 1\\right)$\n",
"solucion_latex: $s\\equiv 2 x - y + 4 = 0$\n"
]
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"pasa por P$\\left( 1, \\ -2\\right)$ y es perpendicular a $\\overrightarrow{v}\\left( 5, \\ -4\\right)$ $\\to$ $s\\equiv - 5 x + 4 y + 13 = 0$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: pasa por P$\\left( 1, \\ -2\\right)$ y es perpendicular a $\\overrightarrow{v}\\left( 5, \\ -4\\right)$\n",
"solucion_latex: $s\\equiv - 5 x + 4 y + 13 = 0$\n"
]
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"pasa por P$\\left( 1, \\ -2\\right)$ y es perpendicular a $\\overrightarrow{v}\\left( -1, \\ 0\\right)$ $\\to$ $s\\equiv x - 1 = 0$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: pasa por P$\\left( 1, \\ -2\\right)$ y es perpendicular a $\\overrightarrow{v}\\left( -1, \\ 0\\right)$\n",
"solucion_latex: $s\\equiv x - 1 = 0$\n",
"\\question Calcula la recta $s$ que:\\begin{parts} \\part[1] pasa por P$\\left( -1, \\ 2\\right)$ y es perpendicular a $\\overrightarrow{v}\\left( -2, \\ 1\\right)$\\begin{solution} $s\\equiv 2 x - y + 4 = 0$\\end{solution} \\part[1] pasa por P$\\left( 1, \\ -2\\right)$ y es perpendicular a $\\overrightarrow{v}\\left( 5, \\ -4\\right)$\\begin{solution} $s\\equiv - 5 x + 4 y + 13 = 0$\\end{solution} \\part[1] pasa por P$\\left( 1, \\ -2\\right)$ y es perpendicular a $\\overrightarrow{v}\\left( -1, \\ 0\\right)$\\begin{solution} $s\\equiv x - 1 = 0$\\end{solution} \\end{parts} \n"
]
}
],
"source": [
"# Calcular rectas y cortes\n",
"\n",
"texto_ejercicio = 'Calcula la recta $s$ que:'\n",
"display(md(\"**\"+texto_ejercicio+\"**\"))\n",
"\n",
"pre_enunciado_latex = [\n",
"# [(3,-2), (2,1)],\n",
"# [(3,-2), (-5,4)],\n",
"# [(3,-2), (-1,0)],\n",
" [(-1,2), (-2,1)],\n",
" [(1,-2), (5,-4)],\n",
" [(1,-2), (-1,0)],\n",
" ]\n",
"question=texto_ejercicio\n",
"parts=[]\n",
"for i in pre_enunciado_latex :\n",
" enun = r\"pasa por P$\"+latex(i[0])+\"$ y es perpendicular a $\\overrightarrow{v}\"+latex(i[1])+\"$\"\n",
" p, v = map(Point,i)\n",
" s=Line(p,p+v).perpendicular_line(p)\n",
" sol=r\"$s\\equiv \"+latex(Eq(s.equation(),0))+\"$\"\n",
"# sol+=r\"$\\to s\\equiv \"+latex(Eq(y,solve(s.equation(y=y),y)[0]))+r\"$\"\n",
" mostrar_ejercicio(enun, sol) \n",
" parts.append([enun, sol]) \n",
"\n",
"print(latex_exam(question, parts).replace(r'\\frac',r'\\dfrac'))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"**Calcula la recta $s$ que:**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"pasa por P$\\left( 3, \\ 1\\right)$ y es perpendicular a $r \\equiv 4 x - 2 y + 1 = 0$ $\\to$ $s\\equiv y = \\frac{5}{2} - \\frac{x}{2}$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: pasa por P$\\left( 3, \\ 1\\right)$ y es perpendicular a $r \\equiv 4 x - 2 y + 1 = 0$\n",
"solucion_latex: $s\\equiv y = \\frac{5}{2} - \\frac{x}{2}$\n"
]
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"pasa por P$\\left( -1, \\ 2\\right)$ y es perpendicular a $r \\equiv 2 x - 3 y + 1 = 0$ $\\to$ $s\\equiv y = \\frac{1}{2} - \\frac{3 x}{2}$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: pasa por P$\\left( -1, \\ 2\\right)$ y es perpendicular a $r \\equiv 2 x - 3 y + 1 = 0$\n",
"solucion_latex: $s\\equiv y = \\frac{1}{2} - \\frac{3 x}{2}$\n",
"\\question Calcula la recta $s$ que:\\begin{parts} \\part[1] pasa por P$\\left( 3, \\ 1\\right)$ y es perpendicular a $r \\equiv 4 x - 2 y + 1 = 0$\\begin{solution} $s\\equiv y = \\dfrac{5}{2} - \\dfrac{x}{2}$\\end{solution} \\part[1] pasa por P$\\left( -1, \\ 2\\right)$ y es perpendicular a $r \\equiv 2 x - 3 y + 1 = 0$\\begin{solution} $s\\equiv y = \\dfrac{1}{2} - \\dfrac{3 x}{2}$\\end{solution} \\end{parts} \n"
]
}
],
"source": [
"# Calcular rectas y cortes\n",
"\n",
"texto_ejercicio = 'Calcula la recta $s$ que:'\n",
"display(md(\"**\"+texto_ejercicio+\"**\"))\n",
"\n",
"pre_enunciado_latex = [\n",
"# [(-3, 2), 8*x-3*y+6],\n",
" [(3, 1), 4*x-2*y+1],\n",
" [(-1, 2), 2*x-3*y+1],\n",
"\n",
" ]\n",
"question=texto_ejercicio\n",
"parts=[]\n",
"for i in pre_enunciado_latex :\n",
" enun = r\"pasa por P${}$ y es perpendicular a $r \\equiv {} = 0$\".format(latex(i[0]),latex(i[1]))\n",
" s=Line(i[1]).perpendicular_line(Point(i[0]))\n",
" sol=r\"$s\\equiv \"+latex(Eq(y,solve(s.equation(y=y),y)[0]))+r\"$\"\n",
" mostrar_ejercicio(enun, sol) \n",
" parts.append([enun, sol]) \n",
"\n",
"print(latex_exam(question, parts).replace(r'\\frac',r'\\dfrac'))"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/markdown": [
"**Obtén las ecuaciones de las rectas $r$ y $s$ y su punto de intersección sabiendo que:**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"r pasa por $\\left( 1, \\ -2\\right)$ y es perpendicular a $6 x - 3 y + 6 = 0$. Y s pasa por $\\left( 3, \\ 1\\right)$ y es paralela a $2 x + y - 7= 0$ $\\to$ Solución: \\\\ $r\\equiv y = - \\frac{x}{2} - \\frac{3}{2}$ \\\\ $s\\equiv y = 7 - 2 x\\to $$\\left[ Point2D\\left(\\frac{17}{3}, - \\frac{13}{3}\\right)\\right] $ "
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: r pasa por $\\left( 1, \\ -2\\right)$ y es perpendicular a $6 x - 3 y + 6 = 0$. Y s pasa por $\\left( 3, \\ 1\\right)$ y es paralela a $2 x + y - 7= 0$\n",
"solucion_latex: Solución: \\\\ $r\\equiv y = - \\frac{x}{2} - \\frac{3}{2}$ \\\\ $s\\equiv y = 7 - 2 x\\to $$\\left[ Point2D\\left(\\frac{17}{3}, - \\frac{13}{3}\\right)\\right] $ \n"
]
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"r pasa por $\\left( 1, \\ 3\\right)$ y es perpendicular a $4 x - 2 y + 1 = 0$. Y s pasa por $\\left( 3, \\ 1\\right)$ y es paralela a $2 x + y - 3= 0$ $\\to$ Solución: \\\\ $r\\equiv y = \\frac{7}{2} - \\frac{x}{2}$ \\\\ $s\\equiv y = 7 - 2 x\\to $$\\left[ Point2D\\left(\\frac{7}{3}, \\frac{7}{3}\\right)\\right] $ "
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: r pasa por $\\left( 1, \\ 3\\right)$ y es perpendicular a $4 x - 2 y + 1 = 0$. Y s pasa por $\\left( 3, \\ 1\\right)$ y es paralela a $2 x + y - 3= 0$\n",
"solucion_latex: Solución: \\\\ $r\\equiv y = \\frac{7}{2} - \\frac{x}{2}$ \\\\ $s\\equiv y = 7 - 2 x\\to $$\\left[ Point2D\\left(\\frac{7}{3}, \\frac{7}{3}\\right)\\right] $ \n",
"\\question Obtén las ecuaciones de las rectas $r$ y $s$ y su punto de intersección sabiendo que:\\begin{parts} \\part[1] r pasa por $\\left( 1, \\ -2\\right)$ y es perpendicular a $6 x - 3 y + 6 = 0$. Y s pasa por $\\left( 3, \\ 1\\right)$ y es paralela a $2 x + y - 7= 0$\\begin{solution} Solución: \\\\ $r\\equiv y = - \\dfrac{x}{2} - \\dfrac{3}{2}$ \\\\ $s\\equiv y = 7 - 2 x\\to $$\\left[ Point2D\\left(\\dfrac{17}{3}, - \\dfrac{13}{3}\\right)\\right] $ \\end{solution} \\part[1] r pasa por $\\left( 1, \\ 3\\right)$ y es perpendicular a $4 x - 2 y + 1 = 0$. Y s pasa por $\\left( 3, \\ 1\\right)$ y es paralela a $2 x + y - 3= 0$\\begin{solution} Solución: \\\\ $r\\equiv y = \\dfrac{7}{2} - \\dfrac{x}{2}$ \\\\ $s\\equiv y = 7 - 2 x\\to $$\\left[ Point2D\\left(\\dfrac{7}{3}, \\dfrac{7}{3}\\right)\\right] $ \\end{solution} \\end{parts} \n"
]
}
],
"source": [
"# Calcular rectas y cortes\n",
"\n",
"texto_ejercicio = 'Obtén las ecuaciones de las rectas $r$ y $s$ y su punto de intersección sabiendo que:'\n",
"display(md(\"**\"+texto_ejercicio+\"**\"))\n",
"\n",
"pre_enunciado_latex = [\n",
"# [(-3, 2), 8*x-3*y+6, (9, -5/2), 2*x+y-7],\n",
" [(1, -2), 6*x-3*y+6, (3, 1), 2*x+y-7],\n",
" [(1, 3), 4*x-2*y+1, (3, 1), 2*x+y-3],\n",
" \n",
" ]\n",
"question=texto_ejercicio\n",
"parts=[]\n",
"for i in pre_enunciado_latex :\n",
" enun = r\"r pasa por ${}$ y es perpendicular a ${} = 0$. Y s pasa por ${}$ y es paralela a ${}= 0$\".format(latex(i[0]),latex(i[1]),latex(i[2]),latex(i[3]))\n",
" r=Line(i[1]).perpendicular_line(Point(i[0]))\n",
" Eq(y,solve(r.equation(y=y),y)[0])\n",
"\n",
" s=Line(i[3]).parallel_line(Point(i[2]))\n",
" Eq(y,solve(s.equation(y=y),y)[0])\n",
" sol=r\"Solución: \\\\ $r\\equiv \"+latex(Eq(y,solve(r.equation(y=y),y)[0]))+r\"$ \\\\ $s\\equiv \"+latex(Eq(y,solve(s.equation(y=y),y)[0]))+r\"\\to $$\"+latex(r.intersection(s))+r\" $ \"\n",
" mostrar_ejercicio(enun, sol) \n",
" parts.append([enun, sol]) \n",
"\n",
"print(latex_exam(question, parts).replace(r'\\frac',r'\\dfrac'))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"**Calcula la distancia entre $P$ y $Q$ siendo:**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"Siendo $P\\left( -2, \\ 0\\right)$ y $Q\\left( 12, \\ 0\\right)$ $\\to$ $dist(P,Q)=|Point2D\\left(14, 0\\right)|=14$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: Siendo $P\\left( -2, \\ 0\\right)$ y $Q\\left( 12, \\ 0\\right)$\n",
"solucion_latex: $dist(P,Q)=|Point2D\\left(14, 0\\right)|=14$\n"
]
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"Siendo $P\\left( -1, \\ 1\\right)$ y $Q\\left( 3, \\ 1\\right)$ $\\to$ $dist(P,Q)=|Point2D\\left(4, 0\\right)|=4$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: Siendo $P\\left( -1, \\ 1\\right)$ y $Q\\left( 3, \\ 1\\right)$\n",
"solucion_latex: $dist(P,Q)=|Point2D\\left(4, 0\\right)|=4$\n"
]
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"Siendo $P\\left( -2, \\ 2\\right)$ y $Q\\left( 3, \\ -4\\right)$ $\\to$ $dist(P,Q)=|Point2D\\left(5, -6\\right)|=\\sqrt{61}$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: Siendo $P\\left( -2, \\ 2\\right)$ y $Q\\left( 3, \\ -4\\right)$\n",
"solucion_latex: $dist(P,Q)=|Point2D\\left(5, -6\\right)|=\\sqrt{61}$\n",
"\\question Calcula la distancia entre $P$ y $Q$ siendo:\\begin{parts} \\part[1] Siendo $P\\left( -2, \\ 0\\right)$ y $Q\\left( 12, \\ 0\\right)$\\begin{solution} $dist(P,Q)=|Point2D\\left(14, 0\\right)|=14$\\end{solution} \\part[1] Siendo $P\\left( -1, \\ 1\\right)$ y $Q\\left( 3, \\ 1\\right)$\\begin{solution} $dist(P,Q)=|Point2D\\left(4, 0\\right)|=4$\\end{solution} \\part[1] Siendo $P\\left( -2, \\ 2\\right)$ y $Q\\left( 3, \\ -4\\right)$\\begin{solution} $dist(P,Q)=|Point2D\\left(5, -6\\right)|=\\sqrt{61}$\\end{solution} \\end{parts} \n"
]
}
],
"source": [
"# Calcular distancias\n",
"\n",
"texto_ejercicio = 'Calcula la distancia entre $P$ y $Q$ siendo:'\n",
"display(md(\"**\"+texto_ejercicio+\"**\"))\n",
"\n",
"pre_enunciado_latex = [\n",
"# [(3, 5), (3,-7)],\n",
"# [(-8, 3), (-6,1)],\n",
"# [(0, -3), (-5,1)],\n",
"# [(-3, 0), (15,0)],\n",
" [(-2, 0), (12,0)],\n",
" [(-1, 1), (3,1)],\n",
" [(-2, 2), (3,-4)],\n",
" ]\n",
"question=texto_ejercicio\n",
"parts=[]\n",
"for i in pre_enunciado_latex :\n",
" enun = r\"Siendo $P\"+latex(i[0])+\"$ y $Q\"+latex(i[1])+\"$\"\n",
" P,Q=map(Point,i)\n",
" d=P.distance(Q)\n",
" sol=r\"$dist(P,Q)=|\"+latex((Q-P))+\"|=\"+latex(d)+r\"$\"\n",
" mostrar_ejercicio(enun, sol) \n",
" parts.append([enun, sol]) \n",
"\n",
"print(latex_exam(question, parts).replace(r'\\frac',r'\\dfrac'))"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"**Calcula el perímetro del triángulo de vértices $A$, $B$ y $C$ siendo:**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"#### Ejercicio:"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"Siendo $A\\left( -2, \\ 1\\right)$, $B\\left( 4, \\ 1\\right)$ y $C\\left( -1, \\ -2\\right)$ $\\to$ Los lados miden $6$, $\\sqrt{10}$ y $\\sqrt{34}\\to Perímetro\\approx14.99$"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"enunciado_latex: Siendo $A\\left( -2, \\ 1\\right)$, $B\\left( 4, \\ 1\\right)$ y $C\\left( -1, \\ -2\\right)$\n",
"solucion_latex: Los lados miden $6$, $\\sqrt{10}$ y $\\sqrt{34}\\to Perímetro\\approx14.99$\n",
"\\question Calcula el perímetro del triángulo de vértices $A$, $B$ y $C$ siendo:\\begin{parts} \\part[1] Siendo $A\\left( -2, \\ 1\\right)$, $B\\left( 4, \\ 1\\right)$ y $C\\left( -1, \\ -2\\right)$\\begin{solution} Los lados miden $6$, $\\sqrt{10}$ y $\\sqrt{34}\\to Perímetro\\approx14.99$\\end{solution} \\end{parts} \n"
]
}
],
"source": [
"# Calcular distancias de un triángulo\n",
"\n",
"texto_ejercicio = 'Calcula el perímetro del triángulo de vértices $A$, $B$ y $C$ siendo:'\n",
"display(md(\"**\"+texto_ejercicio+\"**\"))\n",
"\n",
"pre_enunciado_latex = [\n",
"# [(-4, 1), (6,3),(-2,-3)],\n",
" [(-2, 1), (4,1),(-1,-2)],\n",
" ]\n",
"question=texto_ejercicio\n",
"parts=[]\n",
"for i in pre_enunciado_latex :\n",
" enun = r\"Siendo $A\"+latex(i[0])+\"$, $B\"+latex(i[1])+\"$ y $C\"+latex(i[2])+\"$\"\n",
" A,B,C=map(Point,i)\n",
" d1=A.distance(B)\n",
" d2=A.distance(C)\n",
" d3=B.distance(C)\n",
" sol=r\"Los lados miden $\"+latex(d1)+\"$, $\"+latex(d2)+\"$ y $\"+latex(d3)+r\"\\to Perímetro\\approx\"+latex(round(d1+d2+d3,2))+\"$\"\n",
"# sol=enun\n",
" mostrar_ejercicio(enun, sol) \n",
" parts.append([enun, sol]) \n",
"\n",
"print(latex_exam(question, parts).replace(r'\\frac',r'\\dfrac'))"
]
}
],
"metadata": {
"colab": {
"name": "7 Sistemas.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.12"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| gpl-3.0 |
Autodesk/molecular-design-toolkit | moldesign/_notebooks/Example 4. HIV Protease bound to an inhibitor.ipynb | 1 | 10729 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<span style=\"float:right\"><a href=\"http://moldesign.bionano.autodesk.com/\" target=\"_blank\" title=\"About\">About</a> <a href=\"https://github.com/autodesk/molecular-design-toolkit/issues\" target=\"_blank\" title=\"Issues\">Issues</a> <a href=\"http://bionano.autodesk.com/MolecularDesignToolkit/explore.html\" target=\"_blank\" title=\"Tutorials\">Tutorials</a> <a href=\"http://autodesk.github.io/molecular-design-toolkit/\" target=\"_blank\" title=\"Documentation\">Documentation</a></span>\n",
"</span>\n",
"\n",
"<br>\n",
"\n",
"<center><h1>Example 4: The Dynamics of HIV Protease bound to a small molecule </h1> </center>\n",
"\n",
"This notebook prepares a co-crystallized protein / small molecule ligand structure from [the PDB database](http://www.rcsb.org/pdb/home/home.do) and prepares it for molecular dynamics simulation. \n",
"\n",
" - _Author_: [Aaron Virshup](https://github.com/avirshup), Autodesk Research<br>\n",
" - _Created on_: August 9, 2016\n",
" - _Tags_: HIV Protease, small molecule, ligand, drug, PDB, MD"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import moldesign as mdt\n",
"import moldesign.units as u"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Contents\n",
"=======\n",
"---\n",
" - [I. The crystal structure](#I.-The-crystal-structure)\n",
" - [A. Download and visualize](#A.-Download-and-visualize)\n",
" - [B. Try assigning a forcefield](#B.-Try-assigning-a-forcefield)\n",
" - [II. Parameterizing a small molecule](#II.-Parameterizing-a-small-molecule)\n",
" - [A. Isolate the ligand](#A.-Isolate-the-ligand)\n",
" - [B. Assign bond orders and hydrogens](#B.-Assign-bond-orders-and-hydrogens)\n",
" - [C. Generate forcefield parameters](#C.-Generate-forcefield-parameters)\n",
" - [III. Prepping the protein](#III.-Prepping-the-protein)\n",
" - [A. Strip waters](#A.-Strip-waters)\n",
" - [B. Histidine](#B.-Histidine)\n",
" - [IV. Prep for dynamics](#IV.-Prep-for-dynamics)\n",
" - [A. Assign the forcefield](#A.-Assign-the-forcefield)\n",
" - [B. Attach and configure simulation methods](#B.-Attach-and-configure-simulation-methods)\n",
" - [D. Equilibrate the protein](#D.-Equilibrate-the-protein)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## I. The crystal structure\n",
"\n",
"First, we'll download and investigate the [3AID crystal structure](http://www.rcsb.org/pdb/explore.do?structureId=3aid).\n",
"\n",
"### A. Download and visualize"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"protease = mdt.from_pdb('3AID')\n",
"protease"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"protease.draw()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### B. Try assigning a forcefield"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This structure is not ready for MD - this command will raise a `ParameterizationError` Exception. After running this calculation, click on the **Errors/Warnings** tab to see why."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"amber_ff = mdt.forcefields.DefaultAmber()\n",
"newmol = amber_ff.create_prepped_molecule(protease)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You should see 3 errors: \n",
" 1. The residue name `ARQ` not recognized\n",
" 1. Atom `HD1` in residue `HIS69`, chain `A` was not recognized\n",
" 1. Atom `HD1` in residue `HIS69`, chain `B` was not recognized\n",
" \n",
"(There's also a warning about bond distances, but these can be generally be fixed with an energy minimization before running dynamics)\n",
"\n",
"We'll start by tackling the small molecule \"ARQ\"."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## II. Parameterizing a small molecule\n",
"We'll use the GAFF (generalized Amber force field) to create force field parameters for the small ligand.\n",
"\n",
"### A. Isolate the ligand\n",
"Click on the ligand to select it, then we'll use that selection to create a new molecule."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sel = mdt.widgets.ResidueSelector(protease)\n",
"sel"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"drugres = mdt.Molecule(sel.selected_residues[0])\n",
"drugres.draw2d(width=700, show_hydrogens=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### B. Assign bond orders and hydrogens\n",
"A PDB file provides only limited information; they often don't provide indicate bond orders, hydrogen locations, or formal charges. These can be added, however, with the `add_missing_pdb_data` tool:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"drugmol = mdt.tools.set_hybridization_and_saturate(drugres)\n",
"drugmol.draw(width=500)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"drugmol.draw2d(width=700, show_hydrogens=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### C. Generate forcefield parameters\n",
"\n",
"We'll next generate forcefield parameters using this ready-to-simulate structure.\n",
"\n",
"**NOTE**: for computational speed, we use the `gasteiger` charge model. This is not advisable for production work! `am1-bcc` or `esp` are far likelier to produce sensible results."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"drug_parameters = mdt.create_ff_parameters(drugmol, charges='gasteiger')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## III. Prepping the protein\n",
"\n",
"Section II. dealt with getting forcefield parameters for an unknown small molecule. Next, we'll prep the other part of the structure.\n",
"\n",
"### A. Strip waters\n",
"\n",
"Waters in crystal structures are usually stripped from a simulation as artifacts of the crystallization process. Here, we'll remove the waters from the protein structure."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dehydrated = mdt.Molecule([atom for atom in protease.atoms if atom.residue.type != 'water'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### B. Histidine\n",
"Histidine is notoriously tricky, because it exists in no less than three different protonation states at biological pH (7.4) - the \"delta-protonated\" form, referred to with residue name `HID`; the \"epsilon-protonated\" form aka `HIE`; and the doubly-protonated form `HIP`, which has a +1 charge. Unfortunately, crystallography isn't usually able to resolve the difference between these three.\n",
"\n",
"Luckily, these histidines are pretty far from the ligand binding site, so their protonation is unlikely to affect the dynamics. We'll therefore use the `guess_histidine_states` function to assign a reasonable starting guess."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mdt.guess_histidine_states(dehydrated)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## IV. Prep for dynamics\n",
"\n",
"With these problems fixed, we can succesfully assigne a forcefield and set up the simulation.\n",
"\n",
"### A. Assign the forcefield\n",
"Now that we have parameters for the drug and have dealt with histidine, the forcefield assignment will succeed:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"amber_ff = mdt.forcefields.DefaultAmber()\n",
"amber_ff.add_ff(drug_parameters)\n",
"sim_mol = amber_ff.create_prepped_molecule(dehydrated)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### B. Attach and configure simulation methods\n",
"\n",
"Armed with the forcefield parameters, we can connect an energy model to compute energies and forces, and an integrator to create trajectories:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sim_mol.set_energy_model(mdt.models.OpenMMPotential, implicit_solvent='obc', cutoff=8.0*u.angstrom)\n",
"sim_mol.set_integrator(mdt.integrators.OpenMMLangevin, timestep=2.0*u.fs)\n",
"sim_mol.configure_methods()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### C. Equilibrate the protein\n",
"The next series of cells first minimize the crystal structure to remove clashes, then heats the system to 300K."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mintraj = sim_mol.minimize()\n",
"mintraj.draw()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"traj = sim_mol.run(20*u.ps)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"viewer = traj.draw(display=True)\n",
"viewer.autostyle()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3"
}
},
"nbformat": 4,
"nbformat_minor": 1
} | apache-2.0 |
thehyve/transmart-api-training | R analysis.ipynb | 1 | 2457781 | null | gpl-3.0 |
wesleyktatum/Nanowire_Measurements | NaRWHAL/Test/.ipynb_checkpoints/WesTestWriting-checkpoint.ipynb | 1 | 2834 | {
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ImportError",
"evalue": "No module named 'background_removal'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-5f4002e88b8f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mbackground_removal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mhistogram_equalization\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mImportError\u001b[0m: No module named 'background_removal'"
]
}
],
"source": [
"import background_removal\n",
"import histogram_equalization\n",
"\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def test_background_removal():\n",
" \"\"\"\n",
" This function tests the background removal function\n",
" \"\"\"\n",
" testdata = np.genfromtxt('../Data/UnbackgroundedTXT/5umUniformNetwork')\n",
" bkgd = background_removal(testdata)\n",
" \n",
" assert bkgd != None, 'Backgrounding has deleted datafile'\n",
" return"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def test_histogram_equalization():\n",
" \"\"\"\n",
" This function tests the histogram equalization function\n",
" \"\"\"\n",
" \n",
" testdata = np.genfromtxt('../Data/UnbackgroundedTXT/5umUniformNetwork')\n",
" \n",
" equ = histogram_equalization(testdata)\n",
" \n",
" assert equ != None, 'Equalization has deleted datafile'\n",
" return"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
newlawrence/poliastro | docs/source/examples/Natural and artificial perturbations.ipynb | 1 | 633712 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Natural and artificial perturbations"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
],
"text/vnd.plotly.v1+html": [
"<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Temporary hack, see https://github.com/poliastro/poliastro/issues/281\n",
"from IPython.display import HTML\n",
"HTML('<script type=\"text/javascript\" src=\"https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js\"></script>')\n",
"\n",
"import numpy as np\n",
"\n",
"from plotly.offline import init_notebook_mode\n",
"init_notebook_mode(connected=True)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import functools\n",
"\n",
"import numpy as np\n",
"from astropy import units as u\n",
"from astropy.time import Time\n",
"from astropy.coordinates import solar_system_ephemeris\n",
"\n",
"from poliastro.twobody.propagation import cowell\n",
"from poliastro.ephem import build_ephem_interpolant\n",
"from poliastro.core.elements import rv2coe\n",
"\n",
"from poliastro.core.util import norm\n",
"from poliastro.util import time_range\n",
"from poliastro.core.perturbations import (\n",
" atmospheric_drag, third_body, J2_perturbation\n",
")\n",
"from poliastro.bodies import Earth, Moon\n",
"from poliastro.twobody import Orbit\n",
"from poliastro.plotting import OrbitPlotter, plot, OrbitPlotter3D"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Atmospheric drag ###\n",
"The poliastro package now has several commonly used natural perturbations. One of them is atmospheric drag! See how one can monitor decay of the near-Earth orbit over time using our new module poliastro.twobody.perturbations!"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"R = Earth.R.to(u.km).value\n",
"k = Earth.k.to(u.km**3 / u.s**2).value\n",
"\n",
"orbit = Orbit.circular(Earth, 250 * u.km, epoch=Time(0.0, format='jd', scale='tdb'))\n",
"\n",
"# parameters of a body\n",
"C_D = 2.2 # dimentionless (any value would do)\n",
"A = ((np.pi / 4.0) * (u.m**2)).to(u.km**2).value # km^2\n",
"m = 100 # kg\n",
"B = C_D * A / m\n",
"\n",
"# parameters of the atmosphere\n",
"rho0 = Earth.rho0.to(u.kg / u.km**3).value # kg/km^3\n",
"H0 = Earth.H0.to(u.km).value\n",
"tof = (100000 * u.s).to(u.day).value\n",
"tr = time_range(0.0, periods=2000, end=tof, format='jd', scale='tdb')\n",
"cowell_with_ad = functools.partial(cowell, ad=atmospheric_drag,\n",
" R=R, C_D=C_D, A=A, m=m, H0=H0, rho0=rho0)\n",
"\n",
"rr = orbit.sample(tr, method=cowell_with_ad)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7ff1bfb52630>]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.ylabel('h(t)')\n",
"plt.xlabel('t, days')\n",
"plt.plot(tr.value, rr.data.norm() - Earth.R)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Evolution of RAAN due to the J2 perturbation ###\n",
"We can also see how the J2 perturbation changes RAAN over time!"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7ff1bfddc400>]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEKCAYAAAA8QgPpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3Xd8VfX9x/HXJ5sAIUDCCoQhYU8JQ3DgxomriFvRH87+1Far1lqtttZZW0dVxD0Kiov+qiIVRKWgDEH2MCgEGWGEPRL4/P64F3qNBMNNbu5N8n4+Hnlwz/d8zz2fe5LwyTnfZe6OiIjIoYqLdgAiIlI1KYGIiEhYlEBERCQsSiAiIhIWJRAREQmLEoiIiIRFCURERMKiBCIiImFRAhERkbAkRDuAipKRkeGtWrWKdhgiIlXKjBkz1rl7ZjjHVpsE0qpVK6ZPnx7tMEREqhQz+z7cY/UIS0REwqIEIiIiYVECERGRsCiBiIhIWJRAREQkLEogIiISFiUQEREJS41PINt3F/PAhwtZsWF7tEMREalSanwC2bSjiFenfMfdY+eh9eFFRMquxieQpvVqcfOJ7ZiwcC0fzFkd7XBERKqMajOVSXlc3r8V78/6gZvfnMXUvPW0zqhNRt1kmtVL4fDs+sTFWbRDFBGJOUogQEJ8HC8P68Nd789lzIx8dhTt2b+vbaM6PDakB12b14tihCIiscci+dzfzAYBfwPigZHu/kCJ/Y8BxwY3U4FG7p4e3PcQcBqBx2zjgRv9IMHm5uZ6RUymuHevs2lHEeu27mLeD5t56KOFrN+2mxev6E3/wzLK/f4iIrHEzGa4e244x0asDcTM4oGngFOATsAFZtYptI673+zuPdy9B/AE8E7w2P7AAKAb0AXoDRwTqVhDxcUZ9WsnkdO4Lmf1zOKfvzySlg1Tuerl6UzNW18ZIYiIVAmRbETvAyx19zx33w2MAgYfpP4FwD+Crx1IAZKAZCARWBPBWEvVsE4yr13Zl2bptbj0+a+4adTXjPpqObuL90YjHBGRmBHJBJIFrAjZzg+W/YSZtQRaAxMA3H0KMBFYFfwa5+4LIhjrQTVKS+HNq4/g7J5ZfLF0Hbe/M4ffvTcnWuGIiMSEWOnGOxQY4+57AMysLdARaE4g6RxnZkeVPMjMhpvZdDObXlBQENEAG9RO4sHzujHtzhO45pjDeHN6Ph/NVbdfEam5IplAVgItQrabB8sOZCj/fXwFcDYw1d23uvtW4EPgiJIHufsId89199zMzLBWZDxkZsavT2pHl6w0fvvuHAq27KqU84qIxJpIJpBpQI6ZtTazJAJJYmzJSmbWAagPTAkpXg4cY2YJZpZIoAE9ao+wSkqMj+OxIT3YuquY616fwbh5q5m9opCVhTso2qO2ERGpGSI2DsTdi83sBmAcgW68L7j7PDO7F5ju7vuSyVBgVIkuumOA44A5BBrUP3L3f0Yq1nDkNK7L/Wd35a735nL1qzP2lzeoncR1Aw9j2IDWGoAoItVaRMeBVKaKGgdyqLbtKmbp2q0UbNlFwdZdfDh3NZ8tLuD0bk356/k9SIiPlWYmEZGfKs84EI1EL6fayQl0b5G+f3to7xY8MymPBz9aSEKc8cgvuiuJiEi1pARSwcyMawcexl53Hh63iNWbd3JMu0YM6tKE1hm1ox2eiEiF0Z/GEXL9sW25/+yuLFmzlQc/Wshpj3/O0rVboh2WiEiFUQKJoAv7ZjPjrhP57NZjSU6I49dvfcOevdWjzUlERAmkEmQ3TOXuMzoze0Uhb3z5fbTDERGpEEoglWRwj2Yc2TaDBz9axLh5q1m3dRfFGjMiIlWYGtEriZnx4HnduGTklz8ZN3Jy5yb86sR2ZNZNjmKEIiKHRgmkEmWl1+LDm47iiyXryN+4g8LtRXy3fhtvz8jn43mreXlYH7pkaeEqEakaNJAwBixZs4XLX5zG5p1FvHplX3qEjCsREYmkmFxQSsoup3FdRl/dj/TURC4Z+SVvfLmc2SsKqS7JXUSqJyWQGNG8fiqjhh9Bs/Ra/PbdOQx+ajK3vz1HSUREYpbaQGJIVnotPrjxKL4t2MroaSt4/otlDMjJ4MzuzaIdmojIT+gOJMbExxntGtflt6d2pFvzetz7z3kUbt8d7bBERH5CCSRGxccZfz6nKxu3F3HX+/PYVbwn2iGJiPyIHmHFsM7N6nHT8Tk8On4x4+evJqNOMnVTEunQpC4X98umV8sG0Q5RRGowJZAY98vjc+iRnc7EhQVs3L6bTTuKmLhoLe9+vZKrj2nDbSd30MJVIhIVSiBVwFE5mRyV898137fvLuaP/1rAs5Py2LhtN38+pxvxSiIiUsmUQKqg1KQE/nRWFzLqJPP4J0vYXbyXu8/oTP3aSdEOTURqkIgmEDMbBPyNwJroI939gRL7HwOODW6mAo3cPT24LxsYCbQgsC76qe7+XSTjrUrMjF+d2I6keOORjxfz3qwfaNuoDk9e2JMOTdKiHZ6I1AARm8rEzOKBxcCJQD4wDbjA3eeXUv+XQE93Hxbc/hT4k7uPN7M6wF53317a+aryVCblNSd/E58tKeDFycuon5rEBzceRaKW0RWRMojVqUz6AEvdPc/ddwOjgMEHqX8B8A8AM+sEJLj7eAB333qw5FHTdW1ej+uPbcufz+nGkrVbeWnyd9EOSURqgEgmkCxgRch2frDsJ8ysJdAamBAsagcUmtk7Zva1mT0cvKORgzihYyNO6NiIR8cvYskaLZ8rIpEVK43oQ4Ex7r5vtFwCcBTQE1gOjAYuB54PPcjMhgPDAbKzsysr1phlZtx3VhfOeOILznxyMrmt6tOwdhLpqUn0bd2AEzs1JkGPtkSkgkTyf5OVBBrA92keLDuQoQQfXwXlA7OCj7+KgfeAw0se5O4j3D3X3XMzMzNL7q6RmtarxbvXDWBwj2Zs2lHEjOUbeWv6Cq59fSZn/X0yKzboSaCIVIxI3oFMA3LMrDWBxDEUuLBkJTPrANQHppQ4Nt3MMt29ADgOqJkt5GFo0SCVB87ttn+7eM9ePpi7mrvem8t5z/yH16/qS9tGdaMYoYhUBxG7AwneOdwAjAMWAG+6+zwzu9fMzgypOhQY5SHdwYKPsm4BPjGzOYABz0Uq1uouIT6OM7s3Y/TV/dizF85/dirzftgU7bBEpIrTioQ1TF7BVi587ksKtu6ifeO6nNq1Cdcf2xYzjWQXqYlitRuvxKA2mXV4/4YBXD/wMFIS43jk48W8OX3Fzx8oIlKCEkgN1DgthV+d1J63r+1Pn1YNePCjRWzcpjVHROTQKIHUYGbGHwZ3ZvOOIu7713wtnysihyRWxoFIlHRsmsY1xxzGkxOXUri9iMOz06lfO4nMOskcmZNBapJ+RETkwPS/g/CrE9uRnBDHq1O/Z8LCtfvL66Yk8PvTO/GL3BYHOVpEaiolECEuzvjl8Tn88vgcdhbtYdOOIvIKtvHXfy/m1jHfsLJwBzcen6OeWiLyI0og8iMpifGkJMbTOC2FPq37cdvb3/DXfy9hZ9FebhvUXklERPZTApFSxccZD53bjeSEOJ6Z9C1Tvl1Hr5YNuLx/K7IbpkY7PBGJMvXCkoOKizP+eFYX/nBmZzDj5SnfccFzU9m2qzjaoYlIlCmByM8yMy7r34r3rx/A6OH9WFm4gycmLI12WCISZUogckhyWzXgvF7NGfl5HgtXb452OCISRUogcsh+e2pH0molctXL03lz+gr+8+06FqzazOadRdEOTUQqkRrR5ZA1qJ3Ei5f35vo3ZvKbMd/sL0+Kj+PcXlncfkpH6tVKjGKEIlIZlEAkLN1bpDPp1mNZtm4ba7fspHB7EVO+Xc8bXy3nq2UbeP2qfjSplxLtMEUkgjSdu1SoqXnrufKlaTSsk8zrV/WlRQN19xWJZZrOXWJGvzYNee2qvhRu3825T/+HpyYuZebyjdEOS0QiQAlEKlzP7PqMGn4ETdNr8fC4RZzz9/8w4rNvox2WiFQwJRCJiE7N0nj/+gHM+N0JHN+hEQ+PW8TiNVuiHZaIVCAlEImohnWSeei8btRNSeTWt2ZTvGdvtEMSkQoS0QRiZoPMbJGZLTWz2w+w/zEzmxX8WmxmhSX2p5lZvpk9Gck4JbIa1knmD2d2Znb+Jv784UJ+KNyhRCJSDUSsG6+ZxQNPAScC+cA0Mxvr7vP31XH3m0Pq/xLoWeJt7gM+i1SMUnlO79aUSYsLeP6LZTz/xTLMoGHtJLpm1eOmE9rRvUV6tEMUkUMUyXEgfYCl7p4HYGajgMHA/FLqXwDcvW/DzHoBjYGPgLC6mEnsMDMePq8bF/drydyVm1i7ZRdrN+/k3wvWcO7T/+H3Z3Ti0iNaRTtMETkEkUwgWcCKkO18oO+BKppZS6A1MCG4HQc8ClwMnFDaCcxsODAcIDs7u0KClsgxM3q0SKdHyN3Gph1F/PrNWfz+/Xms3LiD2wZ1IC5Oa46IVAWx0og+FBjj7nuC29cBH7h7/sEOcvcR7p7r7rmZmZkRD1IqXr1aiTx7SS6X9GvJs5/lcd3rM5m4aC1bNV28SMyL5B3ISiB0Me3mwbIDGQpcH7J9BHCUmV0H1AGSzGyru/+kIV6qvvg4497BnWlSL4VHPl7ER/NWk90glfevH0D92knRDk9EShHJO5BpQI6ZtTazJAJJYmzJSmbWAagPTNlX5u4XuXu2u7cCbgFeUfKo3syM649ty7Q7T+CZiw9n1aYd3Pt/pTWXiUgsiFgCcfdi4AZgHLAAeNPd55nZvWZ2ZkjVocAory6Tckm5ZNRJZlCXplxzzGG8+/VKvsxbH+2QRKQUmkxRYtKO3Xs44S+TSE6M4w3N7CsSMeWZTFHTuUtMqpUUzyO/6M4VL33FEQ98QtO0FBrWSaZR3WRO796Uwd2z1FtLJMp0ByIx7bt123hv1kqWb9jOhm27ySvYxvIN2xnQtiF/v6iXFq4SKafy3IEogUiV4u6MnraCu96fS8emabwyrA/pqeqpJRIurQciNYaZMbRPNs9c3IuFq7Zw/rNTmbtyE3v3Vo8/hESqEiUQqZKO79iYF6/ozcrCHZz+xBd0v/djXpnyXbTDEqlRlECkyhrQNoPPfnMsD53bjY5N0rhn7DwWrNoc7bBEagwlEKnSGtROYkjvFoy4tBdptRK595/zqS7teiKxTglEqoX01CRuOak9U/LW8+rU76MdjkiNoHEgUm1c2Ceb8fPX8Pv35/H2zJU0TUuhXq1EOjVL46yeWeryK1LB1I1XqpWdRXsY8Vke//l2HRu27WbDtiLWbd1Fw9pJPDqkOwPbN4p2iCIxReNAUAKR0n2TX8htb89hyZotPDqkO4N7ZEU7JJGYoXEgIgfRrXk6b17dj96tGnDT6Fm8NvV7NbSLVAAlEKkR6qYk8uIVvTk6J5PfvTeXjr//iEue/5KCLbuiHZpIlaUEIjVGSmI8Iy/L5ZFfdGdo72y+WraBX705S3cjImEqUwIxsyPM7Ckz+8bMCsxsuZl9YGbXm1m9SAcpUlES4+M4r1dz7jmzM3ee1pHPl6zj/Vk/RDsskSrpZxOImX0IXEVgYahBQFOgE/A7IAV4v8QCUSJVwkV9W9K9RTp//Nd8Nm0vinY4IlXOz/bCMrMMd19X3jqRpl5YEo55P2zizCcn0yajNsd1bERaSiKZdZI5MieDZum1oh2eSMRFdEGpfYnBzB5099tKnPhBd78t2slDJFydm9Vj5GW5PPjhQl6c/B27i/cCEB9nXHpES+44pSNJCWoqFDmQQxmJfiJwW4myUw5Qtp+ZDQL+BsQDI939gRL7HwOODW6mAo3cPd3MegBPA2nAHuBP7j76EGIVKbNj2zfi2OAAw51Fe8jfuJ0XJ3/Hi5O/Y8GqzYy4NJe0FI1iFympLI+wrgWuA9oA34bsqgtMdveLSzkuHlhMIPHkA9OAC9x9fin1fwn0dPdhZtYOcHdfYmbNgBlAR3cvLC1OPcKSivbe1yu55a3Z5DSuy8vDetOortZll+on0muivwF8CPwZuD2kfIu7bzjIcX2Ape6eFwxyFDAYOGACAS4A7gZw98X7Ct39BzNbC2QCpSYQkYp2Vs8s6tdO4trXZjDor5/Tu1V9zu6ZxaAuTaMdmkhMKMvD3T3u/p27X+Du34d87U8eZlbnAMdlAStCtvODZT9hZi2B1sCEA+zrAyTx47sfkUpxTLtMRg8/gn5tGvBN/iaueW0mU/PWRzsskZhQlgTyvpk9amZHm1ntfYVm1sbMrjSzfd17y2MoMMbd94QWmllT4FXgCnffW/IgMxtuZtPNbHpBQUE5QxA5sK7N6/H3i3ox4dcDyW6Qym/fncOu4j0/f6BINfezCcTdjwc+Aa4G5pnZZjNbD7wGNAEuc/cxBzh0JdAiZLt5sOxAhgL/CC0wszTgX8Cd7j61lNhGuHuuu+dmZmb+3EcRKZdaSfHcO7gzeQXbeP6LZdEORyTqytQLy90/AD44xPeeBuSYWWsCiWMocGHJSmbWAagPTAkpSwLeBV4pJTmJRMXA9o04uXNjHhu/mOI9TsemadSrlUjz+rU0bkRqnJ9NIGaWfbD97r68lPJiM7uBwAj2eOAFd59nZvcC0919bLDqUGCU/7g72BDgaKChmV0eLLvc3Wf9XLwikfbQud3531Ff85fxi39U3v+whvzhzM7kNK4bpchEKldZuvHOARywkGIn0CuqkbvHRy68slM3XqlsazfvZPXmnWzeUcw3Kwt57rM8dhfv5ZlLenFUjh6pStUQ0fVA3L2ru3cL/tsVOAOYDGwFbgrnpCLVQaO0FLo1T+fInAyuG9iWD288mhYNUhn20jQ+mLMq2uGJRFyZ52gwsxwze4nAmJAZQCd3fyJSgYlUNU3qpTD66iPo1jydG96YyW/GzGbUV8vZuqs42qGJRERZZuPtYmb/AN4G/g10cfeR7q7pS0VKqFcrkVev7MMverXggzmruf2dOQx7cRp792rNEal+ytIGsofAgMB/EZiX6kfc/X8jE9qhURuIxJq9e53Xv/yeu96fx4PnduX83gftjyISFZGeymRYOG8sUtPFxRkX92vJe7N+4MGPFnFSpybUr50U7bBEKkxZpnN/+UDlZpZCoEFdREphZvzp7C6c/vgX/O+or7n9lA40SUuhbkqipomXKu9QpnPfN8PuyQQmPjwJ+Bx4KwJxiVQbHZqk8aezu/Dbd+dy2uNf7C/v1DSNq49pw5ndm2FmB3kHkdhUpgRiZscQGEV+KvAVMABo7e7bIxibSLVxfu9sjszJZOb3G9mwbTfrt+3m43mruXHULCYtLuDBc7uRGK87EqlayjISPR9YTmCBp1vcfYuZLVPyEDk0Wem1yAqZ7uTG43N4YsIS/vrvJWzYtpunL+pFraSYGJcrUiZl+ZNnDNAMOB84Izgjr/okipRTfJxx0wntuP/srkxaXMCFI6fywZxVrCzcEe3QRMqkLCPRbyKwVsejwEBgEZBpZkNKWQdERA7BhX2zeeKCnny7divXvT6Tox+ayISFa6IdlsjP+tlxID85wCyRwPofQ4GT3T0jEoEdKo0DkapuZ9EeFq7ewm1jvmHzziI++fUxpCYdUj8XkUMW0bmwSnL3Inf/J3AH8Hg4JxWRn0pJjKdHi3T+dHYXVm3ayRMTlkY7JJGDOqQEYmaZZnadmX0OTATSIhOWSM2V26oB5/VqznOf5fH5Eq20KbGrLL2w6gLnEOjG2w54h0AX3uYRjk2kxrrz1I7MWlHIJc9/Rb1aiTSsnUSD2kkMbJ/J5QNaUydZj7Yk+soyF9YOAmM/fgd84e5uZnnu3qYyAiwrtYFIdbN1VzHvzMxnyZqtbNy+m5WFO/h6eSHN6qUw8rLedGqmBwBSfuVpAylLArmJQIN5bQLrlo8GxiuBiFS+mcs3cv3rM9m6q5jnL+tNn9YNoh2SVHGRXlDqr+7eDxgcLHoPaGZmt5lZu3BOKiLhOTy7PmOu7U9m3WQuef5LxszIZ8O23dEOS2qoQ+7GC4E1Qgi0iQxx97YVHlUYdAciNcn6rbsY9vJ0Zq8oBOCYdpmMuLQXyQkayS6HplK78QK4+1zgLuCeg9Uzs0FmtsjMlprZ7QfY/5iZzQp+LTazwpB9l5nZkuDXZeHEKVJdNayTzDvX9ueNq/pyzTGHMWlxASMm5UU7LKlhytILKw24HsgCxgLjgRuAXwHfAK+Vclw88BRwIpAPTDOzse4+f18dd785pP4vgZ7B1w2Au4FcAtOmzAgeuzGMzyhSLcXHGf3bZtC/bQbLN2zjqU+XcvbhWTSvnxrt0KSGKMsdyKtAe2AOcBWB8R/nAWe7++CDHNcHWOruee6+GxjFf9tRDuQCAo30EJgyfry7bwgmjfEERr+LyAHceVonDOOu9+ZSvGdvtMORGqIsncnbuHtXADMbCawCst19588cl0VgKdx98oG+B6poZi0JzLc14SDHZh3guOHAcIDsbC0XKjVXVnotbj+lA3ePncexj35Ku0Z1qZeaSPP6qZzdM4vWGbWjHaJUQ2W5Ayna98Ld9wD5ZUgeh2ooMCb4/mXm7iPcPdfdczMzMys4JJGq5bL+rfj7RYfTvnFdVm3ayZd5G3hywhJO/MsknpywhHA6zIgcTFnuQLqb2ebgawNqBbcNcHcvbTTTSqBFyHbzYNmBDCXQzhJ67MASx35ahlhFarRTuzbl1K5N92+v3byT+/61gEc+Xkz+xh386eyuxMdp9UOpGGVZEz3cfoHTgBwza00gIQwl0PX3R8ysA1AfmBJSPA6438zqB7dPIjB5o4gcgkZpKTw+tActG6Ty5MSlFG4v4pEh3TUVilSIiP0UuXuxmd1AIBnEAy+4+zwzuxeY7u5jg1WHAqM85P7a3TeY2X0EkhDAve6+IVKxilRnZsYtJ7enfu0k7vu/+Uy4by1dmqXxqxPbc2ROTKzGIFVUWAMJY5EGEor8vK+Xb+SDOav4YM5qNu8oYsItA8msmxztsCSKKn0goYhUTT2z63PnaZ149co+7Cjaw2P/XhztkKQKUwIRqYHaZNbh4n4tGfXVcr5apqfDEh61pInUULec3J6Ji9Zy5UvTGNyzGQ1Sk6hfO4nclg3okpWGmXprycEpgYjUUHWSE3jtyr7cPXYe73/9A1t3F7OvSfSonAwePq87TeqlRDdIiWlqRBcRAPbuddZt3cXY2T/w2PjF1E1J5OVhfWjfpG60Q5MIUiO6iJRbXJzRKC2Fq45qw1vX9GevO0OencLM5ZrDVA5MCUREfqJTszTevrY/6amJDHlmCheP/JInPlnC7mJN1Cj/pQQiIgfUokEqb1/bn8v7t2L9tt08On4x93+wINphSQxRAhGRUmXUSeZ3p3fiwxuP4uJ+2bw69XuWrt0S7bAkRiiBiEiZ3HxCO1IT4/nde3PZu7d6dL6R8lECEZEyaVgnmd+e1pGpeRu4cfQsxs7+gUmLC1iwarOmiq+hNA5ERMpsaO8WrNq0k2c+/ZZ/zv5hf3nLhqnceWpHTurcJIrRSWXTOBAROWRbdxWzqnAHm3YUsWzdNp7/YhkLV2/h5hPa8b/Ht9Uo9iqkPONAdAciIoesTnICOY0DAwxzWzVgcI8sbn/nGx7792LWb9vFPWd0Jk4LV1V7SiAiUm5JCXE8+ovuZNZJ5tnP8pizchPdm6czJLcFnZqVtmipVHVqRBeRCmFm3HFqR/54Vhe279rDqGnLOe+Z//Ddum3RDk0iRAlERCrUxf1aMu7mo5nw64HEmfH7sfPUS6uaUgIRkYholl6Lm07I4bPFBUxYuDba4UgERDSBmNkgM1tkZkvN7PZS6gwxs/lmNs/M3ggpfyhYtsDMHjd16xCpci7r34rDMmtzxztzmLS4gDWbd2o+rWokYo3oZhYPPAWcCOQD08xsrLvPD6mTA9wBDHD3jWbWKFjeHxgAdAtW/QI4Bvg0UvGKSMVLjI/j6Yt7cfHIL7nsha/2lzdJS+H83i24duBhpCTGRzFCKY9I9sLqAyx19zwAMxsFDAbmh9T5H+Apd98I4O777nMdSAGSAAMSgTURjFVEIqRd47p8eutApuat54fCnWzctpvZ+YX87ZMljJu3mleG9aFRmhauqooimUCygBUh2/lA3xJ12gGY2WQgHrjH3T9y9ylmNhFYRSCBPOnumgZUpIpKTUrguA6Nf1Q2ceFarn9jJuc9M4VXhvWhVUbtKEUn4Yp2I3oCkAMMBC4AnjOzdDNrC3QEmhNIRMeZ2VElDzaz4WY23cymFxQUVGLYIlJex3ZoxOtX9WXLziLOffo//P3TpUxctFYTNVYhkUwgK4EWIdvNg2Wh8oGx7l7k7suAxQQSytnAVHff6u5bgQ+BI0qewN1HuHuuu+dmZmZG5EOISOT0zK7PmGv70zQ9hYc+WsQVL07jzvfmRDssKaNIJpBpQI6ZtTazJGAoMLZEnfcI3H1gZhkEHmnlAcuBY8wswcwSCTSg6xGWSDV0WGYd/nnDkcz+/UlcMaAV//hqBZ8v0ROFqiBiCcTdi4EbgHEE/vN/093nmdm9ZnZmsNo4YL2ZzQcmAre6+3pgDPAtMAeYDcx2939GKlYRiS4zo15qIrcN6kDLhqncM3aeuvtWAZqNV0RiyoSFaxj20nSGH92G35zcnoT4aDfVVm+ajVdEqo3jOjTm/NwWjPgsjxcnLyM9NYn0Won0bdOA4UcdRnbD1GiHKEG6AxGRmOPujJ+/hq9XFFK4fTdrN+9i8rfr2Ovw4LldObtn82iHWG3oDkREqhUz46TOTX60wuGazTu5cdTX3Dx6Nis37uD6Y7VwVbTp4aKIVAmN01J4ZVhfzumZxSMfL+aOd+awdO0WiveosT1adAciIlVGUkIcj/yiO5lpyTw7KY9R01aQUSeJ167qS4cmWriqsukORESqlLg4445TOjLp1oE8dG5gvtXbxnyjEexRoDsQEamSWjasTcuGtYmPM3791mzGzMxnSG6Lnz9QKozuQESkSju7ZxaHZ6fzwIcLWb1pZ7TDqVF0ByIiVVpcnHH/OV057+kpnPiXSXRqlka9Wok0rZfCKV2b0q9Nw2iHWG3pDkREqrwOTdJ4+9r+DOrSBHf4fv123pqRz9ARU7nm1RlOSp1UAAAOfUlEQVRs3VUc7RCrJQ0kFJFqaWfRHl6YvIxHP15Mu8Z1eemK3jTWwlU/UZ6BhLoDEZFqKSUxnusGtuWFy3uzfP02zvn7f5j3w6Zoh1WtKIGISLV2TLtMRg0/gl3Fezjt8S/odd94HvhwIdXl6Us0KYGISLXXtXk9PrrpaO45oxPdW6TzzKRvGTv7h2iHVeUpgYhIjZBRJ5nLB7TmuUtz6ZpVj/s/WMA2Na6XixKIiNQo8XHGPWd2Ys3mXTw+YUm0w6nSNA5ERGqcXi0bMCS3Oc9OyuP7ddtpnVmbuikJdGySxoC2GSQl6G/rslACEZEa6Y9ndSUtJZF/zVnFvxesoTg4l1arhqk8dF53+rRuEOUIY19Ex4GY2SDgb0A8MNLdHzhAnSHAPYATWPv8wmB5NjASaBHcd6q7f1fauTQORETC5e5s372HL5au488fLGBl4Q4eOKcb5/aq/gtXxeSCUmYWDzwFnAjkA9PMbKy7zw+pkwPcAQxw941m1ijkLV4B/uTu482sDqBJ/0UkIsyM2skJnNy5Cf1aN+Ta12fw67dms2bLTq495jAtXFWKSD7o6wMsdfc8d98NjAIGl6jzP8BT7r4RwN3XAphZJyDB3ccHy7e6+/YIxioiAkC91EReuqIPZ3ZvxkMfLeLYRz7lhjdm8m3B1miHFnMimUCygBUh2/nBslDtgHZmNtnMpgYfee0rLzSzd8zsazN7OHhHIyIScUkJcfz1/B48cE5X2jaqy8SFaxn20jR2Fu2JdmgxJdpdDRKAHGAgcAHwnJmlB8uPAm4BegNtgMtLHmxmw81suplNLygoqKyYRaQGiIszhvbJZuRluTx7SS7fr9/OyM/zoh1WTIlkAllJoAF8n+bBslD5wFh3L3L3ZcBiAgklH5gVfPxVDLwHHF7yBO4+wt1z3T03MzMzIh9CROTInAwGdW7CUxO/ZdWmHdEOJ2ZEMoFMA3LMrLWZJQFDgbEl6rxH4O4DM8sg8OgqL3hsupntywrHAfMREYmSO0/riONc/sI0Rk9bzicL1jDj+401+rFWxHphuXuxmd0AjCPQjfcFd59nZvcC0919bHDfSWY2H9gD3Oru6wHM7BbgEwt0f5gBPBepWEVEfk6LBqmMuCSXW96azW1vz9lfXjc5gWsGHsbVR7chIT7arQKVS+uBiIgcguI9e/mhcCcbtu9m7eadvDUjn/Hz1zCwfSZPXXg4tZOr1vhsrQciIlJJEuLjyG6YSo8W6ZzUuQnPXZrLn8/pyudL1nH+iCms3VJz1mWvWqlSRCQGXdAnmyZpKVz3+kxOe/wLTu/WlCPaNOTETo2r9SBE3YGIiFSAYzs04s2rj6BDk7q88eVyhr86g1enfh/tsCJKCUREpIJ0bV6PV6/sy9w/nMyAtg15ZNwiNm7bHe2wIkYJRESkgiXGx/H70zuzdVcxj45fFO1wIkZtICIiEdC+SV0u79+aFyYvo01GHY5ul0FarUTqpyaRWE26+yqBiIhEyK0nt2fxmi3c+3//HQedmhTPoC5N+PVJ7clKrxXF6MpPCUREJEJqJcXzyrA+fL2ikPyN29m8s5gFqzbzzsx8PlmwlqcvOpz+bTOiHWbYNJBQRKSSfb9+G1e9PJ3v1m/j4fO6c1bPkhOVVx4NJBQRqUJaNqzNmGv7c3h2fW4aPYvfvTeH8fPXsGVnUbRDOyRKICIiUVCvViKvXNmHC/q0YNRXK/ifV6Zz6uOfU7i96nT7VQIREYmS5IR4/nxON7655ySevuhwVm7cweOfLI12WGWmBCIiEmWpSQmc0rUpQ3Jb8MqU71i6tmosn6sEIiISI245uT21kuK5dcxsNlSBEezqxisiEiMy6iTz0LnduOEfX3P4feNJT02kXq1EOjSpy2VHtIq5Lr9KICIiMeSUrk35V2ZtPp63hoItuyjcUcTUvPWMm/cll/Rryd1ndIqZhauUQEREYkyHJml0aJK2f3tn0R4e/XgRz32+jBUbt8fMwlWxkcZERKRUKYnx3HlaJ+4/uyufLS5g6IipTF66js1RHjcS/RQmIiJlcmHfbBqnJXPTqFlcNPJL4gzuOr0TVwxoHZV4InoHYmaDzGyRmS01s9tLqTPEzOab2Twze6PEvjQzyzezJyMZp4hIVXF8x8ZM+e3xvHRFb444rCH3f7CA79dvi0osEUsgZhYPPAWcAnQCLjCzTiXq5AB3AAPcvTNwU4m3uQ/4LFIxiohURXWSExjYvhGPDelBQlwcD42LzpojkbwD6QMsdfc8d98NjAIGl6jzP8BT7r4RwN3X7tthZr2AxsDHEYxRRKTKapSWwg3HtaVNRm2iMTFuJNtAsoAVIdv5QN8SddoBmNlkIB64x90/MrM44FHgYuCE0k5gZsOB4QDZ2dkVF7mISBVx/bFto3buaPfCSgBygIHABcBzZpYOXAd84O75BzvY3Ue4e66752ZmZkY8WBER+a9I3oGsBFqEbDcPloXKB7509yJgmZktJpBQjgCOMrPrgDpAkpltdfcDNsSLiEjli+QdyDQgx8xam1kSMBQYW6LOewTuPjCzDAKPtPLc/SJ3z3b3VsAtwCtKHiIisSViCcTdi4EbgHHAAuBNd59nZvea2ZnBauOA9WY2H5gI3Oru6yMVk4iIVBwtaSsiUoNpSVsREal0SiAiIhIWJRAREQlLtWkDMbMC4PtyvEUGsK6CwqkMijeyFG9kKd7IOpR4W7p7WAPpqk0CKS8zmx5uQ1I0KN7IUryRpXgjq7Li1SMsEREJixKIiIiERQnkv0ZEO4BDpHgjS/FGluKNrEqJV20gIiISFt2BiIhIWGp8AinLsrsRPHcLM5sYsqTvjcHye8xspZnNCn6dGnLMHcFYF5nZyT/3OYKTWX4ZLB8dnNiyPDF/Z2ZzgnFND5Y1MLPxZrYk+G/9YLmZ2ePBc39jZoeHvM9lwfpLzOyykPJewfdfGjzWyhFr+5BrOMvMNpvZTbF0fc3sBTNba2ZzQ8oifj1LO0eY8T5sZguDMb1rgSUZMLNWZrYj5Do/E25cB/vsYcQb8e+/mSUHt5cG97cqR7yjQ2L9zsxmxcr1xd1r7BeBRay+BdoAScBsoFMlnr8pcHjwdV1gMYHlf+8BbjlA/U7BGJOB1sHY4w/2OYA3gaHB188A15Yz5u+AjBJlDwG3B1/fDjwYfH0q8CFgQD8CU/cDNADygv/WD76uH9z3VbCuBY89pQK/16uBlrF0fYGjgcOBuZV5PUs7R5jxngQkBF8/GBJvq9B6Jd7nkOIq7bOHGW/Ev/8E1jR6Jvh6KDA63HhL7H8U+H2sXN+afgdSlmV3I8bdV7n7zODrLQRmLc46yCGDgVHuvsvdlwFLCXyGA36O4F8dxwFjgse/DJwVgY8yOPjeJc8xmMBU/O7uU4F0M2sKnAyMd/cNHljOeDwwKLgvzd2neuCn+pUKjPd44Ft3P9hg00q/vu7+GbDhAHFE+nqWdo5DjtfdP/bA7NsAUwms/VOqMOMq7bMfcrwHUZHf/9DPMQY4ft9dQLjxBo8fAvzjYO9Rmde3pieQAy27e7D/wCMmeIvbE/gyWHRD8FbyhZDHC6XFW1p5Q6Aw5Je7Ij6fAx+b2QwLLCkM0NjdVwVfryawln048WYFX5csrwhD+fEvXqxeX6ic61naOcprGIG/ZPdpbWZfm9kkMzsqWBZOXBX9uxrp7//+Y4L7NwXrl8dRwBp3XxJSFtXrW9MTSEwwszrA28BN7r4ZeBo4DOgBrCJw2xorjnT3w4FTgOvN7OjQncG/eGKqa1/wufSZwFvBoli+vj9SGdezos5hZncCxcDrwaJVQLa79wR+BbxhZmmVHdcBVJnvfwkX8OM/gqJ+fWt6AinLsrsRZWaJBJLH6+7+DoC7r3H3Pe6+F3iOwC30weItrXw9gVvRhBLlYXP3lcF/1wLvBmNbs+92N/jv2jDjXcmPH39U1PfjFGCmu68Jxh6z1zeoMq5naecIi5ldDpwOXBT8j4ngo6D1wdczCLQjtAszrgr7Xa2k7//+Y4L76wXrhyX4HucAo0M+R9Svb01PIGVZdjdigs80nwcWuPtfQspDnz2eDezrkTEWGBrs4dGawPrxX1HK5wj+Ik8EzgsefxnwfjnirW1mdfe9JtB4OjcY176eP6HnGAtcGuzh0Q/YFLx9HgecZGb1g48PTgLGBfdtNrN+wWtzaXniDfGjv9xi9fqGqIzrWdo5DpmZDQJ+A5zp7ttDyjPNLD74ug2B65kXZlylffZw4q2M73/o5zgPmLAvsYbpBGChu+9/NBUT17dkq3pN+yLQ+2Axgex9ZyWf+0gCt5DfALOCX6cCrwJzguVjgaYhx9wZjHURIT2USvscBHqOfEWgQfAtILkc8bYh0ANlNjBv33kIPNv9BFgC/BtoECw34KlgTHOA3JD3GhaMaSlwRUh5LoFf6G+BJwkOdi1HzLUJ/OVXL6QsZq4vgcS2Cigi8Nz5ysq4nqWdI8x4lxJ4fr7vZ3hf76Nzgz8ns4CZwBnhxnWwzx5GvBH//gMpwe2lwf1two03WP4ScE2JulG/vhqJLiIiYanpj7BERCRMSiAiIhIWJRAREQmLEoiIiIRFCURERMKiBCJSAcws3cyui3YcIpVJCUSkYqQTmIFVpMZQAhGpGA8Ah1lgXYaHS6tkZvFm9pKZzbXAeg03V2KMIhUq4eeriEgZ3A50cfceP1OvB5Dl7l0g8Ogr4pGJRIjuQEQqVx7QxsyeCM4htTnaAYmESwlEpBJ5YMGn7sCnwDXAyKgGJFIOSiAiFWMLgWWJ9zOzhSUrmVkGEOfubwO/I7B8qUiVpDYQkQrg7uvNbLKZzSWwIt+DBGY4LSkLeNHM9v3xdkdlxShS0TQbr0gEmNnpBKbwfjzasYhEihKIiIiERW0gIiISFiUQEREJixKIiIiERQlERETCogQiIiJhUQIREZGwKIGIiEhY/h/9PNN5CFbOjgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"r0 = np.array([-2384.46, 5729.01, 3050.46]) # km\n",
"v0 = np.array([-7.36138, -2.98997, 1.64354]) # km/s\n",
"k = Earth.k.to(u.km**3 / u.s**2).value\n",
"\n",
"orbit = Orbit.from_vectors(Earth, r0 * u.km, v0 * u.km / u.s)\n",
"\n",
"tof = (48.0 * u.h).to(u.s).value\n",
"rr, vv = cowell(orbit, np.linspace(0, tof, 2000), ad=J2_perturbation, J2=Earth.J2.value, R=Earth.R.to(u.km).value)\n",
"raans = [rv2coe(k, r, v)[3] for r, v in zip(rr, vv)]\n",
"plt.ylabel('RAAN(t)')\n",
"plt.xlabel('t, s')\n",
"plt.plot(np.linspace(0, tof, 2000), raans)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3rd body ###\n",
"Apart from time-independent perturbations such as atmospheric drag, J2/J3, we have time-dependend perturbations. Lets's see how Moon changes the orbit of GEO satellite over time!"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# database keeping positions of bodies in Solar system over time\n",
"solar_system_ephemeris.set('de432s')\n",
"\n",
"j_date = 2454283.0 * u.day # setting the exact event date is important\n",
"\n",
"tof = (60 * u.day).to(u.s).value\n",
"\n",
"# create interpolant of 3rd body coordinates (calling in on every iteration will be just too slow)\n",
"body_r = build_ephem_interpolant(Moon, 28 * u.day, (j_date, j_date + 60 * u.day), rtol=1e-2)\n",
"\n",
"epoch = Time(j_date, format='jd', scale='tdb')\n",
"initial = Orbit.from_classical(Earth, 42164.0 * u.km, 0.0001 * u.one, 1 * u.deg, \n",
" 0.0 * u.deg, 0.0 * u.deg, 0.0 * u.rad, epoch=epoch)\n",
"\n",
"# multiply Moon gravity by 400 so that effect is visible :)\n",
"cowell_with_3rdbody = functools.partial(cowell, rtol=1e-6, ad=third_body,\n",
" k_third=400 * Moon.k.to(u.km**3 / u.s**2).value, \n",
" third_body=body_r)\n",
"\n",
"tr = time_range(j_date.value, periods=1000, end=j_date.value + 60, format='jd', scale='tdb')\n",
"rr = initial.sample(tr, method=cowell_with_3rdbody)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"data": [
{
"line": {
"dash": "solid",
"width": 5
},
"mode": "lines",
"name": "orbit influenced by Moon",
"type": "scatter3d",
"uid": "b4d2ead8-b277-11e8-b505-a0afbda902ef",
"x": [
42159.783599999995,
39176.33313050093,
30648.34603837001,
17783.008684663506,
2401.2249728817246,
-13320.363219830044,
-27157.46178851618,
-37152.860734058035,
-41892.85979071243,
-40707.27937602602,
-33763.61472102067,
-22043.987824470438,
-7205.82947952589,
8651.757978035002,
23285.088474672662,
34623.305235890366,
41061.45649430606,
41688.28390883917,
36414.86483194732,
25987.934440893845,
11883.207397572452,
-3902.9889686326196,
-19137.017670261284,
-31663.77867150318,
-39711.42197154506,
-42141.94065227597,
-38611.52820470689,
-29619.54638639312,
-16437.556020633823,
-930.1668316167464,
14708.798182536722,
28266.38921137929,
37823.76402739217,
42028.10260384963,
40284.14735665196,
32838.864881292495,
20746.12229922393,
5717.60520211664,
-10119.98209269242,
-24525.841792373874,
-35462.179469314986,
-41382.256857506814,
-41448.884585737746,
-35652.63382907132,
-24813.188019914207,
-10463.587567263648,
5366.358490835878,
20436.942366895517,
32615.499083144536,
40178.22648981217,
42054.63384793369,
37978.950923577824,
28528.30802123554,
15040.324988052333,
-575.9243139274541,
-16110.729671893676,
-29366.35706959656,
-38467.78174598678,
-42127.973108672566,
-39829.162862363446,
-31896.63962427619,
-19451.99853009428,
-4255.590717149522,
11542.905030438475,
25708.044279219095,
36235.04986232976,
41633.94556238677,
41140.30826111758,
34824.17954857387,
23579.515448783404,
8998.091964665991,
-6856.622486492597,
-21741.281806367813,
-33550.25493543009,
-40613.38100169364,
-41931.73828483307,
-37318.985638958075,
-27427.33180503151,
-13655.815380528431,
2047.6541298608722,
17461.353140263724,
30404.120479599474,
39044.11158380027,
42158.268325485966,
39305.73809426212,
30890.331497868952,
18103.328150578494,
2754.5259951419785,
-12984.049763362627,
-26885.740267922043,
-36984.106617570185,
-41851.006696940494,
-40798.11732451042,
-33974.426392310525,
-22344.820380821824,
-7554.215667268249,
8305.13805646534,
22989.310557287,
34420.15852537943,
40979.80171441247,
41739.50956364618,
36591.90570845013,
26265.50480790668,
12222.189818315672,
-3550.666289207576,
-18821.189818431718,
-31429.06123917744,
-39591.100844044035,
-42152.89477405712,
-38752.35015462737,
-29870.1457693241,
-16762.62988226459,
-1283.6247575081127,
14376.931303621086,
28003.046212569217,
37666.25884005016,
41998.654683028784,
40386.998153693836,
33059.35011865029,
21053.112587257318,
6067.57971116985,
-9776.517140006938,
-24237.47193157102,
-35269.70075707248,
-41312.85816521044,
-41512.38807107915,
-35840.02641379248,
-25097.990516714875,
-10805.477087570558,
5015.7226075170765,
20127.196429071748,
32390.45198142653,
40069.762461740844,
42078.02784524413,
38130.973858626225,
28787.328574089242,
15369.752527682327,
-222.75083913334484,
-15783.789856430674,
-29111.85169706765,
-38321.783671780126,
-42110.99233903175,
-39943.75661725837,
-32126.394359744776,
-19764.5935707179,
-4606.680772970673,
11202.949968187648,
25427.299399334224,
36053.331269462244,
41576.846243674336,
41216.06294857437,
35021.859783977525,
23871.319284234993,
9342.555748303734,
-6508.161692082818,
-21438.123073192,
-33335.319923987416,
-40516.980531426205,
-41967.58853211467,
-37481.89632801874,
-27694.373213008745,
-13989.1088465023,
1695.2031177060976,
17139.62420764959,
30158.65618624187,
38909.6147788178,
42153.80750065348,
39431.88722050544,
31129.277134389078,
18421.18459327842,
3106.347768738171,
-12648.070793236551,
-26613.117351003904,
-36813.40149250679,
-41806.3238097086,
-40885.803757804955,
-34182.02236228202,
-22642.99860142505,
-7900.777783782384,
7959.231498403331,
22692.97973612324,
34215.39074965476,
40895.491565523356,
41787.70399882338,
36765.610485676654,
26540.282705549398,
12559.013092596731,
-3199.3990688900744,
-18505.17461636978,
-31193.05812202745,
-39468.37407197977,
-42160.94171411494,
-38889.824844918134,
-30117.81184506893,
-17085.25048857724,
-1635.6935089606777,
14045.270100494441,
27738.7591663093,
37506.67005959764,
41966.451603413974,
40486.60130311142,
33276.79468086079,
21357.46896939464,
6415.866883454748,
-9433.629437355505,
-23948.491877172637,
-35075.451563598064,
-41240.85452511276,
-41572.73552417208,
-36024.25734997304,
-25379.961060057998,
-11145.365858370698,
4666.042431657759,
19817.19180511566,
32163.985399014276,
39958.88759959271,
42098.42676178947,
38279.746945955136,
29043.40390583562,
15696.887629397073,
129.12422550038204,
-15456.93987921344,
-28856.289177874904,
-38173.59827332985,
-42091.2050153212,
-40055.0411324366,
-32353.129355330402,
-20074.59152024731,
-4956.1575586313975,
10863.455184112818,
25145.8643400068,
35869.70923389763,
41517.14151295139,
41288.54956415172,
35216.467685193216,
24160.283140811563,
9685.13304449285,
-6160.531453135585,
-21134.62463018192,
-33118.81366355257,
-40418.184694920645,
-42000.325389134414,
-37641.68981054293,
-27958.437332699697,
-14320.240265705359,
1343.947656749712,
16817.918580026373,
29911.987021287598,
38772.948546252475,
42146.401938713214,
39554.877209491075,
31365.140001693726,
18736.61692161505,
3456.639180552086,
-12312.461348758805,
-26339.655726525154,
-36640.7778901453,
-41758.91157807431,
-40970.32427229467,
-34386.477288754366,
-22938.506968516584,
-8245.492763199309,
7614.0494799623375,
22396.19409638966,
34008.98044271151,
40808.69944147032,
41832.747366921016,
36936.18055250649,
26812.147293909245,
12893.740539991019,
-2849.199474825707,
-18189.06881033959,
-30955.70810668819,
-39343.42479013778,
-42165.92256930076,
-39024.18814593672,
-30362.35228147116,
-17405.54481881375,
-1986.3230625686558,
13713.888495461077,
27473.47731377999,
37345.12642779574,
41931.37205221622,
40583.11653208445,
33491.02619272064,
21659.277003005263,
6762.382452309398,
-9091.370189757981,
-23658.896939182643,
-34879.53008333232,
-41166.22276051942,
-41630.03385246661,
-36205.25862238809,
-25659.175518665,
-11483.196414280741,
4317.340723953969,
19506.96098515828,
31936.15000066513,
39845.64859323215,
42115.85645939441,
38425.306072619176,
29296.545916653424,
16021.710914923959,
479.7012174891581,
-15130.235751796714,
-28599.6603509553,
-38023.33894929775,
-42068.53060857178,
-40163.18152320191,
-32576.740578259745,
-20382.069543766145,
-5303.996949331426,
10524.482239195686,
24863.694482957893,
35684.32769643502,
41454.69695052938,
41357.99429244861,
35407.80514853182,
24446.570298990155,
10025.731993351093,
-5813.771873009333,
-20830.747919958718,
-32900.8672142189,
-40316.87081851172,
-42030.12065615351,
-37798.16236319913,
-28219.66905422684,
-14649.078530708031,
993.9012574863758,
16496.221398221784,
29664.20846830073,
38634.06663993009,
42136.183344690005,
39674.622731503536,
31598.03641180598,
19049.553022820193,
3805.4135132217284,
-11977.23574793466,
-26065.407941225487,
-36466.24424481385,
-41708.820347860325,
-41051.67852544202,
-34587.84165202491,
-23231.312556816687,
-8588.381856676839,
7269.62406023295,
22098.96100292243,
33800.99296828227,
40719.39762429853,
41874.758405918255,
37103.54894496078,
27081.164313347082,
13226.339093312305,
-2500.113287279013,
-17872.86235318143,
-30717.136730103404,
-39216.17117831653,
-42168.05187665393,
-39155.26999248814,
-30603.965431113367,
-17723.40218073192,
-2335.518116568542,
13382.768740208663,
27207.33394826103,
37181.539327588616,
41893.620316564105,
40676.35525532079,
33702.256016084735,
21958.383466430783,
7107.177757988002,
-8749.743906296786,
-23368.78507654438,
-34681.891006172715,
-41089.10905201398,
-41684.19772863927,
-36383.19522584402,
-25935.535285809055,
-11819.027872655837,
3969.6337206451512,
19196.562391477153,
31706.940693561788,
39730.12842631006,
42130.2874111128,
38567.733021772365,
29546.694917159697,
16344.257300681445,
828.9326665254841,
-14803.712543856338,
-28342.02093330418,
-37871.03392182038,
-42043.06143545029,
-40268.16482401768,
-32797.28544237618,
-20687.017101904465,
-5650.166319766291,
10186.039824118721,
24580.889694559628,
35497.1583628035,
41389.68806105005,
41424.27369661224,
35596.06078061772,
24730.068757846282,
10364.391741161382,
-5467.889944687463,
-20526.602327932473,
-32681.421014949923,
-40213.24196596971,
-42056.813939380045,
-37951.56932819325,
-28477.886761374368,
-14975.746425792473,
645.095967148338,
16174.616342650708,
29415.263647823987,
38493.115644624544,
42123.03015690611,
39791.29724270873,
31827.80716537975,
19360.077135691066,
4152.600768335257,
-11642.45563502713,
-25790.37200161503,
-36289.9035259951,
-41656.021696088115,
-41129.975346009574,
-34786.03876515555,
-23521.482723176247,
-8929.386107580322,
6925.990028349207,
21801.316843824072,
33591.48581059153,
40627.63246918675,
41913.76788769105,
37267.73867782762,
27347.35090550087,
13556.776925974085,
-2152.1471755384327,
-17556.619748395082,
-30477.3386545952,
-39086.73322667409,
-42167.23852337757,
-39283.22846479495,
-30842.542612005018,
-18038.881173126436,
-2683.258838949601,
13051.982837654843,
26940.27772386088,
37016.06625554717,
41853.050254770336,
40766.55390576576,
33910.28124017386,
22254.93262259079,
7450.167822754703,
-8408.809536233173,
-23078.114668078084,
-34482.670080066564,
-41009.38472781306,
-41735.39965350434,
-36557.86116381084,
-26209.17035124355,
-12152.73618610371,
3622.9566985326537,
18885.98426313966,
31476.465522199145,
39612.28113743487,
42141.85807348376,
38706.937253886,
29793.96352324228,
16664.448300271473,
1176.8139219991406,
-14477.389767100693,
-28083.429686740743,
-37716.69530299971,
-42014.84717268828,
-40369.98490177513,
-33014.80918148177,
-20989.39385950048,
-5994.677777417854,
9848.166196852715,
24297.458596478067,
35308.2707615767,
41322.07656086023,
41487.49970168344,
35781.160356418295,
25010.82727852774,
10701.080035356017,
-5122.941213863042,
-20222.17294631668,
-32460.601328301847,
-40107.198028051076,
-42080.61691621145,
-38101.72130532198,
-28733.267745435696,
-15300.129060195732,
297.55163693633284,
15853.083050953175,
29165.29402133467,
38349.99350734382,
42107.14524093962,
39904.700904017576,
32054.647113136994,
19668.033488878104,
4498.226194748497,
-11308.120660214698,
-25514.65165554077,
-36111.71357769948,
-41600.667021382375,
-41205.12828546132,
-34981.22794567282,
-23808.921934266225,
-9268.545919072882,
6583.163513695248,
21503.328401755458,
33380.45407054175,
40533.48930636017,
41949.74506854782,
37428.833209563934,
27610.64546865204,
13885.086089696433,
-1805.3461443908113,
-17240.377395800915,
-30236.370330404407,
-38955.13290691096,
-42163.576348950155,
-39408.04267166684,
-31078.134318164703,
-18351.965306348186,
-3029.503640728119,
12721.540863323129,
26672.416351493743,
36848.66799369755,
41809.842242855964,
40853.57632413723,
34115.281320113274,
22548.8157389931,
7791.374203465721,
-8068.569423469403,
-22787.00479188802,
-34281.805225458564,
-40927.25655365689,
-41783.470069101655,
-36729.49808776109,
-26479.901594564475,
-12484.41628252153,
3277.3333605880994,
18575.320903550557,
31244.66723747242,
39492.25993004646,
42150.449859777895,
38843.095412552655,
30038.1988918293,
16982.363855004227,
1523.289815016102,
-14151.336054003428,
-27823.887265421505,
-37560.43317224093,
-41983.860795904635,
-40468.75958554683,
-33229.24027282051,
-21289.266175190303,
-6337.476745671029,
9510.901534803472,
24013.44158101065,
35117.723010011585,
41251.915418487064,
41547.703382049214,
35963.12790432845,
25288.863639639985,
11035.762130308807,
-4778.938380947175,
-19917.533053914518,
-32238.40660206855,
-39998.86901908532,
-42101.440049552206,
-38248.77415175858,
-28985.713088831304,
-15622.273395042484,
-48.717954427698835,
15531.702578155038,
28914.24663083384,
38204.86539556785,
42088.376604115016,
40015.06573842994,
32278.358780092756,
19973.553416025417,
4842.223128236119,
-10974.30048110409,
-25238.19916451848,
-35931.808667447614,
-41542.61947128564,
-41277.30603489173,
-35173.203455122515,
-24093.741711161383,
-9605.753057593283,
6241.191073850677,
21204.978231417706,
33168.00961517921,
40436.9163906376,
41982.83075778131,
37586.73486101586,
27871.15649754529,
14211.184044232994,
-1459.7277766187622,
-16924.15754913699,
-29994.29914644118,
-38821.385622706955,
-42157.12317079167,
-39529.70685905558,
-31310.788447237475,
-18662.615606301628,
-3374.2581650512266,
12391.484437997546,
26403.763197134715,
36679.42103414735,
41763.96109320079,
40937.535082623006,
34317.194830666485,
22840.075120998637,
8130.7754886049715,
-7729.0812753518885,
-22495.43565269424,
-34079.42032438292,
-40842.6195186085,
-41828.61439048038,
-36897.92767862762,
-26747.888431685922,
-12813.975345815976,
2932.7962635606,
18264.543286158812,
31011.68466774314,
39369.9486279515,
42156.256267917735,
38975.99507561737,
30279.577108949343,
17297.853904655287,
1868.3638201974395,
-13825.540814295084,
-27563.4985439396,
-37402.18415085556,
-41950.25144302199,
-40564.36957107177,
-33440.72333749119,
-21586.521303558988,
-6678.587218750899,
9174.259574991067,
23728.909146432197,
34925.50839898878,
41179.29488952383,
41604.85164531924,
36142.05464037725,
25564.11909122553,
11368.470378605212,
-4435.916247891232,
-19612.716396628242,
-32014.885237158338,
-39888.277407751484,
-42119.367103243654,
-38392.720555220956,
-29235.261417651498,
-15942.17231347695,
-393.6686779761868,
15210.48358388222,
28662.2288120906,
38057.69220909468,
42066.907719693845,
40122.26980667829,
32499.113070301242,
20276.549812452846,
5184.597439508058,
-10640.990424446405,
-24961.139244947295,
-35750.12056001782,
-41482.08640037805,
-41346.33405490097,
-35362.1962999256,
-24375.7861121173,
-9941.082752611024,
5900.080132346802,
20906.366016023494,
32954.08747463287,
40338.07251574228,
42012.89172481428,
37741.624517878874,
28128.726393972687,
14535.151666932949,
-1115.3276828169019,
-16608.024496474773,
-29751.106949902583,
-38685.60102759474,
-42147.836258334,
-39648.34436654602,
-31540.430384851603,
-18970.895325885056,
-3717.477148581867,
12061.854430951487,
26134.349285469805,
36508.38350588398,
41715.45000037322,
41018.47017553291,
34516.031556050635,
23128.736034791928,
8468.33376499746,
-7390.358467495255,
-22203.478301262854,
-33875.51936152143,
-40755.604164599106,
-41870.76580565034,
-37063.295139332746,
-27013.061065939797,
-13141.444621763601,
2589.3489046730374,
17953.737567063712,
30777.466638316306,
39245.52381792619,
42159.12964776565,
39105.88033502572,
30517.925457683552,
17611.04928580993,
2212.000964412135,
-13500.074069161921,
-27302.200298632444,
-37242.09697385265,
-41913.872738404156,
-40657.01127058653,
-33649.06027886518,
-21881.28460763021,
-7017.92385825658,
8838.28969349376,
23443.8321246164,
34731.73637353697,
41104.14635841184,
41659.084595100976,
36317.82758558568,
25836.69631447276,
11699.127437162715,
-4093.897278155987,
-19307.734247119748,
-31790.105107331747,
-39775.42511482028,
-42134.463806289175,
-38533.534708111736,
-29481.962249546505,
-16259.780601025019,
-737.3023745032407,
14889.46332123037,
28409.25314954265,
37908.54473999976,
42042.7020905969,
40226.41323037361,
32716.86228980834,
20577.054852974103,
5525.346637691787,
-10308.240177390537,
-24683.437216637503,
-35566.77154205243,
-41418.95588122264,
-41412.41968140501,
-35548.038461595344,
-24655.19181114536,
-10274.459376936367,
5559.876990254017,
20607.4545761233,
32738.82973219528,
40236.8309937671,
42040.13754759983,
37893.29012184608,
28383.542749131127,
14856.85732104061,
-772.1512397597788,
-16291.955760540464,
-29506.905753891107,
-38547.69990278982,
-42135.86535554523,
-39763.81520357963,
-31767.193407567498,
-19276.690411453874,
-4059.170123515413,
11732.659002064393,
25864.24868488866,
36335.54184248512,
41664.40205765348,
41096.33761699158,
34711.88822124738,
23414.741374949463,
8804.082581752084,
-7052.422865467478,
-21911.15927840532,
-33670.13573922943,
-40666.22358430505,
-41909.978840123425,
-37225.59057702302,
-27275.42770891119,
-13466.816831927365,
2247.0422942324567,
17642.909988324496,
30542.118165171683,
39118.93567468779,
42159.25407705278,
39232.62241482025,
30753.400793262692,
17921.866207285602,
2554.1878803531763,
-13174.927113793035,
-27040.127136416428,
-37080.089753245295,
-41874.94076519528,
-40746.4949886676,
-33854.477379998374,
-22173.398917590617,
-7355.537525780842,
8502.994086571598,
23158.323929520542,
34536.34678140991,
41026.644751637716,
41710.27182649208,
36490.63897959762,
26106.45025372759,
12027.808220780818,
-3752.9049692839335,
-19002.658498448724,
-31564.04630112831,
-39660.427912195824,
-42146.67542821833,
-38671.343793970096,
-29725.73812974861,
-16575.154175928954,
-1079.5764533942468,
14568.69128778207,
28155.352484929917,
37757.48275539089,
42015.801470185535,
40327.53425612027,
32931.60482952924,
20875.086927183387,
5864.421979322249,
-9976.07180404712,
-24405.17423452268,
-35381.764235139824,
-41353.36415568638,
-41475.4897691096,
-35730.86466274888,
-24931.89388169214,
-10605.895072213747,
5220.5863469009355,
20308.34405942276,
32522.188739887762,
40133.38152061662,
42064.407813751815,
38041.973390876,
28635.42585687997,
15176.408022083882,
-430.2293485921099,
-15976.03877084462,
-29261.63094008911,
-38407.84945294688,
-42121.065870929364,
-39876.33198830697,
-31990.889670793225,
-19580.11921955105,
-4399.264486177355,
11403.962577621505,
25593.436781704037,
36161.015271541226,
41610.751782316234,
41171.28196411244,
34904.65244542785,
23698.181300538025,
9137.947558889173,
-6715.314695010485,
-21618.499213832947,
-33463.34743161707,
-40574.485091686875,
-41946.32672574381,
-37384.786233396415,
-27535.04130790314,
-13790.043474975379,
1905.885229207546,
17332.10649124747,
30305.657429780582,
38990.263829285854,
42156.58541224006,
39356.317584540695,
30985.94849570868,
18230.323971657785,
2894.9184835212677,
-12850.16084855242,
-26777.241804997728,
-36916.295672815235,
-41833.330839914495,
-40833.02876805111,
-34056.80060111579,
-22462.985776180525,
-7691.360868819307,
8168.433499833112,
22872.341267952794,
34339.481776124085,
40946.65065797256,
41758.61623923643,
36660.268265111015,
26373.54381675992,
12354.386138833403,
-3412.9678232645365,
-18697.465228365603,
-31336.825446896513,
-39543.203494359936,
-42156.158241588695,
-38806.005462032495,
-29966.716759718856,
-16888.17907355944,
-1420.4833374063976,
14248.175833747322,
27900.608052525167,
37604.495107172115,
41986.30140459179,
40425.587557267194,
33143.433887248546,
21170.588723804067,
6201.846064801572,
-9644.510753874862,
-24126.379387957724,
-35195.13636597016,
-41285.31875201121,
-41535.598169662735,
-35910.66268692378,
-25205.89324662513,
-10935.383150771278,
4882.260870216297,
20009.035907263613,
32304.26573832591,
40027.66049017502,
42085.88256683641,
38187.53676142994,
28884.516936874265,
15493.723945037014,
-89.58928156050467,
-15660.257994590367,
-29015.419212039174,
-38265.95183226153,
-42103.64874795469,
-39985.691506957766,
-32211.72603590705,
-19881.027550594183,
-4737.785879587253,
11075.759597365692,
25322.031189268666,
35984.736187741575,
41554.670708001075,
41243.159939198085,
35094.501391044134,
23978.910262720463,
9469.97934840661,
-6379.051662316248,
-21325.575717179978,
-33255.13469709755,
-40480.51088458044,
-41979.754732231566,
-37541.01607017647,
-27791.826359165072,
-14111.177154077877,
1565.913320045021,
17021.37956455058,
30068.115388238934,
38859.56579614628,
42151.16638699154,
39477.00475943747,
31215.567864130706,
18536.438412643925,
3234.144603537944,
-12525.798354527195,
-26513.629417491356,
-36750.712762268464,
-41789.18450366474,
-40916.53843475917,
-34256.158716767364,
-22749.986599641077,
-8025.393102024463,
7834.611791474242,
22585.99353787937,
34141.092508863985,
40864.357223882966,
41803.95717678483,
36826.94366718821,
26637.81471077822,
12678.944956652784,
-3074.103766033754,
-18392.25066971488,
-31108.37589914831,
-39423.922363862635,
-42162.75801794828,
-38937.726523482044,
-30204.708374512593,
-17198.962055043427,
-1759.9671465715558,
13927.9876932287,
27644.987344452496,
37449.70593364901,
41954.12829735327,
40520.72545432158,
33352.24015594893,
21463.61097835466
],
"y": [
0,
15576.050639839646,
28947.673255183683,
38222.67821692621,
42088.91797509229,
39999.967566771404,
32251.94264774194,
19941.490737583095,
4810.225586419786,
-11001.437683135267,
-25256.92812691474,
-35939.721100098606,
-41538.111858435186,
-41259.703301843045,
-35143.06852148851,
-24053.36249514699,
-9559.52839398408,
6287.2253452990835,
21244.147604295555,
33194.566481971466,
40447.39483958242,
41977.006975830125,
37567.375139508884,
27843.141926045617,
14180.209675709226,
-1488.497860903827,
-16946.658173238702,
-30007.703864714287,
-38823.79836393347,
-42147.427048606296,
-39507.633495937596,
-31277.43331574384,
-18620.939142118554,
-3329.1297384597483,
12433.858253014154,
26437.11233403431,
36698.947900978295,
41767.585461208815,
40926.29558885071,
34294.81004188983,
22811.76527590234,
8101.892196232001,
-7754.0009458026025,
-22513.08355685165,
-34087.596798950006,
-40839.88590534016,
-41814.201833932784,
-36872.04104984537,
-26712.17990909279,
-12771.981766553896,
2975.797840743753,
18302.408728537772,
31038.801388640015,
39382.615775853985,
42153.73201318321,
38960.48555344125,
30255.43151305792,
17270.35589236439,
1842.3910950816717,
-13846.189457359425,
-27576.24770784686,
-37405.33690959409,
-41942.78577524175,
-40545.83422674302,
-33411.74618791657,
-21549.356444581823,
-6637.270223828384,
9214.199478020904,
23761.605964433234,
34946.1620534729,
41185.49891056899,
41597.077717728935,
36123.47047538086,
25539.477663374204,
11342.781612979716,
-4458.3651097848015,
-19628.880532673564,
-32022.804183201057,
-39886.73149660911,
-42107.59558769052,
-38370.654464885345,
-29204.003874440157,
-15904.4668934768,
-354.00847324343886,
15246.541319846427,
28689.303204814827,
38071.9770687892,
42067.212919785314,
40110.279024556636,
32478.671550851952,
20252.509483571146,
5161.558640832761,
-10659.490550346804,
-24972.77753615514,
-35753.45656320402,
-41476.231962125785,
-41330.806922843876,
-35337.24955271186,
-24343.020254506595,
-9903.750822589453,
5937.204609432009,
20937.884600842262,
32975.2912267578,
40346.30245475368,
42008.151443271876,
37726.54637706942,
28107.625946152075,
14512.692866801188,
-1135.113340799736,
-16622.35299652693,
-29758.29484673034,
-38684.69055914065,
-42138.12923858937,
-39629.63583562469,
-31513.334259449177,
-18937.44235464832,
-3681.401137217969,
12095.658607264611,
26160.827089694307,
36523.719030096225,
41718.10542353399,
41009.62796790298,
34499.06419363465,
23108.079033552734,
8448.306368650372,
-7406.4624886965885,
-22213.59664391943,
-33878.51229794544,
-40750.781836881695,
-41857.69887885809,
-37041.97634813994,
-26984.506596343912,
-13108.16750793774,
2623.3381918950295,
17983.577217606296,
30798.648928004837,
39255.21264025826,
42156.96646683212,
39093.9921722402,
30500.194186197907,
17591.81769620327,
2195.0122628971835,
-13512.27809729236,
-27308.216259930185,
-37241.237130858906,
-41905.636711047446,
-40641.11170896649,
-33625.78048146429,
-21852.00335301007,
-6985.601144911633,
8869.448692882808,
23469.200811562947,
34747.57977860899,
41108.6412366521,
41653.005849396875,
36304.052035888315,
25819.282730566312,
11682.140288913708,
-4107.421769600331,
-19315.98792076222,
-31792.271675793785,
-39771.07707133135,
-42123.29868061276,
-38515.38210369842,
-29457.360559433833,
-16230.550493746696,
-706.7005355886004,
14917.17407280092,
28429.867340640278,
37919.15392746285,
42042.644816208194,
40217.371624762294,
32702.281381099885,
20560.96907130696,
5511.234719531205,
-10318.073628812184,
-24687.876165188365,
-35565.422896907265,
-41411.60581418324,
-41398.79859383189,
-35528.18095138767,
-24629.929660530255,
-10245.980952456215,
5588.0692433928225,
20631.230017345166,
32754.597415578202,
40242.62978987015,
42036.33310518886,
37882.38635568207,
28369.210795194067,
14842.887241061258,
-782.9560049211356,
-16298.05647251092,
-29507.80297493676,
-38543.27355646938,
-42126.0179527848,
-39748.3162135203,
-31746.21040393671,
-19251.429032690394,
-4032.1277483650283,
11757.853710013173,
25883.781965388094,
36346.5406495966,
41665.98005530692,
41089.75033444438,
34700.200304010505,
23401.668385099863,
8792.866090129553,
-7059.696672225593,
-21913.671512840207,
-33667.82092461174,
-40659.19043019838,
-41898.09326213735,
-37208.71120765418,
-27253.9397783264,
-13442.197450783724,
2272.0081063105445,
17664.64576638358,
30557.270789105874,
39125.50744309074,
42157.284394977614,
39224.24525613517,
30741.951966903234,
17910.835525202187,
2546.1761231319624,
-13178.634639349859,
-27039.336768682497,
-37075.049238465006,
-41865.79302128527,
-40733.10765630863,
-33836.7365425152,
-22151.956006086537,
-7332.172870764851,
8525.345761981123,
23176.2978632954,
34547.200120362824,
41029.31402535306,
41705.70453420742,
36481.51232995132,
26096.212285240817,
12019.458123180762,
-3757.4909552180566,
-19002.946106537318,
-31560.326377451463,
-39653.18155289527,
-42135.94565130783,
-38656.96692492097,
-29707.70771540186,
-16554.320322987103,
-1058.0291387788766,
14587.997405853524,
28169.398383506483,
37764.305439570184,
42015.213221807026,
40321.30679956986,
32922.77709009441,
20866.86651296848,
5859.2201166172135,
-9977.198280782373,
-24402.3403459567,
-35375.61451544841,
-41344.36196925194,
-41463.66144471696,
-35715.95318575456,
-24914.048495624724,
-10586.237349532137,
5239.827700329867,
20324.314788335152,
32532.373006805457,
40136.61744278784,
42061.41015094634,
38035.07865044619,
28627.804561289824,
15170.857687173911,
-432.0561893125544,
-15973.863351843718,
-29256.09785308405,
-38399.81000381712,
-42110.89518837169,
-39863.88711733731,
-31975.94315139739,
-19562.94823519246,
-4381.254691143195,
11420.504199065537,
25605.91182493048,
36167.583939785785,
41611.047722083866,
41166.80925708146,
34898.12675346918,
23692.576210762658,
9135.522247440234,
-6713.734390830239,
-21613.325098249356,
-33455.63618071333,
-40565.08033860222,
-41935.493416222984,
-37372.22078089918,
-27520.503739196658,
-13774.04302481858,
1921.815243772053,
17345.68138846244,
30314.678015831105,
38993.56893537114,
42154.69934897975,
39351.29905178191,
30980.684221406646,
18227.43764867251,
2895.868747755701,
-12845.332615937778,
-26769.531888512825,
-36906.95734378534,
-41823.14009035011,
-40821.98560544834,
-34044.51985996596,
-22449.290101881757,
-7676.932853263716,
8181.941758438482,
22882.81268132721,
34345.25700820911,
40947.31869056858,
41755.414708224336,
36655.697241095244,
26370.35185336736,
12354.652873664887,
-3408.6023123783148,
-18689.711618576523,
-31327.141664927534,
-39532.85190632634,
-42145.73073854592,
-38795.251262285594,
-29955.11712086175,
-16875.716059585473,
-1407.987962499891,
14259.021315202217,
27908.002178270905,
37607.37094404862,
41985.066476159234,
40422.03448589653,
33140.223506783346,
21170.175706133836,
6205.528592537047,
-9636.897882199826,
-24116.18582183415,
-35184.04502288821,
-41274.544220330696,
-41525.39887629461,
-35900.57937396434,
-25195.390286076283,
-10924.50701484136,
4892.527397570687,
20017.111429149474,
32308.758537158013,
40028.20134419828,
42083.52293150415,
38184.554249794615,
28883.491621584915,
15496.5446248265,
-82.4288527037226,
-15649.737399096462,
-29003.380049866726,
-38254.134858152865,
-42093.01671762894,
-39976.19303556531,
-32202.675937346314,
-19871.913484008193,
-4728.7957079349835,
11083.603280710187,
25327.375405007406,
35986.687070865584,
41553.57089193667,
41240.63048855227,
35092.991826179765,
23980.740092046522,
9476.306261294298,
-6368.5851914747755,
-21312.646249523303,
-33241.875037829865,
-40468.62595186069,
-41969.80515016697,
-37532.62077685523,
-27784.163535623396,
-14103.730857910607,
1572.7967181210217,
17026.72136537317,
30070.87786399026,
38859.49006359347,
42149.191969402964,
39475.211981891895,
31216.39025832505,
18541.623513319213,
3244.0532427916455,
-12512.376146880744,
-26498.91225444073,
-36736.95364136078,
-41777.788117525444,
-40907.7274308023,
-34249.20724093115,
-22743.96094660725,
-8019.88508862319,
7839.239935419415,
22588.893883722634,
34141.63893272524,
40862.892665072664,
41802.00103635723,
36826.78003709748,
26641.618375476282,
12687.784766092751,
-3060.7751618110606,
-18376.399504230834,
-31092.572674949715,
-39410.37063261503,
-42152.45933411768,
-38930.452880594275,
-30199.483881984677,
-17194.79318255448,
-1756.5344354412157,
13930.318500596237,
27645.606536178806,
37448.53952582213,
41952.05227266833,
40519.70128877243,
33354.54425254981,
21470.9334404906,
6550.110784109218,
-9297.211942564485,
-23829.42333181529,
-34990.836670203316,
-41202.11643768236,
-41584.11574150454,
-36082.03083427641,
-25473.97456778995,
-11260.751366151666,
4546.182353746481,
19709.69385607981,
32083.779545921392,
39917.4461660724,
42102.69406237838,
38330.86057323641,
29136.247437622835,
15819.963672746844,
265.9104008594165,
-15325.768763278982,
-28749.563292438306,
-38106.43266921472,
-42072.2630203721,
-40085.38866384176,
-32426.31213434473,
-20178.394859512387,
-5074.701648264032,
10747.221506131957,
25048.075011418394,
35804.09518123131,
41493.343659271966,
41311.44929042856,
35284.642892114345,
24266.228894602602,
9815.136782465914,
-6024.296713401798,
-21011.584369707914,
-33026.727647778636,
-40369.63730905309,
-42001.21753952667,
-37689.76445278017,
-28044.9810013123,
-14431.197274882152,
1224.9738301127525,
16707.77590187253,
29825.996675006372,
38723.20405296741,
42140.910356877335,
39595.93053712391,
31449.118588434394,
18853.35635655502,
3590.7303750294054,
-12179.809978029938,
-26227.504590123546,
-36565.056073199725,
-41729.78460328344,
-40990.33841617203,
-34450.805894231984,
-23035.97693744669,
-8361.015506804004,
7497.295762203545,
22294.503555521016,
33936.481416953124,
40775.96234543106,
41845.59028841822,
36994.68102395751,
26910.078224203673,
13018.786208296533,
-2714.0446823411517,
-18062.964363318337,
-30856.8356454215,
-39285.55963730699,
-42156.37910122463,
-39062.40770384592,
-30440.931131722933,
-17511.458418378716,
-2103.6637764860416,
13601.872682300638,
27382.384883931416,
37287.63056646435,
41916.41910462835,
40614.129721022204,
33565.858880301465,
21769.074633598917,
6892.979177819853,
-8958.155495973055,
-23542.163689526806,
-34795.91235015624,
-41127.25850180197,
-41639.729947983076,
-36260.41611735856,
-25749.766024960998,
-11594.994427160103,
4200.826964105702,
19402.101225375805,
31857.434616860963,
39804.445762452415,
42118.899008160435,
38474.0480732399,
29386.067324432766,
16141.10738008785,
612.9163829340634,
-15001.961783742636,
-28494.765800795736,
-37956.69287869103,
-42048.74537196904,
-40191.44831769816,
-32646.924886623292,
-20482.342340611016,
-5418.966491684122,
10411.352806340365,
24768.171333167793,
35619.68615613258,
41430.599342747526,
41379.1283834496,
35473.23669170757,
24548.957333551418,
10152.053531758249,
-5680.886016957364,
-20710.276523076787,
-32810.04161030744,
-40268.39481950695,
-42029.5401542634,
-37843.85061228052,
-28302.861874300284,
-14756.490910362816,
878.3787469504631,
16388.928140933163,
29579.948928747082,
38584.910546098006,
42129.71062547396,
39713.59847289446,
31678.80817503406,
19162.67328257283,
3935.8538946348413,
-11847.679256420904,
-25955.318186351426,
-36391.39611759615,
-41679.097829063154,
-41069.924538937776,
-34649.29364847761,
-23325.35396886523,
-8700.284249675522,
7156.126034369262,
21999.721734020444,
33729.81773790738,
40686.598155151776,
41886.20767540934,
37159.4506180191,
27175.706406992515,
13347.665146058767,
-2368.4243167605564,
-17749.510927593532,
-30619.84576850421,
-39158.60874613951,
-42157.379013718426,
-39191.26789327764,
-30679.370247107272,
-17825.774869253066,
-2449.3369434158567,
13273.767570952252,
27118.22224338097,
37124.897132315076,
41877.96590363274,
40705.54316526222,
33774.029301880124,
22064.65756739776,
7234.062927321132,
-8619.784939688121,
-23254.338869992433,
-34599.46045184966,
-41049.77672162906,
-41692.41504422576,
-36435.597071968725,
-26022.81954729778,
-11927.171888173998,
3856.49227119977,
19094.340726982824,
31629.865019238074,
39689.126227338405,
42132.2869879726,
38614.064226560295,
29632.997166732013,
16459.937585064385,
958.5833943647058,
-14678.364069162732,
-28239.027375186935,
-37804.937522634114,
-42022.51972858117,
-40294.38327303435,
-32864.52109040999,
-20783.762651742018,
-5761.578729806551,
10076.059810126435,
24487.62583822356,
35433.59677903695,
41365.2753298676,
41443.7876472755,
35658.70430596184,
24828.985822753646,
10487.002483486063,
-5338.395628702867,
-20408.660560331748,
-32592.032055136187,
-40164.724892264494,
-42055.00618927224,
-37994.72659385558,
-28557.90889613557,
-15079.521130593606,
533.0317568470863,
16070.146274261318,
29332.918191448567,
38444.41222347489,
42115.824492295564,
39828.036567266514,
31905.556945265853,
19469.496459728292,
4279.424338205098,
-11515.993243688812,
-25682.471666544763,
-36215.87078106635,
-41625.900942338565,
-41146.384091226166,
-34844.76627488382,
-23612.056135120103,
-9037.71091683417,
6815.761168101583,
21704.595614578644,
33521.63667959824,
40594.90047259416,
41923.826520708506,
37321.140465980185,
27438.49885900789,
13674.421551823267,
-2023.957865878273,
-17436.04016498307,
-30381.710988910476,
-39029.51562950352,
-42155.557131385925,
-39317.01434612896,
-30914.86653434667,
-18137.699989145192,
-2793.53821618344,
12945.988831734143,
26853.2923201948,
36960.22746219509,
41836.92125784539,
40793.82515115885,
33979.20097823473,
22357.60600100942,
7573.391252974513,
-8282.099903869594,
-22966.096344620066,
-34401.32351022127,
-40969.946151975375,
-41741.98828128521,
-36607.756932685494,
-26293.047778095108,
-12257.328472165023,
3513.198769768928,
18786.50180894272,
31400.95577292128,
39571.69138911412,
42142.701573162856,
38751.04902047248,
29876.97645055813,
16776.49500619295,
1302.8775260555908,
-14355.021899626705,
-27982.33607294019,
-37651.29889947184,
-41993.53951743205,
-40394.295503870475,
-33079.07807800116,
-21082.674311376762,
-6102.499301292181,
9741.358131388135,
24206.509379857504,
35245.869647411826,
41297.42603402057,
41505.46135020327,
35841.08899710849,
25106.30072517925,
10819.9850191905,
-4996.829765575325,
-20106.8492300663,
-32372.62957731715,
-40058.8102672656,
-42077.52661226417,
-38142.537442967485,
-28810.05328281993,
-15400.351526912995,
188.9515148580975,
15751.522885380544,
29084.771781724314,
38301.97050308671,
42099.06252521396,
39939.468692327064,
32129.247082905327,
19773.900056046215,
4621.390795181904,
-11184.81111118216,
-25408.87467650989,
-36038.69045864304,
-41569.99873027921,
-41219.90613828881,
-35037.09857788548,
-23896.136328072505,
-9373.239616040326,
6476.233556552065,
21409.10932465502,
33312.08059210873,
40500.7711959096,
41958.58792501223,
37479.691686606326,
27698.498177511206,
13999.015004841393,
-1680.6539217700222,
-17122.591376324435,
-30142.48089784744,
-38898.28575217292,
-42150.976292025414,
-39439.6504495407,
-31147.430241994818,
-18447.23792295335,
-3136.267587253536,
12618.595673075959,
26587.54391439428,
36793.73721009856,
41793.222557193614,
40879.076465725,
34181.311823711105,
22647.97805808541,
7910.923346647105,
-7945.1544446453,
-22677.36192305138,
-34201.71814083619,
-40887.597425376174,
-41788.67445830308,
-36776.755675177104,
-26560.548030430913,
-12585.385736325881,
3170.970404628738,
18478.5306713366,
31170.911006432332,
39451.93491074059,
42150.38382837721,
38884.829692883955,
30118.099728952562,
17090.69949775844,
1645.78756741726,
-14031.934440777464,
-27724.82253038277,
-37495.659903591055,
-41961.97863099892,
-40491.079972775064,
-33290.681426675495,
-21379.04086647305,
-6441.746090376211,
9407.268545464427,
23924.871448091195,
35056.47381961391,
41227.150584222254,
41564.11233001389,
36020.43686499709,
25380.88901665517,
11151.002539614912,
-4656.236170856969,
-19804.839007508046,
-32151.920148578432,
-39950.64636454436,
-42097.17686032502,
-38287.25926794004,
-29059.346879378707,
-15718.93895303926,
-153.8344865362429,
15433.033114587786,
28835.694496607375,
38157.4653047315,
42079.644386791275,
40047.773585010764,
32350.00915038555,
20075.79961244419,
4961.765197361708,
-10854.125748543867,
-25134.69957703618,
-35859.68498290374,
-41511.67027456439,
-41290.305947804365,
-35226.46092659132,
-24177.514827544775,
-9706.905796626155,
6137.557687728533,
21113.3724767567,
33101.02993232001,
40404.43155661719,
41990.347278612906,
37635.24267470367,
27955.65208300237,
14321.481206222088,
-1338.5428365527505,
-16809.222134512616,
-29902.133610259334,
-38765.06080825069,
-42143.585996874885,
-39559.27386745494,
-31377.035770580653,
-18754.401043205587,
-3477.477974970847,
12291.609452384375,
26321.05174070239,
36625.474228715015,
41746.9209865033,
40961.32799418419,
34380.39903705918,
22935.75223569157,
8246.646772800703,
-7608.952958844614,
-22388.263789322355,
-34000.57736307665,
-40802.912699738146,
-41832.39036114801,
-36942.72711140302,
-26825.248917588342,
-12911.389215191579,
2829.828398487832,
18170.547743211675,
30939.588183642572,
39330.129169270076,
42155.141154044744,
39015.6227568376,
30356.254484612935,
17402.62186172231,
1987.2711864013302,
-13709.177928603433,
-27466.38818959167,
-37338.24922470437,
-41927.64934485883,
-40584.924387862804,
-33499.220645501555,
-21672.906341765818,
-6779.262788104524,
9073.84040693501,
23642.698166215483,
34865.56819166212,
41154.36181469117,
41619.881908212345,
36196.69847783841,
25652.787336498426,
11480.009688104936,
-4316.631583072423,
-19502.674079084656,
-31929.969690324247,
-39840.2415920846,
-42114.02442026473,
-38428.895469043986,
-29305.79056524921,
-16035.275457539787,
-495.3171943426989,
15114.754407367014,
28585.637026919667,
38011.020972454346,
42057.507305976935,
40153.05343934054,
32567.77845058499,
20375.2488208309,
5300.510820655191,
-10524.00444707142,
-24859.85439072662,
-35679.07699343195,
-41450.73716886512,
-41357.79630343636,
-35412.711719085906,
-24456.268538358956,
-10038.629884692045,
5799.7775778548585,
20817.32143685089,
32888.69980870174,
40305.67046021573,
42019.33136822634,
37787.627232969375,
28210.036761974534,
14641.741728868124,
-997.6479775007024,
-16495.925037734396,
-29660.815557968468,
-38629.714098643264,
-42133.55613700482,
-39675.779451725044,
-31603.755241649338,
-19059.14545093319,
-3817.1735536670635,
11965.058801151446,
26053.880555252526,
36455.41519959457,
41698.121484338175,
41040.55052745209,
34576.50327344475,
23220.916338082447,
8580.562635391294,
-7273.544363816833,
-22098.79189267723,
-33797.98125372471,
-40715.88139334558,
-41873.199368966125,
-37105.64011812326,
-27087.19109024175,
-13235.294605321738,
2489.811142538988,
17862.516384460876,
30707.177264999737,
39206.1450970461,
42157.183771534554,
39143.302261348814,
30591.554906598565,
17712.175166732988,
2327.3246515112824,
-13386.731927618644,
-27207.216739127805,
-37178.879912164164,
-41890.81453387819,
-40675.6426677307,
-33704.83773442015,
-21964.1861848575,
-7115.06529454865,
8741.085616270511,
23360.114395085384,
34673.02389224609,
41079.27256772929,
41672.62601004007,
36369.995797616044,
25921.9478890706,
11807.038784962851,
-3978.041661417711,
-19200.420101330274,
-31706.747852314762,
-39727.74515467195,
-42128.01845196933,
-38567.5425411613,
-29549.36180862285,
-16349.372167152522,
-835.4509801537313,
14796.708106602959,
28334.672845201567,
37862.6847610647,
42032.702491933414,
40255.33367269746,
32782.59243973373,
20672.223862194798,
5637.607813163532,
-10194.447888345358,
-24584.479046729026,
-35496.80209486443,
-41387.38199828606,
-41422.30121077587,
-35595.97710301386,
-24732.329949588657,
-10368.443734439214,
5462.904236853813,
20521.090918875365,
32674.943883963373,
40204.75449089201,
42045.35821810486,
37937.042954553646,
28461.55016500748,
14959.85531220913,
-658.003096738898,
-16182.78915771736,
-29418.40687257345,
-38492.4800124085,
-42120.69746879619,
-39789.345632498014,
-31827.48687563094,
-19361.515640008296,
-4155.305602863448,
11638.99847457548,
25786.00963494035,
36283.68635415888
],
"z": [
0,
271.8809752273574,
505.28351629006517,
667.179330564762,
734.6647974819019,
698.2020338073938,
562.9597573621555,
348.0800215014314,
83.96280693950011,
-192.03080175563363,
-440.86131382148017,
-627.3301607774125,
-725.0504380732515,
-720.1908025647986,
-613.4245495494754,
-419.8530146906875,
-166.8622022284004,
109.74391199405069,
370.81796199376726,
579.4133024281035,
706.0118972767777,
732.711382782014,
655.7409796921601,
486.0038640680303,
247.51649956751075,
-25.981805134587333,
-295.8049973231742,
-523.7864013266419,
-677.671909554177,
-735.6860725533392,
-689.6083142808403,
-545.9496450788378,
-325.02972516050426,
-58.11020388453996,
217.03377403161946,
461.46148645597873,
640.5824997411348,
729.0559077581014,
714.3711505089786,
598.6181519315342,
398.18087052255623,
141.419088097769,
-135.34655344045282,
-392.96730108788495,
-595.0011886754583,
-712.8628450778355,
-729.869607214266,
-643.6038849303853,
-466.2628627339212,
-222.93580926455925,
51.942701119569435,
319.46969019863246,
541.784258089016,
687.4260924016568,
735.7961224721014,
680.0578159999827,
528.1105494138676,
301.45522485600407,
32.15910564607834,
-241.68608530626008,
-481.3451495433315,
-652.9125523641227,
-732.1140345605952,
-707.7301748656733,
-583.2042250451519,
-376.1454588896023,
-115.85403708161563,
160.83439182220403,
414.7603208660527,
609.9874857480944,
718.8955342075558,
726.079691402821,
630.5375447706364,
445.7932836911129,
197.9890474665874,
-77.82098728864752,
-342.623320879536,
-558.9600739002027,
-696.2254544952799,
-734.9908054325017,
-669.7622815087102,
-509.75782458901926,
-277.61356216098284,
-6.179311778025371,
266.12929662860114,
500.7735872587002,
664.5487870701246,
734.2859133673045,
700.1275336462115,
566.9173575382954,
353.5089280604322,
90.0954164556461,
-186.06201961919686,
-435.9013798103141,
-624.0788493393867,
-723.9702910449445,
-721.431918028201,
-616.8140161574747,
-424.9090573500021,
-172.8706915866774,
103.63420502885714,
365.4720514802586,
575.5857809293182,
704.2472857831048,
733.2549997069933,
658.5193394279095,
490.6204903082792,
253.3200751015017,
-19.813384612949086,
-290.1441576463324,
-519.4328880679094,
-675.2437290426358,
-735.5237581243618,
-691.7378795687126,
-550.0673440492728,
-330.5543635186931,
-64.2591921754159,
211.1304047715862,
456.63884295614275,
637.5238170323164,
728.192201551277,
715.8257201761394,
602.1834458427097,
403.3530942415586,
147.4658344884871,
-129.2801749813285,
-387.7396679857839,
-591.3515478581137,
-711.3074908641534,
-730.6288395920003,
-646.5701323693817,
-471.01638342944983,
-228.80401308208383,
45.790424430422235,
313.9043940168338,
537.5923190653331,
685.2022181429323,
735.8525600099995,
682.3881896475451,
532.3829307383484,
307.06641586964724,
38.31419911746771,
-235.85756858207364,
-476.66657695925227,
-650.048129121791,
-731.4655667502599,
-709.3932474866606,
-586.9402322602826,
-381.42822830852606,
-121.93424041034338,
154.81667256991548,
409.6563007117701,
606.521162444049,
717.5539421245961,
727.0559096978491,
633.689623801485,
450.67734023489834,
203.91263511817172,
-71.69517799939685,
-337.16168886529687,
-554.9360522027074,
-694.2066552126058,
-735.2648843409686,
-672.2885191737494,
-514.1802151996872,
-283.30542701077445,
-12.335642609375407,
260.3800979806774,
496.24505158030894,
661.8811987883347,
733.8570475807231,
701.9968403990714,
570.8205067210628,
358.8931583496576,
96.199099573824,
-180.10249338905803,
-430.9283390294616,
-620.7966531559292,
-722.8422015452576,
-722.6187066465885,
-620.1467526386796,
-429.91712078259707,
-178.8444006550856,
97.53995424114916,
360.1193050292711,
571.7334948154963,
702.4376294710119,
733.746893396232,
661.2395441599436,
495.1865043515287,
259.0836938724544,
-13.666387583392062,
-284.4834691051085,
-515.060470805876,
-672.7752361752135,
-735.312327422585,
-693.8094521787701,
-554.1322376531365,
-336.03506964553014,
-70.3812123149443,
205.23392695300302,
451.80293393099663,
634.4311011350218,
727.2823145701748,
717.2242514150773,
605.6943066383313,
408.47775677442934,
153.48020672626544,
-123.22728505628051,
-382.5043849657134,
-587.6738554686066,
-709.7087142628442,
-731.333907650942,
-649.4805083653803,
-475.7193908691209,
-234.6345819751653,
39.65786730651168,
308.3373535672737,
533.3789831507074,
682.9381562249275,
735.8580826581494,
684.6617651070421,
536.6028542210967,
312.63494118364935,
44.443851352026954,
-230.03372928938543,
-471.97320008641776,
-647.1472534948299,
-730.7700578548546,
-710.9990333583876,
-590.6225012863277,
-386.66396282846006,
-127.98373222767151,
148.81026380030016,
404.543590852635,
603.0234623386472,
716.1692385148802,
727.975750747212,
636.7872134178815,
455.5111975990809,
209.80059437526245,
-65.58704548476915,
-331.69745285793044,
-550.8873677176662,
-692.1487326404816,
-735.4856118235973,
-674.7598913133986,
-518.5500670592347,
-288.9568988339757,
-18.468170230813993,
254.63422725128441,
491.69848173276216,
659.178253896817,
733.3781933008585,
703.8110268397746,
574.6692913092885,
364.2326596820547,
102.2732669903413,
-174.1524226561072,
-425.9442234422808,
-617.4834760331715,
-721.6684145476485,
-723.750874582371,
-623.4243382473866,
-434.87646767525405,
-184.78365262721655,
91.46130841120203,
354.762009521493,
567.8544897850245,
700.587129166671,
734.1845893258532,
663.9047984931705,
499.7003016142252,
264.8084877490254,
-7.541344738863946,
-278.8245869949609,
-510.6668749265149,
-670.2710167832978,
-735.0483252425005,
-695.8267415579063,
-558.1422556632144,
-341.4726992292175,
-76.47530525914023,
199.34540041252768,
446.9526239322164,
631.3073414128567,
726.3234265701432,
718.5693032216935,
609.1491351691957,
413.5556061052058,
159.46134654092563,
-117.18842428840182,
-377.2617491548263,
-583.9700941751223,
-708.0659693485818,
-731.986704888298,
-652.3345794587846,
-480.37230860386336,
-240.4270163758399,
33.54516474176789,
302.7697401072354,
529.1444446700096,
680.6351080569443,
735.8129279215387,
686.8794896938875,
540.7699595420804,
318.16129351199436,
50.54781803431101,
-224.21585244432575,
-467.2636704479797,
-644.2131406198114,
-730.0255083712235,
-712.5503876428883,
-594.2493824543336,
-391.85398319735305,
-134.00159421781376,
142.81605844668547,
399.4207190238894,
599.4984702292035,
714.7379583045056,
728.8434209005883,
639.8276237246911,
460.29634825417156,
215.65148874957305,
-59.49710858115252,
-326.2298556605472,
-546.8170483498221,
-690.0483187228144,
-735.6563792942363,
-677.1736462624477,
-522.8686328635815,
-294.5669239249417,
-24.576783059469797,
248.89186048353795,
487.13572687938915,
656.4388950343555,
732.8519409224272,
705.5692197110384,
578.464842888028,
369.52697582496626,
108.31815776333475,
-168.2123914902742,
-420.9493094897114,
-614.1395569887364,
-720.4496957177289,
-724.8284819745359,
-626.6470021516052,
-439.78733370567477,
-190.68821962584417,
85.39909660656953,
349.3996868255953,
563.9512205220872,
698.6946679089807,
734.5705382764577,
666.5138982117065,
504.16335467721854,
270.4934159437043,
-1.438518459485797,
-273.16688038694,
-506.25560299694706,
-667.7281914969764,
-734.7362172035057,
-697.7870338187329,
-562.0999017300863,
-346.86574216433553,
-82.54170975099169,
193.46470804726582,
442.0906853384963,
628.1497146500511,
725.3201201775946,
719.857834853838,
612.5505199203229,
418.5855522868043,
165.4098014141576,
-111.16376069477931,
-372.0133130080036,
-580.2388162839502,
-706.3823014450006,
-732.5855881150512,
-655.134365729852,
-484.9745264698732,
-246.1817802853151,
27.452965803415047,
297.2022059069106,
524.8888318563462,
678.2946974488681,
735.716758945715,
689.042383740138,
544.8842346108532,
323.6454625973515,
56.62545408711214,
-218.40401138637068,
-462.5399207486233,
-641.2456557321826,
-729.2338430253518,
-714.046994691911,
-597.8222166164485,
-396.9975088816209,
-139.9878952144467,
136.83411460515282,
394.29028087864384,
595.9442412207543,
713.2642471874522,
729.6565587676583,
642.8138794940628,
465.0313421928121,
221.46636874727008,
-53.4257057073374,
-320.76090687271443,
-542.7225670112506,
-687.9103458958139,
-735.7737908847339,
-679.5335856737807,
-527.1340816868476,
-300.13647012803216,
-30.66073699339464,
243.15426799760078,
482.5555284417704,
653.6664240999418,
732.2756330690886,
707.2739787337073,
582.2058284619578,
374.7767485963913,
114.33290941041975,
-162.2830907520925,
-415.94378604631754,
-610.7670313494581,
-719.1854156434778,
-725.8533599072621,
-629.814244095091,
-444.6500597245941,
-196.55742753433486,
79.35356460132701,
344.0336284977359,
560.024134235189,
696.7613758488652,
734.9051359993674,
669.0676715844538,
508.5752352649702,
276.13875061123156,
4.641817964663134,
-267.51192407468676,
-501.82515050181377,
-665.1499829767449,
-734.3739181733448,
-699.6930298989117,
-566.0034894485508,
-352.2154214306727,
-88.57956711641481,
187.59308244312476,
437.2154142116286,
624.9625010935079,
724.2688024760863,
721.0939536694245,
615.8957971344423,
423.5687972874895,
171.32415082083673,
-105.15412678867996,
-366.7581945854725,
-576.4833422291283,
-704.65439600355,
-733.1338586248222,
-657.8773063473391,
-489.5271238794601,
-251.8977618661882,
21.38164310678335,
291.6349302144427,
520.6143512844322,
675.9157600984906,
735.5721618954614,
691.1495139542762,
548.9465264646856,
329.0868065498709,
62.67677225368599,
-212.59898815223073,
-457.8024186598057,
-638.2451132652515,
-728.3958920665979,
-715.488947577372,
-601.3411300476776,
-402.0946864424411,
-145.94238915863335,
130.86540134577058,
389.15161572602955,
592.3631421028041,
711.7468215775442,
730.4173780689764,
645.7445902569463,
469.71733811877033,
227.2440542560455,
-47.37345160505758,
-315.28981503737964,
-538.6076993212805,
-685.7317066913905,
-735.8421705964754,
-681.8368463181167,
-531.3485631554828,
-305.6639092404967,
-36.7199480504892,
237.42116650639267,
477.96091469133887,
650.857689558647,
731.6536082854373,
708.9222066978343,
585.8943353214065,
379.98084204558427,
120.31773952718672,
-156.36478359503116,
-410.92958837228605,
-607.364554338394,
-717.878715258078,
-726.8240763835221,
-632.9279594617664,
-449.4640295274219,
-202.3916955034298,
73.32531334430514,
338.66451890007284,
556.0732072791964,
694.7888923267379,
735.1879552248041,
671.5670000073098,
512.9358619926211,
281.74436168249014,
10.698875351318124,
-261.85979455998057,
-497.3775819725884,
-662.5362080192897,
-733.9633715109951,
-701.5442823192896,
-569.854296090616,
-357.52086405808,
-94.58877201910843,
181.73041828224646,
432.3296162817332,
621.743570272934,
723.1735471162469,
722.2752643733891,
619.1877033869245,
428.50385276223096,
177.20508800681523,
-99.15982082536493,
-361.49878712966085,
-572.7010128410811,
-702.8871486402289,
-733.6281934286808,
-660.5668722102307,
-494.0284559378907,
-257.5758101180047,
15.331758441005842,
286.069364943634,
516.3195110971131,
673.5017818063842,
735.3766262454797,
693.2033904540211,
552.955770289837,
334.4859732604286,
68.70096509136582,
-206.80157129503382,
-453.0513271532127,
-635.2138091589736,
-727.5111028686898,
-716.8780970669723,
-604.8057364468531,
-407.1457937004381,
-151.86438033391212,
124.91021219917037,
384.0061213065673,
588.7557546386059,
710.1869117334757,
731.126319836771,
648.6206368338824,
474.3539003020844,
232.98468413408835,
-41.340581132180475,
-309.81840881066245,
-534.4709657808061,
-683.5157155092853,
-735.8595814598283,
-684.0860850591187,
-535.5105309883369,
-311.1503430265174,
-42.75373468431189,
231.6940176055651,
473.34987940587143,
648.0171096276898,
730.9823579146964,
710.5178037008625,
589.5279538739132,
385.14027393636206,
126.27141157382295,
-150.45845471802755,
-405.9055297042421,
-603.9354151600551,
-716.5262146631896,
-727.743661684064,
-635.9857376487458,
-454.23020179293934,
-208.1899012368401,
67.31488482356555,
333.29248402838874,
552.1009062967114,
692.7759282675651,
735.4216015848726,
674.010967592967,
517.246036469736,
287.30958341331495,
16.732554225447483,
-256.2113232212366,
-492.9135928095542,
-659.8872521749361,
-733.5055549418835,
-703.3409888670142,
-573.6524428191002,
-362.78218792444636,
-100.56912372361144,
175.87780898688212,
427.43261955034666,
618.4953105980062,
722.0332621878814,
723.4038604776387,
622.4250365664625,
433.3917738611051,
183.05167023067548,
-93.18158520609788,
-356.23400628906245,
-568.8955839907609,
-701.0775302810969,
-734.0726639855368,
-663.2003995475714,
-498.48031673642475,
-263.21434343394276,
9.303665901139048,
280.50495522837116,
512.0075161355106,
671.0493192731677,
735.1341976665749,
695.2008859106651,
556.9136762382477,
339.8416047080008,
74.69804213605684,
-201.0119227024575,
-448.2887173928046,
-632.1499465241594,
-726.5824987580561,
-718.2126688864807,
-608.2177084184669,
-412.15020456984195,
-157.75421115140506,
118.96907818046051,
378.85462189971815,
585.1218838530119,
708.5862707710038,
731.7829194995692,
651.4429217051385,
478.9409585490833,
238.6882583245311,
-35.32785880514571,
-304.34670546004907,
-530.3142512933105,
-681.2623326573868,
-735.8277361988697,
-686.2809695006711,
-539.6211259654459,
-316.59502938684363,
-48.7618226576683,
225.97257133849004,
468.72545526242425,
645.1427344349587,
730.2658730620751,
712.0587378372957,
593.1092240930425,
390.2537141385591,
132.19443587693814,
-144.56409780549927,
-400.87418470281074,
-600.4768661213869,
-715.1327160294256,
-728.6089270417311,
-638.9907780479225,
-458.94705247225966,
-213.9528257839438,
61.322636793730055,
327.919085640047,
548.1052251037448,
690.7260425560349,
735.6033457582291,
676.402018915814,
521.5047032911931,
292.83512707624106,
22.742262809195562,
-250.56731820786828,
-488.43297527447214,
-657.2054257322955,
-732.9996566964251,
-705.0849306491391,
-577.3975349323501,
-367.9997169171908,
-106.51994064594689,
170.03552731198386,
422.52566139871874,
615.218466573798,
720.8488986393911,
724.4803413268386,
625.6085528687361,
438.23232330917307,
188.86392196882034,
-87.21949363146126,
-350.96582063124964,
-565.0658451147352,
-699.2287318069446,
-734.4657174543885,
-665.7804212647031,
-502.8815065156358,
-268.81447229331303,
3.29769108125902,
274.94331754612267,
507.6760512710373,
668.5629366810576,
734.8415708540726,
697.145909219894,
560.818188793579,
345.154996113977,
80.66711490180579,
-195.23107552520815,
-443.513012989229,
-629.0571988925166,
-725.6066735357339,
-719.4959644319708,
-611.5748617364073,
-417.10884301863706,
-163.61090630352132,
113.04256490221805,
373.69684110049155,
581.4640154937118,
706.9431875625899,
732.3896412726864,
654.2104182535759,
483.4792572544752,
244.35407171146088,
-29.335434656825864,
-298.8753915256871,
-526.1384224248972,
-678.9716547475585,
-735.7477329751553,
-688.4215600021316,
-543.68052794743,
-321.9980412382976,
-54.74421191794736,
220.2578616124752,
464.0867494271376,
642.2365839133133,
729.503062204167,
713.5467696531886,
596.637063348885,
395.3221779066789,
138.0861003490585,
-138.68265669923497,
-395.8342591371421,
-596.992689389684,
-713.6952643761235,
-729.423797694003,
-641.940637704212,
-463.616281005867,
-219.67910028799724,
55.348987361630165,
322.54338530708395,
544.089727833206,
688.6356348871517,
735.737396933926,
678.7371396690078,
525.7135138469735,
298.3195942383698,
28.727805420106957,
-244.92776548026006,
-483.93801060078613,
-654.4886858882185,
-732.4487134886677,
-706.7742755713836,
-581.0910672663997,
-373.17281540932413,
-112.4415351094624,
164.20393627523234,
417.60960654098574,
611.9124993368662,
719.622188184515,
725.5040596510418,
628.7390581566614,
443.025274575503,
194.64186798177565,
-81.27437969388872,
-345.6941693723278,
-561.2132980019142,
-697.3406746658438,
-734.8086720337607,
-668.3065216135916,
-507.23293117829445,
-274.37544404483833,
-2.685686066612159,
269.3840185740526,
503.3283431964794,
666.0405393748957,
734.5025814910526,
699.0363450897653,
564.6715880035184,
350.4246738673703,
86.6083917564886,
-189.4589061055402,
-438.72722344385164,
-625.9325972719818,
-724.5885000476474,
-720.7247606137901,
-614.8801713915931,
-422.02031830747114,
-169.4350848277395,
107.13092554402411,
368.53469300290504,
577.7800641349183,
705.2615251527648,
732.943962450522,
656.9255434563323,
487.96788480316667,
249.98274789673636,
-23.36383860335418,
-293.4054725989451,
-521.943098271152,
-676.646156447876,
-735.6186918145526,
-690.5095626631102,
-547.6882911554476,
-327.35959514778125,
-60.70008071225752,
214.55026171404867,
459.43506702495006,
639.299494654422,
728.6948202556407,
714.9824396830855,
600.112122129132,
400.34529066939683,
143.94618816792348,
-132.81420846565013,
-390.7879912975597,
-593.4817170429696,
-712.2170407314073,
-730.1868226036867,
-644.8376535969145,
-468.23664224834806,
-225.36951997686955,
49.39430711014085,
317.16748579605775,
540.0519276246838,
686.5094610151974,
735.8203947610867,
681.0201104655854,
529.8705051917044,
303.7642213451239,
34.6884431964232,
-239.29399624066755,
-479.4269794360086,
-651.7410257539796,
-731.8494500543338,
-708.4123015141577,
-584.7311123673524,
-378.3022526127099,
-118.33292447207299,
158.38389768113186,
412.6842135873142,
608.5801836449344,
718.3516107804307,
726.4774836843081,
631.8156908683753,
447.7712681111171,
200.38471698457465,
-75.34653569502461,
-340.41982444951213,
-557.3390944102175,
-695.4135069992741,
-735.1027071779134,
-670.7787612981482,
-511.53460712114725,
-279.897120491206,
-8.646307582074119,
263.82841060747324,
498.9635321721558,
663.4842990805635,
734.1161353564308,
700.8739787294797,
568.4727457397065,
355.65157592519773,
92.52123192822225,
-183.69658930749387,
-433.9297440824325,
-622.7800290324258,
-723.5248656098254,
-721.9027730186517,
-618.1311669750247,
-426.8859737278515,
-175.22535819116626,
101.23492850939539,
363.3670674948133,
574.073785559641,
703.5376002156428,
733.4498430457371,
659.5853976638977,
492.40817208175184,
255.5729185416928,
-17.41347842635703,
-287.93681991887365,
-517.7308357110262,
-674.2836274654302,
-735.4435795880494,
-692.5431434989339,
-551.6456756354947,
-332.6789256316005,
-66.62950218938218,
208.8502716532293,
454.77153911607564,
636.3310607635755,
727.8429802863086,
716.3652394844552,
603.5351113223054,
405.3228404726296,
149.77471907151414,
-126.95960700976566,
-385.73520630857297,
-589.9453478183846,
-710.6978616753471,
-730.8991094555956,
-647.6812837839526,
-472.80884681224427,
-231.02331217219594,
43.45926601436968,
311.79074792704324,
535.9951410845263,
684.3452692413247,
735.8560100307128,
683.2487341790833,
533.9776716616557,
309.1674918039325,
40.62411133950084,
-233.66565396957756,
-474.90308873936783,
-648.9591831572893,
-731.2064561938604,
-709.9957617836781,
-588.3201500944774,
-383.38655360137085,
-124.19438678527564,
152.5756192411376,
407.751662475037,
605.2192709609005,
717.0408828210393,
727.3980999464966,
634.8405811557784,
452.46947070279293,
206.09303381230265,
-69.43640931257961,
-335.1439321965488,
-553.4427053324213,
-693.4498342957438,
-735.3469367806064,
-673.1988259702143,
-515.7861422112305,
-285.3796966842516,
-14.583377410357109,
258.27686629117403,
494.58290124919483,
660.8950416144572,
733.6831275516632,
702.6592551311314,
572.2223170374897,
360.8352874919471,
98.40528946924768,
-177.94413694001247,
-429.123017774615,
-619.5983709819476,
-722.4189584041042,
-723.0286764646034,
-621.3300516826674,
-431.70463195072205,
-180.98228314113274,
95.35476570151201,
358.19631622322225,
570.3426261120741,
701.7760670245827,
733.9041079142626,
662.1934311766942,
496.79833897750683,
261.1256098209652,
-11.484942814537193,
-282.47098825305096,
-513.4995429281331,
-671.8881599683028,
-735.2190942127885,
-694.5254183098161,
-555.5509060477673,
-337.9568103310634,
-72.53162655706699,
203.1588470111559,
450.0958042850076,
633.3334863577608
]
},
{
"cauto": false,
"cmax": 1,
"cmin": 1,
"colorscale": [
[
0,
"#204a87"
],
[
1,
"#204a87"
]
],
"name": "Earth",
"showscale": false,
"type": "surface",
"uid": "b4d2ead9-b277-11e8-b505-a0afbda902ef",
"x": [
[
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
[
2070.9775685349646,
1958.7662914956272,
1634.2922933821865,
1132.717366623061,
508.3949372694737,
-171.0199720974995,
-831.9022138394263,
-1402.6349424190162,
-1821.3704108639388,
-2042.7321338179481,
-2042.7321338179484,
-1821.3704108639388,
-1402.6349424190173,
-831.9022138394275,
-171.0199720975005,
508.39493726947273,
1132.7173666230603,
1634.2922933821858,
1958.7662914956272,
2070.9775685349646
],
[
3917.5325829912545,
3705.269861917151,
3091.4836581186883,
2142.6872306516607,
961.6973945255617,
-323.507276569953,
-1573.6549145165156,
-2653.272624703365,
-3445.3670762369647,
-3864.102544681887,
-3864.1025446818876,
-3445.3670762369647,
-2653.2726247033675,
-1573.654914516518,
-323.50727656995485,
961.69739452556,
2142.687230651659,
3091.483658118687,
3705.269861917151,
3917.5325829912545
],
[
5339.562155299338,
5050.2499496143155,
4213.664799186625,
2920.4636860211895,
1310.7850168122338,
-440.9375478934551,
-2144.8776874338914,
-3616.387048334464,
-4696.004758521314,
-5266.737487100903,
-5266.737487100904,
-4696.004758521314,
-3616.3870483344667,
-2144.877687433894,
-440.93754789345763,
1310.7850168122313,
2920.463686021187,
4213.664799186623,
5050.2499496143155,
5339.562155299338
],
[
6182.9673162373765,
5847.957092568811,
4879.229977516816,
3381.7625853471986,
1517.8287436021724,
-510.58539405170535,
-2483.6696817114025,
-4187.60982125184,
-5437.757459198403,
-6098.63970094033,
-6098.639700940331,
-5437.757459198403,
-4187.609821251843,
-2483.669681711406,
-510.58539405170836,
1517.8287436021697,
3381.762585347196,
4879.229977516814,
5847.957092568811,
6182.9673162373765
],
[
6356.352029838285,
6011.947344139877,
5016.054878729385,
3476.5950350978,
1560.3921744832594,
-524.9033902157762,
-2553.317527869653,
-4305.040092575342,
-5590.2447636708575,
-6269.65967303783,
-6269.6596730378305,
-5590.2447636708575,
-4305.040092575346,
-2553.3175278696567,
-524.9033902157793,
1560.3921744832564,
3476.5950350977973,
5016.054878729383,
6011.947344139877,
6356.352029838285
],
[
5840.927372042378,
5524.449815998859,
4609.31240172086,
3194.6844678654465,
1433.8629012798515,
-482.33995933468935,
-2346.2738010797148,
-3955.95247028867,
-5136.9423064147695,
-5761.264735768357,
-5761.264735768358,
-5136.9423064147695,
-3955.952470288674,
-2346.273801079718,
-482.33995933469214,
1433.8629012798488,
3194.684467865444,
4609.312401720858,
5524.449815998859,
5840.927372042378
],
[
4692.547602159432,
4438.292429623361,
3703.07940513492,
2566.580267902913,
1151.9523340474977,
-387.50750958408815,
-1884.974901753706,
-3178.1760149191423,
-4126.972442386172,
-4628.547369145297,
-4628.547369145297,
-4126.972442386172,
-3178.176014919145,
-1884.9749017537085,
-387.5075095840904,
1151.9523340474955,
2566.580267902911,
3703.0794051349185,
4438.292429623361,
4692.547602159432
],
[
3035.6574872043448,
2871.1771912954932,
2395.560295805413,
1660.3472713169713,
745.2098570389732,
-250.68260837151965,
-1219.4097234235148,
-2055.994873851206,
-2669.7810776496694,
-2994.25507576311,
-2994.2550757631107,
-2669.7810776496694,
-2055.9948738512076,
-1219.4097234235167,
-250.6826083715211,
745.2098570389717,
1660.3472713169701,
2395.560295805412,
2871.1771912954932,
3035.6574872043448
],
[
1049.806780431554,
992.9253533863962,
828.4450574775444,
574.1898849414736,
257.7123288979541,
-86.69235680045416,
-421.70258046901955,
-711.014786154042,
-923.2775072281457,
-1035.488784267483,
-1035.4887842674832,
-923.2775072281457,
-711.0147861540426,
-421.7025804690202,
-86.69235680045466,
257.7123288979536,
574.1898849414731,
828.4450574775441,
992.9253533863962,
1049.806780431554
],
[
-1049.8067804315524,
-992.9253533863947,
-828.4450574775432,
-574.1898849414728,
-257.71232889795374,
86.69235680045404,
421.7025804690189,
711.0147861540408,
923.2775072281444,
1035.4887842674814,
1035.4887842674816,
923.2775072281444,
711.0147861540415,
421.7025804690195,
86.69235680045453,
-257.7123288979532,
-574.1898849414723,
-828.4450574775428,
-992.9253533863947,
-1049.8067804315524
],
[
-3035.6574872043434,
-2871.177191295492,
-2395.560295805412,
-1660.3472713169708,
-745.2098570389728,
250.68260837151956,
1219.4097234235144,
2055.9948738512053,
2669.7810776496685,
2994.255075763109,
2994.2550757631093,
2669.7810776496685,
2055.994873851207,
1219.4097234235162,
250.682608371521,
-745.2098570389714,
-1660.3472713169695,
-2395.560295805411,
-2871.177191295492,
-3035.6574872043434
],
[
-4692.547602159429,
-4438.292429623359,
-3703.079405134917,
-2566.580267902911,
-1151.9523340474968,
387.50750958408787,
1884.9749017537044,
3178.17601491914,
4126.972442386168,
4628.5473691452935,
4628.547369145294,
4126.972442386168,
3178.1760149191427,
1884.9749017537072,
387.50750958409014,
-1151.9523340474948,
-2566.580267902909,
-3703.0794051349158,
-4438.292429623359,
-4692.547602159429
],
[
-5840.927372042376,
-5524.449815998857,
-4609.312401720859,
-3194.6844678654456,
-1433.862901279851,
482.33995933468924,
2346.2738010797143,
3955.9524702886692,
5136.942306414769,
5761.264735768355,
5761.264735768356,
5136.942306414769,
3955.9524702886724,
2346.2738010797175,
482.339959334692,
-1433.8629012798485,
-3194.6844678654434,
-4609.312401720857,
-5524.449815998857,
-5840.927372042376
],
[
-6356.352029838285,
-6011.947344139877,
-5016.054878729385,
-3476.5950350978,
-1560.3921744832594,
524.9033902157762,
2553.317527869653,
4305.040092575342,
5590.2447636708575,
6269.65967303783,
6269.6596730378305,
5590.2447636708575,
4305.040092575346,
2553.3175278696567,
524.9033902157793,
-1560.3921744832564,
-3476.5950350977973,
-5016.054878729383,
-6011.947344139877,
-6356.352029838285
],
[
-6182.967316237377,
-5847.957092568812,
-4879.229977516816,
-3381.762585347199,
-1517.8287436021726,
510.5853940517054,
2483.669681711403,
4187.60982125184,
5437.757459198404,
6098.639700940331,
6098.639700940332,
5437.757459198404,
4187.609821251844,
2483.6696817114066,
510.5853940517084,
-1517.8287436021699,
-3381.7625853471964,
-4879.229977516815,
-5847.957092568812,
-6182.967316237377
],
[
-5339.562155299339,
-5050.249949614317,
-4213.664799186626,
-2920.46368602119,
-1310.7850168122343,
440.9375478934552,
2144.877687433892,
3616.387048334465,
4696.004758521315,
5266.737487100905,
5266.737487100906,
4696.004758521315,
3616.3870483344676,
2144.877687433895,
440.93754789345775,
-1310.7850168122318,
-2920.4636860211876,
-4213.664799186624,
-5050.249949614317,
-5339.562155299339
],
[
-3917.532582991256,
-3705.2698619171524,
-3091.483658118689,
-2142.6872306516616,
-961.6973945255621,
323.5072765699531,
1573.654914516516,
2653.272624703366,
3445.367076236966,
3864.1025446818885,
3864.102544681889,
3445.367076236966,
2653.2726247033684,
1573.6549145165184,
323.507276569955,
-961.6973945255603,
-2142.68723065166,
-3091.4836581186883,
-3705.2698619171524,
-3917.532582991256
],
[
-2070.977568534966,
-1958.7662914956288,
-1634.2922933821878,
-1132.7173666230622,
-508.39493726947416,
171.01997209749965,
831.902213839427,
1402.6349424190175,
1821.3704108639404,
2042.7321338179497,
2042.7321338179502,
1821.3704108639404,
1402.6349424190187,
831.9022138394282,
171.01997209750064,
-508.3949372694732,
-1132.7173666230613,
-1634.2922933821872,
-1958.7662914956288,
-2070.977568534966
],
[
-1.5621929143429165e-12,
-1.4775489932480931e-12,
-1.2327897122000057e-12,
-8.544385371317645e-13,
-3.8349568858537574e-13,
1.2900486836795326e-13,
6.275257461168375e-13,
1.0580444722087668e-12,
1.373907662485362e-12,
1.5408866391577764e-12,
1.5408866391577766e-12,
1.373907662485362e-12,
1.0580444722087678e-12,
6.275257461168384e-13,
1.2900486836795402e-13,
-3.83495688585375e-13,
-8.544385371317639e-13,
-1.2327897122000053e-12,
-1.4775489932480931e-12,
-1.5621929143429165e-12
]
],
"y": [
[
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
[
0,
672.4453172381089,
1272.020750289313,
1733.7529976111107,
2007.6062056921824,
2063.9041301666034,
1896.5460173652668,
1523.6677156388778,
985.6763747824739,
340.8717043736032,
-340.8717043736027,
-985.6763747824734,
-1523.6677156388766,
-1896.5460173652662,
-2063.9041301666034,
-2007.6062056921826,
-1733.752997611111,
-1272.0207502893134,
-672.4453172381094,
-5.072432100824625e-13
],
[
0,
1272.0207502893131,
2406.1983148492195,
3279.626955981496,
3797.6571277777143,
3904.152223057449,
3587.5718458054816,
2882.222392147741,
1864.539420012481,
644.8046704088713,
-644.8046704088703,
-1864.5394200124804,
-2882.222392147739,
-3587.5718458054803,
-3904.152223057449,
-3797.657127777715,
-3279.6269559814964,
-2406.1983148492204,
-1272.0207502893138,
-9.595187476631407e-13
],
[
0,
1733.7529976111107,
3279.6269559814955,
4470.102445015823,
5176.172973346762,
5321.324843416592,
4889.8285978399235,
3928.443550179085,
2541.350687774138,
878.8630452300076,
-878.8630452300063,
-2541.350687774137,
-3928.4435501790817,
-4889.828597839922,
-5321.324843416592,
-5176.172973346763,
-4470.102445015824,
-3279.6269559814964,
-1733.7529976111118,
-1.3078155404671353e-12
],
[
0,
2007.6062056921824,
3797.657127777714,
5176.172973346762,
5993.770160654701,
6161.849348129235,
5662.196547790195,
4548.95689346632,
2942.767175396611,
1017.6829721352607,
-1017.6829721352591,
-2942.7671753966097,
-4548.9568934663175,
-5662.196547790194,
-6161.849348129235,
-5993.770160654702,
-5176.172973346763,
-3797.6571277777152,
-2007.6062056921837,
-1.5143902266125608e-12
],
[
0,
2063.9041301666034,
3904.152223057449,
5321.324843416592,
6161.849348129236,
6334.641865028304,
5820.977643755634,
4676.520173007722,
3025.2891778274434,
1046.2211580313447,
-1046.221158031343,
-3025.289177827442,
-4676.520173007719,
-5820.977643755632,
-6334.641865028304,
-6161.849348129237,
-5321.324843416593,
-3904.1522230574506,
-2063.904130166605,
-1.5568572335190474e-12
],
[
0,
1896.5460173652668,
3587.571845805481,
4889.8285978399235,
5662.196547790195,
5820.977643755633,
5348.9654902458315,
4297.309928116757,
2779.9741556424556,
961.3850476608397,
-961.3850476608383,
-2779.974155642454,
-4297.309928116753,
-5348.96549024583,
-5820.977643755633,
-5662.196547790196,
-4889.828597839924,
-3587.5718458054826,
-1896.5460173652682,
-1.430614602044772e-12
],
[
0,
1523.667715638878,
2882.222392147741,
3928.4435501790854,
4548.956893466321,
4676.520173007722,
4297.309928116757,
3452.419472880565,
2233.405797950153,
772.3679499502728,
-772.3679499502715,
-2233.405797950152,
-3452.4194728805624,
-4297.309928116756,
-4676.520173007722,
-4548.956893466322,
-3928.443550179086,
-2882.222392147742,
-1523.667715638879,
-1.1493426801662274e-12
],
[
0,
985.676374782474,
1864.539420012481,
2541.3506877741384,
2942.767175396611,
3025.289177827443,
2779.9741556424556,
2233.405797950153,
1444.8132671883818,
499.65280033904173,
-499.652800339041,
-1444.8132671883811,
-2233.4057979501513,
-2779.9741556424547,
-3025.289177827443,
-2942.7671753966115,
-2541.350687774139,
-1864.5394200124817,
-985.6763747824746,
-7.435216450024997e-13
],
[
0,
340.8717043736033,
644.8046704088712,
878.8630452300076,
1017.6829721352607,
1046.2211580313447,
961.3850476608397,
772.3679499502726,
499.65280033904173,
172.79251689906815,
-172.7925168990679,
-499.6528003390415,
-772.3679499502721,
-961.3850476608395,
-1046.2211580313447,
-1017.6829721352608,
-878.8630452300079,
-644.8046704088714,
-340.8717043736035,
-2.5712850267573817e-13
],
[
0,
-340.87170437360277,
-644.8046704088703,
-878.8630452300064,
-1017.6829721352591,
-1046.221158031343,
-961.3850476608383,
-772.3679499502715,
-499.652800339041,
-172.7925168990679,
172.7925168990676,
499.65280033904077,
772.3679499502709,
961.385047660838,
1046.221158031343,
1017.6829721352593,
878.8630452300065,
644.8046704088705,
340.871704373603,
2.5712850267573776e-13
],
[
0,
-985.6763747824737,
-1864.5394200124804,
-2541.3506877741374,
-2942.76717539661,
-3025.289177827442,
-2779.9741556424547,
-2233.405797950152,
-1444.8132671883811,
-499.65280033904156,
499.6528003390408,
1444.8132671883807,
2233.4057979501504,
2779.9741556424538,
3025.289177827442,
2942.7671753966106,
2541.350687774138,
1864.539420012481,
985.6763747824742,
7.435216450024994e-13
],
[
0,
-1523.6677156388769,
-2882.222392147739,
-3928.443550179082,
-4548.9568934663175,
-4676.520173007719,
-4297.309928116754,
-3452.4194728805624,
-2233.4057979501513,
-772.3679499502722,
772.3679499502709,
2233.4057979501504,
3452.4194728805596,
4297.309928116752,
4676.520173007719,
4548.956893466318,
3928.443550179083,
2882.22239214774,
1523.6677156388778,
1.1493426801662265e-12
],
[
0,
-1896.5460173652664,
-3587.5718458054803,
-4889.828597839923,
-5662.196547790194,
-5820.977643755632,
-5348.96549024583,
-4297.309928116755,
-2779.9741556424547,
-961.3850476608395,
961.385047660838,
2779.9741556424538,
4297.309928116752,
5348.965490245829,
5820.977643755632,
5662.196547790195,
4889.8285978399235,
3587.5718458054816,
1896.5460173652677,
1.4306146020447716e-12
],
[
0,
-2063.9041301666034,
-3904.152223057449,
-5321.324843416592,
-6161.849348129236,
-6334.641865028304,
-5820.977643755634,
-4676.520173007722,
-3025.2891778274434,
-1046.2211580313447,
1046.221158031343,
3025.289177827442,
4676.520173007719,
5820.977643755632,
6334.641865028304,
6161.849348129237,
5321.324843416593,
3904.1522230574506,
2063.904130166605,
1.5568572335190474e-12
],
[
0,
-2007.6062056921826,
-3797.6571277777143,
-5176.172973346763,
-5993.770160654702,
-6161.849348129236,
-5662.196547790196,
-4548.956893466321,
-2942.7671753966115,
-1017.6829721352608,
1017.6829721352592,
2942.76717539661,
4548.9568934663175,
5662.196547790194,
6161.849348129236,
5993.7701606547025,
5176.172973346764,
3797.6571277777157,
2007.606205692184,
1.514390226612561e-12
],
[
0,
-1733.7529976111111,
-3279.6269559814964,
-4470.102445015825,
-5176.172973346764,
-5321.324843416593,
-4889.828597839924,
-3928.443550179086,
-2541.350687774139,
-878.8630452300079,
878.8630452300065,
2541.3506877741374,
3928.4435501790826,
4889.8285978399235,
5321.324843416593,
5176.172973346765,
4470.102445015826,
3279.6269559814973,
1733.7529976111123,
1.3078155404671357e-12
],
[
0,
-1272.0207502893136,
-2406.1983148492204,
-3279.626955981497,
-3797.6571277777157,
-3904.1522230574506,
-3587.571845805483,
-2882.222392147742,
-1864.5394200124817,
-644.8046704088715,
644.8046704088705,
1864.539420012481,
2882.22239214774,
3587.5718458054816,
3904.1522230574506,
3797.657127777716,
3279.6269559814973,
2406.1983148492213,
1272.0207502893143,
9.595187476631409e-13
],
[
0,
-672.4453172381095,
-1272.020750289314,
-1733.752997611112,
-2007.606205692184,
-2063.9041301666052,
-1896.5460173652684,
-1523.6677156388791,
-985.6763747824748,
-340.87170437360356,
340.87170437360305,
985.6763747824743,
1523.6677156388778,
1896.5460173652677,
2063.9041301666052,
2007.6062056921844,
1733.7529976111123,
1272.0207502893145,
672.44531723811,
5.07243210082463e-13
],
[
0,
-5.072432100824626e-13,
-9.595187476631405e-13,
-1.3078155404671353e-12,
-1.5143902266125608e-12,
-1.5568572335190474e-12,
-1.430614602044772e-12,
-1.1493426801662272e-12,
-7.435216450024997e-13,
-2.5712850267573817e-13,
2.5712850267573776e-13,
7.435216450024993e-13,
1.1493426801662263e-12,
1.4306146020447714e-12,
1.5568572335190474e-12,
1.514390226612561e-12,
1.3078155404671357e-12,
9.595187476631409e-13,
5.072432100824629e-13,
3.826269104401456e-28
]
],
"z": [
[
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366
],
[
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864
],
[
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782
],
[
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239
],
[
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561
],
[
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267
],
[
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456
],
[
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595
],
[
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402
],
[
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607
],
[
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608
],
[
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402
],
[
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663
],
[
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649
],
[
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298
],
[
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558
],
[
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236
],
[
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378
],
[
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864
],
[
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366
]
]
}
],
"layout": {
"autosize": true,
"scene": {
"aspectmode": "data",
"xaxis": {
"title": "x (km)"
},
"yaxis": {
"title": "y (km)"
},
"zaxis": {
"title": "z (km)"
}
}
}
},
"text/html": [
"<div id=\"00191c7b-0fb1-48be-b77a-8d853315f77a\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"00191c7b-0fb1-48be-b77a-8d853315f77a\", [{\"line\": {\"dash\": \"solid\", \"width\": 5}, \"mode\": \"lines\", \"name\": \"orbit influenced by Moon\", \"x\": [42159.783599999995, 39176.33313050093, 30648.34603837001, 17783.008684663506, 2401.2249728817246, -13320.363219830044, -27157.46178851618, -37152.860734058035, -41892.85979071243, -40707.27937602602, -33763.61472102067, -22043.987824470438, -7205.82947952589, 8651.757978035002, 23285.088474672662, 34623.305235890366, 41061.45649430606, 41688.28390883917, 36414.86483194732, 25987.934440893845, 11883.207397572452, -3902.9889686326196, -19137.017670261284, -31663.77867150318, -39711.42197154506, -42141.94065227597, -38611.52820470689, -29619.54638639312, -16437.556020633823, -930.1668316167464, 14708.798182536722, 28266.38921137929, 37823.76402739217, 42028.10260384963, 40284.14735665196, 32838.864881292495, 20746.12229922393, 5717.60520211664, -10119.98209269242, -24525.841792373874, -35462.179469314986, -41382.256857506814, -41448.884585737746, -35652.63382907132, -24813.188019914207, -10463.587567263648, 5366.358490835878, 20436.942366895517, 32615.499083144536, 40178.22648981217, 42054.63384793369, 37978.950923577824, 28528.30802123554, 15040.324988052333, -575.9243139274541, -16110.729671893676, -29366.35706959656, -38467.78174598678, -42127.973108672566, -39829.162862363446, -31896.63962427619, -19451.99853009428, -4255.590717149522, 11542.905030438475, 25708.044279219095, 36235.04986232976, 41633.94556238677, 41140.30826111758, 34824.17954857387, 23579.515448783404, 8998.091964665991, -6856.622486492597, -21741.281806367813, -33550.25493543009, -40613.38100169364, -41931.73828483307, -37318.985638958075, -27427.33180503151, -13655.815380528431, 2047.6541298608722, 17461.353140263724, 30404.120479599474, 39044.11158380027, 42158.268325485966, 39305.73809426212, 30890.331497868952, 18103.328150578494, 2754.5259951419785, -12984.049763362627, -26885.740267922043, -36984.106617570185, -41851.006696940494, -40798.11732451042, -33974.426392310525, -22344.820380821824, -7554.215667268249, 8305.13805646534, 22989.310557287, 34420.15852537943, 40979.80171441247, 41739.50956364618, 36591.90570845013, 26265.50480790668, 12222.189818315672, -3550.666289207576, -18821.189818431718, -31429.06123917744, -39591.100844044035, -42152.89477405712, -38752.35015462737, -29870.1457693241, -16762.62988226459, -1283.6247575081127, 14376.931303621086, 28003.046212569217, 37666.25884005016, 41998.654683028784, 40386.998153693836, 33059.35011865029, 21053.112587257318, 6067.57971116985, -9776.517140006938, -24237.47193157102, -35269.70075707248, -41312.85816521044, -41512.38807107915, -35840.02641379248, -25097.990516714875, -10805.477087570558, 5015.7226075170765, 20127.196429071748, 32390.45198142653, 40069.762461740844, 42078.02784524413, 38130.973858626225, 28787.328574089242, 15369.752527682327, -222.75083913334484, -15783.789856430674, -29111.85169706765, -38321.783671780126, -42110.99233903175, -39943.75661725837, -32126.394359744776, -19764.5935707179, -4606.680772970673, 11202.949968187648, 25427.299399334224, 36053.331269462244, 41576.846243674336, 41216.06294857437, 35021.859783977525, 23871.319284234993, 9342.555748303734, -6508.161692082818, -21438.123073192, -33335.319923987416, -40516.980531426205, -41967.58853211467, -37481.89632801874, -27694.373213008745, -13989.1088465023, 1695.2031177060976, 17139.62420764959, 30158.65618624187, 38909.6147788178, 42153.80750065348, 39431.88722050544, 31129.277134389078, 18421.18459327842, 3106.347768738171, -12648.070793236551, -26613.117351003904, -36813.40149250679, -41806.3238097086, -40885.803757804955, -34182.02236228202, -22642.99860142505, -7900.777783782384, 7959.231498403331, 22692.97973612324, 34215.39074965476, 40895.491565523356, 41787.70399882338, 36765.610485676654, 26540.282705549398, 12559.013092596731, -3199.3990688900744, -18505.17461636978, -31193.05812202745, -39468.37407197977, -42160.94171411494, -38889.824844918134, -30117.81184506893, -17085.25048857724, -1635.6935089606777, 14045.270100494441, 27738.7591663093, 37506.67005959764, 41966.451603413974, 40486.60130311142, 33276.79468086079, 21357.46896939464, 6415.866883454748, -9433.629437355505, -23948.491877172637, -35075.451563598064, -41240.85452511276, -41572.73552417208, -36024.25734997304, -25379.961060057998, -11145.365858370698, 4666.042431657759, 19817.19180511566, 32163.985399014276, 39958.88759959271, 42098.42676178947, 38279.746945955136, 29043.40390583562, 15696.887629397073, 129.12422550038204, -15456.93987921344, -28856.289177874904, -38173.59827332985, -42091.2050153212, -40055.0411324366, -32353.129355330402, -20074.59152024731, -4956.1575586313975, 10863.455184112818, 25145.8643400068, 35869.70923389763, 41517.14151295139, 41288.54956415172, 35216.467685193216, 24160.283140811563, 9685.13304449285, -6160.531453135585, -21134.62463018192, -33118.81366355257, -40418.184694920645, -42000.325389134414, -37641.68981054293, -27958.437332699697, -14320.240265705359, 1343.947656749712, 16817.918580026373, 29911.987021287598, 38772.948546252475, 42146.401938713214, 39554.877209491075, 31365.140001693726, 18736.61692161505, 3456.639180552086, -12312.461348758805, -26339.655726525154, -36640.7778901453, -41758.91157807431, -40970.32427229467, -34386.477288754366, -22938.506968516584, -8245.492763199309, 7614.0494799623375, 22396.19409638966, 34008.98044271151, 40808.69944147032, 41832.747366921016, 36936.18055250649, 26812.147293909245, 12893.740539991019, -2849.199474825707, -18189.06881033959, -30955.70810668819, -39343.42479013778, -42165.92256930076, -39024.18814593672, -30362.35228147116, -17405.54481881375, -1986.3230625686558, 13713.888495461077, 27473.47731377999, 37345.12642779574, 41931.37205221622, 40583.11653208445, 33491.02619272064, 21659.277003005263, 6762.382452309398, -9091.370189757981, -23658.896939182643, -34879.53008333232, -41166.22276051942, -41630.03385246661, -36205.25862238809, -25659.175518665, -11483.196414280741, 4317.340723953969, 19506.96098515828, 31936.15000066513, 39845.64859323215, 42115.85645939441, 38425.306072619176, 29296.545916653424, 16021.710914923959, 479.7012174891581, -15130.235751796714, -28599.6603509553, -38023.33894929775, -42068.53060857178, -40163.18152320191, -32576.740578259745, -20382.069543766145, -5303.996949331426, 10524.482239195686, 24863.694482957893, 35684.32769643502, 41454.69695052938, 41357.99429244861, 35407.80514853182, 24446.570298990155, 10025.731993351093, -5813.771873009333, -20830.747919958718, -32900.8672142189, -40316.87081851172, -42030.12065615351, -37798.16236319913, -28219.66905422684, -14649.078530708031, 993.9012574863758, 16496.221398221784, 29664.20846830073, 38634.06663993009, 42136.183344690005, 39674.622731503536, 31598.03641180598, 19049.553022820193, 3805.4135132217284, -11977.23574793466, -26065.407941225487, -36466.24424481385, -41708.820347860325, -41051.67852544202, -34587.84165202491, -23231.312556816687, -8588.381856676839, 7269.62406023295, 22098.96100292243, 33800.99296828227, 40719.39762429853, 41874.758405918255, 37103.54894496078, 27081.164313347082, 13226.339093312305, -2500.113287279013, -17872.86235318143, -30717.136730103404, -39216.17117831653, -42168.05187665393, -39155.26999248814, -30603.965431113367, -17723.40218073192, -2335.518116568542, 13382.768740208663, 27207.33394826103, 37181.539327588616, 41893.620316564105, 40676.35525532079, 33702.256016084735, 21958.383466430783, 7107.177757988002, -8749.743906296786, -23368.78507654438, -34681.891006172715, -41089.10905201398, -41684.19772863927, -36383.19522584402, -25935.535285809055, -11819.027872655837, 3969.6337206451512, 19196.562391477153, 31706.940693561788, 39730.12842631006, 42130.2874111128, 38567.733021772365, 29546.694917159697, 16344.257300681445, 828.9326665254841, -14803.712543856338, -28342.02093330418, -37871.03392182038, -42043.06143545029, -40268.16482401768, -32797.28544237618, -20687.017101904465, -5650.166319766291, 10186.039824118721, 24580.889694559628, 35497.1583628035, 41389.68806105005, 41424.27369661224, 35596.06078061772, 24730.068757846282, 10364.391741161382, -5467.889944687463, -20526.602327932473, -32681.421014949923, -40213.24196596971, -42056.813939380045, -37951.56932819325, -28477.886761374368, -14975.746425792473, 645.095967148338, 16174.616342650708, 29415.263647823987, 38493.115644624544, 42123.03015690611, 39791.29724270873, 31827.80716537975, 19360.077135691066, 4152.600768335257, -11642.45563502713, -25790.37200161503, -36289.9035259951, -41656.021696088115, -41129.975346009574, -34786.03876515555, -23521.482723176247, -8929.386107580322, 6925.990028349207, 21801.316843824072, 33591.48581059153, 40627.63246918675, 41913.76788769105, 37267.73867782762, 27347.35090550087, 13556.776925974085, -2152.1471755384327, -17556.619748395082, -30477.3386545952, -39086.73322667409, -42167.23852337757, -39283.22846479495, -30842.542612005018, -18038.881173126436, -2683.258838949601, 13051.982837654843, 26940.27772386088, 37016.06625554717, 41853.050254770336, 40766.55390576576, 33910.28124017386, 22254.93262259079, 7450.167822754703, -8408.809536233173, -23078.114668078084, -34482.670080066564, -41009.38472781306, -41735.39965350434, -36557.86116381084, -26209.17035124355, -12152.73618610371, 3622.9566985326537, 18885.98426313966, 31476.465522199145, 39612.28113743487, 42141.85807348376, 38706.937253886, 29793.96352324228, 16664.448300271473, 1176.8139219991406, -14477.389767100693, -28083.429686740743, -37716.69530299971, -42014.84717268828, -40369.98490177513, -33014.80918148177, -20989.39385950048, -5994.677777417854, 9848.166196852715, 24297.458596478067, 35308.2707615767, 41322.07656086023, 41487.49970168344, 35781.160356418295, 25010.82727852774, 10701.080035356017, -5122.941213863042, -20222.17294631668, -32460.601328301847, -40107.198028051076, -42080.61691621145, -38101.72130532198, -28733.267745435696, -15300.129060195732, 297.55163693633284, 15853.083050953175, 29165.29402133467, 38349.99350734382, 42107.14524093962, 39904.700904017576, 32054.647113136994, 19668.033488878104, 4498.226194748497, -11308.120660214698, -25514.65165554077, -36111.71357769948, -41600.667021382375, -41205.12828546132, -34981.22794567282, -23808.921934266225, -9268.545919072882, 6583.163513695248, 21503.328401755458, 33380.45407054175, 40533.48930636017, 41949.74506854782, 37428.833209563934, 27610.64546865204, 13885.086089696433, -1805.3461443908113, -17240.377395800915, -30236.370330404407, -38955.13290691096, -42163.576348950155, -39408.04267166684, -31078.134318164703, -18351.965306348186, -3029.503640728119, 12721.540863323129, 26672.416351493743, 36848.66799369755, 41809.842242855964, 40853.57632413723, 34115.281320113274, 22548.8157389931, 7791.374203465721, -8068.569423469403, -22787.00479188802, -34281.805225458564, -40927.25655365689, -41783.470069101655, -36729.49808776109, -26479.901594564475, -12484.41628252153, 3277.3333605880994, 18575.320903550557, 31244.66723747242, 39492.25993004646, 42150.449859777895, 38843.095412552655, 30038.1988918293, 16982.363855004227, 1523.289815016102, -14151.336054003428, -27823.887265421505, -37560.43317224093, -41983.860795904635, -40468.75958554683, -33229.24027282051, -21289.266175190303, -6337.476745671029, 9510.901534803472, 24013.44158101065, 35117.723010011585, 41251.915418487064, 41547.703382049214, 35963.12790432845, 25288.863639639985, 11035.762130308807, -4778.938380947175, -19917.533053914518, -32238.40660206855, -39998.86901908532, -42101.440049552206, -38248.77415175858, -28985.713088831304, -15622.273395042484, -48.717954427698835, 15531.702578155038, 28914.24663083384, 38204.86539556785, 42088.376604115016, 40015.06573842994, 32278.358780092756, 19973.553416025417, 4842.223128236119, -10974.30048110409, -25238.19916451848, -35931.808667447614, -41542.61947128564, -41277.30603489173, -35173.203455122515, -24093.741711161383, -9605.753057593283, 6241.191073850677, 21204.978231417706, 33168.00961517921, 40436.9163906376, 41982.83075778131, 37586.73486101586, 27871.15649754529, 14211.184044232994, -1459.7277766187622, -16924.15754913699, -29994.29914644118, -38821.385622706955, -42157.12317079167, -39529.70685905558, -31310.788447237475, -18662.615606301628, -3374.2581650512266, 12391.484437997546, 26403.763197134715, 36679.42103414735, 41763.96109320079, 40937.535082623006, 34317.194830666485, 22840.075120998637, 8130.7754886049715, -7729.0812753518885, -22495.43565269424, -34079.42032438292, -40842.6195186085, -41828.61439048038, -36897.92767862762, -26747.888431685922, -12813.975345815976, 2932.7962635606, 18264.543286158812, 31011.68466774314, 39369.9486279515, 42156.256267917735, 38975.99507561737, 30279.577108949343, 17297.853904655287, 1868.3638201974395, -13825.540814295084, -27563.4985439396, -37402.18415085556, -41950.25144302199, -40564.36957107177, -33440.72333749119, -21586.521303558988, -6678.587218750899, 9174.259574991067, 23728.909146432197, 34925.50839898878, 41179.29488952383, 41604.85164531924, 36142.05464037725, 25564.11909122553, 11368.470378605212, -4435.916247891232, -19612.716396628242, -32014.885237158338, -39888.277407751484, -42119.367103243654, -38392.720555220956, -29235.261417651498, -15942.17231347695, -393.6686779761868, 15210.48358388222, 28662.2288120906, 38057.69220909468, 42066.907719693845, 40122.26980667829, 32499.113070301242, 20276.549812452846, 5184.597439508058, -10640.990424446405, -24961.139244947295, -35750.12056001782, -41482.08640037805, -41346.33405490097, -35362.1962999256, -24375.7861121173, -9941.082752611024, 5900.080132346802, 20906.366016023494, 32954.08747463287, 40338.07251574228, 42012.89172481428, 37741.624517878874, 28128.726393972687, 14535.151666932949, -1115.3276828169019, -16608.024496474773, -29751.106949902583, -38685.60102759474, -42147.836258334, -39648.34436654602, -31540.430384851603, -18970.895325885056, -3717.477148581867, 12061.854430951487, 26134.349285469805, 36508.38350588398, 41715.45000037322, 41018.47017553291, 34516.031556050635, 23128.736034791928, 8468.33376499746, -7390.358467495255, -22203.478301262854, -33875.51936152143, -40755.604164599106, -41870.76580565034, -37063.295139332746, -27013.061065939797, -13141.444621763601, 2589.3489046730374, 17953.737567063712, 30777.466638316306, 39245.52381792619, 42159.12964776565, 39105.88033502572, 30517.925457683552, 17611.04928580993, 2212.000964412135, -13500.074069161921, -27302.200298632444, -37242.09697385265, -41913.872738404156, -40657.01127058653, -33649.06027886518, -21881.28460763021, -7017.92385825658, 8838.28969349376, 23443.8321246164, 34731.73637353697, 41104.14635841184, 41659.084595100976, 36317.82758558568, 25836.69631447276, 11699.127437162715, -4093.897278155987, -19307.734247119748, -31790.105107331747, -39775.42511482028, -42134.463806289175, -38533.534708111736, -29481.962249546505, -16259.780601025019, -737.3023745032407, 14889.46332123037, 28409.25314954265, 37908.54473999976, 42042.7020905969, 40226.41323037361, 32716.86228980834, 20577.054852974103, 5525.346637691787, -10308.240177390537, -24683.437216637503, -35566.77154205243, -41418.95588122264, -41412.41968140501, -35548.038461595344, -24655.19181114536, -10274.459376936367, 5559.876990254017, 20607.4545761233, 32738.82973219528, 40236.8309937671, 42040.13754759983, 37893.29012184608, 28383.542749131127, 14856.85732104061, -772.1512397597788, -16291.955760540464, -29506.905753891107, -38547.69990278982, -42135.86535554523, -39763.81520357963, -31767.193407567498, -19276.690411453874, -4059.170123515413, 11732.659002064393, 25864.24868488866, 36335.54184248512, 41664.40205765348, 41096.33761699158, 34711.88822124738, 23414.741374949463, 8804.082581752084, -7052.422865467478, -21911.15927840532, -33670.13573922943, -40666.22358430505, -41909.978840123425, -37225.59057702302, -27275.42770891119, -13466.816831927365, 2247.0422942324567, 17642.909988324496, 30542.118165171683, 39118.93567468779, 42159.25407705278, 39232.62241482025, 30753.400793262692, 17921.866207285602, 2554.1878803531763, -13174.927113793035, -27040.127136416428, -37080.089753245295, -41874.94076519528, -40746.4949886676, -33854.477379998374, -22173.398917590617, -7355.537525780842, 8502.994086571598, 23158.323929520542, 34536.34678140991, 41026.644751637716, 41710.27182649208, 36490.63897959762, 26106.45025372759, 12027.808220780818, -3752.9049692839335, -19002.658498448724, -31564.04630112831, -39660.427912195824, -42146.67542821833, -38671.343793970096, -29725.73812974861, -16575.154175928954, -1079.5764533942468, 14568.69128778207, 28155.352484929917, 37757.48275539089, 42015.801470185535, 40327.53425612027, 32931.60482952924, 20875.086927183387, 5864.421979322249, -9976.07180404712, -24405.17423452268, -35381.764235139824, -41353.36415568638, -41475.4897691096, -35730.86466274888, -24931.89388169214, -10605.895072213747, 5220.5863469009355, 20308.34405942276, 32522.188739887762, 40133.38152061662, 42064.407813751815, 38041.973390876, 28635.42585687997, 15176.408022083882, -430.2293485921099, -15976.03877084462, -29261.63094008911, -38407.84945294688, -42121.065870929364, -39876.33198830697, -31990.889670793225, -19580.11921955105, -4399.264486177355, 11403.962577621505, 25593.436781704037, 36161.015271541226, 41610.751782316234, 41171.28196411244, 34904.65244542785, 23698.181300538025, 9137.947558889173, -6715.314695010485, -21618.499213832947, -33463.34743161707, -40574.485091686875, -41946.32672574381, -37384.786233396415, -27535.04130790314, -13790.043474975379, 1905.885229207546, 17332.10649124747, 30305.657429780582, 38990.263829285854, 42156.58541224006, 39356.317584540695, 30985.94849570868, 18230.323971657785, 2894.9184835212677, -12850.16084855242, -26777.241804997728, -36916.295672815235, -41833.330839914495, -40833.02876805111, -34056.80060111579, -22462.985776180525, -7691.360868819307, 8168.433499833112, 22872.341267952794, 34339.481776124085, 40946.65065797256, 41758.61623923643, 36660.268265111015, 26373.54381675992, 12354.386138833403, -3412.9678232645365, -18697.465228365603, -31336.825446896513, -39543.203494359936, -42156.158241588695, -38806.005462032495, -29966.716759718856, -16888.17907355944, -1420.4833374063976, 14248.175833747322, 27900.608052525167, 37604.495107172115, 41986.30140459179, 40425.587557267194, 33143.433887248546, 21170.588723804067, 6201.846064801572, -9644.510753874862, -24126.379387957724, -35195.13636597016, -41285.31875201121, -41535.598169662735, -35910.66268692378, -25205.89324662513, -10935.383150771278, 4882.260870216297, 20009.035907263613, 32304.26573832591, 40027.66049017502, 42085.88256683641, 38187.53676142994, 28884.516936874265, 15493.723945037014, -89.58928156050467, -15660.257994590367, -29015.419212039174, -38265.95183226153, -42103.64874795469, -39985.691506957766, -32211.72603590705, -19881.027550594183, -4737.785879587253, 11075.759597365692, 25322.031189268666, 35984.736187741575, 41554.670708001075, 41243.159939198085, 35094.501391044134, 23978.910262720463, 9469.97934840661, -6379.051662316248, -21325.575717179978, -33255.13469709755, -40480.51088458044, -41979.754732231566, -37541.01607017647, -27791.826359165072, -14111.177154077877, 1565.913320045021, 17021.37956455058, 30068.115388238934, 38859.56579614628, 42151.16638699154, 39477.00475943747, 31215.567864130706, 18536.438412643925, 3234.144603537944, -12525.798354527195, -26513.629417491356, -36750.712762268464, -41789.18450366474, -40916.53843475917, -34256.158716767364, -22749.986599641077, -8025.393102024463, 7834.611791474242, 22585.99353787937, 34141.092508863985, 40864.357223882966, 41803.95717678483, 36826.94366718821, 26637.81471077822, 12678.944956652784, -3074.103766033754, -18392.25066971488, -31108.37589914831, -39423.922363862635, -42162.75801794828, -38937.726523482044, -30204.708374512593, -17198.962055043427, -1759.9671465715558, 13927.9876932287, 27644.987344452496, 37449.70593364901, 41954.12829735327, 40520.72545432158, 33352.24015594893, 21463.61097835466], \"y\": [0.0, 15576.050639839646, 28947.673255183683, 38222.67821692621, 42088.91797509229, 39999.967566771404, 32251.94264774194, 19941.490737583095, 4810.225586419786, -11001.437683135267, -25256.92812691474, -35939.721100098606, -41538.111858435186, -41259.703301843045, -35143.06852148851, -24053.36249514699, -9559.52839398408, 6287.2253452990835, 21244.147604295555, 33194.566481971466, 40447.39483958242, 41977.006975830125, 37567.375139508884, 27843.141926045617, 14180.209675709226, -1488.497860903827, -16946.658173238702, -30007.703864714287, -38823.79836393347, -42147.427048606296, -39507.633495937596, -31277.43331574384, -18620.939142118554, -3329.1297384597483, 12433.858253014154, 26437.11233403431, 36698.947900978295, 41767.585461208815, 40926.29558885071, 34294.81004188983, 22811.76527590234, 8101.892196232001, -7754.0009458026025, -22513.08355685165, -34087.596798950006, -40839.88590534016, -41814.201833932784, -36872.04104984537, -26712.17990909279, -12771.981766553896, 2975.797840743753, 18302.408728537772, 31038.801388640015, 39382.615775853985, 42153.73201318321, 38960.48555344125, 30255.43151305792, 17270.35589236439, 1842.3910950816717, -13846.189457359425, -27576.24770784686, -37405.33690959409, -41942.78577524175, -40545.83422674302, -33411.74618791657, -21549.356444581823, -6637.270223828384, 9214.199478020904, 23761.605964433234, 34946.1620534729, 41185.49891056899, 41597.077717728935, 36123.47047538086, 25539.477663374204, 11342.781612979716, -4458.3651097848015, -19628.880532673564, -32022.804183201057, -39886.73149660911, -42107.59558769052, -38370.654464885345, -29204.003874440157, -15904.4668934768, -354.00847324343886, 15246.541319846427, 28689.303204814827, 38071.9770687892, 42067.212919785314, 40110.279024556636, 32478.671550851952, 20252.509483571146, 5161.558640832761, -10659.490550346804, -24972.77753615514, -35753.45656320402, -41476.231962125785, -41330.806922843876, -35337.24955271186, -24343.020254506595, -9903.750822589453, 5937.204609432009, 20937.884600842262, 32975.2912267578, 40346.30245475368, 42008.151443271876, 37726.54637706942, 28107.625946152075, 14512.692866801188, -1135.113340799736, -16622.35299652693, -29758.29484673034, -38684.69055914065, -42138.12923858937, -39629.63583562469, -31513.334259449177, -18937.44235464832, -3681.401137217969, 12095.658607264611, 26160.827089694307, 36523.719030096225, 41718.10542353399, 41009.62796790298, 34499.06419363465, 23108.079033552734, 8448.306368650372, -7406.4624886965885, -22213.59664391943, -33878.51229794544, -40750.781836881695, -41857.69887885809, -37041.97634813994, -26984.506596343912, -13108.16750793774, 2623.3381918950295, 17983.577217606296, 30798.648928004837, 39255.21264025826, 42156.96646683212, 39093.9921722402, 30500.194186197907, 17591.81769620327, 2195.0122628971835, -13512.27809729236, -27308.216259930185, -37241.237130858906, -41905.636711047446, -40641.11170896649, -33625.78048146429, -21852.00335301007, -6985.601144911633, 8869.448692882808, 23469.200811562947, 34747.57977860899, 41108.6412366521, 41653.005849396875, 36304.052035888315, 25819.282730566312, 11682.140288913708, -4107.421769600331, -19315.98792076222, -31792.271675793785, -39771.07707133135, -42123.29868061276, -38515.38210369842, -29457.360559433833, -16230.550493746696, -706.7005355886004, 14917.17407280092, 28429.867340640278, 37919.15392746285, 42042.644816208194, 40217.371624762294, 32702.281381099885, 20560.96907130696, 5511.234719531205, -10318.073628812184, -24687.876165188365, -35565.422896907265, -41411.60581418324, -41398.79859383189, -35528.18095138767, -24629.929660530255, -10245.980952456215, 5588.0692433928225, 20631.230017345166, 32754.597415578202, 40242.62978987015, 42036.33310518886, 37882.38635568207, 28369.210795194067, 14842.887241061258, -782.9560049211356, -16298.05647251092, -29507.80297493676, -38543.27355646938, -42126.0179527848, -39748.3162135203, -31746.21040393671, -19251.429032690394, -4032.1277483650283, 11757.853710013173, 25883.781965388094, 36346.5406495966, 41665.98005530692, 41089.75033444438, 34700.200304010505, 23401.668385099863, 8792.866090129553, -7059.696672225593, -21913.671512840207, -33667.82092461174, -40659.19043019838, -41898.09326213735, -37208.71120765418, -27253.9397783264, -13442.197450783724, 2272.0081063105445, 17664.64576638358, 30557.270789105874, 39125.50744309074, 42157.284394977614, 39224.24525613517, 30741.951966903234, 17910.835525202187, 2546.1761231319624, -13178.634639349859, -27039.336768682497, -37075.049238465006, -41865.79302128527, -40733.10765630863, -33836.7365425152, -22151.956006086537, -7332.172870764851, 8525.345761981123, 23176.2978632954, 34547.200120362824, 41029.31402535306, 41705.70453420742, 36481.51232995132, 26096.212285240817, 12019.458123180762, -3757.4909552180566, -19002.946106537318, -31560.326377451463, -39653.18155289527, -42135.94565130783, -38656.96692492097, -29707.70771540186, -16554.320322987103, -1058.0291387788766, 14587.997405853524, 28169.398383506483, 37764.305439570184, 42015.213221807026, 40321.30679956986, 32922.77709009441, 20866.86651296848, 5859.2201166172135, -9977.198280782373, -24402.3403459567, -35375.61451544841, -41344.36196925194, -41463.66144471696, -35715.95318575456, -24914.048495624724, -10586.237349532137, 5239.827700329867, 20324.314788335152, 32532.373006805457, 40136.61744278784, 42061.41015094634, 38035.07865044619, 28627.804561289824, 15170.857687173911, -432.0561893125544, -15973.863351843718, -29256.09785308405, -38399.81000381712, -42110.89518837169, -39863.88711733731, -31975.94315139739, -19562.94823519246, -4381.254691143195, 11420.504199065537, 25605.91182493048, 36167.583939785785, 41611.047722083866, 41166.80925708146, 34898.12675346918, 23692.576210762658, 9135.522247440234, -6713.734390830239, -21613.325098249356, -33455.63618071333, -40565.08033860222, -41935.493416222984, -37372.22078089918, -27520.503739196658, -13774.04302481858, 1921.815243772053, 17345.68138846244, 30314.678015831105, 38993.56893537114, 42154.69934897975, 39351.29905178191, 30980.684221406646, 18227.43764867251, 2895.868747755701, -12845.332615937778, -26769.531888512825, -36906.95734378534, -41823.14009035011, -40821.98560544834, -34044.51985996596, -22449.290101881757, -7676.932853263716, 8181.941758438482, 22882.81268132721, 34345.25700820911, 40947.31869056858, 41755.414708224336, 36655.697241095244, 26370.35185336736, 12354.652873664887, -3408.6023123783148, -18689.711618576523, -31327.141664927534, -39532.85190632634, -42145.73073854592, -38795.251262285594, -29955.11712086175, -16875.716059585473, -1407.987962499891, 14259.021315202217, 27908.002178270905, 37607.37094404862, 41985.066476159234, 40422.03448589653, 33140.223506783346, 21170.175706133836, 6205.528592537047, -9636.897882199826, -24116.18582183415, -35184.04502288821, -41274.544220330696, -41525.39887629461, -35900.57937396434, -25195.390286076283, -10924.50701484136, 4892.527397570687, 20017.111429149474, 32308.758537158013, 40028.20134419828, 42083.52293150415, 38184.554249794615, 28883.491621584915, 15496.5446248265, -82.4288527037226, -15649.737399096462, -29003.380049866726, -38254.134858152865, -42093.01671762894, -39976.19303556531, -32202.675937346314, -19871.913484008193, -4728.7957079349835, 11083.603280710187, 25327.375405007406, 35986.687070865584, 41553.57089193667, 41240.63048855227, 35092.991826179765, 23980.740092046522, 9476.306261294298, -6368.5851914747755, -21312.646249523303, -33241.875037829865, -40468.62595186069, -41969.80515016697, -37532.62077685523, -27784.163535623396, -14103.730857910607, 1572.7967181210217, 17026.72136537317, 30070.87786399026, 38859.49006359347, 42149.191969402964, 39475.211981891895, 31216.39025832505, 18541.623513319213, 3244.0532427916455, -12512.376146880744, -26498.91225444073, -36736.95364136078, -41777.788117525444, -40907.7274308023, -34249.20724093115, -22743.96094660725, -8019.88508862319, 7839.239935419415, 22588.893883722634, 34141.63893272524, 40862.892665072664, 41802.00103635723, 36826.78003709748, 26641.618375476282, 12687.784766092751, -3060.7751618110606, -18376.399504230834, -31092.572674949715, -39410.37063261503, -42152.45933411768, -38930.452880594275, -30199.483881984677, -17194.79318255448, -1756.5344354412157, 13930.318500596237, 27645.606536178806, 37448.53952582213, 41952.05227266833, 40519.70128877243, 33354.54425254981, 21470.9334404906, 6550.110784109218, -9297.211942564485, -23829.42333181529, -34990.836670203316, -41202.11643768236, -41584.11574150454, -36082.03083427641, -25473.97456778995, -11260.751366151666, 4546.182353746481, 19709.69385607981, 32083.779545921392, 39917.4461660724, 42102.69406237838, 38330.86057323641, 29136.247437622835, 15819.963672746844, 265.9104008594165, -15325.768763278982, -28749.563292438306, -38106.43266921472, -42072.2630203721, -40085.38866384176, -32426.31213434473, -20178.394859512387, -5074.701648264032, 10747.221506131957, 25048.075011418394, 35804.09518123131, 41493.343659271966, 41311.44929042856, 35284.642892114345, 24266.228894602602, 9815.136782465914, -6024.296713401798, -21011.584369707914, -33026.727647778636, -40369.63730905309, -42001.21753952667, -37689.76445278017, -28044.9810013123, -14431.197274882152, 1224.9738301127525, 16707.77590187253, 29825.996675006372, 38723.20405296741, 42140.910356877335, 39595.93053712391, 31449.118588434394, 18853.35635655502, 3590.7303750294054, -12179.809978029938, -26227.504590123546, -36565.056073199725, -41729.78460328344, -40990.33841617203, -34450.805894231984, -23035.97693744669, -8361.015506804004, 7497.295762203545, 22294.503555521016, 33936.481416953124, 40775.96234543106, 41845.59028841822, 36994.68102395751, 26910.078224203673, 13018.786208296533, -2714.0446823411517, -18062.964363318337, -30856.8356454215, -39285.55963730699, -42156.37910122463, -39062.40770384592, -30440.931131722933, -17511.458418378716, -2103.6637764860416, 13601.872682300638, 27382.384883931416, 37287.63056646435, 41916.41910462835, 40614.129721022204, 33565.858880301465, 21769.074633598917, 6892.979177819853, -8958.155495973055, -23542.163689526806, -34795.91235015624, -41127.25850180197, -41639.729947983076, -36260.41611735856, -25749.766024960998, -11594.994427160103, 4200.826964105702, 19402.101225375805, 31857.434616860963, 39804.445762452415, 42118.899008160435, 38474.0480732399, 29386.067324432766, 16141.10738008785, 612.9163829340634, -15001.961783742636, -28494.765800795736, -37956.69287869103, -42048.74537196904, -40191.44831769816, -32646.924886623292, -20482.342340611016, -5418.966491684122, 10411.352806340365, 24768.171333167793, 35619.68615613258, 41430.599342747526, 41379.1283834496, 35473.23669170757, 24548.957333551418, 10152.053531758249, -5680.886016957364, -20710.276523076787, -32810.04161030744, -40268.39481950695, -42029.5401542634, -37843.85061228052, -28302.861874300284, -14756.490910362816, 878.3787469504631, 16388.928140933163, 29579.948928747082, 38584.910546098006, 42129.71062547396, 39713.59847289446, 31678.80817503406, 19162.67328257283, 3935.8538946348413, -11847.679256420904, -25955.318186351426, -36391.39611759615, -41679.097829063154, -41069.924538937776, -34649.29364847761, -23325.35396886523, -8700.284249675522, 7156.126034369262, 21999.721734020444, 33729.81773790738, 40686.598155151776, 41886.20767540934, 37159.4506180191, 27175.706406992515, 13347.665146058767, -2368.4243167605564, -17749.510927593532, -30619.84576850421, -39158.60874613951, -42157.379013718426, -39191.26789327764, -30679.370247107272, -17825.774869253066, -2449.3369434158567, 13273.767570952252, 27118.22224338097, 37124.897132315076, 41877.96590363274, 40705.54316526222, 33774.029301880124, 22064.65756739776, 7234.062927321132, -8619.784939688121, -23254.338869992433, -34599.46045184966, -41049.77672162906, -41692.41504422576, -36435.597071968725, -26022.81954729778, -11927.171888173998, 3856.49227119977, 19094.340726982824, 31629.865019238074, 39689.126227338405, 42132.2869879726, 38614.064226560295, 29632.997166732013, 16459.937585064385, 958.5833943647058, -14678.364069162732, -28239.027375186935, -37804.937522634114, -42022.51972858117, -40294.38327303435, -32864.52109040999, -20783.762651742018, -5761.578729806551, 10076.059810126435, 24487.62583822356, 35433.59677903695, 41365.2753298676, 41443.7876472755, 35658.70430596184, 24828.985822753646, 10487.002483486063, -5338.395628702867, -20408.660560331748, -32592.032055136187, -40164.724892264494, -42055.00618927224, -37994.72659385558, -28557.90889613557, -15079.521130593606, 533.0317568470863, 16070.146274261318, 29332.918191448567, 38444.41222347489, 42115.824492295564, 39828.036567266514, 31905.556945265853, 19469.496459728292, 4279.424338205098, -11515.993243688812, -25682.471666544763, -36215.87078106635, -41625.900942338565, -41146.384091226166, -34844.76627488382, -23612.056135120103, -9037.71091683417, 6815.761168101583, 21704.595614578644, 33521.63667959824, 40594.90047259416, 41923.826520708506, 37321.140465980185, 27438.49885900789, 13674.421551823267, -2023.957865878273, -17436.04016498307, -30381.710988910476, -39029.51562950352, -42155.557131385925, -39317.01434612896, -30914.86653434667, -18137.699989145192, -2793.53821618344, 12945.988831734143, 26853.2923201948, 36960.22746219509, 41836.92125784539, 40793.82515115885, 33979.20097823473, 22357.60600100942, 7573.391252974513, -8282.099903869594, -22966.096344620066, -34401.32351022127, -40969.946151975375, -41741.98828128521, -36607.756932685494, -26293.047778095108, -12257.328472165023, 3513.198769768928, 18786.50180894272, 31400.95577292128, 39571.69138911412, 42142.701573162856, 38751.04902047248, 29876.97645055813, 16776.49500619295, 1302.8775260555908, -14355.021899626705, -27982.33607294019, -37651.29889947184, -41993.53951743205, -40394.295503870475, -33079.07807800116, -21082.674311376762, -6102.499301292181, 9741.358131388135, 24206.509379857504, 35245.869647411826, 41297.42603402057, 41505.46135020327, 35841.08899710849, 25106.30072517925, 10819.9850191905, -4996.829765575325, -20106.8492300663, -32372.62957731715, -40058.8102672656, -42077.52661226417, -38142.537442967485, -28810.05328281993, -15400.351526912995, 188.9515148580975, 15751.522885380544, 29084.771781724314, 38301.97050308671, 42099.06252521396, 39939.468692327064, 32129.247082905327, 19773.900056046215, 4621.390795181904, -11184.81111118216, -25408.87467650989, -36038.69045864304, -41569.99873027921, -41219.90613828881, -35037.09857788548, -23896.136328072505, -9373.239616040326, 6476.233556552065, 21409.10932465502, 33312.08059210873, 40500.7711959096, 41958.58792501223, 37479.691686606326, 27698.498177511206, 13999.015004841393, -1680.6539217700222, -17122.591376324435, -30142.48089784744, -38898.28575217292, -42150.976292025414, -39439.6504495407, -31147.430241994818, -18447.23792295335, -3136.267587253536, 12618.595673075959, 26587.54391439428, 36793.73721009856, 41793.222557193614, 40879.076465725, 34181.311823711105, 22647.97805808541, 7910.923346647105, -7945.1544446453, -22677.36192305138, -34201.71814083619, -40887.597425376174, -41788.67445830308, -36776.755675177104, -26560.548030430913, -12585.385736325881, 3170.970404628738, 18478.5306713366, 31170.911006432332, 39451.93491074059, 42150.38382837721, 38884.829692883955, 30118.099728952562, 17090.69949775844, 1645.78756741726, -14031.934440777464, -27724.82253038277, -37495.659903591055, -41961.97863099892, -40491.079972775064, -33290.681426675495, -21379.04086647305, -6441.746090376211, 9407.268545464427, 23924.871448091195, 35056.47381961391, 41227.150584222254, 41564.11233001389, 36020.43686499709, 25380.88901665517, 11151.002539614912, -4656.236170856969, -19804.839007508046, -32151.920148578432, -39950.64636454436, -42097.17686032502, -38287.25926794004, -29059.346879378707, -15718.93895303926, -153.8344865362429, 15433.033114587786, 28835.694496607375, 38157.4653047315, 42079.644386791275, 40047.773585010764, 32350.00915038555, 20075.79961244419, 4961.765197361708, -10854.125748543867, -25134.69957703618, -35859.68498290374, -41511.67027456439, -41290.305947804365, -35226.46092659132, -24177.514827544775, -9706.905796626155, 6137.557687728533, 21113.3724767567, 33101.02993232001, 40404.43155661719, 41990.347278612906, 37635.24267470367, 27955.65208300237, 14321.481206222088, -1338.5428365527505, -16809.222134512616, -29902.133610259334, -38765.06080825069, -42143.585996874885, -39559.27386745494, -31377.035770580653, -18754.401043205587, -3477.477974970847, 12291.609452384375, 26321.05174070239, 36625.474228715015, 41746.9209865033, 40961.32799418419, 34380.39903705918, 22935.75223569157, 8246.646772800703, -7608.952958844614, -22388.263789322355, -34000.57736307665, -40802.912699738146, -41832.39036114801, -36942.72711140302, -26825.248917588342, -12911.389215191579, 2829.828398487832, 18170.547743211675, 30939.588183642572, 39330.129169270076, 42155.141154044744, 39015.6227568376, 30356.254484612935, 17402.62186172231, 1987.2711864013302, -13709.177928603433, -27466.38818959167, -37338.24922470437, -41927.64934485883, -40584.924387862804, -33499.220645501555, -21672.906341765818, -6779.262788104524, 9073.84040693501, 23642.698166215483, 34865.56819166212, 41154.36181469117, 41619.881908212345, 36196.69847783841, 25652.787336498426, 11480.009688104936, -4316.631583072423, -19502.674079084656, -31929.969690324247, -39840.2415920846, -42114.02442026473, -38428.895469043986, -29305.79056524921, -16035.275457539787, -495.3171943426989, 15114.754407367014, 28585.637026919667, 38011.020972454346, 42057.507305976935, 40153.05343934054, 32567.77845058499, 20375.2488208309, 5300.510820655191, -10524.00444707142, -24859.85439072662, -35679.07699343195, -41450.73716886512, -41357.79630343636, -35412.711719085906, -24456.268538358956, -10038.629884692045, 5799.7775778548585, 20817.32143685089, 32888.69980870174, 40305.67046021573, 42019.33136822634, 37787.627232969375, 28210.036761974534, 14641.741728868124, -997.6479775007024, -16495.925037734396, -29660.815557968468, -38629.714098643264, -42133.55613700482, -39675.779451725044, -31603.755241649338, -19059.14545093319, -3817.1735536670635, 11965.058801151446, 26053.880555252526, 36455.41519959457, 41698.121484338175, 41040.55052745209, 34576.50327344475, 23220.916338082447, 8580.562635391294, -7273.544363816833, -22098.79189267723, -33797.98125372471, -40715.88139334558, -41873.199368966125, -37105.64011812326, -27087.19109024175, -13235.294605321738, 2489.811142538988, 17862.516384460876, 30707.177264999737, 39206.1450970461, 42157.183771534554, 39143.302261348814, 30591.554906598565, 17712.175166732988, 2327.3246515112824, -13386.731927618644, -27207.216739127805, -37178.879912164164, -41890.81453387819, -40675.6426677307, -33704.83773442015, -21964.1861848575, -7115.06529454865, 8741.085616270511, 23360.114395085384, 34673.02389224609, 41079.27256772929, 41672.62601004007, 36369.995797616044, 25921.9478890706, 11807.038784962851, -3978.041661417711, -19200.420101330274, -31706.747852314762, -39727.74515467195, -42128.01845196933, -38567.5425411613, -29549.36180862285, -16349.372167152522, -835.4509801537313, 14796.708106602959, 28334.672845201567, 37862.6847610647, 42032.702491933414, 40255.33367269746, 32782.59243973373, 20672.223862194798, 5637.607813163532, -10194.447888345358, -24584.479046729026, -35496.80209486443, -41387.38199828606, -41422.30121077587, -35595.97710301386, -24732.329949588657, -10368.443734439214, 5462.904236853813, 20521.090918875365, 32674.943883963373, 40204.75449089201, 42045.35821810486, 37937.042954553646, 28461.55016500748, 14959.85531220913, -658.003096738898, -16182.78915771736, -29418.40687257345, -38492.4800124085, -42120.69746879619, -39789.345632498014, -31827.48687563094, -19361.515640008296, -4155.305602863448, 11638.99847457548, 25786.00963494035, 36283.68635415888], \"z\": [0.0, 271.8809752273574, 505.28351629006517, 667.179330564762, 734.6647974819019, 698.2020338073938, 562.9597573621555, 348.0800215014314, 83.96280693950011, -192.03080175563363, -440.86131382148017, -627.3301607774125, -725.0504380732515, -720.1908025647986, -613.4245495494754, -419.8530146906875, -166.8622022284004, 109.74391199405069, 370.81796199376726, 579.4133024281035, 706.0118972767777, 732.711382782014, 655.7409796921601, 486.0038640680303, 247.51649956751075, -25.981805134587333, -295.8049973231742, -523.7864013266419, -677.671909554177, -735.6860725533392, -689.6083142808403, -545.9496450788378, -325.02972516050426, -58.11020388453996, 217.03377403161946, 461.46148645597873, 640.5824997411348, 729.0559077581014, 714.3711505089786, 598.6181519315342, 398.18087052255623, 141.419088097769, -135.34655344045282, -392.96730108788495, -595.0011886754583, -712.8628450778355, -729.869607214266, -643.6038849303853, -466.2628627339212, -222.93580926455925, 51.942701119569435, 319.46969019863246, 541.784258089016, 687.4260924016568, 735.7961224721014, 680.0578159999827, 528.1105494138676, 301.45522485600407, 32.15910564607834, -241.68608530626008, -481.3451495433315, -652.9125523641227, -732.1140345605952, -707.7301748656733, -583.2042250451519, -376.1454588896023, -115.85403708161563, 160.83439182220403, 414.7603208660527, 609.9874857480944, 718.8955342075558, 726.079691402821, 630.5375447706364, 445.7932836911129, 197.9890474665874, -77.82098728864752, -342.623320879536, -558.9600739002027, -696.2254544952799, -734.9908054325017, -669.7622815087102, -509.75782458901926, -277.61356216098284, -6.179311778025371, 266.12929662860114, 500.7735872587002, 664.5487870701246, 734.2859133673045, 700.1275336462115, 566.9173575382954, 353.5089280604322, 90.0954164556461, -186.06201961919686, -435.9013798103141, -624.0788493393867, -723.9702910449445, -721.431918028201, -616.8140161574747, -424.9090573500021, -172.8706915866774, 103.63420502885714, 365.4720514802586, 575.5857809293182, 704.2472857831048, 733.2549997069933, 658.5193394279095, 490.6204903082792, 253.3200751015017, -19.813384612949086, -290.1441576463324, -519.4328880679094, -675.2437290426358, -735.5237581243618, -691.7378795687126, -550.0673440492728, -330.5543635186931, -64.2591921754159, 211.1304047715862, 456.63884295614275, 637.5238170323164, 728.192201551277, 715.8257201761394, 602.1834458427097, 403.3530942415586, 147.4658344884871, -129.2801749813285, -387.7396679857839, -591.3515478581137, -711.3074908641534, -730.6288395920003, -646.5701323693817, -471.01638342944983, -228.80401308208383, 45.790424430422235, 313.9043940168338, 537.5923190653331, 685.2022181429323, 735.8525600099995, 682.3881896475451, 532.3829307383484, 307.06641586964724, 38.31419911746771, -235.85756858207364, -476.66657695925227, -650.048129121791, -731.4655667502599, -709.3932474866606, -586.9402322602826, -381.42822830852606, -121.93424041034338, 154.81667256991548, 409.6563007117701, 606.521162444049, 717.5539421245961, 727.0559096978491, 633.689623801485, 450.67734023489834, 203.91263511817172, -71.69517799939685, -337.16168886529687, -554.9360522027074, -694.2066552126058, -735.2648843409686, -672.2885191737494, -514.1802151996872, -283.30542701077445, -12.335642609375407, 260.3800979806774, 496.24505158030894, 661.8811987883347, 733.8570475807231, 701.9968403990714, 570.8205067210628, 358.8931583496576, 96.199099573824, -180.10249338905803, -430.9283390294616, -620.7966531559292, -722.8422015452576, -722.6187066465885, -620.1467526386796, -429.91712078259707, -178.8444006550856, 97.53995424114916, 360.1193050292711, 571.7334948154963, 702.4376294710119, 733.746893396232, 661.2395441599436, 495.1865043515287, 259.0836938724544, -13.666387583392062, -284.4834691051085, -515.060470805876, -672.7752361752135, -735.312327422585, -693.8094521787701, -554.1322376531365, -336.03506964553014, -70.3812123149443, 205.23392695300302, 451.80293393099663, 634.4311011350218, 727.2823145701748, 717.2242514150773, 605.6943066383313, 408.47775677442934, 153.48020672626544, -123.22728505628051, -382.5043849657134, -587.6738554686066, -709.7087142628442, -731.333907650942, -649.4805083653803, -475.7193908691209, -234.6345819751653, 39.65786730651168, 308.3373535672737, 533.3789831507074, 682.9381562249275, 735.8580826581494, 684.6617651070421, 536.6028542210967, 312.63494118364935, 44.443851352026954, -230.03372928938543, -471.97320008641776, -647.1472534948299, -730.7700578548546, -710.9990333583876, -590.6225012863277, -386.66396282846006, -127.98373222767151, 148.81026380030016, 404.543590852635, 603.0234623386472, 716.1692385148802, 727.975750747212, 636.7872134178815, 455.5111975990809, 209.80059437526245, -65.58704548476915, -331.69745285793044, -550.8873677176662, -692.1487326404816, -735.4856118235973, -674.7598913133986, -518.5500670592347, -288.9568988339757, -18.468170230813993, 254.63422725128441, 491.69848173276216, 659.178253896817, 733.3781933008585, 703.8110268397746, 574.6692913092885, 364.2326596820547, 102.2732669903413, -174.1524226561072, -425.9442234422808, -617.4834760331715, -721.6684145476485, -723.750874582371, -623.4243382473866, -434.87646767525405, -184.78365262721655, 91.46130841120203, 354.762009521493, 567.8544897850245, 700.587129166671, 734.1845893258532, 663.9047984931705, 499.7003016142252, 264.8084877490254, -7.541344738863946, -278.8245869949609, -510.6668749265149, -670.2710167832978, -735.0483252425005, -695.8267415579063, -558.1422556632144, -341.4726992292175, -76.47530525914023, 199.34540041252768, 446.9526239322164, 631.3073414128567, 726.3234265701432, 718.5693032216935, 609.1491351691957, 413.5556061052058, 159.46134654092563, -117.18842428840182, -377.2617491548263, -583.9700941751223, -708.0659693485818, -731.986704888298, -652.3345794587846, -480.37230860386336, -240.4270163758399, 33.54516474176789, 302.7697401072354, 529.1444446700096, 680.6351080569443, 735.8129279215387, 686.8794896938875, 540.7699595420804, 318.16129351199436, 50.54781803431101, -224.21585244432575, -467.2636704479797, -644.2131406198114, -730.0255083712235, -712.5503876428883, -594.2493824543336, -391.85398319735305, -134.00159421781376, 142.81605844668547, 399.4207190238894, 599.4984702292035, 714.7379583045056, 728.8434209005883, 639.8276237246911, 460.29634825417156, 215.65148874957305, -59.49710858115252, -326.2298556605472, -546.8170483498221, -690.0483187228144, -735.6563792942363, -677.1736462624477, -522.8686328635815, -294.5669239249417, -24.576783059469797, 248.89186048353795, 487.13572687938915, 656.4388950343555, 732.8519409224272, 705.5692197110384, 578.464842888028, 369.52697582496626, 108.31815776333475, -168.2123914902742, -420.9493094897114, -614.1395569887364, -720.4496957177289, -724.8284819745359, -626.6470021516052, -439.78733370567477, -190.68821962584417, 85.39909660656953, 349.3996868255953, 563.9512205220872, 698.6946679089807, 734.5705382764577, 666.5138982117065, 504.16335467721854, 270.4934159437043, -1.438518459485797, -273.16688038694, -506.25560299694706, -667.7281914969764, -734.7362172035057, -697.7870338187329, -562.0999017300863, -346.86574216433553, -82.54170975099169, 193.46470804726582, 442.0906853384963, 628.1497146500511, 725.3201201775946, 719.857834853838, 612.5505199203229, 418.5855522868043, 165.4098014141576, -111.16376069477931, -372.0133130080036, -580.2388162839502, -706.3823014450006, -732.5855881150512, -655.134365729852, -484.9745264698732, -246.1817802853151, 27.452965803415047, 297.2022059069106, 524.8888318563462, 678.2946974488681, 735.716758945715, 689.042383740138, 544.8842346108532, 323.6454625973515, 56.62545408711214, -218.40401138637068, -462.5399207486233, -641.2456557321826, -729.2338430253518, -714.046994691911, -597.8222166164485, -396.9975088816209, -139.9878952144467, 136.83411460515282, 394.29028087864384, 595.9442412207543, 713.2642471874522, 729.6565587676583, 642.8138794940628, 465.0313421928121, 221.46636874727008, -53.4257057073374, -320.76090687271443, -542.7225670112506, -687.9103458958139, -735.7737908847339, -679.5335856737807, -527.1340816868476, -300.13647012803216, -30.66073699339464, 243.15426799760078, 482.5555284417704, 653.6664240999418, 732.2756330690886, 707.2739787337073, 582.2058284619578, 374.7767485963913, 114.33290941041975, -162.2830907520925, -415.94378604631754, -610.7670313494581, -719.1854156434778, -725.8533599072621, -629.814244095091, -444.6500597245941, -196.55742753433486, 79.35356460132701, 344.0336284977359, 560.024134235189, 696.7613758488652, 734.9051359993674, 669.0676715844538, 508.5752352649702, 276.13875061123156, 4.641817964663134, -267.51192407468676, -501.82515050181377, -665.1499829767449, -734.3739181733448, -699.6930298989117, -566.0034894485508, -352.2154214306727, -88.57956711641481, 187.59308244312476, 437.2154142116286, 624.9625010935079, 724.2688024760863, 721.0939536694245, 615.8957971344423, 423.5687972874895, 171.32415082083673, -105.15412678867996, -366.7581945854725, -576.4833422291283, -704.65439600355, -733.1338586248222, -657.8773063473391, -489.5271238794601, -251.8977618661882, 21.38164310678335, 291.6349302144427, 520.6143512844322, 675.9157600984906, 735.5721618954614, 691.1495139542762, 548.9465264646856, 329.0868065498709, 62.67677225368599, -212.59898815223073, -457.8024186598057, -638.2451132652515, -728.3958920665979, -715.488947577372, -601.3411300476776, -402.0946864424411, -145.94238915863335, 130.86540134577058, 389.15161572602955, 592.3631421028041, 711.7468215775442, 730.4173780689764, 645.7445902569463, 469.71733811877033, 227.2440542560455, -47.37345160505758, -315.28981503737964, -538.6076993212805, -685.7317066913905, -735.8421705964754, -681.8368463181167, -531.3485631554828, -305.6639092404967, -36.7199480504892, 237.42116650639267, 477.96091469133887, 650.857689558647, 731.6536082854373, 708.9222066978343, 585.8943353214065, 379.98084204558427, 120.31773952718672, -156.36478359503116, -410.92958837228605, -607.364554338394, -717.878715258078, -726.8240763835221, -632.9279594617664, -449.4640295274219, -202.3916955034298, 73.32531334430514, 338.66451890007284, 556.0732072791964, 694.7888923267379, 735.1879552248041, 671.5670000073098, 512.9358619926211, 281.74436168249014, 10.698875351318124, -261.85979455998057, -497.3775819725884, -662.5362080192897, -733.9633715109951, -701.5442823192896, -569.854296090616, -357.52086405808, -94.58877201910843, 181.73041828224646, 432.3296162817332, 621.743570272934, 723.1735471162469, 722.2752643733891, 619.1877033869245, 428.50385276223096, 177.20508800681523, -99.15982082536493, -361.49878712966085, -572.7010128410811, -702.8871486402289, -733.6281934286808, -660.5668722102307, -494.0284559378907, -257.5758101180047, 15.331758441005842, 286.069364943634, 516.3195110971131, 673.5017818063842, 735.3766262454797, 693.2033904540211, 552.955770289837, 334.4859732604286, 68.70096509136582, -206.80157129503382, -453.0513271532127, -635.2138091589736, -727.5111028686898, -716.8780970669723, -604.8057364468531, -407.1457937004381, -151.86438033391212, 124.91021219917037, 384.0061213065673, 588.7557546386059, 710.1869117334757, 731.126319836771, 648.6206368338824, 474.3539003020844, 232.98468413408835, -41.340581132180475, -309.81840881066245, -534.4709657808061, -683.5157155092853, -735.8595814598283, -684.0860850591187, -535.5105309883369, -311.1503430265174, -42.75373468431189, 231.6940176055651, 473.34987940587143, 648.0171096276898, 730.9823579146964, 710.5178037008625, 589.5279538739132, 385.14027393636206, 126.27141157382295, -150.45845471802755, -405.9055297042421, -603.9354151600551, -716.5262146631896, -727.743661684064, -635.9857376487458, -454.23020179293934, -208.1899012368401, 67.31488482356555, 333.29248402838874, 552.1009062967114, 692.7759282675651, 735.4216015848726, 674.010967592967, 517.246036469736, 287.30958341331495, 16.732554225447483, -256.2113232212366, -492.9135928095542, -659.8872521749361, -733.5055549418835, -703.3409888670142, -573.6524428191002, -362.78218792444636, -100.56912372361144, 175.87780898688212, 427.43261955034666, 618.4953105980062, 722.0332621878814, 723.4038604776387, 622.4250365664625, 433.3917738611051, 183.05167023067548, -93.18158520609788, -356.23400628906245, -568.8955839907609, -701.0775302810969, -734.0726639855368, -663.2003995475714, -498.48031673642475, -263.21434343394276, 9.303665901139048, 280.50495522837116, 512.0075161355106, 671.0493192731677, 735.1341976665749, 695.2008859106651, 556.9136762382477, 339.8416047080008, 74.69804213605684, -201.0119227024575, -448.2887173928046, -632.1499465241594, -726.5824987580561, -718.2126688864807, -608.2177084184669, -412.15020456984195, -157.75421115140506, 118.96907818046051, 378.85462189971815, 585.1218838530119, 708.5862707710038, 731.7829194995692, 651.4429217051385, 478.9409585490833, 238.6882583245311, -35.32785880514571, -304.34670546004907, -530.3142512933105, -681.2623326573868, -735.8277361988697, -686.2809695006711, -539.6211259654459, -316.59502938684363, -48.7618226576683, 225.97257133849004, 468.72545526242425, 645.1427344349587, 730.2658730620751, 712.0587378372957, 593.1092240930425, 390.2537141385591, 132.19443587693814, -144.56409780549927, -400.87418470281074, -600.4768661213869, -715.1327160294256, -728.6089270417311, -638.9907780479225, -458.94705247225966, -213.9528257839438, 61.322636793730055, 327.919085640047, 548.1052251037448, 690.7260425560349, 735.6033457582291, 676.402018915814, 521.5047032911931, 292.83512707624106, 22.742262809195562, -250.56731820786828, -488.43297527447214, -657.2054257322955, -732.9996566964251, -705.0849306491391, -577.3975349323501, -367.9997169171908, -106.51994064594689, 170.03552731198386, 422.52566139871874, 615.218466573798, 720.8488986393911, 724.4803413268386, 625.6085528687361, 438.23232330917307, 188.86392196882034, -87.21949363146126, -350.96582063124964, -565.0658451147352, -699.2287318069446, -734.4657174543885, -665.7804212647031, -502.8815065156358, -268.81447229331303, 3.29769108125902, 274.94331754612267, 507.6760512710373, 668.5629366810576, 734.8415708540726, 697.145909219894, 560.818188793579, 345.154996113977, 80.66711490180579, -195.23107552520815, -443.513012989229, -629.0571988925166, -725.6066735357339, -719.4959644319708, -611.5748617364073, -417.10884301863706, -163.61090630352132, 113.04256490221805, 373.69684110049155, 581.4640154937118, 706.9431875625899, 732.3896412726864, 654.2104182535759, 483.4792572544752, 244.35407171146088, -29.335434656825864, -298.8753915256871, -526.1384224248972, -678.9716547475585, -735.7477329751553, -688.4215600021316, -543.68052794743, -321.9980412382976, -54.74421191794736, 220.2578616124752, 464.0867494271376, 642.2365839133133, 729.503062204167, 713.5467696531886, 596.637063348885, 395.3221779066789, 138.0861003490585, -138.68265669923497, -395.8342591371421, -596.992689389684, -713.6952643761235, -729.423797694003, -641.940637704212, -463.616281005867, -219.67910028799724, 55.348987361630165, 322.54338530708395, 544.089727833206, 688.6356348871517, 735.737396933926, 678.7371396690078, 525.7135138469735, 298.3195942383698, 28.727805420106957, -244.92776548026006, -483.93801060078613, -654.4886858882185, -732.4487134886677, -706.7742755713836, -581.0910672663997, -373.17281540932413, -112.4415351094624, 164.20393627523234, 417.60960654098574, 611.9124993368662, 719.622188184515, 725.5040596510418, 628.7390581566614, 443.025274575503, 194.64186798177565, -81.27437969388872, -345.6941693723278, -561.2132980019142, -697.3406746658438, -734.8086720337607, -668.3065216135916, -507.23293117829445, -274.37544404483833, -2.685686066612159, 269.3840185740526, 503.3283431964794, 666.0405393748957, 734.5025814910526, 699.0363450897653, 564.6715880035184, 350.4246738673703, 86.6083917564886, -189.4589061055402, -438.72722344385164, -625.9325972719818, -724.5885000476474, -720.7247606137901, -614.8801713915931, -422.02031830747114, -169.4350848277395, 107.13092554402411, 368.53469300290504, 577.7800641349183, 705.2615251527648, 732.943962450522, 656.9255434563323, 487.96788480316667, 249.98274789673636, -23.36383860335418, -293.4054725989451, -521.943098271152, -676.646156447876, -735.6186918145526, -690.5095626631102, -547.6882911554476, -327.35959514778125, -60.70008071225752, 214.55026171404867, 459.43506702495006, 639.299494654422, 728.6948202556407, 714.9824396830855, 600.112122129132, 400.34529066939683, 143.94618816792348, -132.81420846565013, -390.7879912975597, -593.4817170429696, -712.2170407314073, -730.1868226036867, -644.8376535969145, -468.23664224834806, -225.36951997686955, 49.39430711014085, 317.16748579605775, 540.0519276246838, 686.5094610151974, 735.8203947610867, 681.0201104655854, 529.8705051917044, 303.7642213451239, 34.6884431964232, -239.29399624066755, -479.4269794360086, -651.7410257539796, -731.8494500543338, -708.4123015141577, -584.7311123673524, -378.3022526127099, -118.33292447207299, 158.38389768113186, 412.6842135873142, 608.5801836449344, 718.3516107804307, 726.4774836843081, 631.8156908683753, 447.7712681111171, 200.38471698457465, -75.34653569502461, -340.41982444951213, -557.3390944102175, -695.4135069992741, -735.1027071779134, -670.7787612981482, -511.53460712114725, -279.897120491206, -8.646307582074119, 263.82841060747324, 498.9635321721558, 663.4842990805635, 734.1161353564308, 700.8739787294797, 568.4727457397065, 355.65157592519773, 92.52123192822225, -183.69658930749387, -433.9297440824325, -622.7800290324258, -723.5248656098254, -721.9027730186517, -618.1311669750247, -426.8859737278515, -175.22535819116626, 101.23492850939539, 363.3670674948133, 574.073785559641, 703.5376002156428, 733.4498430457371, 659.5853976638977, 492.40817208175184, 255.5729185416928, -17.41347842635703, -287.93681991887365, -517.7308357110262, -674.2836274654302, -735.4435795880494, -692.5431434989339, -551.6456756354947, -332.6789256316005, -66.62950218938218, 208.8502716532293, 454.77153911607564, 636.3310607635755, 727.8429802863086, 716.3652394844552, 603.5351113223054, 405.3228404726296, 149.77471907151414, -126.95960700976566, -385.73520630857297, -589.9453478183846, -710.6978616753471, -730.8991094555956, -647.6812837839526, -472.80884681224427, -231.02331217219594, 43.45926601436968, 311.79074792704324, 535.9951410845263, 684.3452692413247, 735.8560100307128, 683.2487341790833, 533.9776716616557, 309.1674918039325, 40.62411133950084, -233.66565396957756, -474.90308873936783, -648.9591831572893, -731.2064561938604, -709.9957617836781, -588.3201500944774, -383.38655360137085, -124.19438678527564, 152.5756192411376, 407.751662475037, 605.2192709609005, 717.0408828210393, 727.3980999464966, 634.8405811557784, 452.46947070279293, 206.09303381230265, -69.43640931257961, -335.1439321965488, -553.4427053324213, -693.4498342957438, -735.3469367806064, -673.1988259702143, -515.7861422112305, -285.3796966842516, -14.583377410357109, 258.27686629117403, 494.58290124919483, 660.8950416144572, 733.6831275516632, 702.6592551311314, 572.2223170374897, 360.8352874919471, 98.40528946924768, -177.94413694001247, -429.123017774615, -619.5983709819476, -722.4189584041042, -723.0286764646034, -621.3300516826674, -431.70463195072205, -180.98228314113274, 95.35476570151201, 358.19631622322225, 570.3426261120741, 701.7760670245827, 733.9041079142626, 662.1934311766942, 496.79833897750683, 261.1256098209652, -11.484942814537193, -282.47098825305096, -513.4995429281331, -671.8881599683028, -735.2190942127885, -694.5254183098161, -555.5509060477673, -337.9568103310634, -72.53162655706699, 203.1588470111559, 450.0958042850076, 633.3334863577608], \"type\": \"scatter3d\", \"uid\": \"b4d2eada-b277-11e8-b505-a0afbda902ef\"}, {\"cauto\": false, \"cmax\": 1, \"cmin\": 1, \"colorscale\": [[0, \"#204a87\"], [1, \"#204a87\"]], \"name\": \"Earth\", \"showscale\": false, \"x\": [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [2070.9775685349646, 1958.7662914956272, 1634.2922933821865, 1132.717366623061, 508.3949372694737, -171.0199720974995, -831.9022138394263, -1402.6349424190162, -1821.3704108639388, -2042.7321338179481, -2042.7321338179484, -1821.3704108639388, -1402.6349424190173, -831.9022138394275, -171.0199720975005, 508.39493726947273, 1132.7173666230603, 1634.2922933821858, 1958.7662914956272, 2070.9775685349646], [3917.5325829912545, 3705.269861917151, 3091.4836581186883, 2142.6872306516607, 961.6973945255617, -323.507276569953, -1573.6549145165156, -2653.272624703365, -3445.3670762369647, -3864.102544681887, -3864.1025446818876, -3445.3670762369647, -2653.2726247033675, -1573.654914516518, -323.50727656995485, 961.69739452556, 2142.687230651659, 3091.483658118687, 3705.269861917151, 3917.5325829912545], [5339.562155299338, 5050.2499496143155, 4213.664799186625, 2920.4636860211895, 1310.7850168122338, -440.9375478934551, -2144.8776874338914, -3616.387048334464, -4696.004758521314, -5266.737487100903, -5266.737487100904, -4696.004758521314, -3616.3870483344667, -2144.877687433894, -440.93754789345763, 1310.7850168122313, 2920.463686021187, 4213.664799186623, 5050.2499496143155, 5339.562155299338], [6182.9673162373765, 5847.957092568811, 4879.229977516816, 3381.7625853471986, 1517.8287436021724, -510.58539405170535, -2483.6696817114025, -4187.60982125184, -5437.757459198403, -6098.63970094033, -6098.639700940331, -5437.757459198403, -4187.609821251843, -2483.669681711406, -510.58539405170836, 1517.8287436021697, 3381.762585347196, 4879.229977516814, 5847.957092568811, 6182.9673162373765], [6356.352029838285, 6011.947344139877, 5016.054878729385, 3476.5950350978, 1560.3921744832594, -524.9033902157762, -2553.317527869653, -4305.040092575342, -5590.2447636708575, -6269.65967303783, -6269.6596730378305, -5590.2447636708575, -4305.040092575346, -2553.3175278696567, -524.9033902157793, 1560.3921744832564, 3476.5950350977973, 5016.054878729383, 6011.947344139877, 6356.352029838285], [5840.927372042378, 5524.449815998859, 4609.31240172086, 3194.6844678654465, 1433.8629012798515, -482.33995933468935, -2346.2738010797148, -3955.95247028867, -5136.9423064147695, -5761.264735768357, -5761.264735768358, -5136.9423064147695, -3955.952470288674, -2346.273801079718, -482.33995933469214, 1433.8629012798488, 3194.684467865444, 4609.312401720858, 5524.449815998859, 5840.927372042378], [4692.547602159432, 4438.292429623361, 3703.07940513492, 2566.580267902913, 1151.9523340474977, -387.50750958408815, -1884.974901753706, -3178.1760149191423, -4126.972442386172, -4628.547369145297, -4628.547369145297, -4126.972442386172, -3178.176014919145, -1884.9749017537085, -387.5075095840904, 1151.9523340474955, 2566.580267902911, 3703.0794051349185, 4438.292429623361, 4692.547602159432], [3035.6574872043448, 2871.1771912954932, 2395.560295805413, 1660.3472713169713, 745.2098570389732, -250.68260837151965, -1219.4097234235148, -2055.994873851206, -2669.7810776496694, -2994.25507576311, -2994.2550757631107, -2669.7810776496694, -2055.9948738512076, -1219.4097234235167, -250.6826083715211, 745.2098570389717, 1660.3472713169701, 2395.560295805412, 2871.1771912954932, 3035.6574872043448], [1049.806780431554, 992.9253533863962, 828.4450574775444, 574.1898849414736, 257.7123288979541, -86.69235680045416, -421.70258046901955, -711.014786154042, -923.2775072281457, -1035.488784267483, -1035.4887842674832, -923.2775072281457, -711.0147861540426, -421.7025804690202, -86.69235680045466, 257.7123288979536, 574.1898849414731, 828.4450574775441, 992.9253533863962, 1049.806780431554], [-1049.8067804315524, -992.9253533863947, -828.4450574775432, -574.1898849414728, -257.71232889795374, 86.69235680045404, 421.7025804690189, 711.0147861540408, 923.2775072281444, 1035.4887842674814, 1035.4887842674816, 923.2775072281444, 711.0147861540415, 421.7025804690195, 86.69235680045453, -257.7123288979532, -574.1898849414723, -828.4450574775428, -992.9253533863947, -1049.8067804315524], [-3035.6574872043434, -2871.177191295492, -2395.560295805412, -1660.3472713169708, -745.2098570389728, 250.68260837151956, 1219.4097234235144, 2055.9948738512053, 2669.7810776496685, 2994.255075763109, 2994.2550757631093, 2669.7810776496685, 2055.994873851207, 1219.4097234235162, 250.682608371521, -745.2098570389714, -1660.3472713169695, -2395.560295805411, -2871.177191295492, -3035.6574872043434], [-4692.547602159429, -4438.292429623359, -3703.079405134917, -2566.580267902911, -1151.9523340474968, 387.50750958408787, 1884.9749017537044, 3178.17601491914, 4126.972442386168, 4628.5473691452935, 4628.547369145294, 4126.972442386168, 3178.1760149191427, 1884.9749017537072, 387.50750958409014, -1151.9523340474948, -2566.580267902909, -3703.0794051349158, -4438.292429623359, -4692.547602159429], [-5840.927372042376, -5524.449815998857, -4609.312401720859, -3194.6844678654456, -1433.862901279851, 482.33995933468924, 2346.2738010797143, 3955.9524702886692, 5136.942306414769, 5761.264735768355, 5761.264735768356, 5136.942306414769, 3955.9524702886724, 2346.2738010797175, 482.339959334692, -1433.8629012798485, -3194.6844678654434, -4609.312401720857, -5524.449815998857, -5840.927372042376], [-6356.352029838285, -6011.947344139877, -5016.054878729385, -3476.5950350978, -1560.3921744832594, 524.9033902157762, 2553.317527869653, 4305.040092575342, 5590.2447636708575, 6269.65967303783, 6269.6596730378305, 5590.2447636708575, 4305.040092575346, 2553.3175278696567, 524.9033902157793, -1560.3921744832564, -3476.5950350977973, -5016.054878729383, -6011.947344139877, -6356.352029838285], [-6182.967316237377, -5847.957092568812, -4879.229977516816, -3381.762585347199, -1517.8287436021726, 510.5853940517054, 2483.669681711403, 4187.60982125184, 5437.757459198404, 6098.639700940331, 6098.639700940332, 5437.757459198404, 4187.609821251844, 2483.6696817114066, 510.5853940517084, -1517.8287436021699, -3381.7625853471964, -4879.229977516815, -5847.957092568812, -6182.967316237377], [-5339.562155299339, -5050.249949614317, -4213.664799186626, -2920.46368602119, -1310.7850168122343, 440.9375478934552, 2144.877687433892, 3616.387048334465, 4696.004758521315, 5266.737487100905, 5266.737487100906, 4696.004758521315, 3616.3870483344676, 2144.877687433895, 440.93754789345775, -1310.7850168122318, -2920.4636860211876, -4213.664799186624, -5050.249949614317, -5339.562155299339], [-3917.532582991256, -3705.2698619171524, -3091.483658118689, -2142.6872306516616, -961.6973945255621, 323.5072765699531, 1573.654914516516, 2653.272624703366, 3445.367076236966, 3864.1025446818885, 3864.102544681889, 3445.367076236966, 2653.2726247033684, 1573.6549145165184, 323.507276569955, -961.6973945255603, -2142.68723065166, -3091.4836581186883, -3705.2698619171524, -3917.532582991256], [-2070.977568534966, -1958.7662914956288, -1634.2922933821878, -1132.7173666230622, -508.39493726947416, 171.01997209749965, 831.902213839427, 1402.6349424190175, 1821.3704108639404, 2042.7321338179497, 2042.7321338179502, 1821.3704108639404, 1402.6349424190187, 831.9022138394282, 171.01997209750064, -508.3949372694732, -1132.7173666230613, -1634.2922933821872, -1958.7662914956288, -2070.977568534966], [-1.5621929143429165e-12, -1.4775489932480931e-12, -1.2327897122000057e-12, -8.544385371317645e-13, -3.8349568858537574e-13, 1.2900486836795326e-13, 6.275257461168375e-13, 1.0580444722087668e-12, 1.373907662485362e-12, 1.5408866391577764e-12, 1.5408866391577766e-12, 1.373907662485362e-12, 1.0580444722087678e-12, 6.275257461168384e-13, 1.2900486836795402e-13, -3.83495688585375e-13, -8.544385371317639e-13, -1.2327897122000053e-12, -1.4775489932480931e-12, -1.5621929143429165e-12]], \"y\": [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 672.4453172381089, 1272.020750289313, 1733.7529976111107, 2007.6062056921824, 2063.9041301666034, 1896.5460173652668, 1523.6677156388778, 985.6763747824739, 340.8717043736032, -340.8717043736027, -985.6763747824734, -1523.6677156388766, -1896.5460173652662, -2063.9041301666034, -2007.6062056921826, -1733.752997611111, -1272.0207502893134, -672.4453172381094, -5.072432100824625e-13], [0.0, 1272.0207502893131, 2406.1983148492195, 3279.626955981496, 3797.6571277777143, 3904.152223057449, 3587.5718458054816, 2882.222392147741, 1864.539420012481, 644.8046704088713, -644.8046704088703, -1864.5394200124804, -2882.222392147739, -3587.5718458054803, -3904.152223057449, -3797.657127777715, -3279.6269559814964, -2406.1983148492204, -1272.0207502893138, -9.595187476631407e-13], [0.0, 1733.7529976111107, 3279.6269559814955, 4470.102445015823, 5176.172973346762, 5321.324843416592, 4889.8285978399235, 3928.443550179085, 2541.350687774138, 878.8630452300076, -878.8630452300063, -2541.350687774137, -3928.4435501790817, -4889.828597839922, -5321.324843416592, -5176.172973346763, -4470.102445015824, -3279.6269559814964, -1733.7529976111118, -1.3078155404671353e-12], [0.0, 2007.6062056921824, 3797.657127777714, 5176.172973346762, 5993.770160654701, 6161.849348129235, 5662.196547790195, 4548.95689346632, 2942.767175396611, 1017.6829721352607, -1017.6829721352591, -2942.7671753966097, -4548.9568934663175, -5662.196547790194, -6161.849348129235, -5993.770160654702, -5176.172973346763, -3797.6571277777152, -2007.6062056921837, -1.5143902266125608e-12], [0.0, 2063.9041301666034, 3904.152223057449, 5321.324843416592, 6161.849348129236, 6334.641865028304, 5820.977643755634, 4676.520173007722, 3025.2891778274434, 1046.2211580313447, -1046.221158031343, -3025.289177827442, -4676.520173007719, -5820.977643755632, -6334.641865028304, -6161.849348129237, -5321.324843416593, -3904.1522230574506, -2063.904130166605, -1.5568572335190474e-12], [0.0, 1896.5460173652668, 3587.571845805481, 4889.8285978399235, 5662.196547790195, 5820.977643755633, 5348.9654902458315, 4297.309928116757, 2779.9741556424556, 961.3850476608397, -961.3850476608383, -2779.974155642454, -4297.309928116753, -5348.96549024583, -5820.977643755633, -5662.196547790196, -4889.828597839924, -3587.5718458054826, -1896.5460173652682, -1.430614602044772e-12], [0.0, 1523.667715638878, 2882.222392147741, 3928.4435501790854, 4548.956893466321, 4676.520173007722, 4297.309928116757, 3452.419472880565, 2233.405797950153, 772.3679499502728, -772.3679499502715, -2233.405797950152, -3452.4194728805624, -4297.309928116756, -4676.520173007722, -4548.956893466322, -3928.443550179086, -2882.222392147742, -1523.667715638879, -1.1493426801662274e-12], [0.0, 985.676374782474, 1864.539420012481, 2541.3506877741384, 2942.767175396611, 3025.289177827443, 2779.9741556424556, 2233.405797950153, 1444.8132671883818, 499.65280033904173, -499.652800339041, -1444.8132671883811, -2233.4057979501513, -2779.9741556424547, -3025.289177827443, -2942.7671753966115, -2541.350687774139, -1864.5394200124817, -985.6763747824746, -7.435216450024997e-13], [0.0, 340.8717043736033, 644.8046704088712, 878.8630452300076, 1017.6829721352607, 1046.2211580313447, 961.3850476608397, 772.3679499502726, 499.65280033904173, 172.79251689906815, -172.7925168990679, -499.6528003390415, -772.3679499502721, -961.3850476608395, -1046.2211580313447, -1017.6829721352608, -878.8630452300079, -644.8046704088714, -340.8717043736035, -2.5712850267573817e-13], [0.0, -340.87170437360277, -644.8046704088703, -878.8630452300064, -1017.6829721352591, -1046.221158031343, -961.3850476608383, -772.3679499502715, -499.652800339041, -172.7925168990679, 172.7925168990676, 499.65280033904077, 772.3679499502709, 961.385047660838, 1046.221158031343, 1017.6829721352593, 878.8630452300065, 644.8046704088705, 340.871704373603, 2.5712850267573776e-13], [0.0, -985.6763747824737, -1864.5394200124804, -2541.3506877741374, -2942.76717539661, -3025.289177827442, -2779.9741556424547, -2233.405797950152, -1444.8132671883811, -499.65280033904156, 499.6528003390408, 1444.8132671883807, 2233.4057979501504, 2779.9741556424538, 3025.289177827442, 2942.7671753966106, 2541.350687774138, 1864.539420012481, 985.6763747824742, 7.435216450024994e-13], [0.0, -1523.6677156388769, -2882.222392147739, -3928.443550179082, -4548.9568934663175, -4676.520173007719, -4297.309928116754, -3452.4194728805624, -2233.4057979501513, -772.3679499502722, 772.3679499502709, 2233.4057979501504, 3452.4194728805596, 4297.309928116752, 4676.520173007719, 4548.956893466318, 3928.443550179083, 2882.22239214774, 1523.6677156388778, 1.1493426801662265e-12], [0.0, -1896.5460173652664, -3587.5718458054803, -4889.828597839923, -5662.196547790194, -5820.977643755632, -5348.96549024583, -4297.309928116755, -2779.9741556424547, -961.3850476608395, 961.385047660838, 2779.9741556424538, 4297.309928116752, 5348.965490245829, 5820.977643755632, 5662.196547790195, 4889.8285978399235, 3587.5718458054816, 1896.5460173652677, 1.4306146020447716e-12], [0.0, -2063.9041301666034, -3904.152223057449, -5321.324843416592, -6161.849348129236, -6334.641865028304, -5820.977643755634, -4676.520173007722, -3025.2891778274434, -1046.2211580313447, 1046.221158031343, 3025.289177827442, 4676.520173007719, 5820.977643755632, 6334.641865028304, 6161.849348129237, 5321.324843416593, 3904.1522230574506, 2063.904130166605, 1.5568572335190474e-12], [0.0, -2007.6062056921826, -3797.6571277777143, -5176.172973346763, -5993.770160654702, -6161.849348129236, -5662.196547790196, -4548.956893466321, -2942.7671753966115, -1017.6829721352608, 1017.6829721352592, 2942.76717539661, 4548.9568934663175, 5662.196547790194, 6161.849348129236, 5993.7701606547025, 5176.172973346764, 3797.6571277777157, 2007.606205692184, 1.514390226612561e-12], [0.0, -1733.7529976111111, -3279.6269559814964, -4470.102445015825, -5176.172973346764, -5321.324843416593, -4889.828597839924, -3928.443550179086, -2541.350687774139, -878.8630452300079, 878.8630452300065, 2541.3506877741374, 3928.4435501790826, 4889.8285978399235, 5321.324843416593, 5176.172973346765, 4470.102445015826, 3279.6269559814973, 1733.7529976111123, 1.3078155404671357e-12], [0.0, -1272.0207502893136, -2406.1983148492204, -3279.626955981497, -3797.6571277777157, -3904.1522230574506, -3587.571845805483, -2882.222392147742, -1864.5394200124817, -644.8046704088715, 644.8046704088705, 1864.539420012481, 2882.22239214774, 3587.5718458054816, 3904.1522230574506, 3797.657127777716, 3279.6269559814973, 2406.1983148492213, 1272.0207502893143, 9.595187476631409e-13], [0.0, -672.4453172381095, -1272.020750289314, -1733.752997611112, -2007.606205692184, -2063.9041301666052, -1896.5460173652684, -1523.6677156388791, -985.6763747824748, -340.87170437360356, 340.87170437360305, 985.6763747824743, 1523.6677156388778, 1896.5460173652677, 2063.9041301666052, 2007.6062056921844, 1733.7529976111123, 1272.0207502893145, 672.44531723811, 5.07243210082463e-13], [0.0, -5.072432100824626e-13, -9.595187476631405e-13, -1.3078155404671353e-12, -1.5143902266125608e-12, -1.5568572335190474e-12, -1.430614602044772e-12, -1.1493426801662272e-12, -7.435216450024997e-13, -2.5712850267573817e-13, 2.5712850267573776e-13, 7.435216450024993e-13, 1.1493426801662263e-12, 1.4306146020447714e-12, 1.5568572335190474e-12, 1.514390226612561e-12, 1.3078155404671357e-12, 9.595187476631409e-13, 5.072432100824629e-13, 3.826269104401456e-28]], \"z\": [[6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366], [6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864], [5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782], [3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239], [1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561], [-526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267], [-2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456], [-4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595], [-5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402], [-6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607], [-6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608], [-5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402], [-4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663], [-2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649], [-526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298], [1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558], [3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236], [5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378], [6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864], [6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366]], \"type\": \"surface\", \"uid\": \"b4d2eadb-b277-11e8-b505-a0afbda902ef\"}], {\"autosize\": true, \"scene\": {\"aspectmode\": \"data\", \"xaxis\": {\"title\": \"x (km)\"}, \"yaxis\": {\"title\": \"y (km)\"}, \"zaxis\": {\"title\": \"z (km)\"}}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
],
"text/vnd.plotly.v1+html": [
"<div id=\"00191c7b-0fb1-48be-b77a-8d853315f77a\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"00191c7b-0fb1-48be-b77a-8d853315f77a\", [{\"line\": {\"dash\": \"solid\", \"width\": 5}, \"mode\": \"lines\", \"name\": \"orbit influenced by Moon\", \"x\": [42159.783599999995, 39176.33313050093, 30648.34603837001, 17783.008684663506, 2401.2249728817246, -13320.363219830044, -27157.46178851618, -37152.860734058035, -41892.85979071243, -40707.27937602602, -33763.61472102067, -22043.987824470438, -7205.82947952589, 8651.757978035002, 23285.088474672662, 34623.305235890366, 41061.45649430606, 41688.28390883917, 36414.86483194732, 25987.934440893845, 11883.207397572452, -3902.9889686326196, -19137.017670261284, -31663.77867150318, -39711.42197154506, -42141.94065227597, -38611.52820470689, -29619.54638639312, -16437.556020633823, -930.1668316167464, 14708.798182536722, 28266.38921137929, 37823.76402739217, 42028.10260384963, 40284.14735665196, 32838.864881292495, 20746.12229922393, 5717.60520211664, -10119.98209269242, -24525.841792373874, -35462.179469314986, -41382.256857506814, -41448.884585737746, -35652.63382907132, -24813.188019914207, -10463.587567263648, 5366.358490835878, 20436.942366895517, 32615.499083144536, 40178.22648981217, 42054.63384793369, 37978.950923577824, 28528.30802123554, 15040.324988052333, -575.9243139274541, -16110.729671893676, -29366.35706959656, -38467.78174598678, -42127.973108672566, -39829.162862363446, -31896.63962427619, -19451.99853009428, -4255.590717149522, 11542.905030438475, 25708.044279219095, 36235.04986232976, 41633.94556238677, 41140.30826111758, 34824.17954857387, 23579.515448783404, 8998.091964665991, -6856.622486492597, -21741.281806367813, -33550.25493543009, -40613.38100169364, -41931.73828483307, -37318.985638958075, -27427.33180503151, -13655.815380528431, 2047.6541298608722, 17461.353140263724, 30404.120479599474, 39044.11158380027, 42158.268325485966, 39305.73809426212, 30890.331497868952, 18103.328150578494, 2754.5259951419785, -12984.049763362627, -26885.740267922043, -36984.106617570185, -41851.006696940494, -40798.11732451042, -33974.426392310525, -22344.820380821824, -7554.215667268249, 8305.13805646534, 22989.310557287, 34420.15852537943, 40979.80171441247, 41739.50956364618, 36591.90570845013, 26265.50480790668, 12222.189818315672, -3550.666289207576, -18821.189818431718, -31429.06123917744, -39591.100844044035, -42152.89477405712, -38752.35015462737, -29870.1457693241, -16762.62988226459, -1283.6247575081127, 14376.931303621086, 28003.046212569217, 37666.25884005016, 41998.654683028784, 40386.998153693836, 33059.35011865029, 21053.112587257318, 6067.57971116985, -9776.517140006938, -24237.47193157102, -35269.70075707248, -41312.85816521044, -41512.38807107915, -35840.02641379248, -25097.990516714875, -10805.477087570558, 5015.7226075170765, 20127.196429071748, 32390.45198142653, 40069.762461740844, 42078.02784524413, 38130.973858626225, 28787.328574089242, 15369.752527682327, -222.75083913334484, -15783.789856430674, -29111.85169706765, -38321.783671780126, -42110.99233903175, -39943.75661725837, -32126.394359744776, -19764.5935707179, -4606.680772970673, 11202.949968187648, 25427.299399334224, 36053.331269462244, 41576.846243674336, 41216.06294857437, 35021.859783977525, 23871.319284234993, 9342.555748303734, -6508.161692082818, -21438.123073192, -33335.319923987416, -40516.980531426205, -41967.58853211467, -37481.89632801874, -27694.373213008745, -13989.1088465023, 1695.2031177060976, 17139.62420764959, 30158.65618624187, 38909.6147788178, 42153.80750065348, 39431.88722050544, 31129.277134389078, 18421.18459327842, 3106.347768738171, -12648.070793236551, -26613.117351003904, -36813.40149250679, -41806.3238097086, -40885.803757804955, -34182.02236228202, -22642.99860142505, -7900.777783782384, 7959.231498403331, 22692.97973612324, 34215.39074965476, 40895.491565523356, 41787.70399882338, 36765.610485676654, 26540.282705549398, 12559.013092596731, -3199.3990688900744, -18505.17461636978, -31193.05812202745, -39468.37407197977, -42160.94171411494, -38889.824844918134, -30117.81184506893, -17085.25048857724, -1635.6935089606777, 14045.270100494441, 27738.7591663093, 37506.67005959764, 41966.451603413974, 40486.60130311142, 33276.79468086079, 21357.46896939464, 6415.866883454748, -9433.629437355505, -23948.491877172637, -35075.451563598064, -41240.85452511276, -41572.73552417208, -36024.25734997304, -25379.961060057998, -11145.365858370698, 4666.042431657759, 19817.19180511566, 32163.985399014276, 39958.88759959271, 42098.42676178947, 38279.746945955136, 29043.40390583562, 15696.887629397073, 129.12422550038204, -15456.93987921344, -28856.289177874904, -38173.59827332985, -42091.2050153212, -40055.0411324366, -32353.129355330402, -20074.59152024731, -4956.1575586313975, 10863.455184112818, 25145.8643400068, 35869.70923389763, 41517.14151295139, 41288.54956415172, 35216.467685193216, 24160.283140811563, 9685.13304449285, -6160.531453135585, -21134.62463018192, -33118.81366355257, -40418.184694920645, -42000.325389134414, -37641.68981054293, -27958.437332699697, -14320.240265705359, 1343.947656749712, 16817.918580026373, 29911.987021287598, 38772.948546252475, 42146.401938713214, 39554.877209491075, 31365.140001693726, 18736.61692161505, 3456.639180552086, -12312.461348758805, -26339.655726525154, -36640.7778901453, -41758.91157807431, -40970.32427229467, -34386.477288754366, -22938.506968516584, -8245.492763199309, 7614.0494799623375, 22396.19409638966, 34008.98044271151, 40808.69944147032, 41832.747366921016, 36936.18055250649, 26812.147293909245, 12893.740539991019, -2849.199474825707, -18189.06881033959, -30955.70810668819, -39343.42479013778, -42165.92256930076, -39024.18814593672, -30362.35228147116, -17405.54481881375, -1986.3230625686558, 13713.888495461077, 27473.47731377999, 37345.12642779574, 41931.37205221622, 40583.11653208445, 33491.02619272064, 21659.277003005263, 6762.382452309398, -9091.370189757981, -23658.896939182643, -34879.53008333232, -41166.22276051942, -41630.03385246661, -36205.25862238809, -25659.175518665, -11483.196414280741, 4317.340723953969, 19506.96098515828, 31936.15000066513, 39845.64859323215, 42115.85645939441, 38425.306072619176, 29296.545916653424, 16021.710914923959, 479.7012174891581, -15130.235751796714, -28599.6603509553, -38023.33894929775, -42068.53060857178, -40163.18152320191, -32576.740578259745, -20382.069543766145, -5303.996949331426, 10524.482239195686, 24863.694482957893, 35684.32769643502, 41454.69695052938, 41357.99429244861, 35407.80514853182, 24446.570298990155, 10025.731993351093, -5813.771873009333, -20830.747919958718, -32900.8672142189, -40316.87081851172, -42030.12065615351, -37798.16236319913, -28219.66905422684, -14649.078530708031, 993.9012574863758, 16496.221398221784, 29664.20846830073, 38634.06663993009, 42136.183344690005, 39674.622731503536, 31598.03641180598, 19049.553022820193, 3805.4135132217284, -11977.23574793466, -26065.407941225487, -36466.24424481385, -41708.820347860325, -41051.67852544202, -34587.84165202491, -23231.312556816687, -8588.381856676839, 7269.62406023295, 22098.96100292243, 33800.99296828227, 40719.39762429853, 41874.758405918255, 37103.54894496078, 27081.164313347082, 13226.339093312305, -2500.113287279013, -17872.86235318143, -30717.136730103404, -39216.17117831653, -42168.05187665393, -39155.26999248814, -30603.965431113367, -17723.40218073192, -2335.518116568542, 13382.768740208663, 27207.33394826103, 37181.539327588616, 41893.620316564105, 40676.35525532079, 33702.256016084735, 21958.383466430783, 7107.177757988002, -8749.743906296786, -23368.78507654438, -34681.891006172715, -41089.10905201398, -41684.19772863927, -36383.19522584402, -25935.535285809055, -11819.027872655837, 3969.6337206451512, 19196.562391477153, 31706.940693561788, 39730.12842631006, 42130.2874111128, 38567.733021772365, 29546.694917159697, 16344.257300681445, 828.9326665254841, -14803.712543856338, -28342.02093330418, -37871.03392182038, -42043.06143545029, -40268.16482401768, -32797.28544237618, -20687.017101904465, -5650.166319766291, 10186.039824118721, 24580.889694559628, 35497.1583628035, 41389.68806105005, 41424.27369661224, 35596.06078061772, 24730.068757846282, 10364.391741161382, -5467.889944687463, -20526.602327932473, -32681.421014949923, -40213.24196596971, -42056.813939380045, -37951.56932819325, -28477.886761374368, -14975.746425792473, 645.095967148338, 16174.616342650708, 29415.263647823987, 38493.115644624544, 42123.03015690611, 39791.29724270873, 31827.80716537975, 19360.077135691066, 4152.600768335257, -11642.45563502713, -25790.37200161503, -36289.9035259951, -41656.021696088115, -41129.975346009574, -34786.03876515555, -23521.482723176247, -8929.386107580322, 6925.990028349207, 21801.316843824072, 33591.48581059153, 40627.63246918675, 41913.76788769105, 37267.73867782762, 27347.35090550087, 13556.776925974085, -2152.1471755384327, -17556.619748395082, -30477.3386545952, -39086.73322667409, -42167.23852337757, -39283.22846479495, -30842.542612005018, -18038.881173126436, -2683.258838949601, 13051.982837654843, 26940.27772386088, 37016.06625554717, 41853.050254770336, 40766.55390576576, 33910.28124017386, 22254.93262259079, 7450.167822754703, -8408.809536233173, -23078.114668078084, -34482.670080066564, -41009.38472781306, -41735.39965350434, -36557.86116381084, -26209.17035124355, -12152.73618610371, 3622.9566985326537, 18885.98426313966, 31476.465522199145, 39612.28113743487, 42141.85807348376, 38706.937253886, 29793.96352324228, 16664.448300271473, 1176.8139219991406, -14477.389767100693, -28083.429686740743, -37716.69530299971, -42014.84717268828, -40369.98490177513, -33014.80918148177, -20989.39385950048, -5994.677777417854, 9848.166196852715, 24297.458596478067, 35308.2707615767, 41322.07656086023, 41487.49970168344, 35781.160356418295, 25010.82727852774, 10701.080035356017, -5122.941213863042, -20222.17294631668, -32460.601328301847, -40107.198028051076, -42080.61691621145, -38101.72130532198, -28733.267745435696, -15300.129060195732, 297.55163693633284, 15853.083050953175, 29165.29402133467, 38349.99350734382, 42107.14524093962, 39904.700904017576, 32054.647113136994, 19668.033488878104, 4498.226194748497, -11308.120660214698, -25514.65165554077, -36111.71357769948, -41600.667021382375, -41205.12828546132, -34981.22794567282, -23808.921934266225, -9268.545919072882, 6583.163513695248, 21503.328401755458, 33380.45407054175, 40533.48930636017, 41949.74506854782, 37428.833209563934, 27610.64546865204, 13885.086089696433, -1805.3461443908113, -17240.377395800915, -30236.370330404407, -38955.13290691096, -42163.576348950155, -39408.04267166684, -31078.134318164703, -18351.965306348186, -3029.503640728119, 12721.540863323129, 26672.416351493743, 36848.66799369755, 41809.842242855964, 40853.57632413723, 34115.281320113274, 22548.8157389931, 7791.374203465721, -8068.569423469403, -22787.00479188802, -34281.805225458564, -40927.25655365689, -41783.470069101655, -36729.49808776109, -26479.901594564475, -12484.41628252153, 3277.3333605880994, 18575.320903550557, 31244.66723747242, 39492.25993004646, 42150.449859777895, 38843.095412552655, 30038.1988918293, 16982.363855004227, 1523.289815016102, -14151.336054003428, -27823.887265421505, -37560.43317224093, -41983.860795904635, -40468.75958554683, -33229.24027282051, -21289.266175190303, -6337.476745671029, 9510.901534803472, 24013.44158101065, 35117.723010011585, 41251.915418487064, 41547.703382049214, 35963.12790432845, 25288.863639639985, 11035.762130308807, -4778.938380947175, -19917.533053914518, -32238.40660206855, -39998.86901908532, -42101.440049552206, -38248.77415175858, -28985.713088831304, -15622.273395042484, -48.717954427698835, 15531.702578155038, 28914.24663083384, 38204.86539556785, 42088.376604115016, 40015.06573842994, 32278.358780092756, 19973.553416025417, 4842.223128236119, -10974.30048110409, -25238.19916451848, -35931.808667447614, -41542.61947128564, -41277.30603489173, -35173.203455122515, -24093.741711161383, -9605.753057593283, 6241.191073850677, 21204.978231417706, 33168.00961517921, 40436.9163906376, 41982.83075778131, 37586.73486101586, 27871.15649754529, 14211.184044232994, -1459.7277766187622, -16924.15754913699, -29994.29914644118, -38821.385622706955, -42157.12317079167, -39529.70685905558, -31310.788447237475, -18662.615606301628, -3374.2581650512266, 12391.484437997546, 26403.763197134715, 36679.42103414735, 41763.96109320079, 40937.535082623006, 34317.194830666485, 22840.075120998637, 8130.7754886049715, -7729.0812753518885, -22495.43565269424, -34079.42032438292, -40842.6195186085, -41828.61439048038, -36897.92767862762, -26747.888431685922, -12813.975345815976, 2932.7962635606, 18264.543286158812, 31011.68466774314, 39369.9486279515, 42156.256267917735, 38975.99507561737, 30279.577108949343, 17297.853904655287, 1868.3638201974395, -13825.540814295084, -27563.4985439396, -37402.18415085556, -41950.25144302199, -40564.36957107177, -33440.72333749119, -21586.521303558988, -6678.587218750899, 9174.259574991067, 23728.909146432197, 34925.50839898878, 41179.29488952383, 41604.85164531924, 36142.05464037725, 25564.11909122553, 11368.470378605212, -4435.916247891232, -19612.716396628242, -32014.885237158338, -39888.277407751484, -42119.367103243654, -38392.720555220956, -29235.261417651498, -15942.17231347695, -393.6686779761868, 15210.48358388222, 28662.2288120906, 38057.69220909468, 42066.907719693845, 40122.26980667829, 32499.113070301242, 20276.549812452846, 5184.597439508058, -10640.990424446405, -24961.139244947295, -35750.12056001782, -41482.08640037805, -41346.33405490097, -35362.1962999256, -24375.7861121173, -9941.082752611024, 5900.080132346802, 20906.366016023494, 32954.08747463287, 40338.07251574228, 42012.89172481428, 37741.624517878874, 28128.726393972687, 14535.151666932949, -1115.3276828169019, -16608.024496474773, -29751.106949902583, -38685.60102759474, -42147.836258334, -39648.34436654602, -31540.430384851603, -18970.895325885056, -3717.477148581867, 12061.854430951487, 26134.349285469805, 36508.38350588398, 41715.45000037322, 41018.47017553291, 34516.031556050635, 23128.736034791928, 8468.33376499746, -7390.358467495255, -22203.478301262854, -33875.51936152143, -40755.604164599106, -41870.76580565034, -37063.295139332746, -27013.061065939797, -13141.444621763601, 2589.3489046730374, 17953.737567063712, 30777.466638316306, 39245.52381792619, 42159.12964776565, 39105.88033502572, 30517.925457683552, 17611.04928580993, 2212.000964412135, -13500.074069161921, -27302.200298632444, -37242.09697385265, -41913.872738404156, -40657.01127058653, -33649.06027886518, -21881.28460763021, -7017.92385825658, 8838.28969349376, 23443.8321246164, 34731.73637353697, 41104.14635841184, 41659.084595100976, 36317.82758558568, 25836.69631447276, 11699.127437162715, -4093.897278155987, -19307.734247119748, -31790.105107331747, -39775.42511482028, -42134.463806289175, -38533.534708111736, -29481.962249546505, -16259.780601025019, -737.3023745032407, 14889.46332123037, 28409.25314954265, 37908.54473999976, 42042.7020905969, 40226.41323037361, 32716.86228980834, 20577.054852974103, 5525.346637691787, -10308.240177390537, -24683.437216637503, -35566.77154205243, -41418.95588122264, -41412.41968140501, -35548.038461595344, -24655.19181114536, -10274.459376936367, 5559.876990254017, 20607.4545761233, 32738.82973219528, 40236.8309937671, 42040.13754759983, 37893.29012184608, 28383.542749131127, 14856.85732104061, -772.1512397597788, -16291.955760540464, -29506.905753891107, -38547.69990278982, -42135.86535554523, -39763.81520357963, -31767.193407567498, -19276.690411453874, -4059.170123515413, 11732.659002064393, 25864.24868488866, 36335.54184248512, 41664.40205765348, 41096.33761699158, 34711.88822124738, 23414.741374949463, 8804.082581752084, -7052.422865467478, -21911.15927840532, -33670.13573922943, -40666.22358430505, -41909.978840123425, -37225.59057702302, -27275.42770891119, -13466.816831927365, 2247.0422942324567, 17642.909988324496, 30542.118165171683, 39118.93567468779, 42159.25407705278, 39232.62241482025, 30753.400793262692, 17921.866207285602, 2554.1878803531763, -13174.927113793035, -27040.127136416428, -37080.089753245295, -41874.94076519528, -40746.4949886676, -33854.477379998374, -22173.398917590617, -7355.537525780842, 8502.994086571598, 23158.323929520542, 34536.34678140991, 41026.644751637716, 41710.27182649208, 36490.63897959762, 26106.45025372759, 12027.808220780818, -3752.9049692839335, -19002.658498448724, -31564.04630112831, -39660.427912195824, -42146.67542821833, -38671.343793970096, -29725.73812974861, -16575.154175928954, -1079.5764533942468, 14568.69128778207, 28155.352484929917, 37757.48275539089, 42015.801470185535, 40327.53425612027, 32931.60482952924, 20875.086927183387, 5864.421979322249, -9976.07180404712, -24405.17423452268, -35381.764235139824, -41353.36415568638, -41475.4897691096, -35730.86466274888, -24931.89388169214, -10605.895072213747, 5220.5863469009355, 20308.34405942276, 32522.188739887762, 40133.38152061662, 42064.407813751815, 38041.973390876, 28635.42585687997, 15176.408022083882, -430.2293485921099, -15976.03877084462, -29261.63094008911, -38407.84945294688, -42121.065870929364, -39876.33198830697, -31990.889670793225, -19580.11921955105, -4399.264486177355, 11403.962577621505, 25593.436781704037, 36161.015271541226, 41610.751782316234, 41171.28196411244, 34904.65244542785, 23698.181300538025, 9137.947558889173, -6715.314695010485, -21618.499213832947, -33463.34743161707, -40574.485091686875, -41946.32672574381, -37384.786233396415, -27535.04130790314, -13790.043474975379, 1905.885229207546, 17332.10649124747, 30305.657429780582, 38990.263829285854, 42156.58541224006, 39356.317584540695, 30985.94849570868, 18230.323971657785, 2894.9184835212677, -12850.16084855242, -26777.241804997728, -36916.295672815235, -41833.330839914495, -40833.02876805111, -34056.80060111579, -22462.985776180525, -7691.360868819307, 8168.433499833112, 22872.341267952794, 34339.481776124085, 40946.65065797256, 41758.61623923643, 36660.268265111015, 26373.54381675992, 12354.386138833403, -3412.9678232645365, -18697.465228365603, -31336.825446896513, -39543.203494359936, -42156.158241588695, -38806.005462032495, -29966.716759718856, -16888.17907355944, -1420.4833374063976, 14248.175833747322, 27900.608052525167, 37604.495107172115, 41986.30140459179, 40425.587557267194, 33143.433887248546, 21170.588723804067, 6201.846064801572, -9644.510753874862, -24126.379387957724, -35195.13636597016, -41285.31875201121, -41535.598169662735, -35910.66268692378, -25205.89324662513, -10935.383150771278, 4882.260870216297, 20009.035907263613, 32304.26573832591, 40027.66049017502, 42085.88256683641, 38187.53676142994, 28884.516936874265, 15493.723945037014, -89.58928156050467, -15660.257994590367, -29015.419212039174, -38265.95183226153, -42103.64874795469, -39985.691506957766, -32211.72603590705, -19881.027550594183, -4737.785879587253, 11075.759597365692, 25322.031189268666, 35984.736187741575, 41554.670708001075, 41243.159939198085, 35094.501391044134, 23978.910262720463, 9469.97934840661, -6379.051662316248, -21325.575717179978, -33255.13469709755, -40480.51088458044, -41979.754732231566, -37541.01607017647, -27791.826359165072, -14111.177154077877, 1565.913320045021, 17021.37956455058, 30068.115388238934, 38859.56579614628, 42151.16638699154, 39477.00475943747, 31215.567864130706, 18536.438412643925, 3234.144603537944, -12525.798354527195, -26513.629417491356, -36750.712762268464, -41789.18450366474, -40916.53843475917, -34256.158716767364, -22749.986599641077, -8025.393102024463, 7834.611791474242, 22585.99353787937, 34141.092508863985, 40864.357223882966, 41803.95717678483, 36826.94366718821, 26637.81471077822, 12678.944956652784, -3074.103766033754, -18392.25066971488, -31108.37589914831, -39423.922363862635, -42162.75801794828, -38937.726523482044, -30204.708374512593, -17198.962055043427, -1759.9671465715558, 13927.9876932287, 27644.987344452496, 37449.70593364901, 41954.12829735327, 40520.72545432158, 33352.24015594893, 21463.61097835466], \"y\": [0.0, 15576.050639839646, 28947.673255183683, 38222.67821692621, 42088.91797509229, 39999.967566771404, 32251.94264774194, 19941.490737583095, 4810.225586419786, -11001.437683135267, -25256.92812691474, -35939.721100098606, -41538.111858435186, -41259.703301843045, -35143.06852148851, -24053.36249514699, -9559.52839398408, 6287.2253452990835, 21244.147604295555, 33194.566481971466, 40447.39483958242, 41977.006975830125, 37567.375139508884, 27843.141926045617, 14180.209675709226, -1488.497860903827, -16946.658173238702, -30007.703864714287, -38823.79836393347, -42147.427048606296, -39507.633495937596, -31277.43331574384, -18620.939142118554, -3329.1297384597483, 12433.858253014154, 26437.11233403431, 36698.947900978295, 41767.585461208815, 40926.29558885071, 34294.81004188983, 22811.76527590234, 8101.892196232001, -7754.0009458026025, -22513.08355685165, -34087.596798950006, -40839.88590534016, -41814.201833932784, -36872.04104984537, -26712.17990909279, -12771.981766553896, 2975.797840743753, 18302.408728537772, 31038.801388640015, 39382.615775853985, 42153.73201318321, 38960.48555344125, 30255.43151305792, 17270.35589236439, 1842.3910950816717, -13846.189457359425, -27576.24770784686, -37405.33690959409, -41942.78577524175, -40545.83422674302, -33411.74618791657, -21549.356444581823, -6637.270223828384, 9214.199478020904, 23761.605964433234, 34946.1620534729, 41185.49891056899, 41597.077717728935, 36123.47047538086, 25539.477663374204, 11342.781612979716, -4458.3651097848015, -19628.880532673564, -32022.804183201057, -39886.73149660911, -42107.59558769052, -38370.654464885345, -29204.003874440157, -15904.4668934768, -354.00847324343886, 15246.541319846427, 28689.303204814827, 38071.9770687892, 42067.212919785314, 40110.279024556636, 32478.671550851952, 20252.509483571146, 5161.558640832761, -10659.490550346804, -24972.77753615514, -35753.45656320402, -41476.231962125785, -41330.806922843876, -35337.24955271186, -24343.020254506595, -9903.750822589453, 5937.204609432009, 20937.884600842262, 32975.2912267578, 40346.30245475368, 42008.151443271876, 37726.54637706942, 28107.625946152075, 14512.692866801188, -1135.113340799736, -16622.35299652693, -29758.29484673034, -38684.69055914065, -42138.12923858937, -39629.63583562469, -31513.334259449177, -18937.44235464832, -3681.401137217969, 12095.658607264611, 26160.827089694307, 36523.719030096225, 41718.10542353399, 41009.62796790298, 34499.06419363465, 23108.079033552734, 8448.306368650372, -7406.4624886965885, -22213.59664391943, -33878.51229794544, -40750.781836881695, -41857.69887885809, -37041.97634813994, -26984.506596343912, -13108.16750793774, 2623.3381918950295, 17983.577217606296, 30798.648928004837, 39255.21264025826, 42156.96646683212, 39093.9921722402, 30500.194186197907, 17591.81769620327, 2195.0122628971835, -13512.27809729236, -27308.216259930185, -37241.237130858906, -41905.636711047446, -40641.11170896649, -33625.78048146429, -21852.00335301007, -6985.601144911633, 8869.448692882808, 23469.200811562947, 34747.57977860899, 41108.6412366521, 41653.005849396875, 36304.052035888315, 25819.282730566312, 11682.140288913708, -4107.421769600331, -19315.98792076222, -31792.271675793785, -39771.07707133135, -42123.29868061276, -38515.38210369842, -29457.360559433833, -16230.550493746696, -706.7005355886004, 14917.17407280092, 28429.867340640278, 37919.15392746285, 42042.644816208194, 40217.371624762294, 32702.281381099885, 20560.96907130696, 5511.234719531205, -10318.073628812184, -24687.876165188365, -35565.422896907265, -41411.60581418324, -41398.79859383189, -35528.18095138767, -24629.929660530255, -10245.980952456215, 5588.0692433928225, 20631.230017345166, 32754.597415578202, 40242.62978987015, 42036.33310518886, 37882.38635568207, 28369.210795194067, 14842.887241061258, -782.9560049211356, -16298.05647251092, -29507.80297493676, -38543.27355646938, -42126.0179527848, -39748.3162135203, -31746.21040393671, -19251.429032690394, -4032.1277483650283, 11757.853710013173, 25883.781965388094, 36346.5406495966, 41665.98005530692, 41089.75033444438, 34700.200304010505, 23401.668385099863, 8792.866090129553, -7059.696672225593, -21913.671512840207, -33667.82092461174, -40659.19043019838, -41898.09326213735, -37208.71120765418, -27253.9397783264, -13442.197450783724, 2272.0081063105445, 17664.64576638358, 30557.270789105874, 39125.50744309074, 42157.284394977614, 39224.24525613517, 30741.951966903234, 17910.835525202187, 2546.1761231319624, -13178.634639349859, -27039.336768682497, -37075.049238465006, -41865.79302128527, -40733.10765630863, -33836.7365425152, -22151.956006086537, -7332.172870764851, 8525.345761981123, 23176.2978632954, 34547.200120362824, 41029.31402535306, 41705.70453420742, 36481.51232995132, 26096.212285240817, 12019.458123180762, -3757.4909552180566, -19002.946106537318, -31560.326377451463, -39653.18155289527, -42135.94565130783, -38656.96692492097, -29707.70771540186, -16554.320322987103, -1058.0291387788766, 14587.997405853524, 28169.398383506483, 37764.305439570184, 42015.213221807026, 40321.30679956986, 32922.77709009441, 20866.86651296848, 5859.2201166172135, -9977.198280782373, -24402.3403459567, -35375.61451544841, -41344.36196925194, -41463.66144471696, -35715.95318575456, -24914.048495624724, -10586.237349532137, 5239.827700329867, 20324.314788335152, 32532.373006805457, 40136.61744278784, 42061.41015094634, 38035.07865044619, 28627.804561289824, 15170.857687173911, -432.0561893125544, -15973.863351843718, -29256.09785308405, -38399.81000381712, -42110.89518837169, -39863.88711733731, -31975.94315139739, -19562.94823519246, -4381.254691143195, 11420.504199065537, 25605.91182493048, 36167.583939785785, 41611.047722083866, 41166.80925708146, 34898.12675346918, 23692.576210762658, 9135.522247440234, -6713.734390830239, -21613.325098249356, -33455.63618071333, -40565.08033860222, -41935.493416222984, -37372.22078089918, -27520.503739196658, -13774.04302481858, 1921.815243772053, 17345.68138846244, 30314.678015831105, 38993.56893537114, 42154.69934897975, 39351.29905178191, 30980.684221406646, 18227.43764867251, 2895.868747755701, -12845.332615937778, -26769.531888512825, -36906.95734378534, -41823.14009035011, -40821.98560544834, -34044.51985996596, -22449.290101881757, -7676.932853263716, 8181.941758438482, 22882.81268132721, 34345.25700820911, 40947.31869056858, 41755.414708224336, 36655.697241095244, 26370.35185336736, 12354.652873664887, -3408.6023123783148, -18689.711618576523, -31327.141664927534, -39532.85190632634, -42145.73073854592, -38795.251262285594, -29955.11712086175, -16875.716059585473, -1407.987962499891, 14259.021315202217, 27908.002178270905, 37607.37094404862, 41985.066476159234, 40422.03448589653, 33140.223506783346, 21170.175706133836, 6205.528592537047, -9636.897882199826, -24116.18582183415, -35184.04502288821, -41274.544220330696, -41525.39887629461, -35900.57937396434, -25195.390286076283, -10924.50701484136, 4892.527397570687, 20017.111429149474, 32308.758537158013, 40028.20134419828, 42083.52293150415, 38184.554249794615, 28883.491621584915, 15496.5446248265, -82.4288527037226, -15649.737399096462, -29003.380049866726, -38254.134858152865, -42093.01671762894, -39976.19303556531, -32202.675937346314, -19871.913484008193, -4728.7957079349835, 11083.603280710187, 25327.375405007406, 35986.687070865584, 41553.57089193667, 41240.63048855227, 35092.991826179765, 23980.740092046522, 9476.306261294298, -6368.5851914747755, -21312.646249523303, -33241.875037829865, -40468.62595186069, -41969.80515016697, -37532.62077685523, -27784.163535623396, -14103.730857910607, 1572.7967181210217, 17026.72136537317, 30070.87786399026, 38859.49006359347, 42149.191969402964, 39475.211981891895, 31216.39025832505, 18541.623513319213, 3244.0532427916455, -12512.376146880744, -26498.91225444073, -36736.95364136078, -41777.788117525444, -40907.7274308023, -34249.20724093115, -22743.96094660725, -8019.88508862319, 7839.239935419415, 22588.893883722634, 34141.63893272524, 40862.892665072664, 41802.00103635723, 36826.78003709748, 26641.618375476282, 12687.784766092751, -3060.7751618110606, -18376.399504230834, -31092.572674949715, -39410.37063261503, -42152.45933411768, -38930.452880594275, -30199.483881984677, -17194.79318255448, -1756.5344354412157, 13930.318500596237, 27645.606536178806, 37448.53952582213, 41952.05227266833, 40519.70128877243, 33354.54425254981, 21470.9334404906, 6550.110784109218, -9297.211942564485, -23829.42333181529, -34990.836670203316, -41202.11643768236, -41584.11574150454, -36082.03083427641, -25473.97456778995, -11260.751366151666, 4546.182353746481, 19709.69385607981, 32083.779545921392, 39917.4461660724, 42102.69406237838, 38330.86057323641, 29136.247437622835, 15819.963672746844, 265.9104008594165, -15325.768763278982, -28749.563292438306, -38106.43266921472, -42072.2630203721, -40085.38866384176, -32426.31213434473, -20178.394859512387, -5074.701648264032, 10747.221506131957, 25048.075011418394, 35804.09518123131, 41493.343659271966, 41311.44929042856, 35284.642892114345, 24266.228894602602, 9815.136782465914, -6024.296713401798, -21011.584369707914, -33026.727647778636, -40369.63730905309, -42001.21753952667, -37689.76445278017, -28044.9810013123, -14431.197274882152, 1224.9738301127525, 16707.77590187253, 29825.996675006372, 38723.20405296741, 42140.910356877335, 39595.93053712391, 31449.118588434394, 18853.35635655502, 3590.7303750294054, -12179.809978029938, -26227.504590123546, -36565.056073199725, -41729.78460328344, -40990.33841617203, -34450.805894231984, -23035.97693744669, -8361.015506804004, 7497.295762203545, 22294.503555521016, 33936.481416953124, 40775.96234543106, 41845.59028841822, 36994.68102395751, 26910.078224203673, 13018.786208296533, -2714.0446823411517, -18062.964363318337, -30856.8356454215, -39285.55963730699, -42156.37910122463, -39062.40770384592, -30440.931131722933, -17511.458418378716, -2103.6637764860416, 13601.872682300638, 27382.384883931416, 37287.63056646435, 41916.41910462835, 40614.129721022204, 33565.858880301465, 21769.074633598917, 6892.979177819853, -8958.155495973055, -23542.163689526806, -34795.91235015624, -41127.25850180197, -41639.729947983076, -36260.41611735856, -25749.766024960998, -11594.994427160103, 4200.826964105702, 19402.101225375805, 31857.434616860963, 39804.445762452415, 42118.899008160435, 38474.0480732399, 29386.067324432766, 16141.10738008785, 612.9163829340634, -15001.961783742636, -28494.765800795736, -37956.69287869103, -42048.74537196904, -40191.44831769816, -32646.924886623292, -20482.342340611016, -5418.966491684122, 10411.352806340365, 24768.171333167793, 35619.68615613258, 41430.599342747526, 41379.1283834496, 35473.23669170757, 24548.957333551418, 10152.053531758249, -5680.886016957364, -20710.276523076787, -32810.04161030744, -40268.39481950695, -42029.5401542634, -37843.85061228052, -28302.861874300284, -14756.490910362816, 878.3787469504631, 16388.928140933163, 29579.948928747082, 38584.910546098006, 42129.71062547396, 39713.59847289446, 31678.80817503406, 19162.67328257283, 3935.8538946348413, -11847.679256420904, -25955.318186351426, -36391.39611759615, -41679.097829063154, -41069.924538937776, -34649.29364847761, -23325.35396886523, -8700.284249675522, 7156.126034369262, 21999.721734020444, 33729.81773790738, 40686.598155151776, 41886.20767540934, 37159.4506180191, 27175.706406992515, 13347.665146058767, -2368.4243167605564, -17749.510927593532, -30619.84576850421, -39158.60874613951, -42157.379013718426, -39191.26789327764, -30679.370247107272, -17825.774869253066, -2449.3369434158567, 13273.767570952252, 27118.22224338097, 37124.897132315076, 41877.96590363274, 40705.54316526222, 33774.029301880124, 22064.65756739776, 7234.062927321132, -8619.784939688121, -23254.338869992433, -34599.46045184966, -41049.77672162906, -41692.41504422576, -36435.597071968725, -26022.81954729778, -11927.171888173998, 3856.49227119977, 19094.340726982824, 31629.865019238074, 39689.126227338405, 42132.2869879726, 38614.064226560295, 29632.997166732013, 16459.937585064385, 958.5833943647058, -14678.364069162732, -28239.027375186935, -37804.937522634114, -42022.51972858117, -40294.38327303435, -32864.52109040999, -20783.762651742018, -5761.578729806551, 10076.059810126435, 24487.62583822356, 35433.59677903695, 41365.2753298676, 41443.7876472755, 35658.70430596184, 24828.985822753646, 10487.002483486063, -5338.395628702867, -20408.660560331748, -32592.032055136187, -40164.724892264494, -42055.00618927224, -37994.72659385558, -28557.90889613557, -15079.521130593606, 533.0317568470863, 16070.146274261318, 29332.918191448567, 38444.41222347489, 42115.824492295564, 39828.036567266514, 31905.556945265853, 19469.496459728292, 4279.424338205098, -11515.993243688812, -25682.471666544763, -36215.87078106635, -41625.900942338565, -41146.384091226166, -34844.76627488382, -23612.056135120103, -9037.71091683417, 6815.761168101583, 21704.595614578644, 33521.63667959824, 40594.90047259416, 41923.826520708506, 37321.140465980185, 27438.49885900789, 13674.421551823267, -2023.957865878273, -17436.04016498307, -30381.710988910476, -39029.51562950352, -42155.557131385925, -39317.01434612896, -30914.86653434667, -18137.699989145192, -2793.53821618344, 12945.988831734143, 26853.2923201948, 36960.22746219509, 41836.92125784539, 40793.82515115885, 33979.20097823473, 22357.60600100942, 7573.391252974513, -8282.099903869594, -22966.096344620066, -34401.32351022127, -40969.946151975375, -41741.98828128521, -36607.756932685494, -26293.047778095108, -12257.328472165023, 3513.198769768928, 18786.50180894272, 31400.95577292128, 39571.69138911412, 42142.701573162856, 38751.04902047248, 29876.97645055813, 16776.49500619295, 1302.8775260555908, -14355.021899626705, -27982.33607294019, -37651.29889947184, -41993.53951743205, -40394.295503870475, -33079.07807800116, -21082.674311376762, -6102.499301292181, 9741.358131388135, 24206.509379857504, 35245.869647411826, 41297.42603402057, 41505.46135020327, 35841.08899710849, 25106.30072517925, 10819.9850191905, -4996.829765575325, -20106.8492300663, -32372.62957731715, -40058.8102672656, -42077.52661226417, -38142.537442967485, -28810.05328281993, -15400.351526912995, 188.9515148580975, 15751.522885380544, 29084.771781724314, 38301.97050308671, 42099.06252521396, 39939.468692327064, 32129.247082905327, 19773.900056046215, 4621.390795181904, -11184.81111118216, -25408.87467650989, -36038.69045864304, -41569.99873027921, -41219.90613828881, -35037.09857788548, -23896.136328072505, -9373.239616040326, 6476.233556552065, 21409.10932465502, 33312.08059210873, 40500.7711959096, 41958.58792501223, 37479.691686606326, 27698.498177511206, 13999.015004841393, -1680.6539217700222, -17122.591376324435, -30142.48089784744, -38898.28575217292, -42150.976292025414, -39439.6504495407, -31147.430241994818, -18447.23792295335, -3136.267587253536, 12618.595673075959, 26587.54391439428, 36793.73721009856, 41793.222557193614, 40879.076465725, 34181.311823711105, 22647.97805808541, 7910.923346647105, -7945.1544446453, -22677.36192305138, -34201.71814083619, -40887.597425376174, -41788.67445830308, -36776.755675177104, -26560.548030430913, -12585.385736325881, 3170.970404628738, 18478.5306713366, 31170.911006432332, 39451.93491074059, 42150.38382837721, 38884.829692883955, 30118.099728952562, 17090.69949775844, 1645.78756741726, -14031.934440777464, -27724.82253038277, -37495.659903591055, -41961.97863099892, -40491.079972775064, -33290.681426675495, -21379.04086647305, -6441.746090376211, 9407.268545464427, 23924.871448091195, 35056.47381961391, 41227.150584222254, 41564.11233001389, 36020.43686499709, 25380.88901665517, 11151.002539614912, -4656.236170856969, -19804.839007508046, -32151.920148578432, -39950.64636454436, -42097.17686032502, -38287.25926794004, -29059.346879378707, -15718.93895303926, -153.8344865362429, 15433.033114587786, 28835.694496607375, 38157.4653047315, 42079.644386791275, 40047.773585010764, 32350.00915038555, 20075.79961244419, 4961.765197361708, -10854.125748543867, -25134.69957703618, -35859.68498290374, -41511.67027456439, -41290.305947804365, -35226.46092659132, -24177.514827544775, -9706.905796626155, 6137.557687728533, 21113.3724767567, 33101.02993232001, 40404.43155661719, 41990.347278612906, 37635.24267470367, 27955.65208300237, 14321.481206222088, -1338.5428365527505, -16809.222134512616, -29902.133610259334, -38765.06080825069, -42143.585996874885, -39559.27386745494, -31377.035770580653, -18754.401043205587, -3477.477974970847, 12291.609452384375, 26321.05174070239, 36625.474228715015, 41746.9209865033, 40961.32799418419, 34380.39903705918, 22935.75223569157, 8246.646772800703, -7608.952958844614, -22388.263789322355, -34000.57736307665, -40802.912699738146, -41832.39036114801, -36942.72711140302, -26825.248917588342, -12911.389215191579, 2829.828398487832, 18170.547743211675, 30939.588183642572, 39330.129169270076, 42155.141154044744, 39015.6227568376, 30356.254484612935, 17402.62186172231, 1987.2711864013302, -13709.177928603433, -27466.38818959167, -37338.24922470437, -41927.64934485883, -40584.924387862804, -33499.220645501555, -21672.906341765818, -6779.262788104524, 9073.84040693501, 23642.698166215483, 34865.56819166212, 41154.36181469117, 41619.881908212345, 36196.69847783841, 25652.787336498426, 11480.009688104936, -4316.631583072423, -19502.674079084656, -31929.969690324247, -39840.2415920846, -42114.02442026473, -38428.895469043986, -29305.79056524921, -16035.275457539787, -495.3171943426989, 15114.754407367014, 28585.637026919667, 38011.020972454346, 42057.507305976935, 40153.05343934054, 32567.77845058499, 20375.2488208309, 5300.510820655191, -10524.00444707142, -24859.85439072662, -35679.07699343195, -41450.73716886512, -41357.79630343636, -35412.711719085906, -24456.268538358956, -10038.629884692045, 5799.7775778548585, 20817.32143685089, 32888.69980870174, 40305.67046021573, 42019.33136822634, 37787.627232969375, 28210.036761974534, 14641.741728868124, -997.6479775007024, -16495.925037734396, -29660.815557968468, -38629.714098643264, -42133.55613700482, -39675.779451725044, -31603.755241649338, -19059.14545093319, -3817.1735536670635, 11965.058801151446, 26053.880555252526, 36455.41519959457, 41698.121484338175, 41040.55052745209, 34576.50327344475, 23220.916338082447, 8580.562635391294, -7273.544363816833, -22098.79189267723, -33797.98125372471, -40715.88139334558, -41873.199368966125, -37105.64011812326, -27087.19109024175, -13235.294605321738, 2489.811142538988, 17862.516384460876, 30707.177264999737, 39206.1450970461, 42157.183771534554, 39143.302261348814, 30591.554906598565, 17712.175166732988, 2327.3246515112824, -13386.731927618644, -27207.216739127805, -37178.879912164164, -41890.81453387819, -40675.6426677307, -33704.83773442015, -21964.1861848575, -7115.06529454865, 8741.085616270511, 23360.114395085384, 34673.02389224609, 41079.27256772929, 41672.62601004007, 36369.995797616044, 25921.9478890706, 11807.038784962851, -3978.041661417711, -19200.420101330274, -31706.747852314762, -39727.74515467195, -42128.01845196933, -38567.5425411613, -29549.36180862285, -16349.372167152522, -835.4509801537313, 14796.708106602959, 28334.672845201567, 37862.6847610647, 42032.702491933414, 40255.33367269746, 32782.59243973373, 20672.223862194798, 5637.607813163532, -10194.447888345358, -24584.479046729026, -35496.80209486443, -41387.38199828606, -41422.30121077587, -35595.97710301386, -24732.329949588657, -10368.443734439214, 5462.904236853813, 20521.090918875365, 32674.943883963373, 40204.75449089201, 42045.35821810486, 37937.042954553646, 28461.55016500748, 14959.85531220913, -658.003096738898, -16182.78915771736, -29418.40687257345, -38492.4800124085, -42120.69746879619, -39789.345632498014, -31827.48687563094, -19361.515640008296, -4155.305602863448, 11638.99847457548, 25786.00963494035, 36283.68635415888], \"z\": [0.0, 271.8809752273574, 505.28351629006517, 667.179330564762, 734.6647974819019, 698.2020338073938, 562.9597573621555, 348.0800215014314, 83.96280693950011, -192.03080175563363, -440.86131382148017, -627.3301607774125, -725.0504380732515, -720.1908025647986, -613.4245495494754, -419.8530146906875, -166.8622022284004, 109.74391199405069, 370.81796199376726, 579.4133024281035, 706.0118972767777, 732.711382782014, 655.7409796921601, 486.0038640680303, 247.51649956751075, -25.981805134587333, -295.8049973231742, -523.7864013266419, -677.671909554177, -735.6860725533392, -689.6083142808403, -545.9496450788378, -325.02972516050426, -58.11020388453996, 217.03377403161946, 461.46148645597873, 640.5824997411348, 729.0559077581014, 714.3711505089786, 598.6181519315342, 398.18087052255623, 141.419088097769, -135.34655344045282, -392.96730108788495, -595.0011886754583, -712.8628450778355, -729.869607214266, -643.6038849303853, -466.2628627339212, -222.93580926455925, 51.942701119569435, 319.46969019863246, 541.784258089016, 687.4260924016568, 735.7961224721014, 680.0578159999827, 528.1105494138676, 301.45522485600407, 32.15910564607834, -241.68608530626008, -481.3451495433315, -652.9125523641227, -732.1140345605952, -707.7301748656733, -583.2042250451519, -376.1454588896023, -115.85403708161563, 160.83439182220403, 414.7603208660527, 609.9874857480944, 718.8955342075558, 726.079691402821, 630.5375447706364, 445.7932836911129, 197.9890474665874, -77.82098728864752, -342.623320879536, -558.9600739002027, -696.2254544952799, -734.9908054325017, -669.7622815087102, -509.75782458901926, -277.61356216098284, -6.179311778025371, 266.12929662860114, 500.7735872587002, 664.5487870701246, 734.2859133673045, 700.1275336462115, 566.9173575382954, 353.5089280604322, 90.0954164556461, -186.06201961919686, -435.9013798103141, -624.0788493393867, -723.9702910449445, -721.431918028201, -616.8140161574747, -424.9090573500021, -172.8706915866774, 103.63420502885714, 365.4720514802586, 575.5857809293182, 704.2472857831048, 733.2549997069933, 658.5193394279095, 490.6204903082792, 253.3200751015017, -19.813384612949086, -290.1441576463324, -519.4328880679094, -675.2437290426358, -735.5237581243618, -691.7378795687126, -550.0673440492728, -330.5543635186931, -64.2591921754159, 211.1304047715862, 456.63884295614275, 637.5238170323164, 728.192201551277, 715.8257201761394, 602.1834458427097, 403.3530942415586, 147.4658344884871, -129.2801749813285, -387.7396679857839, -591.3515478581137, -711.3074908641534, -730.6288395920003, -646.5701323693817, -471.01638342944983, -228.80401308208383, 45.790424430422235, 313.9043940168338, 537.5923190653331, 685.2022181429323, 735.8525600099995, 682.3881896475451, 532.3829307383484, 307.06641586964724, 38.31419911746771, -235.85756858207364, -476.66657695925227, -650.048129121791, -731.4655667502599, -709.3932474866606, -586.9402322602826, -381.42822830852606, -121.93424041034338, 154.81667256991548, 409.6563007117701, 606.521162444049, 717.5539421245961, 727.0559096978491, 633.689623801485, 450.67734023489834, 203.91263511817172, -71.69517799939685, -337.16168886529687, -554.9360522027074, -694.2066552126058, -735.2648843409686, -672.2885191737494, -514.1802151996872, -283.30542701077445, -12.335642609375407, 260.3800979806774, 496.24505158030894, 661.8811987883347, 733.8570475807231, 701.9968403990714, 570.8205067210628, 358.8931583496576, 96.199099573824, -180.10249338905803, -430.9283390294616, -620.7966531559292, -722.8422015452576, -722.6187066465885, -620.1467526386796, -429.91712078259707, -178.8444006550856, 97.53995424114916, 360.1193050292711, 571.7334948154963, 702.4376294710119, 733.746893396232, 661.2395441599436, 495.1865043515287, 259.0836938724544, -13.666387583392062, -284.4834691051085, -515.060470805876, -672.7752361752135, -735.312327422585, -693.8094521787701, -554.1322376531365, -336.03506964553014, -70.3812123149443, 205.23392695300302, 451.80293393099663, 634.4311011350218, 727.2823145701748, 717.2242514150773, 605.6943066383313, 408.47775677442934, 153.48020672626544, -123.22728505628051, -382.5043849657134, -587.6738554686066, -709.7087142628442, -731.333907650942, -649.4805083653803, -475.7193908691209, -234.6345819751653, 39.65786730651168, 308.3373535672737, 533.3789831507074, 682.9381562249275, 735.8580826581494, 684.6617651070421, 536.6028542210967, 312.63494118364935, 44.443851352026954, -230.03372928938543, -471.97320008641776, -647.1472534948299, -730.7700578548546, -710.9990333583876, -590.6225012863277, -386.66396282846006, -127.98373222767151, 148.81026380030016, 404.543590852635, 603.0234623386472, 716.1692385148802, 727.975750747212, 636.7872134178815, 455.5111975990809, 209.80059437526245, -65.58704548476915, -331.69745285793044, -550.8873677176662, -692.1487326404816, -735.4856118235973, -674.7598913133986, -518.5500670592347, -288.9568988339757, -18.468170230813993, 254.63422725128441, 491.69848173276216, 659.178253896817, 733.3781933008585, 703.8110268397746, 574.6692913092885, 364.2326596820547, 102.2732669903413, -174.1524226561072, -425.9442234422808, -617.4834760331715, -721.6684145476485, -723.750874582371, -623.4243382473866, -434.87646767525405, -184.78365262721655, 91.46130841120203, 354.762009521493, 567.8544897850245, 700.587129166671, 734.1845893258532, 663.9047984931705, 499.7003016142252, 264.8084877490254, -7.541344738863946, -278.8245869949609, -510.6668749265149, -670.2710167832978, -735.0483252425005, -695.8267415579063, -558.1422556632144, -341.4726992292175, -76.47530525914023, 199.34540041252768, 446.9526239322164, 631.3073414128567, 726.3234265701432, 718.5693032216935, 609.1491351691957, 413.5556061052058, 159.46134654092563, -117.18842428840182, -377.2617491548263, -583.9700941751223, -708.0659693485818, -731.986704888298, -652.3345794587846, -480.37230860386336, -240.4270163758399, 33.54516474176789, 302.7697401072354, 529.1444446700096, 680.6351080569443, 735.8129279215387, 686.8794896938875, 540.7699595420804, 318.16129351199436, 50.54781803431101, -224.21585244432575, -467.2636704479797, -644.2131406198114, -730.0255083712235, -712.5503876428883, -594.2493824543336, -391.85398319735305, -134.00159421781376, 142.81605844668547, 399.4207190238894, 599.4984702292035, 714.7379583045056, 728.8434209005883, 639.8276237246911, 460.29634825417156, 215.65148874957305, -59.49710858115252, -326.2298556605472, -546.8170483498221, -690.0483187228144, -735.6563792942363, -677.1736462624477, -522.8686328635815, -294.5669239249417, -24.576783059469797, 248.89186048353795, 487.13572687938915, 656.4388950343555, 732.8519409224272, 705.5692197110384, 578.464842888028, 369.52697582496626, 108.31815776333475, -168.2123914902742, -420.9493094897114, -614.1395569887364, -720.4496957177289, -724.8284819745359, -626.6470021516052, -439.78733370567477, -190.68821962584417, 85.39909660656953, 349.3996868255953, 563.9512205220872, 698.6946679089807, 734.5705382764577, 666.5138982117065, 504.16335467721854, 270.4934159437043, -1.438518459485797, -273.16688038694, -506.25560299694706, -667.7281914969764, -734.7362172035057, -697.7870338187329, -562.0999017300863, -346.86574216433553, -82.54170975099169, 193.46470804726582, 442.0906853384963, 628.1497146500511, 725.3201201775946, 719.857834853838, 612.5505199203229, 418.5855522868043, 165.4098014141576, -111.16376069477931, -372.0133130080036, -580.2388162839502, -706.3823014450006, -732.5855881150512, -655.134365729852, -484.9745264698732, -246.1817802853151, 27.452965803415047, 297.2022059069106, 524.8888318563462, 678.2946974488681, 735.716758945715, 689.042383740138, 544.8842346108532, 323.6454625973515, 56.62545408711214, -218.40401138637068, -462.5399207486233, -641.2456557321826, -729.2338430253518, -714.046994691911, -597.8222166164485, -396.9975088816209, -139.9878952144467, 136.83411460515282, 394.29028087864384, 595.9442412207543, 713.2642471874522, 729.6565587676583, 642.8138794940628, 465.0313421928121, 221.46636874727008, -53.4257057073374, -320.76090687271443, -542.7225670112506, -687.9103458958139, -735.7737908847339, -679.5335856737807, -527.1340816868476, -300.13647012803216, -30.66073699339464, 243.15426799760078, 482.5555284417704, 653.6664240999418, 732.2756330690886, 707.2739787337073, 582.2058284619578, 374.7767485963913, 114.33290941041975, -162.2830907520925, -415.94378604631754, -610.7670313494581, -719.1854156434778, -725.8533599072621, -629.814244095091, -444.6500597245941, -196.55742753433486, 79.35356460132701, 344.0336284977359, 560.024134235189, 696.7613758488652, 734.9051359993674, 669.0676715844538, 508.5752352649702, 276.13875061123156, 4.641817964663134, -267.51192407468676, -501.82515050181377, -665.1499829767449, -734.3739181733448, -699.6930298989117, -566.0034894485508, -352.2154214306727, -88.57956711641481, 187.59308244312476, 437.2154142116286, 624.9625010935079, 724.2688024760863, 721.0939536694245, 615.8957971344423, 423.5687972874895, 171.32415082083673, -105.15412678867996, -366.7581945854725, -576.4833422291283, -704.65439600355, -733.1338586248222, -657.8773063473391, -489.5271238794601, -251.8977618661882, 21.38164310678335, 291.6349302144427, 520.6143512844322, 675.9157600984906, 735.5721618954614, 691.1495139542762, 548.9465264646856, 329.0868065498709, 62.67677225368599, -212.59898815223073, -457.8024186598057, -638.2451132652515, -728.3958920665979, -715.488947577372, -601.3411300476776, -402.0946864424411, -145.94238915863335, 130.86540134577058, 389.15161572602955, 592.3631421028041, 711.7468215775442, 730.4173780689764, 645.7445902569463, 469.71733811877033, 227.2440542560455, -47.37345160505758, -315.28981503737964, -538.6076993212805, -685.7317066913905, -735.8421705964754, -681.8368463181167, -531.3485631554828, -305.6639092404967, -36.7199480504892, 237.42116650639267, 477.96091469133887, 650.857689558647, 731.6536082854373, 708.9222066978343, 585.8943353214065, 379.98084204558427, 120.31773952718672, -156.36478359503116, -410.92958837228605, -607.364554338394, -717.878715258078, -726.8240763835221, -632.9279594617664, -449.4640295274219, -202.3916955034298, 73.32531334430514, 338.66451890007284, 556.0732072791964, 694.7888923267379, 735.1879552248041, 671.5670000073098, 512.9358619926211, 281.74436168249014, 10.698875351318124, -261.85979455998057, -497.3775819725884, -662.5362080192897, -733.9633715109951, -701.5442823192896, -569.854296090616, -357.52086405808, -94.58877201910843, 181.73041828224646, 432.3296162817332, 621.743570272934, 723.1735471162469, 722.2752643733891, 619.1877033869245, 428.50385276223096, 177.20508800681523, -99.15982082536493, -361.49878712966085, -572.7010128410811, -702.8871486402289, -733.6281934286808, -660.5668722102307, -494.0284559378907, -257.5758101180047, 15.331758441005842, 286.069364943634, 516.3195110971131, 673.5017818063842, 735.3766262454797, 693.2033904540211, 552.955770289837, 334.4859732604286, 68.70096509136582, -206.80157129503382, -453.0513271532127, -635.2138091589736, -727.5111028686898, -716.8780970669723, -604.8057364468531, -407.1457937004381, -151.86438033391212, 124.91021219917037, 384.0061213065673, 588.7557546386059, 710.1869117334757, 731.126319836771, 648.6206368338824, 474.3539003020844, 232.98468413408835, -41.340581132180475, -309.81840881066245, -534.4709657808061, -683.5157155092853, -735.8595814598283, -684.0860850591187, -535.5105309883369, -311.1503430265174, -42.75373468431189, 231.6940176055651, 473.34987940587143, 648.0171096276898, 730.9823579146964, 710.5178037008625, 589.5279538739132, 385.14027393636206, 126.27141157382295, -150.45845471802755, -405.9055297042421, -603.9354151600551, -716.5262146631896, -727.743661684064, -635.9857376487458, -454.23020179293934, -208.1899012368401, 67.31488482356555, 333.29248402838874, 552.1009062967114, 692.7759282675651, 735.4216015848726, 674.010967592967, 517.246036469736, 287.30958341331495, 16.732554225447483, -256.2113232212366, -492.9135928095542, -659.8872521749361, -733.5055549418835, -703.3409888670142, -573.6524428191002, -362.78218792444636, -100.56912372361144, 175.87780898688212, 427.43261955034666, 618.4953105980062, 722.0332621878814, 723.4038604776387, 622.4250365664625, 433.3917738611051, 183.05167023067548, -93.18158520609788, -356.23400628906245, -568.8955839907609, -701.0775302810969, -734.0726639855368, -663.2003995475714, -498.48031673642475, -263.21434343394276, 9.303665901139048, 280.50495522837116, 512.0075161355106, 671.0493192731677, 735.1341976665749, 695.2008859106651, 556.9136762382477, 339.8416047080008, 74.69804213605684, -201.0119227024575, -448.2887173928046, -632.1499465241594, -726.5824987580561, -718.2126688864807, -608.2177084184669, -412.15020456984195, -157.75421115140506, 118.96907818046051, 378.85462189971815, 585.1218838530119, 708.5862707710038, 731.7829194995692, 651.4429217051385, 478.9409585490833, 238.6882583245311, -35.32785880514571, -304.34670546004907, -530.3142512933105, -681.2623326573868, -735.8277361988697, -686.2809695006711, -539.6211259654459, -316.59502938684363, -48.7618226576683, 225.97257133849004, 468.72545526242425, 645.1427344349587, 730.2658730620751, 712.0587378372957, 593.1092240930425, 390.2537141385591, 132.19443587693814, -144.56409780549927, -400.87418470281074, -600.4768661213869, -715.1327160294256, -728.6089270417311, -638.9907780479225, -458.94705247225966, -213.9528257839438, 61.322636793730055, 327.919085640047, 548.1052251037448, 690.7260425560349, 735.6033457582291, 676.402018915814, 521.5047032911931, 292.83512707624106, 22.742262809195562, -250.56731820786828, -488.43297527447214, -657.2054257322955, -732.9996566964251, -705.0849306491391, -577.3975349323501, -367.9997169171908, -106.51994064594689, 170.03552731198386, 422.52566139871874, 615.218466573798, 720.8488986393911, 724.4803413268386, 625.6085528687361, 438.23232330917307, 188.86392196882034, -87.21949363146126, -350.96582063124964, -565.0658451147352, -699.2287318069446, -734.4657174543885, -665.7804212647031, -502.8815065156358, -268.81447229331303, 3.29769108125902, 274.94331754612267, 507.6760512710373, 668.5629366810576, 734.8415708540726, 697.145909219894, 560.818188793579, 345.154996113977, 80.66711490180579, -195.23107552520815, -443.513012989229, -629.0571988925166, -725.6066735357339, -719.4959644319708, -611.5748617364073, -417.10884301863706, -163.61090630352132, 113.04256490221805, 373.69684110049155, 581.4640154937118, 706.9431875625899, 732.3896412726864, 654.2104182535759, 483.4792572544752, 244.35407171146088, -29.335434656825864, -298.8753915256871, -526.1384224248972, -678.9716547475585, -735.7477329751553, -688.4215600021316, -543.68052794743, -321.9980412382976, -54.74421191794736, 220.2578616124752, 464.0867494271376, 642.2365839133133, 729.503062204167, 713.5467696531886, 596.637063348885, 395.3221779066789, 138.0861003490585, -138.68265669923497, -395.8342591371421, -596.992689389684, -713.6952643761235, -729.423797694003, -641.940637704212, -463.616281005867, -219.67910028799724, 55.348987361630165, 322.54338530708395, 544.089727833206, 688.6356348871517, 735.737396933926, 678.7371396690078, 525.7135138469735, 298.3195942383698, 28.727805420106957, -244.92776548026006, -483.93801060078613, -654.4886858882185, -732.4487134886677, -706.7742755713836, -581.0910672663997, -373.17281540932413, -112.4415351094624, 164.20393627523234, 417.60960654098574, 611.9124993368662, 719.622188184515, 725.5040596510418, 628.7390581566614, 443.025274575503, 194.64186798177565, -81.27437969388872, -345.6941693723278, -561.2132980019142, -697.3406746658438, -734.8086720337607, -668.3065216135916, -507.23293117829445, -274.37544404483833, -2.685686066612159, 269.3840185740526, 503.3283431964794, 666.0405393748957, 734.5025814910526, 699.0363450897653, 564.6715880035184, 350.4246738673703, 86.6083917564886, -189.4589061055402, -438.72722344385164, -625.9325972719818, -724.5885000476474, -720.7247606137901, -614.8801713915931, -422.02031830747114, -169.4350848277395, 107.13092554402411, 368.53469300290504, 577.7800641349183, 705.2615251527648, 732.943962450522, 656.9255434563323, 487.96788480316667, 249.98274789673636, -23.36383860335418, -293.4054725989451, -521.943098271152, -676.646156447876, -735.6186918145526, -690.5095626631102, -547.6882911554476, -327.35959514778125, -60.70008071225752, 214.55026171404867, 459.43506702495006, 639.299494654422, 728.6948202556407, 714.9824396830855, 600.112122129132, 400.34529066939683, 143.94618816792348, -132.81420846565013, -390.7879912975597, -593.4817170429696, -712.2170407314073, -730.1868226036867, -644.8376535969145, -468.23664224834806, -225.36951997686955, 49.39430711014085, 317.16748579605775, 540.0519276246838, 686.5094610151974, 735.8203947610867, 681.0201104655854, 529.8705051917044, 303.7642213451239, 34.6884431964232, -239.29399624066755, -479.4269794360086, -651.7410257539796, -731.8494500543338, -708.4123015141577, -584.7311123673524, -378.3022526127099, -118.33292447207299, 158.38389768113186, 412.6842135873142, 608.5801836449344, 718.3516107804307, 726.4774836843081, 631.8156908683753, 447.7712681111171, 200.38471698457465, -75.34653569502461, -340.41982444951213, -557.3390944102175, -695.4135069992741, -735.1027071779134, -670.7787612981482, -511.53460712114725, -279.897120491206, -8.646307582074119, 263.82841060747324, 498.9635321721558, 663.4842990805635, 734.1161353564308, 700.8739787294797, 568.4727457397065, 355.65157592519773, 92.52123192822225, -183.69658930749387, -433.9297440824325, -622.7800290324258, -723.5248656098254, -721.9027730186517, -618.1311669750247, -426.8859737278515, -175.22535819116626, 101.23492850939539, 363.3670674948133, 574.073785559641, 703.5376002156428, 733.4498430457371, 659.5853976638977, 492.40817208175184, 255.5729185416928, -17.41347842635703, -287.93681991887365, -517.7308357110262, -674.2836274654302, -735.4435795880494, -692.5431434989339, -551.6456756354947, -332.6789256316005, -66.62950218938218, 208.8502716532293, 454.77153911607564, 636.3310607635755, 727.8429802863086, 716.3652394844552, 603.5351113223054, 405.3228404726296, 149.77471907151414, -126.95960700976566, -385.73520630857297, -589.9453478183846, -710.6978616753471, -730.8991094555956, -647.6812837839526, -472.80884681224427, -231.02331217219594, 43.45926601436968, 311.79074792704324, 535.9951410845263, 684.3452692413247, 735.8560100307128, 683.2487341790833, 533.9776716616557, 309.1674918039325, 40.62411133950084, -233.66565396957756, -474.90308873936783, -648.9591831572893, -731.2064561938604, -709.9957617836781, -588.3201500944774, -383.38655360137085, -124.19438678527564, 152.5756192411376, 407.751662475037, 605.2192709609005, 717.0408828210393, 727.3980999464966, 634.8405811557784, 452.46947070279293, 206.09303381230265, -69.43640931257961, -335.1439321965488, -553.4427053324213, -693.4498342957438, -735.3469367806064, -673.1988259702143, -515.7861422112305, -285.3796966842516, -14.583377410357109, 258.27686629117403, 494.58290124919483, 660.8950416144572, 733.6831275516632, 702.6592551311314, 572.2223170374897, 360.8352874919471, 98.40528946924768, -177.94413694001247, -429.123017774615, -619.5983709819476, -722.4189584041042, -723.0286764646034, -621.3300516826674, -431.70463195072205, -180.98228314113274, 95.35476570151201, 358.19631622322225, 570.3426261120741, 701.7760670245827, 733.9041079142626, 662.1934311766942, 496.79833897750683, 261.1256098209652, -11.484942814537193, -282.47098825305096, -513.4995429281331, -671.8881599683028, -735.2190942127885, -694.5254183098161, -555.5509060477673, -337.9568103310634, -72.53162655706699, 203.1588470111559, 450.0958042850076, 633.3334863577608], \"type\": \"scatter3d\", \"uid\": \"b4d2eada-b277-11e8-b505-a0afbda902ef\"}, {\"cauto\": false, \"cmax\": 1, \"cmin\": 1, \"colorscale\": [[0, \"#204a87\"], [1, \"#204a87\"]], \"name\": \"Earth\", \"showscale\": false, \"x\": [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [2070.9775685349646, 1958.7662914956272, 1634.2922933821865, 1132.717366623061, 508.3949372694737, -171.0199720974995, -831.9022138394263, -1402.6349424190162, -1821.3704108639388, -2042.7321338179481, -2042.7321338179484, -1821.3704108639388, -1402.6349424190173, -831.9022138394275, -171.0199720975005, 508.39493726947273, 1132.7173666230603, 1634.2922933821858, 1958.7662914956272, 2070.9775685349646], [3917.5325829912545, 3705.269861917151, 3091.4836581186883, 2142.6872306516607, 961.6973945255617, -323.507276569953, -1573.6549145165156, -2653.272624703365, -3445.3670762369647, -3864.102544681887, -3864.1025446818876, -3445.3670762369647, -2653.2726247033675, -1573.654914516518, -323.50727656995485, 961.69739452556, 2142.687230651659, 3091.483658118687, 3705.269861917151, 3917.5325829912545], [5339.562155299338, 5050.2499496143155, 4213.664799186625, 2920.4636860211895, 1310.7850168122338, -440.9375478934551, -2144.8776874338914, -3616.387048334464, -4696.004758521314, -5266.737487100903, -5266.737487100904, -4696.004758521314, -3616.3870483344667, -2144.877687433894, -440.93754789345763, 1310.7850168122313, 2920.463686021187, 4213.664799186623, 5050.2499496143155, 5339.562155299338], [6182.9673162373765, 5847.957092568811, 4879.229977516816, 3381.7625853471986, 1517.8287436021724, -510.58539405170535, -2483.6696817114025, -4187.60982125184, -5437.757459198403, -6098.63970094033, -6098.639700940331, -5437.757459198403, -4187.609821251843, -2483.669681711406, -510.58539405170836, 1517.8287436021697, 3381.762585347196, 4879.229977516814, 5847.957092568811, 6182.9673162373765], [6356.352029838285, 6011.947344139877, 5016.054878729385, 3476.5950350978, 1560.3921744832594, -524.9033902157762, -2553.317527869653, -4305.040092575342, -5590.2447636708575, -6269.65967303783, -6269.6596730378305, -5590.2447636708575, -4305.040092575346, -2553.3175278696567, -524.9033902157793, 1560.3921744832564, 3476.5950350977973, 5016.054878729383, 6011.947344139877, 6356.352029838285], [5840.927372042378, 5524.449815998859, 4609.31240172086, 3194.6844678654465, 1433.8629012798515, -482.33995933468935, -2346.2738010797148, -3955.95247028867, -5136.9423064147695, -5761.264735768357, -5761.264735768358, -5136.9423064147695, -3955.952470288674, -2346.273801079718, -482.33995933469214, 1433.8629012798488, 3194.684467865444, 4609.312401720858, 5524.449815998859, 5840.927372042378], [4692.547602159432, 4438.292429623361, 3703.07940513492, 2566.580267902913, 1151.9523340474977, -387.50750958408815, -1884.974901753706, -3178.1760149191423, -4126.972442386172, -4628.547369145297, -4628.547369145297, -4126.972442386172, -3178.176014919145, -1884.9749017537085, -387.5075095840904, 1151.9523340474955, 2566.580267902911, 3703.0794051349185, 4438.292429623361, 4692.547602159432], [3035.6574872043448, 2871.1771912954932, 2395.560295805413, 1660.3472713169713, 745.2098570389732, -250.68260837151965, -1219.4097234235148, -2055.994873851206, -2669.7810776496694, -2994.25507576311, -2994.2550757631107, -2669.7810776496694, -2055.9948738512076, -1219.4097234235167, -250.6826083715211, 745.2098570389717, 1660.3472713169701, 2395.560295805412, 2871.1771912954932, 3035.6574872043448], [1049.806780431554, 992.9253533863962, 828.4450574775444, 574.1898849414736, 257.7123288979541, -86.69235680045416, -421.70258046901955, -711.014786154042, -923.2775072281457, -1035.488784267483, -1035.4887842674832, -923.2775072281457, -711.0147861540426, -421.7025804690202, -86.69235680045466, 257.7123288979536, 574.1898849414731, 828.4450574775441, 992.9253533863962, 1049.806780431554], [-1049.8067804315524, -992.9253533863947, -828.4450574775432, -574.1898849414728, -257.71232889795374, 86.69235680045404, 421.7025804690189, 711.0147861540408, 923.2775072281444, 1035.4887842674814, 1035.4887842674816, 923.2775072281444, 711.0147861540415, 421.7025804690195, 86.69235680045453, -257.7123288979532, -574.1898849414723, -828.4450574775428, -992.9253533863947, -1049.8067804315524], [-3035.6574872043434, -2871.177191295492, -2395.560295805412, -1660.3472713169708, -745.2098570389728, 250.68260837151956, 1219.4097234235144, 2055.9948738512053, 2669.7810776496685, 2994.255075763109, 2994.2550757631093, 2669.7810776496685, 2055.994873851207, 1219.4097234235162, 250.682608371521, -745.2098570389714, -1660.3472713169695, -2395.560295805411, -2871.177191295492, -3035.6574872043434], [-4692.547602159429, -4438.292429623359, -3703.079405134917, -2566.580267902911, -1151.9523340474968, 387.50750958408787, 1884.9749017537044, 3178.17601491914, 4126.972442386168, 4628.5473691452935, 4628.547369145294, 4126.972442386168, 3178.1760149191427, 1884.9749017537072, 387.50750958409014, -1151.9523340474948, -2566.580267902909, -3703.0794051349158, -4438.292429623359, -4692.547602159429], [-5840.927372042376, -5524.449815998857, -4609.312401720859, -3194.6844678654456, -1433.862901279851, 482.33995933468924, 2346.2738010797143, 3955.9524702886692, 5136.942306414769, 5761.264735768355, 5761.264735768356, 5136.942306414769, 3955.9524702886724, 2346.2738010797175, 482.339959334692, -1433.8629012798485, -3194.6844678654434, -4609.312401720857, -5524.449815998857, -5840.927372042376], [-6356.352029838285, -6011.947344139877, -5016.054878729385, -3476.5950350978, -1560.3921744832594, 524.9033902157762, 2553.317527869653, 4305.040092575342, 5590.2447636708575, 6269.65967303783, 6269.6596730378305, 5590.2447636708575, 4305.040092575346, 2553.3175278696567, 524.9033902157793, -1560.3921744832564, -3476.5950350977973, -5016.054878729383, -6011.947344139877, -6356.352029838285], [-6182.967316237377, -5847.957092568812, -4879.229977516816, -3381.762585347199, -1517.8287436021726, 510.5853940517054, 2483.669681711403, 4187.60982125184, 5437.757459198404, 6098.639700940331, 6098.639700940332, 5437.757459198404, 4187.609821251844, 2483.6696817114066, 510.5853940517084, -1517.8287436021699, -3381.7625853471964, -4879.229977516815, -5847.957092568812, -6182.967316237377], [-5339.562155299339, -5050.249949614317, -4213.664799186626, -2920.46368602119, -1310.7850168122343, 440.9375478934552, 2144.877687433892, 3616.387048334465, 4696.004758521315, 5266.737487100905, 5266.737487100906, 4696.004758521315, 3616.3870483344676, 2144.877687433895, 440.93754789345775, -1310.7850168122318, -2920.4636860211876, -4213.664799186624, -5050.249949614317, -5339.562155299339], [-3917.532582991256, -3705.2698619171524, -3091.483658118689, -2142.6872306516616, -961.6973945255621, 323.5072765699531, 1573.654914516516, 2653.272624703366, 3445.367076236966, 3864.1025446818885, 3864.102544681889, 3445.367076236966, 2653.2726247033684, 1573.6549145165184, 323.507276569955, -961.6973945255603, -2142.68723065166, -3091.4836581186883, -3705.2698619171524, -3917.532582991256], [-2070.977568534966, -1958.7662914956288, -1634.2922933821878, -1132.7173666230622, -508.39493726947416, 171.01997209749965, 831.902213839427, 1402.6349424190175, 1821.3704108639404, 2042.7321338179497, 2042.7321338179502, 1821.3704108639404, 1402.6349424190187, 831.9022138394282, 171.01997209750064, -508.3949372694732, -1132.7173666230613, -1634.2922933821872, -1958.7662914956288, -2070.977568534966], [-1.5621929143429165e-12, -1.4775489932480931e-12, -1.2327897122000057e-12, -8.544385371317645e-13, -3.8349568858537574e-13, 1.2900486836795326e-13, 6.275257461168375e-13, 1.0580444722087668e-12, 1.373907662485362e-12, 1.5408866391577764e-12, 1.5408866391577766e-12, 1.373907662485362e-12, 1.0580444722087678e-12, 6.275257461168384e-13, 1.2900486836795402e-13, -3.83495688585375e-13, -8.544385371317639e-13, -1.2327897122000053e-12, -1.4775489932480931e-12, -1.5621929143429165e-12]], \"y\": [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 672.4453172381089, 1272.020750289313, 1733.7529976111107, 2007.6062056921824, 2063.9041301666034, 1896.5460173652668, 1523.6677156388778, 985.6763747824739, 340.8717043736032, -340.8717043736027, -985.6763747824734, -1523.6677156388766, -1896.5460173652662, -2063.9041301666034, -2007.6062056921826, -1733.752997611111, -1272.0207502893134, -672.4453172381094, -5.072432100824625e-13], [0.0, 1272.0207502893131, 2406.1983148492195, 3279.626955981496, 3797.6571277777143, 3904.152223057449, 3587.5718458054816, 2882.222392147741, 1864.539420012481, 644.8046704088713, -644.8046704088703, -1864.5394200124804, -2882.222392147739, -3587.5718458054803, -3904.152223057449, -3797.657127777715, -3279.6269559814964, -2406.1983148492204, -1272.0207502893138, -9.595187476631407e-13], [0.0, 1733.7529976111107, 3279.6269559814955, 4470.102445015823, 5176.172973346762, 5321.324843416592, 4889.8285978399235, 3928.443550179085, 2541.350687774138, 878.8630452300076, -878.8630452300063, -2541.350687774137, -3928.4435501790817, -4889.828597839922, -5321.324843416592, -5176.172973346763, -4470.102445015824, -3279.6269559814964, -1733.7529976111118, -1.3078155404671353e-12], [0.0, 2007.6062056921824, 3797.657127777714, 5176.172973346762, 5993.770160654701, 6161.849348129235, 5662.196547790195, 4548.95689346632, 2942.767175396611, 1017.6829721352607, -1017.6829721352591, -2942.7671753966097, -4548.9568934663175, -5662.196547790194, -6161.849348129235, -5993.770160654702, -5176.172973346763, -3797.6571277777152, -2007.6062056921837, -1.5143902266125608e-12], [0.0, 2063.9041301666034, 3904.152223057449, 5321.324843416592, 6161.849348129236, 6334.641865028304, 5820.977643755634, 4676.520173007722, 3025.2891778274434, 1046.2211580313447, -1046.221158031343, -3025.289177827442, -4676.520173007719, -5820.977643755632, -6334.641865028304, -6161.849348129237, -5321.324843416593, -3904.1522230574506, -2063.904130166605, -1.5568572335190474e-12], [0.0, 1896.5460173652668, 3587.571845805481, 4889.8285978399235, 5662.196547790195, 5820.977643755633, 5348.9654902458315, 4297.309928116757, 2779.9741556424556, 961.3850476608397, -961.3850476608383, -2779.974155642454, -4297.309928116753, -5348.96549024583, -5820.977643755633, -5662.196547790196, -4889.828597839924, -3587.5718458054826, -1896.5460173652682, -1.430614602044772e-12], [0.0, 1523.667715638878, 2882.222392147741, 3928.4435501790854, 4548.956893466321, 4676.520173007722, 4297.309928116757, 3452.419472880565, 2233.405797950153, 772.3679499502728, -772.3679499502715, -2233.405797950152, -3452.4194728805624, -4297.309928116756, -4676.520173007722, -4548.956893466322, -3928.443550179086, -2882.222392147742, -1523.667715638879, -1.1493426801662274e-12], [0.0, 985.676374782474, 1864.539420012481, 2541.3506877741384, 2942.767175396611, 3025.289177827443, 2779.9741556424556, 2233.405797950153, 1444.8132671883818, 499.65280033904173, -499.652800339041, -1444.8132671883811, -2233.4057979501513, -2779.9741556424547, -3025.289177827443, -2942.7671753966115, -2541.350687774139, -1864.5394200124817, -985.6763747824746, -7.435216450024997e-13], [0.0, 340.8717043736033, 644.8046704088712, 878.8630452300076, 1017.6829721352607, 1046.2211580313447, 961.3850476608397, 772.3679499502726, 499.65280033904173, 172.79251689906815, -172.7925168990679, -499.6528003390415, -772.3679499502721, -961.3850476608395, -1046.2211580313447, -1017.6829721352608, -878.8630452300079, -644.8046704088714, -340.8717043736035, -2.5712850267573817e-13], [0.0, -340.87170437360277, -644.8046704088703, -878.8630452300064, -1017.6829721352591, -1046.221158031343, -961.3850476608383, -772.3679499502715, -499.652800339041, -172.7925168990679, 172.7925168990676, 499.65280033904077, 772.3679499502709, 961.385047660838, 1046.221158031343, 1017.6829721352593, 878.8630452300065, 644.8046704088705, 340.871704373603, 2.5712850267573776e-13], [0.0, -985.6763747824737, -1864.5394200124804, -2541.3506877741374, -2942.76717539661, -3025.289177827442, -2779.9741556424547, -2233.405797950152, -1444.8132671883811, -499.65280033904156, 499.6528003390408, 1444.8132671883807, 2233.4057979501504, 2779.9741556424538, 3025.289177827442, 2942.7671753966106, 2541.350687774138, 1864.539420012481, 985.6763747824742, 7.435216450024994e-13], [0.0, -1523.6677156388769, -2882.222392147739, -3928.443550179082, -4548.9568934663175, -4676.520173007719, -4297.309928116754, -3452.4194728805624, -2233.4057979501513, -772.3679499502722, 772.3679499502709, 2233.4057979501504, 3452.4194728805596, 4297.309928116752, 4676.520173007719, 4548.956893466318, 3928.443550179083, 2882.22239214774, 1523.6677156388778, 1.1493426801662265e-12], [0.0, -1896.5460173652664, -3587.5718458054803, -4889.828597839923, -5662.196547790194, -5820.977643755632, -5348.96549024583, -4297.309928116755, -2779.9741556424547, -961.3850476608395, 961.385047660838, 2779.9741556424538, 4297.309928116752, 5348.965490245829, 5820.977643755632, 5662.196547790195, 4889.8285978399235, 3587.5718458054816, 1896.5460173652677, 1.4306146020447716e-12], [0.0, -2063.9041301666034, -3904.152223057449, -5321.324843416592, -6161.849348129236, -6334.641865028304, -5820.977643755634, -4676.520173007722, -3025.2891778274434, -1046.2211580313447, 1046.221158031343, 3025.289177827442, 4676.520173007719, 5820.977643755632, 6334.641865028304, 6161.849348129237, 5321.324843416593, 3904.1522230574506, 2063.904130166605, 1.5568572335190474e-12], [0.0, -2007.6062056921826, -3797.6571277777143, -5176.172973346763, -5993.770160654702, -6161.849348129236, -5662.196547790196, -4548.956893466321, -2942.7671753966115, -1017.6829721352608, 1017.6829721352592, 2942.76717539661, 4548.9568934663175, 5662.196547790194, 6161.849348129236, 5993.7701606547025, 5176.172973346764, 3797.6571277777157, 2007.606205692184, 1.514390226612561e-12], [0.0, -1733.7529976111111, -3279.6269559814964, -4470.102445015825, -5176.172973346764, -5321.324843416593, -4889.828597839924, -3928.443550179086, -2541.350687774139, -878.8630452300079, 878.8630452300065, 2541.3506877741374, 3928.4435501790826, 4889.8285978399235, 5321.324843416593, 5176.172973346765, 4470.102445015826, 3279.6269559814973, 1733.7529976111123, 1.3078155404671357e-12], [0.0, -1272.0207502893136, -2406.1983148492204, -3279.626955981497, -3797.6571277777157, -3904.1522230574506, -3587.571845805483, -2882.222392147742, -1864.5394200124817, -644.8046704088715, 644.8046704088705, 1864.539420012481, 2882.22239214774, 3587.5718458054816, 3904.1522230574506, 3797.657127777716, 3279.6269559814973, 2406.1983148492213, 1272.0207502893143, 9.595187476631409e-13], [0.0, -672.4453172381095, -1272.020750289314, -1733.752997611112, -2007.606205692184, -2063.9041301666052, -1896.5460173652684, -1523.6677156388791, -985.6763747824748, -340.87170437360356, 340.87170437360305, 985.6763747824743, 1523.6677156388778, 1896.5460173652677, 2063.9041301666052, 2007.6062056921844, 1733.7529976111123, 1272.0207502893145, 672.44531723811, 5.07243210082463e-13], [0.0, -5.072432100824626e-13, -9.595187476631405e-13, -1.3078155404671353e-12, -1.5143902266125608e-12, -1.5568572335190474e-12, -1.430614602044772e-12, -1.1493426801662272e-12, -7.435216450024997e-13, -2.5712850267573817e-13, 2.5712850267573776e-13, 7.435216450024993e-13, 1.1493426801662263e-12, 1.4306146020447714e-12, 1.5568572335190474e-12, 1.514390226612561e-12, 1.3078155404671357e-12, 9.595187476631409e-13, 5.072432100824629e-13, 3.826269104401456e-28]], \"z\": [[6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366], [6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864], [5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782], [3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239], [1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561], [-526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267], [-2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456], [-4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595], [-5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402], [-6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607], [-6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608], [-5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402], [-4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663], [-2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649], [-526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298], [1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558], [3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236], [5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378], [6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864], [6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366]], \"type\": \"surface\", \"uid\": \"b4d2eadb-b277-11e8-b505-a0afbda902ef\"}], {\"autosize\": true, \"scene\": {\"aspectmode\": \"data\", \"xaxis\": {\"title\": \"x (km)\"}, \"yaxis\": {\"title\": \"y (km)\"}, \"zaxis\": {\"title\": \"z (km)\"}}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"frame = OrbitPlotter3D()\n",
"\n",
"frame.set_attractor(Earth)\n",
"frame.plot_trajectory(rr, label='orbit influenced by Moon')\n",
"frame.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Thrusts ###\n",
"Apart from natural perturbations, there are artificial thrusts aimed at intentional change of orbit parameters. One of such changes is simultaineous change of eccenricy and inclination."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from poliastro.twobody.thrust import change_inc_ecc\n",
"\n",
"ecc_0, ecc_f = 0.4, 0.0\n",
"a = 42164 # km\n",
"inc_0 = 0.0 # rad, baseline\n",
"inc_f = (20.0 * u.deg).to(u.rad).value # rad\n",
"argp = 0.0 # rad, the method is efficient for 0 and 180\n",
"f = 2.4e-6 # km / s2\n",
"\n",
"k = Earth.k.to(u.km**3 / u.s**2).value\n",
"s0 = Orbit.from_classical(\n",
" Earth,\n",
" a * u.km, ecc_0 * u.one, inc_0 * u.deg,\n",
" 0 * u.deg, argp * u.deg, 0 * u.deg,\n",
" epoch=Time(0, format='jd', scale='tdb')\n",
")\n",
" \n",
"a_d, _, _, t_f = change_inc_ecc(s0, ecc_f, inc_f, f)\n",
"\n",
"cowell_with_ad = functools.partial(cowell, rtol=1e-6, ad=a_d)\n",
"\n",
"tr = time_range(0.0, periods=1000, end=(t_f * u.s).to(u.day).value, format='jd', scale='tdb')\n",
"rr = s0.sample(tr, method=cowell_with_ad)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"data": [
{
"line": {
"dash": "solid",
"width": 5
},
"mode": "lines",
"name": "orbit with artificial thrust",
"type": "scatter3d",
"uid": "bd507856-b277-11e8-b505-a0afbda902ef",
"x": [
25298.4,
25129.95094301439,
24629.473005922027,
23811.06399499985,
22696.654854560566,
21313.9848693321,
19694.427888362825,
17870.86426367357,
15875.970445808323,
13740.902604479561,
11494.428404909075,
9162.582761252159,
6768.391050500591,
4331.976246042497,
1870.723137333599,
-600.4766510219104,
-3068.9566022446206,
-5523.989582266942,
-7956.528317196312,
-10358.96863016055,
-12724.938763101389,
-15049.110451277978,
-17327.038293213045,
-19555.0268240598,
-21730.01457645421,
-23849.4644894363,
-25911.281416495345,
-27913.75331287907,
-29855.48100525764,
-31735.329571718663,
-33552.386524054906,
-35305.92692542771,
-36995.382170397854,
-38620.31161901917,
-40180.384994708635,
-41675.362556762455,
-43105.079905868755,
-44469.43343409295,
-45768.36905028565,
-47001.87347980603,
-48169.96722371393,
-49272.698262584396,
-50310.13559009795,
-51282.36166156018,
-52189.46360936794,
-53031.54007330621,
-53808.6949358683,
-54521.03266252771,
-55168.65716534234,
-55751.66910235091,
-56270.16403305576,
-56724.23124971992,
-57113.95310420657,
-57439.40465008878,
-57700.653419757466,
-57897.75915625567,
-58030.773319567146,
-58099.738187087096,
-58104.68536800328,
-58045.63355131515,
-57922.58717282707,
-57735.5521370458,
-57484.52211171138,
-57169.48292868814,
-56790.414269747525,
-56347.28918469497,
-55840.07439959519,
-55268.731323835476,
-54633.217664766256,
-53933.489558658635,
-53169.50412671837,
-52341.222364895504,
-51448.61227622951,
-50491.65215446916,
-49470.33392770672,
-48384.666470765835,
-47234.678795082946,
-46020.42302482095,
-44741.97706795563,
-43399.44691434107,
-41992.99621188644,
-40522.85940424613,
-38989.326276037726,
-37392.765333584364,
-35733.63815141309,
-34012.51609700226,
-32230.09939598289,
-30387.238664475757,
-28484.961246145704,
-26524.50101779782,
-24507.33262971259,
-22435.211745458397,
-20310.22171516925,
-18134.82687744492,
-15911.93483271752,
-13644.968207335696,
-11337.946625811837,
-8995.580628273095,
-6623.378197754873,
-4227.764587285679,
-1816.215438414089,
602.5981021741972,
3018.659821025472,
5420.437363558957,
7794.725955749116,
10126.507438470911,
12398.838624348067,
14592.794127542471,
16687.50330524782,
18660.289817410674,
20487.008813849996,
22142.565247160226,
23601.59933179125,
24839.459357893382,
25833.48032844497,
26564.075710986384,
27016.03969559695,
27179.613475776237,
27051.16081645905,
26633.469189174168,
25935.559051684497,
24971.935748698023,
23761.640653054652,
22327.032968049614,
20692.397068237977,
18882.931321839802,
16923.863113621777,
14839.427222826092,
12652.506687119168,
10384.267125748678,
8053.932793200496,
5678.769347852585,
3274.203526942929,
853.7947569292202,
-1570.5554036466615,
-3988.4692648668024,
-6390.925648068066,
-8770.112424588777,
-11119.284509051698,
-13432.628411772399,
-15705.143666323489,
-17932.539841385933,
-20111.142850322467,
-22237.80429649911,
-24309.828787910108,
-26324.923879338385,
-28281.135084735146,
-30176.800766534394,
-32010.51181612771,
-33781.07703741762,
-35487.49185151589,
-37128.91055270189,
-38704.626131649165,
-40214.049738639515,
-41656.69356715635,
-43032.15536370977,
-44340.106035434175,
-45580.27950969844,
-46752.46397225443,
-47856.49361044931,
-48892.2399880266,
-49859.60237772237,
-50758.50777752945,
-51588.90657340656,
-52350.76646819533,
-53044.0699666992,
-53668.810453815546,
-54224.98937497695,
-54712.61429514207,
-55131.69761257534,
-55482.255703655144,
-55764.3082749501,
-55977.877698803066,
-56122.988108662415,
-56199.66403040018,
-56207.92832585675,
-56147.799224851595,
-56019.29231768679,
-55822.43538258486,
-55557.251579873315,
-55223.76749933492,
-54822.013186202974,
-54352.022309199674,
-53813.83320896769,
-53207.490715408785,
-52533.04862244259,
-51790.57270869854,
-50980.14419265545,
-50101.863510741045,
-49155.85430690529,
-48142.267522181144,
-47061.28547274552,
-45913.12580499476,
-44698.04521614694,
-43416.342828885485,
-42068.365514329606,
-40654.54653483676,
-39175.3940832112,
-37631.499308987346,
-36023.55408144464,
-34352.36616868075,
-32618.876549567976,
-30824.178753146243,
-28969.54018406831,
-27056.427700139113,
-25086.536398375036,
-23061.821263858576,
-20984.533829309326,
-18857.26339890003,
-16682.983215323067,
-14465.102754462298,
-12207.52577294925,
-9914.714951124826,
-7591.763110186755,
-5244.47098972793,
-2879.4310628751964,
-504.11637473951276,
1873.0306445458398,
4242.505440685997,
6593.643098069291,
8914.55005759924,
11192.036560046889,
13411.591156286098,
15557.392880214547,
17612.372298664377,
19558.34518774212,
21376.242586522312,
23046.46086811814,
24549.29912292404,
25865.526242417425,
26976.961133057102,
27867.28811104045,
28522.78338910723,
28932.883844463442,
29090.669541457806,
28993.331482643713,
28642.61915208613,
28044.344151350222,
27208.10277730485,
26146.900334905655,
24876.47686331361,
23414.500894596702,
21779.85014509213,
19992.181589942713,
18071.147403300278,
16035.931839616904,
13904.97410301877,
11695.695207627075,
9424.296830980102,
7105.68536423625,
4753.5424235606015,
2380.224221530778,
-3.1410417767706216,
-2386.5839206685982,
-4761.209470659042,
-7119.105257100164,
-9453.255966275972,
-11757.457022749615,
-14026.230944761382,
-16254.748493811918,
-18438.759326282096,
-20574.52913642925,
-22658.779841857446,
-24688.62993871674,
-26661.55315274725,
-28575.33969867971,
-30428.05395030827,
-32218.002488588372,
-33943.70536272275,
-35603.87093005484,
-37197.372845716905,
-38723.22823550548,
-40180.58198703324,
-41568.69107880478,
-42886.91099228018,
-44134.68288567263,
-45311.52278614133,
-46417.01276404026,
-47450.793507231174,
-48412.55771347624,
-49302.04371891886,
-50119.028780666085,
-50863.3214335638,
-51534.76047506098,
-52133.21503883293,
-52658.57680087174,
-53110.75935347859,
-53489.69509004395,
-53795.33308211952,
-54027.63776908957,
-54186.58828074709,
-54272.178213082785,
-54284.41567759253,
-54223.32344441068,
-54088.938999575665,
-53881.31433673456,
-53600.515303593565,
-53246.62032342066,
-52819.720831174716,
-52319.93743324885,
-51747.40808588868,
-51102.29522964449,
-50384.78831699325,
-49595.10563722538,
-48733.497473411524,
-47800.25043633439,
-46795.69282027057,
-45720.20082550623,
-44574.205492472836,
-43358.20019238503,
-42072.74851926828,
-40718.49242825867,
-39296.160465060246,
-37806.57593144565,
-36250.66483168175,
-34629.46571252091,
-32944.17538263138,
-31196.14594956652,
-29386.898352590964,
-27518.148261817587,
-25591.82957853986,
-23610.12037625255,
-21575.471034293125,
-19490.635722119092,
-17358.707600178837,
-15183.156199843446,
-12967.868729665366,
-10717.194476104982,
-8435.992407721227,
-6129.681500072957,
-3804.293256123249,
-1466.525482223645,
876.2048431708114,
3215.7151483525577,
5542.998334745137,
7848.184340565847,
10120.524089775536,
12348.361730216937,
14519.155806137751,
16619.530117818722,
18635.343109339923,
20551.79409176176,
22353.584653205307,
24025.15360731413,
25551.003380054648,
26916.00359273623,
28105.73284171606,
29106.888251386114,
29907.75621358617,
30498.632690534578,
30872.13179682373,
31023.419918658466,
30950.412628514478,
30653.97165339472,
30137.992721720584,
29408.884515300295,
28475.48906523878,
27348.767767021312,
26041.464004664016,
24567.620153867556,
22942.068858827668,
21180.035333462973,
19296.987437827524,
17308.57825197944,
15229.730218471072,
13074.869363423566,
10857.742910659721,
8591.315510430077,
6287.693307168683,
3958.0860538296315,
1612.8338611568138,
-738.5143616581285,
-3087.283777438253,
-5425.566924206873,
-7746.179407592045,
-10042.615338230613,
-12308.996416536653,
-14540.021056869227,
-16730.913403042643,
-18877.377580649532,
-20975.555585720205,
-23021.986640681436,
-25013.56584857255,
-26947.504265629683,
-28821.309326957173,
-30632.749078677385,
-32379.826060769516,
-34060.75354655943,
-35673.93459032342,
-37217.943413212066,
-38691.50817406807,
-40093.49417170515,
-41422.888196619744,
-42678.790481386095,
-43860.40125472553,
-44967.011341863574,
-45997.99268734245,
-46952.790431797956,
-47830.91639676657,
-48631.94358829877,
-49355.501330161635,
-50001.27063740816,
-50568.97944109356,
-51058.39727491849,
-51469.33102636553,
-51801.63056721397,
-52055.18072054704,
-52229.90146230831,
-52325.74642284643,
-52342.70171955294,
-52280.78564885503,
-52140.0490893788,
-51920.57646809803,
-51622.4871412839,
-51245.937042071215,
-50791.120446455585,
-50258.271709537905,
-49647.66682383085,
-48959.62465144243,
-48194.50810068982,
-47352.739716655,
-46434.79742451044,
-45441.22235920652,
-44372.62639753037,
-43229.695735712856,
-42013.19676115766,
-40723.98394553594,
-39363.00948648838,
-37931.334425178255,
-36430.14096693815,
-34860.74573225128,
-33224.613665313474,
-31523.372327414498,
-29758.826302385598,
-27932.97144135189,
-26048.008674035602,
-24106.35711385147,
-22110.676616320656,
-20063.946463405257,
-17969.436529353374,
-15830.741536830741,
-13651.814207882664,
-11436.995109512818,
-9191.043188305723,
-6919.16755551075,
-4627.058956654446,
-2320.9207234157125,
-7.498098135412635,
2305.900234295661,
4611.373902534852,
6900.409634857354,
9163.881788033139,
11392.053628259411,
13574.581956204851,
15700.567956300887,
17758.61004438368,
19736.867048207394,
21623.143713304198,
23405.010526667847,
25069.969850731784,
26605.680360121558,
28000.151431506787,
29241.887746007596,
30320.17604505196,
31225.331630881024,
31948.97462237822,
32484.231804314706,
32825.88566503006,
32970.49283952015,
32916.49417490522,
32664.33863524935,
32216.60104891623,
31577.60556758501,
30753.39405999474,
29751.59137644803,
28581.237109339938,
27252.549498944652,
25776.63163790524,
24165.182945828245,
22430.278885385585,
20584.281891330087,
18639.83514438343,
16609.221485946917,
14504.48059480156,
12337.311765328048,
10118.961458075117,
7860.1610779973425,
5571.084955511163,
3261.3252994134964,
939.881643550692,
-1384.8403389330936,
-3705.0216852787566,
-6013.40222326928,
-8303.264107317918,
-10568.406654642533,
-12803.118746552936,
-15002.150331623756,
-17160.68107725312,
-19274.291693903935,
-21338.93715629589,
-23350.920347537874,
-25306.864652200748,
-27203.684039625532,
-29038.561959290757,
-30808.93717670874,
-32512.476351865487,
-34147.05679104959,
-35710.74893396576,
-37201.801043127954,
-38618.62545731584,
-39959.78577087048,
-41223.98430061149,
-42410.04920215532,
-43516.92406553346,
-44543.664961007154,
-45489.42788167426,
-46353.462831586105,
-47135.107016498936,
-47833.77923328132,
-48448.9753091107,
-48980.26434428051,
-49427.28551243838,
-49789.745172077986,
-50067.414043103556,
-50260.12420228979,
-50367.765765181044,
-50390.290601125125,
-50327.71175798892,
-50180.10138506786,
-49947.59220470789,
-49630.377996047304,
-49228.71500320271,
-48742.92414362792,
-48173.39389237583,
-47520.5837179921,
-46785.02794577028,
-45967.33992409769,
-45068.21636962056,
-44088.441766959404,
-43028.89269870255,
-41890.54264872077,
-40674.47834294951,
-39381.90083288478,
-38014.136552660544,
-36572.64625803516,
-35059.033256714574,
-33475.05391689239,
-31822.630037258376,
-30103.862661738865,
-28321.04692222051,
-26476.68749251759,
-24573.514236835174,
-22614.49763599029,
-20602.863574643725,
-18542.10707280034,
-16436.004544836418,
-14288.636327566162,
-12104.43595157008,
-9888.173247124303,
-7644.981096664003,
-5380.373444932911,
-3100.2604731192864,
-810.9618650968106,
1480.7852078873718,
3767.821443291313,
6042.553001894759,
8296.959113354405,
10522.615064166508,
12710.690090823433,
14851.99020864466,
16937.006003010505,
18955.959391691275,
20898.857039089642,
22755.55810322267,
24515.863996265038,
26169.637839478273,
27706.961293348522,
29118.24513442138,
30394.287112947008,
31526.4731288991,
32506.901488047144,
33328.55826785625,
33985.444678670014,
34472.66903175195,
34786.516777389515,
34924.51207626531,
34885.48436730163,
34669.65339518279,
34278.735042617685,
33715.67366274823,
32984.59296781168,
32090.801841474156,
31040.68379521685,
29841.59044377116,
28501.68774280391,
27029.78428630566,
25435.170961132746,
23727.501256157666,
21916.74152347667,
20013.174437115773,
18026.964452079825,
15968.202487745144,
13846.870632451632,
11672.739468720207,
9455.310530543986,
7203.769596772714,
4926.951425013526,
2633.3138452339667,
330.9206097940601,
-1972.5725216923668,
-4269.918977392774,
-6554.273329691396,
-8819.190860572095,
-11058.61792688046,
-13266.881859867894,
-15438.676300134415,
-17569.045172955008,
-19653.367942377172,
-21687.34507075831,
-23666.982577844403,
-25588.574593493202,
-27448.682798142836,
-29244.120212284197,
-30971.94853107281,
-32629.453873599734,
-34214.13555630986,
-35723.69280979512,
-37156.01300672637,
-38509.16111161878,
-39781.36996897628,
-40971.03104636708,
-42076.68524897434,
-43097.01342217371,
-44030.826797486254,
-44877.06699722133,
-45634.79786181244,
-46303.19893774602,
-46881.561555191525,
-47369.28484604567,
-47765.87256017546,
-48070.93057708212,
-48284.16501020525,
-48405.38080108918,
-48434.48070063189,
-48371.4645346375,
-48216.42865089303,
-47969.56583107051,
-47631.1695562955,
-47201.63386603493,
-46681.455524104495,
-46071.23663924553,
-45371.68766337912,
-44583.63115223972,
-43708.00619347265,
-42745.873407280014,
-41698.420424697244,
-40566.96774858702,
-39352.974902432456,
-38058.046772015376,
-36683.940045061485,
-35232.570815917454,
-33706.02743110211,
-32106.57349427184,
-30436.663276965082,
-28698.948439917407,
-26896.285728133276,
-25031.74672379495,
-23108.62900986269,
-21130.46809722376,
-19101.0494682452,
-17024.420089584735,
-14904.898747120329,
-12747.08455584827,
-10555.86299761031,
-8336.40883950406,
-6094.185285830912,
-3834.9443804755065,
-1564.7756902582512,
709.9316438778413,
2982.4987901391623,
5245.947961807267,
7493.016419279126,
9716.178606277132,
11907.657151823902,
14059.45744053036,
16163.403311479276,
18211.172132621952,
20194.33367283418,
22104.397193772074,
23932.871183677238,
25671.340155271755,
27311.557314819303,
28845.46792189937,
30265.31920836255,
31563.709907421344,
32733.69615389196,
33768.89188401064,
34663.543820719395,
35412.58897020106,
36011.702555446005,
36457.344312629444,
36746.81107608234,
36878.303577635284,
36851.01538611709,
36665.19762392298,
36321.8610227337,
35822.95909239614,
35171.33662048008,
34370.699322760236,
33425.57065406535,
32341.219002055885,
31123.568829344556,
29779.10932937554,
28314.814161478294,
26738.085830510496,
25056.738276506567,
23278.99125345696,
21413.122579843613,
19467.549803267346,
17450.78017693739,
15371.340113751061,
13237.723117960517,
11058.333904367211,
8841.435833363892,
6595.108790134634,
4327.224636335781,
2045.4473625581284,
-242.75058084295054,
-2530.2155581753786,
-4810.066435092627,
-7075.689957574445,
-9320.758066433336,
-11539.231059752188,
-13725.35793266819,
-15873.672656139837,
-17978.990848279318,
-20036.406267078808,
-22041.28631808797,
-23989.26577061853,
-25876.237876030686,
-27698.342081668066,
-29451.95767811929,
-31133.706892228274,
-32740.436413754418,
-34269.212437112685,
-35717.31295637883,
-37082.220569840734,
-38361.615759215485,
-39553.37058834072,
-40655.54276615369,
-41666.37001877109,
-42584.2647154792,
-43407.80869344996,
-44135.748226311436,
-44766.99114474091,
-45300.605628655416,
-45735.81464213854,
-46071.99433465354,
-46308.67268906147,
-46445.52829736347,
-46482.38938119211,
-46419.23317407652,
-46256.18578250575,
-45993.522642814714,
-45631.66969091775,
-45171.2053619137,
-44612.86353658787,
-43957.535416921,
-43206.268117040745,
-42360.27210636666,
-41420.92317711944,
-40389.76616830193,
-39268.51905971408,
-38059.077301926154,
-36763.518344419295,
-35384.10632410743,
-33923.29687644513,
-32383.74203133994,
-30768.295156068925,
-29080.015907422057,
-27322.175158026857,
-25498.25990776402,
-23611.97737126224,
-21667.258837160436,
-19668.26317351684,
-17619.37965079976,
-15525.229843462866,
-13390.668688373748,
-11220.784462659614,
-9020.897378366048,
-6796.55682796515,
-4553.536875638436,
-2297.8299728504962,
-35.63859950105572,
2226.6403050766658,
4482.42144793332,
6724.945106994718,
8947.298573559783,
11142.437730433045,
13303.21128500558,
15422.386920376286,
17492.680990571745,
19506.793299510107,
21457.438764492148,
23337.401704760003,
25139.55118867293,
26856.893359525297,
28482.629495390185,
30010.203017524247,
31433.33944133321,
32746.08326388277,
33942.83578195403,
35018.39783463181,
35968.02146442084,
36787.47449087969,
37473.12199076825,
38021.8965632246,
38431.24227649981,
38699.25851230085,
38824.6805238706,
38806.8991953652,
38645.96388056756,
38342.57367755292,
37898.06250730922,
37314.38336431379,
36594.097107068,
35740.371156594076,
34756.97761946378,
33648.16485334259,
32418.67421597067,
31073.701366766112,
29618.85789018959,
28060.130322479537,
26403.83117665899,
24656.547486558396,
22825.092390606296,
20916.46527613019,
18937.826004916787,
16896.485854508726,
14799.810510027139,
12655.183058466804,
10470.005264374824,
8251.653231659064,
6007.448960108469,
3744.6342214984115,
1470.3470860637383,
-808.4002122485981,
-3084.744122952825,
-5351.975951881122,
-7603.560118746364,
-9833.1477082603,
-12034.587192487123,
-14201.932404741103,
-16329.449923875105,
-18411.625618267382,
-20443.16992867957,
-22419.021469180603,
-24334.348525316494,
-26184.54836074266,
-27965.25274058885,
-29672.32911264127,
-31301.87624017809,
-32850.225999074035,
-34313.94377929138,
-35689.82764787186,
-36974.90670283751,
-38166.43904740221,
-39261.90981390164,
-40259.02966684371,
-41155.73421448941,
-41950.184758363415,
-42640.770810106456,
-43226.11241324072,
-43705.03982126812,
-44076.60713447272,
-44340.089958925055,
-44494.98514565281,
-44541.01209607157,
-44478.112768296574,
-44306.45093057146,
-44026.41120805146,
-43638.5984691778,
-43143.838097880674,
-42543.177697847146,
-41837.89077509239,
-41029.48294506953,
-40119.69127372695,
-39110.462010641866,
-38003.97475283827,
-36802.63524649073,
-35509.076632900105,
-34126.16023158729,
-32656.97520523481,
-31104.83732189326,
-29473.28702988702,
-27766.08706084147,
-25987.219776256545,
-24140.884473061018,
-22231.49486356543,
-20263.67694524405,
-18242.267475775054,
-16172.305159660793,
-14059.009157861696,
-11907.787972335651,
-9724.225611435142,
-7514.071099492537,
-5283.226984572263,
-3037.7366958402326,
-783.7705887425582,
1472.392424625919,
3724.3796337906197,
5965.736156625713,
8189.947298716857,
10390.464396654068,
12560.717761320602,
14694.14232166579,
16784.205521670636,
18824.431769162384,
20808.425013789478,
22729.891031491592,
24582.660992774287,
26360.717892118453,
28058.22741584039,
29669.574825727566,
31189.405596197408,
32612.60188526328,
33934.33198345628,
35150.072844597205,
36255.634629459535,
37247.18845233602,
38121.2854641808,
38874.86992322531,
39505.288903967565,
40010.301295431906,
40388.08873960031,
40637.27116091233,
40756.92953773323,
40746.63856668866,
40606.42242976296,
40336.71095384731,
39938.40745580666,
39412.85675184476,
38761.8486614893,
37987.6106212516,
37092.792444414765,
36080.446265084734,
34954.00470464073,
33717.26029871535,
32374.34922284346,
30929.74235491122,
29388.246712537784,
27755.014393382065,
26035.410965212242,
24235.028716023346,
22359.70432180356,
20415.47447391746,
18408.55219450519,
16345.303683234108,
14232.224641728484,
12075.9172642356,
9883.06749761596,
7660.422678597997,
5414.769784857234,
3152.91411098178,
881.6586322627558,
-1392.2199634054527,
-3661.9856806911575,
-5920.959857583914,
-8162.541973302598,
-10380.226822679517,
-12567.620994289005,
-14718.458660326962,
-16826.616640875334,
-18886.128171207718,
-20891.19789748445,
-22836.215496768687,
-24715.7632093442,
-26524.631699520138,
-28257.832963219793,
-29910.610396371914,
-31478.446964567287,
-32957.07241442789,
-34342.47046715261,
-35630.88693468788,
-36818.83969898363,
-37903.13149479078,
-38880.86643645045,
-39749.445325937486,
-40506.54171880617,
-41150.14387656782,
-41678.54626529345,
-42090.35737675067,
-42384.50362263096,
-42560.2302565388,
-42617.10063782596,
-42554.995151358315,
-42374.1110972996,
-42074.964864998154,
-41658.39770506154,
-41125.58641370567
],
"y": [
0,
3450.5350348436245,
6854.9797701458565,
10170.551303244134,
13359.40466527167,
16390.16925051825,
19238.652155234755,
21887.799623507435,
24327.103209099885,
26551.661011151366,
28561.106657472395,
30358.598637607614,
31949.838416969757,
33342.298992841344,
34544.583309359965,
35565.93234388592,
36415.8620896293,
37103.89068948638,
37639.35494061142,
38031.28805016374,
38288.34380530761,
38418.750187610975,
38430.288710258086,
38330.29546180485,
38125.67132202903,
37822.88886598989,
37428.01251661776,
36946.733060536644,
36384.38278591029,
35745.96168701508,
35036.16176371231,
34259.39063743965,
33419.79317972634,
32521.270880211443,
31567.50342221243,
30561.9658500426,
29507.945386574982,
28408.55704837182,
27266.75809823618,
26085.361444010254,
24867.048034756324,
23614.378305455928,
22329.802721363114,
21015.67147314745,
19674.243374695026,
18307.694616556844,
16918.12711317954,
15507.574991044543,
14078.01173738312,
12631.356933515528,
11169.482473595375,
9694.218346665873,
8207.358059935248,
6710.663781177992,
5205.871278169173,
3694.694733058612,
2178.831509591918,
659.9669510853286,
-860.2207129386114,
-2380.055273547654,
-3897.85767585202,
-5411.945652758044,
-6920.625721181304,
-8422.189676319462,
-9914.910189582672,
-11397.035761147914,
-12866.78576119655,
-14322.345487332886,
-15761.86116568247,
-17183.434823168653,
-18585.118958465097,
-19964.910939123427,
-21320.747052373885,
-22650.496137097558,
-23951.95272446828,
-25222.829614763243,
-26460.749817839573,
-27663.23778477666,
-28827.70985818129,
-29951.46387570372,
-31031.676044998487,
-32065.391196668897,
-33049.50394875843,
-33980.75039444435,
-34855.695773487736,
-35670.72156196536,
-36422.01181778891,
-37105.53870933355,
-37717.04832506117,
-38252.04599349244,
-38705.78147032048,
-39073.23501940146,
-39349.104749668986,
-39527.79514523362,
-39603.4092333958,
-39569.74523901489,
-39420.299112897,
-39148.27607925946,
-38746.61404736036,
-38208.023027236704,
-37525.04550967057,
-36690.1482278171,
-35695.908715277415,
-34535.06600575609,
-33200.758401750594,
-31686.830236644302,
-29988.185216151633,
-28101.21367836703,
-26024.297347387594,
-23758.385444315565,
-21307.590148590665,
-18679.801466309727,
-15887.241010235604,
-12946.820714463096,
-9880.217318426252,
-6713.7352235527605,
-3477.6498344264633,
-205.16324678876472,
3068.840001069659,
6309.385470634159,
9483.015839696714,
12559.003404085293,
15510.497763142497,
18315.152318970126,
20955.387069230277,
23418.320454624547,
25695.40683435402,
27781.91937715619,
29676.372988464518,
31379.923802467347,
32895.77016744762,
34228.662032274486,
35384.51609819832,
36369.976872339066,
37192.20622204074,
37858.67787504789,
38376.868243885765,
38754.20217793787,
38997.96746948468,
39115.24858936353,
39112.8835730188,
38997.43941825284,
38775.198839093304,
38452.15123219643,
38033.992776144936,
37526.140098128475,
36933.7346858328,
36261.655628773966,
35514.532936187556,
34696.76169570615,
33812.51583709316,
32865.76140148159,
31860.271679277066,
30799.639781938222,
29687.291565108193,
28526.497820422697,
27320.385905130486,
26071.950795026605,
24784.065537834897,
23459.49108417697,
22100.885473264843,
20710.812347226576,
19291.749245850508,
17846.096152014088,
16376.18179419558,
14884.270851074467,
13372.570894383749,
11843.238797684293,
10298.386701787,
8740.087627550287,
7170.380826781162,
5591.276961966323,
4004.763205560667,
2412.8083495614233,
817.3680160944297,
-779.609940260129,
-2376.179747674272,
-3970.393591937499,
-5560.301342334877,
-7143.940447924826,
-8719.333598269825,
-10284.483470634474,
-11837.367446451812,
-13375.932492730059,
-14898.090126237645,
-16401.711378308144,
-17884.621678105057,
-19344.59557218473,
-20779.35119819906,
-22186.544430577513,
-23563.762616027605,
-24908.517816694908,
-26218.239478821364,
-27490.26644474299,
-28721.838226065902,
-29910.08645010826,
-31052.03916733831,
-32144.60339429046,
-33184.55531738875,
-34168.533504332925,
-35093.03018237318,
-35954.38250904627,
-36748.76373090629,
-37472.17416591811,
-38120.43340013314,
-38689.17315765472,
-39173.830703404055,
-39569.64437695592,
-39871.65130190642,
-40074.68785117987,
-40173.394651258845,
-40162.226508189764,
-40035.46933911892,
-39787.26576687134,
-39411.651753109654,
-38902.60689600644,
-38254.12157931147,
-37460.354523790906,
-36515.73639941647,
-35414.91831848276,
-34153.07115194714,
-32726.101361588204,
-31130.884548018716,
-29365.553473566946,
-27429.828161251287,
-25325.363661327232,
-23056.091076157307,
-20628.52761022603,
-18052.10683660989,
-15339.369104058362,
-12506.143585073305,
-9571.304768207177,
-6556.5179703035665,
-3485.9705808203785,
-385.8587285231653,
2716.600939164317,
5794.689565264754,
8822.120319363425,
11774.066314824342,
14628.070471287338,
17364.441804980288,
19966.549406324073,
22420.945086571846,
24717.333256842823,
26848.3466929044,
28809.28582644543,
30597.80721653893,
32213.57660856715,
33657.9076618448,
34933.446806328284,
36043.93757596281,
36993.90121800355,
37788.449346470305,
38433.22654754744,
38934.12210495697,
39297.065779845994,
39528.03796126002,
39632.99747474989,
39617.826729889544,
39488.29074698527,
39250.00817328598,
38908.43179482676,
38468.83599554131,
37936.30869862381,
37315.748998499424,
36611.86882443251,
35829.19549511522,
34972.07685314863,
34044.687769325385,
33051.03764864729,
31994.978352803526,
30880.212038469046,
29710.300044432563,
28488.67145288254,
27218.63122557628,
25903.368682052245,
24545.96560054936,
23149.404000807353,
21716.573656503635,
20250.27938507428,
18753.248162674914,
17228.136112031316,
15677.535410529516,
14103.980016782049,
12509.95188450504,
10897.886892384151,
9270.1802849509,
7629.1926775428365,
5977.25561519167,
4316.676782090269,
2649.7449582954514,
978.7348203165267,
-694.0883177543659,
-2366.463725917937,
-4036.1303460959325,
-5700.821649457716,
-7358.260166592039,
-9006.151593872139,
-10642.17955960806,
-12264.007507035822,
-13869.26725065574,
-15455.556261896114,
-17020.43242841902,
-18561.40822611898,
-20075.945234651554,
-21561.44889069863,
-23015.26337317945,
-24434.666514616336,
-25816.864632862103,
-27158.987177400926,
-28458.081084427253,
-29711.10473491448,
-30914.92140988077,
-32066.29213705964,
-33161.86782318668,
-34198.1818232465,
-35171.661701816105,
-36078.613466086295,
-36915.214680552876,
-37677.513984703925,
-38361.42913635495,
-38962.74642047676,
-39477.1214244172,
-39900.08237861661,
-40227.036885238536,
-40453.281314275715,
-40574.01504927937,
-40584.35986991272,
-40479.38581903404,
-40254.144691054455,
-39903.71263707619,
-39423.24344407285,
-38808.060359039926,
-38053.90622990958,
-37156.66954246777,
-36112.61793883996,
-34918.57972374202,
-33572.100147601996,
-32071.598703092313,
-30416.552712354922,
-28607.702909147756,
-26647.26451733484,
-24539.12731659813,
-22289.02818625085,
-19904.680079299615,
-17395.925216385687,
-14774.764582295704,
-12055.388752936422,
-9253.949000753948,
-6388.310958923619,
-3477.834246559661,
-543.075985909967,
2394.6838574939193,
5314.197380769024,
8195.054507581279,
11017.26054479752,
13762.231897496902,
16413.092416173677,
18954.90352734047,
21374.79730393622,
23662.019052165586,
25807.91451263142,
27805.89677361041,
29651.35022925756,
31341.275205839574,
32874.22801348527,
34250.10703140955,
35469.95664367627,
36535.76867706314,
37450.301892819334,
38216.94510409196,
38839.64312892522,
39322.89725510655,
39671.317831919485,
39889.642409049535,
39982.728848568935,
39955.49772163263,
39812.88569243096,
39559.80892460948,
39201.1336285538,
38741.65314619476,
38186.07116724647,
37538.9906707912,
36804.90694077806,
35988.19863927786,
35093.1274661711,
34123.83732633087,
33084.35516893643,
31978.59311716208,
30810.351590962695,
29583.32326737544,
28301.097722762348,
26967.166399177913,
25584.927409556763,
24157.691120441268,
22688.685469557357,
21181.06186924407,
19637.900818066006,
18062.217283900238,
16456.965973432074,
14825.04660298326,
13169.309285605406,
11492.560149366145,
9797.567301756655,
8087.066401482512,
6363.762487078722,
4630.337570341944,
2889.454482759321,
1143.7621103336674,
-604.0995058832128,
-2351.4944242315432,
-4095.786308166102,
-5834.33407957216,
-7564.487615980202,
-9283.583387988561,
-10988.939933205833,
-12677.853063016388,
-14347.590698480926,
-15995.387231680917,
-17618.43758794645,
-19213.900416754692,
-20778.88751229239,
-22310.4616199069,
-23805.633293484934,
-25261.354672420195,
-26674.5144475918,
-28041.933851191905,
-29360.36350524317,
-30626.48096364447,
-31836.8887825849,
-32988.11295416659,
-34076.601538073395,
-35098.72332612808,
-36050.76637457437,
-36928.93623892623,
-37729.35374622184,
-38448.05213952251,
-39080.98224744977,
-39624.06589640341,
-40073.16037045509,
-40424.0772353145,
-40672.60241531373,
-40814.51641635016,
-40845.61894255378,
-40761.75920355292,
-40558.8719865937,
-40233.0205434591,
-39780.447357682744,
-39197.84326108994,
-38482.168897784606,
-37630.58752870279,
-36640.7264276259,
-35510.776055304625,
-34239.591455241636,
-32826.79609460467,
-31272.899902800687,
-29579.423300226295,
-27749.018107893873,
-25785.576228631006,
-23694.31699055621,
-21481.844043525954,
-19156.217462802306,
-16726.97775999894,
-14205.143917345604,
-11603.116401096999,
-8934.505464202535,
-6213.994901752184,
-3457.19746462195,
-680.4576129049917,
2099.442709293245,
4865.719596105535,
7602.132633002184,
10292.680640326216,
12922.072490885255,
15476.072860205362,
17941.638320440605,
20307.047196404095,
22561.971144509967,
24697.506957633126,
26706.187098888906,
28581.987467330433,
30320.313312638053,
31917.74604538728,
33372.04765078576,
34682.04976872009,
35847.53045918515,
36869.10130070176,
37748.098084452315,
38486.477335240634,
39086.720395388445,
39551.83290882805,
39885.39410951279,
40090.993696815494,
40172.421335979714,
40133.639320739014,
39978.73679369944,
39711.89011940345,
39337.33138891869,
38859.32065328888,
38282.1220377505,
37609.98444632915,
36847.12756642825,
35997.73387171775,
35065.93790680986,
34055.81408823286,
32971.377318045095,
31816.578645899357,
30595.30367739938,
29311.371640278823,
27968.53528923243,
26570.48183023447,
25120.835045174932,
23623.15879764543,
22080.959967349507,
20497.688135883276,
18876.741619375196,
17221.470245467797,
15535.17961282538,
13821.135009072948,
12082.565093481468,
10322.665516314944,
8544.60264675937,
6751.517581344081,
4946.530604780991,
3132.7462751322373,
1313.2592038364028,
-508.8460941839508,
-2330.4910117171507,
-4148.596773707559,
-5960.081421570779,
-7761.855529932633,
-9550.818336206303,
-11323.854193691453,
-13077.82925886919,
-14809.588323576821,
-16515.95170273972,
-18193.712088340544,
-19839.631280306687,
-21450.43670499216,
-23022.81763193671,
-24553.421492394984,
-26038.85911085571,
-27475.69839205353,
-28860.463883898326,
-30189.63628925834,
-31459.651320120458,
-32666.90006978271,
-33807.7308080009,
-34878.45210400073,
-35875.33718227752,
-36794.62941609831,
-37632.54886362619,
-38385.299751581304,
-39049.0788113583,
-39620.084372516576,
-40094.52611856078,
-40468.64467364551,
-40738.75451840474,
-40901.24045602264,
-40952.59088955137,
-40889.42895583599,
-40708.54694834182,
-40406.945100785684,
-39981.99321558558,
-39431.57662896307,
-38753.61610053915,
-37946.4185457412,
-37008.75822090606,
-35939.94411970421,
-34739.88136737674,
-33409.1382191748,
-31949.016718216288,
-30361.622322712235,
-28649.92781251152,
-26817.826784915058,
-24870.172049708417,
-22812.794233363344,
-20652.53563801713,
-18397.25379262632,
-16055.806418390177,
-13638.00556716816,
-11154.507206701846,
-8616.722890075675,
-6036.735442311443,
-3427.20004837866,
-801.2121269075395,
1827.876626119419,
4446.712639409996,
7042.2688735567035,
9601.675752635234,
12112.387333121302,
14562.507952268918,
16940.856290276453,
19237.070716246624,
21441.679152305816,
23546.14337262764,
25542.88765411187,
27425.321695462448,
29187.867721417624,
30825.982288397652,
32335.97835845207,
33715.05199615428,
34961.24427312206,
36073.36699199796,
37050.93970828137,
37894.12518668419,
38603.66542722766,
39180.81921516401,
39627.302411305434,
39945.445469240534,
40138.00092285012,
40207.73218728319,
40157.65479670117,
39990.99898245317,
39711.17212868428,
39321.72801713397,
38826.34020156413,
38228.77630079011,
37532.8745948638,
36742.52435219775,
35861.65131541842,
34894.20977373751,
33844.17199578209,
32715.504198945826,
31512.170837244717,
30238.125493549614,
28897.30697392655,
27493.63534847778,
26031.00830847212,
24513.298330700538,
22944.351139987146,
21327.985960793612,
19667.998048847265,
17968.16292110023,
16232.226716881201,
14463.91183905371,
12666.920026892323,
10844.933605714758,
9001.617898057468,
7140.623229760097,
5265.5867417641175,
3380.1342184415194,
1487.8821432591712,
-407.5598074098297,
-2302.585621516216,
-4193.58894880067,
-6076.958986729713,
-7949.086575772831,
-9806.357443761899,
-11645.149543215848,
-13461.832005632617,
-15252.763493720333,
-17014.290957760255,
-18742.748778142348,
-20434.458276113368,
-22085.72757478295,
-23692.85179242359,
-25252.11355011019,
-26759.783775737575,
-28212.12278646067,
-29605.382080241383,
-30935.811079688247,
-32199.6597138532,
-33393.18257882725,
-34512.6447247956,
-35554.328625799535,
-36514.54265583053,
-37389.63109600567,
-38175.98569757144,
-38870.05882548437,
-39468.37820731685,
-39967.56331223451,
-40364.343384795226,
-40655.577158316046,
-40838.274272556926,
-40909.618420469036,
-40866.993936832565,
-40708.02877274131,
-40430.656364760944,
-40033.47393928991,
-39515.103324334224,
-38874.43256356914,
-38110.75716417525,
-37223.818976870585,
-36213.84423356514,
-35081.58238456662,
-33828.344378446614,
-32456.038788035676,
-30967.2041860186,
-29365.036173596603,
-27653.407465687997,
-25836.881379014405,
-23920.730422919736,
-21910.91610349894,
-19814.111260282738,
-17637.65647541731,
-15389.513933582853,
-13078.227156040883,
-10712.87965783612,
-8303.045577173763,
-5858.725326000807,
-3390.259310809651,
-908.2127726922804,
1576.7752043307057,
4054.136665970773,
6513.197289763585,
8943.461889716005,
11334.697054610086,
13677.011578127525,
15960.938101470285,
18177.496107660343,
20318.24160772675,
22375.308858980137,
24341.449455576723,
26210.074131575806,
27975.302616691468,
29632.00478229801,
31175.647710904603,
32602.38056366526,
33909.02285738946,
35093.03858802257,
36152.51115587774,
37086.10787391111,
37893.0400942302,
38573.024988030214,
39126.25501414847,
39553.381111434406,
39855.51524457963,
40034.47973084869,
40092.238322079545,
40030.88005465333,
39852.77239041432,
39560.53154745735,
39156.99491251203,
38645.19959476789,
38028.359442117806,
37309.84191564686,
36493.14666820749,
35581.88767291625,
34579.780747413504,
33490.63831972508,
32318.353433069453,
31066.870716813566,
29740.193788362267,
28342.371486802982,
26877.49302110561,
25349.68172392097,
23763.08817380769,
22121.88349385099,
20430.25363463274,
18692.395449511652,
16912.51537017805,
15094.831490437959,
13243.57986027176,
11362.999864968773,
9457.319434801817,
7530.775810721467,
5587.607269268674,
3632.0530573192996,
1668.352755390422,
-299.25473180856676,
-2266.5323588975166,
-4229.2460987284285,
-6183.165877997957,
-8124.066090869236,
-10047.725491933174,
-11949.926269813184,
-13826.456121257703,
-15673.119133830298,
-17485.728707622442,
-19260.111887198236,
-20992.113041413584,
-22677.59749078806,
-24312.45539811311,
-25892.606086178606,
-27414.002946493973,
-28872.639102891488,
-30264.553993883055,
-31585.841037662227,
-32832.65654362004,
-34001.22997453896,
-35087.87091957768,
-36088.97968107387,
-37001.06160781041,
-37820.73914349989,
-38544.76577271323,
-39170.04118367364,
-39693.627385685824,
-40112.76582142743,
-40424.89561240652,
-40627.6726473895,
-40718.9896360832,
-40696.99689026512,
-40560.127134436385,
-40307.48693934911,
-39938.383770869455,
-39452.213463883505,
-38848.73730125093,
-38128.10360170954,
-37290.86848274832,
-36338.01510888712,
-35270.97147719494,
-34091.62683728865,
-32802.34602155205,
-31405.97615277957,
-29905.86026988392,
-28305.838342418665,
-26610.244132105054,
-24823.900522426426,
-22952.112002086295,
-21000.652988141614,
-18975.75067460693,
-16884.06109233768,
-14732.637065991848,
-12528.886753878614,
-10280.52145649045,
-7995.5342946652545,
-5682.17849070205,
-3348.879312949901,
-1004.1940291733945,
1343.2450121249847,
3684.783623356197,
6011.7968935587905,
8315.739733085238,
10588.198402180022,
12820.944578359096,
15005.993517498373,
17135.66010937608,
19202.552294113564,
21199.634972356194,
23120.261523813457,
24958.20261399352,
26707.669275448985,
28363.327629185256,
29920.30811862628,
31374.21212852494,
32721.118861214454,
33957.59534258963,
35080.710729133214,
36087.99463844789,
36977.43048302263,
37747.46453229661,
38396.98480135898,
38925.30642618314,
39332.155530803815,
39617.65215011816,
39782.37891140733,
39827.69490880182,
39754.87290339423,
39565.4223818361,
39261.152743410144,
38844.15421699616,
38316.78253126776,
37681.64139282156,
36941.563971049545,
36099.59511950246,
35158.976063487666,
34123.1332836484,
32995.67137990788,
31780.34592080139,
30481.05778963771,
29101.83708750134,
27646.835287468464,
26120.31800624728,
24526.655177554265,
22870.309761239074,
21155.826122178,
19387.819212948823,
17570.96569430802,
15709.998127478948,
13809.703372277509,
11874.92632507802,
9910.573907073465,
7921.551966020701,
5912.793727257159,
3889.2592731199197,
1855.9235752537202,
-182.22902751528488,
-2220.210803303603,
-4253.036921723319,
-6275.7319373154,
-8283.336330301014,
-10270.912973095446,
-12233.553391028841,
-14166.38368571212,
-16064.569989497006,
-17923.325131428654,
-19737.925839873405,
-21503.713049865462,
-23216.09810200337,
-24870.575110494865,
-26462.73170032382,
-27988.258608070188,
-29442.958687643317,
-30822.755862167287,
-32123.70456327128,
-33342.0001990336,
-34473.9911918211,
-35516.19312727909,
-36465.30555571602,
-37318.23198713002,
-38072.0859797301,
-38724.173192349714,
-39272.04194113019,
-39713.48556887928,
-40046.55486846047,
-40269.5710799992,
-40381.13843629809,
-40380.15645624408,
-40266.117039026576,
-40038.9680634644,
-39698.56409595535,
-39245.09110528898,
-38679.08181459273,
-38001.418810304094,
-37213.342002243284,
-36316.455443629326,
-35312.73054928263,
-34204.50640391875,
-32994.48785242795,
-31685.742064046426,
-30281.69426231503,
-28786.123312728723,
-27203.157859974774,
-25537.272421642465,
-23793.255814713248,
-21976.20246578226,
-20091.502651837287,
-18144.82072976271,
-16142.074864817674,
-14089.415201185542,
-11993.200545577023,
-9859.973634866734,
-7696.43505875003,
-5509.4159083951745,
-3305.8492220779663,
-1092.7402987765638,
1122.864049285814,
3333.8991463379107,
5533.31408122904,
7714.10023346039,
9869.318329069509,
11992.13108638125,
14075.831006138023,
16113.864527413036,
18099.853770928774,
20027.61709140296,
21891.189660555323,
23684.84530239145,
25403.120802395173,
27040.843912255797,
28593.163229718983,
30055.505299263477,
31423.608276027368,
32693.552593701672,
33861.75871484259,
34924.994245513786,
35880.37989589513,
36725.3933705603,
37457.872187952365,
38076.015058100566,
38578.38192265436,
38963.89293118619,
39231.82611225955,
39381.816636008276,
39414.28272721756,
39329.87111218684,
39129.24595637082,
38813.401767090705,
38383.65635187915,
37841.642844407506,
37189.300748495174,
36428.86702517175,
35562.86708069725,
34594.105514554816,
33525.65665550482,
32360.847404617572,
31103.252399122168,
29756.685526024903,
28325.187862187744,
26813.013401066477,
25224.613926320122,
23564.624392489277,
21837.850172955597,
20049.257535382658,
18203.9687048415,
16307.262874833978,
14364.550509250235,
12381.314276316942,
10363.150588005035,
8315.739269490363,
6244.829722010987,
4156.226847600616,
2055.7764967059834,
-50.64938434984034,
-2157.1677291116903,
-4257.899272992977,
-6346.983244569971,
-8418.59164923028,
-10466.942968210366
],
"z": [
0,
0.5710083111363927,
2.276538609770986,
5.094890987553403,
8.99234012546611,
13.926210521491704,
19.84832026394841,
26.708178247478973,
34.45564037845436,
43.04286454506144,
52.425574705160194,
62.563618823181145,
73.4212327564129,
84.96687410683451,
97.17291071015126,
109.92800909526488,
121.61187948392516,
131.61477867461136,
139.93618064378103,
146.5836157692417,
151.5708309292577,
154.91629500540608,
156.6420246838213,
156.77269505486075,
155.3349356488859,
152.35671285353732,
147.8669047511696,
141.89505711372172,
134.47107605884597,
125.625060533133,
115.38717493786794,
103.78756326435348,
90.85628719454593,
76.62327950328533,
61.11834628509148,
44.3711567351915,
26.41125641022018,
7.26808962643235,
-13.028973086856475,
-34.450602219238284,
-56.96747138029197,
-80.55021445348692,
-105.16937798661414,
-130.79536900245526,
-157.39839998419566,
-184.94845088504013,
-213.41519603355806,
-242.76796470367444,
-272.97567400572115,
-304.0067575522286,
-335.82909831722367,
-368.40996359132424,
-401.71593993491143,
-435.71286603167135,
-470.36576134478736,
-505.63874847807176,
-541.4949671443235,
-577.8964776431984,
-614.8041517508747,
-652.1775489238057,
-689.9747974244195,
-728.1526569276197,
-766.6662375530398,
-805.4689332763568,
-844.5122883806315,
-883.7458314640702,
-923.1169114255455,
-962.5705317169596,
-1002.0491791515569,
-1041.4926435572856,
-1080.8378245642866,
-1120.0185218156346,
-1158.9652048904065,
-1197.6047592281855,
-1235.8602043440872,
-1273.6503806234282,
-1310.8896009850962,
-1347.4872637027634,
-1383.3474226730034,
-1418.3683118077663,
-1452.4422698092053,
-1485.4552922959228,
-1517.2860833848674,
-1547.805686507723,
-1576.8768856076854,
-1604.3535653641054,
-1630.0800213126688,
-1653.8902144222245,
-1675.6070123215065,
-1695.0413810033456,
-1711.9915379885367,
-1726.242104712123,
-1737.5632674010496,
-1745.7099378205162,
-1750.421009136402,
-1751.4187347486463,
-1748.4082809466508,
-1741.0775773705213,
-1729.0975781271366,
-1712.1231028223697,
-1689.7944649500123,
-1661.6485481148027,
-1625.7014341871172,
-1580.9938798489404,
-1527.1722754917203,
-1463.9154369023463,
-1390.9516748215874,
-1308.079556437275,
-1215.1925657310708,
-1112.3073504877023,
-999.5932536954255,
-877.4029547028485,
-746.3002854831423,
-607.079041420919,
-460.7684853078961,
-308.6277460509879,
-152.11563157444647,
7.159032299143161,
167.4971529196493,
327.1868227136427,
484.5821540046289,
638.1632409263773,
786.5936699805974,
928.7526747660552,
1063.7494613414133,
1190.921548762759,
1309.8183806071675,
1420.1767815179903,
1521.893608662795,
1614.996776141869,
1699.6162088153922,
1775.9598948555597,
1844.2949627269825,
1904.9261322566938,
1957.6493331452257,
2001.1768659277834,
2035.7550674230083,
2061.7500315996813,
2079.527744607375,
2089.4495000911843,
2091.8686958198614,
2087.1287507043317,
2075.561736204683,
2057.4873168063828,
2033.212278591812,
2003.0308825787522,
1967.2247452919505,
1926.0632423348934,
1879.8039858154057,
1828.6933872228044,
1772.967231439545,
1712.8512513850985,
1648.5618330562627,
1580.3065966535494,
1508.2850205298855,
1432.6890398807686,
1353.7036261317644,
1271.5073461872823,
1186.2729002472595,
1098.1676368984079,
1007.3540441867175,
913.9902145486941,
818.2302827165591,
720.2249095215413,
620.1216241563619,
518.0652392431249,
414.1982621954546,
308.66127472626124,
201.59328756313266,
93.13207743346769,
-16.585486618485994,
-127.42312052064396,
-239.24479900604263,
-351.9144189275796,
-465.2954449924904,
-579.2505308544562,
-693.6411085011838,
-808.3271117635644,
-923.1670266897264,
-1038.0170677965036,
-1152.731046704333,
-1267.1599612297184,
-1381.1515794146671,
-1494.5500358794627,
-1607.1954325698352,
-1718.923435969879,
-1829.5648628519368,
-1938.945246634555,
-2046.8843764198507,
-2153.1958007814874,
-2257.6862883744166,
-2360.1552374377034,
-2460.394026261545,
-2558.18529668983,
-2653.3021627293347,
-2745.507432535427,
-2834.554068290979,
-2920.183526680181,
-3002.1248380873662,
-3080.0939673241946,
-3153.7929731592017,
-3222.9091518223727,
-3287.1141531775284,
-3346.063061789543,
-3399.3935628916847,
-3446.725141624762,
-3487.658300210476,
-3521.7739298149363,
-3548.6328365021527,
-3567.7754678863553,
-3578.7219920905786,
-3580.9727579140454,
-3574.009314429108,
-3557.2961314456797,
-3530.283226552976,
-3492.4099283739697,
-3443.1100581461374,
-3381.041777297948,
-3304.188878474727,
-3211.997822539289,
-3103.9978169189917,
-2979.786328596471,
-2839.0511723128866,
-2681.59768681591,
-2507.379829992789,
-2316.5330330624233,
-2109.4066539461055,
-1886.5938859706498,
-1648.963318632138,
-1397.676959287458,
-1134.208607111664,
-860.3215381231004,
-578.0452574848915,
-289.65084122721527,
2.3969046623059844,
295.53223983277263,
587.2315141944671,
875.0072847090788,
1156.505528689601,
1429.5921440721286,
1692.3910886562053,
1943.3126796692854,
2181.065936529911,
2404.656966158731,
2613.36789079167,
2806.732790098342,
2984.508801042167,
3146.643571763733,
3293.241307001176,
3424.533097864314,
3540.8547035725815,
3642.6163263361937,
3730.2829996196156,
3803.1358418991845,
3860.6642675560906,
3903.426862424302,
3931.9939045863593,
3946.9393728183404,
3948.834761152639,
3938.244354566419,
3915.7217782792845,
3881.8075634199217,
3837.027465851232,
3781.8912838062843,
3716.892306640697,
3642.507208046222,
3559.19606064051,
3467.40264895819,
3367.5549476857914,
3260.0657268439277,
3145.333222520867,
3023.7418197888833,
2895.6628565370715,
2761.4554107496742,
2621.467057397015,
2476.034678594433,
2325.4852423017464,
2170.1365554669796,
2010.2979977581133,
1846.2712420257728,
1678.350967640636,
1506.825572848726,
1331.977892234255,
1154.0857680427912,
973.4226899647264,
790.2584242797659,
604.859583513634,
417.4902793678616,
228.4127262204431,
37.88780748756389,
-153.82438286265764,
-346.4640113543202,
-539.7709763149428,
-733.4843671633607,
-927.3418993837873,
-1121.079312895434,
-1314.429721527212,
-1507.122901307177,
-1698.8846618337593,
-1889.4371500593054,
-2078.4973858947387,
-2265.7769582390374,
-2450.981367761548,
-2633.8092889751597,
-2813.951875231427,
-2991.0920916412156,
-3164.9040609246836,
-3335.05240719444,
-3501.191582675452,
-3662.9651623658638,
-3820.0050916420246,
-3971.93087181188,
-4118.348668620354,
-4258.850328710392,
-4393.012289043758,
-4520.394535125101,
-4640.54215218866,
-4752.983111111788,
-4857.227248294453,
-4952.766093075885,
-5039.072467518536,
-5115.600242158428,
-5181.784245240013,
-5237.040480186485,
-5280.766755988209,
-5312.343632820983,
-5331.135966346469,
-5336.495085436856,
-5327.761777723427,
-5304.270230499,
-5265.353122732271,
-5210.348073854586,
-5138.407825084185,
-5047.2223770388955,
-4935.823766411963,
-4803.6867785161485,
-4650.3627951216395,
-4475.501155983634,
-4278.87085365258,
-4060.3858287042117,
-3820.1332088407516,
-3558.4023142161836,
-3275.7122500911364,
-2972.835907923175,
-2650.8182575441997,
-2310.997475804819,
-1955.008829891215,
-1584.789916867792,
-1202.5493420297253,
-810.7332030641265,
-411.9952011384621,
-9.156242425899558,
394.8606064221001,
797.1339453466526,
1194.8550137637355,
1585.2692944140395,
1965.8137151663664,
2334.1578407360926,
2688.2360064184872,
3026.2657407197107,
3346.75368448568,
3648.4941645606373,
3930.5655800056807,
4192.318152043663,
4433.323117240832,
4653.366077037044,
4852.417494156581,
5030.605762224003,
5188.189934895622,
5325.534914122338,
5443.09263837178,
5541.242553984026,
5618.90577736613,
5676.236907932582,
5713.888190719548,
5732.534586159165,
5732.86495909997,
5715.574885893962,
5681.36094375709,
5630.9160699169925,
5564.925901497737,
5484.066035205341,
5389.000145878038,
5280.378724862097,
5158.837575120273,
5024.997562256731,
4879.464325002761,
4722.828248872601,
4555.664643661249,
4378.5340821281625,
4191.982877561836,
3996.5436779209795,
3792.736120254283,
3581.0674601304822,
3362.033337589672,
3136.118508890403,
2903.7976804771833,
2665.5363028039774,
2421.7913343679047,
2173.0119947810686,
1919.6405257067795,
1662.112978487845,
1400.8600472937983,
1136.3079666141089,
868.879350250377,
598.9934199271196,
327.0671904810666,
53.51603881195974,
-221.24548255597884,
-496.80285092453784,
-772.7408343512151,
-1048.6427902074117,
-1324.089983515152,
-1598.6609072113317,
-1871.9305864890914,
-2143.4698493663327,
-2412.844545630072,
-2679.6146963066526,
-2943.3335558073722,
-3203.5466173787995,
-3459.7921946144265,
-3711.5996046763316,
-3958.4888150194865,
-4199.969904573075,
-4435.541983709956,
-4664.692315907277,
-4886.895610974244,
-5101.613459723436,
-5308.293879961679,
-5506.370943676781,
-5695.264455296478,
-5874.379650895398,
-6043.106888226734,
-6200.821297454356,
-6346.882362461978,
-6480.6334026152645,
-6601.40092485322,
-6708.495353940525,
-6801.2201480719405,
-6878.865365036504,
-6940.710685138013,
-6986.028617127008,
-7014.087693012296,
-7024.156390965365,
-7015.508009775797,
-6987.426508285595,
-6939.2134923678595,
-6870.194143722704,
-6778.507600810039,
-6662.541536333676,
-6521.777719615316,
-6355.780721709816,
-6164.2156050957265,
-5946.866030197832,
-5703.652904544616,
-5434.655502379511,
-5140.133623851696,
-4820.549214053933,
-4476.5858621745765,
-4109.164601031916,
-3719.4544272575095,
-3308.8853143539272,
-2879.1520371839574,
-2432.214695005346,
-1970.2813552308603,
-1495.7774530477213,
-1011.3216534495583,
-519.7002593654363,
-23.832157362708163,
473.2837076009268,
968.6463368986477,
1459.3505321667049,
1942.5318485382932,
2415.4510265484705,
2875.556010516545,
3320.506661184559,
3748.198094514585,
4156.77341080537,
4544.6293483708505,
4910.4183960221735,
5253.050898594135,
5571.693465093883,
5865.721628756655,
6134.721951629297,
6378.472308635085,
6596.919784917529,
6790.160531297423,
6958.420246564514,
7102.035687686537,
7221.4375204547005,
7316.663337244423,
7386.65084386547,
7431.893115821661,
7453.0698280472925,
7450.897321109322,
7426.119729440364,
7379.501270758323,
7311.820069742575,
7223.862698043761,
7116.419458989712,
6990.280549179927,
6846.233229160697,
6685.060133262135,
6507.537093361739,
6314.430706180539,
6106.498300754951,
5884.487007982855,
5649.133354700521,
5401.162990511237,
5141.2905829735655,
4870.219916758211,
4588.6442323815845,
4297.246840124377,
3996.7016279584977,
3687.672943442506,
3370.816713471332,
3046.7809391622377,
2716.2064884693173,
2379.7278238167414,
2037.9736866813107,
1691.5677739341816,
1341.1294407525402,
987.274464910137,
630.6159072584708,
271.7651032075717,
-88.66720063937115,
-450.07090828521183,
-811.8357448402178,
-1173.3500575470596,
-1534.0002171862902,
-1893.169757469542,
-2250.238597789166,
-2604.582329974649,
-2955.5715497041388,
-3302.5712132214585,
-3644.9400000076153,
-3982.0296620564454,
-4313.1843404041265,
-4637.739829561369,
-4955.022770498422,
-5264.349858508805,
-5565.02894989203,
-5856.3576838711515,
-6137.623366377648,
-6408.1028210940085,
-6667.062088921161,
-6913.756442570832,
-7147.4306942507355,
-7367.31977440838,
-7572.649559501451,
-7762.637926761234,
-7936.496013916589,
-8093.429661844874,
-8232.641018117287,
-8353.330279405462,
-8454.697550716264,
-8535.946794434212,
-8596.294778970558,
-8634.969996072314,
-8651.219472693572,
-8644.315094433372,
-8613.560610264087,
-8558.29955099045,
-8477.365597924792,
-8368.629630331561,
-8231.524325422732,
-8065.667620887417,
-7870.7798235036935,
-7646.698329716853,
-7393.391126567547,
-7110.97150002102,
-6799.713517837531,
-6460.067282508887,
-6092.6729497850765,
-5698.37250831565,
-5278.218315929916,
-4833.477388080416,
-4365.638883730688,
-3876.414508399302,
-3367.7358724643755,
-2841.744416899491,
-2300.767203498261,
-1747.297678157779,
-1183.9773839055888,
-613.5744810466747,
-38.954931807896685,
536.9577931449797,
1111.2386289401215,
1681.0332544600285,
2243.5202602208988,
2795.9476233147616,
3335.7045577044755,
3860.3357171402113,
4367.56443133306,
4855.307962124547,
5321.68709777867,
5765.032403524536,
6183.88944647593,
6577.0253130562805,
6943.434202852454,
7282.296499214983,
7592.98561202928,
7875.059973731593,
8128.246617878707,
8352.427424830486,
8547.62501347358,
8713.988750378478,
8851.781084393804,
8961.36447478559,
9042.242843134205,
9093.668802243274,
9116.23668110842,
9110.609543356617,
9077.503177761662,
9017.677333060974,
8931.928500405325,
8821.083643637518,
8685.994148399357,
8527.53030580135,
8346.576656227106,
8144.028518844368,
7920.790032394727,
7677.771607841014,
7415.884335393386,
7136.040833878056,
6839.153075198672,
6526.131406800462,
6197.8835649323455,
5855.313765815692,
5499.321989462649,
5130.803570885562,
4750.649213440984,
4359.7455390498135,
3958.976039218502,
3549.2187916932035,
3131.3477509577897,
2706.233436050391,
2274.7432078785523,
1837.7418185615793,
1396.0918663437842,
950.6542067794655,
502.28837089286105,
51.85304101393701,
-399.7933650077844,
-851.7919505091031,
-1303.2823869379342,
-1753.4019313259157,
-2201.286895167361,
-2646.071018361386,
-3086.8848320426428,
-3522.855396157589,
-3953.105891828186,
-4376.7553111023335,
-4792.918238718218,
-5200.7047205095105,
-5599.220213079678,
-5987.565609371384,
-6364.837334759317,
-6730.127508292996,
-7082.524163717662,
-7421.111642801323,
-7744.972250038648,
-8053.186798724357,
-8344.835595315133,
-8618.99979457899,
-8874.763039817377,
-9111.213474889693,
-9327.446133131225,
-9522.565708255206,
-9695.689712329864,
-9845.952025921495,
-9972.506845493883,
-10074.5330331555,
-10151.238873844894,
-10201.867245045276,
-10225.701204119156,
-10222.070439274063,
-10190.361990798609,
-10129.853762803024,
-10038.427841584415,
-9915.279550108704,
-9760.108776139592,
-9572.720953342165,
-9353.037128738022,
-9101.10383088987,
-8817.10314177993,
-8501.362623261826,
-8154.364690471191,
-7776.755024581109,
-7369.349617287147,
-6933.140039408128,
-6469.297029976576,
-5979.175292300937,
-5464.308255125287,
-4926.414162513929,
-4367.384724222437,
-3789.2731704218677,
-3194.2839139233915,
-2584.761981434948,
-1963.1803753802117,
-1332.1235278122024,
-694.2650079512025,
-52.33764487917391,
590.9057730780814,
1232.7302100421678,
1870.3713413090686,
2501.1101814049143,
3122.294319696382,
3731.358958645686,
4325.848152368505,
4903.431058550666,
5461.914681665608,
5999.25458543262,
6513.565052456316,
7003.130168988293,
7466.4173127510985,
7902.088461523484,
8308.958311600876,
8686.017641112336,
9032.430485253286,
9347.527344168882,
9630.79874385072,
9881.886043613942,
10100.571092101947,
10286.76633376745,
10440.506967777248,
10561.946761290075,
10651.31312355685,
10707.508793086632,
10730.270242716188,
10720.134512344057,
10677.716929011136,
10603.70301624867,
10498.84095540002,
10363.935692645038,
10199.842635095218,
10007.461311244997,
9787.729491733067,
9541.618266365947,
9270.128573357806,
8974.289676739809,
8655.154763092845,
8313.793063791787,
7951.291725667589,
7568.752032306485,
7167.288031325886,
6748.0247841412975,
6312.096446409577,
5860.644399465427,
5394.815653066833,
4915.761739765066,
4424.638321216302,
3922.605726749461,
3410.830642892528,
2890.482102222573,
2362.72743095064,
1828.7378885046205,
1289.6863967870145,
746.7475107075926,
201.09723227876944,
-346.0872768344626,
-893.6280294183048,
-1440.3465385734532,
-1985.0640804492239,
-2526.6018389518254,
-3063.7808775385856,
-3595.421883202671,
-4120.345740449001,
-4637.376549160767,
-5145.33966086568,
-5643.062875239662,
-6129.37746192718,
-6603.119167069213,
-7063.129292770676,
-7508.255895810212,
-7937.355151893713,
-8349.292931756063,
-8742.94663541105,
-9117.207330855153,
-9470.982243524337,
-9803.197626134299,
-10112.800713605553,
-10398.762689705189,
-10660.082706507046,
-10895.7912717085,
-11104.954171701349,
-11286.676743135598,
-11440.108418673553,
-11564.447559546741,
-11658.946615786355,
-11722.917532000287,
-11755.737435738292,
-11756.854541352495,
-11725.782492813427,
-11660.828501253043,
-11560.81238424088,
-11425.539383808895,
-11254.924056362363,
-11048.996740417788,
-10807.909781896647,
-10531.943313286512,
-10221.510598936384,
-9877.162972715556,
-9499.594175279954,
-9089.642330852514,
-8648.294170180357,
-8176.6853468325235,
-7676.099499101662,
-7147.96690335524,
-6593.862308017045,
-6015.501537364339,
-5414.736454321438,
-4793.547871434016,
-4154.035999206022,
-3498.408020983579,
-2828.9623835669076,
-2148.0826917787067,
-1458.2315212725996,
-761.9237889723694,
-61.71489989746716,
639.8163167294101,
1340.0834854377401,
2036.5078862726834,
2726.5334912977164,
3407.642346770435,
4077.370811922639,
4733.327165275168,
5373.208629129074,
5994.798569367785,
6595.986106599416,
7174.775590478549,
7729.295274125195,
8257.804271800342,
8758.696958427558,
9230.50571237662,
9671.90290292278,
10081.70302379812,
10458.865874249115,
10802.50115432837,
11111.855510914607,
11386.310594797756,
11625.38612276546,
11828.733483117252,
11996.1314003335,
12127.481144806967,
12222.801458475948,
12281.942832760222,
12303.505668013993,
12287.569282802111,
12234.580777621648,
12145.083281258558,
12019.709988279998,
11859.17934943122,
11664.289580022676,
11435.91285760828,
11174.98974429192,
10882.52436899783,
10559.580905041616,
10207.281276289148,
9826.79668322067,
9419.345780082886,
8986.189594549754,
8528.629035063283,
8048.002586293604,
7545.683195838178,
7023.074705545149,
6481.608180847961,
5922.738491496383,
5347.941497071412,
4758.712190667759,
4156.564154133248,
3543.030678246885,
2919.665962824224,
2288.025412681251,
1649.6744524169646,
1006.1883342201428,
359.1483592460423,
-289.8598904186712,
-939.2484570894667,
-1587.4290110500992,
-2232.8149132920275,
-2873.8232983633416,
-3508.877138265618,
-4136.407248337096,
-4754.854196058548,
-5362.670073721785,
-5958.320620041348,
-6540.2906147370395,
-7107.084096994782,
-7657.226297663791,
-8189.2676211663975,
-8701.787098017028,
-9193.395464750736,
-9662.73804861966,
-10108.497634408703,
-10529.397490726491,
-10924.204733126104,
-11291.734201407937,
-11630.85302846138,
-11940.486077998452,
-12219.622428533412,
-12467.317314825203,
-12682.686216094675,
-12864.921340148967,
-13013.292363044493,
-13127.150460562854,
-13205.93253446733,
-13249.165298363341,
-13256.469289994293,
-13226.693807239315,
-13158.36995981761,
-13051.418634908337,
-12905.88682708958,
-12721.939396593169,
-12499.860228744721,
-12240.054779347625,
-11943.052394246244,
-11609.507442399117,
-11240.199483565257,
-10836.032691703384,
-10398.034755187578,
-9927.355474939532,
-9425.265281580096,
-8893.153892700979,
-8332.528923852566,
-7745.005572924492,
-7132.303534346728,
-6496.243758220996,
-5838.741071239712,
-5161.797276540059,
-4467.493783335877,
-3757.983765775592,
-3035.4838504735853,
-2302.2653321647813,
-1560.6449169281957,
-812.9749924293694,
-61.633424628322686,
690.9871195968636,
1442.486794599879,
2190.468951806075,
2932.5504066386106,
3666.3704108293555,
4389.601866128764,
5099.960799048941,
5795.214542661,
6473.189071556925,
7131.77593608626,
7768.9392419816295,
8382.723121481331,
8971.260142062407,
9532.781098895844,
10065.625537580216,
10568.22599469257,
11039.11989150088,
11476.960447800664,
11880.515725638495,
12248.671089149528,
12580.431279136932,
12874.921776587416,
13131.389807933885,
13349.204859597501,
13527.85873738301,
13666.965266132189,
13766.259543532506,
13825.589778577356,
13843.690838827206,
13819.747206857935,
13753.975335782965,
13646.710791894442,
13498.405786548949,
13309.626372462953,
13081.049287453987,
12813.458805553297,
12507.743545729416,
12164.89319035207,
11785.995123989842,
11372.22836305473,
10924.861805495413,
10445.251194352508,
9934.834821996656,
9395.128452471496,
8827.719939367758,
8234.26401664981,
7616.477739861062,
6976.137055130567,
6315.074973404016,
5635.181827325977,
4938.396160833807,
4226.6838839995635,
3502.052752560892,
2766.541615041006,
2022.2154181201934,
1271.1601422776898,
515.4775775919759,
-242.72000478250743,
-1001.3152299497979,
-1758.1909899039122,
-2511.2357519391458,
-3258.3487493292632,
-3997.444998756482
]
},
{
"cauto": false,
"cmax": 1,
"cmin": 1,
"colorscale": [
[
0,
"#204a87"
],
[
1,
"#204a87"
]
],
"name": "Earth",
"showscale": false,
"type": "surface",
"uid": "bd507857-b277-11e8-b505-a0afbda902ef",
"x": [
[
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
[
2070.9775685349646,
1958.7662914956272,
1634.2922933821865,
1132.717366623061,
508.3949372694737,
-171.0199720974995,
-831.9022138394263,
-1402.6349424190162,
-1821.3704108639388,
-2042.7321338179481,
-2042.7321338179484,
-1821.3704108639388,
-1402.6349424190173,
-831.9022138394275,
-171.0199720975005,
508.39493726947273,
1132.7173666230603,
1634.2922933821858,
1958.7662914956272,
2070.9775685349646
],
[
3917.5325829912545,
3705.269861917151,
3091.4836581186883,
2142.6872306516607,
961.6973945255617,
-323.507276569953,
-1573.6549145165156,
-2653.272624703365,
-3445.3670762369647,
-3864.102544681887,
-3864.1025446818876,
-3445.3670762369647,
-2653.2726247033675,
-1573.654914516518,
-323.50727656995485,
961.69739452556,
2142.687230651659,
3091.483658118687,
3705.269861917151,
3917.5325829912545
],
[
5339.562155299338,
5050.2499496143155,
4213.664799186625,
2920.4636860211895,
1310.7850168122338,
-440.9375478934551,
-2144.8776874338914,
-3616.387048334464,
-4696.004758521314,
-5266.737487100903,
-5266.737487100904,
-4696.004758521314,
-3616.3870483344667,
-2144.877687433894,
-440.93754789345763,
1310.7850168122313,
2920.463686021187,
4213.664799186623,
5050.2499496143155,
5339.562155299338
],
[
6182.9673162373765,
5847.957092568811,
4879.229977516816,
3381.7625853471986,
1517.8287436021724,
-510.58539405170535,
-2483.6696817114025,
-4187.60982125184,
-5437.757459198403,
-6098.63970094033,
-6098.639700940331,
-5437.757459198403,
-4187.609821251843,
-2483.669681711406,
-510.58539405170836,
1517.8287436021697,
3381.762585347196,
4879.229977516814,
5847.957092568811,
6182.9673162373765
],
[
6356.352029838285,
6011.947344139877,
5016.054878729385,
3476.5950350978,
1560.3921744832594,
-524.9033902157762,
-2553.317527869653,
-4305.040092575342,
-5590.2447636708575,
-6269.65967303783,
-6269.6596730378305,
-5590.2447636708575,
-4305.040092575346,
-2553.3175278696567,
-524.9033902157793,
1560.3921744832564,
3476.5950350977973,
5016.054878729383,
6011.947344139877,
6356.352029838285
],
[
5840.927372042378,
5524.449815998859,
4609.31240172086,
3194.6844678654465,
1433.8629012798515,
-482.33995933468935,
-2346.2738010797148,
-3955.95247028867,
-5136.9423064147695,
-5761.264735768357,
-5761.264735768358,
-5136.9423064147695,
-3955.952470288674,
-2346.273801079718,
-482.33995933469214,
1433.8629012798488,
3194.684467865444,
4609.312401720858,
5524.449815998859,
5840.927372042378
],
[
4692.547602159432,
4438.292429623361,
3703.07940513492,
2566.580267902913,
1151.9523340474977,
-387.50750958408815,
-1884.974901753706,
-3178.1760149191423,
-4126.972442386172,
-4628.547369145297,
-4628.547369145297,
-4126.972442386172,
-3178.176014919145,
-1884.9749017537085,
-387.5075095840904,
1151.9523340474955,
2566.580267902911,
3703.0794051349185,
4438.292429623361,
4692.547602159432
],
[
3035.6574872043448,
2871.1771912954932,
2395.560295805413,
1660.3472713169713,
745.2098570389732,
-250.68260837151965,
-1219.4097234235148,
-2055.994873851206,
-2669.7810776496694,
-2994.25507576311,
-2994.2550757631107,
-2669.7810776496694,
-2055.9948738512076,
-1219.4097234235167,
-250.6826083715211,
745.2098570389717,
1660.3472713169701,
2395.560295805412,
2871.1771912954932,
3035.6574872043448
],
[
1049.806780431554,
992.9253533863962,
828.4450574775444,
574.1898849414736,
257.7123288979541,
-86.69235680045416,
-421.70258046901955,
-711.014786154042,
-923.2775072281457,
-1035.488784267483,
-1035.4887842674832,
-923.2775072281457,
-711.0147861540426,
-421.7025804690202,
-86.69235680045466,
257.7123288979536,
574.1898849414731,
828.4450574775441,
992.9253533863962,
1049.806780431554
],
[
-1049.8067804315524,
-992.9253533863947,
-828.4450574775432,
-574.1898849414728,
-257.71232889795374,
86.69235680045404,
421.7025804690189,
711.0147861540408,
923.2775072281444,
1035.4887842674814,
1035.4887842674816,
923.2775072281444,
711.0147861540415,
421.7025804690195,
86.69235680045453,
-257.7123288979532,
-574.1898849414723,
-828.4450574775428,
-992.9253533863947,
-1049.8067804315524
],
[
-3035.6574872043434,
-2871.177191295492,
-2395.560295805412,
-1660.3472713169708,
-745.2098570389728,
250.68260837151956,
1219.4097234235144,
2055.9948738512053,
2669.7810776496685,
2994.255075763109,
2994.2550757631093,
2669.7810776496685,
2055.994873851207,
1219.4097234235162,
250.682608371521,
-745.2098570389714,
-1660.3472713169695,
-2395.560295805411,
-2871.177191295492,
-3035.6574872043434
],
[
-4692.547602159429,
-4438.292429623359,
-3703.079405134917,
-2566.580267902911,
-1151.9523340474968,
387.50750958408787,
1884.9749017537044,
3178.17601491914,
4126.972442386168,
4628.5473691452935,
4628.547369145294,
4126.972442386168,
3178.1760149191427,
1884.9749017537072,
387.50750958409014,
-1151.9523340474948,
-2566.580267902909,
-3703.0794051349158,
-4438.292429623359,
-4692.547602159429
],
[
-5840.927372042376,
-5524.449815998857,
-4609.312401720859,
-3194.6844678654456,
-1433.862901279851,
482.33995933468924,
2346.2738010797143,
3955.9524702886692,
5136.942306414769,
5761.264735768355,
5761.264735768356,
5136.942306414769,
3955.9524702886724,
2346.2738010797175,
482.339959334692,
-1433.8629012798485,
-3194.6844678654434,
-4609.312401720857,
-5524.449815998857,
-5840.927372042376
],
[
-6356.352029838285,
-6011.947344139877,
-5016.054878729385,
-3476.5950350978,
-1560.3921744832594,
524.9033902157762,
2553.317527869653,
4305.040092575342,
5590.2447636708575,
6269.65967303783,
6269.6596730378305,
5590.2447636708575,
4305.040092575346,
2553.3175278696567,
524.9033902157793,
-1560.3921744832564,
-3476.5950350977973,
-5016.054878729383,
-6011.947344139877,
-6356.352029838285
],
[
-6182.967316237377,
-5847.957092568812,
-4879.229977516816,
-3381.762585347199,
-1517.8287436021726,
510.5853940517054,
2483.669681711403,
4187.60982125184,
5437.757459198404,
6098.639700940331,
6098.639700940332,
5437.757459198404,
4187.609821251844,
2483.6696817114066,
510.5853940517084,
-1517.8287436021699,
-3381.7625853471964,
-4879.229977516815,
-5847.957092568812,
-6182.967316237377
],
[
-5339.562155299339,
-5050.249949614317,
-4213.664799186626,
-2920.46368602119,
-1310.7850168122343,
440.9375478934552,
2144.877687433892,
3616.387048334465,
4696.004758521315,
5266.737487100905,
5266.737487100906,
4696.004758521315,
3616.3870483344676,
2144.877687433895,
440.93754789345775,
-1310.7850168122318,
-2920.4636860211876,
-4213.664799186624,
-5050.249949614317,
-5339.562155299339
],
[
-3917.532582991256,
-3705.2698619171524,
-3091.483658118689,
-2142.6872306516616,
-961.6973945255621,
323.5072765699531,
1573.654914516516,
2653.272624703366,
3445.367076236966,
3864.1025446818885,
3864.102544681889,
3445.367076236966,
2653.2726247033684,
1573.6549145165184,
323.507276569955,
-961.6973945255603,
-2142.68723065166,
-3091.4836581186883,
-3705.2698619171524,
-3917.532582991256
],
[
-2070.977568534966,
-1958.7662914956288,
-1634.2922933821878,
-1132.7173666230622,
-508.39493726947416,
171.01997209749965,
831.902213839427,
1402.6349424190175,
1821.3704108639404,
2042.7321338179497,
2042.7321338179502,
1821.3704108639404,
1402.6349424190187,
831.9022138394282,
171.01997209750064,
-508.3949372694732,
-1132.7173666230613,
-1634.2922933821872,
-1958.7662914956288,
-2070.977568534966
],
[
-1.5621929143429165e-12,
-1.4775489932480931e-12,
-1.2327897122000057e-12,
-8.544385371317645e-13,
-3.8349568858537574e-13,
1.2900486836795326e-13,
6.275257461168375e-13,
1.0580444722087668e-12,
1.373907662485362e-12,
1.5408866391577764e-12,
1.5408866391577766e-12,
1.373907662485362e-12,
1.0580444722087678e-12,
6.275257461168384e-13,
1.2900486836795402e-13,
-3.83495688585375e-13,
-8.544385371317639e-13,
-1.2327897122000053e-12,
-1.4775489932480931e-12,
-1.5621929143429165e-12
]
],
"y": [
[
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
[
0,
672.4453172381089,
1272.020750289313,
1733.7529976111107,
2007.6062056921824,
2063.9041301666034,
1896.5460173652668,
1523.6677156388778,
985.6763747824739,
340.8717043736032,
-340.8717043736027,
-985.6763747824734,
-1523.6677156388766,
-1896.5460173652662,
-2063.9041301666034,
-2007.6062056921826,
-1733.752997611111,
-1272.0207502893134,
-672.4453172381094,
-5.072432100824625e-13
],
[
0,
1272.0207502893131,
2406.1983148492195,
3279.626955981496,
3797.6571277777143,
3904.152223057449,
3587.5718458054816,
2882.222392147741,
1864.539420012481,
644.8046704088713,
-644.8046704088703,
-1864.5394200124804,
-2882.222392147739,
-3587.5718458054803,
-3904.152223057449,
-3797.657127777715,
-3279.6269559814964,
-2406.1983148492204,
-1272.0207502893138,
-9.595187476631407e-13
],
[
0,
1733.7529976111107,
3279.6269559814955,
4470.102445015823,
5176.172973346762,
5321.324843416592,
4889.8285978399235,
3928.443550179085,
2541.350687774138,
878.8630452300076,
-878.8630452300063,
-2541.350687774137,
-3928.4435501790817,
-4889.828597839922,
-5321.324843416592,
-5176.172973346763,
-4470.102445015824,
-3279.6269559814964,
-1733.7529976111118,
-1.3078155404671353e-12
],
[
0,
2007.6062056921824,
3797.657127777714,
5176.172973346762,
5993.770160654701,
6161.849348129235,
5662.196547790195,
4548.95689346632,
2942.767175396611,
1017.6829721352607,
-1017.6829721352591,
-2942.7671753966097,
-4548.9568934663175,
-5662.196547790194,
-6161.849348129235,
-5993.770160654702,
-5176.172973346763,
-3797.6571277777152,
-2007.6062056921837,
-1.5143902266125608e-12
],
[
0,
2063.9041301666034,
3904.152223057449,
5321.324843416592,
6161.849348129236,
6334.641865028304,
5820.977643755634,
4676.520173007722,
3025.2891778274434,
1046.2211580313447,
-1046.221158031343,
-3025.289177827442,
-4676.520173007719,
-5820.977643755632,
-6334.641865028304,
-6161.849348129237,
-5321.324843416593,
-3904.1522230574506,
-2063.904130166605,
-1.5568572335190474e-12
],
[
0,
1896.5460173652668,
3587.571845805481,
4889.8285978399235,
5662.196547790195,
5820.977643755633,
5348.9654902458315,
4297.309928116757,
2779.9741556424556,
961.3850476608397,
-961.3850476608383,
-2779.974155642454,
-4297.309928116753,
-5348.96549024583,
-5820.977643755633,
-5662.196547790196,
-4889.828597839924,
-3587.5718458054826,
-1896.5460173652682,
-1.430614602044772e-12
],
[
0,
1523.667715638878,
2882.222392147741,
3928.4435501790854,
4548.956893466321,
4676.520173007722,
4297.309928116757,
3452.419472880565,
2233.405797950153,
772.3679499502728,
-772.3679499502715,
-2233.405797950152,
-3452.4194728805624,
-4297.309928116756,
-4676.520173007722,
-4548.956893466322,
-3928.443550179086,
-2882.222392147742,
-1523.667715638879,
-1.1493426801662274e-12
],
[
0,
985.676374782474,
1864.539420012481,
2541.3506877741384,
2942.767175396611,
3025.289177827443,
2779.9741556424556,
2233.405797950153,
1444.8132671883818,
499.65280033904173,
-499.652800339041,
-1444.8132671883811,
-2233.4057979501513,
-2779.9741556424547,
-3025.289177827443,
-2942.7671753966115,
-2541.350687774139,
-1864.5394200124817,
-985.6763747824746,
-7.435216450024997e-13
],
[
0,
340.8717043736033,
644.8046704088712,
878.8630452300076,
1017.6829721352607,
1046.2211580313447,
961.3850476608397,
772.3679499502726,
499.65280033904173,
172.79251689906815,
-172.7925168990679,
-499.6528003390415,
-772.3679499502721,
-961.3850476608395,
-1046.2211580313447,
-1017.6829721352608,
-878.8630452300079,
-644.8046704088714,
-340.8717043736035,
-2.5712850267573817e-13
],
[
0,
-340.87170437360277,
-644.8046704088703,
-878.8630452300064,
-1017.6829721352591,
-1046.221158031343,
-961.3850476608383,
-772.3679499502715,
-499.652800339041,
-172.7925168990679,
172.7925168990676,
499.65280033904077,
772.3679499502709,
961.385047660838,
1046.221158031343,
1017.6829721352593,
878.8630452300065,
644.8046704088705,
340.871704373603,
2.5712850267573776e-13
],
[
0,
-985.6763747824737,
-1864.5394200124804,
-2541.3506877741374,
-2942.76717539661,
-3025.289177827442,
-2779.9741556424547,
-2233.405797950152,
-1444.8132671883811,
-499.65280033904156,
499.6528003390408,
1444.8132671883807,
2233.4057979501504,
2779.9741556424538,
3025.289177827442,
2942.7671753966106,
2541.350687774138,
1864.539420012481,
985.6763747824742,
7.435216450024994e-13
],
[
0,
-1523.6677156388769,
-2882.222392147739,
-3928.443550179082,
-4548.9568934663175,
-4676.520173007719,
-4297.309928116754,
-3452.4194728805624,
-2233.4057979501513,
-772.3679499502722,
772.3679499502709,
2233.4057979501504,
3452.4194728805596,
4297.309928116752,
4676.520173007719,
4548.956893466318,
3928.443550179083,
2882.22239214774,
1523.6677156388778,
1.1493426801662265e-12
],
[
0,
-1896.5460173652664,
-3587.5718458054803,
-4889.828597839923,
-5662.196547790194,
-5820.977643755632,
-5348.96549024583,
-4297.309928116755,
-2779.9741556424547,
-961.3850476608395,
961.385047660838,
2779.9741556424538,
4297.309928116752,
5348.965490245829,
5820.977643755632,
5662.196547790195,
4889.8285978399235,
3587.5718458054816,
1896.5460173652677,
1.4306146020447716e-12
],
[
0,
-2063.9041301666034,
-3904.152223057449,
-5321.324843416592,
-6161.849348129236,
-6334.641865028304,
-5820.977643755634,
-4676.520173007722,
-3025.2891778274434,
-1046.2211580313447,
1046.221158031343,
3025.289177827442,
4676.520173007719,
5820.977643755632,
6334.641865028304,
6161.849348129237,
5321.324843416593,
3904.1522230574506,
2063.904130166605,
1.5568572335190474e-12
],
[
0,
-2007.6062056921826,
-3797.6571277777143,
-5176.172973346763,
-5993.770160654702,
-6161.849348129236,
-5662.196547790196,
-4548.956893466321,
-2942.7671753966115,
-1017.6829721352608,
1017.6829721352592,
2942.76717539661,
4548.9568934663175,
5662.196547790194,
6161.849348129236,
5993.7701606547025,
5176.172973346764,
3797.6571277777157,
2007.606205692184,
1.514390226612561e-12
],
[
0,
-1733.7529976111111,
-3279.6269559814964,
-4470.102445015825,
-5176.172973346764,
-5321.324843416593,
-4889.828597839924,
-3928.443550179086,
-2541.350687774139,
-878.8630452300079,
878.8630452300065,
2541.3506877741374,
3928.4435501790826,
4889.8285978399235,
5321.324843416593,
5176.172973346765,
4470.102445015826,
3279.6269559814973,
1733.7529976111123,
1.3078155404671357e-12
],
[
0,
-1272.0207502893136,
-2406.1983148492204,
-3279.626955981497,
-3797.6571277777157,
-3904.1522230574506,
-3587.571845805483,
-2882.222392147742,
-1864.5394200124817,
-644.8046704088715,
644.8046704088705,
1864.539420012481,
2882.22239214774,
3587.5718458054816,
3904.1522230574506,
3797.657127777716,
3279.6269559814973,
2406.1983148492213,
1272.0207502893143,
9.595187476631409e-13
],
[
0,
-672.4453172381095,
-1272.020750289314,
-1733.752997611112,
-2007.606205692184,
-2063.9041301666052,
-1896.5460173652684,
-1523.6677156388791,
-985.6763747824748,
-340.87170437360356,
340.87170437360305,
985.6763747824743,
1523.6677156388778,
1896.5460173652677,
2063.9041301666052,
2007.6062056921844,
1733.7529976111123,
1272.0207502893145,
672.44531723811,
5.07243210082463e-13
],
[
0,
-5.072432100824626e-13,
-9.595187476631405e-13,
-1.3078155404671353e-12,
-1.5143902266125608e-12,
-1.5568572335190474e-12,
-1.430614602044772e-12,
-1.1493426801662272e-12,
-7.435216450024997e-13,
-2.5712850267573817e-13,
2.5712850267573776e-13,
7.435216450024993e-13,
1.1493426801662263e-12,
1.4306146020447714e-12,
1.5568572335190474e-12,
1.514390226612561e-12,
1.3078155404671357e-12,
9.595187476631409e-13,
5.072432100824629e-13,
3.826269104401456e-28
]
],
"z": [
[
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366
],
[
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864
],
[
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782,
5033.245965523782
],
[
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239,
3488.510065623239
],
[
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561,
1565.739970301561
],
[
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267,
-526.7023457611267
],
[
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456,
-2562.0682900316456
],
[
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595,
-4319.7943804916595
],
[
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402
],
[
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607,
-6291.147130056607
],
[
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608,
-6291.147130056608
],
[
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402,
-5609.403721309402
],
[
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663,
-4319.794380491663
],
[
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649,
-2562.068290031649
],
[
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298,
-526.7023457611298
],
[
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558,
1565.739970301558
],
[
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236,
3488.510065623236
],
[
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378,
5033.24596552378
],
[
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864,
6032.551566201864
],
[
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366,
6378.1366
]
]
}
],
"layout": {
"autosize": true,
"scene": {
"aspectmode": "data",
"xaxis": {
"title": "x (km)"
},
"yaxis": {
"title": "y (km)"
},
"zaxis": {
"title": "z (km)"
}
}
}
},
"text/html": [
"<div id=\"d35cb9d8-a09b-41c2-b214-8494e23f9b57\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"d35cb9d8-a09b-41c2-b214-8494e23f9b57\", [{\"line\": {\"dash\": \"solid\", \"width\": 5}, \"mode\": \"lines\", \"name\": \"orbit with artificial thrust\", \"x\": [25298.4, 25129.95094301439, 24629.473005922027, 23811.06399499985, 22696.654854560566, 21313.9848693321, 19694.427888362825, 17870.86426367357, 15875.970445808323, 13740.902604479561, 11494.428404909075, 9162.582761252159, 6768.391050500591, 4331.976246042497, 1870.723137333599, -600.4766510219104, -3068.9566022446206, -5523.989582266942, -7956.528317196312, -10358.96863016055, -12724.938763101389, -15049.110451277978, -17327.038293213045, -19555.0268240598, -21730.01457645421, -23849.4644894363, -25911.281416495345, -27913.75331287907, -29855.48100525764, -31735.329571718663, -33552.386524054906, -35305.92692542771, -36995.382170397854, -38620.31161901917, -40180.384994708635, -41675.362556762455, -43105.079905868755, -44469.43343409295, -45768.36905028565, -47001.87347980603, -48169.96722371393, -49272.698262584396, -50310.13559009795, -51282.36166156018, -52189.46360936794, -53031.54007330621, -53808.6949358683, -54521.03266252771, -55168.65716534234, -55751.66910235091, -56270.16403305576, -56724.23124971992, -57113.95310420657, -57439.40465008878, -57700.653419757466, -57897.75915625567, -58030.773319567146, -58099.738187087096, -58104.68536800328, -58045.63355131515, -57922.58717282707, -57735.5521370458, -57484.52211171138, -57169.48292868814, -56790.414269747525, -56347.28918469497, -55840.07439959519, -55268.731323835476, -54633.217664766256, -53933.489558658635, -53169.50412671837, -52341.222364895504, -51448.61227622951, -50491.65215446916, -49470.33392770672, -48384.666470765835, -47234.678795082946, -46020.42302482095, -44741.97706795563, -43399.44691434107, -41992.99621188644, -40522.85940424613, -38989.326276037726, -37392.765333584364, -35733.63815141309, -34012.51609700226, -32230.09939598289, -30387.238664475757, -28484.961246145704, -26524.50101779782, -24507.33262971259, -22435.211745458397, -20310.22171516925, -18134.82687744492, -15911.93483271752, -13644.968207335696, -11337.946625811837, -8995.580628273095, -6623.378197754873, -4227.764587285679, -1816.215438414089, 602.5981021741972, 3018.659821025472, 5420.437363558957, 7794.725955749116, 10126.507438470911, 12398.838624348067, 14592.794127542471, 16687.50330524782, 18660.289817410674, 20487.008813849996, 22142.565247160226, 23601.59933179125, 24839.459357893382, 25833.48032844497, 26564.075710986384, 27016.03969559695, 27179.613475776237, 27051.16081645905, 26633.469189174168, 25935.559051684497, 24971.935748698023, 23761.640653054652, 22327.032968049614, 20692.397068237977, 18882.931321839802, 16923.863113621777, 14839.427222826092, 12652.506687119168, 10384.267125748678, 8053.932793200496, 5678.769347852585, 3274.203526942929, 853.7947569292202, -1570.5554036466615, -3988.4692648668024, -6390.925648068066, -8770.112424588777, -11119.284509051698, -13432.628411772399, -15705.143666323489, -17932.539841385933, -20111.142850322467, -22237.80429649911, -24309.828787910108, -26324.923879338385, -28281.135084735146, -30176.800766534394, -32010.51181612771, -33781.07703741762, -35487.49185151589, -37128.91055270189, -38704.626131649165, -40214.049738639515, -41656.69356715635, -43032.15536370977, -44340.106035434175, -45580.27950969844, -46752.46397225443, -47856.49361044931, -48892.2399880266, -49859.60237772237, -50758.50777752945, -51588.90657340656, -52350.76646819533, -53044.0699666992, -53668.810453815546, -54224.98937497695, -54712.61429514207, -55131.69761257534, -55482.255703655144, -55764.3082749501, -55977.877698803066, -56122.988108662415, -56199.66403040018, -56207.92832585675, -56147.799224851595, -56019.29231768679, -55822.43538258486, -55557.251579873315, -55223.76749933492, -54822.013186202974, -54352.022309199674, -53813.83320896769, -53207.490715408785, -52533.04862244259, -51790.57270869854, -50980.14419265545, -50101.863510741045, -49155.85430690529, -48142.267522181144, -47061.28547274552, -45913.12580499476, -44698.04521614694, -43416.342828885485, -42068.365514329606, -40654.54653483676, -39175.3940832112, -37631.499308987346, -36023.55408144464, -34352.36616868075, -32618.876549567976, -30824.178753146243, -28969.54018406831, -27056.427700139113, -25086.536398375036, -23061.821263858576, -20984.533829309326, -18857.26339890003, -16682.983215323067, -14465.102754462298, -12207.52577294925, -9914.714951124826, -7591.763110186755, -5244.47098972793, -2879.4310628751964, -504.11637473951276, 1873.0306445458398, 4242.505440685997, 6593.643098069291, 8914.55005759924, 11192.036560046889, 13411.591156286098, 15557.392880214547, 17612.372298664377, 19558.34518774212, 21376.242586522312, 23046.46086811814, 24549.29912292404, 25865.526242417425, 26976.961133057102, 27867.28811104045, 28522.78338910723, 28932.883844463442, 29090.669541457806, 28993.331482643713, 28642.61915208613, 28044.344151350222, 27208.10277730485, 26146.900334905655, 24876.47686331361, 23414.500894596702, 21779.85014509213, 19992.181589942713, 18071.147403300278, 16035.931839616904, 13904.97410301877, 11695.695207627075, 9424.296830980102, 7105.68536423625, 4753.5424235606015, 2380.224221530778, -3.1410417767706216, -2386.5839206685982, -4761.209470659042, -7119.105257100164, -9453.255966275972, -11757.457022749615, -14026.230944761382, -16254.748493811918, -18438.759326282096, -20574.52913642925, -22658.779841857446, -24688.62993871674, -26661.55315274725, -28575.33969867971, -30428.05395030827, -32218.002488588372, -33943.70536272275, -35603.87093005484, -37197.372845716905, -38723.22823550548, -40180.58198703324, -41568.69107880478, -42886.91099228018, -44134.68288567263, -45311.52278614133, -46417.01276404026, -47450.793507231174, -48412.55771347624, -49302.04371891886, -50119.028780666085, -50863.3214335638, -51534.76047506098, -52133.21503883293, -52658.57680087174, -53110.75935347859, -53489.69509004395, -53795.33308211952, -54027.63776908957, -54186.58828074709, -54272.178213082785, -54284.41567759253, -54223.32344441068, -54088.938999575665, -53881.31433673456, -53600.515303593565, -53246.62032342066, -52819.720831174716, -52319.93743324885, -51747.40808588868, -51102.29522964449, -50384.78831699325, -49595.10563722538, -48733.497473411524, -47800.25043633439, -46795.69282027057, -45720.20082550623, -44574.205492472836, -43358.20019238503, -42072.74851926828, -40718.49242825867, -39296.160465060246, -37806.57593144565, -36250.66483168175, -34629.46571252091, -32944.17538263138, -31196.14594956652, -29386.898352590964, -27518.148261817587, -25591.82957853986, -23610.12037625255, -21575.471034293125, -19490.635722119092, -17358.707600178837, -15183.156199843446, -12967.868729665366, -10717.194476104982, -8435.992407721227, -6129.681500072957, -3804.293256123249, -1466.525482223645, 876.2048431708114, 3215.7151483525577, 5542.998334745137, 7848.184340565847, 10120.524089775536, 12348.361730216937, 14519.155806137751, 16619.530117818722, 18635.343109339923, 20551.79409176176, 22353.584653205307, 24025.15360731413, 25551.003380054648, 26916.00359273623, 28105.73284171606, 29106.888251386114, 29907.75621358617, 30498.632690534578, 30872.13179682373, 31023.419918658466, 30950.412628514478, 30653.97165339472, 30137.992721720584, 29408.884515300295, 28475.48906523878, 27348.767767021312, 26041.464004664016, 24567.620153867556, 22942.068858827668, 21180.035333462973, 19296.987437827524, 17308.57825197944, 15229.730218471072, 13074.869363423566, 10857.742910659721, 8591.315510430077, 6287.693307168683, 3958.0860538296315, 1612.8338611568138, -738.5143616581285, -3087.283777438253, -5425.566924206873, -7746.179407592045, -10042.615338230613, -12308.996416536653, -14540.021056869227, -16730.913403042643, -18877.377580649532, -20975.555585720205, -23021.986640681436, -25013.56584857255, -26947.504265629683, -28821.309326957173, -30632.749078677385, -32379.826060769516, -34060.75354655943, -35673.93459032342, -37217.943413212066, -38691.50817406807, -40093.49417170515, -41422.888196619744, -42678.790481386095, -43860.40125472553, -44967.011341863574, -45997.99268734245, -46952.790431797956, -47830.91639676657, -48631.94358829877, -49355.501330161635, -50001.27063740816, -50568.97944109356, -51058.39727491849, -51469.33102636553, -51801.63056721397, -52055.18072054704, -52229.90146230831, -52325.74642284643, -52342.70171955294, -52280.78564885503, -52140.0490893788, -51920.57646809803, -51622.4871412839, -51245.937042071215, -50791.120446455585, -50258.271709537905, -49647.66682383085, -48959.62465144243, -48194.50810068982, -47352.739716655, -46434.79742451044, -45441.22235920652, -44372.62639753037, -43229.695735712856, -42013.19676115766, -40723.98394553594, -39363.00948648838, -37931.334425178255, -36430.14096693815, -34860.74573225128, -33224.613665313474, -31523.372327414498, -29758.826302385598, -27932.97144135189, -26048.008674035602, -24106.35711385147, -22110.676616320656, -20063.946463405257, -17969.436529353374, -15830.741536830741, -13651.814207882664, -11436.995109512818, -9191.043188305723, -6919.16755551075, -4627.058956654446, -2320.9207234157125, -7.498098135412635, 2305.900234295661, 4611.373902534852, 6900.409634857354, 9163.881788033139, 11392.053628259411, 13574.581956204851, 15700.567956300887, 17758.61004438368, 19736.867048207394, 21623.143713304198, 23405.010526667847, 25069.969850731784, 26605.680360121558, 28000.151431506787, 29241.887746007596, 30320.17604505196, 31225.331630881024, 31948.97462237822, 32484.231804314706, 32825.88566503006, 32970.49283952015, 32916.49417490522, 32664.33863524935, 32216.60104891623, 31577.60556758501, 30753.39405999474, 29751.59137644803, 28581.237109339938, 27252.549498944652, 25776.63163790524, 24165.182945828245, 22430.278885385585, 20584.281891330087, 18639.83514438343, 16609.221485946917, 14504.48059480156, 12337.311765328048, 10118.961458075117, 7860.1610779973425, 5571.084955511163, 3261.3252994134964, 939.881643550692, -1384.8403389330936, -3705.0216852787566, -6013.40222326928, -8303.264107317918, -10568.406654642533, -12803.118746552936, -15002.150331623756, -17160.68107725312, -19274.291693903935, -21338.93715629589, -23350.920347537874, -25306.864652200748, -27203.684039625532, -29038.561959290757, -30808.93717670874, -32512.476351865487, -34147.05679104959, -35710.74893396576, -37201.801043127954, -38618.62545731584, -39959.78577087048, -41223.98430061149, -42410.04920215532, -43516.92406553346, -44543.664961007154, -45489.42788167426, -46353.462831586105, -47135.107016498936, -47833.77923328132, -48448.9753091107, -48980.26434428051, -49427.28551243838, -49789.745172077986, -50067.414043103556, -50260.12420228979, -50367.765765181044, -50390.290601125125, -50327.71175798892, -50180.10138506786, -49947.59220470789, -49630.377996047304, -49228.71500320271, -48742.92414362792, -48173.39389237583, -47520.5837179921, -46785.02794577028, -45967.33992409769, -45068.21636962056, -44088.441766959404, -43028.89269870255, -41890.54264872077, -40674.47834294951, -39381.90083288478, -38014.136552660544, -36572.64625803516, -35059.033256714574, -33475.05391689239, -31822.630037258376, -30103.862661738865, -28321.04692222051, -26476.68749251759, -24573.514236835174, -22614.49763599029, -20602.863574643725, -18542.10707280034, -16436.004544836418, -14288.636327566162, -12104.43595157008, -9888.173247124303, -7644.981096664003, -5380.373444932911, -3100.2604731192864, -810.9618650968106, 1480.7852078873718, 3767.821443291313, 6042.553001894759, 8296.959113354405, 10522.615064166508, 12710.690090823433, 14851.99020864466, 16937.006003010505, 18955.959391691275, 20898.857039089642, 22755.55810322267, 24515.863996265038, 26169.637839478273, 27706.961293348522, 29118.24513442138, 30394.287112947008, 31526.4731288991, 32506.901488047144, 33328.55826785625, 33985.444678670014, 34472.66903175195, 34786.516777389515, 34924.51207626531, 34885.48436730163, 34669.65339518279, 34278.735042617685, 33715.67366274823, 32984.59296781168, 32090.801841474156, 31040.68379521685, 29841.59044377116, 28501.68774280391, 27029.78428630566, 25435.170961132746, 23727.501256157666, 21916.74152347667, 20013.174437115773, 18026.964452079825, 15968.202487745144, 13846.870632451632, 11672.739468720207, 9455.310530543986, 7203.769596772714, 4926.951425013526, 2633.3138452339667, 330.9206097940601, -1972.5725216923668, -4269.918977392774, -6554.273329691396, -8819.190860572095, -11058.61792688046, -13266.881859867894, -15438.676300134415, -17569.045172955008, -19653.367942377172, -21687.34507075831, -23666.982577844403, -25588.574593493202, -27448.682798142836, -29244.120212284197, -30971.94853107281, -32629.453873599734, -34214.13555630986, -35723.69280979512, -37156.01300672637, -38509.16111161878, -39781.36996897628, -40971.03104636708, -42076.68524897434, -43097.01342217371, -44030.826797486254, -44877.06699722133, -45634.79786181244, -46303.19893774602, -46881.561555191525, -47369.28484604567, -47765.87256017546, -48070.93057708212, -48284.16501020525, -48405.38080108918, -48434.48070063189, -48371.4645346375, -48216.42865089303, -47969.56583107051, -47631.1695562955, -47201.63386603493, -46681.455524104495, -46071.23663924553, -45371.68766337912, -44583.63115223972, -43708.00619347265, -42745.873407280014, -41698.420424697244, -40566.96774858702, -39352.974902432456, -38058.046772015376, -36683.940045061485, -35232.570815917454, -33706.02743110211, -32106.57349427184, -30436.663276965082, -28698.948439917407, -26896.285728133276, -25031.74672379495, -23108.62900986269, -21130.46809722376, -19101.0494682452, -17024.420089584735, -14904.898747120329, -12747.08455584827, -10555.86299761031, -8336.40883950406, -6094.185285830912, -3834.9443804755065, -1564.7756902582512, 709.9316438778413, 2982.4987901391623, 5245.947961807267, 7493.016419279126, 9716.178606277132, 11907.657151823902, 14059.45744053036, 16163.403311479276, 18211.172132621952, 20194.33367283418, 22104.397193772074, 23932.871183677238, 25671.340155271755, 27311.557314819303, 28845.46792189937, 30265.31920836255, 31563.709907421344, 32733.69615389196, 33768.89188401064, 34663.543820719395, 35412.58897020106, 36011.702555446005, 36457.344312629444, 36746.81107608234, 36878.303577635284, 36851.01538611709, 36665.19762392298, 36321.8610227337, 35822.95909239614, 35171.33662048008, 34370.699322760236, 33425.57065406535, 32341.219002055885, 31123.568829344556, 29779.10932937554, 28314.814161478294, 26738.085830510496, 25056.738276506567, 23278.99125345696, 21413.122579843613, 19467.549803267346, 17450.78017693739, 15371.340113751061, 13237.723117960517, 11058.333904367211, 8841.435833363892, 6595.108790134634, 4327.224636335781, 2045.4473625581284, -242.75058084295054, -2530.2155581753786, -4810.066435092627, -7075.689957574445, -9320.758066433336, -11539.231059752188, -13725.35793266819, -15873.672656139837, -17978.990848279318, -20036.406267078808, -22041.28631808797, -23989.26577061853, -25876.237876030686, -27698.342081668066, -29451.95767811929, -31133.706892228274, -32740.436413754418, -34269.212437112685, -35717.31295637883, -37082.220569840734, -38361.615759215485, -39553.37058834072, -40655.54276615369, -41666.37001877109, -42584.2647154792, -43407.80869344996, -44135.748226311436, -44766.99114474091, -45300.605628655416, -45735.81464213854, -46071.99433465354, -46308.67268906147, -46445.52829736347, -46482.38938119211, -46419.23317407652, -46256.18578250575, -45993.522642814714, -45631.66969091775, -45171.2053619137, -44612.86353658787, -43957.535416921, -43206.268117040745, -42360.27210636666, -41420.92317711944, -40389.76616830193, -39268.51905971408, -38059.077301926154, -36763.518344419295, -35384.10632410743, -33923.29687644513, -32383.74203133994, -30768.295156068925, -29080.015907422057, -27322.175158026857, -25498.25990776402, -23611.97737126224, -21667.258837160436, -19668.26317351684, -17619.37965079976, -15525.229843462866, -13390.668688373748, -11220.784462659614, -9020.897378366048, -6796.55682796515, -4553.536875638436, -2297.8299728504962, -35.63859950105572, 2226.6403050766658, 4482.42144793332, 6724.945106994718, 8947.298573559783, 11142.437730433045, 13303.21128500558, 15422.386920376286, 17492.680990571745, 19506.793299510107, 21457.438764492148, 23337.401704760003, 25139.55118867293, 26856.893359525297, 28482.629495390185, 30010.203017524247, 31433.33944133321, 32746.08326388277, 33942.83578195403, 35018.39783463181, 35968.02146442084, 36787.47449087969, 37473.12199076825, 38021.8965632246, 38431.24227649981, 38699.25851230085, 38824.6805238706, 38806.8991953652, 38645.96388056756, 38342.57367755292, 37898.06250730922, 37314.38336431379, 36594.097107068, 35740.371156594076, 34756.97761946378, 33648.16485334259, 32418.67421597067, 31073.701366766112, 29618.85789018959, 28060.130322479537, 26403.83117665899, 24656.547486558396, 22825.092390606296, 20916.46527613019, 18937.826004916787, 16896.485854508726, 14799.810510027139, 12655.183058466804, 10470.005264374824, 8251.653231659064, 6007.448960108469, 3744.6342214984115, 1470.3470860637383, -808.4002122485981, -3084.744122952825, -5351.975951881122, -7603.560118746364, -9833.1477082603, -12034.587192487123, -14201.932404741103, -16329.449923875105, -18411.625618267382, -20443.16992867957, -22419.021469180603, -24334.348525316494, -26184.54836074266, -27965.25274058885, -29672.32911264127, -31301.87624017809, -32850.225999074035, -34313.94377929138, -35689.82764787186, -36974.90670283751, -38166.43904740221, -39261.90981390164, -40259.02966684371, -41155.73421448941, -41950.184758363415, -42640.770810106456, -43226.11241324072, -43705.03982126812, -44076.60713447272, -44340.089958925055, -44494.98514565281, -44541.01209607157, -44478.112768296574, -44306.45093057146, -44026.41120805146, -43638.5984691778, -43143.838097880674, -42543.177697847146, -41837.89077509239, -41029.48294506953, -40119.69127372695, -39110.462010641866, -38003.97475283827, -36802.63524649073, -35509.076632900105, -34126.16023158729, -32656.97520523481, -31104.83732189326, -29473.28702988702, -27766.08706084147, -25987.219776256545, -24140.884473061018, -22231.49486356543, -20263.67694524405, -18242.267475775054, -16172.305159660793, -14059.009157861696, -11907.787972335651, -9724.225611435142, -7514.071099492537, -5283.226984572263, -3037.7366958402326, -783.7705887425582, 1472.392424625919, 3724.3796337906197, 5965.736156625713, 8189.947298716857, 10390.464396654068, 12560.717761320602, 14694.14232166579, 16784.205521670636, 18824.431769162384, 20808.425013789478, 22729.891031491592, 24582.660992774287, 26360.717892118453, 28058.22741584039, 29669.574825727566, 31189.405596197408, 32612.60188526328, 33934.33198345628, 35150.072844597205, 36255.634629459535, 37247.18845233602, 38121.2854641808, 38874.86992322531, 39505.288903967565, 40010.301295431906, 40388.08873960031, 40637.27116091233, 40756.92953773323, 40746.63856668866, 40606.42242976296, 40336.71095384731, 39938.40745580666, 39412.85675184476, 38761.8486614893, 37987.6106212516, 37092.792444414765, 36080.446265084734, 34954.00470464073, 33717.26029871535, 32374.34922284346, 30929.74235491122, 29388.246712537784, 27755.014393382065, 26035.410965212242, 24235.028716023346, 22359.70432180356, 20415.47447391746, 18408.55219450519, 16345.303683234108, 14232.224641728484, 12075.9172642356, 9883.06749761596, 7660.422678597997, 5414.769784857234, 3152.91411098178, 881.6586322627558, -1392.2199634054527, -3661.9856806911575, -5920.959857583914, -8162.541973302598, -10380.226822679517, -12567.620994289005, -14718.458660326962, -16826.616640875334, -18886.128171207718, -20891.19789748445, -22836.215496768687, -24715.7632093442, -26524.631699520138, -28257.832963219793, -29910.610396371914, -31478.446964567287, -32957.07241442789, -34342.47046715261, -35630.88693468788, -36818.83969898363, -37903.13149479078, -38880.86643645045, -39749.445325937486, -40506.54171880617, -41150.14387656782, -41678.54626529345, -42090.35737675067, -42384.50362263096, -42560.2302565388, -42617.10063782596, -42554.995151358315, -42374.1110972996, -42074.964864998154, -41658.39770506154, -41125.58641370567], \"y\": [0.0, 3450.5350348436245, 6854.9797701458565, 10170.551303244134, 13359.40466527167, 16390.16925051825, 19238.652155234755, 21887.799623507435, 24327.103209099885, 26551.661011151366, 28561.106657472395, 30358.598637607614, 31949.838416969757, 33342.298992841344, 34544.583309359965, 35565.93234388592, 36415.8620896293, 37103.89068948638, 37639.35494061142, 38031.28805016374, 38288.34380530761, 38418.750187610975, 38430.288710258086, 38330.29546180485, 38125.67132202903, 37822.88886598989, 37428.01251661776, 36946.733060536644, 36384.38278591029, 35745.96168701508, 35036.16176371231, 34259.39063743965, 33419.79317972634, 32521.270880211443, 31567.50342221243, 30561.9658500426, 29507.945386574982, 28408.55704837182, 27266.75809823618, 26085.361444010254, 24867.048034756324, 23614.378305455928, 22329.802721363114, 21015.67147314745, 19674.243374695026, 18307.694616556844, 16918.12711317954, 15507.574991044543, 14078.01173738312, 12631.356933515528, 11169.482473595375, 9694.218346665873, 8207.358059935248, 6710.663781177992, 5205.871278169173, 3694.694733058612, 2178.831509591918, 659.9669510853286, -860.2207129386114, -2380.055273547654, -3897.85767585202, -5411.945652758044, -6920.625721181304, -8422.189676319462, -9914.910189582672, -11397.035761147914, -12866.78576119655, -14322.345487332886, -15761.86116568247, -17183.434823168653, -18585.118958465097, -19964.910939123427, -21320.747052373885, -22650.496137097558, -23951.95272446828, -25222.829614763243, -26460.749817839573, -27663.23778477666, -28827.70985818129, -29951.46387570372, -31031.676044998487, -32065.391196668897, -33049.50394875843, -33980.75039444435, -34855.695773487736, -35670.72156196536, -36422.01181778891, -37105.53870933355, -37717.04832506117, -38252.04599349244, -38705.78147032048, -39073.23501940146, -39349.104749668986, -39527.79514523362, -39603.4092333958, -39569.74523901489, -39420.299112897, -39148.27607925946, -38746.61404736036, -38208.023027236704, -37525.04550967057, -36690.1482278171, -35695.908715277415, -34535.06600575609, -33200.758401750594, -31686.830236644302, -29988.185216151633, -28101.21367836703, -26024.297347387594, -23758.385444315565, -21307.590148590665, -18679.801466309727, -15887.241010235604, -12946.820714463096, -9880.217318426252, -6713.7352235527605, -3477.6498344264633, -205.16324678876472, 3068.840001069659, 6309.385470634159, 9483.015839696714, 12559.003404085293, 15510.497763142497, 18315.152318970126, 20955.387069230277, 23418.320454624547, 25695.40683435402, 27781.91937715619, 29676.372988464518, 31379.923802467347, 32895.77016744762, 34228.662032274486, 35384.51609819832, 36369.976872339066, 37192.20622204074, 37858.67787504789, 38376.868243885765, 38754.20217793787, 38997.96746948468, 39115.24858936353, 39112.8835730188, 38997.43941825284, 38775.198839093304, 38452.15123219643, 38033.992776144936, 37526.140098128475, 36933.7346858328, 36261.655628773966, 35514.532936187556, 34696.76169570615, 33812.51583709316, 32865.76140148159, 31860.271679277066, 30799.639781938222, 29687.291565108193, 28526.497820422697, 27320.385905130486, 26071.950795026605, 24784.065537834897, 23459.49108417697, 22100.885473264843, 20710.812347226576, 19291.749245850508, 17846.096152014088, 16376.18179419558, 14884.270851074467, 13372.570894383749, 11843.238797684293, 10298.386701787, 8740.087627550287, 7170.380826781162, 5591.276961966323, 4004.763205560667, 2412.8083495614233, 817.3680160944297, -779.609940260129, -2376.179747674272, -3970.393591937499, -5560.301342334877, -7143.940447924826, -8719.333598269825, -10284.483470634474, -11837.367446451812, -13375.932492730059, -14898.090126237645, -16401.711378308144, -17884.621678105057, -19344.59557218473, -20779.35119819906, -22186.544430577513, -23563.762616027605, -24908.517816694908, -26218.239478821364, -27490.26644474299, -28721.838226065902, -29910.08645010826, -31052.03916733831, -32144.60339429046, -33184.55531738875, -34168.533504332925, -35093.03018237318, -35954.38250904627, -36748.76373090629, -37472.17416591811, -38120.43340013314, -38689.17315765472, -39173.830703404055, -39569.64437695592, -39871.65130190642, -40074.68785117987, -40173.394651258845, -40162.226508189764, -40035.46933911892, -39787.26576687134, -39411.651753109654, -38902.60689600644, -38254.12157931147, -37460.354523790906, -36515.73639941647, -35414.91831848276, -34153.07115194714, -32726.101361588204, -31130.884548018716, -29365.553473566946, -27429.828161251287, -25325.363661327232, -23056.091076157307, -20628.52761022603, -18052.10683660989, -15339.369104058362, -12506.143585073305, -9571.304768207177, -6556.5179703035665, -3485.9705808203785, -385.8587285231653, 2716.600939164317, 5794.689565264754, 8822.120319363425, 11774.066314824342, 14628.070471287338, 17364.441804980288, 19966.549406324073, 22420.945086571846, 24717.333256842823, 26848.3466929044, 28809.28582644543, 30597.80721653893, 32213.57660856715, 33657.9076618448, 34933.446806328284, 36043.93757596281, 36993.90121800355, 37788.449346470305, 38433.22654754744, 38934.12210495697, 39297.065779845994, 39528.03796126002, 39632.99747474989, 39617.826729889544, 39488.29074698527, 39250.00817328598, 38908.43179482676, 38468.83599554131, 37936.30869862381, 37315.748998499424, 36611.86882443251, 35829.19549511522, 34972.07685314863, 34044.687769325385, 33051.03764864729, 31994.978352803526, 30880.212038469046, 29710.300044432563, 28488.67145288254, 27218.63122557628, 25903.368682052245, 24545.96560054936, 23149.404000807353, 21716.573656503635, 20250.27938507428, 18753.248162674914, 17228.136112031316, 15677.535410529516, 14103.980016782049, 12509.95188450504, 10897.886892384151, 9270.1802849509, 7629.1926775428365, 5977.25561519167, 4316.676782090269, 2649.7449582954514, 978.7348203165267, -694.0883177543659, -2366.463725917937, -4036.1303460959325, -5700.821649457716, -7358.260166592039, -9006.151593872139, -10642.17955960806, -12264.007507035822, -13869.26725065574, -15455.556261896114, -17020.43242841902, -18561.40822611898, -20075.945234651554, -21561.44889069863, -23015.26337317945, -24434.666514616336, -25816.864632862103, -27158.987177400926, -28458.081084427253, -29711.10473491448, -30914.92140988077, -32066.29213705964, -33161.86782318668, -34198.1818232465, -35171.661701816105, -36078.613466086295, -36915.214680552876, -37677.513984703925, -38361.42913635495, -38962.74642047676, -39477.1214244172, -39900.08237861661, -40227.036885238536, -40453.281314275715, -40574.01504927937, -40584.35986991272, -40479.38581903404, -40254.144691054455, -39903.71263707619, -39423.24344407285, -38808.060359039926, -38053.90622990958, -37156.66954246777, -36112.61793883996, -34918.57972374202, -33572.100147601996, -32071.598703092313, -30416.552712354922, -28607.702909147756, -26647.26451733484, -24539.12731659813, -22289.02818625085, -19904.680079299615, -17395.925216385687, -14774.764582295704, -12055.388752936422, -9253.949000753948, -6388.310958923619, -3477.834246559661, -543.075985909967, 2394.6838574939193, 5314.197380769024, 8195.054507581279, 11017.26054479752, 13762.231897496902, 16413.092416173677, 18954.90352734047, 21374.79730393622, 23662.019052165586, 25807.91451263142, 27805.89677361041, 29651.35022925756, 31341.275205839574, 32874.22801348527, 34250.10703140955, 35469.95664367627, 36535.76867706314, 37450.301892819334, 38216.94510409196, 38839.64312892522, 39322.89725510655, 39671.317831919485, 39889.642409049535, 39982.728848568935, 39955.49772163263, 39812.88569243096, 39559.80892460948, 39201.1336285538, 38741.65314619476, 38186.07116724647, 37538.9906707912, 36804.90694077806, 35988.19863927786, 35093.1274661711, 34123.83732633087, 33084.35516893643, 31978.59311716208, 30810.351590962695, 29583.32326737544, 28301.097722762348, 26967.166399177913, 25584.927409556763, 24157.691120441268, 22688.685469557357, 21181.06186924407, 19637.900818066006, 18062.217283900238, 16456.965973432074, 14825.04660298326, 13169.309285605406, 11492.560149366145, 9797.567301756655, 8087.066401482512, 6363.762487078722, 4630.337570341944, 2889.454482759321, 1143.7621103336674, -604.0995058832128, -2351.4944242315432, -4095.786308166102, -5834.33407957216, -7564.487615980202, -9283.583387988561, -10988.939933205833, -12677.853063016388, -14347.590698480926, -15995.387231680917, -17618.43758794645, -19213.900416754692, -20778.88751229239, -22310.4616199069, -23805.633293484934, -25261.354672420195, -26674.5144475918, -28041.933851191905, -29360.36350524317, -30626.48096364447, -31836.8887825849, -32988.11295416659, -34076.601538073395, -35098.72332612808, -36050.76637457437, -36928.93623892623, -37729.35374622184, -38448.05213952251, -39080.98224744977, -39624.06589640341, -40073.16037045509, -40424.0772353145, -40672.60241531373, -40814.51641635016, -40845.61894255378, -40761.75920355292, -40558.8719865937, -40233.0205434591, -39780.447357682744, -39197.84326108994, -38482.168897784606, -37630.58752870279, -36640.7264276259, -35510.776055304625, -34239.591455241636, -32826.79609460467, -31272.899902800687, -29579.423300226295, -27749.018107893873, -25785.576228631006, -23694.31699055621, -21481.844043525954, -19156.217462802306, -16726.97775999894, -14205.143917345604, -11603.116401096999, -8934.505464202535, -6213.994901752184, -3457.19746462195, -680.4576129049917, 2099.442709293245, 4865.719596105535, 7602.132633002184, 10292.680640326216, 12922.072490885255, 15476.072860205362, 17941.638320440605, 20307.047196404095, 22561.971144509967, 24697.506957633126, 26706.187098888906, 28581.987467330433, 30320.313312638053, 31917.74604538728, 33372.04765078576, 34682.04976872009, 35847.53045918515, 36869.10130070176, 37748.098084452315, 38486.477335240634, 39086.720395388445, 39551.83290882805, 39885.39410951279, 40090.993696815494, 40172.421335979714, 40133.639320739014, 39978.73679369944, 39711.89011940345, 39337.33138891869, 38859.32065328888, 38282.1220377505, 37609.98444632915, 36847.12756642825, 35997.73387171775, 35065.93790680986, 34055.81408823286, 32971.377318045095, 31816.578645899357, 30595.30367739938, 29311.371640278823, 27968.53528923243, 26570.48183023447, 25120.835045174932, 23623.15879764543, 22080.959967349507, 20497.688135883276, 18876.741619375196, 17221.470245467797, 15535.17961282538, 13821.135009072948, 12082.565093481468, 10322.665516314944, 8544.60264675937, 6751.517581344081, 4946.530604780991, 3132.7462751322373, 1313.2592038364028, -508.8460941839508, -2330.4910117171507, -4148.596773707559, -5960.081421570779, -7761.855529932633, -9550.818336206303, -11323.854193691453, -13077.82925886919, -14809.588323576821, -16515.95170273972, -18193.712088340544, -19839.631280306687, -21450.43670499216, -23022.81763193671, -24553.421492394984, -26038.85911085571, -27475.69839205353, -28860.463883898326, -30189.63628925834, -31459.651320120458, -32666.90006978271, -33807.7308080009, -34878.45210400073, -35875.33718227752, -36794.62941609831, -37632.54886362619, -38385.299751581304, -39049.0788113583, -39620.084372516576, -40094.52611856078, -40468.64467364551, -40738.75451840474, -40901.24045602264, -40952.59088955137, -40889.42895583599, -40708.54694834182, -40406.945100785684, -39981.99321558558, -39431.57662896307, -38753.61610053915, -37946.4185457412, -37008.75822090606, -35939.94411970421, -34739.88136737674, -33409.1382191748, -31949.016718216288, -30361.622322712235, -28649.92781251152, -26817.826784915058, -24870.172049708417, -22812.794233363344, -20652.53563801713, -18397.25379262632, -16055.806418390177, -13638.00556716816, -11154.507206701846, -8616.722890075675, -6036.735442311443, -3427.20004837866, -801.2121269075395, 1827.876626119419, 4446.712639409996, 7042.2688735567035, 9601.675752635234, 12112.387333121302, 14562.507952268918, 16940.856290276453, 19237.070716246624, 21441.679152305816, 23546.14337262764, 25542.88765411187, 27425.321695462448, 29187.867721417624, 30825.982288397652, 32335.97835845207, 33715.05199615428, 34961.24427312206, 36073.36699199796, 37050.93970828137, 37894.12518668419, 38603.66542722766, 39180.81921516401, 39627.302411305434, 39945.445469240534, 40138.00092285012, 40207.73218728319, 40157.65479670117, 39990.99898245317, 39711.17212868428, 39321.72801713397, 38826.34020156413, 38228.77630079011, 37532.8745948638, 36742.52435219775, 35861.65131541842, 34894.20977373751, 33844.17199578209, 32715.504198945826, 31512.170837244717, 30238.125493549614, 28897.30697392655, 27493.63534847778, 26031.00830847212, 24513.298330700538, 22944.351139987146, 21327.985960793612, 19667.998048847265, 17968.16292110023, 16232.226716881201, 14463.91183905371, 12666.920026892323, 10844.933605714758, 9001.617898057468, 7140.623229760097, 5265.5867417641175, 3380.1342184415194, 1487.8821432591712, -407.5598074098297, -2302.585621516216, -4193.58894880067, -6076.958986729713, -7949.086575772831, -9806.357443761899, -11645.149543215848, -13461.832005632617, -15252.763493720333, -17014.290957760255, -18742.748778142348, -20434.458276113368, -22085.72757478295, -23692.85179242359, -25252.11355011019, -26759.783775737575, -28212.12278646067, -29605.382080241383, -30935.811079688247, -32199.6597138532, -33393.18257882725, -34512.6447247956, -35554.328625799535, -36514.54265583053, -37389.63109600567, -38175.98569757144, -38870.05882548437, -39468.37820731685, -39967.56331223451, -40364.343384795226, -40655.577158316046, -40838.274272556926, -40909.618420469036, -40866.993936832565, -40708.02877274131, -40430.656364760944, -40033.47393928991, -39515.103324334224, -38874.43256356914, -38110.75716417525, -37223.818976870585, -36213.84423356514, -35081.58238456662, -33828.344378446614, -32456.038788035676, -30967.2041860186, -29365.036173596603, -27653.407465687997, -25836.881379014405, -23920.730422919736, -21910.91610349894, -19814.111260282738, -17637.65647541731, -15389.513933582853, -13078.227156040883, -10712.87965783612, -8303.045577173763, -5858.725326000807, -3390.259310809651, -908.2127726922804, 1576.7752043307057, 4054.136665970773, 6513.197289763585, 8943.461889716005, 11334.697054610086, 13677.011578127525, 15960.938101470285, 18177.496107660343, 20318.24160772675, 22375.308858980137, 24341.449455576723, 26210.074131575806, 27975.302616691468, 29632.00478229801, 31175.647710904603, 32602.38056366526, 33909.02285738946, 35093.03858802257, 36152.51115587774, 37086.10787391111, 37893.0400942302, 38573.024988030214, 39126.25501414847, 39553.381111434406, 39855.51524457963, 40034.47973084869, 40092.238322079545, 40030.88005465333, 39852.77239041432, 39560.53154745735, 39156.99491251203, 38645.19959476789, 38028.359442117806, 37309.84191564686, 36493.14666820749, 35581.88767291625, 34579.780747413504, 33490.63831972508, 32318.353433069453, 31066.870716813566, 29740.193788362267, 28342.371486802982, 26877.49302110561, 25349.68172392097, 23763.08817380769, 22121.88349385099, 20430.25363463274, 18692.395449511652, 16912.51537017805, 15094.831490437959, 13243.57986027176, 11362.999864968773, 9457.319434801817, 7530.775810721467, 5587.607269268674, 3632.0530573192996, 1668.352755390422, -299.25473180856676, -2266.5323588975166, -4229.2460987284285, -6183.165877997957, -8124.066090869236, -10047.725491933174, -11949.926269813184, -13826.456121257703, -15673.119133830298, -17485.728707622442, -19260.111887198236, -20992.113041413584, -22677.59749078806, -24312.45539811311, -25892.606086178606, -27414.002946493973, -28872.639102891488, -30264.553993883055, -31585.841037662227, -32832.65654362004, -34001.22997453896, -35087.87091957768, -36088.97968107387, -37001.06160781041, -37820.73914349989, -38544.76577271323, -39170.04118367364, -39693.627385685824, -40112.76582142743, -40424.89561240652, -40627.6726473895, -40718.9896360832, -40696.99689026512, -40560.127134436385, -40307.48693934911, -39938.383770869455, -39452.213463883505, -38848.73730125093, -38128.10360170954, -37290.86848274832, -36338.01510888712, -35270.97147719494, -34091.62683728865, -32802.34602155205, -31405.97615277957, -29905.86026988392, -28305.838342418665, -26610.244132105054, -24823.900522426426, -22952.112002086295, -21000.652988141614, -18975.75067460693, -16884.06109233768, -14732.637065991848, -12528.886753878614, -10280.52145649045, -7995.5342946652545, -5682.17849070205, -3348.879312949901, -1004.1940291733945, 1343.2450121249847, 3684.783623356197, 6011.7968935587905, 8315.739733085238, 10588.198402180022, 12820.944578359096, 15005.993517498373, 17135.66010937608, 19202.552294113564, 21199.634972356194, 23120.261523813457, 24958.20261399352, 26707.669275448985, 28363.327629185256, 29920.30811862628, 31374.21212852494, 32721.118861214454, 33957.59534258963, 35080.710729133214, 36087.99463844789, 36977.43048302263, 37747.46453229661, 38396.98480135898, 38925.30642618314, 39332.155530803815, 39617.65215011816, 39782.37891140733, 39827.69490880182, 39754.87290339423, 39565.4223818361, 39261.152743410144, 38844.15421699616, 38316.78253126776, 37681.64139282156, 36941.563971049545, 36099.59511950246, 35158.976063487666, 34123.1332836484, 32995.67137990788, 31780.34592080139, 30481.05778963771, 29101.83708750134, 27646.835287468464, 26120.31800624728, 24526.655177554265, 22870.309761239074, 21155.826122178, 19387.819212948823, 17570.96569430802, 15709.998127478948, 13809.703372277509, 11874.92632507802, 9910.573907073465, 7921.551966020701, 5912.793727257159, 3889.2592731199197, 1855.9235752537202, -182.22902751528488, -2220.210803303603, -4253.036921723319, -6275.7319373154, -8283.336330301014, -10270.912973095446, -12233.553391028841, -14166.38368571212, -16064.569989497006, -17923.325131428654, -19737.925839873405, -21503.713049865462, -23216.09810200337, -24870.575110494865, -26462.73170032382, -27988.258608070188, -29442.958687643317, -30822.755862167287, -32123.70456327128, -33342.0001990336, -34473.9911918211, -35516.19312727909, -36465.30555571602, -37318.23198713002, -38072.0859797301, -38724.173192349714, -39272.04194113019, -39713.48556887928, -40046.55486846047, -40269.5710799992, -40381.13843629809, -40380.15645624408, -40266.117039026576, -40038.9680634644, -39698.56409595535, -39245.09110528898, -38679.08181459273, -38001.418810304094, -37213.342002243284, -36316.455443629326, -35312.73054928263, -34204.50640391875, -32994.48785242795, -31685.742064046426, -30281.69426231503, -28786.123312728723, -27203.157859974774, -25537.272421642465, -23793.255814713248, -21976.20246578226, -20091.502651837287, -18144.82072976271, -16142.074864817674, -14089.415201185542, -11993.200545577023, -9859.973634866734, -7696.43505875003, -5509.4159083951745, -3305.8492220779663, -1092.7402987765638, 1122.864049285814, 3333.8991463379107, 5533.31408122904, 7714.10023346039, 9869.318329069509, 11992.13108638125, 14075.831006138023, 16113.864527413036, 18099.853770928774, 20027.61709140296, 21891.189660555323, 23684.84530239145, 25403.120802395173, 27040.843912255797, 28593.163229718983, 30055.505299263477, 31423.608276027368, 32693.552593701672, 33861.75871484259, 34924.994245513786, 35880.37989589513, 36725.3933705603, 37457.872187952365, 38076.015058100566, 38578.38192265436, 38963.89293118619, 39231.82611225955, 39381.816636008276, 39414.28272721756, 39329.87111218684, 39129.24595637082, 38813.401767090705, 38383.65635187915, 37841.642844407506, 37189.300748495174, 36428.86702517175, 35562.86708069725, 34594.105514554816, 33525.65665550482, 32360.847404617572, 31103.252399122168, 29756.685526024903, 28325.187862187744, 26813.013401066477, 25224.613926320122, 23564.624392489277, 21837.850172955597, 20049.257535382658, 18203.9687048415, 16307.262874833978, 14364.550509250235, 12381.314276316942, 10363.150588005035, 8315.739269490363, 6244.829722010987, 4156.226847600616, 2055.7764967059834, -50.64938434984034, -2157.1677291116903, -4257.899272992977, -6346.983244569971, -8418.59164923028, -10466.942968210366], \"z\": [0.0, 0.5710083111363927, 2.276538609770986, 5.094890987553403, 8.99234012546611, 13.926210521491704, 19.84832026394841, 26.708178247478973, 34.45564037845436, 43.04286454506144, 52.425574705160194, 62.563618823181145, 73.4212327564129, 84.96687410683451, 97.17291071015126, 109.92800909526488, 121.61187948392516, 131.61477867461136, 139.93618064378103, 146.5836157692417, 151.5708309292577, 154.91629500540608, 156.6420246838213, 156.77269505486075, 155.3349356488859, 152.35671285353732, 147.8669047511696, 141.89505711372172, 134.47107605884597, 125.625060533133, 115.38717493786794, 103.78756326435348, 90.85628719454593, 76.62327950328533, 61.11834628509148, 44.3711567351915, 26.41125641022018, 7.26808962643235, -13.028973086856475, -34.450602219238284, -56.96747138029197, -80.55021445348692, -105.16937798661414, -130.79536900245526, -157.39839998419566, -184.94845088504013, -213.41519603355806, -242.76796470367444, -272.97567400572115, -304.0067575522286, -335.82909831722367, -368.40996359132424, -401.71593993491143, -435.71286603167135, -470.36576134478736, -505.63874847807176, -541.4949671443235, -577.8964776431984, -614.8041517508747, -652.1775489238057, -689.9747974244195, -728.1526569276197, -766.6662375530398, -805.4689332763568, -844.5122883806315, -883.7458314640702, -923.1169114255455, -962.5705317169596, -1002.0491791515569, -1041.4926435572856, -1080.8378245642866, -1120.0185218156346, -1158.9652048904065, -1197.6047592281855, -1235.8602043440872, -1273.6503806234282, -1310.8896009850962, -1347.4872637027634, -1383.3474226730034, -1418.3683118077663, -1452.4422698092053, -1485.4552922959228, -1517.2860833848674, -1547.805686507723, -1576.8768856076854, -1604.3535653641054, -1630.0800213126688, -1653.8902144222245, -1675.6070123215065, -1695.0413810033456, -1711.9915379885367, -1726.242104712123, -1737.5632674010496, -1745.7099378205162, -1750.421009136402, -1751.4187347486463, -1748.4082809466508, -1741.0775773705213, -1729.0975781271366, -1712.1231028223697, -1689.7944649500123, -1661.6485481148027, -1625.7014341871172, -1580.9938798489404, -1527.1722754917203, -1463.9154369023463, -1390.9516748215874, -1308.079556437275, -1215.1925657310708, -1112.3073504877023, -999.5932536954255, -877.4029547028485, -746.3002854831423, -607.079041420919, -460.7684853078961, -308.6277460509879, -152.11563157444647, 7.159032299143161, 167.4971529196493, 327.1868227136427, 484.5821540046289, 638.1632409263773, 786.5936699805974, 928.7526747660552, 1063.7494613414133, 1190.921548762759, 1309.8183806071675, 1420.1767815179903, 1521.893608662795, 1614.996776141869, 1699.6162088153922, 1775.9598948555597, 1844.2949627269825, 1904.9261322566938, 1957.6493331452257, 2001.1768659277834, 2035.7550674230083, 2061.7500315996813, 2079.527744607375, 2089.4495000911843, 2091.8686958198614, 2087.1287507043317, 2075.561736204683, 2057.4873168063828, 2033.212278591812, 2003.0308825787522, 1967.2247452919505, 1926.0632423348934, 1879.8039858154057, 1828.6933872228044, 1772.967231439545, 1712.8512513850985, 1648.5618330562627, 1580.3065966535494, 1508.2850205298855, 1432.6890398807686, 1353.7036261317644, 1271.5073461872823, 1186.2729002472595, 1098.1676368984079, 1007.3540441867175, 913.9902145486941, 818.2302827165591, 720.2249095215413, 620.1216241563619, 518.0652392431249, 414.1982621954546, 308.66127472626124, 201.59328756313266, 93.13207743346769, -16.585486618485994, -127.42312052064396, -239.24479900604263, -351.9144189275796, -465.2954449924904, -579.2505308544562, -693.6411085011838, -808.3271117635644, -923.1670266897264, -1038.0170677965036, -1152.731046704333, -1267.1599612297184, -1381.1515794146671, -1494.5500358794627, -1607.1954325698352, -1718.923435969879, -1829.5648628519368, -1938.945246634555, -2046.8843764198507, -2153.1958007814874, -2257.6862883744166, -2360.1552374377034, -2460.394026261545, -2558.18529668983, -2653.3021627293347, -2745.507432535427, -2834.554068290979, -2920.183526680181, -3002.1248380873662, -3080.0939673241946, -3153.7929731592017, -3222.9091518223727, -3287.1141531775284, -3346.063061789543, -3399.3935628916847, -3446.725141624762, -3487.658300210476, -3521.7739298149363, -3548.6328365021527, -3567.7754678863553, -3578.7219920905786, -3580.9727579140454, -3574.009314429108, -3557.2961314456797, -3530.283226552976, -3492.4099283739697, -3443.1100581461374, -3381.041777297948, -3304.188878474727, -3211.997822539289, -3103.9978169189917, -2979.786328596471, -2839.0511723128866, -2681.59768681591, -2507.379829992789, -2316.5330330624233, -2109.4066539461055, -1886.5938859706498, -1648.963318632138, -1397.676959287458, -1134.208607111664, -860.3215381231004, -578.0452574848915, -289.65084122721527, 2.3969046623059844, 295.53223983277263, 587.2315141944671, 875.0072847090788, 1156.505528689601, 1429.5921440721286, 1692.3910886562053, 1943.3126796692854, 2181.065936529911, 2404.656966158731, 2613.36789079167, 2806.732790098342, 2984.508801042167, 3146.643571763733, 3293.241307001176, 3424.533097864314, 3540.8547035725815, 3642.6163263361937, 3730.2829996196156, 3803.1358418991845, 3860.6642675560906, 3903.426862424302, 3931.9939045863593, 3946.9393728183404, 3948.834761152639, 3938.244354566419, 3915.7217782792845, 3881.8075634199217, 3837.027465851232, 3781.8912838062843, 3716.892306640697, 3642.507208046222, 3559.19606064051, 3467.40264895819, 3367.5549476857914, 3260.0657268439277, 3145.333222520867, 3023.7418197888833, 2895.6628565370715, 2761.4554107496742, 2621.467057397015, 2476.034678594433, 2325.4852423017464, 2170.1365554669796, 2010.2979977581133, 1846.2712420257728, 1678.350967640636, 1506.825572848726, 1331.977892234255, 1154.0857680427912, 973.4226899647264, 790.2584242797659, 604.859583513634, 417.4902793678616, 228.4127262204431, 37.88780748756389, -153.82438286265764, -346.4640113543202, -539.7709763149428, -733.4843671633607, -927.3418993837873, -1121.079312895434, -1314.429721527212, -1507.122901307177, -1698.8846618337593, -1889.4371500593054, -2078.4973858947387, -2265.7769582390374, -2450.981367761548, -2633.8092889751597, -2813.951875231427, -2991.0920916412156, -3164.9040609246836, -3335.05240719444, -3501.191582675452, -3662.9651623658638, -3820.0050916420246, -3971.93087181188, -4118.348668620354, -4258.850328710392, -4393.012289043758, -4520.394535125101, -4640.54215218866, -4752.983111111788, -4857.227248294453, -4952.766093075885, -5039.072467518536, -5115.600242158428, -5181.784245240013, -5237.040480186485, -5280.766755988209, -5312.343632820983, -5331.135966346469, -5336.495085436856, -5327.761777723427, -5304.270230499, -5265.353122732271, -5210.348073854586, -5138.407825084185, -5047.2223770388955, -4935.823766411963, -4803.6867785161485, -4650.3627951216395, -4475.501155983634, -4278.87085365258, -4060.3858287042117, -3820.1332088407516, -3558.4023142161836, -3275.7122500911364, -2972.835907923175, -2650.8182575441997, -2310.997475804819, -1955.008829891215, -1584.789916867792, -1202.5493420297253, -810.7332030641265, -411.9952011384621, -9.156242425899558, 394.8606064221001, 797.1339453466526, 1194.8550137637355, 1585.2692944140395, 1965.8137151663664, 2334.1578407360926, 2688.2360064184872, 3026.2657407197107, 3346.75368448568, 3648.4941645606373, 3930.5655800056807, 4192.318152043663, 4433.323117240832, 4653.366077037044, 4852.417494156581, 5030.605762224003, 5188.189934895622, 5325.534914122338, 5443.09263837178, 5541.242553984026, 5618.90577736613, 5676.236907932582, 5713.888190719548, 5732.534586159165, 5732.86495909997, 5715.574885893962, 5681.36094375709, 5630.9160699169925, 5564.925901497737, 5484.066035205341, 5389.000145878038, 5280.378724862097, 5158.837575120273, 5024.997562256731, 4879.464325002761, 4722.828248872601, 4555.664643661249, 4378.5340821281625, 4191.982877561836, 3996.5436779209795, 3792.736120254283, 3581.0674601304822, 3362.033337589672, 3136.118508890403, 2903.7976804771833, 2665.5363028039774, 2421.7913343679047, 2173.0119947810686, 1919.6405257067795, 1662.112978487845, 1400.8600472937983, 1136.3079666141089, 868.879350250377, 598.9934199271196, 327.0671904810666, 53.51603881195974, -221.24548255597884, -496.80285092453784, -772.7408343512151, -1048.6427902074117, -1324.089983515152, -1598.6609072113317, -1871.9305864890914, -2143.4698493663327, -2412.844545630072, -2679.6146963066526, -2943.3335558073722, -3203.5466173787995, -3459.7921946144265, -3711.5996046763316, -3958.4888150194865, -4199.969904573075, -4435.541983709956, -4664.692315907277, -4886.895610974244, -5101.613459723436, -5308.293879961679, -5506.370943676781, -5695.264455296478, -5874.379650895398, -6043.106888226734, -6200.821297454356, -6346.882362461978, -6480.6334026152645, -6601.40092485322, -6708.495353940525, -6801.2201480719405, -6878.865365036504, -6940.710685138013, -6986.028617127008, -7014.087693012296, -7024.156390965365, -7015.508009775797, -6987.426508285595, -6939.2134923678595, -6870.194143722704, -6778.507600810039, -6662.541536333676, -6521.777719615316, -6355.780721709816, -6164.2156050957265, -5946.866030197832, -5703.652904544616, -5434.655502379511, -5140.133623851696, -4820.549214053933, -4476.5858621745765, -4109.164601031916, -3719.4544272575095, -3308.8853143539272, -2879.1520371839574, -2432.214695005346, -1970.2813552308603, -1495.7774530477213, -1011.3216534495583, -519.7002593654363, -23.832157362708163, 473.2837076009268, 968.6463368986477, 1459.3505321667049, 1942.5318485382932, 2415.4510265484705, 2875.556010516545, 3320.506661184559, 3748.198094514585, 4156.77341080537, 4544.6293483708505, 4910.4183960221735, 5253.050898594135, 5571.693465093883, 5865.721628756655, 6134.721951629297, 6378.472308635085, 6596.919784917529, 6790.160531297423, 6958.420246564514, 7102.035687686537, 7221.4375204547005, 7316.663337244423, 7386.65084386547, 7431.893115821661, 7453.0698280472925, 7450.897321109322, 7426.119729440364, 7379.501270758323, 7311.820069742575, 7223.862698043761, 7116.419458989712, 6990.280549179927, 6846.233229160697, 6685.060133262135, 6507.537093361739, 6314.430706180539, 6106.498300754951, 5884.487007982855, 5649.133354700521, 5401.162990511237, 5141.2905829735655, 4870.219916758211, 4588.6442323815845, 4297.246840124377, 3996.7016279584977, 3687.672943442506, 3370.816713471332, 3046.7809391622377, 2716.2064884693173, 2379.7278238167414, 2037.9736866813107, 1691.5677739341816, 1341.1294407525402, 987.274464910137, 630.6159072584708, 271.7651032075717, -88.66720063937115, -450.07090828521183, -811.8357448402178, -1173.3500575470596, -1534.0002171862902, -1893.169757469542, -2250.238597789166, -2604.582329974649, -2955.5715497041388, -3302.5712132214585, -3644.9400000076153, -3982.0296620564454, -4313.1843404041265, -4637.739829561369, -4955.022770498422, -5264.349858508805, -5565.02894989203, -5856.3576838711515, -6137.623366377648, -6408.1028210940085, -6667.062088921161, -6913.756442570832, -7147.4306942507355, -7367.31977440838, -7572.649559501451, -7762.637926761234, -7936.496013916589, -8093.429661844874, -8232.641018117287, -8353.330279405462, -8454.697550716264, -8535.946794434212, -8596.294778970558, -8634.969996072314, -8651.219472693572, -8644.315094433372, -8613.560610264087, -8558.29955099045, -8477.365597924792, -8368.629630331561, -8231.524325422732, -8065.667620887417, -7870.7798235036935, -7646.698329716853, -7393.391126567547, -7110.97150002102, -6799.713517837531, -6460.067282508887, -6092.6729497850765, -5698.37250831565, -5278.218315929916, -4833.477388080416, -4365.638883730688, -3876.414508399302, -3367.7358724643755, -2841.744416899491, -2300.767203498261, -1747.297678157779, -1183.9773839055888, -613.5744810466747, -38.954931807896685, 536.9577931449797, 1111.2386289401215, 1681.0332544600285, 2243.5202602208988, 2795.9476233147616, 3335.7045577044755, 3860.3357171402113, 4367.56443133306, 4855.307962124547, 5321.68709777867, 5765.032403524536, 6183.88944647593, 6577.0253130562805, 6943.434202852454, 7282.296499214983, 7592.98561202928, 7875.059973731593, 8128.246617878707, 8352.427424830486, 8547.62501347358, 8713.988750378478, 8851.781084393804, 8961.36447478559, 9042.242843134205, 9093.668802243274, 9116.23668110842, 9110.609543356617, 9077.503177761662, 9017.677333060974, 8931.928500405325, 8821.083643637518, 8685.994148399357, 8527.53030580135, 8346.576656227106, 8144.028518844368, 7920.790032394727, 7677.771607841014, 7415.884335393386, 7136.040833878056, 6839.153075198672, 6526.131406800462, 6197.8835649323455, 5855.313765815692, 5499.321989462649, 5130.803570885562, 4750.649213440984, 4359.7455390498135, 3958.976039218502, 3549.2187916932035, 3131.3477509577897, 2706.233436050391, 2274.7432078785523, 1837.7418185615793, 1396.0918663437842, 950.6542067794655, 502.28837089286105, 51.85304101393701, -399.7933650077844, -851.7919505091031, -1303.2823869379342, -1753.4019313259157, -2201.286895167361, -2646.071018361386, -3086.8848320426428, -3522.855396157589, -3953.105891828186, -4376.7553111023335, -4792.918238718218, -5200.7047205095105, -5599.220213079678, -5987.565609371384, -6364.837334759317, -6730.127508292996, -7082.524163717662, -7421.111642801323, -7744.972250038648, -8053.186798724357, -8344.835595315133, -8618.99979457899, -8874.763039817377, -9111.213474889693, -9327.446133131225, -9522.565708255206, -9695.689712329864, -9845.952025921495, -9972.506845493883, -10074.5330331555, -10151.238873844894, -10201.867245045276, -10225.701204119156, -10222.070439274063, -10190.361990798609, -10129.853762803024, -10038.427841584415, -9915.279550108704, -9760.108776139592, -9572.720953342165, -9353.037128738022, -9101.10383088987, -8817.10314177993, -8501.362623261826, -8154.364690471191, -7776.755024581109, -7369.349617287147, -6933.140039408128, -6469.297029976576, -5979.175292300937, -5464.308255125287, -4926.414162513929, -4367.384724222437, -3789.2731704218677, -3194.2839139233915, -2584.761981434948, -1963.1803753802117, -1332.1235278122024, -694.2650079512025, -52.33764487917391, 590.9057730780814, 1232.7302100421678, 1870.3713413090686, 2501.1101814049143, 3122.294319696382, 3731.358958645686, 4325.848152368505, 4903.431058550666, 5461.914681665608, 5999.25458543262, 6513.565052456316, 7003.130168988293, 7466.4173127510985, 7902.088461523484, 8308.958311600876, 8686.017641112336, 9032.430485253286, 9347.527344168882, 9630.79874385072, 9881.886043613942, 10100.571092101947, 10286.76633376745, 10440.506967777248, 10561.946761290075, 10651.31312355685, 10707.508793086632, 10730.270242716188, 10720.134512344057, 10677.716929011136, 10603.70301624867, 10498.84095540002, 10363.935692645038, 10199.842635095218, 10007.461311244997, 9787.729491733067, 9541.618266365947, 9270.128573357806, 8974.289676739809, 8655.154763092845, 8313.793063791787, 7951.291725667589, 7568.752032306485, 7167.288031325886, 6748.0247841412975, 6312.096446409577, 5860.644399465427, 5394.815653066833, 4915.761739765066, 4424.638321216302, 3922.605726749461, 3410.830642892528, 2890.482102222573, 2362.72743095064, 1828.7378885046205, 1289.6863967870145, 746.7475107075926, 201.09723227876944, -346.0872768344626, -893.6280294183048, -1440.3465385734532, -1985.0640804492239, -2526.6018389518254, -3063.7808775385856, -3595.421883202671, -4120.345740449001, -4637.376549160767, -5145.33966086568, -5643.062875239662, -6129.37746192718, -6603.119167069213, -7063.129292770676, -7508.255895810212, -7937.355151893713, -8349.292931756063, -8742.94663541105, -9117.207330855153, -9470.982243524337, -9803.197626134299, -10112.800713605553, -10398.762689705189, -10660.082706507046, -10895.7912717085, -11104.954171701349, -11286.676743135598, -11440.108418673553, -11564.447559546741, -11658.946615786355, -11722.917532000287, -11755.737435738292, -11756.854541352495, -11725.782492813427, -11660.828501253043, -11560.81238424088, -11425.539383808895, -11254.924056362363, -11048.996740417788, -10807.909781896647, -10531.943313286512, -10221.510598936384, -9877.162972715556, -9499.594175279954, -9089.642330852514, -8648.294170180357, -8176.6853468325235, -7676.099499101662, -7147.96690335524, -6593.862308017045, -6015.501537364339, -5414.736454321438, -4793.547871434016, -4154.035999206022, -3498.408020983579, -2828.9623835669076, -2148.0826917787067, -1458.2315212725996, -761.9237889723694, -61.71489989746716, 639.8163167294101, 1340.0834854377401, 2036.5078862726834, 2726.5334912977164, 3407.642346770435, 4077.370811922639, 4733.327165275168, 5373.208629129074, 5994.798569367785, 6595.986106599416, 7174.775590478549, 7729.295274125195, 8257.804271800342, 8758.696958427558, 9230.50571237662, 9671.90290292278, 10081.70302379812, 10458.865874249115, 10802.50115432837, 11111.855510914607, 11386.310594797756, 11625.38612276546, 11828.733483117252, 11996.1314003335, 12127.481144806967, 12222.801458475948, 12281.942832760222, 12303.505668013993, 12287.569282802111, 12234.580777621648, 12145.083281258558, 12019.709988279998, 11859.17934943122, 11664.289580022676, 11435.91285760828, 11174.98974429192, 10882.52436899783, 10559.580905041616, 10207.281276289148, 9826.79668322067, 9419.345780082886, 8986.189594549754, 8528.629035063283, 8048.002586293604, 7545.683195838178, 7023.074705545149, 6481.608180847961, 5922.738491496383, 5347.941497071412, 4758.712190667759, 4156.564154133248, 3543.030678246885, 2919.665962824224, 2288.025412681251, 1649.6744524169646, 1006.1883342201428, 359.1483592460423, -289.8598904186712, -939.2484570894667, -1587.4290110500992, -2232.8149132920275, -2873.8232983633416, -3508.877138265618, -4136.407248337096, -4754.854196058548, -5362.670073721785, -5958.320620041348, -6540.2906147370395, -7107.084096994782, -7657.226297663791, -8189.2676211663975, -8701.787098017028, -9193.395464750736, -9662.73804861966, -10108.497634408703, -10529.397490726491, -10924.204733126104, -11291.734201407937, -11630.85302846138, -11940.486077998452, -12219.622428533412, -12467.317314825203, -12682.686216094675, -12864.921340148967, -13013.292363044493, -13127.150460562854, -13205.93253446733, -13249.165298363341, -13256.469289994293, -13226.693807239315, -13158.36995981761, -13051.418634908337, -12905.88682708958, -12721.939396593169, -12499.860228744721, -12240.054779347625, -11943.052394246244, -11609.507442399117, -11240.199483565257, -10836.032691703384, -10398.034755187578, -9927.355474939532, -9425.265281580096, -8893.153892700979, -8332.528923852566, -7745.005572924492, -7132.303534346728, -6496.243758220996, -5838.741071239712, -5161.797276540059, -4467.493783335877, -3757.983765775592, -3035.4838504735853, -2302.2653321647813, -1560.6449169281957, -812.9749924293694, -61.633424628322686, 690.9871195968636, 1442.486794599879, 2190.468951806075, 2932.5504066386106, 3666.3704108293555, 4389.601866128764, 5099.960799048941, 5795.214542661, 6473.189071556925, 7131.77593608626, 7768.9392419816295, 8382.723121481331, 8971.260142062407, 9532.781098895844, 10065.625537580216, 10568.22599469257, 11039.11989150088, 11476.960447800664, 11880.515725638495, 12248.671089149528, 12580.431279136932, 12874.921776587416, 13131.389807933885, 13349.204859597501, 13527.85873738301, 13666.965266132189, 13766.259543532506, 13825.589778577356, 13843.690838827206, 13819.747206857935, 13753.975335782965, 13646.710791894442, 13498.405786548949, 13309.626372462953, 13081.049287453987, 12813.458805553297, 12507.743545729416, 12164.89319035207, 11785.995123989842, 11372.22836305473, 10924.861805495413, 10445.251194352508, 9934.834821996656, 9395.128452471496, 8827.719939367758, 8234.26401664981, 7616.477739861062, 6976.137055130567, 6315.074973404016, 5635.181827325977, 4938.396160833807, 4226.6838839995635, 3502.052752560892, 2766.541615041006, 2022.2154181201934, 1271.1601422776898, 515.4775775919759, -242.72000478250743, -1001.3152299497979, -1758.1909899039122, -2511.2357519391458, -3258.3487493292632, -3997.444998756482], \"type\": \"scatter3d\", \"uid\": \"bd507858-b277-11e8-b505-a0afbda902ef\"}, {\"cauto\": false, \"cmax\": 1, \"cmin\": 1, \"colorscale\": [[0, \"#204a87\"], [1, \"#204a87\"]], \"name\": \"Earth\", \"showscale\": false, \"x\": [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [2070.9775685349646, 1958.7662914956272, 1634.2922933821865, 1132.717366623061, 508.3949372694737, -171.0199720974995, -831.9022138394263, -1402.6349424190162, -1821.3704108639388, -2042.7321338179481, -2042.7321338179484, -1821.3704108639388, -1402.6349424190173, -831.9022138394275, -171.0199720975005, 508.39493726947273, 1132.7173666230603, 1634.2922933821858, 1958.7662914956272, 2070.9775685349646], [3917.5325829912545, 3705.269861917151, 3091.4836581186883, 2142.6872306516607, 961.6973945255617, -323.507276569953, -1573.6549145165156, -2653.272624703365, -3445.3670762369647, -3864.102544681887, -3864.1025446818876, -3445.3670762369647, -2653.2726247033675, -1573.654914516518, -323.50727656995485, 961.69739452556, 2142.687230651659, 3091.483658118687, 3705.269861917151, 3917.5325829912545], [5339.562155299338, 5050.2499496143155, 4213.664799186625, 2920.4636860211895, 1310.7850168122338, -440.9375478934551, -2144.8776874338914, -3616.387048334464, -4696.004758521314, -5266.737487100903, -5266.737487100904, -4696.004758521314, -3616.3870483344667, -2144.877687433894, -440.93754789345763, 1310.7850168122313, 2920.463686021187, 4213.664799186623, 5050.2499496143155, 5339.562155299338], [6182.9673162373765, 5847.957092568811, 4879.229977516816, 3381.7625853471986, 1517.8287436021724, -510.58539405170535, -2483.6696817114025, -4187.60982125184, -5437.757459198403, -6098.63970094033, -6098.639700940331, -5437.757459198403, -4187.609821251843, -2483.669681711406, -510.58539405170836, 1517.8287436021697, 3381.762585347196, 4879.229977516814, 5847.957092568811, 6182.9673162373765], [6356.352029838285, 6011.947344139877, 5016.054878729385, 3476.5950350978, 1560.3921744832594, -524.9033902157762, -2553.317527869653, -4305.040092575342, -5590.2447636708575, -6269.65967303783, -6269.6596730378305, -5590.2447636708575, -4305.040092575346, -2553.3175278696567, -524.9033902157793, 1560.3921744832564, 3476.5950350977973, 5016.054878729383, 6011.947344139877, 6356.352029838285], [5840.927372042378, 5524.449815998859, 4609.31240172086, 3194.6844678654465, 1433.8629012798515, -482.33995933468935, -2346.2738010797148, -3955.95247028867, -5136.9423064147695, -5761.264735768357, -5761.264735768358, -5136.9423064147695, -3955.952470288674, -2346.273801079718, -482.33995933469214, 1433.8629012798488, 3194.684467865444, 4609.312401720858, 5524.449815998859, 5840.927372042378], [4692.547602159432, 4438.292429623361, 3703.07940513492, 2566.580267902913, 1151.9523340474977, -387.50750958408815, -1884.974901753706, -3178.1760149191423, -4126.972442386172, -4628.547369145297, -4628.547369145297, -4126.972442386172, -3178.176014919145, -1884.9749017537085, -387.5075095840904, 1151.9523340474955, 2566.580267902911, 3703.0794051349185, 4438.292429623361, 4692.547602159432], [3035.6574872043448, 2871.1771912954932, 2395.560295805413, 1660.3472713169713, 745.2098570389732, -250.68260837151965, -1219.4097234235148, -2055.994873851206, -2669.7810776496694, -2994.25507576311, -2994.2550757631107, -2669.7810776496694, -2055.9948738512076, -1219.4097234235167, -250.6826083715211, 745.2098570389717, 1660.3472713169701, 2395.560295805412, 2871.1771912954932, 3035.6574872043448], [1049.806780431554, 992.9253533863962, 828.4450574775444, 574.1898849414736, 257.7123288979541, -86.69235680045416, -421.70258046901955, -711.014786154042, -923.2775072281457, -1035.488784267483, -1035.4887842674832, -923.2775072281457, -711.0147861540426, -421.7025804690202, -86.69235680045466, 257.7123288979536, 574.1898849414731, 828.4450574775441, 992.9253533863962, 1049.806780431554], [-1049.8067804315524, -992.9253533863947, -828.4450574775432, -574.1898849414728, -257.71232889795374, 86.69235680045404, 421.7025804690189, 711.0147861540408, 923.2775072281444, 1035.4887842674814, 1035.4887842674816, 923.2775072281444, 711.0147861540415, 421.7025804690195, 86.69235680045453, -257.7123288979532, -574.1898849414723, -828.4450574775428, -992.9253533863947, -1049.8067804315524], [-3035.6574872043434, -2871.177191295492, -2395.560295805412, -1660.3472713169708, -745.2098570389728, 250.68260837151956, 1219.4097234235144, 2055.9948738512053, 2669.7810776496685, 2994.255075763109, 2994.2550757631093, 2669.7810776496685, 2055.994873851207, 1219.4097234235162, 250.682608371521, -745.2098570389714, -1660.3472713169695, -2395.560295805411, -2871.177191295492, -3035.6574872043434], [-4692.547602159429, -4438.292429623359, -3703.079405134917, -2566.580267902911, -1151.9523340474968, 387.50750958408787, 1884.9749017537044, 3178.17601491914, 4126.972442386168, 4628.5473691452935, 4628.547369145294, 4126.972442386168, 3178.1760149191427, 1884.9749017537072, 387.50750958409014, -1151.9523340474948, -2566.580267902909, -3703.0794051349158, -4438.292429623359, -4692.547602159429], [-5840.927372042376, -5524.449815998857, -4609.312401720859, -3194.6844678654456, -1433.862901279851, 482.33995933468924, 2346.2738010797143, 3955.9524702886692, 5136.942306414769, 5761.264735768355, 5761.264735768356, 5136.942306414769, 3955.9524702886724, 2346.2738010797175, 482.339959334692, -1433.8629012798485, -3194.6844678654434, -4609.312401720857, -5524.449815998857, -5840.927372042376], [-6356.352029838285, -6011.947344139877, -5016.054878729385, -3476.5950350978, -1560.3921744832594, 524.9033902157762, 2553.317527869653, 4305.040092575342, 5590.2447636708575, 6269.65967303783, 6269.6596730378305, 5590.2447636708575, 4305.040092575346, 2553.3175278696567, 524.9033902157793, -1560.3921744832564, -3476.5950350977973, -5016.054878729383, -6011.947344139877, -6356.352029838285], [-6182.967316237377, -5847.957092568812, -4879.229977516816, -3381.762585347199, -1517.8287436021726, 510.5853940517054, 2483.669681711403, 4187.60982125184, 5437.757459198404, 6098.639700940331, 6098.639700940332, 5437.757459198404, 4187.609821251844, 2483.6696817114066, 510.5853940517084, -1517.8287436021699, -3381.7625853471964, -4879.229977516815, -5847.957092568812, -6182.967316237377], [-5339.562155299339, -5050.249949614317, -4213.664799186626, -2920.46368602119, -1310.7850168122343, 440.9375478934552, 2144.877687433892, 3616.387048334465, 4696.004758521315, 5266.737487100905, 5266.737487100906, 4696.004758521315, 3616.3870483344676, 2144.877687433895, 440.93754789345775, -1310.7850168122318, -2920.4636860211876, -4213.664799186624, -5050.249949614317, -5339.562155299339], [-3917.532582991256, -3705.2698619171524, -3091.483658118689, -2142.6872306516616, -961.6973945255621, 323.5072765699531, 1573.654914516516, 2653.272624703366, 3445.367076236966, 3864.1025446818885, 3864.102544681889, 3445.367076236966, 2653.2726247033684, 1573.6549145165184, 323.507276569955, -961.6973945255603, -2142.68723065166, -3091.4836581186883, -3705.2698619171524, -3917.532582991256], [-2070.977568534966, -1958.7662914956288, -1634.2922933821878, -1132.7173666230622, -508.39493726947416, 171.01997209749965, 831.902213839427, 1402.6349424190175, 1821.3704108639404, 2042.7321338179497, 2042.7321338179502, 1821.3704108639404, 1402.6349424190187, 831.9022138394282, 171.01997209750064, -508.3949372694732, -1132.7173666230613, -1634.2922933821872, -1958.7662914956288, -2070.977568534966], [-1.5621929143429165e-12, -1.4775489932480931e-12, -1.2327897122000057e-12, -8.544385371317645e-13, -3.8349568858537574e-13, 1.2900486836795326e-13, 6.275257461168375e-13, 1.0580444722087668e-12, 1.373907662485362e-12, 1.5408866391577764e-12, 1.5408866391577766e-12, 1.373907662485362e-12, 1.0580444722087678e-12, 6.275257461168384e-13, 1.2900486836795402e-13, -3.83495688585375e-13, -8.544385371317639e-13, -1.2327897122000053e-12, -1.4775489932480931e-12, -1.5621929143429165e-12]], \"y\": [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 672.4453172381089, 1272.020750289313, 1733.7529976111107, 2007.6062056921824, 2063.9041301666034, 1896.5460173652668, 1523.6677156388778, 985.6763747824739, 340.8717043736032, -340.8717043736027, -985.6763747824734, -1523.6677156388766, -1896.5460173652662, -2063.9041301666034, -2007.6062056921826, -1733.752997611111, -1272.0207502893134, -672.4453172381094, -5.072432100824625e-13], [0.0, 1272.0207502893131, 2406.1983148492195, 3279.626955981496, 3797.6571277777143, 3904.152223057449, 3587.5718458054816, 2882.222392147741, 1864.539420012481, 644.8046704088713, -644.8046704088703, -1864.5394200124804, -2882.222392147739, -3587.5718458054803, -3904.152223057449, -3797.657127777715, -3279.6269559814964, -2406.1983148492204, -1272.0207502893138, -9.595187476631407e-13], [0.0, 1733.7529976111107, 3279.6269559814955, 4470.102445015823, 5176.172973346762, 5321.324843416592, 4889.8285978399235, 3928.443550179085, 2541.350687774138, 878.8630452300076, -878.8630452300063, -2541.350687774137, -3928.4435501790817, -4889.828597839922, -5321.324843416592, -5176.172973346763, -4470.102445015824, -3279.6269559814964, -1733.7529976111118, -1.3078155404671353e-12], [0.0, 2007.6062056921824, 3797.657127777714, 5176.172973346762, 5993.770160654701, 6161.849348129235, 5662.196547790195, 4548.95689346632, 2942.767175396611, 1017.6829721352607, -1017.6829721352591, -2942.7671753966097, -4548.9568934663175, -5662.196547790194, -6161.849348129235, -5993.770160654702, -5176.172973346763, -3797.6571277777152, -2007.6062056921837, -1.5143902266125608e-12], [0.0, 2063.9041301666034, 3904.152223057449, 5321.324843416592, 6161.849348129236, 6334.641865028304, 5820.977643755634, 4676.520173007722, 3025.2891778274434, 1046.2211580313447, -1046.221158031343, -3025.289177827442, -4676.520173007719, -5820.977643755632, -6334.641865028304, -6161.849348129237, -5321.324843416593, -3904.1522230574506, -2063.904130166605, -1.5568572335190474e-12], [0.0, 1896.5460173652668, 3587.571845805481, 4889.8285978399235, 5662.196547790195, 5820.977643755633, 5348.9654902458315, 4297.309928116757, 2779.9741556424556, 961.3850476608397, -961.3850476608383, -2779.974155642454, -4297.309928116753, -5348.96549024583, -5820.977643755633, -5662.196547790196, -4889.828597839924, -3587.5718458054826, -1896.5460173652682, -1.430614602044772e-12], [0.0, 1523.667715638878, 2882.222392147741, 3928.4435501790854, 4548.956893466321, 4676.520173007722, 4297.309928116757, 3452.419472880565, 2233.405797950153, 772.3679499502728, -772.3679499502715, -2233.405797950152, -3452.4194728805624, -4297.309928116756, -4676.520173007722, -4548.956893466322, -3928.443550179086, -2882.222392147742, -1523.667715638879, -1.1493426801662274e-12], [0.0, 985.676374782474, 1864.539420012481, 2541.3506877741384, 2942.767175396611, 3025.289177827443, 2779.9741556424556, 2233.405797950153, 1444.8132671883818, 499.65280033904173, -499.652800339041, -1444.8132671883811, -2233.4057979501513, -2779.9741556424547, -3025.289177827443, -2942.7671753966115, -2541.350687774139, -1864.5394200124817, -985.6763747824746, -7.435216450024997e-13], [0.0, 340.8717043736033, 644.8046704088712, 878.8630452300076, 1017.6829721352607, 1046.2211580313447, 961.3850476608397, 772.3679499502726, 499.65280033904173, 172.79251689906815, -172.7925168990679, -499.6528003390415, -772.3679499502721, -961.3850476608395, -1046.2211580313447, -1017.6829721352608, -878.8630452300079, -644.8046704088714, -340.8717043736035, -2.5712850267573817e-13], [0.0, -340.87170437360277, -644.8046704088703, -878.8630452300064, -1017.6829721352591, -1046.221158031343, -961.3850476608383, -772.3679499502715, -499.652800339041, -172.7925168990679, 172.7925168990676, 499.65280033904077, 772.3679499502709, 961.385047660838, 1046.221158031343, 1017.6829721352593, 878.8630452300065, 644.8046704088705, 340.871704373603, 2.5712850267573776e-13], [0.0, -985.6763747824737, -1864.5394200124804, -2541.3506877741374, -2942.76717539661, -3025.289177827442, -2779.9741556424547, -2233.405797950152, -1444.8132671883811, -499.65280033904156, 499.6528003390408, 1444.8132671883807, 2233.4057979501504, 2779.9741556424538, 3025.289177827442, 2942.7671753966106, 2541.350687774138, 1864.539420012481, 985.6763747824742, 7.435216450024994e-13], [0.0, -1523.6677156388769, -2882.222392147739, -3928.443550179082, -4548.9568934663175, -4676.520173007719, -4297.309928116754, -3452.4194728805624, -2233.4057979501513, -772.3679499502722, 772.3679499502709, 2233.4057979501504, 3452.4194728805596, 4297.309928116752, 4676.520173007719, 4548.956893466318, 3928.443550179083, 2882.22239214774, 1523.6677156388778, 1.1493426801662265e-12], [0.0, -1896.5460173652664, -3587.5718458054803, -4889.828597839923, -5662.196547790194, -5820.977643755632, -5348.96549024583, -4297.309928116755, -2779.9741556424547, -961.3850476608395, 961.385047660838, 2779.9741556424538, 4297.309928116752, 5348.965490245829, 5820.977643755632, 5662.196547790195, 4889.8285978399235, 3587.5718458054816, 1896.5460173652677, 1.4306146020447716e-12], [0.0, -2063.9041301666034, -3904.152223057449, -5321.324843416592, -6161.849348129236, -6334.641865028304, -5820.977643755634, -4676.520173007722, -3025.2891778274434, -1046.2211580313447, 1046.221158031343, 3025.289177827442, 4676.520173007719, 5820.977643755632, 6334.641865028304, 6161.849348129237, 5321.324843416593, 3904.1522230574506, 2063.904130166605, 1.5568572335190474e-12], [0.0, -2007.6062056921826, -3797.6571277777143, -5176.172973346763, -5993.770160654702, -6161.849348129236, -5662.196547790196, -4548.956893466321, -2942.7671753966115, -1017.6829721352608, 1017.6829721352592, 2942.76717539661, 4548.9568934663175, 5662.196547790194, 6161.849348129236, 5993.7701606547025, 5176.172973346764, 3797.6571277777157, 2007.606205692184, 1.514390226612561e-12], [0.0, -1733.7529976111111, -3279.6269559814964, -4470.102445015825, -5176.172973346764, -5321.324843416593, -4889.828597839924, -3928.443550179086, -2541.350687774139, -878.8630452300079, 878.8630452300065, 2541.3506877741374, 3928.4435501790826, 4889.8285978399235, 5321.324843416593, 5176.172973346765, 4470.102445015826, 3279.6269559814973, 1733.7529976111123, 1.3078155404671357e-12], [0.0, -1272.0207502893136, -2406.1983148492204, -3279.626955981497, -3797.6571277777157, -3904.1522230574506, -3587.571845805483, -2882.222392147742, -1864.5394200124817, -644.8046704088715, 644.8046704088705, 1864.539420012481, 2882.22239214774, 3587.5718458054816, 3904.1522230574506, 3797.657127777716, 3279.6269559814973, 2406.1983148492213, 1272.0207502893143, 9.595187476631409e-13], [0.0, -672.4453172381095, -1272.020750289314, -1733.752997611112, -2007.606205692184, -2063.9041301666052, -1896.5460173652684, -1523.6677156388791, -985.6763747824748, -340.87170437360356, 340.87170437360305, 985.6763747824743, 1523.6677156388778, 1896.5460173652677, 2063.9041301666052, 2007.6062056921844, 1733.7529976111123, 1272.0207502893145, 672.44531723811, 5.07243210082463e-13], [0.0, -5.072432100824626e-13, -9.595187476631405e-13, -1.3078155404671353e-12, -1.5143902266125608e-12, -1.5568572335190474e-12, -1.430614602044772e-12, -1.1493426801662272e-12, -7.435216450024997e-13, -2.5712850267573817e-13, 2.5712850267573776e-13, 7.435216450024993e-13, 1.1493426801662263e-12, 1.4306146020447714e-12, 1.5568572335190474e-12, 1.514390226612561e-12, 1.3078155404671357e-12, 9.595187476631409e-13, 5.072432100824629e-13, 3.826269104401456e-28]], \"z\": [[6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366], [6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864], [5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782], [3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239], [1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561], [-526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267], [-2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456], [-4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595], [-5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402], [-6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607], [-6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608], [-5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402], [-4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663], [-2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649], [-526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298], [1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558], [3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236], [5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378], [6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864], [6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366]], \"type\": \"surface\", \"uid\": \"bd507859-b277-11e8-b505-a0afbda902ef\"}], {\"autosize\": true, \"scene\": {\"aspectmode\": \"data\", \"xaxis\": {\"title\": \"x (km)\"}, \"yaxis\": {\"title\": \"y (km)\"}, \"zaxis\": {\"title\": \"z (km)\"}}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
],
"text/vnd.plotly.v1+html": [
"<div id=\"d35cb9d8-a09b-41c2-b214-8494e23f9b57\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"d35cb9d8-a09b-41c2-b214-8494e23f9b57\", [{\"line\": {\"dash\": \"solid\", \"width\": 5}, \"mode\": \"lines\", \"name\": \"orbit with artificial thrust\", \"x\": [25298.4, 25129.95094301439, 24629.473005922027, 23811.06399499985, 22696.654854560566, 21313.9848693321, 19694.427888362825, 17870.86426367357, 15875.970445808323, 13740.902604479561, 11494.428404909075, 9162.582761252159, 6768.391050500591, 4331.976246042497, 1870.723137333599, -600.4766510219104, -3068.9566022446206, -5523.989582266942, -7956.528317196312, -10358.96863016055, -12724.938763101389, -15049.110451277978, -17327.038293213045, -19555.0268240598, -21730.01457645421, -23849.4644894363, -25911.281416495345, -27913.75331287907, -29855.48100525764, -31735.329571718663, -33552.386524054906, -35305.92692542771, -36995.382170397854, -38620.31161901917, -40180.384994708635, -41675.362556762455, -43105.079905868755, -44469.43343409295, -45768.36905028565, -47001.87347980603, -48169.96722371393, -49272.698262584396, -50310.13559009795, -51282.36166156018, -52189.46360936794, -53031.54007330621, -53808.6949358683, -54521.03266252771, -55168.65716534234, -55751.66910235091, -56270.16403305576, -56724.23124971992, -57113.95310420657, -57439.40465008878, -57700.653419757466, -57897.75915625567, -58030.773319567146, -58099.738187087096, -58104.68536800328, -58045.63355131515, -57922.58717282707, -57735.5521370458, -57484.52211171138, -57169.48292868814, -56790.414269747525, -56347.28918469497, -55840.07439959519, -55268.731323835476, -54633.217664766256, -53933.489558658635, -53169.50412671837, -52341.222364895504, -51448.61227622951, -50491.65215446916, -49470.33392770672, -48384.666470765835, -47234.678795082946, -46020.42302482095, -44741.97706795563, -43399.44691434107, -41992.99621188644, -40522.85940424613, -38989.326276037726, -37392.765333584364, -35733.63815141309, -34012.51609700226, -32230.09939598289, -30387.238664475757, -28484.961246145704, -26524.50101779782, -24507.33262971259, -22435.211745458397, -20310.22171516925, -18134.82687744492, -15911.93483271752, -13644.968207335696, -11337.946625811837, -8995.580628273095, -6623.378197754873, -4227.764587285679, -1816.215438414089, 602.5981021741972, 3018.659821025472, 5420.437363558957, 7794.725955749116, 10126.507438470911, 12398.838624348067, 14592.794127542471, 16687.50330524782, 18660.289817410674, 20487.008813849996, 22142.565247160226, 23601.59933179125, 24839.459357893382, 25833.48032844497, 26564.075710986384, 27016.03969559695, 27179.613475776237, 27051.16081645905, 26633.469189174168, 25935.559051684497, 24971.935748698023, 23761.640653054652, 22327.032968049614, 20692.397068237977, 18882.931321839802, 16923.863113621777, 14839.427222826092, 12652.506687119168, 10384.267125748678, 8053.932793200496, 5678.769347852585, 3274.203526942929, 853.7947569292202, -1570.5554036466615, -3988.4692648668024, -6390.925648068066, -8770.112424588777, -11119.284509051698, -13432.628411772399, -15705.143666323489, -17932.539841385933, -20111.142850322467, -22237.80429649911, -24309.828787910108, -26324.923879338385, -28281.135084735146, -30176.800766534394, -32010.51181612771, -33781.07703741762, -35487.49185151589, -37128.91055270189, -38704.626131649165, -40214.049738639515, -41656.69356715635, -43032.15536370977, -44340.106035434175, -45580.27950969844, -46752.46397225443, -47856.49361044931, -48892.2399880266, -49859.60237772237, -50758.50777752945, -51588.90657340656, -52350.76646819533, -53044.0699666992, -53668.810453815546, -54224.98937497695, -54712.61429514207, -55131.69761257534, -55482.255703655144, -55764.3082749501, -55977.877698803066, -56122.988108662415, -56199.66403040018, -56207.92832585675, -56147.799224851595, -56019.29231768679, -55822.43538258486, -55557.251579873315, -55223.76749933492, -54822.013186202974, -54352.022309199674, -53813.83320896769, -53207.490715408785, -52533.04862244259, -51790.57270869854, -50980.14419265545, -50101.863510741045, -49155.85430690529, -48142.267522181144, -47061.28547274552, -45913.12580499476, -44698.04521614694, -43416.342828885485, -42068.365514329606, -40654.54653483676, -39175.3940832112, -37631.499308987346, -36023.55408144464, -34352.36616868075, -32618.876549567976, -30824.178753146243, -28969.54018406831, -27056.427700139113, -25086.536398375036, -23061.821263858576, -20984.533829309326, -18857.26339890003, -16682.983215323067, -14465.102754462298, -12207.52577294925, -9914.714951124826, -7591.763110186755, -5244.47098972793, -2879.4310628751964, -504.11637473951276, 1873.0306445458398, 4242.505440685997, 6593.643098069291, 8914.55005759924, 11192.036560046889, 13411.591156286098, 15557.392880214547, 17612.372298664377, 19558.34518774212, 21376.242586522312, 23046.46086811814, 24549.29912292404, 25865.526242417425, 26976.961133057102, 27867.28811104045, 28522.78338910723, 28932.883844463442, 29090.669541457806, 28993.331482643713, 28642.61915208613, 28044.344151350222, 27208.10277730485, 26146.900334905655, 24876.47686331361, 23414.500894596702, 21779.85014509213, 19992.181589942713, 18071.147403300278, 16035.931839616904, 13904.97410301877, 11695.695207627075, 9424.296830980102, 7105.68536423625, 4753.5424235606015, 2380.224221530778, -3.1410417767706216, -2386.5839206685982, -4761.209470659042, -7119.105257100164, -9453.255966275972, -11757.457022749615, -14026.230944761382, -16254.748493811918, -18438.759326282096, -20574.52913642925, -22658.779841857446, -24688.62993871674, -26661.55315274725, -28575.33969867971, -30428.05395030827, -32218.002488588372, -33943.70536272275, -35603.87093005484, -37197.372845716905, -38723.22823550548, -40180.58198703324, -41568.69107880478, -42886.91099228018, -44134.68288567263, -45311.52278614133, -46417.01276404026, -47450.793507231174, -48412.55771347624, -49302.04371891886, -50119.028780666085, -50863.3214335638, -51534.76047506098, -52133.21503883293, -52658.57680087174, -53110.75935347859, -53489.69509004395, -53795.33308211952, -54027.63776908957, -54186.58828074709, -54272.178213082785, -54284.41567759253, -54223.32344441068, -54088.938999575665, -53881.31433673456, -53600.515303593565, -53246.62032342066, -52819.720831174716, -52319.93743324885, -51747.40808588868, -51102.29522964449, -50384.78831699325, -49595.10563722538, -48733.497473411524, -47800.25043633439, -46795.69282027057, -45720.20082550623, -44574.205492472836, -43358.20019238503, -42072.74851926828, -40718.49242825867, -39296.160465060246, -37806.57593144565, -36250.66483168175, -34629.46571252091, -32944.17538263138, -31196.14594956652, -29386.898352590964, -27518.148261817587, -25591.82957853986, -23610.12037625255, -21575.471034293125, -19490.635722119092, -17358.707600178837, -15183.156199843446, -12967.868729665366, -10717.194476104982, -8435.992407721227, -6129.681500072957, -3804.293256123249, -1466.525482223645, 876.2048431708114, 3215.7151483525577, 5542.998334745137, 7848.184340565847, 10120.524089775536, 12348.361730216937, 14519.155806137751, 16619.530117818722, 18635.343109339923, 20551.79409176176, 22353.584653205307, 24025.15360731413, 25551.003380054648, 26916.00359273623, 28105.73284171606, 29106.888251386114, 29907.75621358617, 30498.632690534578, 30872.13179682373, 31023.419918658466, 30950.412628514478, 30653.97165339472, 30137.992721720584, 29408.884515300295, 28475.48906523878, 27348.767767021312, 26041.464004664016, 24567.620153867556, 22942.068858827668, 21180.035333462973, 19296.987437827524, 17308.57825197944, 15229.730218471072, 13074.869363423566, 10857.742910659721, 8591.315510430077, 6287.693307168683, 3958.0860538296315, 1612.8338611568138, -738.5143616581285, -3087.283777438253, -5425.566924206873, -7746.179407592045, -10042.615338230613, -12308.996416536653, -14540.021056869227, -16730.913403042643, -18877.377580649532, -20975.555585720205, -23021.986640681436, -25013.56584857255, -26947.504265629683, -28821.309326957173, -30632.749078677385, -32379.826060769516, -34060.75354655943, -35673.93459032342, -37217.943413212066, -38691.50817406807, -40093.49417170515, -41422.888196619744, -42678.790481386095, -43860.40125472553, -44967.011341863574, -45997.99268734245, -46952.790431797956, -47830.91639676657, -48631.94358829877, -49355.501330161635, -50001.27063740816, -50568.97944109356, -51058.39727491849, -51469.33102636553, -51801.63056721397, -52055.18072054704, -52229.90146230831, -52325.74642284643, -52342.70171955294, -52280.78564885503, -52140.0490893788, -51920.57646809803, -51622.4871412839, -51245.937042071215, -50791.120446455585, -50258.271709537905, -49647.66682383085, -48959.62465144243, -48194.50810068982, -47352.739716655, -46434.79742451044, -45441.22235920652, -44372.62639753037, -43229.695735712856, -42013.19676115766, -40723.98394553594, -39363.00948648838, -37931.334425178255, -36430.14096693815, -34860.74573225128, -33224.613665313474, -31523.372327414498, -29758.826302385598, -27932.97144135189, -26048.008674035602, -24106.35711385147, -22110.676616320656, -20063.946463405257, -17969.436529353374, -15830.741536830741, -13651.814207882664, -11436.995109512818, -9191.043188305723, -6919.16755551075, -4627.058956654446, -2320.9207234157125, -7.498098135412635, 2305.900234295661, 4611.373902534852, 6900.409634857354, 9163.881788033139, 11392.053628259411, 13574.581956204851, 15700.567956300887, 17758.61004438368, 19736.867048207394, 21623.143713304198, 23405.010526667847, 25069.969850731784, 26605.680360121558, 28000.151431506787, 29241.887746007596, 30320.17604505196, 31225.331630881024, 31948.97462237822, 32484.231804314706, 32825.88566503006, 32970.49283952015, 32916.49417490522, 32664.33863524935, 32216.60104891623, 31577.60556758501, 30753.39405999474, 29751.59137644803, 28581.237109339938, 27252.549498944652, 25776.63163790524, 24165.182945828245, 22430.278885385585, 20584.281891330087, 18639.83514438343, 16609.221485946917, 14504.48059480156, 12337.311765328048, 10118.961458075117, 7860.1610779973425, 5571.084955511163, 3261.3252994134964, 939.881643550692, -1384.8403389330936, -3705.0216852787566, -6013.40222326928, -8303.264107317918, -10568.406654642533, -12803.118746552936, -15002.150331623756, -17160.68107725312, -19274.291693903935, -21338.93715629589, -23350.920347537874, -25306.864652200748, -27203.684039625532, -29038.561959290757, -30808.93717670874, -32512.476351865487, -34147.05679104959, -35710.74893396576, -37201.801043127954, -38618.62545731584, -39959.78577087048, -41223.98430061149, -42410.04920215532, -43516.92406553346, -44543.664961007154, -45489.42788167426, -46353.462831586105, -47135.107016498936, -47833.77923328132, -48448.9753091107, -48980.26434428051, -49427.28551243838, -49789.745172077986, -50067.414043103556, -50260.12420228979, -50367.765765181044, -50390.290601125125, -50327.71175798892, -50180.10138506786, -49947.59220470789, -49630.377996047304, -49228.71500320271, -48742.92414362792, -48173.39389237583, -47520.5837179921, -46785.02794577028, -45967.33992409769, -45068.21636962056, -44088.441766959404, -43028.89269870255, -41890.54264872077, -40674.47834294951, -39381.90083288478, -38014.136552660544, -36572.64625803516, -35059.033256714574, -33475.05391689239, -31822.630037258376, -30103.862661738865, -28321.04692222051, -26476.68749251759, -24573.514236835174, -22614.49763599029, -20602.863574643725, -18542.10707280034, -16436.004544836418, -14288.636327566162, -12104.43595157008, -9888.173247124303, -7644.981096664003, -5380.373444932911, -3100.2604731192864, -810.9618650968106, 1480.7852078873718, 3767.821443291313, 6042.553001894759, 8296.959113354405, 10522.615064166508, 12710.690090823433, 14851.99020864466, 16937.006003010505, 18955.959391691275, 20898.857039089642, 22755.55810322267, 24515.863996265038, 26169.637839478273, 27706.961293348522, 29118.24513442138, 30394.287112947008, 31526.4731288991, 32506.901488047144, 33328.55826785625, 33985.444678670014, 34472.66903175195, 34786.516777389515, 34924.51207626531, 34885.48436730163, 34669.65339518279, 34278.735042617685, 33715.67366274823, 32984.59296781168, 32090.801841474156, 31040.68379521685, 29841.59044377116, 28501.68774280391, 27029.78428630566, 25435.170961132746, 23727.501256157666, 21916.74152347667, 20013.174437115773, 18026.964452079825, 15968.202487745144, 13846.870632451632, 11672.739468720207, 9455.310530543986, 7203.769596772714, 4926.951425013526, 2633.3138452339667, 330.9206097940601, -1972.5725216923668, -4269.918977392774, -6554.273329691396, -8819.190860572095, -11058.61792688046, -13266.881859867894, -15438.676300134415, -17569.045172955008, -19653.367942377172, -21687.34507075831, -23666.982577844403, -25588.574593493202, -27448.682798142836, -29244.120212284197, -30971.94853107281, -32629.453873599734, -34214.13555630986, -35723.69280979512, -37156.01300672637, -38509.16111161878, -39781.36996897628, -40971.03104636708, -42076.68524897434, -43097.01342217371, -44030.826797486254, -44877.06699722133, -45634.79786181244, -46303.19893774602, -46881.561555191525, -47369.28484604567, -47765.87256017546, -48070.93057708212, -48284.16501020525, -48405.38080108918, -48434.48070063189, -48371.4645346375, -48216.42865089303, -47969.56583107051, -47631.1695562955, -47201.63386603493, -46681.455524104495, -46071.23663924553, -45371.68766337912, -44583.63115223972, -43708.00619347265, -42745.873407280014, -41698.420424697244, -40566.96774858702, -39352.974902432456, -38058.046772015376, -36683.940045061485, -35232.570815917454, -33706.02743110211, -32106.57349427184, -30436.663276965082, -28698.948439917407, -26896.285728133276, -25031.74672379495, -23108.62900986269, -21130.46809722376, -19101.0494682452, -17024.420089584735, -14904.898747120329, -12747.08455584827, -10555.86299761031, -8336.40883950406, -6094.185285830912, -3834.9443804755065, -1564.7756902582512, 709.9316438778413, 2982.4987901391623, 5245.947961807267, 7493.016419279126, 9716.178606277132, 11907.657151823902, 14059.45744053036, 16163.403311479276, 18211.172132621952, 20194.33367283418, 22104.397193772074, 23932.871183677238, 25671.340155271755, 27311.557314819303, 28845.46792189937, 30265.31920836255, 31563.709907421344, 32733.69615389196, 33768.89188401064, 34663.543820719395, 35412.58897020106, 36011.702555446005, 36457.344312629444, 36746.81107608234, 36878.303577635284, 36851.01538611709, 36665.19762392298, 36321.8610227337, 35822.95909239614, 35171.33662048008, 34370.699322760236, 33425.57065406535, 32341.219002055885, 31123.568829344556, 29779.10932937554, 28314.814161478294, 26738.085830510496, 25056.738276506567, 23278.99125345696, 21413.122579843613, 19467.549803267346, 17450.78017693739, 15371.340113751061, 13237.723117960517, 11058.333904367211, 8841.435833363892, 6595.108790134634, 4327.224636335781, 2045.4473625581284, -242.75058084295054, -2530.2155581753786, -4810.066435092627, -7075.689957574445, -9320.758066433336, -11539.231059752188, -13725.35793266819, -15873.672656139837, -17978.990848279318, -20036.406267078808, -22041.28631808797, -23989.26577061853, -25876.237876030686, -27698.342081668066, -29451.95767811929, -31133.706892228274, -32740.436413754418, -34269.212437112685, -35717.31295637883, -37082.220569840734, -38361.615759215485, -39553.37058834072, -40655.54276615369, -41666.37001877109, -42584.2647154792, -43407.80869344996, -44135.748226311436, -44766.99114474091, -45300.605628655416, -45735.81464213854, -46071.99433465354, -46308.67268906147, -46445.52829736347, -46482.38938119211, -46419.23317407652, -46256.18578250575, -45993.522642814714, -45631.66969091775, -45171.2053619137, -44612.86353658787, -43957.535416921, -43206.268117040745, -42360.27210636666, -41420.92317711944, -40389.76616830193, -39268.51905971408, -38059.077301926154, -36763.518344419295, -35384.10632410743, -33923.29687644513, -32383.74203133994, -30768.295156068925, -29080.015907422057, -27322.175158026857, -25498.25990776402, -23611.97737126224, -21667.258837160436, -19668.26317351684, -17619.37965079976, -15525.229843462866, -13390.668688373748, -11220.784462659614, -9020.897378366048, -6796.55682796515, -4553.536875638436, -2297.8299728504962, -35.63859950105572, 2226.6403050766658, 4482.42144793332, 6724.945106994718, 8947.298573559783, 11142.437730433045, 13303.21128500558, 15422.386920376286, 17492.680990571745, 19506.793299510107, 21457.438764492148, 23337.401704760003, 25139.55118867293, 26856.893359525297, 28482.629495390185, 30010.203017524247, 31433.33944133321, 32746.08326388277, 33942.83578195403, 35018.39783463181, 35968.02146442084, 36787.47449087969, 37473.12199076825, 38021.8965632246, 38431.24227649981, 38699.25851230085, 38824.6805238706, 38806.8991953652, 38645.96388056756, 38342.57367755292, 37898.06250730922, 37314.38336431379, 36594.097107068, 35740.371156594076, 34756.97761946378, 33648.16485334259, 32418.67421597067, 31073.701366766112, 29618.85789018959, 28060.130322479537, 26403.83117665899, 24656.547486558396, 22825.092390606296, 20916.46527613019, 18937.826004916787, 16896.485854508726, 14799.810510027139, 12655.183058466804, 10470.005264374824, 8251.653231659064, 6007.448960108469, 3744.6342214984115, 1470.3470860637383, -808.4002122485981, -3084.744122952825, -5351.975951881122, -7603.560118746364, -9833.1477082603, -12034.587192487123, -14201.932404741103, -16329.449923875105, -18411.625618267382, -20443.16992867957, -22419.021469180603, -24334.348525316494, -26184.54836074266, -27965.25274058885, -29672.32911264127, -31301.87624017809, -32850.225999074035, -34313.94377929138, -35689.82764787186, -36974.90670283751, -38166.43904740221, -39261.90981390164, -40259.02966684371, -41155.73421448941, -41950.184758363415, -42640.770810106456, -43226.11241324072, -43705.03982126812, -44076.60713447272, -44340.089958925055, -44494.98514565281, -44541.01209607157, -44478.112768296574, -44306.45093057146, -44026.41120805146, -43638.5984691778, -43143.838097880674, -42543.177697847146, -41837.89077509239, -41029.48294506953, -40119.69127372695, -39110.462010641866, -38003.97475283827, -36802.63524649073, -35509.076632900105, -34126.16023158729, -32656.97520523481, -31104.83732189326, -29473.28702988702, -27766.08706084147, -25987.219776256545, -24140.884473061018, -22231.49486356543, -20263.67694524405, -18242.267475775054, -16172.305159660793, -14059.009157861696, -11907.787972335651, -9724.225611435142, -7514.071099492537, -5283.226984572263, -3037.7366958402326, -783.7705887425582, 1472.392424625919, 3724.3796337906197, 5965.736156625713, 8189.947298716857, 10390.464396654068, 12560.717761320602, 14694.14232166579, 16784.205521670636, 18824.431769162384, 20808.425013789478, 22729.891031491592, 24582.660992774287, 26360.717892118453, 28058.22741584039, 29669.574825727566, 31189.405596197408, 32612.60188526328, 33934.33198345628, 35150.072844597205, 36255.634629459535, 37247.18845233602, 38121.2854641808, 38874.86992322531, 39505.288903967565, 40010.301295431906, 40388.08873960031, 40637.27116091233, 40756.92953773323, 40746.63856668866, 40606.42242976296, 40336.71095384731, 39938.40745580666, 39412.85675184476, 38761.8486614893, 37987.6106212516, 37092.792444414765, 36080.446265084734, 34954.00470464073, 33717.26029871535, 32374.34922284346, 30929.74235491122, 29388.246712537784, 27755.014393382065, 26035.410965212242, 24235.028716023346, 22359.70432180356, 20415.47447391746, 18408.55219450519, 16345.303683234108, 14232.224641728484, 12075.9172642356, 9883.06749761596, 7660.422678597997, 5414.769784857234, 3152.91411098178, 881.6586322627558, -1392.2199634054527, -3661.9856806911575, -5920.959857583914, -8162.541973302598, -10380.226822679517, -12567.620994289005, -14718.458660326962, -16826.616640875334, -18886.128171207718, -20891.19789748445, -22836.215496768687, -24715.7632093442, -26524.631699520138, -28257.832963219793, -29910.610396371914, -31478.446964567287, -32957.07241442789, -34342.47046715261, -35630.88693468788, -36818.83969898363, -37903.13149479078, -38880.86643645045, -39749.445325937486, -40506.54171880617, -41150.14387656782, -41678.54626529345, -42090.35737675067, -42384.50362263096, -42560.2302565388, -42617.10063782596, -42554.995151358315, -42374.1110972996, -42074.964864998154, -41658.39770506154, -41125.58641370567], \"y\": [0.0, 3450.5350348436245, 6854.9797701458565, 10170.551303244134, 13359.40466527167, 16390.16925051825, 19238.652155234755, 21887.799623507435, 24327.103209099885, 26551.661011151366, 28561.106657472395, 30358.598637607614, 31949.838416969757, 33342.298992841344, 34544.583309359965, 35565.93234388592, 36415.8620896293, 37103.89068948638, 37639.35494061142, 38031.28805016374, 38288.34380530761, 38418.750187610975, 38430.288710258086, 38330.29546180485, 38125.67132202903, 37822.88886598989, 37428.01251661776, 36946.733060536644, 36384.38278591029, 35745.96168701508, 35036.16176371231, 34259.39063743965, 33419.79317972634, 32521.270880211443, 31567.50342221243, 30561.9658500426, 29507.945386574982, 28408.55704837182, 27266.75809823618, 26085.361444010254, 24867.048034756324, 23614.378305455928, 22329.802721363114, 21015.67147314745, 19674.243374695026, 18307.694616556844, 16918.12711317954, 15507.574991044543, 14078.01173738312, 12631.356933515528, 11169.482473595375, 9694.218346665873, 8207.358059935248, 6710.663781177992, 5205.871278169173, 3694.694733058612, 2178.831509591918, 659.9669510853286, -860.2207129386114, -2380.055273547654, -3897.85767585202, -5411.945652758044, -6920.625721181304, -8422.189676319462, -9914.910189582672, -11397.035761147914, -12866.78576119655, -14322.345487332886, -15761.86116568247, -17183.434823168653, -18585.118958465097, -19964.910939123427, -21320.747052373885, -22650.496137097558, -23951.95272446828, -25222.829614763243, -26460.749817839573, -27663.23778477666, -28827.70985818129, -29951.46387570372, -31031.676044998487, -32065.391196668897, -33049.50394875843, -33980.75039444435, -34855.695773487736, -35670.72156196536, -36422.01181778891, -37105.53870933355, -37717.04832506117, -38252.04599349244, -38705.78147032048, -39073.23501940146, -39349.104749668986, -39527.79514523362, -39603.4092333958, -39569.74523901489, -39420.299112897, -39148.27607925946, -38746.61404736036, -38208.023027236704, -37525.04550967057, -36690.1482278171, -35695.908715277415, -34535.06600575609, -33200.758401750594, -31686.830236644302, -29988.185216151633, -28101.21367836703, -26024.297347387594, -23758.385444315565, -21307.590148590665, -18679.801466309727, -15887.241010235604, -12946.820714463096, -9880.217318426252, -6713.7352235527605, -3477.6498344264633, -205.16324678876472, 3068.840001069659, 6309.385470634159, 9483.015839696714, 12559.003404085293, 15510.497763142497, 18315.152318970126, 20955.387069230277, 23418.320454624547, 25695.40683435402, 27781.91937715619, 29676.372988464518, 31379.923802467347, 32895.77016744762, 34228.662032274486, 35384.51609819832, 36369.976872339066, 37192.20622204074, 37858.67787504789, 38376.868243885765, 38754.20217793787, 38997.96746948468, 39115.24858936353, 39112.8835730188, 38997.43941825284, 38775.198839093304, 38452.15123219643, 38033.992776144936, 37526.140098128475, 36933.7346858328, 36261.655628773966, 35514.532936187556, 34696.76169570615, 33812.51583709316, 32865.76140148159, 31860.271679277066, 30799.639781938222, 29687.291565108193, 28526.497820422697, 27320.385905130486, 26071.950795026605, 24784.065537834897, 23459.49108417697, 22100.885473264843, 20710.812347226576, 19291.749245850508, 17846.096152014088, 16376.18179419558, 14884.270851074467, 13372.570894383749, 11843.238797684293, 10298.386701787, 8740.087627550287, 7170.380826781162, 5591.276961966323, 4004.763205560667, 2412.8083495614233, 817.3680160944297, -779.609940260129, -2376.179747674272, -3970.393591937499, -5560.301342334877, -7143.940447924826, -8719.333598269825, -10284.483470634474, -11837.367446451812, -13375.932492730059, -14898.090126237645, -16401.711378308144, -17884.621678105057, -19344.59557218473, -20779.35119819906, -22186.544430577513, -23563.762616027605, -24908.517816694908, -26218.239478821364, -27490.26644474299, -28721.838226065902, -29910.08645010826, -31052.03916733831, -32144.60339429046, -33184.55531738875, -34168.533504332925, -35093.03018237318, -35954.38250904627, -36748.76373090629, -37472.17416591811, -38120.43340013314, -38689.17315765472, -39173.830703404055, -39569.64437695592, -39871.65130190642, -40074.68785117987, -40173.394651258845, -40162.226508189764, -40035.46933911892, -39787.26576687134, -39411.651753109654, -38902.60689600644, -38254.12157931147, -37460.354523790906, -36515.73639941647, -35414.91831848276, -34153.07115194714, -32726.101361588204, -31130.884548018716, -29365.553473566946, -27429.828161251287, -25325.363661327232, -23056.091076157307, -20628.52761022603, -18052.10683660989, -15339.369104058362, -12506.143585073305, -9571.304768207177, -6556.5179703035665, -3485.9705808203785, -385.8587285231653, 2716.600939164317, 5794.689565264754, 8822.120319363425, 11774.066314824342, 14628.070471287338, 17364.441804980288, 19966.549406324073, 22420.945086571846, 24717.333256842823, 26848.3466929044, 28809.28582644543, 30597.80721653893, 32213.57660856715, 33657.9076618448, 34933.446806328284, 36043.93757596281, 36993.90121800355, 37788.449346470305, 38433.22654754744, 38934.12210495697, 39297.065779845994, 39528.03796126002, 39632.99747474989, 39617.826729889544, 39488.29074698527, 39250.00817328598, 38908.43179482676, 38468.83599554131, 37936.30869862381, 37315.748998499424, 36611.86882443251, 35829.19549511522, 34972.07685314863, 34044.687769325385, 33051.03764864729, 31994.978352803526, 30880.212038469046, 29710.300044432563, 28488.67145288254, 27218.63122557628, 25903.368682052245, 24545.96560054936, 23149.404000807353, 21716.573656503635, 20250.27938507428, 18753.248162674914, 17228.136112031316, 15677.535410529516, 14103.980016782049, 12509.95188450504, 10897.886892384151, 9270.1802849509, 7629.1926775428365, 5977.25561519167, 4316.676782090269, 2649.7449582954514, 978.7348203165267, -694.0883177543659, -2366.463725917937, -4036.1303460959325, -5700.821649457716, -7358.260166592039, -9006.151593872139, -10642.17955960806, -12264.007507035822, -13869.26725065574, -15455.556261896114, -17020.43242841902, -18561.40822611898, -20075.945234651554, -21561.44889069863, -23015.26337317945, -24434.666514616336, -25816.864632862103, -27158.987177400926, -28458.081084427253, -29711.10473491448, -30914.92140988077, -32066.29213705964, -33161.86782318668, -34198.1818232465, -35171.661701816105, -36078.613466086295, -36915.214680552876, -37677.513984703925, -38361.42913635495, -38962.74642047676, -39477.1214244172, -39900.08237861661, -40227.036885238536, -40453.281314275715, -40574.01504927937, -40584.35986991272, -40479.38581903404, -40254.144691054455, -39903.71263707619, -39423.24344407285, -38808.060359039926, -38053.90622990958, -37156.66954246777, -36112.61793883996, -34918.57972374202, -33572.100147601996, -32071.598703092313, -30416.552712354922, -28607.702909147756, -26647.26451733484, -24539.12731659813, -22289.02818625085, -19904.680079299615, -17395.925216385687, -14774.764582295704, -12055.388752936422, -9253.949000753948, -6388.310958923619, -3477.834246559661, -543.075985909967, 2394.6838574939193, 5314.197380769024, 8195.054507581279, 11017.26054479752, 13762.231897496902, 16413.092416173677, 18954.90352734047, 21374.79730393622, 23662.019052165586, 25807.91451263142, 27805.89677361041, 29651.35022925756, 31341.275205839574, 32874.22801348527, 34250.10703140955, 35469.95664367627, 36535.76867706314, 37450.301892819334, 38216.94510409196, 38839.64312892522, 39322.89725510655, 39671.317831919485, 39889.642409049535, 39982.728848568935, 39955.49772163263, 39812.88569243096, 39559.80892460948, 39201.1336285538, 38741.65314619476, 38186.07116724647, 37538.9906707912, 36804.90694077806, 35988.19863927786, 35093.1274661711, 34123.83732633087, 33084.35516893643, 31978.59311716208, 30810.351590962695, 29583.32326737544, 28301.097722762348, 26967.166399177913, 25584.927409556763, 24157.691120441268, 22688.685469557357, 21181.06186924407, 19637.900818066006, 18062.217283900238, 16456.965973432074, 14825.04660298326, 13169.309285605406, 11492.560149366145, 9797.567301756655, 8087.066401482512, 6363.762487078722, 4630.337570341944, 2889.454482759321, 1143.7621103336674, -604.0995058832128, -2351.4944242315432, -4095.786308166102, -5834.33407957216, -7564.487615980202, -9283.583387988561, -10988.939933205833, -12677.853063016388, -14347.590698480926, -15995.387231680917, -17618.43758794645, -19213.900416754692, -20778.88751229239, -22310.4616199069, -23805.633293484934, -25261.354672420195, -26674.5144475918, -28041.933851191905, -29360.36350524317, -30626.48096364447, -31836.8887825849, -32988.11295416659, -34076.601538073395, -35098.72332612808, -36050.76637457437, -36928.93623892623, -37729.35374622184, -38448.05213952251, -39080.98224744977, -39624.06589640341, -40073.16037045509, -40424.0772353145, -40672.60241531373, -40814.51641635016, -40845.61894255378, -40761.75920355292, -40558.8719865937, -40233.0205434591, -39780.447357682744, -39197.84326108994, -38482.168897784606, -37630.58752870279, -36640.7264276259, -35510.776055304625, -34239.591455241636, -32826.79609460467, -31272.899902800687, -29579.423300226295, -27749.018107893873, -25785.576228631006, -23694.31699055621, -21481.844043525954, -19156.217462802306, -16726.97775999894, -14205.143917345604, -11603.116401096999, -8934.505464202535, -6213.994901752184, -3457.19746462195, -680.4576129049917, 2099.442709293245, 4865.719596105535, 7602.132633002184, 10292.680640326216, 12922.072490885255, 15476.072860205362, 17941.638320440605, 20307.047196404095, 22561.971144509967, 24697.506957633126, 26706.187098888906, 28581.987467330433, 30320.313312638053, 31917.74604538728, 33372.04765078576, 34682.04976872009, 35847.53045918515, 36869.10130070176, 37748.098084452315, 38486.477335240634, 39086.720395388445, 39551.83290882805, 39885.39410951279, 40090.993696815494, 40172.421335979714, 40133.639320739014, 39978.73679369944, 39711.89011940345, 39337.33138891869, 38859.32065328888, 38282.1220377505, 37609.98444632915, 36847.12756642825, 35997.73387171775, 35065.93790680986, 34055.81408823286, 32971.377318045095, 31816.578645899357, 30595.30367739938, 29311.371640278823, 27968.53528923243, 26570.48183023447, 25120.835045174932, 23623.15879764543, 22080.959967349507, 20497.688135883276, 18876.741619375196, 17221.470245467797, 15535.17961282538, 13821.135009072948, 12082.565093481468, 10322.665516314944, 8544.60264675937, 6751.517581344081, 4946.530604780991, 3132.7462751322373, 1313.2592038364028, -508.8460941839508, -2330.4910117171507, -4148.596773707559, -5960.081421570779, -7761.855529932633, -9550.818336206303, -11323.854193691453, -13077.82925886919, -14809.588323576821, -16515.95170273972, -18193.712088340544, -19839.631280306687, -21450.43670499216, -23022.81763193671, -24553.421492394984, -26038.85911085571, -27475.69839205353, -28860.463883898326, -30189.63628925834, -31459.651320120458, -32666.90006978271, -33807.7308080009, -34878.45210400073, -35875.33718227752, -36794.62941609831, -37632.54886362619, -38385.299751581304, -39049.0788113583, -39620.084372516576, -40094.52611856078, -40468.64467364551, -40738.75451840474, -40901.24045602264, -40952.59088955137, -40889.42895583599, -40708.54694834182, -40406.945100785684, -39981.99321558558, -39431.57662896307, -38753.61610053915, -37946.4185457412, -37008.75822090606, -35939.94411970421, -34739.88136737674, -33409.1382191748, -31949.016718216288, -30361.622322712235, -28649.92781251152, -26817.826784915058, -24870.172049708417, -22812.794233363344, -20652.53563801713, -18397.25379262632, -16055.806418390177, -13638.00556716816, -11154.507206701846, -8616.722890075675, -6036.735442311443, -3427.20004837866, -801.2121269075395, 1827.876626119419, 4446.712639409996, 7042.2688735567035, 9601.675752635234, 12112.387333121302, 14562.507952268918, 16940.856290276453, 19237.070716246624, 21441.679152305816, 23546.14337262764, 25542.88765411187, 27425.321695462448, 29187.867721417624, 30825.982288397652, 32335.97835845207, 33715.05199615428, 34961.24427312206, 36073.36699199796, 37050.93970828137, 37894.12518668419, 38603.66542722766, 39180.81921516401, 39627.302411305434, 39945.445469240534, 40138.00092285012, 40207.73218728319, 40157.65479670117, 39990.99898245317, 39711.17212868428, 39321.72801713397, 38826.34020156413, 38228.77630079011, 37532.8745948638, 36742.52435219775, 35861.65131541842, 34894.20977373751, 33844.17199578209, 32715.504198945826, 31512.170837244717, 30238.125493549614, 28897.30697392655, 27493.63534847778, 26031.00830847212, 24513.298330700538, 22944.351139987146, 21327.985960793612, 19667.998048847265, 17968.16292110023, 16232.226716881201, 14463.91183905371, 12666.920026892323, 10844.933605714758, 9001.617898057468, 7140.623229760097, 5265.5867417641175, 3380.1342184415194, 1487.8821432591712, -407.5598074098297, -2302.585621516216, -4193.58894880067, -6076.958986729713, -7949.086575772831, -9806.357443761899, -11645.149543215848, -13461.832005632617, -15252.763493720333, -17014.290957760255, -18742.748778142348, -20434.458276113368, -22085.72757478295, -23692.85179242359, -25252.11355011019, -26759.783775737575, -28212.12278646067, -29605.382080241383, -30935.811079688247, -32199.6597138532, -33393.18257882725, -34512.6447247956, -35554.328625799535, -36514.54265583053, -37389.63109600567, -38175.98569757144, -38870.05882548437, -39468.37820731685, -39967.56331223451, -40364.343384795226, -40655.577158316046, -40838.274272556926, -40909.618420469036, -40866.993936832565, -40708.02877274131, -40430.656364760944, -40033.47393928991, -39515.103324334224, -38874.43256356914, -38110.75716417525, -37223.818976870585, -36213.84423356514, -35081.58238456662, -33828.344378446614, -32456.038788035676, -30967.2041860186, -29365.036173596603, -27653.407465687997, -25836.881379014405, -23920.730422919736, -21910.91610349894, -19814.111260282738, -17637.65647541731, -15389.513933582853, -13078.227156040883, -10712.87965783612, -8303.045577173763, -5858.725326000807, -3390.259310809651, -908.2127726922804, 1576.7752043307057, 4054.136665970773, 6513.197289763585, 8943.461889716005, 11334.697054610086, 13677.011578127525, 15960.938101470285, 18177.496107660343, 20318.24160772675, 22375.308858980137, 24341.449455576723, 26210.074131575806, 27975.302616691468, 29632.00478229801, 31175.647710904603, 32602.38056366526, 33909.02285738946, 35093.03858802257, 36152.51115587774, 37086.10787391111, 37893.0400942302, 38573.024988030214, 39126.25501414847, 39553.381111434406, 39855.51524457963, 40034.47973084869, 40092.238322079545, 40030.88005465333, 39852.77239041432, 39560.53154745735, 39156.99491251203, 38645.19959476789, 38028.359442117806, 37309.84191564686, 36493.14666820749, 35581.88767291625, 34579.780747413504, 33490.63831972508, 32318.353433069453, 31066.870716813566, 29740.193788362267, 28342.371486802982, 26877.49302110561, 25349.68172392097, 23763.08817380769, 22121.88349385099, 20430.25363463274, 18692.395449511652, 16912.51537017805, 15094.831490437959, 13243.57986027176, 11362.999864968773, 9457.319434801817, 7530.775810721467, 5587.607269268674, 3632.0530573192996, 1668.352755390422, -299.25473180856676, -2266.5323588975166, -4229.2460987284285, -6183.165877997957, -8124.066090869236, -10047.725491933174, -11949.926269813184, -13826.456121257703, -15673.119133830298, -17485.728707622442, -19260.111887198236, -20992.113041413584, -22677.59749078806, -24312.45539811311, -25892.606086178606, -27414.002946493973, -28872.639102891488, -30264.553993883055, -31585.841037662227, -32832.65654362004, -34001.22997453896, -35087.87091957768, -36088.97968107387, -37001.06160781041, -37820.73914349989, -38544.76577271323, -39170.04118367364, -39693.627385685824, -40112.76582142743, -40424.89561240652, -40627.6726473895, -40718.9896360832, -40696.99689026512, -40560.127134436385, -40307.48693934911, -39938.383770869455, -39452.213463883505, -38848.73730125093, -38128.10360170954, -37290.86848274832, -36338.01510888712, -35270.97147719494, -34091.62683728865, -32802.34602155205, -31405.97615277957, -29905.86026988392, -28305.838342418665, -26610.244132105054, -24823.900522426426, -22952.112002086295, -21000.652988141614, -18975.75067460693, -16884.06109233768, -14732.637065991848, -12528.886753878614, -10280.52145649045, -7995.5342946652545, -5682.17849070205, -3348.879312949901, -1004.1940291733945, 1343.2450121249847, 3684.783623356197, 6011.7968935587905, 8315.739733085238, 10588.198402180022, 12820.944578359096, 15005.993517498373, 17135.66010937608, 19202.552294113564, 21199.634972356194, 23120.261523813457, 24958.20261399352, 26707.669275448985, 28363.327629185256, 29920.30811862628, 31374.21212852494, 32721.118861214454, 33957.59534258963, 35080.710729133214, 36087.99463844789, 36977.43048302263, 37747.46453229661, 38396.98480135898, 38925.30642618314, 39332.155530803815, 39617.65215011816, 39782.37891140733, 39827.69490880182, 39754.87290339423, 39565.4223818361, 39261.152743410144, 38844.15421699616, 38316.78253126776, 37681.64139282156, 36941.563971049545, 36099.59511950246, 35158.976063487666, 34123.1332836484, 32995.67137990788, 31780.34592080139, 30481.05778963771, 29101.83708750134, 27646.835287468464, 26120.31800624728, 24526.655177554265, 22870.309761239074, 21155.826122178, 19387.819212948823, 17570.96569430802, 15709.998127478948, 13809.703372277509, 11874.92632507802, 9910.573907073465, 7921.551966020701, 5912.793727257159, 3889.2592731199197, 1855.9235752537202, -182.22902751528488, -2220.210803303603, -4253.036921723319, -6275.7319373154, -8283.336330301014, -10270.912973095446, -12233.553391028841, -14166.38368571212, -16064.569989497006, -17923.325131428654, -19737.925839873405, -21503.713049865462, -23216.09810200337, -24870.575110494865, -26462.73170032382, -27988.258608070188, -29442.958687643317, -30822.755862167287, -32123.70456327128, -33342.0001990336, -34473.9911918211, -35516.19312727909, -36465.30555571602, -37318.23198713002, -38072.0859797301, -38724.173192349714, -39272.04194113019, -39713.48556887928, -40046.55486846047, -40269.5710799992, -40381.13843629809, -40380.15645624408, -40266.117039026576, -40038.9680634644, -39698.56409595535, -39245.09110528898, -38679.08181459273, -38001.418810304094, -37213.342002243284, -36316.455443629326, -35312.73054928263, -34204.50640391875, -32994.48785242795, -31685.742064046426, -30281.69426231503, -28786.123312728723, -27203.157859974774, -25537.272421642465, -23793.255814713248, -21976.20246578226, -20091.502651837287, -18144.82072976271, -16142.074864817674, -14089.415201185542, -11993.200545577023, -9859.973634866734, -7696.43505875003, -5509.4159083951745, -3305.8492220779663, -1092.7402987765638, 1122.864049285814, 3333.8991463379107, 5533.31408122904, 7714.10023346039, 9869.318329069509, 11992.13108638125, 14075.831006138023, 16113.864527413036, 18099.853770928774, 20027.61709140296, 21891.189660555323, 23684.84530239145, 25403.120802395173, 27040.843912255797, 28593.163229718983, 30055.505299263477, 31423.608276027368, 32693.552593701672, 33861.75871484259, 34924.994245513786, 35880.37989589513, 36725.3933705603, 37457.872187952365, 38076.015058100566, 38578.38192265436, 38963.89293118619, 39231.82611225955, 39381.816636008276, 39414.28272721756, 39329.87111218684, 39129.24595637082, 38813.401767090705, 38383.65635187915, 37841.642844407506, 37189.300748495174, 36428.86702517175, 35562.86708069725, 34594.105514554816, 33525.65665550482, 32360.847404617572, 31103.252399122168, 29756.685526024903, 28325.187862187744, 26813.013401066477, 25224.613926320122, 23564.624392489277, 21837.850172955597, 20049.257535382658, 18203.9687048415, 16307.262874833978, 14364.550509250235, 12381.314276316942, 10363.150588005035, 8315.739269490363, 6244.829722010987, 4156.226847600616, 2055.7764967059834, -50.64938434984034, -2157.1677291116903, -4257.899272992977, -6346.983244569971, -8418.59164923028, -10466.942968210366], \"z\": [0.0, 0.5710083111363927, 2.276538609770986, 5.094890987553403, 8.99234012546611, 13.926210521491704, 19.84832026394841, 26.708178247478973, 34.45564037845436, 43.04286454506144, 52.425574705160194, 62.563618823181145, 73.4212327564129, 84.96687410683451, 97.17291071015126, 109.92800909526488, 121.61187948392516, 131.61477867461136, 139.93618064378103, 146.5836157692417, 151.5708309292577, 154.91629500540608, 156.6420246838213, 156.77269505486075, 155.3349356488859, 152.35671285353732, 147.8669047511696, 141.89505711372172, 134.47107605884597, 125.625060533133, 115.38717493786794, 103.78756326435348, 90.85628719454593, 76.62327950328533, 61.11834628509148, 44.3711567351915, 26.41125641022018, 7.26808962643235, -13.028973086856475, -34.450602219238284, -56.96747138029197, -80.55021445348692, -105.16937798661414, -130.79536900245526, -157.39839998419566, -184.94845088504013, -213.41519603355806, -242.76796470367444, -272.97567400572115, -304.0067575522286, -335.82909831722367, -368.40996359132424, -401.71593993491143, -435.71286603167135, -470.36576134478736, -505.63874847807176, -541.4949671443235, -577.8964776431984, -614.8041517508747, -652.1775489238057, -689.9747974244195, -728.1526569276197, -766.6662375530398, -805.4689332763568, -844.5122883806315, -883.7458314640702, -923.1169114255455, -962.5705317169596, -1002.0491791515569, -1041.4926435572856, -1080.8378245642866, -1120.0185218156346, -1158.9652048904065, -1197.6047592281855, -1235.8602043440872, -1273.6503806234282, -1310.8896009850962, -1347.4872637027634, -1383.3474226730034, -1418.3683118077663, -1452.4422698092053, -1485.4552922959228, -1517.2860833848674, -1547.805686507723, -1576.8768856076854, -1604.3535653641054, -1630.0800213126688, -1653.8902144222245, -1675.6070123215065, -1695.0413810033456, -1711.9915379885367, -1726.242104712123, -1737.5632674010496, -1745.7099378205162, -1750.421009136402, -1751.4187347486463, -1748.4082809466508, -1741.0775773705213, -1729.0975781271366, -1712.1231028223697, -1689.7944649500123, -1661.6485481148027, -1625.7014341871172, -1580.9938798489404, -1527.1722754917203, -1463.9154369023463, -1390.9516748215874, -1308.079556437275, -1215.1925657310708, -1112.3073504877023, -999.5932536954255, -877.4029547028485, -746.3002854831423, -607.079041420919, -460.7684853078961, -308.6277460509879, -152.11563157444647, 7.159032299143161, 167.4971529196493, 327.1868227136427, 484.5821540046289, 638.1632409263773, 786.5936699805974, 928.7526747660552, 1063.7494613414133, 1190.921548762759, 1309.8183806071675, 1420.1767815179903, 1521.893608662795, 1614.996776141869, 1699.6162088153922, 1775.9598948555597, 1844.2949627269825, 1904.9261322566938, 1957.6493331452257, 2001.1768659277834, 2035.7550674230083, 2061.7500315996813, 2079.527744607375, 2089.4495000911843, 2091.8686958198614, 2087.1287507043317, 2075.561736204683, 2057.4873168063828, 2033.212278591812, 2003.0308825787522, 1967.2247452919505, 1926.0632423348934, 1879.8039858154057, 1828.6933872228044, 1772.967231439545, 1712.8512513850985, 1648.5618330562627, 1580.3065966535494, 1508.2850205298855, 1432.6890398807686, 1353.7036261317644, 1271.5073461872823, 1186.2729002472595, 1098.1676368984079, 1007.3540441867175, 913.9902145486941, 818.2302827165591, 720.2249095215413, 620.1216241563619, 518.0652392431249, 414.1982621954546, 308.66127472626124, 201.59328756313266, 93.13207743346769, -16.585486618485994, -127.42312052064396, -239.24479900604263, -351.9144189275796, -465.2954449924904, -579.2505308544562, -693.6411085011838, -808.3271117635644, -923.1670266897264, -1038.0170677965036, -1152.731046704333, -1267.1599612297184, -1381.1515794146671, -1494.5500358794627, -1607.1954325698352, -1718.923435969879, -1829.5648628519368, -1938.945246634555, -2046.8843764198507, -2153.1958007814874, -2257.6862883744166, -2360.1552374377034, -2460.394026261545, -2558.18529668983, -2653.3021627293347, -2745.507432535427, -2834.554068290979, -2920.183526680181, -3002.1248380873662, -3080.0939673241946, -3153.7929731592017, -3222.9091518223727, -3287.1141531775284, -3346.063061789543, -3399.3935628916847, -3446.725141624762, -3487.658300210476, -3521.7739298149363, -3548.6328365021527, -3567.7754678863553, -3578.7219920905786, -3580.9727579140454, -3574.009314429108, -3557.2961314456797, -3530.283226552976, -3492.4099283739697, -3443.1100581461374, -3381.041777297948, -3304.188878474727, -3211.997822539289, -3103.9978169189917, -2979.786328596471, -2839.0511723128866, -2681.59768681591, -2507.379829992789, -2316.5330330624233, -2109.4066539461055, -1886.5938859706498, -1648.963318632138, -1397.676959287458, -1134.208607111664, -860.3215381231004, -578.0452574848915, -289.65084122721527, 2.3969046623059844, 295.53223983277263, 587.2315141944671, 875.0072847090788, 1156.505528689601, 1429.5921440721286, 1692.3910886562053, 1943.3126796692854, 2181.065936529911, 2404.656966158731, 2613.36789079167, 2806.732790098342, 2984.508801042167, 3146.643571763733, 3293.241307001176, 3424.533097864314, 3540.8547035725815, 3642.6163263361937, 3730.2829996196156, 3803.1358418991845, 3860.6642675560906, 3903.426862424302, 3931.9939045863593, 3946.9393728183404, 3948.834761152639, 3938.244354566419, 3915.7217782792845, 3881.8075634199217, 3837.027465851232, 3781.8912838062843, 3716.892306640697, 3642.507208046222, 3559.19606064051, 3467.40264895819, 3367.5549476857914, 3260.0657268439277, 3145.333222520867, 3023.7418197888833, 2895.6628565370715, 2761.4554107496742, 2621.467057397015, 2476.034678594433, 2325.4852423017464, 2170.1365554669796, 2010.2979977581133, 1846.2712420257728, 1678.350967640636, 1506.825572848726, 1331.977892234255, 1154.0857680427912, 973.4226899647264, 790.2584242797659, 604.859583513634, 417.4902793678616, 228.4127262204431, 37.88780748756389, -153.82438286265764, -346.4640113543202, -539.7709763149428, -733.4843671633607, -927.3418993837873, -1121.079312895434, -1314.429721527212, -1507.122901307177, -1698.8846618337593, -1889.4371500593054, -2078.4973858947387, -2265.7769582390374, -2450.981367761548, -2633.8092889751597, -2813.951875231427, -2991.0920916412156, -3164.9040609246836, -3335.05240719444, -3501.191582675452, -3662.9651623658638, -3820.0050916420246, -3971.93087181188, -4118.348668620354, -4258.850328710392, -4393.012289043758, -4520.394535125101, -4640.54215218866, -4752.983111111788, -4857.227248294453, -4952.766093075885, -5039.072467518536, -5115.600242158428, -5181.784245240013, -5237.040480186485, -5280.766755988209, -5312.343632820983, -5331.135966346469, -5336.495085436856, -5327.761777723427, -5304.270230499, -5265.353122732271, -5210.348073854586, -5138.407825084185, -5047.2223770388955, -4935.823766411963, -4803.6867785161485, -4650.3627951216395, -4475.501155983634, -4278.87085365258, -4060.3858287042117, -3820.1332088407516, -3558.4023142161836, -3275.7122500911364, -2972.835907923175, -2650.8182575441997, -2310.997475804819, -1955.008829891215, -1584.789916867792, -1202.5493420297253, -810.7332030641265, -411.9952011384621, -9.156242425899558, 394.8606064221001, 797.1339453466526, 1194.8550137637355, 1585.2692944140395, 1965.8137151663664, 2334.1578407360926, 2688.2360064184872, 3026.2657407197107, 3346.75368448568, 3648.4941645606373, 3930.5655800056807, 4192.318152043663, 4433.323117240832, 4653.366077037044, 4852.417494156581, 5030.605762224003, 5188.189934895622, 5325.534914122338, 5443.09263837178, 5541.242553984026, 5618.90577736613, 5676.236907932582, 5713.888190719548, 5732.534586159165, 5732.86495909997, 5715.574885893962, 5681.36094375709, 5630.9160699169925, 5564.925901497737, 5484.066035205341, 5389.000145878038, 5280.378724862097, 5158.837575120273, 5024.997562256731, 4879.464325002761, 4722.828248872601, 4555.664643661249, 4378.5340821281625, 4191.982877561836, 3996.5436779209795, 3792.736120254283, 3581.0674601304822, 3362.033337589672, 3136.118508890403, 2903.7976804771833, 2665.5363028039774, 2421.7913343679047, 2173.0119947810686, 1919.6405257067795, 1662.112978487845, 1400.8600472937983, 1136.3079666141089, 868.879350250377, 598.9934199271196, 327.0671904810666, 53.51603881195974, -221.24548255597884, -496.80285092453784, -772.7408343512151, -1048.6427902074117, -1324.089983515152, -1598.6609072113317, -1871.9305864890914, -2143.4698493663327, -2412.844545630072, -2679.6146963066526, -2943.3335558073722, -3203.5466173787995, -3459.7921946144265, -3711.5996046763316, -3958.4888150194865, -4199.969904573075, -4435.541983709956, -4664.692315907277, -4886.895610974244, -5101.613459723436, -5308.293879961679, -5506.370943676781, -5695.264455296478, -5874.379650895398, -6043.106888226734, -6200.821297454356, -6346.882362461978, -6480.6334026152645, -6601.40092485322, -6708.495353940525, -6801.2201480719405, -6878.865365036504, -6940.710685138013, -6986.028617127008, -7014.087693012296, -7024.156390965365, -7015.508009775797, -6987.426508285595, -6939.2134923678595, -6870.194143722704, -6778.507600810039, -6662.541536333676, -6521.777719615316, -6355.780721709816, -6164.2156050957265, -5946.866030197832, -5703.652904544616, -5434.655502379511, -5140.133623851696, -4820.549214053933, -4476.5858621745765, -4109.164601031916, -3719.4544272575095, -3308.8853143539272, -2879.1520371839574, -2432.214695005346, -1970.2813552308603, -1495.7774530477213, -1011.3216534495583, -519.7002593654363, -23.832157362708163, 473.2837076009268, 968.6463368986477, 1459.3505321667049, 1942.5318485382932, 2415.4510265484705, 2875.556010516545, 3320.506661184559, 3748.198094514585, 4156.77341080537, 4544.6293483708505, 4910.4183960221735, 5253.050898594135, 5571.693465093883, 5865.721628756655, 6134.721951629297, 6378.472308635085, 6596.919784917529, 6790.160531297423, 6958.420246564514, 7102.035687686537, 7221.4375204547005, 7316.663337244423, 7386.65084386547, 7431.893115821661, 7453.0698280472925, 7450.897321109322, 7426.119729440364, 7379.501270758323, 7311.820069742575, 7223.862698043761, 7116.419458989712, 6990.280549179927, 6846.233229160697, 6685.060133262135, 6507.537093361739, 6314.430706180539, 6106.498300754951, 5884.487007982855, 5649.133354700521, 5401.162990511237, 5141.2905829735655, 4870.219916758211, 4588.6442323815845, 4297.246840124377, 3996.7016279584977, 3687.672943442506, 3370.816713471332, 3046.7809391622377, 2716.2064884693173, 2379.7278238167414, 2037.9736866813107, 1691.5677739341816, 1341.1294407525402, 987.274464910137, 630.6159072584708, 271.7651032075717, -88.66720063937115, -450.07090828521183, -811.8357448402178, -1173.3500575470596, -1534.0002171862902, -1893.169757469542, -2250.238597789166, -2604.582329974649, -2955.5715497041388, -3302.5712132214585, -3644.9400000076153, -3982.0296620564454, -4313.1843404041265, -4637.739829561369, -4955.022770498422, -5264.349858508805, -5565.02894989203, -5856.3576838711515, -6137.623366377648, -6408.1028210940085, -6667.062088921161, -6913.756442570832, -7147.4306942507355, -7367.31977440838, -7572.649559501451, -7762.637926761234, -7936.496013916589, -8093.429661844874, -8232.641018117287, -8353.330279405462, -8454.697550716264, -8535.946794434212, -8596.294778970558, -8634.969996072314, -8651.219472693572, -8644.315094433372, -8613.560610264087, -8558.29955099045, -8477.365597924792, -8368.629630331561, -8231.524325422732, -8065.667620887417, -7870.7798235036935, -7646.698329716853, -7393.391126567547, -7110.97150002102, -6799.713517837531, -6460.067282508887, -6092.6729497850765, -5698.37250831565, -5278.218315929916, -4833.477388080416, -4365.638883730688, -3876.414508399302, -3367.7358724643755, -2841.744416899491, -2300.767203498261, -1747.297678157779, -1183.9773839055888, -613.5744810466747, -38.954931807896685, 536.9577931449797, 1111.2386289401215, 1681.0332544600285, 2243.5202602208988, 2795.9476233147616, 3335.7045577044755, 3860.3357171402113, 4367.56443133306, 4855.307962124547, 5321.68709777867, 5765.032403524536, 6183.88944647593, 6577.0253130562805, 6943.434202852454, 7282.296499214983, 7592.98561202928, 7875.059973731593, 8128.246617878707, 8352.427424830486, 8547.62501347358, 8713.988750378478, 8851.781084393804, 8961.36447478559, 9042.242843134205, 9093.668802243274, 9116.23668110842, 9110.609543356617, 9077.503177761662, 9017.677333060974, 8931.928500405325, 8821.083643637518, 8685.994148399357, 8527.53030580135, 8346.576656227106, 8144.028518844368, 7920.790032394727, 7677.771607841014, 7415.884335393386, 7136.040833878056, 6839.153075198672, 6526.131406800462, 6197.8835649323455, 5855.313765815692, 5499.321989462649, 5130.803570885562, 4750.649213440984, 4359.7455390498135, 3958.976039218502, 3549.2187916932035, 3131.3477509577897, 2706.233436050391, 2274.7432078785523, 1837.7418185615793, 1396.0918663437842, 950.6542067794655, 502.28837089286105, 51.85304101393701, -399.7933650077844, -851.7919505091031, -1303.2823869379342, -1753.4019313259157, -2201.286895167361, -2646.071018361386, -3086.8848320426428, -3522.855396157589, -3953.105891828186, -4376.7553111023335, -4792.918238718218, -5200.7047205095105, -5599.220213079678, -5987.565609371384, -6364.837334759317, -6730.127508292996, -7082.524163717662, -7421.111642801323, -7744.972250038648, -8053.186798724357, -8344.835595315133, -8618.99979457899, -8874.763039817377, -9111.213474889693, -9327.446133131225, -9522.565708255206, -9695.689712329864, -9845.952025921495, -9972.506845493883, -10074.5330331555, -10151.238873844894, -10201.867245045276, -10225.701204119156, -10222.070439274063, -10190.361990798609, -10129.853762803024, -10038.427841584415, -9915.279550108704, -9760.108776139592, -9572.720953342165, -9353.037128738022, -9101.10383088987, -8817.10314177993, -8501.362623261826, -8154.364690471191, -7776.755024581109, -7369.349617287147, -6933.140039408128, -6469.297029976576, -5979.175292300937, -5464.308255125287, -4926.414162513929, -4367.384724222437, -3789.2731704218677, -3194.2839139233915, -2584.761981434948, -1963.1803753802117, -1332.1235278122024, -694.2650079512025, -52.33764487917391, 590.9057730780814, 1232.7302100421678, 1870.3713413090686, 2501.1101814049143, 3122.294319696382, 3731.358958645686, 4325.848152368505, 4903.431058550666, 5461.914681665608, 5999.25458543262, 6513.565052456316, 7003.130168988293, 7466.4173127510985, 7902.088461523484, 8308.958311600876, 8686.017641112336, 9032.430485253286, 9347.527344168882, 9630.79874385072, 9881.886043613942, 10100.571092101947, 10286.76633376745, 10440.506967777248, 10561.946761290075, 10651.31312355685, 10707.508793086632, 10730.270242716188, 10720.134512344057, 10677.716929011136, 10603.70301624867, 10498.84095540002, 10363.935692645038, 10199.842635095218, 10007.461311244997, 9787.729491733067, 9541.618266365947, 9270.128573357806, 8974.289676739809, 8655.154763092845, 8313.793063791787, 7951.291725667589, 7568.752032306485, 7167.288031325886, 6748.0247841412975, 6312.096446409577, 5860.644399465427, 5394.815653066833, 4915.761739765066, 4424.638321216302, 3922.605726749461, 3410.830642892528, 2890.482102222573, 2362.72743095064, 1828.7378885046205, 1289.6863967870145, 746.7475107075926, 201.09723227876944, -346.0872768344626, -893.6280294183048, -1440.3465385734532, -1985.0640804492239, -2526.6018389518254, -3063.7808775385856, -3595.421883202671, -4120.345740449001, -4637.376549160767, -5145.33966086568, -5643.062875239662, -6129.37746192718, -6603.119167069213, -7063.129292770676, -7508.255895810212, -7937.355151893713, -8349.292931756063, -8742.94663541105, -9117.207330855153, -9470.982243524337, -9803.197626134299, -10112.800713605553, -10398.762689705189, -10660.082706507046, -10895.7912717085, -11104.954171701349, -11286.676743135598, -11440.108418673553, -11564.447559546741, -11658.946615786355, -11722.917532000287, -11755.737435738292, -11756.854541352495, -11725.782492813427, -11660.828501253043, -11560.81238424088, -11425.539383808895, -11254.924056362363, -11048.996740417788, -10807.909781896647, -10531.943313286512, -10221.510598936384, -9877.162972715556, -9499.594175279954, -9089.642330852514, -8648.294170180357, -8176.6853468325235, -7676.099499101662, -7147.96690335524, -6593.862308017045, -6015.501537364339, -5414.736454321438, -4793.547871434016, -4154.035999206022, -3498.408020983579, -2828.9623835669076, -2148.0826917787067, -1458.2315212725996, -761.9237889723694, -61.71489989746716, 639.8163167294101, 1340.0834854377401, 2036.5078862726834, 2726.5334912977164, 3407.642346770435, 4077.370811922639, 4733.327165275168, 5373.208629129074, 5994.798569367785, 6595.986106599416, 7174.775590478549, 7729.295274125195, 8257.804271800342, 8758.696958427558, 9230.50571237662, 9671.90290292278, 10081.70302379812, 10458.865874249115, 10802.50115432837, 11111.855510914607, 11386.310594797756, 11625.38612276546, 11828.733483117252, 11996.1314003335, 12127.481144806967, 12222.801458475948, 12281.942832760222, 12303.505668013993, 12287.569282802111, 12234.580777621648, 12145.083281258558, 12019.709988279998, 11859.17934943122, 11664.289580022676, 11435.91285760828, 11174.98974429192, 10882.52436899783, 10559.580905041616, 10207.281276289148, 9826.79668322067, 9419.345780082886, 8986.189594549754, 8528.629035063283, 8048.002586293604, 7545.683195838178, 7023.074705545149, 6481.608180847961, 5922.738491496383, 5347.941497071412, 4758.712190667759, 4156.564154133248, 3543.030678246885, 2919.665962824224, 2288.025412681251, 1649.6744524169646, 1006.1883342201428, 359.1483592460423, -289.8598904186712, -939.2484570894667, -1587.4290110500992, -2232.8149132920275, -2873.8232983633416, -3508.877138265618, -4136.407248337096, -4754.854196058548, -5362.670073721785, -5958.320620041348, -6540.2906147370395, -7107.084096994782, -7657.226297663791, -8189.2676211663975, -8701.787098017028, -9193.395464750736, -9662.73804861966, -10108.497634408703, -10529.397490726491, -10924.204733126104, -11291.734201407937, -11630.85302846138, -11940.486077998452, -12219.622428533412, -12467.317314825203, -12682.686216094675, -12864.921340148967, -13013.292363044493, -13127.150460562854, -13205.93253446733, -13249.165298363341, -13256.469289994293, -13226.693807239315, -13158.36995981761, -13051.418634908337, -12905.88682708958, -12721.939396593169, -12499.860228744721, -12240.054779347625, -11943.052394246244, -11609.507442399117, -11240.199483565257, -10836.032691703384, -10398.034755187578, -9927.355474939532, -9425.265281580096, -8893.153892700979, -8332.528923852566, -7745.005572924492, -7132.303534346728, -6496.243758220996, -5838.741071239712, -5161.797276540059, -4467.493783335877, -3757.983765775592, -3035.4838504735853, -2302.2653321647813, -1560.6449169281957, -812.9749924293694, -61.633424628322686, 690.9871195968636, 1442.486794599879, 2190.468951806075, 2932.5504066386106, 3666.3704108293555, 4389.601866128764, 5099.960799048941, 5795.214542661, 6473.189071556925, 7131.77593608626, 7768.9392419816295, 8382.723121481331, 8971.260142062407, 9532.781098895844, 10065.625537580216, 10568.22599469257, 11039.11989150088, 11476.960447800664, 11880.515725638495, 12248.671089149528, 12580.431279136932, 12874.921776587416, 13131.389807933885, 13349.204859597501, 13527.85873738301, 13666.965266132189, 13766.259543532506, 13825.589778577356, 13843.690838827206, 13819.747206857935, 13753.975335782965, 13646.710791894442, 13498.405786548949, 13309.626372462953, 13081.049287453987, 12813.458805553297, 12507.743545729416, 12164.89319035207, 11785.995123989842, 11372.22836305473, 10924.861805495413, 10445.251194352508, 9934.834821996656, 9395.128452471496, 8827.719939367758, 8234.26401664981, 7616.477739861062, 6976.137055130567, 6315.074973404016, 5635.181827325977, 4938.396160833807, 4226.6838839995635, 3502.052752560892, 2766.541615041006, 2022.2154181201934, 1271.1601422776898, 515.4775775919759, -242.72000478250743, -1001.3152299497979, -1758.1909899039122, -2511.2357519391458, -3258.3487493292632, -3997.444998756482], \"type\": \"scatter3d\", \"uid\": \"bd507858-b277-11e8-b505-a0afbda902ef\"}, {\"cauto\": false, \"cmax\": 1, \"cmin\": 1, \"colorscale\": [[0, \"#204a87\"], [1, \"#204a87\"]], \"name\": \"Earth\", \"showscale\": false, \"x\": [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [2070.9775685349646, 1958.7662914956272, 1634.2922933821865, 1132.717366623061, 508.3949372694737, -171.0199720974995, -831.9022138394263, -1402.6349424190162, -1821.3704108639388, -2042.7321338179481, -2042.7321338179484, -1821.3704108639388, -1402.6349424190173, -831.9022138394275, -171.0199720975005, 508.39493726947273, 1132.7173666230603, 1634.2922933821858, 1958.7662914956272, 2070.9775685349646], [3917.5325829912545, 3705.269861917151, 3091.4836581186883, 2142.6872306516607, 961.6973945255617, -323.507276569953, -1573.6549145165156, -2653.272624703365, -3445.3670762369647, -3864.102544681887, -3864.1025446818876, -3445.3670762369647, -2653.2726247033675, -1573.654914516518, -323.50727656995485, 961.69739452556, 2142.687230651659, 3091.483658118687, 3705.269861917151, 3917.5325829912545], [5339.562155299338, 5050.2499496143155, 4213.664799186625, 2920.4636860211895, 1310.7850168122338, -440.9375478934551, -2144.8776874338914, -3616.387048334464, -4696.004758521314, -5266.737487100903, -5266.737487100904, -4696.004758521314, -3616.3870483344667, -2144.877687433894, -440.93754789345763, 1310.7850168122313, 2920.463686021187, 4213.664799186623, 5050.2499496143155, 5339.562155299338], [6182.9673162373765, 5847.957092568811, 4879.229977516816, 3381.7625853471986, 1517.8287436021724, -510.58539405170535, -2483.6696817114025, -4187.60982125184, -5437.757459198403, -6098.63970094033, -6098.639700940331, -5437.757459198403, -4187.609821251843, -2483.669681711406, -510.58539405170836, 1517.8287436021697, 3381.762585347196, 4879.229977516814, 5847.957092568811, 6182.9673162373765], [6356.352029838285, 6011.947344139877, 5016.054878729385, 3476.5950350978, 1560.3921744832594, -524.9033902157762, -2553.317527869653, -4305.040092575342, -5590.2447636708575, -6269.65967303783, -6269.6596730378305, -5590.2447636708575, -4305.040092575346, -2553.3175278696567, -524.9033902157793, 1560.3921744832564, 3476.5950350977973, 5016.054878729383, 6011.947344139877, 6356.352029838285], [5840.927372042378, 5524.449815998859, 4609.31240172086, 3194.6844678654465, 1433.8629012798515, -482.33995933468935, -2346.2738010797148, -3955.95247028867, -5136.9423064147695, -5761.264735768357, -5761.264735768358, -5136.9423064147695, -3955.952470288674, -2346.273801079718, -482.33995933469214, 1433.8629012798488, 3194.684467865444, 4609.312401720858, 5524.449815998859, 5840.927372042378], [4692.547602159432, 4438.292429623361, 3703.07940513492, 2566.580267902913, 1151.9523340474977, -387.50750958408815, -1884.974901753706, -3178.1760149191423, -4126.972442386172, -4628.547369145297, -4628.547369145297, -4126.972442386172, -3178.176014919145, -1884.9749017537085, -387.5075095840904, 1151.9523340474955, 2566.580267902911, 3703.0794051349185, 4438.292429623361, 4692.547602159432], [3035.6574872043448, 2871.1771912954932, 2395.560295805413, 1660.3472713169713, 745.2098570389732, -250.68260837151965, -1219.4097234235148, -2055.994873851206, -2669.7810776496694, -2994.25507576311, -2994.2550757631107, -2669.7810776496694, -2055.9948738512076, -1219.4097234235167, -250.6826083715211, 745.2098570389717, 1660.3472713169701, 2395.560295805412, 2871.1771912954932, 3035.6574872043448], [1049.806780431554, 992.9253533863962, 828.4450574775444, 574.1898849414736, 257.7123288979541, -86.69235680045416, -421.70258046901955, -711.014786154042, -923.2775072281457, -1035.488784267483, -1035.4887842674832, -923.2775072281457, -711.0147861540426, -421.7025804690202, -86.69235680045466, 257.7123288979536, 574.1898849414731, 828.4450574775441, 992.9253533863962, 1049.806780431554], [-1049.8067804315524, -992.9253533863947, -828.4450574775432, -574.1898849414728, -257.71232889795374, 86.69235680045404, 421.7025804690189, 711.0147861540408, 923.2775072281444, 1035.4887842674814, 1035.4887842674816, 923.2775072281444, 711.0147861540415, 421.7025804690195, 86.69235680045453, -257.7123288979532, -574.1898849414723, -828.4450574775428, -992.9253533863947, -1049.8067804315524], [-3035.6574872043434, -2871.177191295492, -2395.560295805412, -1660.3472713169708, -745.2098570389728, 250.68260837151956, 1219.4097234235144, 2055.9948738512053, 2669.7810776496685, 2994.255075763109, 2994.2550757631093, 2669.7810776496685, 2055.994873851207, 1219.4097234235162, 250.682608371521, -745.2098570389714, -1660.3472713169695, -2395.560295805411, -2871.177191295492, -3035.6574872043434], [-4692.547602159429, -4438.292429623359, -3703.079405134917, -2566.580267902911, -1151.9523340474968, 387.50750958408787, 1884.9749017537044, 3178.17601491914, 4126.972442386168, 4628.5473691452935, 4628.547369145294, 4126.972442386168, 3178.1760149191427, 1884.9749017537072, 387.50750958409014, -1151.9523340474948, -2566.580267902909, -3703.0794051349158, -4438.292429623359, -4692.547602159429], [-5840.927372042376, -5524.449815998857, -4609.312401720859, -3194.6844678654456, -1433.862901279851, 482.33995933468924, 2346.2738010797143, 3955.9524702886692, 5136.942306414769, 5761.264735768355, 5761.264735768356, 5136.942306414769, 3955.9524702886724, 2346.2738010797175, 482.339959334692, -1433.8629012798485, -3194.6844678654434, -4609.312401720857, -5524.449815998857, -5840.927372042376], [-6356.352029838285, -6011.947344139877, -5016.054878729385, -3476.5950350978, -1560.3921744832594, 524.9033902157762, 2553.317527869653, 4305.040092575342, 5590.2447636708575, 6269.65967303783, 6269.6596730378305, 5590.2447636708575, 4305.040092575346, 2553.3175278696567, 524.9033902157793, -1560.3921744832564, -3476.5950350977973, -5016.054878729383, -6011.947344139877, -6356.352029838285], [-6182.967316237377, -5847.957092568812, -4879.229977516816, -3381.762585347199, -1517.8287436021726, 510.5853940517054, 2483.669681711403, 4187.60982125184, 5437.757459198404, 6098.639700940331, 6098.639700940332, 5437.757459198404, 4187.609821251844, 2483.6696817114066, 510.5853940517084, -1517.8287436021699, -3381.7625853471964, -4879.229977516815, -5847.957092568812, -6182.967316237377], [-5339.562155299339, -5050.249949614317, -4213.664799186626, -2920.46368602119, -1310.7850168122343, 440.9375478934552, 2144.877687433892, 3616.387048334465, 4696.004758521315, 5266.737487100905, 5266.737487100906, 4696.004758521315, 3616.3870483344676, 2144.877687433895, 440.93754789345775, -1310.7850168122318, -2920.4636860211876, -4213.664799186624, -5050.249949614317, -5339.562155299339], [-3917.532582991256, -3705.2698619171524, -3091.483658118689, -2142.6872306516616, -961.6973945255621, 323.5072765699531, 1573.654914516516, 2653.272624703366, 3445.367076236966, 3864.1025446818885, 3864.102544681889, 3445.367076236966, 2653.2726247033684, 1573.6549145165184, 323.507276569955, -961.6973945255603, -2142.68723065166, -3091.4836581186883, -3705.2698619171524, -3917.532582991256], [-2070.977568534966, -1958.7662914956288, -1634.2922933821878, -1132.7173666230622, -508.39493726947416, 171.01997209749965, 831.902213839427, 1402.6349424190175, 1821.3704108639404, 2042.7321338179497, 2042.7321338179502, 1821.3704108639404, 1402.6349424190187, 831.9022138394282, 171.01997209750064, -508.3949372694732, -1132.7173666230613, -1634.2922933821872, -1958.7662914956288, -2070.977568534966], [-1.5621929143429165e-12, -1.4775489932480931e-12, -1.2327897122000057e-12, -8.544385371317645e-13, -3.8349568858537574e-13, 1.2900486836795326e-13, 6.275257461168375e-13, 1.0580444722087668e-12, 1.373907662485362e-12, 1.5408866391577764e-12, 1.5408866391577766e-12, 1.373907662485362e-12, 1.0580444722087678e-12, 6.275257461168384e-13, 1.2900486836795402e-13, -3.83495688585375e-13, -8.544385371317639e-13, -1.2327897122000053e-12, -1.4775489932480931e-12, -1.5621929143429165e-12]], \"y\": [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 672.4453172381089, 1272.020750289313, 1733.7529976111107, 2007.6062056921824, 2063.9041301666034, 1896.5460173652668, 1523.6677156388778, 985.6763747824739, 340.8717043736032, -340.8717043736027, -985.6763747824734, -1523.6677156388766, -1896.5460173652662, -2063.9041301666034, -2007.6062056921826, -1733.752997611111, -1272.0207502893134, -672.4453172381094, -5.072432100824625e-13], [0.0, 1272.0207502893131, 2406.1983148492195, 3279.626955981496, 3797.6571277777143, 3904.152223057449, 3587.5718458054816, 2882.222392147741, 1864.539420012481, 644.8046704088713, -644.8046704088703, -1864.5394200124804, -2882.222392147739, -3587.5718458054803, -3904.152223057449, -3797.657127777715, -3279.6269559814964, -2406.1983148492204, -1272.0207502893138, -9.595187476631407e-13], [0.0, 1733.7529976111107, 3279.6269559814955, 4470.102445015823, 5176.172973346762, 5321.324843416592, 4889.8285978399235, 3928.443550179085, 2541.350687774138, 878.8630452300076, -878.8630452300063, -2541.350687774137, -3928.4435501790817, -4889.828597839922, -5321.324843416592, -5176.172973346763, -4470.102445015824, -3279.6269559814964, -1733.7529976111118, -1.3078155404671353e-12], [0.0, 2007.6062056921824, 3797.657127777714, 5176.172973346762, 5993.770160654701, 6161.849348129235, 5662.196547790195, 4548.95689346632, 2942.767175396611, 1017.6829721352607, -1017.6829721352591, -2942.7671753966097, -4548.9568934663175, -5662.196547790194, -6161.849348129235, -5993.770160654702, -5176.172973346763, -3797.6571277777152, -2007.6062056921837, -1.5143902266125608e-12], [0.0, 2063.9041301666034, 3904.152223057449, 5321.324843416592, 6161.849348129236, 6334.641865028304, 5820.977643755634, 4676.520173007722, 3025.2891778274434, 1046.2211580313447, -1046.221158031343, -3025.289177827442, -4676.520173007719, -5820.977643755632, -6334.641865028304, -6161.849348129237, -5321.324843416593, -3904.1522230574506, -2063.904130166605, -1.5568572335190474e-12], [0.0, 1896.5460173652668, 3587.571845805481, 4889.8285978399235, 5662.196547790195, 5820.977643755633, 5348.9654902458315, 4297.309928116757, 2779.9741556424556, 961.3850476608397, -961.3850476608383, -2779.974155642454, -4297.309928116753, -5348.96549024583, -5820.977643755633, -5662.196547790196, -4889.828597839924, -3587.5718458054826, -1896.5460173652682, -1.430614602044772e-12], [0.0, 1523.667715638878, 2882.222392147741, 3928.4435501790854, 4548.956893466321, 4676.520173007722, 4297.309928116757, 3452.419472880565, 2233.405797950153, 772.3679499502728, -772.3679499502715, -2233.405797950152, -3452.4194728805624, -4297.309928116756, -4676.520173007722, -4548.956893466322, -3928.443550179086, -2882.222392147742, -1523.667715638879, -1.1493426801662274e-12], [0.0, 985.676374782474, 1864.539420012481, 2541.3506877741384, 2942.767175396611, 3025.289177827443, 2779.9741556424556, 2233.405797950153, 1444.8132671883818, 499.65280033904173, -499.652800339041, -1444.8132671883811, -2233.4057979501513, -2779.9741556424547, -3025.289177827443, -2942.7671753966115, -2541.350687774139, -1864.5394200124817, -985.6763747824746, -7.435216450024997e-13], [0.0, 340.8717043736033, 644.8046704088712, 878.8630452300076, 1017.6829721352607, 1046.2211580313447, 961.3850476608397, 772.3679499502726, 499.65280033904173, 172.79251689906815, -172.7925168990679, -499.6528003390415, -772.3679499502721, -961.3850476608395, -1046.2211580313447, -1017.6829721352608, -878.8630452300079, -644.8046704088714, -340.8717043736035, -2.5712850267573817e-13], [0.0, -340.87170437360277, -644.8046704088703, -878.8630452300064, -1017.6829721352591, -1046.221158031343, -961.3850476608383, -772.3679499502715, -499.652800339041, -172.7925168990679, 172.7925168990676, 499.65280033904077, 772.3679499502709, 961.385047660838, 1046.221158031343, 1017.6829721352593, 878.8630452300065, 644.8046704088705, 340.871704373603, 2.5712850267573776e-13], [0.0, -985.6763747824737, -1864.5394200124804, -2541.3506877741374, -2942.76717539661, -3025.289177827442, -2779.9741556424547, -2233.405797950152, -1444.8132671883811, -499.65280033904156, 499.6528003390408, 1444.8132671883807, 2233.4057979501504, 2779.9741556424538, 3025.289177827442, 2942.7671753966106, 2541.350687774138, 1864.539420012481, 985.6763747824742, 7.435216450024994e-13], [0.0, -1523.6677156388769, -2882.222392147739, -3928.443550179082, -4548.9568934663175, -4676.520173007719, -4297.309928116754, -3452.4194728805624, -2233.4057979501513, -772.3679499502722, 772.3679499502709, 2233.4057979501504, 3452.4194728805596, 4297.309928116752, 4676.520173007719, 4548.956893466318, 3928.443550179083, 2882.22239214774, 1523.6677156388778, 1.1493426801662265e-12], [0.0, -1896.5460173652664, -3587.5718458054803, -4889.828597839923, -5662.196547790194, -5820.977643755632, -5348.96549024583, -4297.309928116755, -2779.9741556424547, -961.3850476608395, 961.385047660838, 2779.9741556424538, 4297.309928116752, 5348.965490245829, 5820.977643755632, 5662.196547790195, 4889.8285978399235, 3587.5718458054816, 1896.5460173652677, 1.4306146020447716e-12], [0.0, -2063.9041301666034, -3904.152223057449, -5321.324843416592, -6161.849348129236, -6334.641865028304, -5820.977643755634, -4676.520173007722, -3025.2891778274434, -1046.2211580313447, 1046.221158031343, 3025.289177827442, 4676.520173007719, 5820.977643755632, 6334.641865028304, 6161.849348129237, 5321.324843416593, 3904.1522230574506, 2063.904130166605, 1.5568572335190474e-12], [0.0, -2007.6062056921826, -3797.6571277777143, -5176.172973346763, -5993.770160654702, -6161.849348129236, -5662.196547790196, -4548.956893466321, -2942.7671753966115, -1017.6829721352608, 1017.6829721352592, 2942.76717539661, 4548.9568934663175, 5662.196547790194, 6161.849348129236, 5993.7701606547025, 5176.172973346764, 3797.6571277777157, 2007.606205692184, 1.514390226612561e-12], [0.0, -1733.7529976111111, -3279.6269559814964, -4470.102445015825, -5176.172973346764, -5321.324843416593, -4889.828597839924, -3928.443550179086, -2541.350687774139, -878.8630452300079, 878.8630452300065, 2541.3506877741374, 3928.4435501790826, 4889.8285978399235, 5321.324843416593, 5176.172973346765, 4470.102445015826, 3279.6269559814973, 1733.7529976111123, 1.3078155404671357e-12], [0.0, -1272.0207502893136, -2406.1983148492204, -3279.626955981497, -3797.6571277777157, -3904.1522230574506, -3587.571845805483, -2882.222392147742, -1864.5394200124817, -644.8046704088715, 644.8046704088705, 1864.539420012481, 2882.22239214774, 3587.5718458054816, 3904.1522230574506, 3797.657127777716, 3279.6269559814973, 2406.1983148492213, 1272.0207502893143, 9.595187476631409e-13], [0.0, -672.4453172381095, -1272.020750289314, -1733.752997611112, -2007.606205692184, -2063.9041301666052, -1896.5460173652684, -1523.6677156388791, -985.6763747824748, -340.87170437360356, 340.87170437360305, 985.6763747824743, 1523.6677156388778, 1896.5460173652677, 2063.9041301666052, 2007.6062056921844, 1733.7529976111123, 1272.0207502893145, 672.44531723811, 5.07243210082463e-13], [0.0, -5.072432100824626e-13, -9.595187476631405e-13, -1.3078155404671353e-12, -1.5143902266125608e-12, -1.5568572335190474e-12, -1.430614602044772e-12, -1.1493426801662272e-12, -7.435216450024997e-13, -2.5712850267573817e-13, 2.5712850267573776e-13, 7.435216450024993e-13, 1.1493426801662263e-12, 1.4306146020447714e-12, 1.5568572335190474e-12, 1.514390226612561e-12, 1.3078155404671357e-12, 9.595187476631409e-13, 5.072432100824629e-13, 3.826269104401456e-28]], \"z\": [[6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366], [6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864], [5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782, 5033.245965523782], [3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239, 3488.510065623239], [1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561, 1565.739970301561], [-526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267, -526.7023457611267], [-2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456, -2562.0682900316456], [-4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595, -4319.7943804916595], [-5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402], [-6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607, -6291.147130056607], [-6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608, -6291.147130056608], [-5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402, -5609.403721309402], [-4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663, -4319.794380491663], [-2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649, -2562.068290031649], [-526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298, -526.7023457611298], [1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558, 1565.739970301558], [3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236, 3488.510065623236], [5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378, 5033.24596552378], [6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864, 6032.551566201864], [6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366, 6378.1366]], \"type\": \"surface\", \"uid\": \"bd507859-b277-11e8-b505-a0afbda902ef\"}], {\"autosize\": true, \"scene\": {\"aspectmode\": \"data\", \"xaxis\": {\"title\": \"x (km)\"}, \"yaxis\": {\"title\": \"y (km)\"}, \"zaxis\": {\"title\": \"z (km)\"}}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"frame = OrbitPlotter3D()\n",
"\n",
"frame.set_attractor(Earth)\n",
"frame.plot_trajectory(rr, label='orbit with artificial thrust')\n",
"frame.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
materialsvirtuallab/matgenb | notebooks/2013-01-01-Units.ipynb | 14 | 5438 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From v2.8.0, pymatgen comes with a fairly robust system of managing units. In essence, subclasses of float and numpy array is provided to attach units to any quantity, as well as provide for conversions. These are loaded at the root level of pymatgen and some properties (e.g., atomic masses, final energies) are returned with attached units. This demo provides an outline of some of the capabilities."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's start with some common units, like Energy."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1000.0 Ha = 27211.383859999998 eV\n",
"Supported energy units are ('kJ', 'J', 'eV', 'Ha', 'Ry')\n"
]
}
],
"source": [
"import pymatgen as mg\n",
"#The constructor is simply the value + a string unit.\n",
"e = mg.Energy(1000, \"Ha\")\n",
"#Let's perform a conversion. Note that when printing, the units are printed as well.\n",
"print \"{} = {}\".format(e, e.to(\"eV\"))\n",
"#To check what units are supported\n",
"print \"Supported energy units are {}\".format(e.supported_units)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Units support all functionality that is supported by floats. Unit combinations are automatically taken care of."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The speed is 2.1666666666666665 mile min^-1\n",
"The speed is 130.0 mile h^-1\n"
]
}
],
"source": [
"dist = mg.Length(65, \"mile\")\n",
"time = mg.Time(30, \"min\")\n",
"speed = dist / time\n",
"print \"The speed is {}\".format(speed)\n",
"#Let's do a more sensible unit.\n",
"print \"The speed is {}\".format(speed.to(\"mile h^-1\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that complex units are specified as **space-separated powers of units**. Powers are specified using \"^\". E.g., \"kg m s^-1\". Only **integer powers** are supported."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's do some basic science."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The force is 19.62 N\n",
"The potential energy is force is 196.20000000000002 J\n"
]
}
],
"source": [
"g = mg.FloatWithUnit(9.81, \"m s^-2\") #Acceleration due to gravity\n",
"m = mg.Mass(2, \"kg\")\n",
"h = mg.Length(10, \"m\")\n",
"print \"The force is {}\".format(m * g)\n",
"print \"The potential energy is force is {}\".format((m * g * h).to(\"J\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some highly complex conversions are possible with this system. Let's do some made up units. We will also demonstrate pymatgen's internal unit consistency checks."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.959243823351516e-50 J^3 ang^-2\n"
]
}
],
"source": [
"made_up = mg.FloatWithUnit(100, \"Ha^3 bohr^-2\")\n",
"print made_up.to(\"J^3 ang^-2\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Units are not compatible!\n"
]
}
],
"source": [
"try:\n",
" made_up.to(\"J^2\")\n",
"except mg.UnitError as ex:\n",
" print ex"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For arrays, we have the equivalent EnergyArray, ... and ArrayWithUnit classes. All other functionality remain the same."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Speeds are [ 9.09090909 16.66666667 13.04347826] mile h^-1\n"
]
}
],
"source": [
"dists = mg.LengthArray([1, 2, 3], \"mile\")\n",
"times = mg.TimeArray([0.11, 0.12, 0.23], \"h\")\n",
"print \"Speeds are {}\".format(dists / times)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## This concludes the tutorial on units in pymatgen."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.8"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-3-clause |
aboSamoor/compsocial | Word_Tracker/3rd_Yr_Paper/PsychoInfo.ipynb | 1 | 55971 | {
"cells": [
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import glob\n",
"from io import open\n",
"import pandas as pd\n",
"from pandas import DataFrame as df\n",
"from os import path\n",
"import re"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Merge CSV databases"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from tools import get_psycinfo_database"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"words_df = get_psycinfo_database()"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Abstract</th>\n",
" <th>Accession Number</th>\n",
" <th>Author(s)</th>\n",
" <th>Type of Book</th>\n",
" <th>PsycINFO Classification Code</th>\n",
" <th>Conference</th>\n",
" <th>Document Type</th>\n",
" <th>Grant/Sponsorship</th>\n",
" <th>Key Concepts</th>\n",
" <th>Institution</th>\n",
" <th>...</th>\n",
" <th>Population Group</th>\n",
" <th>Publication Status</th>\n",
" <th>Publication Type</th>\n",
" <th>Publisher</th>\n",
" <th>Cited References</th>\n",
" <th>Title</th>\n",
" <th>Tests & Measures</th>\n",
" <th>Volume</th>\n",
" <th>Date</th>\n",
" <th>Term</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>PURPOSE: Rates of alcohol use may be increasin...</td>\n",
" <td>Peer Reviewed Journal: 2015-52719-001.</td>\n",
" <td>Kane, Jeremy C\\n\\nJohnson, Renee M\\n\\nRobinson...</td>\n",
" <td>NaN</td>\n",
" <td>Health & Mental Health Treatment & Prevention ...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Acculturation, Intergenerational cultural diss...</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>First Posting</td>\n",
" <td>Journal\\n\\nPeer Reviewed Journal</td>\n",
" <td>Elsevier Science; Netherlands</td>\n",
" <td>NaN</td>\n",
" <td>The impact of intergenerational cultural disso...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2015</td>\n",
" <td>bicultural</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Given the negative developmental risks associa...</td>\n",
" <td>Peer Reviewed Journal: 2015-52548-001.</td>\n",
" <td>Killoren, Sarah E\\n\\nZeiders, Katharine H\\n\\nU...</td>\n",
" <td>NaN</td>\n",
" <td>Developmental Psychology [2800].</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Adolescence, Cultural context, Mexican-America...</td>\n",
" <td>Killoren, Sarah E.: Department of Human Develo...</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>First Posting</td>\n",
" <td>Journal\\n\\nPeer Reviewed Journal</td>\n",
" <td>Springer; Germany</td>\n",
" <td>NaN</td>\n",
" <td>The sociocultural context of mexican-origin pr...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2015</td>\n",
" <td>bicultural</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>(from the chapter) Assessment science is an es...</td>\n",
" <td>Book: 2013-02670-011.</td>\n",
" <td>Dana, Richard H</td>\n",
" <td>Handbook/Manual</td>\n",
" <td>Personality Scales & Inventories [2223].</td>\n",
" <td>NaN</td>\n",
" <td>Chapter</td>\n",
" <td>NaN</td>\n",
" <td>personality tests, psychology, assessment, cul...</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>Human</td>\n",
" <td>NaN</td>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>American Psychological Association; US</td>\n",
" <td>Aiken, L. S., West, S. G., & Millsap, R. E. (2...</td>\n",
" <td>Personality tests and psychological science: I...</td>\n",
" <td>California Brief Multicultural Competency Scal...</td>\n",
" <td>NaN</td>\n",
" <td>2014</td>\n",
" <td>bicultural</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Objective: The aim of the study was to explore...</td>\n",
" <td>Peer Reviewed Journal: 2015-46649-006.</td>\n",
" <td>Goutaudier, N\\n\\nChauchard, E\\n\\nMelioli, T\\n\\...</td>\n",
" <td>NaN</td>\n",
" <td>Psychosocial & Personality Development [2840].</td>\n",
" <td>NaN</td>\n",
" <td>Journal Article</td>\n",
" <td>NaN</td>\n",
" <td>Acculturation, Adolescence, Cluster analysis, ...</td>\n",
" <td>Goutaudier, N.: Laboratoire CERPP-OCTOGONE, UF...</td>\n",
" <td>...</td>\n",
" <td>Human. Male. Female. Adolescence (13-17 yrs)</td>\n",
" <td>NaN</td>\n",
" <td>Journal\\n\\nPeer Reviewed Journal</td>\n",
" <td>Elsevier Masson SAS; France</td>\n",
" <td>Aubry, B., & Tribalat, M. (2009). Les jeunes d...</td>\n",
" <td>Acculturation orientations and psychosocial ad...</td>\n",
" <td>Immigrant Acculturation Scale\\nRosenberg Self-...</td>\n",
" <td>41</td>\n",
" <td>2015</td>\n",
" <td>bicultural</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>(from the chapter) In Germany, the visit of th...</td>\n",
" <td>Book: 2014-27297-015.</td>\n",
" <td>Leyendecker, Birgit\\n\\nWillard, Jessica\\n\\nAga...</td>\n",
" <td>NaN</td>\n",
" <td>Cognitive & Perceptual Development [2820].</td>\n",
" <td>NaN</td>\n",
" <td>Chapter</td>\n",
" <td><b>Sponsor: </b>NORFACE. ERA-NET\\n<b>Grant: </...</td>\n",
" <td>children's bilingual development, parents, imm...</td>\n",
" <td>Leyendecker, Birgit: Ruhr University Bochum, B...</td>\n",
" <td>...</td>\n",
" <td>Human. Childhood (birth-12 yrs)</td>\n",
" <td>NaN</td>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Ashgate Publishing Co; US</td>\n",
" <td>Adesope, O. O., Lavin, T., Thompson, T., & Ung...</td>\n",
" <td>Learning a host country: A plea to strengthen ...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2014</td>\n",
" <td>bicultural</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 29 columns</p>\n",
"</div>"
],
"text/plain": [
" Abstract \\\n",
"0 PURPOSE: Rates of alcohol use may be increasin... \n",
"1 Given the negative developmental risks associa... \n",
"2 (from the chapter) Assessment science is an es... \n",
"3 Objective: The aim of the study was to explore... \n",
"4 (from the chapter) In Germany, the visit of th... \n",
"\n",
" Accession Number \\\n",
"0 Peer Reviewed Journal: 2015-52719-001. \n",
"1 Peer Reviewed Journal: 2015-52548-001. \n",
"2 Book: 2013-02670-011. \n",
"3 Peer Reviewed Journal: 2015-46649-006. \n",
"4 Book: 2014-27297-015. \n",
"\n",
" Author(s) Type of Book \\\n",
"0 Kane, Jeremy C\\n\\nJohnson, Renee M\\n\\nRobinson... NaN \n",
"1 Killoren, Sarah E\\n\\nZeiders, Katharine H\\n\\nU... NaN \n",
"2 Dana, Richard H Handbook/Manual \n",
"3 Goutaudier, N\\n\\nChauchard, E\\n\\nMelioli, T\\n\\... NaN \n",
"4 Leyendecker, Birgit\\n\\nWillard, Jessica\\n\\nAga... NaN \n",
"\n",
" PsycINFO Classification Code Conference \\\n",
"0 Health & Mental Health Treatment & Prevention ... NaN \n",
"1 Developmental Psychology [2800]. NaN \n",
"2 Personality Scales & Inventories [2223]. NaN \n",
"3 Psychosocial & Personality Development [2840]. NaN \n",
"4 Cognitive & Perceptual Development [2820]. NaN \n",
"\n",
" Document Type Grant/Sponsorship \\\n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 Chapter NaN \n",
"3 Journal Article NaN \n",
"4 Chapter <b>Sponsor: </b>NORFACE. ERA-NET\\n<b>Grant: </... \n",
"\n",
" Key Concepts \\\n",
"0 Acculturation, Intergenerational cultural diss... \n",
"1 Adolescence, Cultural context, Mexican-America... \n",
"2 personality tests, psychology, assessment, cul... \n",
"3 Acculturation, Adolescence, Cluster analysis, ... \n",
"4 children's bilingual development, parents, imm... \n",
"\n",
" Institution ... \\\n",
"0 NaN ... \n",
"1 Killoren, Sarah E.: Department of Human Develo... ... \n",
"2 NaN ... \n",
"3 Goutaudier, N.: Laboratoire CERPP-OCTOGONE, UF... ... \n",
"4 Leyendecker, Birgit: Ruhr University Bochum, B... ... \n",
"\n",
" Population Group Publication Status \\\n",
"0 NaN First Posting \n",
"1 NaN First Posting \n",
"2 Human NaN \n",
"3 Human. Male. Female. Adolescence (13-17 yrs) NaN \n",
"4 Human. Childhood (birth-12 yrs) NaN \n",
"\n",
" Publication Type Publisher \\\n",
"0 Journal\\n\\nPeer Reviewed Journal Elsevier Science; Netherlands \n",
"1 Journal\\n\\nPeer Reviewed Journal Springer; Germany \n",
"2 Book\\n\\nEdited Book American Psychological Association; US \n",
"3 Journal\\n\\nPeer Reviewed Journal Elsevier Masson SAS; France \n",
"4 Book\\n\\nEdited Book Ashgate Publishing Co; US \n",
"\n",
" Cited References \\\n",
"0 NaN \n",
"1 NaN \n",
"2 Aiken, L. S., West, S. G., & Millsap, R. E. (2... \n",
"3 Aubry, B., & Tribalat, M. (2009). Les jeunes d... \n",
"4 Adesope, O. O., Lavin, T., Thompson, T., & Ung... \n",
"\n",
" Title \\\n",
"0 The impact of intergenerational cultural disso... \n",
"1 The sociocultural context of mexican-origin pr... \n",
"2 Personality tests and psychological science: I... \n",
"3 Acculturation orientations and psychosocial ad... \n",
"4 Learning a host country: A plea to strengthen ... \n",
"\n",
" Tests & Measures Volume Date Term \n",
"0 NaN NaN 2015 bicultural \n",
"1 NaN NaN 2015 bicultural \n",
"2 California Brief Multicultural Competency Scal... NaN 2014 bicultural \n",
"3 Immigrant Acculturation Scale\\nRosenberg Self-... 41 2015 bicultural \n",
"4 NaN NaN 2014 bicultural \n",
"\n",
"[5 rows x 29 columns]"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"words_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#words_df.to_csv(\"data/PsycInfo/processed/psychinfo_combined.csv.bz2\", encoding='utf-8',compression='bz2')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load PsychINFO unified database"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#psychinfo = pd.read_csv(\"data/PsycInfo/processed/psychinfo_combined.csv.bz2\", encoding='utf-8', compression='bz2')\n",
"psychinfo = words_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Term appearance in abstract and title"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"abstract_occurrence = []\n",
"for x,y in psychinfo[[\"Term\", \"Abstract\"]].fillna(\"\").values:\n",
" if x.lower() in y.lower():\n",
" abstract_occurrence.append(1)\n",
" else:\n",
" abstract_occurrence.append(0)\n",
"psychinfo[\"term_in_abstract\"] = abstract_occurrence"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"title_occurrence = []\n",
"for x,y in psychinfo[[\"Term\", \"Title\"]].fillna(\"\").values:\n",
" if x.lower() in y.lower():\n",
" title_occurrence.append(1)\n",
" else:\n",
" title_occurrence.append(0)\n",
"psychinfo[\"term_in_title\"] = title_occurrence"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"psychinfo_search = psychinfo.drop('Abstract', 1)\n",
"psychinfo_search = psychinfo_search.drop('Title', 1)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"term_ID = {\"multiculturalism\": 1, \"polyculturalism\": 2, \"cultural pluralism\": 3, \n",
" \"monocultural\": 4, \"monoracial\": 5, \"bicultural\": 6, \n",
" \"biracial\": 7, \"biethnic\": 8, \"interracial\": 9, \n",
" \"multicultural\": 10, \"multiracial\": 11, \"polycultural\": 12, \n",
" \"polyracial\": 13, \"polyethnic\": 14, \"mixed race\": 15, \n",
" \"mixed ethnicity\": 16, \"other race\": 17, \"other ethnicity\": 18}"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"psychinfo_search[\"term_ID\"] = psychinfo_search.Term.map(term_ID)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Handbook/Manual 1395\n",
"Textbook/Study Guide 533\n",
"Conference Proceedings 53\n",
"Reference Book 45\n",
"Classic Book 25\n",
"Handbook/Manual\\n\\nTextbook/Study Guide 16\n",
"Reference Book\\n\\nTextbook/Study Guide 6\n",
"Classic Book\\n\\nTextbook/Study Guide 2\n",
"Reference Book\\r\\rTextbook/Study Guide 1\n",
"Conference Proceedings\\n\\nTextbook/Study Guide 1\n",
"Handbook/Manual\\n\\nReference Book 1\n",
"Conference Proceedings\\r\\rTextbook/Study Guide 1\n",
"Name: Type of Book, dtype: int64"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"psychinfo_search[\"Type of Book\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"type_of_book = { 'Handbook/Manual': 1, 'Textbook/Study Guide': 2, 'Conference Proceedings': 3,\n",
" 'Reference Book': 2, 'Classic Book': 4,'Handbook/Manual\\n\\nTextbook/Study Guide': 5,\n",
" 'Reference Book\\n\\nTextbook/Study Guide': 5,'Classic Book\\n\\nTextbook/Study Guide': 5,\n",
" 'Handbook/Manual\\n\\nReference Book': 5,'Conference Proceedings\\n\\nTextbook/Study Guide': 5,\n",
" 'Reference Book\\r\\rTextbook/Study Guide': 5,'Conference Proceedings\\r\\rTextbook/Study Guide': 5}"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"psychinfo_search[\"type_of_book\"] = psychinfo_search[\"Type of Book\"].map(type_of_book)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"psychinfo_search[\"cited_references\"] = psychinfo_search['Cited References'].map(lambda text:len(text.strip().split(\"\\n\")),\"ignore\")"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Journal Article 14369\n",
"Dissertation 4919\n",
"Chapter 4558\n",
"Review-Book 1444\n",
"Comment/Reply 548\n",
"Editorial 228\n",
"Chapter\\n\\nReprint 78\n",
"Erratum/Correction 66\n",
"Review-Media 35\n",
"Abstract Collection 29\n",
"Letter 18\n",
"Obituary 13\n",
"Chapter\\n\\nComment/Reply 10\n",
"Reprint 9\n",
"Column/Opinion 9\n",
"Bibliography 8\n",
"Journal Article\\n\\nReprint 7\n",
"Chapter\\r\\rReprint 6\n",
"Chapter\\n\\nJournal Article\\n\\nReprint 5\n",
"Encyclopedia Entry 5\n",
"Bibliography\\n\\nChapter 5\n",
"Chapter\\r\\rJournal Article\\r\\rReprint 2\n",
"Reprint\\n\\nReview-Book 1\n",
"Publication Information 1\n",
"Review-Software & Other 1\n",
"Journal Article\\r\\rReprint 1\n",
"Name: Document Type, dtype: int64"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"psychinfo_search['Document Type'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"document_type = {'Journal Article': 1, 'Dissertation': 2, 'Chapter': 3, 'Review-Book': 4,\n",
" 'Comment/Reply': 6, 'Editorial': 6, 'Chapter\\n\\nReprint': 3,\n",
" 'Erratum/Correction': 6, 'Review-Media': 6, 'Abstract Collection': 6,\n",
" 'Letter': 6, 'Obituary': 6, 'Chapter\\n\\nComment/Reply': 3, 'Column/Opinion': 6,\n",
" 'Reprint': 5, 'Bibliography': 5, 'Journal Article\\n\\nReprint': 1,\n",
" 'Chapter\\r\\rReprint': 3, 'Chapter\\n\\nJournal Article\\n\\nReprint': 3,\n",
" 'Bibliography\\n\\nChapter': 3, 'Encyclopedia Entry': 5,\n",
" 'Chapter\\r\\rJournal Article\\r\\rReprint': 3, 'Review-Software & Other': 6,\n",
" 'Publication Information': 6, 'Journal Article\\r\\rReprint': 1,\n",
" 'Reprint\\n\\nReview-Book': 4}"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"psychinfo_search['document_type'] = psychinfo_search['Document Type'].map(document_type)"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"psychinfo_search[\"conference_dich\"] = psychinfo_search[\"Conference\"].fillna(\"\").map(lambda x: int((len(x) > 0)))\n"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Journal\\n\\nPeer Reviewed Journal 15714\n",
"Book\\n\\nEdited Book 5402\n",
"Dissertation Abstract 4919\n",
"Book\\n\\nAuthored Book 890\n",
"Journal\\r\\rPeer Reviewed Journal 468\n",
"Electronic Collection 454\n",
"Journal\\n\\nPeer-Reviewed Status-Unknown 234\n",
"Book\\r\\rEdited Book 155\n",
"Book 30\n",
"Journal\\r\\rPeer-Reviewed Status-Unknown 14\n",
"Book\\r\\rAuthored Book 13\n",
"Encyclopedia 11\n",
"Name: Publication Type, dtype: int64"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"psychinfo_search['Publication Type'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"publication_type = {'Journal\\n\\nPeer Reviewed Journal': 1, 'Book\\n\\nEdited Book': 3,\n",
" 'Dissertation Abstract': 2, 'Book\\n\\nAuthored Book': 3,\n",
" 'Journal\\r\\rPeer Reviewed Journal': 1, 'Electronic Collection': 1,\n",
" 'Journal\\n\\nPeer-Reviewed Status-Unknown': 1, 'Book\\r\\rEdited Book': 3,\n",
" 'Book': 3, 'Journal\\r\\rPeer-Reviewed Status-Unknown': 1,\n",
" 'Book\\r\\rAuthored Book': 3, 'Encyclopedia': 4}"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"psychinfo_search['publication_type'] = psychinfo_search['Publication Type'].map(publication_type)"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 27380\n",
"1 773\n",
"3 151\n",
"dtype: int64"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(psychinfo_search[\"publication_type\"] * psychinfo_search[\"conference_dich\"]).value_counts()\n"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"//anaconda/lib/python3.5/site-packages/pandas/computation/expressions.py:190: UserWarning: evaluating in Python space because the '*' operator is not supported by numexpr for the bool dtype, use '&' instead\n",
" unsupported[op_str]))\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Publication Type</th>\n",
" <th>Conference</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>707</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>2005 IEEE International Professional Communica...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>800</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>2006 ACA Annual Convention. 2006. US. The arti...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>801</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>2006 ACA Annual Convention. 2006. US. The arti...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1027</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Ontario Symposium on Personality and Social Ps...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1501</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Bienneial Meeting of the International Society...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1553</th>\n",
" <td>Book\\n\\nAuthored Book</td>\n",
" <td>Earlier versions of several parts of this book...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1591</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Mental Health of Immigrants and Refugees. Mar,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1607</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>International Conference of the International ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1638</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Conference on Childhood Bilingualism. Jun, 198...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>410</th>\n",
" <td>Book\\r\\rEdited Book</td>\n",
" <td>24th Spring Meeting, Division 39, APA. Apr, 20...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>439</th>\n",
" <td>Book\\r\\rEdited Book</td>\n",
" <td>The annual meeting of the Association for Wome...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>727</th>\n",
" <td>Book\\r\\rEdited Book</td>\n",
" <td>The chapters contained in this book emerged fr...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>750</th>\n",
" <td>Book\\r\\rAuthored Book</td>\n",
" <td>Based on the Interactive Forum on Transference...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>808</th>\n",
" <td>Book\\r\\rEdited Book</td>\n",
" <td>An earlier version of this paper was presented...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>152</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>A major portion of the works in this volume or...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>739</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>First Universal Races Conference. 1. Jul, 1911...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>897</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Gender & Empire. Oct, 2004. Otago University. ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>900</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Gender & Empire. Oct, 2004. University of Otag...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>984</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Nebraska Symposium on Motivation. 53. The afor...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1180</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>24th Spring Meeting, Division 39, APA. Apr, 20...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1225</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>The annual meeting of the Association for Wome...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1229</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>The annual meeting of the Midwestern Conferenc...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1302</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Annual Meeting of the Society for the Study of...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1461</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Annual Conference of the American Association ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Joint Conference of the Internataional Society...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>548</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>A portion of the chapter by D. Byrne was read ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>585</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>International Conference of the American Couns...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>276</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Gender & Empire. Oct, 2004. Otago University. ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>438</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Gender and Power in Families. 1987. London. Un...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>163</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Rutgers Invitational Symposium on Education. 1...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>368</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>National Conference on Education & Training in...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>517</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Annual Convention of the American Psychologica...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>640</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Rutgers Invitational Symposium on Education. 1...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>942</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>International Congress of the International As...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1026</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Minnesota Symposium on Child Psychology.. 29th...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1068</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>International Interdisciplinary Conference on ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1455</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>International Conference on Computer Support f...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1498</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Annual Meeting of the National Reading Confere...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1520</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>This volume grew out of a conference entitled ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1568</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>The papers in this volume are expansions of ta...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1578</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>International Conference of the International ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>372</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>European Conference on Traumatic Stress. 10th....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>825</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>2006 ACA Annual Convention. 2006. US. The arti...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>175</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Symposium XXI of the Association for the Advan...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>177</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Youth in Cities: Successful Mediators of Norma...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>228</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Symposium on the Psychosocial Consequences of ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>229</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Symposium on the Psychosocial Consequences of ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>335</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>27th Interamerican Congress of Psychology. Jun...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>336</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>International Congress of the International As...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>381</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Conference on Sexual Orientation and the Law.....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>453</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Annual Meeting of the National Reading Confere...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>477</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>An earlier version of this chapter was present...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>478</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Portions of this chapter were presented at the...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>488</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>The collection presented here stems from a con...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>509</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>The Future of Literacy in a Changing World. Ma...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>180</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>8th annual consumer culture theory conference....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>674</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>2006 ACA Annual Convention. 2006. US. The arti...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>779</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>The annual meeting of the Midwestern Conferenc...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>393</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>Midwestern Psychological Association conventio...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>670</th>\n",
" <td>Book\\n\\nEdited Book</td>\n",
" <td>International Conference on Practical Aspects ...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>151 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" Publication Type Conference\n",
"707 Book\\n\\nEdited Book 2005 IEEE International Professional Communica...\n",
"800 Book\\n\\nEdited Book 2006 ACA Annual Convention. 2006. US. The arti...\n",
"801 Book\\n\\nEdited Book 2006 ACA Annual Convention. 2006. US. The arti...\n",
"1027 Book\\n\\nEdited Book Ontario Symposium on Personality and Social Ps...\n",
"1501 Book\\n\\nEdited Book Bienneial Meeting of the International Society...\n",
"1553 Book\\n\\nAuthored Book Earlier versions of several parts of this book...\n",
"1591 Book\\n\\nEdited Book Mental Health of Immigrants and Refugees. Mar,...\n",
"1607 Book\\n\\nEdited Book International Conference of the International ...\n",
"1638 Book\\n\\nEdited Book Conference on Childhood Bilingualism. Jun, 198...\n",
"410 Book\\r\\rEdited Book 24th Spring Meeting, Division 39, APA. Apr, 20...\n",
"439 Book\\r\\rEdited Book The annual meeting of the Association for Wome...\n",
"727 Book\\r\\rEdited Book The chapters contained in this book emerged fr...\n",
"750 Book\\r\\rAuthored Book Based on the Interactive Forum on Transference...\n",
"808 Book\\r\\rEdited Book An earlier version of this paper was presented...\n",
"152 Book\\n\\nEdited Book A major portion of the works in this volume or...\n",
"739 Book\\n\\nEdited Book First Universal Races Conference. 1. Jul, 1911...\n",
"897 Book\\n\\nEdited Book Gender & Empire. Oct, 2004. Otago University. ...\n",
"900 Book\\n\\nEdited Book Gender & Empire. Oct, 2004. University of Otag...\n",
"984 Book\\n\\nEdited Book Nebraska Symposium on Motivation. 53. The afor...\n",
"1180 Book\\n\\nEdited Book 24th Spring Meeting, Division 39, APA. Apr, 20...\n",
"1225 Book\\n\\nEdited Book The annual meeting of the Association for Wome...\n",
"1229 Book\\n\\nEdited Book The annual meeting of the Midwestern Conferenc...\n",
"1302 Book\\n\\nEdited Book Annual Meeting of the Society for the Study of...\n",
"1461 Book\\n\\nEdited Book Annual Conference of the American Association ...\n",
"34 Book\\n\\nEdited Book Joint Conference of the Internataional Society...\n",
"548 Book\\n\\nEdited Book A portion of the chapter by D. Byrne was read ...\n",
"585 Book\\n\\nEdited Book International Conference of the American Couns...\n",
"276 Book\\n\\nEdited Book Gender & Empire. Oct, 2004. Otago University. ...\n",
"438 Book\\n\\nEdited Book Gender and Power in Families. 1987. London. Un...\n",
"163 Book\\n\\nEdited Book Rutgers Invitational Symposium on Education. 1...\n",
"... ... ...\n",
"368 Book\\n\\nEdited Book National Conference on Education & Training in...\n",
"517 Book\\n\\nEdited Book Annual Convention of the American Psychologica...\n",
"640 Book\\n\\nEdited Book Rutgers Invitational Symposium on Education. 1...\n",
"942 Book\\n\\nEdited Book International Congress of the International As...\n",
"1026 Book\\n\\nEdited Book Minnesota Symposium on Child Psychology.. 29th...\n",
"1068 Book\\n\\nEdited Book International Interdisciplinary Conference on ...\n",
"1455 Book\\n\\nEdited Book International Conference on Computer Support f...\n",
"1498 Book\\n\\nEdited Book Annual Meeting of the National Reading Confere...\n",
"1520 Book\\n\\nEdited Book This volume grew out of a conference entitled ...\n",
"1568 Book\\n\\nEdited Book The papers in this volume are expansions of ta...\n",
"1578 Book\\n\\nEdited Book International Conference of the International ...\n",
"372 Book\\n\\nEdited Book European Conference on Traumatic Stress. 10th....\n",
"825 Book\\n\\nEdited Book 2006 ACA Annual Convention. 2006. US. The arti...\n",
"175 Book\\n\\nEdited Book Symposium XXI of the Association for the Advan...\n",
"177 Book\\n\\nEdited Book Youth in Cities: Successful Mediators of Norma...\n",
"228 Book\\n\\nEdited Book Symposium on the Psychosocial Consequences of ...\n",
"229 Book\\n\\nEdited Book Symposium on the Psychosocial Consequences of ...\n",
"335 Book\\n\\nEdited Book 27th Interamerican Congress of Psychology. Jun...\n",
"336 Book\\n\\nEdited Book International Congress of the International As...\n",
"381 Book\\n\\nEdited Book Conference on Sexual Orientation and the Law.....\n",
"453 Book\\n\\nEdited Book Annual Meeting of the National Reading Confere...\n",
"477 Book\\n\\nEdited Book An earlier version of this chapter was present...\n",
"478 Book\\n\\nEdited Book Portions of this chapter were presented at the...\n",
"488 Book\\n\\nEdited Book The collection presented here stems from a con...\n",
"509 Book\\n\\nEdited Book The Future of Literacy in a Changing World. Ma...\n",
"180 Book\\n\\nEdited Book 8th annual consumer culture theory conference....\n",
"674 Book\\n\\nEdited Book 2006 ACA Annual Convention. 2006. US. The arti...\n",
"779 Book\\n\\nEdited Book The annual meeting of the Midwestern Conferenc...\n",
"393 Book\\n\\nEdited Book Midwestern Psychological Association conventio...\n",
"670 Book\\n\\nEdited Book International Conference on Practical Aspects ...\n",
"\n",
"[151 rows x 2 columns]"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"selection = (psychinfo_search[\"publication_type\"] == 3) * (psychinfo_search[\"conference_dich\"] == 1)\n",
"psychinfo_search[selection][[\"Publication Type\", \"Conference\"]]"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"English 27823\n",
"French 83\n",
"Spanish 78\n",
"Italian 42\n",
"German 41\n",
"Portuguese 31\n",
"Dutch 29\n",
"Chinese 22\n",
"Greek 10\n",
"Hebrew 7\n",
"Turkish 6\n",
"Serbo-Croatian 5\n",
"Russian 5\n",
"Slovak 4\n",
"Japanese 3\n",
"Hungarian 3\n",
"Czech 2\n",
"Polish 2\n",
"Danish 2\n",
"Norwegian 2\n",
"Romanian 2\n",
"Afrikaans 1\n",
"NonEnglish 1\n",
"Swedish 1\n",
"Finnish 1\n",
"Arabic 1\n",
"Name: Language, dtype: int64"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"psychinfo_search['Language'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"language = {'English': 1, 'French': 2, 'Spanish': 3, 'Italian': 4, 'German': 5, 'Portuguese': 6,\n",
" 'Dutch': 7, 'Chinese': 8, 'Greek': 9, 'Hebrew': 10, 'Turkish': 10, 'Russian': 10,\n",
" 'Serbo-Croatian': 10, 'Slovak': 10, 'Japanese': 10, 'Hungarian': 10, 'Czech': 10,\n",
" 'Danish': 10, 'Romanian': 10, 'Polish': 10, 'Norwegian': 10, 'Swedish': 10, 'Finnish': 10,\n",
" 'NonEnglish': 10, 'Arabic': 10, 'Afrikaans': 10}"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"psychinfo_search['language'] = psychinfo_search['Language'].map(language)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#psychinfo_search[\"PsycINFO Classification Code\"].value_counts().to_csv(\"data/PsycInfo/processed/PsycINFO_Classification_Code.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#psychinfo_search[\"Tests & Measures\"].value_counts().to_csv(\"data/PsycInfo/processed/Tests_&_Measures.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#psychinfo_search[\"Key Concepts\"].value_counts().to_csv(\"data/PsycInfo/processed/Key_Concepts.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#psychinfo_search[\"Location\"].value_counts().to_csv(\"data/PsycInfo/processed/Location.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#psychinfo_search[\"MeSH Subject Headings\"].value_counts().to_csv(\"data/PsycInfo/processed/MeSH_Subject_Headings.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#psychinfo_search[\"Journal Name\"].value_counts().to_csv(\"data/PsycInfo/processed/Journal_Name.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#psychinfo_search[\"Institution\"].value_counts().to_csv(\"data/PsycInfo/processed/Institution.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"349"
]
},
"execution_count": 118,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(psychinfo_search[\"Population Group\"].value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#psychinfo_search[\"Methodology\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def GetCats(text):\n",
" pattern = re.compile(\"([0-9]+)\")\n",
" results = [100*(int(x)//100) for x in pattern.findall(text)]\n",
" if len(set(results))>1:\n",
" return 4300 \n",
" else:\n",
" return results[0] "
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"psychinfo_search[\"PsycINFO_Classification_Code\"] = psychinfo_search[\"PsycINFO Classification Code\"].map(GetCats, \"ignore\")"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"23"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lists = psychinfo[\"PsycINFO Classification Code\"].map(GetCats, \"ignore\")\n",
"len(set([x for x in lists.dropna()]))\n",
"#Number of unique categories"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"psychinfo_search[\"grants_sponsorship\"] = psychinfo_search[\"Grant/Sponsorship\"].fillna(\"\").map(lambda x: int(len(x) > 0))"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#psychinfo_search.to_csv(\"data/PsycInfo/processed/psychinfo_term_search.csv.bz2\", encoding='utf-8', compression='bz2')"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#psychinfo_search = psychinfo_search.drop('Title', 1)"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#psychinfo_search[\"Methodology\"].value_counts().to_csv(\"data/PsycInfo/Manual_Mapping/Methodology.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#psychinfo_search[\"Population Group\"].value_counts().to_csv(\"data/PsycInfo/Manual_Mapping/Population_Group.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PsycINFO Tasks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Keep the current spreadsheet and add the following: \n",
"1. ~~Add Term in Abstract to spreadsheet~~ (word co-occurrence and control for the length of the abstract--lambda(len(abstract)) )**do this for NSF/NIH data as well**\n",
"1. ~~Add Term in Title to spreadsheet~~\n",
"1. ~~Copy the word data into a new column (title it 'terms')--> code them as the following: 1 = multiculturalism, 2 = polyculturalism, 3 = cultural pluralism, 4 = monocultural, 5 = monoracial, 6 = bicultural, 7 = biracial, 8 = biethnic, 9 = interracial, 10 = multicultural, 11 = multiracial, 12 = polycultural, 13 = polyracial, 14 = polyethnic, 15 = mixed race, 16 = mixed ethnicity, 17 = other race, 18 = other ethnicity~~\n",
"1. Search all options in set for the following categories: -- I will manually categorize them once you give all options in each set\n",
" 1. ~~\"Type of Book\"~~\n",
" 1. ~~\"PsycINFO Classification Code\"~~\n",
" ~~1. (used the classification codes[recoded to most basic category levels] -- subcategories \n",
" created by PsycInfo (22)-- multiple categories = 4300)~~\n",
" 1. ~~\"Document Type\"~~\n",
" 1. ~~\"Grant/Scholarship\"~~ \n",
" 1. ~~(create a dichotomized variable 0/1)~~\n",
" 1. ~~\"Tests & Measures\"--> csv (no longer necessary)~~\n",
" 1. ~~(Too many categories---needs to be reviewed manually/carefully in excel)~~\n",
" 1. ~~\"Publication Type\"~~\n",
" 1. ~~\"Publication Status\"~~\n",
" 1. \"Population Group\" \n",
" 1. (Need to be mapped manually and then recategorized)\n",
" 1. We need: gender, age (abstract, years)\n",
" 1. \"Methodology\"\n",
" 1. (can make specific methods dichotomous--may remove if unnecessary)\n",
" 1. \"Conference\" \n",
" 1. ~~Right now, this is text (~699 entries)--> dichotomize variable.~~ \n",
" ~~If it is a conference ie there is a text = 1, if there is NaN = 0.~~\n",
" 1. Then, I will incorporate this as a new category in \"Publication Type\" and remove this column).??? [not currently included as a category--overlaps with category 3 in Publication Type = Books]\n",
" 1. \"Key Concepts\"--> csv \n",
" 1. (word co-occurrence)\n",
" 1. \"Location\"-->csv--> sent to Barbara\n",
" 1. (categorized by region--multiple regions)\n",
" 1. ~~\"Language\"~~\n",
" ~~1. I am not sure about my \"other\" language (10) category -- I put everything with less \n",
" than 10 entries into one category.~~\n",
" 1. \"MeSH Subject Headings\"--> csv (may no longer be necessary?)\n",
" 1. (word co-occurrence)\n",
" 1. \"Journal Name\"-->csv--> sent to Jian Xin\n",
" 1. (categorized by H-index in 2014)\n",
" 1. \"Institution\"-->csv --> sent to Barbara\n",
" 1. (categorized by state, region & country)\n",
"1. ~~Count the number of cited references for each entry~~\n",
"\n",
"***Once we extract the csv files for these columns, I will categorize them. \n",
"\n",
"Once all of these corrections have been made, make a new spreadsheet and delete the following information: \n",
"1. Volume\n",
"1. Publisher\n",
"1. Accession Number\n",
"1. Author(s) \n",
"1. Issue\n",
"1. Cited References\n",
"1. Publication Status (had no variance)--only first posting\n",
"1. Document Type???\n"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"349"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(psychinfo_search[\"Population Group\"].value_counts())"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
ptiger10/better-work-done-faster | code_samples/spreadsheet_transformation.ipynb | 1 | 13098 | {
"cells": [
{
"cell_type": "code",
"execution_count": 183,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Import a library called Pandas and assign it to the alias \"pd.\" This is a standard convention.\n",
"# Import a library called Numpy and assign it to the alias \"np.\"\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 221,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Call a built-in Pandas function called .read_csv() to create a new spreadsheet-like object (i.e., a dataframe).\n",
"df = pd.read_csv('resources/sample_data.csv')"
]
},
{
"cell_type": "code",
"execution_count": 222,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Date</th>\n",
" <th>Item</th>\n",
" <th>Product Category</th>\n",
" <th>Time (hrs)</th>\n",
" <th>Amt Picked (lbs)</th>\n",
" <th>Sale Value ($/lb)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Lucas</td>\n",
" <td>1/5/17</td>\n",
" <td>Green Apple</td>\n",
" <td>Apples</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Valter</td>\n",
" <td>1/5/17</td>\n",
" <td>European Pear</td>\n",
" <td>Pears</td>\n",
" <td>4</td>\n",
" <td>10</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Erik</td>\n",
" <td>1/5/17</td>\n",
" <td>Red Apple</td>\n",
" <td>Apples</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>2.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Georg</td>\n",
" <td>1/5/17</td>\n",
" <td>Asian Pear</td>\n",
" <td>Pears</td>\n",
" <td>8</td>\n",
" <td>15</td>\n",
" <td>3.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Lucas</td>\n",
" <td>1/5/17</td>\n",
" <td>Red Apple</td>\n",
" <td>Apples</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>3.6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Date Item Product Category Time (hrs) \\\n",
"0 Lucas 1/5/17 Green Apple Apples 2 \n",
"1 Valter 1/5/17 European Pear Pears 4 \n",
"2 Erik 1/5/17 Red Apple Apples 2 \n",
"3 Georg 1/5/17 Asian Pear Pears 8 \n",
"4 Lucas 1/5/17 Red Apple Apples 6 \n",
"\n",
" Amt Picked (lbs) Sale Value ($/lb) \n",
"0 5 2.5 \n",
"1 10 2.0 \n",
"2 3 2.1 \n",
"3 15 3.8 \n",
"4 6 3.6 "
]
},
"execution_count": 222,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Print the spreadsheet for easy viewing.\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 223,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create a dictionary called item_name_changes and populate it with key/value pairs.\n",
"item_name_changes = {'European Pear':'Continental Fruit', 'Red Apple':'Pomme Rouge'}"
]
},
{
"cell_type": "code",
"execution_count": 224,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Call the dataframe's built-in .replace() function on the Item column to change the item names\n",
"df['Item'].replace(item_name_changes, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 225,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create a function in which an input (lbs, expressed here as x) is converted to grams.\n",
"# Format the results as a floating point object to two decimal points.\n",
"def convert_lb_to_kg(x):\n",
" return x*0.453"
]
},
{
"cell_type": "code",
"execution_count": 226,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create a new column applying the conversion function to every row in the Amt Picked (lbs) column.\n",
"df['Amt Picked (kg)'] = [convert_lb_to_kg(row) for row in df['Amt Picked (lbs)']]"
]
},
{
"cell_type": "code",
"execution_count": 227,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def convert_dollars_per_lb_to_euro_per_kg(x):\n",
" return (x*0.95) / 0.453"
]
},
{
"cell_type": "code",
"execution_count": 228,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df['Sale Value (€/kg)'] = [convert_dollars_per_lb_to_euro_per_kg(row) for row in df['Sale Value ($/lb)']]"
]
},
{
"cell_type": "code",
"execution_count": 229,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create a new column calculating the total amount earned from selling that particular item.\n",
"df['Amt Earned (€)'] = df['Amt Picked (kg)'] * df['Sale Value (€/kg)']"
]
},
{
"cell_type": "code",
"execution_count": 230,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create a new column calculating the cumulative amount earned that day by each person.\n",
"# Note that Lucas' cumulative total increases after factoring in the Pomme Rouge sale.\n",
"df['Cumulative Amt Earned Per Person (€)'] = df.groupby('Name')['Amt Earned (€)'].transform(np.cumsum)"
]
},
{
"cell_type": "code",
"execution_count": 231,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create a new column totaling the time spent harvesting this product category on this day, across all harvesters.\n",
"df['Collective Time Harvesting This Category (hrs)'] = df.groupby('Product Category')['Time (hrs)'].transform(np.sum)"
]
},
{
"cell_type": "code",
"execution_count": 232,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Format the results of the numeric columns as floating point objects to one decimal place.\n",
"numeric_columns = ['Amt Picked (kg)', 'Sale Value (€/kg)', 'Amt Earned (€)', 'Cumulative Amt Earned Per Person (€)']\n",
"df[numeric_columns] = df[numeric_columns].applymap('{:,.1f}'.format)"
]
},
{
"cell_type": "code",
"execution_count": 233,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Drop the unnecessary legacy columns.\n",
"df.drop(['Amt Picked (lbs)', 'Sale Value ($/lb)'], axis=1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 234,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Date</th>\n",
" <th>Item</th>\n",
" <th>Product Category</th>\n",
" <th>Time (hrs)</th>\n",
" <th>Amt Picked (kg)</th>\n",
" <th>Sale Value (€/kg)</th>\n",
" <th>Amt Earned (€)</th>\n",
" <th>Cumulative Amt Earned Per Person (€)</th>\n",
" <th>Collective Time Harvesting This Category (hrs)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Lucas</td>\n",
" <td>1/5/17</td>\n",
" <td>Green Apple</td>\n",
" <td>Apples</td>\n",
" <td>2</td>\n",
" <td>2.3</td>\n",
" <td>5.2</td>\n",
" <td>11.9</td>\n",
" <td>11.9</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Valter</td>\n",
" <td>1/5/17</td>\n",
" <td>Continental Fruit</td>\n",
" <td>Pears</td>\n",
" <td>4</td>\n",
" <td>4.5</td>\n",
" <td>4.2</td>\n",
" <td>19.0</td>\n",
" <td>19.0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Erik</td>\n",
" <td>1/5/17</td>\n",
" <td>Pomme Rouge</td>\n",
" <td>Apples</td>\n",
" <td>2</td>\n",
" <td>1.4</td>\n",
" <td>4.4</td>\n",
" <td>6.0</td>\n",
" <td>6.0</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Georg</td>\n",
" <td>1/5/17</td>\n",
" <td>Asian Pear</td>\n",
" <td>Pears</td>\n",
" <td>8</td>\n",
" <td>6.8</td>\n",
" <td>8.0</td>\n",
" <td>54.1</td>\n",
" <td>54.1</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Lucas</td>\n",
" <td>1/5/17</td>\n",
" <td>Pomme Rouge</td>\n",
" <td>Apples</td>\n",
" <td>6</td>\n",
" <td>2.7</td>\n",
" <td>7.5</td>\n",
" <td>20.5</td>\n",
" <td>32.4</td>\n",
" <td>10</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Date Item Product Category Time (hrs) \\\n",
"0 Lucas 1/5/17 Green Apple Apples 2 \n",
"1 Valter 1/5/17 Continental Fruit Pears 4 \n",
"2 Erik 1/5/17 Pomme Rouge Apples 2 \n",
"3 Georg 1/5/17 Asian Pear Pears 8 \n",
"4 Lucas 1/5/17 Pomme Rouge Apples 6 \n",
"\n",
" Amt Picked (kg) Sale Value (€/kg) Amt Earned (€) \\\n",
"0 2.3 5.2 11.9 \n",
"1 4.5 4.2 19.0 \n",
"2 1.4 4.4 6.0 \n",
"3 6.8 8.0 54.1 \n",
"4 2.7 7.5 20.5 \n",
"\n",
" Cumulative Amt Earned Per Person (€) \\\n",
"0 11.9 \n",
"1 19.0 \n",
"2 6.0 \n",
"3 54.1 \n",
"4 32.4 \n",
"\n",
" Collective Time Harvesting This Category (hrs) \n",
"0 10 \n",
"1 12 \n",
"2 10 \n",
"3 12 \n",
"4 10 "
]
},
"execution_count": 234,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Print the final result.\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda env:better-work-done-faster]",
"language": "python",
"name": "conda-env-better-work-done-faster-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
ipapusha/amnet | examples/example_linear_discrimination_big.ipynb | 1 | 43736 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline \n",
"\n",
"from __future__ import division\n",
"\n",
"import sys\n",
"if '../' not in sys.path: sys.path.append(\"../\")\n",
"\n",
"import numpy as np\n",
"import amnet\n",
"import cvxpy\n",
"\n",
"from numpy.random import seed, randn\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import z3\n",
"\n",
"# set up global z3 parameters\n",
"# parameters from https://stackoverflow.com/a/12516269\n",
"z3.set_param('auto_config', False)\n",
"z3.set_param('smt.case_split', 5)\n",
"z3.set_param('smt.relevancy', 2)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Replicate BV Fig 8.10 using CVXPY\n",
"# data generation\n",
"n = 2\n",
"N = 30\n",
"M = 30\n",
"seed(0)\n",
"Y = np.vstack((\n",
" np.hstack((1.5 + 0.9*randn(1, int(0.6*N)), 1.5 + 0.7*randn(1, int(0.4*N)))),\n",
" np.hstack((2*(randn(1, int(0.6*N)) + 1), 2*(randn(1, int(0.4*N)) - 1)))\n",
"))\n",
"X = np.vstack((\n",
" np.hstack((-1.5 + 0.9*randn(1,int(0.6*M)), -1.5 + 0.7*randn(1, int(0.4*M)))),\n",
" np.hstack((2*(randn(1, int(0.6*M)) - 1), 2*(randn(1, int(0.4*M)) + 1))),\n",
"))\n",
"T = np.array([[-1, 1], [1, 1]])\n",
"Y = np.dot(T, Y)\n",
"X = np.dot(T, Y)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"12.5872977541\n"
]
}
],
"source": [
"# solution via CVXPY\n",
"a = cvxpy.Variable(n)\n",
"b = cvxpy.Variable(1)\n",
"u = cvxpy.Variable(N)\n",
"v = cvxpy.Variable(M)\n",
"obj = cvxpy.Minimize(sum(u) + sum(v))\n",
"cons = [X.T * a - b >= 1 - u,\n",
" Y.T * a - b <= -(1 - v),\n",
" u >= 0,\n",
" v >= 0]\n",
"prob = cvxpy.Problem(obj, cons)\n",
"\n",
"result = prob.solve()\n",
"print result"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(-10, 10)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEKCAYAAAARnO4WAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npXKEBnyllMoR/x+b6F8R2Lz/hgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10ed4fc90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# graph CVXPY solution\n",
"t_min = min(np.hstack((X[0,:], Y[0,:])))\n",
"t_max = max(np.hstack((X[0,:], Y[0,:])))\n",
"tt = np.linspace(t_min-1, t_max+1, 100)\n",
"p = np.ravel(-a.value[0]/a.value[1]*tt + b.value/a.value[1])\n",
"p1 = np.ravel(-a.value[0]*tt/a.value[1] + (b.value+1)/a.value[1])\n",
"p2 = np.ravel(-a.value[0]*tt/a.value[1] + (b.value-1)/a.value[1])\n",
"plt.plot(X[0,:], X[1,:], 'o', Y[0,:], Y[1,:], 'o')\n",
"plt.plot(tt, p, '-r', tt, p1, '--r', tt, p2, '--r')\n",
"plt.title('Appriximate Linear Discrimination')\n",
"plt.axis('equal')\n",
"plt.xlim(-10, 10)\n",
"plt.ylim(-10, 10)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"itr | lo | hi | gam | res | obj | feas \n",
"===============================================================================\n",
" 1 | -1.0486e+06 | 1.0486e+06 | 0 | unsat | None | False \n",
" 2 | 0 | 1.0486e+06 | 5.2429e+05 | sat | 60 | True \n",
" 3 | 0 | 5.2429e+05 | 2.6214e+05 | sat | 60 | True \n",
" 4 | 0 | 2.6214e+05 | 1.3107e+05 | sat | 60 | True \n",
" 5 | 0 | 1.3107e+05 | 65536 | sat | 60 | True \n",
" 6 | 0 | 65536 | 32768 | sat | 60 | True \n",
" 7 | 0 | 32768 | 16384 | sat | 60 | True \n",
" 8 | 0 | 16384 | 8192 | sat | 60 | True \n",
" 9 | 0 | 8192 | 4096 | sat | 60 | True \n",
" 10 | 0 | 4096 | 2048 | sat | 60 | True \n",
" 11 | 0 | 2048 | 1024 | sat | 60 | True \n",
" 12 | 0 | 1024 | 512 | sat | 60 | True \n",
" 13 | 0 | 512 | 256 | sat | 60 | True \n",
" 14 | 0 | 256 | 128 | sat | 60 | True \n",
" 15 | 0 | 128 | 64 | sat | 60 | True \n",
" 16 | 0 | 64 | 32 | sat | 32 | False \n",
" 17 | 0 | 32 | 16 | sat | 16 | False \n",
" 18 | 0 | 16 | 8 | unsat | 16.0 | False \n",
" 19 | 8 | 16 | 12 | unsat | 16.0 | False \n",
" 20 | 12 | 16 | 14 | sat | 14 | False \n",
" 21 | 12 | 14 | 13 | sat | 13 | False \n",
" 22 | 12 | 13 | 12.5 | unsat | 13.0 | False \n",
"Solution found.\n",
" objval: 13.000000\n",
" point: [ 0.76016059 -0.28226778 0.37816368 0. 0.10619464 0. 0.\n",
" 0. 1.74048075 0.31584307 1.79142576 0. 0. 0.\n",
" 0. 0. 2.26283054 0. 0. 0.\n",
" 1.38531071 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 1.08028347 0.29851435\n",
" 1.30519793 0. 0. 0. 0. 0.\n",
" 1.76434876 0. 0. 0. 0.94957003 0. 0.\n",
" 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. ]\n"
]
}
],
"source": [
"# solution via AMNET\n",
"x = amnet.Variable(n + 1 + N + M, name='x')\n",
"a = x[0:n]\n",
"b = x[n:n+1]\n",
"u = x[n+1:n+1+N]\n",
"v = x[n+1+N:n+1+N+M]\n",
"Em = np.ones((M,1))\n",
"En = np.ones((N,1))\n",
"assert len(a) == n and len(b) == 1 and len(u) == N and len(v) == M\n",
"\n",
"obj = amnet.opt.Minimize(amnet.atoms.add_all(u) + amnet.atoms.add_all(v))\n",
"cons = [X.T * a - Em * b >= 1 - u,\n",
" Y.T * a - En * b <= -(1 - v),\n",
" u >= 0,\n",
" v >= 0]\n",
"prob = amnet.opt.Problem(obj, cons)\n",
"\n",
"result = prob.solve()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(-10, 10)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEKCAYAAAARnO4WAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10ef1eb10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# graph AMNET solution\n",
"t_min = min(np.hstack((X[0,:], Y[0,:])))\n",
"t_max = max(np.hstack((X[0,:], Y[0,:])))\n",
"tt = np.linspace(t_min-1, t_max+1, 100)\n",
"av = a.eval(result.value)\n",
"bv = b.eval(result.value)\n",
"p = -av[0]/av[1]*tt + bv/av[1]\n",
"p1 = -av[0]*tt/av[1] + (bv+1)/av[1]\n",
"p2 = -av[0]*tt/av[1] + (bv-1)/av[1]\n",
"plt.plot(X[0,:], X[1,:], 'o', Y[0,:], Y[1,:], 'o')\n",
"plt.plot(tt, p, '-r', tt, p1, '--r', tt, p2, '--r')\n",
"plt.title('Appriximate Linear Discrimination')\n",
"plt.axis('equal')\n",
"plt.xlim(-10, 10)\n",
"plt.ylim(-10, 10)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| bsd-3-clause |
NYUDataBootcamp/Projects | MBA_S17/Lou-US Home Price.ipynb | 1 | 1203988 | null | mit |
LigninTools/Ligpy-Cantera | Ligscipy/Untitled.ipynb | 1 | 8403 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import sciligpy_utils as lig\n",
"import constants as const\n",
"import numpy as np\n",
"import gen_ODE_scipy as gos"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"species_IC = 'Pseudotsuga_menziesii'\n",
"T_rate = 2.7+273.15"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"reactionlist, rateconstantlist, compositionlist = lig.set_paths()\n",
"y_list = lig.get_specieslist(reactionlist)\n",
"speciesindices, indices_to_species = lig.get_speciesindices(y_list)\n",
"kmatrix = lig.build_k_matrix(rateconstantlist)\n",
"rate_list = lig.build_rates_list_kexp(rateconstantlist, reactionlist, speciesindices, indices_to_species)\n",
"species_rxns = lig.build_species_rxns_dict(reactionlist)\n",
"dydt_expressions = lig.build_dydt_list(rate_list, y_list, species_rxns)\n",
"PLIGC_0, PLIGH_0, PLIGO_0 = lig.define_initial_composition(compositionlist, species_IC)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with open('solve_ODEs_%s.py' %species_IC, 'w') as f:\n",
" beginning = \"#!/usr/bin/pythons\\n\" \\\n",
" \"# -*- coding: utf-8 -*-\\n\\n\" \\\n",
" \"from scipy.integrate import odeint\\n\" \\\n",
" \"import numpy as np\\n\\n\\n\" \\\n",
" \"def ODEs(y, t, p):\\n\"\n",
" f.write(beginning)\n",
" \n",
" y = '\\tT'\n",
" for spec in y_list:\n",
" y += (', ' + spec)\n",
" y += ' = y'\n",
" f.write(y + '\\n')\n",
" \n",
" parameters = '\\talpha, R, A0, n0, E0'\n",
" for i in range(1, len(kmatrix)):\n",
" parameters += ', A%s, n%s, E%s' % (i, i, i)\n",
" parameters += ' = p\\n'\n",
" f.write(parameters)\n",
"\n",
" dydt = '\\tdydt = [alpha'\n",
" for ODE in dydt_expressions:\n",
" dydt += (', \\n\\t\\t\\t' + ODE.split('=')[1][1:-2])\n",
" dydt += ']'\n",
" f.write(dydt + '\\n')\n",
" f.write('\\treturn dydt\\n\\n')\n",
" \n",
" f.write('alpha = %s\\nR = %s\\n' %(T_rate, const.GAS_CONST))\n",
" f.write('# A, n, E values\\n')\n",
" for i in range(len(kmatrix)):\n",
" f.write('A%s = %s\\nn%s = %s\\nE%s = %s\\n' %(i, kmatrix[i][0], i, kmatrix[i][1], i, kmatrix[i][2]))\n",
" \n",
" f.write('\\n# Initial conditions\\n')\n",
" f.write('T0 = 298.15\\n')\n",
" f.write('PLIGC = %s\\nPLIGH = %s\\nPLIGO = %s\\n' %(PLIGC_0, PLIGH_0, PLIGO_0))\n",
" IC = ''\n",
" for i in y_list:\n",
" if i == 'PLIGC' or i == 'PLIGH' or i == 'PLIGO':\n",
" continue\n",
" else:\n",
" IC += ('%s = ' %i)\n",
" IC += '0'\n",
" f.write(IC + '\\n\\n')\n",
" \n",
" f.write('# ODE solver parameters\\n')\n",
" f.write('abserr = %s\\n' %const.ABSOLUTE_TOLERANCE)\n",
" f.write('relerr = %s\\n' %const.RELATIVE_TOLERANCE)\n",
" f.write('stoptime = 2000\\n')\n",
" f.write('numpoints = 10000\\n\\n')\n",
" \n",
" f.write('t = [stoptime * float(i) / (numpoints - 1) for i in range(numpoints)]\\n')\n",
" y0 = 'y0 = [T0'\n",
" for spec in y_list:\n",
" y0 += ', %s' %spec\n",
" y0 += ']\\n'\n",
" f.write(y0)\n",
" \n",
" p = 'p = [alpha, R'\n",
" for i in range(len(kmatrix)):\n",
" p += ', A%s, n%s, E%s' %(i, i, i)\n",
" p += ']\\n'\n",
" f.write(p)\n",
" \n",
" f.write('\\nysol = odeint(ODEs, y0, t, args=(p,), atol=abserr, rtol=relerr)\\n\\n')\n",
" \n",
" f.write(\"with open('sol_%s.dat', 'w') as f:\\n\" %(species_IC))\n",
" \n",
" ysol = ''\n",
" for i in range(len(y_list)+1):\n",
" ysol += 'yy[%s], ' %i\n",
" f.write('\\tfor tt, yy in zip(t, ysol):\\n')\n",
" f.write('\\t\\tprint(tt, %sfile=f)\\n' %ysol)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"len(kmatrix)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.exp(0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rate_list"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dydt_expressions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"kmatrix"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"-1*2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a=1; b=2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"b"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"gos.heating()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"gos.isothermal()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def func(a, b, c, d):\n",
" return a, b, c, d"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a, b, c , d= func(2, 4, 6, 8)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"round(3.14)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a = [3, 4, 5, 6]\n",
"print('3', end='\\t')\n",
"[print(a[i], end='\\t') for i in [1, 2, 3]]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(3, \"\\t\".join(str(x) for x in a), sep='\\t')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"a = np.array([3, -4, 5, 6])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a = a.clip(min=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| bsd-2-clause |
FedericoMuciaccia/SistemiComplessi | src/heatmap_and_range.ipynb | 2 | 136105 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"application/javascript": [
"\n",
" require(['base/js/utils'],\n",
" function(utils) {\n",
" utils.load_extensions('gmaps_js/gmaps_views');\n",
" });\n",
" "
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy\n",
"import pandas\n",
"\n",
"import matplotlib\n",
"from matplotlib import pyplot\n",
"%matplotlib inline\n",
"\n",
"import scipy\n",
"from scipy import stats # TODO vedere perché non fa chiamare il modulo direttamente\n",
"\n",
"import gmaps"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Creazione della mappa\n",
"\n",
"invece che uno scatterplot con dei raggi, la libreria ci consente solo di fare una heatmap (eventualmente pesata)\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"roma = pandas.read_csv(\"../data/Roma_towers.csv\")\n",
"coordinate = roma[['lat', 'lon']].values"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"heatmap = gmaps.heatmap(coordinate)\n",
"gmaps.display(heatmap)\n",
"\n",
"# TODO scrivere che dietro queste due semplici linee ci sta un pomeriggio intero di smadonnamenti"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"colosseo = (41.890183, 12.492369)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import gmplot\n",
"from gmplot import GoogleMapPlotter\n",
"\n",
"# gmap = gmplot.from_geocode(\"San Francisco\")\n",
"\n",
"mappa = gmplot.GoogleMapPlotter(41.890183, 12.492369, 11)\n",
"\n",
"#gmap.plot(latitudes, longitudes, 'cornflowerblue', edge_width=10)\n",
"#gmap.plot((41.890183, 41.891183), (12.492369, 12.493369), 'cornflowerblue', edge_width=10)\n",
"#gmap.scatter(more_lats, more_lngs, '#3B0B39', size=40, marker=False)\n",
"#gmap.scatter(marker_lats, marker_lngs, 'k', marker=True)\n",
"#gmap.heatmap(heat_lats, heat_lngs)\n",
"\n",
"#mappa.scatter((41.890183, 41.891183), (12.492369, 12.493369), color='#3B0B39', size=40, marker=False)\n",
"\n",
"#mappa.scatter(roma.lat.values,\n",
"# roma.lon.values,\n",
"# color='#3333ff',\n",
"# size=0,\n",
"# marker=False)\n",
"\n",
"mappa.heatmap(roma.lat.values,roma.lon.values)\n",
"\n",
"mappa.draw(\"../html/heatmap.html\")\n",
"#print a"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### NOTE guardando la mappa\n",
"\n",
"Sembrano esserci dei problemi con la posizione delle antenne: ci sono antenne sul tevere, su ponte Sisto, dentro il parchetto di Castel Sant'Angelo, in mezzo al pratone della Sapienza, in cima al dipartimento di Fisica...\n",
"\n",
"\n",
"Inoltre sembra esserci una strana clusterizzazione lungo le vie di traffico principali. Questo è ragionevole nell'ottica di garantire la copertura in una città con grossi flussi turistici come Roma, ma probabilmente non a tal punto da rendere plausibile la presenza di 7 antenne attorno a piazza Panteon. Ci sono anche coppie di antenne isolate che sembrano distare tra loro pochi metri. Probabilmente sono artefatti di ricostruzione.\n",
"\n",
"Probabilmente l'algoitmo di ricostruzione di Mozilla ha diversi problemi. Se questa è la situazione delle antenne non oso pensare alla situazione dei router wifi.\n",
"\n",
"Queste misure e queste ricostruzioni devono essere precise, perché è su queste che si poggerà il loro futuro servizio di geolocalizzazione.\n",
"\n",
"Bisognerebbe farglielo presente (magari ci prendono a lavorare da loro :-) )\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analisi del raggio di copertura delle antenne\n",
"\n",
"dato che ci servirà fare un grafico con scale logaritmiche teniamo solo i dati con\n",
"> range =! 0"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"41832\n"
]
}
],
"source": [
"\n",
"# condizioni di filtro\n",
"raggioMin = 1\n",
"# raggioMax = 1000\n",
"raggiPositivi = roma.range >= raggioMin\n",
"# raggiCorti = roma.range < raggioMax\n",
"\n",
"# query con le condizioni\n",
"#romaFiltrato = roma[raggiPositivi & raggiCorti]\n",
"romaFiltrato = roma[raggiPositivi]\n",
"raggi = romaFiltrato.range\n",
"\n",
"print max(raggi)\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"# logaritmic (base 2) binning in log-log (base 10) plots of integer histograms\n",
"\n",
"def logBinnedHist(histogramResults):\n",
" \"\"\"\n",
" histogramResults = numpy.histogram(...)\n",
" OR matplotlib.pyplot.hist(...)\n",
" \n",
" returns x, y\n",
" to be used with matplotlib.pyplot.step(x, y, where='post')\n",
" \"\"\"\n",
" \n",
" # TODO così funziona solo con l'istogramma di pyplot;\n",
" # quello di numpy restituisce solo la tupla (values, binEdges)\n",
" values, binEdges, others = histogramResults\n",
" \n",
" # print binEdges\n",
" \n",
" # TODO\n",
" # if 0 in binEdges:\n",
" # return \"error: log2(0) = ?\"\n",
" \n",
" # print len(values), len(binEdges)\n",
" \n",
" # print binEdges # TODO vedere quando non si parte da 1\n",
" \n",
" # int arrotonda all'intero inferiore\n",
" linMin = min(binEdges)\n",
" linMax = max(binEdges)\n",
" \n",
" # print linMin, linMax\n",
" \n",
" logStart = int(numpy.log2(linMin))\n",
" logStop = int(numpy.log2(linMax))\n",
" \n",
" # print logStart, logStop\n",
" \n",
" nLogBins = logStop - logStart + 1\n",
" \n",
" # print nLogBins\n",
" \n",
" logBins = numpy.logspace(logStart, logStop, num=nLogBins, base=2, dtype=int)\n",
" # print logBins\n",
" \n",
" # 1,2,4,8,16,32,64,128,256,512,1024\n",
" \n",
" ######################\n",
" \n",
" linStart = 2**logStop + 1\n",
" linStop = linMax\n",
" \n",
" # print linStart, linStop\n",
" \n",
" nLinBins = linStop - linStart + 1\n",
" \n",
" # print nLinBins\n",
" \n",
" linBins = numpy.linspace(linStart, linStop, num=nLinBins, dtype=int)\n",
" \n",
" # print linBins\n",
" \n",
" ######################\n",
" \n",
" bins = numpy.append(logBins, linBins)\n",
" \n",
" # print bins\n",
" \n",
" # print len(bins)\n",
" \n",
" \n",
" \n",
" \n",
" # TODO rendere generale questa funzione!!!\n",
" totalValues, binEdges, otherBinNumbers = scipy.stats.binned_statistic(raggi.values,\n",
" raggi.values,\n",
" statistic='count',\n",
" bins=bins)\n",
" \n",
" # print totalValues\n",
" # print len(totalValues)\n",
" \n",
" # uso le proprietà dei logaritmi in base 2:\n",
" # 2^(n+1) - 2^n = 2^n\n",
" correzioniDatiCanalizzatiLog = numpy.delete(logBins, -1)\n",
" \n",
" # print correzioniDatiCanalizzatiLog\n",
" \n",
" # print len(correzioniDatiCanalizzatiLog)\n",
" \n",
" correzioniDatiCanalizzatiLin = numpy.ones(nLinBins, dtype=int)\n",
" \n",
" # print correzioniDatiCanalizzatiLin\n",
" \n",
" # print len(correzioniDatiCanalizzatiLin)\n",
" \n",
" correzioniDatiCanalizzati = numpy.append(correzioniDatiCanalizzatiLog, correzioniDatiCanalizzatiLin)\n",
" \n",
" # print correzioniDatiCanalizzati\n",
" \n",
" # print len(correzioniDatiCanalizzati)\n",
" \n",
" \n",
" \n",
" \n",
" x = numpy.concatenate(([0], bins))\n",
" conteggi = totalValues/correzioniDatiCanalizzati\n",
" \n",
" # TODO caso speciale per il grafico di sotto\n",
" # (per non fare vedere la parte oltre l'ultima potenza di 2)\n",
" l = len(correzioniDatiCanalizzatiLin)\n",
" conteggi[-l:] = numpy.zeros(l, dtype='int')\n",
" \n",
" y = numpy.concatenate(([0], conteggi, [0]))\n",
" \n",
" return x, y\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAm0AAAJuCAYAAADinzUQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiLiAEraRERERBzg/wO1fCbmfHGeygAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8134c7be10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"# creazione di un istogramma log-log per la distribuzione del raggio di copertura\n",
"\n",
"# TODO provare a raggruppare le code\n",
"# esempio: con bins=100\n",
"# oppure con canalizzazione a logaritmo di 2, ma mediato\n",
"# in modo che venga equispaziato nel grafico logaritmico\n",
"# il programma vuole pesati i dati e non i canali\n",
"# si potrebbe implementare una mappa che pesa i dati\n",
"# secondo la funzione divisione intera per logaritmo di 2\n",
"# TODO mettere cerchietto che indica il range massimo oppure scritta in rosso \"20341 m!\"\n",
"# TODO spiegare perché ci sono così tanti conteggi a 1,2,4,... metri\n",
"# TODO ricavare il range dai dati grezzi, facendo un algoritmo di clustering\n",
"# sulle varie osservazioni delle antenne. machine learning?\n",
"# TODO scrivere funzione che fa grafici logaritmici con canali\n",
"# equispaziati nel plot logaritmico (canali pesati)\n",
"\n",
"# impostazioni plot complessivo\n",
"# pyplot.figure(figsize=(20,8)) # dimensioni in pollici\n",
"pyplot.figure(figsize=(10,10))\n",
"matplotlib.pyplot.xlim(10**0,10**5)\n",
"matplotlib.pyplot.ylim(10**-3,10**2)\n",
"pyplot.title('Distribuzione del raggio di copertura')\n",
"pyplot.ylabel(\"Numero di antenne\")\n",
"pyplot.xlabel(\"Copertura [m]\")\n",
"# pyplot.gca().set_xscale(\"log\")\n",
"# pyplot.gca().set_yscale(\"log\")\n",
"pyplot.xscale(\"log\")\n",
"pyplot.yscale(\"log\")\n",
"\n",
"# lin binning\n",
"distribuzioneRange = pyplot.hist(raggi.values,\n",
" bins=max(raggi)-min(raggi),\n",
" histtype='step',\n",
" color='#3385ff',\n",
" label='linear binning')\n",
"\n",
"# log_2 binning\n",
"xLog2, yLog2 = logBinnedHist(distribuzioneRange)\n",
"matplotlib.pyplot.step(xLog2, yLog2, where='post', color='#ff3300', linewidth=2, label='log_2 weighted binning') #where = mid OR post\n",
"# matplotlib.pyplot.plot(xLog2, yLog2)\n",
"\n",
"# linea verticale ad indicare il massimo grado\n",
"pyplot.axvline(x=max(raggi), color='#808080', linestyle='dotted', label='max range (41832m)')\n",
"\n",
"# legenda e salvataggio\n",
"pyplot.legend(loc='lower left', frameon=False)\n",
"pyplot.savefig('../img/range/infinite_log_binning.svg', format='svg', dpi=600, transparent=True)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Frequency-rank"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# istogramma sugli interi\n",
"unique, counts = numpy.unique(raggi.values, return_counts=True)\n",
"# print numpy.asarray((unique, counts)).T\n",
"rank = numpy.arange(1,len(unique)+1)\n",
"frequency = numpy.array(sorted(counts, reverse=True))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python2.7/dist-packages/matplotlib/collections.py:571: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" if self._edgecolors == str('face'):\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABJkAAAJuCAYAAAATjdOwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3762b82b50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pyplot.figure(figsize=(20,10))\n",
"pyplot.title('Distribuzione del raggio di copertura')\n",
"pyplot.ylabel(\"Numero di antenne\")\n",
"pyplot.xlabel(\"Copertura [m] o ranking\")\n",
"pyplot.xscale(\"log\")\n",
"pyplot.yscale(\"log\")\n",
"matplotlib.pyplot.xlim(10**0,10**4)\n",
"matplotlib.pyplot.ylim(10**0,10**2)\n",
"\n",
"matplotlib.pyplot.step(x=rank, y=frequency, where='post', label='frequency-rank', color='#00cc44')\n",
"\n",
"matplotlib.pyplot.scatter(x=unique, y=counts, marker='o', color='#3385ff', label='linear binning (scatter)')\n",
"matplotlib.pyplot.step(xLog2, yLog2, where='post', color='#ff3300', label='log_2 weighted binning')\n",
"\n",
"pyplot.legend(loc='lower left', frameon=False)\n",
"pyplot.savefig('../img/range/range_distribution.svg', format='svg', dpi=600, transparent=True)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Cumulative histogram\n",
"\n",
"the cumulative distribution function cdf(x) is the probability that a real-valued random variable X will take a value less than or equal to x"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"conteggi, binEdges = numpy.histogram(raggi.values,\n",
" bins=max(raggi)-min(raggi))\n",
"conteggiCumulativi = numpy.cumsum(conteggi)\n",
"valoriRaggi = numpy.delete(binEdges, -1)\n",
"N = len(raggi.values)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAJuCAYAAABhd85uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKSOWbUmSJCkj/z+pYQqNtG56VQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3762893e50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"pyplot.figure(figsize=(12,10))\n",
"pyplot.title('Raggio di copertura')\n",
"pyplot.ylabel(\"Numero di antenne\")\n",
"pyplot.xlabel(\"Copertura [m]\")\n",
"pyplot.xscale(\"log\")\n",
"pyplot.yscale(\"log\")\n",
"matplotlib.pyplot.xlim(10**0,10**5)\n",
"matplotlib.pyplot.ylim(10**0,10**4)\n",
"\n",
"matplotlib.pyplot.step(x=valoriRaggi, y=conteggiCumulativi, where='post', label='Cumulata', color='#009999')\n",
"matplotlib.pyplot.step(x=valoriRaggi, y=N-conteggiCumulativi, where='post', label='N - Cumulata', color='#ff0066')\n",
"\n",
"pyplot.axhline(y=N, color='#808080', linestyle='dotted', label='N_max = 6505')\n",
"\n",
"pyplot.legend(loc='lower left', frameon=False)\n",
"pyplot.savefig('../img/range/range_cumulated_distribution.svg', format='svg', dpi=600, transparent=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# TODO fare fit a mano e controllare le relazioni tra i vari esponenti"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
NREL/bifacial_radiance | docs/tutorials/9 - Advanced topics - 1 axis torque tube Shading for 1 day (Research documentation).ipynb | 1 | 64357 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 9 - Advanced topics - 1 axis torque tube Shading for 1 day (Research Documentation)\n",
"## Recreating JPV 2019 / PVSC 2018 Fig. 13\n",
"\n",
"\n",
"Calculating and plotting shading from torque tube on 1-axis tracking for 1 day, which is figure 13 in: \n",
"\n",
" Ayala Pelaez S, Deline C, Greenberg P, Stein JS, Kostuk RK. Model and validation of single-axis tracking with bifacial PV. IEEE J Photovoltaics. 2019;9(3):715–21. https://ieeexplore.ieee.org/document/8644027 and https://www.nrel.gov/docs/fy19osti/72039.pdf (pre-print, conference version)\n",
"\n",
"\n",
"This is what we will re-create:\n",
"\n",
"\n",
"Use bifacial_radiance minimum v. 0.3.1 or higher. Many things have been updated since this paper, simplifying the generation of this plot:\n",
"\n",
"<ul>\n",
" <li> Sensor position is now always generated E to W on N-S tracking systems, so same sensor positions can just be added for this calculation at the end without needing to flip the sensors. </li>\n",
" <li> Torquetubes get automatically generated in makeModule. Following PVSC 2018 paper, rotation is around the modules and not around the torque tube axis (which is a new feature) </li>\n",
" <li> Simulating only 1 day on single-axis tracking easier with cumulativesky = False and gendaylit1axis(startdate='06/24', enddate='06/24' </li> \n",
" <li> Sensors get generated very close to surface, so all results are from the module surface and not the torquetube for this 1-UP case. </li>\n",
"</ul>\n",
"\n",
"## Steps:\n",
"\n",
"<ol>\n",
" <li> <a href='#step1'> Running the simulations for all the cases: </li>\n",
" <ol type='A'> \n",
" <li> <a href='#step1a'>Baseline Case: No Torque Tube </a></li>\n",
" <li> <a href='#step1b'> Zgap = 0.1 </a></li>\n",
" <li> <a href='#step1c'> Zgap = 0.2 </a></li>\n",
" <li> <a href='#step1d'> Zgap = 0.3 </a></li>\n",
" </ol>\n",
" <li> <a href='#step2'> Read-back the values and tabulate average values for unshaded, 10cm gap and 30cm gap </a></li>\n",
" <li> <a href='#step3'> Plot spatial loss values for 10cm and 30cm data </a></li>\n",
" <li> <a href='#step4'> Overall Shading Factor (for 1 day) </a></li>\n",
"</ol>\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='step1'></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. Running the simulations for all the cases"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Your simulation will be stored in C:\\Users\\sayala\\Documents\\GitHub\\bifacial_radiance\\bifacial_radiance\\TEMP\\Tutorial_09\n"
]
}
],
"source": [
"import os\n",
"from pathlib import Path\n",
"\n",
"testfolder = str(Path().resolve().parent.parent / 'bifacial_radiance' / 'TEMP' / 'Tutorial_09')\n",
"if not os.path.exists(testfolder):\n",
" os.makedirs(testfolder)\n",
"\n",
"print (\"Your simulation will be stored in %s\" % testfolder)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# VARIABLES of the simulation: \n",
"lat = 35.1 # ABQ\n",
"lon = -106.7 # ABQ\n",
"x=1\n",
"y = 2 \n",
"numpanels=1\n",
"limit_angle = 45 # tracker rotation limit angle\n",
"backtrack = True\n",
"albedo = 'concrete' # ground albedo\n",
"hub_height = y*0.75 # H = 0.75 \n",
"gcr = 0.35 \n",
"pitch = y/gcr\n",
"#pitch = 1.0/gcr # Check from 1Axis_Shading_PVSC2018 file\n",
"cumulativesky = False # needed for set1axis and makeScene1axis so simulation is done hourly not with gencumsky.\n",
"limit_angle = 45 # tracker rotation limit angle\n",
"nMods=10\n",
"nRows=3\n",
"sensorsy = 200\n",
"module_type='test-module'\n",
"datewanted='06_24' # sunny day 6/24/1972 (index 4180 - 4195). Valid formats starting version 0.4.0 for full day sim: mm_dd\n",
"\n",
"## Torque tube info\n",
"tubetype='round'\n",
"material = 'Metal_Grey'\n",
"diameter = 0.1\n",
"axisofrotationTorqueTube = False # Original PVSC version rotated around the modules like most other software.\n",
"# Variables that will get defined on each iteration below:\n",
"zgap = 0 # 0.2, 0.3 values tested. Re-defined on each simulation.\n",
"visible = False # baseline is no torque tube.\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.3.4+387.g07a8343.dirty\n",
"path = C:\\Users\\sayala\\Documents\\GitHub\\bifacial_radiance\\bifacial_radiance\\TEMP\\Tutorial_09\n",
"Loading albedo, 1 value(s), 0.281 avg\n",
"1 nonzero albedo values.\n",
"Getting weather file: USA_NM_Albuquerque.723650_TMY2.epw\n",
" ... OK!\n",
"8760 line in WeatherFile. Assuming this is a standard hourly WeatherFile for the year for purposes of saving Gencumulativesky temporary weather files in EPW folder.\n",
"Coercing year to 2021\n",
"Filtering dates\n",
"Saving file EPWs\\metdata_temp.csv, # points: 8760\n",
"Calculating Sun position for Metdata that is right-labeled with a delta of -30 mins. i.e. 12 is 11:30 sunpos\n",
"Creating ~14 skyfiles. \n",
"Created 13 skyfiles in /skies/\n"
]
}
],
"source": [
"# Simulation Start.\n",
"import bifacial_radiance\n",
"import numpy as np\n",
"\n",
"print(bifacial_radiance.__version__)\n",
"\n",
"demo = bifacial_radiance.RadianceObj(path = testfolder) \n",
"demo.setGround(albedo)\n",
"epwfile = demo.getEPW(lat, lon) \n",
"metdata = demo.readWeatherFile(epwfile, starttime=datewanted, endtime=datewanted) \n",
"trackerdict = demo.set1axis(metdata, limit_angle = limit_angle, backtrack = backtrack, gcr = gcr, cumulativesky = cumulativesky)\n",
"trackerdict = demo.gendaylit1axis() \n",
"sceneDict = {'pitch':pitch,'hub_height':hub_height, 'nMods': nMods, 'nRows': nRows} "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='step1a'></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### A. Baseline Case: No Torque Tube\n",
"\n",
"When torquetube is False, zgap is the distance from axis of torque tube to module surface, but since we are rotating from the module's axis, this Zgap doesn't matter for this baseline case."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Module Name: _NoTT\n",
"Module _NoTT updated in module.json\n",
"Pre-existing .rad file objects\\_NoTT.rad will be overwritten\n",
"Module _NoTT updated in module.json\n",
"Pre-existing .rad file objects\\_NoTT.rad will be overwritten\n",
"\n",
"Making ~13 .rad files for gendaylit 1-axis workflow (this takes a minute..)\n",
"13 Radfiles created in /objects/\n",
"\n",
"Making 13 octfiles in root directory.\n",
"Created 1axis_2021-06-24_0600.oct\n",
"Created 1axis_2021-06-24_0700.oct\n",
"Created 1axis_2021-06-24_0800.oct\n",
"Created 1axis_2021-06-24_0900.oct\n",
"Created 1axis_2021-06-24_1000.oct\n",
"Created 1axis_2021-06-24_1100.oct\n",
"Created 1axis_2021-06-24_1200.oct\n",
"Created 1axis_2021-06-24_1300.oct\n",
"Created 1axis_2021-06-24_1400.oct\n",
"Created 1axis_2021-06-24_1500.oct\n",
"Created 1axis_2021-06-24_1600.oct\n",
"Created 1axis_2021-06-24_1700.oct\n",
"Created 1axis_2021-06-24_1800.oct\n",
"Linescan in process: 1axis_2021-06-24_0600_NoTT_Front\n",
"Linescan in process: 1axis_2021-06-24_0600_NoTT_Back\n",
"Saved: results\\irr_1axis_2021-06-24_0600_NoTT.csv\n",
"Index: 2021-06-24_0600. Wm2Front: 160.73402666666667. Wm2Back: 17.121764033333335\n",
"Linescan in process: 1axis_2021-06-24_0700_NoTT_Front\n",
"Linescan in process: 1axis_2021-06-24_0700_NoTT_Back\n",
"Saved: results\\irr_1axis_2021-06-24_0700_NoTT.csv\n",
"Index: 2021-06-24_0700. Wm2Front: 711.7897250000001. Wm2Back: 15.662987166666667\n",
"Linescan in process: 1axis_2021-06-24_0800_NoTT_Front\n",
"Linescan in process: 1axis_2021-06-24_0800_NoTT_Back\n",
"Saved: results\\irr_1axis_2021-06-24_0800_NoTT.csv\n",
"Index: 2021-06-24_0800. Wm2Front: 924.5799430000001. Wm2Back: 69.3365472\n",
"Linescan in process: 1axis_2021-06-24_0900_NoTT_Front\n",
"Linescan in process: 1axis_2021-06-24_0900_NoTT_Back\n",
"Saved: results\\irr_1axis_2021-06-24_0900_NoTT.csv\n",
"Index: 2021-06-24_0900. Wm2Front: 1039.2877216666666. Wm2Back: 84.73331776666666\n",
"Linescan in process: 1axis_2021-06-24_1000_NoTT_Front\n",
"Linescan in process: 1axis_2021-06-24_1000_NoTT_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1000_NoTT.csv\n",
"Index: 2021-06-24_1000. Wm2Front: 1077.4515266666667. Wm2Back: 107.66131883333334\n",
"Linescan in process: 1axis_2021-06-24_1100_NoTT_Front\n",
"Linescan in process: 1axis_2021-06-24_1100_NoTT_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1100_NoTT.csv\n",
"Index: 2021-06-24_1100. Wm2Front: 1083.8388816666668. Wm2Back: 133.7681057\n",
"Linescan in process: 1axis_2021-06-24_1200_NoTT_Front\n",
"Linescan in process: 1axis_2021-06-24_1200_NoTT_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1200_NoTT.csv\n",
"Index: 2021-06-24_1200. Wm2Front: 1085.4498483333332. Wm2Back: 150.34431866666668\n",
"Linescan in process: 1axis_2021-06-24_1300_NoTT_Front\n",
"Linescan in process: 1axis_2021-06-24_1300_NoTT_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1300_NoTT.csv\n",
"Index: 2021-06-24_1300. Wm2Front: 1083.580191666667. Wm2Back: 152.35295416666665\n",
"Linescan in process: 1axis_2021-06-24_1400_NoTT_Front\n",
"Linescan in process: 1axis_2021-06-24_1400_NoTT_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1400_NoTT.csv\n",
"Index: 2021-06-24_1400. Wm2Front: 1090.0554633333334. Wm2Back: 139.71770083333334\n",
"Linescan in process: 1axis_2021-06-24_1500_NoTT_Front\n",
"Linescan in process: 1axis_2021-06-24_1500_NoTT_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1500_NoTT.csv\n",
"Index: 2021-06-24_1500. Wm2Front: 1088.7448499999998. Wm2Back: 115.78704476666665\n",
"Linescan in process: 1axis_2021-06-24_1600_NoTT_Front\n",
"Linescan in process: 1axis_2021-06-24_1600_NoTT_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1600_NoTT.csv\n",
"Index: 2021-06-24_1600. Wm2Front: 1063.6545199999998. Wm2Back: 88.75401606666667\n",
"Linescan in process: 1axis_2021-06-24_1700_NoTT_Front\n",
"Linescan in process: 1axis_2021-06-24_1700_NoTT_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1700_NoTT.csv\n",
"Index: 2021-06-24_1700. Wm2Front: 973.7315478333334. Wm2Back: 74.38548493333333\n",
"Linescan in process: 1axis_2021-06-24_1800_NoTT_Front\n",
"Linescan in process: 1axis_2021-06-24_1800_NoTT_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1800_NoTT.csv\n",
"Index: 2021-06-24_1800. Wm2Front: 795.1581940000001. Wm2Back: 29.155385466666665\n",
"Saving a cumulative-results file in the main simulation folder.This adds up by sensor location the irradiance over all hours or configurations considered.\n",
"Warning: This file saving routine does not clean results, so if your setup has ygaps, or 2+modules or torque tubes, doing a deeper cleaning and working with the individual results files in the results folder is highly suggested.\n",
"\n",
"Saving Cumulative results\n",
"Saved: cumulative_results__NoTT.csv\n"
]
}
],
"source": [
"#CASE 0 No torque tube\n",
"# When torquetube is False, zgap is the distance from axis of torque tube to module surface, but since we are rotating from the module's axis, this Zgap doesn't matter.\n",
"# zgap = 0.1 + diameter/2.0 \n",
"torquetube = False \n",
"customname = '_NoTT'\n",
"module_NoTT = demo.makeModule(name=customname,x=x,y=y, numpanels=numpanels)\n",
"module_NoTT.addTorquetube(visible=False, axisofrotation=False, diameter=0)\n",
"trackerdict = demo.makeScene1axis(trackerdict, module_NoTT, sceneDict, cumulativesky = cumulativesky) \n",
"trackerdict = demo.makeOct1axis(trackerdict)\n",
"trackerdict = demo.analysis1axis(trackerdict, sensorsy = sensorsy, customname = customname)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='step1b'></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### B. ZGAP = 0.1"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Module Name: _zgap0.1\n",
"Module _zgap0.1 updated in module.json\n",
"\n",
"Making ~13 .rad files for gendaylit 1-axis workflow (this takes a minute..)\n",
"13 Radfiles created in /objects/\n",
"\n",
"Making 13 octfiles in root directory.\n",
"Created 1axis_2021-06-24_0600.oct\n",
"Created 1axis_2021-06-24_0700.oct\n",
"Created 1axis_2021-06-24_0800.oct\n",
"Created 1axis_2021-06-24_0900.oct\n",
"Created 1axis_2021-06-24_1000.oct\n",
"Created 1axis_2021-06-24_1100.oct\n",
"Created 1axis_2021-06-24_1200.oct\n",
"Created 1axis_2021-06-24_1300.oct\n",
"Created 1axis_2021-06-24_1400.oct\n",
"Created 1axis_2021-06-24_1500.oct\n",
"Created 1axis_2021-06-24_1600.oct\n",
"Created 1axis_2021-06-24_1700.oct\n",
"Created 1axis_2021-06-24_1800.oct\n",
"Linescan in process: 1axis_2021-06-24_0600_zgap0.1_Front\n",
"Linescan in process: 1axis_2021-06-24_0600_zgap0.1_Back\n",
"Saved: results\\irr_1axis_2021-06-24_0600_zgap0.1.csv\n",
"Index: 2021-06-24_0600. Wm2Front: 160.58522366666668. Wm2Back: 16.314374041666664\n",
"Linescan in process: 1axis_2021-06-24_0700_zgap0.1_Front\n",
"Linescan in process: 1axis_2021-06-24_0700_zgap0.1_Back\n",
"Saved: results\\irr_1axis_2021-06-24_0700_zgap0.1.csv\n",
"Index: 2021-06-24_0700. Wm2Front: 711.8783271666666. Wm2Back: 14.72640245\n",
"Linescan in process: 1axis_2021-06-24_0800_zgap0.1_Front\n",
"Linescan in process: 1axis_2021-06-24_0800_zgap0.1_Back\n",
"Saved: results\\irr_1axis_2021-06-24_0800_zgap0.1.csv\n",
"Index: 2021-06-24_0800. Wm2Front: 924.6545375000001. Wm2Back: 65.33095958333334\n",
"Linescan in process: 1axis_2021-06-24_0900_zgap0.1_Front\n",
"Linescan in process: 1axis_2021-06-24_0900_zgap0.1_Back\n",
"Saved: results\\irr_1axis_2021-06-24_0900_zgap0.1.csv\n",
"Index: 2021-06-24_0900. Wm2Front: 1039.1562083333333. Wm2Back: 80.46069813333334\n",
"Linescan in process: 1axis_2021-06-24_1000_zgap0.1_Front\n",
"Linescan in process: 1axis_2021-06-24_1000_zgap0.1_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1000_zgap0.1.csv\n",
"Index: 2021-06-24_1000. Wm2Front: 1078.4097983333334. Wm2Back: 102.5569975\n",
"Linescan in process: 1axis_2021-06-24_1100_zgap0.1_Front\n",
"Linescan in process: 1axis_2021-06-24_1100_zgap0.1_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1100_zgap0.1.csv\n",
"Index: 2021-06-24_1100. Wm2Front: 1083.4543766666666. Wm2Back: 126.3588816\n",
"Linescan in process: 1axis_2021-06-24_1200_zgap0.1_Front\n",
"Linescan in process: 1axis_2021-06-24_1200_zgap0.1_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1200_zgap0.1.csv\n",
"Index: 2021-06-24_1200. Wm2Front: 1085.5948533333335. Wm2Back: 143.09100388333331\n",
"Linescan in process: 1axis_2021-06-24_1300_zgap0.1_Front\n",
"Linescan in process: 1axis_2021-06-24_1300_zgap0.1_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1300_zgap0.1.csv\n",
"Index: 2021-06-24_1300. Wm2Front: 1083.3561866666669. Wm2Back: 143.74302033333333\n",
"Linescan in process: 1axis_2021-06-24_1400_zgap0.1_Front\n",
"Linescan in process: 1axis_2021-06-24_1400_zgap0.1_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1400_zgap0.1.csv\n",
"Index: 2021-06-24_1400. Wm2Front: 1089.9818416666667. Wm2Back: 133.16825973333334\n",
"Linescan in process: 1axis_2021-06-24_1500_zgap0.1_Front\n",
"Linescan in process: 1axis_2021-06-24_1500_zgap0.1_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1500_zgap0.1.csv\n",
"Index: 2021-06-24_1500. Wm2Front: 1089.1320249999999. Wm2Back: 110.99724395000001\n",
"Linescan in process: 1axis_2021-06-24_1600_zgap0.1_Front\n",
"Linescan in process: 1axis_2021-06-24_1600_zgap0.1_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1600_zgap0.1.csv\n",
"Index: 2021-06-24_1600. Wm2Front: 1063.6464533333333. Wm2Back: 84.98364443333334\n",
"Linescan in process: 1axis_2021-06-24_1700_zgap0.1_Front\n",
"Linescan in process: 1axis_2021-06-24_1700_zgap0.1_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1700_zgap0.1.csv\n",
"Index: 2021-06-24_1700. Wm2Front: 974.0719039999999. Wm2Back: 71.22193366666666\n",
"Linescan in process: 1axis_2021-06-24_1800_zgap0.1_Front\n",
"Linescan in process: 1axis_2021-06-24_1800_zgap0.1_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1800_zgap0.1.csv\n",
"Index: 2021-06-24_1800. Wm2Front: 795.5270095. Wm2Back: 27.774535033333333\n",
"Saving a cumulative-results file in the main simulation folder.This adds up by sensor location the irradiance over all hours or configurations considered.\n",
"Warning: This file saving routine does not clean results, so if your setup has ygaps, or 2+modules or torque tubes, doing a deeper cleaning and working with the individual results files in the results folder is highly suggested.\n",
"\n",
"Saving Cumulative results\n",
"Saved: cumulative_results__zgap0.1.csv\n"
]
}
],
"source": [
"#ZGAP 0.1 \n",
"zgap = 0.1\n",
"customname = '_zgap0.1'\n",
"tubeParams = {'tubetype':tubetype,\n",
" 'diameter':diameter,\n",
" 'material':material,\n",
" 'axisofrotation':False,\n",
" 'visible':True} # either pass this into makeModule, or separately into module.addTorquetube()\n",
"module_zgap01 = demo.makeModule(name=customname, x=x,y=y, numpanels=numpanels, zgap=zgap, tubeParams=tubeParams)\n",
"trackerdict = demo.makeScene1axis(trackerdict, module_zgap01, sceneDict, cumulativesky = cumulativesky) \n",
"trackerdict = demo.makeOct1axis(trackerdict)\n",
"trackerdict = demo.analysis1axis(trackerdict, sensorsy = sensorsy, customname = customname)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='step1c'></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### C. ZGAP = 0.2"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Module Name: _zgap0.2\n",
"Module _zgap0.2 updated in module.json\n",
"Pre-existing .rad file objects\\_zgap0.2.rad will be overwritten\n",
"\n",
"Making ~13 .rad files for gendaylit 1-axis workflow (this takes a minute..)\n",
"13 Radfiles created in /objects/\n",
"\n",
"Making 13 octfiles in root directory.\n",
"Created 1axis_2021-06-24_0600.oct\n",
"Created 1axis_2021-06-24_0700.oct\n",
"Created 1axis_2021-06-24_0800.oct\n",
"Created 1axis_2021-06-24_0900.oct\n",
"Created 1axis_2021-06-24_1000.oct\n",
"Created 1axis_2021-06-24_1100.oct\n",
"Created 1axis_2021-06-24_1200.oct\n",
"Created 1axis_2021-06-24_1300.oct\n",
"Created 1axis_2021-06-24_1400.oct\n",
"Created 1axis_2021-06-24_1500.oct\n",
"Created 1axis_2021-06-24_1600.oct\n",
"Created 1axis_2021-06-24_1700.oct\n",
"Created 1axis_2021-06-24_1800.oct\n",
"Linescan in process: 1axis_2021-06-24_0600_zgap0.2_Front\n",
"Linescan in process: 1axis_2021-06-24_0600_zgap0.2_Back\n",
"Saved: results\\irr_1axis_2021-06-24_0600_zgap0.2.csv\n",
"Index: 2021-06-24_0600. Wm2Front: 160.6793395. Wm2Back: 16.308693233333333\n",
"Linescan in process: 1axis_2021-06-24_0700_zgap0.2_Front\n",
"Linescan in process: 1axis_2021-06-24_0700_zgap0.2_Back\n",
"Saved: results\\irr_1axis_2021-06-24_0700_zgap0.2.csv\n",
"Index: 2021-06-24_0700. Wm2Front: 711.6508748333333. Wm2Back: 14.997858851666665\n",
"Linescan in process: 1axis_2021-06-24_0800_zgap0.2_Front\n",
"Linescan in process: 1axis_2021-06-24_0800_zgap0.2_Back\n",
"Saved: results\\irr_1axis_2021-06-24_0800_zgap0.2.csv\n",
"Index: 2021-06-24_0800. Wm2Front: 924.6050766666667. Wm2Back: 65.85995245000001\n",
"Linescan in process: 1axis_2021-06-24_0900_zgap0.2_Front\n",
"Linescan in process: 1axis_2021-06-24_0900_zgap0.2_Back\n",
"Saved: results\\irr_1axis_2021-06-24_0900_zgap0.2.csv\n",
"Index: 2021-06-24_0900. Wm2Front: 1039.106215. Wm2Back: 81.24756816666667\n",
"Linescan in process: 1axis_2021-06-24_1000_zgap0.2_Front\n",
"Linescan in process: 1axis_2021-06-24_1000_zgap0.2_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1000_zgap0.2.csv\n",
"Index: 2021-06-24_1000. Wm2Front: 1078.0332999999998. Wm2Back: 103.389067\n",
"Linescan in process: 1axis_2021-06-24_1100_zgap0.2_Front\n",
"Linescan in process: 1axis_2021-06-24_1100_zgap0.2_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1100_zgap0.2.csv\n",
"Index: 2021-06-24_1100. Wm2Front: 1083.9385066666666. Wm2Back: 127.08079916666668\n",
"Linescan in process: 1axis_2021-06-24_1200_zgap0.2_Front\n",
"Linescan in process: 1axis_2021-06-24_1200_zgap0.2_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1200_zgap0.2.csv\n",
"Index: 2021-06-24_1200. Wm2Front: 1085.1381666666668. Wm2Back: 144.71764443333333\n",
"Linescan in process: 1axis_2021-06-24_1300_zgap0.2_Front\n",
"Linescan in process: 1axis_2021-06-24_1300_zgap0.2_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1300_zgap0.2.csv\n",
"Index: 2021-06-24_1300. Wm2Front: 1083.1195133333333. Wm2Back: 146.28812258333335\n",
"Linescan in process: 1axis_2021-06-24_1400_zgap0.2_Front\n",
"Linescan in process: 1axis_2021-06-24_1400_zgap0.2_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1400_zgap0.2.csv\n",
"Index: 2021-06-24_1400. Wm2Front: 1089.849945. Wm2Back: 135.0038261166667\n",
"Linescan in process: 1axis_2021-06-24_1500_zgap0.2_Front\n",
"Linescan in process: 1axis_2021-06-24_1500_zgap0.2_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1500_zgap0.2.csv\n",
"Index: 2021-06-24_1500. Wm2Front: 1090.1313516666667. Wm2Back: 111.87352368333333\n",
"Linescan in process: 1axis_2021-06-24_1600_zgap0.2_Front\n",
"Linescan in process: 1axis_2021-06-24_1600_zgap0.2_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1600_zgap0.2.csv\n",
"Index: 2021-06-24_1600. Wm2Front: 1064.4160566666667. Wm2Back: 86.25420585\n",
"Linescan in process: 1axis_2021-06-24_1700_zgap0.2_Front\n",
"Linescan in process: 1axis_2021-06-24_1700_zgap0.2_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1700_zgap0.2.csv\n",
"Index: 2021-06-24_1700. Wm2Front: 974.0041676666666. Wm2Back: 72.31188941666667\n",
"Linescan in process: 1axis_2021-06-24_1800_zgap0.2_Front\n",
"Linescan in process: 1axis_2021-06-24_1800_zgap0.2_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1800_zgap0.2.csv\n",
"Index: 2021-06-24_1800. Wm2Front: 795.1022419999999. Wm2Back: 27.072133116666663\n",
"Saving a cumulative-results file in the main simulation folder.This adds up by sensor location the irradiance over all hours or configurations considered.\n",
"Warning: This file saving routine does not clean results, so if your setup has ygaps, or 2+modules or torque tubes, doing a deeper cleaning and working with the individual results files in the results folder is highly suggested.\n",
"\n",
"Saving Cumulative results\n",
"Saved: cumulative_results__zgap0.2.csv\n"
]
}
],
"source": [
"#ZGAP 0.2\n",
"zgap = 0.2\n",
"customname = '_zgap0.2'\n",
"tubeParams = {'tubetype':tubetype,\n",
" 'diameter':diameter,\n",
" 'material':material,\n",
" 'axisofrotation':False,\n",
" 'visible':True} # either pass this into makeModule, or separately into module.addTorquetube()\n",
"module_zgap02 = demo.makeModule(name=customname, x=x,y=y, numpanels=numpanels,zgap=zgap, tubeParams=tubeParams)\n",
"trackerdict = demo.makeScene1axis(trackerdict, module_zgap02, sceneDict, cumulativesky = cumulativesky) \n",
"trackerdict = demo.makeOct1axis(trackerdict)\n",
"trackerdict = demo.analysis1axis(trackerdict, sensorsy = sensorsy, customname = customname)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='step1d'></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### D. ZGAP = 0.3"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Module Name: _zgap0.3\n",
"Module _zgap0.3 updated in module.json\n",
"\n",
"Making ~13 .rad files for gendaylit 1-axis workflow (this takes a minute..)\n",
"13 Radfiles created in /objects/\n",
"\n",
"Making 13 octfiles in root directory.\n",
"Created 1axis_2021-06-24_0600.oct\n",
"Created 1axis_2021-06-24_0700.oct\n",
"Created 1axis_2021-06-24_0800.oct\n",
"Created 1axis_2021-06-24_0900.oct\n",
"Created 1axis_2021-06-24_1000.oct\n",
"Created 1axis_2021-06-24_1100.oct\n",
"Created 1axis_2021-06-24_1200.oct\n",
"Created 1axis_2021-06-24_1300.oct\n",
"Created 1axis_2021-06-24_1400.oct\n",
"Created 1axis_2021-06-24_1500.oct\n",
"Created 1axis_2021-06-24_1600.oct\n",
"Created 1axis_2021-06-24_1700.oct\n",
"Created 1axis_2021-06-24_1800.oct\n",
"Linescan in process: 1axis_2021-06-24_0600_zgap0.3_Front\n",
"Linescan in process: 1axis_2021-06-24_0600_zgap0.3_Back\n",
"Saved: results\\irr_1axis_2021-06-24_0600_zgap0.3.csv\n",
"Index: 2021-06-24_0600. Wm2Front: 160.71439716666666. Wm2Back: 16.402217866666664\n",
"Linescan in process: 1axis_2021-06-24_0700_zgap0.3_Front\n",
"Linescan in process: 1axis_2021-06-24_0700_zgap0.3_Back\n",
"Saved: results\\irr_1axis_2021-06-24_0700_zgap0.3.csv\n",
"Index: 2021-06-24_0700. Wm2Front: 711.8272646666668. Wm2Back: 14.969952549999999\n",
"Linescan in process: 1axis_2021-06-24_0800_zgap0.3_Front\n",
"Linescan in process: 1axis_2021-06-24_0800_zgap0.3_Back\n",
"Saved: results\\irr_1axis_2021-06-24_0800_zgap0.3.csv\n",
"Index: 2021-06-24_0800. Wm2Front: 924.5672013333332. Wm2Back: 70.4617007\n",
"Linescan in process: 1axis_2021-06-24_0900_zgap0.3_Front\n",
"Linescan in process: 1axis_2021-06-24_0900_zgap0.3_Back\n",
"Saved: results\\irr_1axis_2021-06-24_0900_zgap0.3.csv\n",
"Index: 2021-06-24_0900. Wm2Front: 1039.4952816666666. Wm2Back: 82.29257826666668\n",
"Linescan in process: 1axis_2021-06-24_1000_zgap0.3_Front\n",
"Linescan in process: 1axis_2021-06-24_1000_zgap0.3_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1000_zgap0.3.csv\n",
"Index: 2021-06-24_1000. Wm2Front: 1077.7083666666667. Wm2Back: 104.50355728333332\n",
"Linescan in process: 1axis_2021-06-24_1100_zgap0.3_Front\n",
"Linescan in process: 1axis_2021-06-24_1100_zgap0.3_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1100_zgap0.3.csv\n",
"Index: 2021-06-24_1100. Wm2Front: 1083.7942533333332. Wm2Back: 130.08286796666667\n",
"Linescan in process: 1axis_2021-06-24_1200_zgap0.3_Front\n",
"Linescan in process: 1axis_2021-06-24_1200_zgap0.3_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1200_zgap0.3.csv\n",
"Index: 2021-06-24_1200. Wm2Front: 1085.1777233333335. Wm2Back: 145.488044\n",
"Linescan in process: 1axis_2021-06-24_1300_zgap0.3_Front\n",
"Linescan in process: 1axis_2021-06-24_1300_zgap0.3_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1300_zgap0.3.csv\n",
"Index: 2021-06-24_1300. Wm2Front: 1083.5996499999999. Wm2Back: 147.47137110000003\n",
"Linescan in process: 1axis_2021-06-24_1400_zgap0.3_Front\n",
"Linescan in process: 1axis_2021-06-24_1400_zgap0.3_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1400_zgap0.3.csv\n",
"Index: 2021-06-24_1400. Wm2Front: 1089.7519816666668. Wm2Back: 136.00649253333333\n",
"Linescan in process: 1axis_2021-06-24_1500_zgap0.3_Front\n",
"Linescan in process: 1axis_2021-06-24_1500_zgap0.3_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1500_zgap0.3.csv\n",
"Index: 2021-06-24_1500. Wm2Front: 1090.3138166666668. Wm2Back: 112.7980408\n",
"Linescan in process: 1axis_2021-06-24_1600_zgap0.3_Front\n",
"Linescan in process: 1axis_2021-06-24_1600_zgap0.3_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1600_zgap0.3.csv\n",
"Index: 2021-06-24_1600. Wm2Front: 1063.9477266666668. Wm2Back: 87.05726741666668\n",
"Linescan in process: 1axis_2021-06-24_1700_zgap0.3_Front\n",
"Linescan in process: 1axis_2021-06-24_1700_zgap0.3_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1700_zgap0.3.csv\n",
"Index: 2021-06-24_1700. Wm2Front: 973.9965698333332. Wm2Back: 72.18619936666667\n",
"Linescan in process: 1axis_2021-06-24_1800_zgap0.3_Front\n",
"Linescan in process: 1axis_2021-06-24_1800_zgap0.3_Back\n",
"Saved: results\\irr_1axis_2021-06-24_1800_zgap0.3.csv\n",
"Index: 2021-06-24_1800. Wm2Front: 795.2513158333334. Wm2Back: 27.609950083333334\n",
"Saving a cumulative-results file in the main simulation folder.This adds up by sensor location the irradiance over all hours or configurations considered.\n",
"Warning: This file saving routine does not clean results, so if your setup has ygaps, or 2+modules or torque tubes, doing a deeper cleaning and working with the individual results files in the results folder is highly suggested.\n",
"\n",
"Saving Cumulative results\n",
"Saved: cumulative_results__zgap0.3.csv\n"
]
}
],
"source": [
"#ZGAP 0.3\n",
"zgap = 0.3\n",
"customname = '_zgap0.3'\n",
"tubeParams = {'tubetype':tubetype,\n",
" 'diameter':diameter,\n",
" 'material':material,\n",
" 'axisofrotation':False,\n",
" 'visible':True} # either pass this into makeModule, or separately into module.addTorquetube()\n",
"module_zgap03 = demo.makeModule(name=customname,x=x,y=y, numpanels=numpanels, zgap=zgap, tubeParams=tubeParams)\n",
"trackerdict = demo.makeScene1axis(trackerdict, module_zgap03, sceneDict, cumulativesky = cumulativesky) \n",
"trackerdict = demo.makeOct1axis(trackerdict)\n",
"trackerdict = demo.analysis1axis(trackerdict, sensorsy = sensorsy, customname = customname)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='step2'></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. Read-back the values and tabulate average values for unshaded, 10cm gap and 30cm gap\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"C:\\Users\\sayala\\Documents\\GitHub\\bifacial_radiance\\bifacial_radiance\\TEMP\\Tutorial_09\\results\n"
]
}
],
"source": [
"import glob\n",
"import pandas as pd\n",
"\n",
"resultsfolder = os.path.join(testfolder, 'results')\n",
"print (resultsfolder)\n",
"filenames = glob.glob(os.path.join(resultsfolder,'*.csv'))\n",
"noTTlist = [k for k in filenames if 'NoTT' in k]\n",
"zgap10cmlist = [k for k in filenames if 'zgap0.1' in k]\n",
"zgap20cmlist = [k for k in filenames if 'zgap0.2' in k]\n",
"zgap30cmlist = [k for k in filenames if 'zgap0.3' in k]\n",
"\n",
"# sum across all hours for each case\n",
"unsh_front = np.array([pd.read_csv(f, engine='python')['Wm2Front'] for f in noTTlist]).sum(axis = 0)\n",
"cm10_front = np.array([pd.read_csv(f, engine='python')['Wm2Front'] for f in zgap10cmlist]).sum(axis = 0)\n",
"cm20_front = np.array([pd.read_csv(f, engine='python')['Wm2Front'] for f in zgap20cmlist]).sum(axis = 0)\n",
"cm30_front = np.array([pd.read_csv(f, engine='python')['Wm2Front'] for f in zgap30cmlist]).sum(axis = 0)\n",
"unsh_back = np.array([pd.read_csv(f, engine='python')['Wm2Back'] for f in noTTlist]).sum(axis = 0)\n",
"cm10_back = np.array([pd.read_csv(f, engine='python')['Wm2Back'] for f in zgap10cmlist]).sum(axis = 0)\n",
"cm20_back = np.array([pd.read_csv(f, engine='python')['Wm2Back'] for f in zgap20cmlist]).sum(axis = 0)\n",
"cm30_back = np.array([pd.read_csv(f, engine='python')['Wm2Back'] for f in zgap30cmlist]).sum(axis = 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='step3'></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3. plot spatial loss values for 10cm and 30cm data"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:matplotlib.font_manager:findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
"WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Helvetica\n",
"WARNING:matplotlib.font_manager:findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
"WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Helvetica\n",
"WARNING:matplotlib.font_manager:findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
"WARNING:matplotlib.font_manager:findfont: Generic family 'sans-serif' not found because none of the following families were found: Helvetica\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 288x180 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.rcParams['font.family'] = 'sans-serif'\n",
"plt.rcParams['font.sans-serif'] = ['Helvetica']\n",
"plt.rcParams['axes.linewidth'] = 0.2 #set the value globally\n",
"\n",
"fig = plt.figure()\n",
"fig.set_size_inches(4, 2.5)\n",
"ax = fig.add_axes((0.15,0.15,0.78,0.75))\n",
"#plt.rc('font', family='sans-serif')\n",
"plt.rc('xtick',labelsize=8)\n",
"plt.rc('ytick',labelsize=8)\n",
"plt.rc('axes',labelsize=8)\n",
"plt.plot(np.linspace(-1,1,unsh_back.__len__()),(cm30_back - unsh_back)/unsh_back*100, label = '30cm gap',color = 'black') #steelblue\n",
"plt.plot(np.linspace(-1,1,unsh_back.__len__()),(cm20_back - unsh_back)/unsh_back*100, label = '20cm gap',color = 'steelblue', linestyle = '--') #steelblue\n",
"plt.plot(np.linspace(-1,1,unsh_back.__len__()),(cm10_back - unsh_back)/unsh_back*100, label = '10cm gap',color = 'darkorange') #steelblue\n",
"#plt.ylabel('$G_{rear}$ vs unshaded [Wm-2]')#(r'$BG_E$ [%]')\n",
"plt.ylabel('$G_{rear}$ / $G_{rear,tubeless}$ -1 [%]')\n",
"plt.xlabel('Module X position [m]')\n",
"plt.legend(fontsize = 8,frameon = False,loc='best')\n",
"#plt.ylim([0, 15])\n",
"plt.title('Torque tube shading loss',fontsize=9)\n",
"#plt.annotate('South',xy=(-10,9.5),fontsize = 8); plt.annotate('North',xy=(8,9.5),fontsize = 8)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a id='step4'></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4. Overall Shading Loss Factor\n",
"\n",
"To calculate shading loss factor, we can use the following equation:\n",
"\n",
"\n",
"\n",
"<img src=\"../images_wiki/AdvancedJournals/Equation_ShadingFactor.PNG\">"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"ShadingFactor = (1 - cm30_back.sum() / unsh_back.sum())*100"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| bsd-3-clause |
uber/pyro | tutorial/source/dmm.ipynb | 1 | 62506 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deep Markov Model \n",
"\n",
"## Introduction\n",
"\n",
"We're going to build a deep probabilistic model for sequential data: the deep markov model. The particular dataset we want to model is composed of snippets of polyphonic music. Each time slice in a sequence spans a quarter note and is represented by an 88-dimensional binary vector that encodes the notes at that time step. \n",
"\n",
"Since music is (obviously) temporally coherent, we need a model that can represent complex time dependencies in the observed data. It would not, for example, be appropriate to consider a model in which the notes at a particular time step are independent of the notes at previous time steps. One way to do this is to build a latent variable model in which the variability and temporal structure of the observations is controlled by the dynamics of the latent variables. \n",
"\n",
"One particular realization of this idea is a markov model, in which we have a chain of latent variables, with each latent variable in the chain conditioned on the previous latent variable. This is a powerful approach, but if we want to represent complex data with complex (and in this case unknown) dynamics, we would like our model to be sufficiently flexible to accommodate dynamics that are potentially highly non-linear. Thus a deep markov model: we allow for the transition probabilities governing the dynamics of the latent variables as well as the the emission probabilities that govern how the observations are generated by the latent dynamics to be parameterized by (non-linear) neural networks.\n",
"\n",
"The specific model we're going to implement is based on the following reference:\n",
"\n",
"[1] `Structured Inference Networks for Nonlinear State Space Models`,<br /> \n",
" Rahul G. Krishnan, Uri Shalit, David Sontag\n",
" \n",
"Please note that while we do not assume that the reader of this tutorial has read the reference, it's definitely a good place to look for a more comprehensive discussion of the deep markov model in the context of other time series models.\n",
"\n",
"We've described the model, but how do we go about training it? The inference strategy we're going to use is variational inference, which requires specifying a parameterized family of distributions that can be used to approximate the posterior distribution over the latent random variables. Given the non-linearities and complex time-dependencies inherent in our model and data, we expect the exact posterior to be highly non-trivial. So we're going to need a flexible family of variational distributions if we hope to learn a good model. Happily, together PyTorch and Pyro provide all the necessary ingredients. As we will see, assembling them will be straightforward. Let's get to work."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Model\n",
" \n",
"A convenient way to describe the high-level structure of the model is with a graphical model."
]
},
{
"cell_type": "raw",
"metadata": {
"raw_mimetype": "text/html"
},
"source": [
"<center><figure><img src=\"_static/img/model.png\" style=\"width: 500px;\"><figcaption> <font size=\"+1\"><b>Figure 1</b>: The model rolled out for T=3 time steps.</font></figcaption></figure></center>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here, we've rolled out the model assuming that the sequence of observations is of length three: $\\{{\\bf x}_1, {\\bf x}_2, {\\bf x}_3\\}$. Mirroring the sequence of observations we also have a sequence of latent random variables: $\\{{\\bf z}_1, {\\bf z}_2, {\\bf z}_3\\}$. The figure encodes the structure of the model. The corresponding joint distribution is\n",
"\n",
"$$p({\\bf x}_{123} , {\\bf z}_{123})=p({\\bf x}_1|{\\bf z}_1)p({\\bf x}_2|{\\bf z}_2)p({\\bf x}_3|{\\bf z}_3)p({\\bf z}_1)p({\\bf z}_2|{\\bf z}_1)p({\\bf z}_3|{\\bf z}_2)$$\n",
"\n",
"Conditioned on ${\\bf z}_t$, each observation ${\\bf x}_t$ is independent of the other observations. This can be read off from the fact that each ${\\bf x}_t$ only depends on the corresponding latent ${\\bf z}_t$, as indicated by the downward pointing arrows. We can also read off the markov property of the model: each latent ${\\bf z}_t$, when conditioned on the previous latent ${\\bf z}_{t-1}$, is independent of all previous latents $\\{ {\\bf z}_{t-2}, {\\bf z}_{t-3}, ...\\}$. This effectively says that everything one needs to know about the state of the system at time $t$ is encapsulated by the latent ${\\bf z}_{t}$.\n",
"\n",
"We will assume that the observation likelihoods, i.e. the probability distributions $p({{\\bf x}_t}|{{\\bf z}_t})$ that control the observations, are given by the bernoulli distribution. This is an appropriate choice since our observations are all 0 or 1. For the probability distributions $p({\\bf z}_t|{\\bf z}_{t-1})$ that control the latent dynamics, we choose (conditional) gaussian distributions with diagonal covariances. This is reasonable since we assume that the latent space is continuous. \n",
" \n",
"\n",
" \n",
"The solid black squares represent non-linear functions parameterized by neural networks. This is what makes this a _deep_ markov model. Note that the black squares appear in two different places: in between pairs of latents and in between latents and observations. The non-linear function that connects the latent variables ('Trans' in Fig. 1) controls the dynamics of the latent variables. Since we allow the conditional probability distribution of ${\\bf z}_{t}$ to depend on ${\\bf z}_{t-1}$ in a complex way, we will be able to capture complex dynamics in our model. Similarly, the non-linear function that connects the latent variables to the observations ('Emit' in Fig. 1) controls how the observations depend on the latent dynamics. \n",
"\n",
"Some additional notes:\n",
"- we can freely choose the dimension of the latent space to suit the problem at hand: small latent spaces for simple problems and larger latent spaces for problems with complex dynamics\n",
"- note the parameter ${\\bf z}_0$ in Fig. 1. as will become more apparent from the code, this is just a convenient way for us to parameterize the probability distribution $p({\\bf z}_1)$ for the first time step, where there are no previous latents to condition on.\n",
"\n",
"### The Gated Transition and the Emitter\n",
"\n",
"Without further ado, let's start writing some code. We first define the two PyTorch Modules that correspond to the black squares in Fig. 1. First the emission function:\n",
"\n",
"```python\n",
"class Emitter(nn.Module):\n",
" \"\"\"\n",
" Parameterizes the bernoulli observation likelihood p(x_t | z_t)\n",
" \"\"\"\n",
" def __init__(self, input_dim, z_dim, emission_dim):\n",
" super().__init__()\n",
" # initialize the three linear transformations used in the neural network\n",
" self.lin_z_to_hidden = nn.Linear(z_dim, emission_dim)\n",
" self.lin_hidden_to_hidden = nn.Linear(emission_dim, emission_dim)\n",
" self.lin_hidden_to_input = nn.Linear(emission_dim, input_dim)\n",
" # initialize the two non-linearities used in the neural network\n",
" self.relu = nn.ReLU()\n",
" self.sigmoid = nn.Sigmoid()\n",
"\n",
" def forward(self, z_t):\n",
" \"\"\"\n",
" Given the latent z at a particular time step t we return the vector of \n",
" probabilities `ps` that parameterizes the bernoulli distribution p(x_t|z_t)\n",
" \"\"\"\n",
" h1 = self.relu(self.lin_z_to_hidden(z_t))\n",
" h2 = self.relu(self.lin_hidden_to_hidden(h1))\n",
" ps = self.sigmoid(self.lin_hidden_to_input(h2))\n",
" return ps\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the constructor we define the linear transformations that will be used in our emission function. Note that `emission_dim` is the number of hidden units in the neural network. We also define the non-linearities that we will be using. The forward call defines the computational flow of the function. We take in the latent ${\\bf z}_{t}$ as input and do a sequence of transformations until we obtain a vector of length 88 that defines the emission probabilities of our bernoulli likelihood. Because of the sigmoid, each element of `ps` will be between 0 and 1 and will define a valid probability. Taken together the elements of `ps` encode which notes we expect to observe at time $t$ given the state of the system (as encoded in ${\\bf z}_{t}$)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we define the gated transition function:\n",
"\n",
"```python\n",
"class GatedTransition(nn.Module):\n",
" \"\"\"\n",
" Parameterizes the gaussian latent transition probability p(z_t | z_{t-1})\n",
" See section 5 in the reference for comparison.\n",
" \"\"\"\n",
" def __init__(self, z_dim, transition_dim):\n",
" super().__init__()\n",
" # initialize the six linear transformations used in the neural network\n",
" self.lin_gate_z_to_hidden = nn.Linear(z_dim, transition_dim)\n",
" self.lin_gate_hidden_to_z = nn.Linear(transition_dim, z_dim)\n",
" self.lin_proposed_mean_z_to_hidden = nn.Linear(z_dim, transition_dim)\n",
" self.lin_proposed_mean_hidden_to_z = nn.Linear(transition_dim, z_dim)\n",
" self.lin_sig = nn.Linear(z_dim, z_dim)\n",
" self.lin_z_to_loc = nn.Linear(z_dim, z_dim)\n",
" # modify the default initialization of lin_z_to_loc\n",
" # so that it's starts out as the identity function\n",
" self.lin_z_to_loc.weight.data = torch.eye(z_dim)\n",
" self.lin_z_to_loc.bias.data = torch.zeros(z_dim)\n",
" # initialize the three non-linearities used in the neural network\n",
" self.relu = nn.ReLU()\n",
" self.sigmoid = nn.Sigmoid()\n",
" self.softplus = nn.Softplus()\n",
"\n",
" def forward(self, z_t_1):\n",
" \"\"\"\n",
" Given the latent z_{t-1} corresponding to the time step t-1\n",
" we return the mean and scale vectors that parameterize the\n",
" (diagonal) gaussian distribution p(z_t | z_{t-1})\n",
" \"\"\"\n",
" # compute the gating function\n",
" _gate = self.relu(self.lin_gate_z_to_hidden(z_t_1))\n",
" gate = self.sigmoid(self.lin_gate_hidden_to_z(_gate))\n",
" # compute the 'proposed mean'\n",
" _proposed_mean = self.relu(self.lin_proposed_mean_z_to_hidden(z_t_1))\n",
" proposed_mean = self.lin_proposed_mean_hidden_to_z(_proposed_mean)\n",
" # assemble the actual mean used to sample z_t, which mixes \n",
" # a linear transformation of z_{t-1} with the proposed mean \n",
" # modulated by the gating function\n",
" loc = (1 - gate) * self.lin_z_to_loc(z_t_1) + gate * proposed_mean\n",
" # compute the scale used to sample z_t, using the proposed \n",
" # mean from above as input. the softplus ensures that scale is positive\n",
" scale = self.softplus(self.lin_sig(self.relu(proposed_mean)))\n",
" # return loc, scale which can be fed into Normal\n",
" return loc, scale\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This mirrors the structure of `Emitter` above, with the difference that the computational flow is a bit more complicated. This is for two reasons. First, the output of `GatedTransition` needs to define a valid (diagonal) gaussian distribution. So we need to output two parameters: the mean `loc`, and the (square root) covariance `scale`. These both need to have the same dimension as the latent space. Second, we don't want to _force_ the dynamics to be non-linear. Thus our mean `loc` is a sum of two terms, only one of which depends non-linearily on the input `z_t_1`. This way we can support both linear and non-linear dynamics (or indeed have the dynamics of part of the latent space be linear, while the remainder of the dynamics is non-linear). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model - a Pyro Stochastic Function\n",
"\n",
"So far everything we've done is pure PyTorch. To finish translating our model into code we need to bring Pyro into the picture. Basically we need to implement the stochastic nodes (i.e. the circles) in Fig. 1. To do this we introduce a callable `model()` that contains the Pyro primitive `pyro.sample`. The `sample` statements will be used to specify the joint distribution over the latents ${\\bf z}_{1:T}$. Additionally, the `obs` argument can be used with the `sample` statements to specify how the observations ${\\bf x}_{1:T}$ depend on the latents. Before we look at the complete code for `model()`, let's look at a stripped down version that contains the main logic:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```python\n",
"def model(...):\n",
" z_prev = self.z_0\n",
"\n",
" # sample the latents z and observed x's one time step at a time\n",
" for t in range(1, T_max + 1): \n",
" # the next two lines of code sample z_t ~ p(z_t | z_{t-1}).\n",
" # first compute the parameters of the diagonal gaussian \n",
" # distribution p(z_t | z_{t-1})\n",
" z_loc, z_scale = self.trans(z_prev)\n",
" # then sample z_t according to dist.Normal(z_loc, z_scale)\n",
" z_t = pyro.sample(\"z_%d\" % t, dist.Normal(z_loc, z_scale))\n",
" \n",
" # compute the probabilities that parameterize the bernoulli likelihood\n",
" emission_probs_t = self.emitter(z_t)\n",
" # the next statement instructs pyro to observe x_t according to the\n",
" # bernoulli distribution p(x_t|z_t) \n",
" pyro.sample(\"obs_x_%d\" % t, \n",
" dist.Bernoulli(emission_probs_t),\n",
" obs=mini_batch[:, t - 1, :])\n",
" # the latent sampled at this time step will be conditioned upon \n",
" # in the next time step so keep track of it\n",
" z_prev = z_t \n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first thing we need to do is sample ${\\bf z}_1$. Once we've sampled ${\\bf z}_1$, we can sample ${\\bf z}_2 \\sim p({\\bf z}_2|{\\bf z}_1)$ and so on. This is the logic implemented in the `for` loop. The parameters `z_loc` and `z_scale` that define the probability distributions $p({\\bf z}_t|{\\bf z}_{t-1})$ are computed using `self.trans`, which is just an instance of the `GatedTransition` module defined above. For the first time step at $t=1$ we condition on `self.z_0`, which is a (trainable) `Parameter`, while for subsequent time steps we condition on the previously drawn latent. Note that each random variable `z_t` is assigned a unique name by the user.\n",
"\n",
"Once we've sampled ${\\bf z}_t$ at a given time step, we need to observe the datapoint ${\\bf x}_t$. So we pass `z_t` through `self.emitter`, an instance of the `Emitter` module defined above to obtain `emission_probs_t`. Together with the argument `dist.Bernoulli()` in the `sample` statement, these probabilities fully specify the observation likelihood. Finally, we also specify the slice of observed data ${\\bf x}_t$: `mini_batch[:, t - 1, :]` using the `obs` argument to `sample`. \n",
"\n",
"This fully specifies our model and encapsulates it in a callable that can be passed to Pyro. Before we move on let's look at the full version of `model()` and go through some of the details we glossed over in our first pass."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```python\n",
"def model(self, mini_batch, mini_batch_reversed, mini_batch_mask,\n",
"\t\t mini_batch_seq_lengths, annealing_factor=1.0):\n",
"\n",
"\t# this is the number of time steps we need to process in the mini-batch\n",
"\tT_max = mini_batch.size(1)\n",
"\n",
"\t# register all PyTorch (sub)modules with pyro\n",
"\t# this needs to happen in both the model and guide\n",
"\tpyro.module(\"dmm\", self)\n",
"\n",
"\t# set z_prev = z_0 to setup the recursive conditioning in p(z_t | z_{t-1})\n",
"\tz_prev = self.z_0.expand(mini_batch.size(0), self.z_0.size(0))\n",
"\n",
"\t# we enclose all the sample statements in the model in a plate.\n",
"\t# this marks that each datapoint is conditionally independent of the others\n",
"\twith pyro.plate(\"z_minibatch\", len(mini_batch)):\n",
"\t\t# sample the latents z and observed x's one time step at a time\n",
"\t\tfor t in range(1, T_max + 1):\n",
"\t\t\t# the next chunk of code samples z_t ~ p(z_t | z_{t-1})\n",
"\t\t\t# note that (both here and elsewhere) we use poutine.scale to take care\n",
"\t\t\t# of KL annealing. we use the mask() method to deal with raggedness\n",
"\t\t\t# in the observed data (i.e. different sequences in the mini-batch\n",
"\t\t\t# have different lengths)\n",
"\n",
"\t\t\t# first compute the parameters of the diagonal gaussian \n",
" # distribution p(z_t | z_{t-1})\n",
"\t\t\tz_loc, z_scale = self.trans(z_prev)\n",
"\n",
"\t\t\t# then sample z_t according to dist.Normal(z_loc, z_scale).\n",
"\t\t\t# note that we use the reshape method so that the univariate \n",
" # Normal distribution is treated as a multivariate Normal \n",
" # distribution with a diagonal covariance.\n",
"\t\t\twith poutine.scale(None, annealing_factor):\n",
"\t\t\t\tz_t = pyro.sample(\"z_%d\" % t,\n",
"\t\t\t\t\t\t\t\t dist.Normal(z_loc, z_scale)\n",
"\t\t\t\t\t\t\t\t\t .mask(mini_batch_mask[:, t - 1:t])\n",
"\t\t\t\t\t\t\t\t\t .to_event(1))\n",
"\n",
"\t\t\t# compute the probabilities that parameterize the bernoulli likelihood\n",
"\t\t\temission_probs_t = self.emitter(z_t)\n",
"\t\t\t# the next statement instructs pyro to observe x_t according to the\n",
"\t\t\t# bernoulli distribution p(x_t|z_t)\n",
"\t\t\tpyro.sample(\"obs_x_%d\" % t,\n",
"\t\t\t\t\t\tdist.Bernoulli(emission_probs_t)\n",
"\t\t\t\t\t\t\t.mask(mini_batch_mask[:, t - 1:t])\n",
"\t\t\t\t\t\t\t.to_event(1),\n",
"\t\t\t\t\t\tobs=mini_batch[:, t - 1, :])\n",
"\t\t\t# the latent sampled at this time step will be conditioned upon\n",
"\t\t\t# in the next time step so keep track of it\n",
"\t\t\tz_prev = z_t\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first thing to note is that `model()` takes a number of arguments. For now let's just take a look at `mini_batch` and `mini_batch_mask`. `mini_batch` is a three dimensional tensor, with the first dimension being the batch dimension, the second dimension being the temporal dimension, and the final dimension being the features (88-dimensional in our case). To speed up the code, whenever we run `model` we're going to process an entire mini-batch of sequences (i.e. we're going to take advantage of vectorization). \n",
"\n",
"This is sensible because our model is implicitly defined over a single observed sequence. The probability of a set of sequences is just given by the products of the individual sequence probabilities. In other words, given the parameters of the model the sequences are conditionally independent.\n",
"\n",
"This vectorization introduces some complications because sequences can be of different lengths. This is where `mini_batch_mask` comes in. `mini_batch_mask` is a two dimensional 0/1 mask of dimensions `mini_batch_size` x `T_max`, where `T_max` is the maximum length of any sequence in the mini-batch. This encodes which parts of `mini_batch` are valid observations. \n",
"\n",
"So the first thing we do is grab `T_max`: we have to unroll our model for at least this many time steps. Note that this will result in a lot of 'wasted' computation, since some of the sequences will be shorter than `T_max`, but this is a small price to pay for the big speed-ups that come with vectorization. We just need to make sure that none of the 'wasted' computations 'pollute' our model computation. We accomplish this by passing the mask appropriate to time step $t$ to the `mask` method (which acts on the distribution that needs masking).\n",
"\n",
"Finally, the line `pyro.module(\"dmm\", self)` is equivalent to a bunch of `pyro.param` statements for each parameter in the model. This lets Pyro know which parameters are part of the model. Just like for the `sample` statement, we give the module a unique name. This name will be incorporated into the name of the `Parameters` in the model. We leave a discussion of the KL annealing factor for later."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Inference\n",
"\n",
"At this point we've fully specified our model. The next step is to set ourselves up for inference. As mentioned in the introduction, our inference strategy is going to be variational inference (see [SVI Part I](svi_part_i.ipynb) for an introduction). So our next task is to build a family of variational distributions appropriate to doing inference in a deep markov model. However, at this point it's worth emphasizing that nothing about the way we've implemented `model()` ties us to variational inference. In principle we could use _any_ inference strategy available in Pyro. For example, in this particular context one could imagine using some variant of Sequential Monte Carlo (although this is not currently supported in Pyro).\n",
"\n",
"### Guide\n",
"\n",
"The purpose of the guide (i.e. the variational distribution) is to provide a (parameterized) approximation to the exact posterior $p({\\bf z}_{1:T}|{\\bf x}_{1:T})$. Actually, there's an implicit assumption here which we should make explicit, so let's take a step back. \n",
"Suppose our dataset $\\mathcal{D}$ consists of $N$ sequences \n",
"$\\{ {\\bf x}_{1:T_1}^1, {\\bf x}_{1:T_2}^2, ..., {\\bf x}_{1:T_N}^N \\}$. Then the posterior we're actually interested in is given by \n",
"$p({\\bf z}_{1:T_1}^1, {\\bf z}_{1:T_2}^2, ..., {\\bf z}_{1:T_N}^N | \\mathcal{D})$, i.e. we want to infer the latents for _all_ $N$ sequences. Even for small $N$ this is a very high-dimensional distribution that will require a very large number of parameters to specify. In particular if we were to directly parameterize the posterior in this form, the number of parameters required would grow (at least) linearly with $N$. One way to avoid this nasty growth with the size of the dataset is *amortization* (see the analogous discussion in [SVI Part II](svi_part_ii.ipynb)).\n",
"\n",
"#### Aside: Amortization\n",
"\n",
"This works as follows. Instead of introducing variational parameters for each sequence in our dataset, we're going to learn a single parametric function $f({\\bf x}_{1:T})$ and work with a variational distribution that has the form $\\prod_{n=1}^N q({\\bf z}_{1:T_n}^n | f({\\bf x}_{1:T_n}^n))$. The function $f(\\cdot)$—which basically maps a given observed sequence to a set of variational parameters tailored to that sequence—will need to be sufficiently rich to capture the posterior accurately, but now we can handle large datasets without having to introduce an obscene number of variational parameters.\n",
"\n",
"So our task is to construct the function $f(\\cdot)$. Since in our case we need to support variable-length sequences, it's only natural that $f(\\cdot)$ have a RNN in the loop. Before we look at the various component parts that make up our $f(\\cdot)$ in detail, let's look at a computational graph that encodes the basic structure: <p>"
]
},
{
"cell_type": "raw",
"metadata": {
"raw_mimetype": "text/html"
},
"source": [
"<center><figure><img src=\"_static/img/guide.png\" style=\"width: 400px;\"><figcaption> <font size=\"+1\"><b>Figure 2</b>: The guide rolled out for T=3 time steps. </font></figcaption></figure></center>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At the bottom of the figure we have our sequence of three observations. These observations will be consumed by a RNN that reads the observations from right to left and outputs three hidden states $\\{ {\\bf h}_1, {\\bf h}_2,{\\bf h}_3\\}$. Note that this computation is done _before_ we sample any latent variables. Next, each of the hidden states will be fed into a `Combiner` module whose job is to output the mean and covariance of the the conditional distribution $q({\\bf z}_t | {\\bf z}_{t-1}, {\\bf x}_{t:T})$, which we take to be given by a diagonal gaussian distribution. (Just like in the model, the conditional structure of ${\\bf z}_{1:T}$ in the guide is such that we sample ${\\bf z}_t$ forward in time.) In addition to the RNN hidden state, the `Combiner` also takes the latent random variable from the previous time step as input, except for $t=1$, where it instead takes the trainable (variational) parameter ${\\bf z}_0^{\\rm{q}}$. \n",
"\n",
"#### Aside: Guide Structure\n",
"Why do we setup the RNN to consume the observations from right to left? Why not left to right? With this choice our conditional distribution $q({\\bf z}_t |...)$ depends on two things:\n",
"\n",
"- the latent ${\\bf z}_{t-1}$ from the previous time step; and \n",
"- the observations ${\\bf x}_{t:T}$, i.e. the current observation together with all future observations\n",
"\n",
"We are free to make other choices; all that is required is that that the guide is a properly normalized distribution that plays nice with autograd. This particular choice is motivated by the dependency structure of the true posterior: see reference [1] for a detailed discussion. In brief, while we could, for example, condition on the entire sequence of observations, because of the markov structure of the model everything that we need to know about the previous observations ${\\bf x}_{1:t-1}$ is encapsulated by ${\\bf z}_{t-1}$. We could condition on more things, but there's no need; and doing so will probably tend to dilute the learning signal. So running the RNN from right to left is the most natural choice for this particular model.\n",
"\n",
"Let's look at the component parts in detail. First, the `Combiner` module:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```python\n",
"class Combiner(nn.Module):\n",
" \"\"\"\n",
" Parameterizes q(z_t | z_{t-1}, x_{t:T}), which is the basic building block\n",
" of the guide (i.e. the variational distribution). The dependence on x_{t:T} is\n",
" through the hidden state of the RNN (see the pytorch module `rnn` below)\n",
" \"\"\"\n",
" def __init__(self, z_dim, rnn_dim):\n",
" super().__init__()\n",
" # initialize the three linear transformations used in the neural network\n",
" self.lin_z_to_hidden = nn.Linear(z_dim, rnn_dim)\n",
" self.lin_hidden_to_loc = nn.Linear(rnn_dim, z_dim)\n",
" self.lin_hidden_to_scale = nn.Linear(rnn_dim, z_dim)\n",
" # initialize the two non-linearities used in the neural network\n",
" self.tanh = nn.Tanh()\n",
" self.softplus = nn.Softplus()\n",
"\n",
" def forward(self, z_t_1, h_rnn):\n",
" \"\"\"\n",
" Given the latent z at at a particular time step t-1 as well as the hidden\n",
" state of the RNN h(x_{t:T}) we return the mean and scale vectors that\n",
" parameterize the (diagonal) gaussian distribution q(z_t | z_{t-1}, x_{t:T})\n",
" \"\"\"\n",
" # combine the rnn hidden state with a transformed version of z_t_1\n",
" h_combined = 0.5 * (self.tanh(self.lin_z_to_hidden(z_t_1)) + h_rnn)\n",
" # use the combined hidden state to compute the mean used to sample z_t\n",
" loc = self.lin_hidden_to_loc(h_combined)\n",
" # use the combined hidden state to compute the scale used to sample z_t\n",
" scale = self.softplus(self.lin_hidden_to_scale(h_combined))\n",
" # return loc, scale which can be fed into Normal\n",
" return loc, scale\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This module has the same general structure as `Emitter` and `GatedTransition` in the model. The only thing of note is that because the `Combiner` needs to consume two inputs at each time step, it transforms the inputs into a single combined hidden state `h_combined` before it computes the outputs. \n",
"\n",
"Apart from the RNN, we now have all the ingredients we need to construct our guide distribution.\n",
"Happily, PyTorch has great built-in RNN modules, so we don't have much work to do here. We'll see where we instantiate the RNN later. Let's instead jump right into the definition of the stochastic function `guide()`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```python\n",
"def guide(self, mini_batch, mini_batch_reversed, mini_batch_mask,\n",
" mini_batch_seq_lengths, annealing_factor=1.0):\n",
"\n",
" # this is the number of time steps we need to process in the mini-batch\n",
" T_max = mini_batch.size(1)\n",
" # register all PyTorch (sub)modules with pyro\n",
" pyro.module(\"dmm\", self)\n",
"\n",
" # if on gpu we need the fully broadcast view of the rnn initial state\n",
" # to be in contiguous gpu memory\n",
" h_0_contig = self.h_0.expand(1, mini_batch.size(0), \n",
" self.rnn.hidden_size).contiguous()\n",
" # push the observed x's through the rnn;\n",
" # rnn_output contains the hidden state at each time step\n",
" rnn_output, _ = self.rnn(mini_batch_reversed, h_0_contig)\n",
" # reverse the time-ordering in the hidden state and un-pack it\n",
" rnn_output = poly.pad_and_reverse(rnn_output, mini_batch_seq_lengths)\n",
" # set z_prev = z_q_0 to setup the recursive conditioning in q(z_t |...)\n",
" z_prev = self.z_q_0.expand(mini_batch.size(0), self.z_q_0.size(0))\n",
"\n",
" # we enclose all the sample statements in the guide in a plate.\n",
" # this marks that each datapoint is conditionally independent of the others.\n",
" with pyro.plate(\"z_minibatch\", len(mini_batch)):\n",
" # sample the latents z one time step at a time\n",
" for t in range(1, T_max + 1):\n",
" # the next two lines assemble the distribution q(z_t | z_{t-1}, x_{t:T})\n",
" z_loc, z_scale = self.combiner(z_prev, rnn_output[:, t - 1, :])\n",
" z_dist = dist.Normal(z_loc, z_scale)\n",
"\n",
" # sample z_t from the distribution z_dist\n",
" with pyro.poutine.scale(None, annealing_factor):\n",
" z_t = pyro.sample(\"z_%d\" % t,\n",
" z_dist.mask(mini_batch_mask[:, t - 1:t])\n",
" .to_event(1))\n",
" # the latent sampled at this time step will be conditioned \n",
" # upon in the next time step so keep track of it\n",
" z_prev = z_t\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The high-level structure of `guide()` is very similar to `model()`. First note that the model and guide take the same arguments: this is a general requirement for model/guide pairs in Pyro. As in the model, there's a call to `pyro.module` that registers all the parameters with Pyro. Also, the `for` loop has the same structure as the one in `model()`, with the difference that the guide only needs to sample latents (there are no `sample` statements with the `obs` keyword). Finally, note that the names of the latent variables in the guide exactly match those in the model. This is how Pyro knows to correctly align random variables. \n",
"\n",
"The RNN logic should be familar to PyTorch users, but let's go through it quickly. First we prepare the initial state of the RNN, `h_0`. Then we invoke the RNN via its forward call; the resulting tensor `rnn_output` contains the hidden states for the entire mini-batch. Note that because we want the RNN to consume the observations from right to left, the input to the RNN is `mini_batch_reversed`, which is a copy of `mini_batch` with all the sequences running in _reverse_ temporal order. Furthermore, `mini_batch_reversed` has been wrapped in a PyTorch `rnn.pack_padded_sequence` so that the RNN can deal with variable-length sequences. Since we do our sampling in latent space in normal temporal order, we use the helper function `pad_and_reverse` to reverse the hidden state sequences in `rnn_output`, so that we can feed the `Combiner` RNN hidden states that are correctly aligned and ordered. This helper function also unpacks the `rnn_output` so that it is no longer in the form of a PyTorch `rnn.pack_padded_sequence`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Packaging the Model and Guide as a PyTorch Module\n",
"\n",
"At this juncture, we're ready to proceed to inference. But before we do so let's quickly go over how we packaged the model and guide as a single PyTorch Module. This is generally good practice, especially for larger models."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```python\n",
"class DMM(nn.Module):\n",
" \"\"\"\n",
" This PyTorch Module encapsulates the model as well as the \n",
" variational distribution (the guide) for the Deep Markov Model\n",
" \"\"\"\n",
" def __init__(self, input_dim=88, z_dim=100, emission_dim=100, \n",
" transition_dim=200, rnn_dim=600, rnn_dropout_rate=0.0, \n",
" num_iafs=0, iaf_dim=50, use_cuda=False):\n",
" super().__init__()\n",
" # instantiate pytorch modules used in the model and guide below\n",
" self.emitter = Emitter(input_dim, z_dim, emission_dim)\n",
" self.trans = GatedTransition(z_dim, transition_dim)\n",
" self.combiner = Combiner(z_dim, rnn_dim)\n",
" self.rnn = nn.RNN(input_size=input_dim, hidden_size=rnn_dim, \n",
" nonlinearity='relu', batch_first=True, \n",
" bidirectional=False, num_layers=1, dropout=rnn_dropout_rate)\n",
"\n",
" # define a (trainable) parameters z_0 and z_q_0 that help define \n",
" # the probability distributions p(z_1) and q(z_1)\n",
" # (since for t = 1 there are no previous latents to condition on)\n",
" self.z_0 = nn.Parameter(torch.zeros(z_dim))\n",
" self.z_q_0 = nn.Parameter(torch.zeros(z_dim))\n",
" # define a (trainable) parameter for the initial hidden state of the rnn\n",
" self.h_0 = nn.Parameter(torch.zeros(1, 1, rnn_dim))\n",
"\n",
" self.use_cuda = use_cuda\n",
" # if on gpu cuda-ize all pytorch (sub)modules\n",
" if use_cuda:\n",
" self.cuda()\n",
"\n",
" # the model p(x_{1:T} | z_{1:T}) p(z_{1:T})\n",
" def model(...):\n",
"\n",
" # ... as above ...\n",
"\n",
" # the guide q(z_{1:T} | x_{1:T}) (i.e. the variational distribution)\n",
" def guide(...):\n",
" \n",
" # ... as above ...\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we've already gone over `model` and `guide`, our focus here is on the constructor. First we instantiate the four PyTorch modules that we use in our model and guide. On the model-side: `Emitter` and `GatedTransition`. On the guide-side: `Combiner` and the RNN. \n",
"\n",
"Next we define PyTorch `Parameter`s for the initial state of the RNN as well as `z_0` and `z_q_0`, which are fed into `self.trans` and `self.combiner`, respectively, in lieu of the non-existent random variable $\\bf z_0$. \n",
"\n",
"The important point to make here is that all of these `Module`s and `Parameter`s are attributes of `DMM` (which itself inherits from `nn.Module`). This has the consequence they are all automatically registered as belonging to the module. So, for example, when we call `parameters()` on an instance of `DMM`, PyTorch will know to return all the relevant parameters. It also means that when we invoke `pyro.module(\"dmm\", self)` in `model()` and `guide()`, all the parameters of both the model and guide will be registered with Pyro. Finally, it means that if we're running on a GPU, the call to `cuda()` will move all the parameters into GPU memory.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Stochastic Variational Inference\n",
"\n",
"With our model and guide at hand, we're finally ready to do inference. Before we look at the full logic that is involved in a complete experimental script, let's first see how to take a single gradient step. First we instantiate an instance of `DMM` and setup an optimizer.\n",
"\n",
"```python\n",
"# instantiate the dmm\n",
"dmm = DMM(input_dim, z_dim, emission_dim, transition_dim, rnn_dim,\n",
" args.rnn_dropout_rate, args.num_iafs, args.iaf_dim, args.cuda)\n",
"\n",
"# setup optimizer\n",
"adam_params = {\"lr\": args.learning_rate, \"betas\": (args.beta1, args.beta2),\n",
" \"clip_norm\": args.clip_norm, \"lrd\": args.lr_decay,\n",
" \"weight_decay\": args.weight_decay}\n",
"optimizer = ClippedAdam(adam_params)\n",
"```\n",
"\n",
"Here we're using an implementation of the Adam optimizer that includes gradient clipping. This mitigates some of the problems that can occur when training recurrent neural networks (e.g. vanishing/exploding gradients). Next we setup the inference algorithm. \n",
"\n",
"```python\n",
"# setup inference algorithm\n",
"svi = SVI(dmm.model, dmm.guide, optimizer, Trace_ELBO())\n",
"```\n",
"\n",
"The inference algorithm `SVI` uses a stochastic gradient estimator to take gradient steps on an objective function, which in this case is given by the ELBO (the evidence lower bound). As the name indicates, the ELBO is a lower bound to the log evidence: $\\log p(\\mathcal{D})$. As we take gradient steps that maximize the ELBO, we move our guide $q(\\cdot)$ closer to the exact posterior. \n",
"\n",
"The argument `Trace_ELBO()` constructs a version of the gradient estimator that doesn't need access to the dependency structure of the model and guide. Since all the latent variables in our model are reparameterizable, this is the appropriate gradient estimator for our use case. (It's also the default option.)\n",
"\n",
"Assuming we've prepared the various arguments of `dmm.model` and `dmm.guide`, taking a gradient step is accomplished by calling\n",
"\n",
"```python\n",
"svi.step(mini_batch, ...)\n",
"```\n",
"\n",
"That's all there is to it!\n",
"\n",
"Well, not quite. This will be the main step in our inference algorithm, but we still need to implement a complete training loop with preparation of mini-batches, evaluation, and so on. This sort of logic will be familiar to any deep learner but let's see how it looks in PyTorch/Pyro."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Black Magic of Optimization\n",
"\n",
"Actually, before we get to the guts of training, let's take a moment and think a bit about the optimization problem we've setup. We've traded Bayesian inference in a non-linear model with a high-dimensional latent space—a hard problem—for a particular optimization problem. Let's not kid ourselves, this optimization problem is pretty hard too. Why? Let's go through some of the reasons:\n",
"\n",
"- the space of parameters we're optimizing over is very high-dimensional (it includes all the weights in all the neural networks we've defined).\n",
"- our objective function (the ELBO) cannot be computed analytically. so our parameter updates will be following noisy Monte Carlo gradient estimates\n",
"- data-subsampling serves as an additional source of stochasticity: even if we wanted to, we couldn't in general take gradient steps on the ELBO defined over the whole dataset (actually in our particular case the dataset isn't so large, but let's ignore that).\n",
"- given all the neural networks and non-linearities we have in the loop, our (stochastic) loss surface is highly non-trivial\n",
"\n",
"The upshot is that if we're going to find reasonable (local) optima of the ELBO, we better take some care in deciding how to do optimization. This isn't the time or place to discuss all the different strategies that one might adopt, but it's important to emphasize how decisive a good or bad choice in learning hyperparameters (the learning rate, the mini-batch size, etc.) can be. \n",
"\n",
"Before we move on, let's discuss one particular optimization strategy that we're making use of in greater detail: KL annealing. In our case the ELBO is the sum of two terms: an expected log likelihood term (which measures model fit) and a sum of KL divergence terms (which serve to regularize the approximate posterior):\n",
"\n",
"$\\rm{ELBO} = \\mathbb{E}_{q({\\bf z}_{1:T})}[\\log p({\\bf x}_{1:T}|{\\bf z}_{1:T})] - \\mathbb{E}_{q({\\bf z}_{1:T})}[ \\log q({\\bf z}_{1:T}) - \\log p({\\bf z}_{1:T})]$\n",
"\n",
"This latter term can be a quite strong regularizer, and in early stages of training it has a tendency to favor regions of the loss surface that contain lots of bad local optima. One strategy to avoid these bad local optima, which was also adopted in reference [1], is to anneal the KL divergence terms by multiplying them by a scalar `annealing_factor` that ranges between zero and one:\n",
"\n",
"$\\mathbb{E}_{q({\\bf z}_{1:T})}[\\log p({\\bf x}_{1:T}|{\\bf z}_{1:T})] - \\rm{annealing\\_factor} \\times \\mathbb{E}_{q({\\bf z}_{1:T})}[ \\log q({\\bf z}_{1:T}) - \\log p({\\bf z}_{1:T})]$\n",
"\n",
"The idea is that during the course of training the `annealing_factor` rises slowly from its initial value at/near zero to its final value at 1.0. The annealing schedule is arbitrary; below we will use a simple linear schedule. In terms of code, to scale the log likelihoods by the appropriate annealing factor we enclose each of the latent sample statements in the model and guide with a `pyro.poutine.scale` context.\n",
"\n",
"Finally, we should mention that the main difference between the DMM implementation described here and the one used in reference [1] is that they take advantage of the analytic formula for the KL divergence between two gaussian distributions (whereas we rely on Monte Carlo estimates). This leads to lower variance gradient estimates of the ELBO, which makes training a bit easier. We can still train the model without making this analytic substitution, but training probably takes somewhat longer because of the higher variance. To use analytic KL divergences use [TraceMeanField_ELBO](http://docs.pyro.ai/en/stable/inference_algos.html#pyro.infer.trace_mean_field_elbo.TraceMeanField_ELBO)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Loading, Training, and Evaluation\n",
"\n",
"First we load the data. There are 229 sequences in the training dataset, each with an average length of ~60 time steps.\n",
"\n",
"```python\n",
"jsb_file_loc = \"./data/jsb_processed.pkl\"\n",
"data = pickle.load(open(jsb_file_loc, \"rb\"))\n",
"training_seq_lengths = data['train']['sequence_lengths']\n",
"training_data_sequences = data['train']['sequences']\n",
"test_seq_lengths = data['test']['sequence_lengths']\n",
"test_data_sequences = data['test']['sequences']\n",
"val_seq_lengths = data['valid']['sequence_lengths']\n",
"val_data_sequences = data['valid']['sequences']\n",
"N_train_data = len(training_seq_lengths)\n",
"N_train_time_slices = np.sum(training_seq_lengths)\n",
"N_mini_batches = int(N_train_data / args.mini_batch_size +\n",
" int(N_train_data % args.mini_batch_size > 0))\n",
"```\n",
"\n",
"For this dataset we will typically use a `mini_batch_size` of 20, so that there will be 12 mini-batches per epoch. Next we define the function `process_minibatch` which prepares a mini-batch for training and takes a gradient step:\n",
"\n",
"```python\n",
"def process_minibatch(epoch, which_mini_batch, shuffled_indices):\n",
" if args.annealing_epochs > 0 and epoch < args.annealing_epochs:\n",
" # compute the KL annealing factor appropriate \n",
" # for the current mini-batch in the current epoch\n",
" min_af = args.minimum_annealing_factor\n",
" annealing_factor = min_af + (1.0 - min_af) * \\ \n",
" (float(which_mini_batch + epoch * N_mini_batches + 1) /\n",
" float(args.annealing_epochs * N_mini_batches))\n",
" else:\n",
" # by default the KL annealing factor is unity\n",
" annealing_factor = 1.0 \n",
"\n",
" # compute which sequences in the training set we should grab\n",
" mini_batch_start = (which_mini_batch * args.mini_batch_size)\n",
" mini_batch_end = np.min([(which_mini_batch + 1) * args.mini_batch_size,\n",
" N_train_data])\n",
" mini_batch_indices = shuffled_indices[mini_batch_start:mini_batch_end]\n",
" # grab the fully prepped mini-batch using the helper function in the data loader\n",
" mini_batch, mini_batch_reversed, mini_batch_mask, mini_batch_seq_lengths \\\n",
" = poly.get_mini_batch(mini_batch_indices, training_data_sequences,\n",
" training_seq_lengths, cuda=args.cuda)\n",
" # do an actual gradient step\n",
" loss = svi.step(mini_batch, mini_batch_reversed, mini_batch_mask,\n",
" mini_batch_seq_lengths, annealing_factor)\n",
" # keep track of the training loss\n",
" return loss\n",
"```\n",
"\n",
"We first compute the KL annealing factor appropriate to the mini-batch (according to a linear schedule as described earlier). We then compute the mini-batch indices, which we pass to the helper function `get_mini_batch`. This helper function takes care of a number of different things:\n",
"\n",
"- it sorts each mini-batch by sequence length\n",
"- it calls another helper function to get a copy of the mini-batch in reversed temporal order\n",
"- it packs each reversed mini-batch in a `rnn.pack_padded_sequence`, which is then ready to be ingested by the RNN\n",
"- it cuda-izes all tensors if we're on a GPU\n",
"- it calls another helper function to get an appropriate 0/1 mask for the mini-batch\n",
"\n",
"We then pipe all the return values of `get_mini_batch()` into `elbo.step(...)`. Recall that these arguments will be further piped to `model(...)` and `guide(...)` during construction of the gradient estimator in `elbo`. Finally, we return a float which is a noisy estimate of the loss for that mini-batch.\n",
"\n",
"We now have all the ingredients required for the main bit of our training loop:\n",
"\n",
"```python\n",
"times = [time.time()]\n",
"for epoch in range(args.num_epochs):\n",
" # accumulator for our estimate of the negative log likelihood \n",
" # (or rather -elbo) for this epoch\n",
" epoch_nll = 0.0 \n",
" # prepare mini-batch subsampling indices for this epoch\n",
" shuffled_indices = np.arange(N_train_data)\n",
" np.random.shuffle(shuffled_indices)\n",
"\n",
" # process each mini-batch; this is where we take gradient steps\n",
" for which_mini_batch in range(N_mini_batches):\n",
" epoch_nll += process_minibatch(epoch, which_mini_batch, shuffled_indices)\n",
"\n",
" # report training diagnostics\n",
" times.append(time.time())\n",
" epoch_time = times[-1] - times[-2]\n",
" log(\"[training epoch %04d] %.4f \\t\\t\\t\\t(dt = %.3f sec)\" %\n",
" (epoch, epoch_nll / N_train_time_slices, epoch_time))\n",
"```\n",
"\n",
"At the beginning of each epoch we shuffle the indices pointing to the training data. We then process each mini-batch until we've gone through the entire training set, accumulating the training loss as we go. Finally we report some diagnostic info. Note that we normalize the loss by the total number of time slices in the training set (this allows us to compare to reference [1]). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluation\n",
"This training loop is still missing any kind of evaluation diagnostics. Let's fix that. First we need to prepare the validation and test data for evaluation. Since the validation and test datasets are small enough that we can easily fit them into memory, we're going to process each dataset batchwise (i.e. we will not be breaking up the dataset into mini-batches). [_Aside: at this point the reader may ask why we don't do the same thing for the training set. The reason is that additional stochasticity due to data-subsampling is often advantageous during optimization: in particular it can help us avoid local optima._] And, in fact, in order to get a lessy noisy estimate of the ELBO, we're going to compute a multi-sample estimate. The simplest way to do this would be as follows:\n",
"\n",
"```python\n",
"val_loss = svi.evaluate_loss(val_batch, ..., num_particles=5)\n",
"```\n",
"\n",
"This, however, would involve an explicit `for` loop with five iterations. For our particular model, we can do better and vectorize the whole computation. The only way to do this currently in Pyro is to explicitly replicate the data `n_eval_samples` many times. This is the strategy we follow:\n",
"\n",
"```python\n",
"# package repeated copies of val/test data for faster evaluation\n",
"# (i.e. set us up for vectorization)\n",
"def rep(x):\n",
" return np.repeat(x, n_eval_samples, axis=0)\n",
"\n",
"# get the validation/test data ready for the dmm: pack into sequences, etc.\n",
"val_seq_lengths = rep(val_seq_lengths)\n",
"test_seq_lengths = rep(test_seq_lengths)\n",
"val_batch, val_batch_reversed, val_batch_mask, val_seq_lengths = poly.get_mini_batch(\n",
" np.arange(n_eval_samples * val_data_sequences.shape[0]), rep(val_data_sequences),\n",
" val_seq_lengths, cuda=args.cuda)\n",
"test_batch, test_batch_reversed, test_batch_mask, test_seq_lengths = \\\n",
" poly.get_mini_batch(np.arange(n_eval_samples * test_data_sequences.shape[0]), \n",
" rep(test_data_sequences),\n",
" test_seq_lengths, cuda=args.cuda)\n",
"```\n",
"\n",
"With the test and validation data now fully prepped, we define the helper function that does the evaluation: \n",
"\n",
"```python\n",
"def do_evaluation():\n",
" # put the RNN into evaluation mode (i.e. turn off drop-out if applicable)\n",
" dmm.rnn.eval()\n",
"\n",
" # compute the validation and test loss\n",
" val_nll = svi.evaluate_loss(val_batch, val_batch_reversed, val_batch_mask,\n",
" val_seq_lengths) / np.sum(val_seq_lengths)\n",
" test_nll = svi.evaluate_loss(test_batch, test_batch_reversed, test_batch_mask,\n",
" test_seq_lengths) / np.sum(test_seq_lengths)\n",
"\n",
" # put the RNN back into training mode (i.e. turn on drop-out if applicable)\n",
" dmm.rnn.train()\n",
" return val_nll, test_nll\n",
"```\n",
"\n",
"We simply call the `evaluate_loss` method of `elbo`, which takes the same arguments as `step()`, namely the arguments that are passed to the model and guide. Note that we have to put the RNN into and out of evaluation mode to account for dropout. We can now stick `do_evaluation()` into the training loop; see [the source code](https://github.com/pyro-ppl/pyro/blob/dev/examples/dmm.py) for details."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Results\n",
"\n",
"Let's make sure that our implementation gives reasonable results. We can use the numbers reported in reference [1] as a sanity check. For the same dataset and a similar model/guide setup (dimension of the latent space, number of hidden units in the RNN, etc.) they report a normalized negative log likelihood (NLL) of `6.93` on the testset (lower is better$)^{\\S}$. This is to be compared to our result of `6.87`. These numbers are very much in the same ball park, which is reassuring. It seems that, at least for this dataset, not using analytic expressions for the KL divergences doesn't degrade the quality of the learned model (although, as discussed above, the training probably takes somewhat longer)."
]
},
{
"cell_type": "raw",
"metadata": {
"raw_mimetype": "text/html"
},
"source": [
"<figure><img src=\"_static/img/test_nll.png\" style=\"width: 400px;\"><center><figcaption> <font size=\"-1\"><b>Figure 3</b>: Progress on the test set NLL as training progresses for a sample training run. </font></figcaption></figure></center>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the figure we show how the test NLL progresses during training for a single sample run (one with a rather conservative learning rate). Most of the progress is during the first 3000 epochs or so, with some marginal gains if we let training go on for longer. On a GeForce GTX 1080, 5000 epochs takes about 20 hours.\n",
"\n",
"\n",
"| `num_iafs` | test NLL |\n",
"|---|---|\n",
"| `0` | `6.87` | \n",
"| `1` | `6.82` |\n",
"| `2` | `6.80` |\n",
"\n",
"Finally, we also report results for guides with normalizing flows in the mix (details to be found in the next section). \n",
"\n",
"${ \\S\\;}$ Actually, they seem to report two numbers—6.93 and 7.03—for the same model/guide and it's not entirely clear how the two reported numbers are different."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bells, whistles, and other improvements\n",
"\n",
"### Inverse Autoregressive Flows\n",
"\n",
"One of the great things about a probabilistic programming language is that it encourages modularity. Let's showcase an example in the context of the DMM. We're going to make our variational distribution richer by adding normalizing flows to the mix (see reference [2] for a discussion). **This will only cost us four additional lines of code!**\n",
"\n",
"First, in the `DMM` constructor we add\n",
"\n",
"```python\n",
"iafs = [AffineAutoregressive(AutoRegressiveNN(z_dim, [iaf_dim])) for _ in range(num_iafs)]\n",
"self.iafs = nn.ModuleList(iafs)\n",
"```\n",
"\n",
"This instantiates `num_iafs` many bijective transforms of the `AffineAutoregressive` type (see references [3,4]); each normalizing flow will have `iaf_dim` many hidden units. We then bundle the normalizing flows in a `nn.ModuleList`; this is just the PyTorchy way to package a list of `nn.Module`s. Next, in the guide we add the lines\n",
"\n",
"```python\n",
"if self.iafs.__len__() > 0:\n",
" z_dist = TransformedDistribution(z_dist, self.iafs)\n",
"```\n",
"\n",
"Here we're taking the base distribution `z_dist`, which in our case is a conditional gaussian distribution, and using the `TransformedDistribution` construct we transform it into a non-gaussian distribution that is, by construction, richer than the base distribution. Voila!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Checkpointing\n",
"\n",
"If we want to recover from a catastrophic failure in our training loop, there are two kinds of state we need to keep track of. The first is the various parameters of the model and guide. The second is the state of the optimizers (e.g. in Adam this will include the running average of recent gradient estimates for each parameter).\n",
"\n",
"In Pyro, the parameters can all be found in the `ParamStore`. However, PyTorch also keeps track of them for us via the `parameters()` method of `nn.Module`. So one simple way we can save the parameters of the model and guide is to make use of the `state_dict()` method of `dmm` in conjunction with `torch.save()`; see below. In the case that we have `AffineAutoregressive`'s in the loop, this is in fact the only option at our disposal. This is because the `AffineAutoregressive` module contains what are called 'persistent buffers' in PyTorch parlance. These are things that carry state but are not `Parameter`s. The `state_dict()` and `load_state_dict()` methods of `nn.Module` know how to deal with buffers correctly.\n",
"\n",
"To save the state of the optimizers, we have to use functionality inside of `pyro.optim.PyroOptim`. Recall that the typical user never interacts directly with PyTorch `Optimizers` when using Pyro; since parameters can be created dynamically in an arbitrary probabilistic program, Pyro needs to manage `Optimizers` for us. In our case saving the optimizer state will be as easy as calling `optimizer.save()`. The loading logic is entirely analagous. So our entire logic for saving and loading checkpoints only takes a few lines:\n",
"\n",
"```python\n",
"# saves the model and optimizer states to disk\n",
"def save_checkpoint():\n",
" log(\"saving model to %s...\" % args.save_model)\n",
" torch.save(dmm.state_dict(), args.save_model)\n",
" log(\"saving optimizer states to %s...\" % args.save_opt)\n",
" optimizer.save(args.save_opt)\n",
" log(\"done saving model and optimizer checkpoints to disk.\")\n",
"\n",
"# loads the model and optimizer states from disk\n",
"def load_checkpoint():\n",
" assert exists(args.load_opt) and exists(args.load_model), \\\n",
" \"--load-model and/or --load-opt misspecified\"\n",
" log(\"loading model from %s...\" % args.load_model)\n",
" dmm.load_state_dict(torch.load(args.load_model))\n",
" log(\"loading optimizer states from %s...\" % args.load_opt)\n",
" optimizer.load(args.load_opt)\n",
" log(\"done loading model and optimizer states.\")\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Some final comments\n",
"\n",
"A deep markov model is a relatively complex model. Now that we've taken the effort to implement a version of the deep markov model tailored to the polyphonic music dataset, we should ask ourselves what else we can do. What if we're handed a different sequential dataset? Do we have to start all over?\n",
"\n",
"Not at all! The beauty of probalistic programming is that it enables—and encourages—modular approaches to modeling and inference. Adapting our polyphonic music model to a dataset with continuous observations is as simple as changing the observation likelihood. The vast majority of the code could be taken over unchanged. This means that with a little bit of extra work, the code in this tutorial could be repurposed to enable a huge variety of different models. \n",
"\n",
"See the complete code on [Github](https://github.com/pyro-ppl/pyro/blob/dev/examples/dmm.py).\n",
"\n",
"## References\n",
"\n",
"[1] `Structured Inference Networks for Nonlinear State Space Models`,<br /> \n",
" Rahul G. Krishnan, Uri Shalit, David Sontag\n",
" \n",
"[2] `Variational Inference with Normalizing Flows`,\n",
"<br /> \n",
"Danilo Jimenez Rezende, Shakir Mohamed \n",
" \n",
"[3] `Improving Variational Inference with Inverse Autoregressive Flow`,\n",
"<br /> \n",
"Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling \n",
"\n",
"[4] `MADE: Masked Autoencoder for Distribution Estimation`,\n",
"<br /> \n",
"Mathieu Germain, Karol Gregor, Iain Murray, Hugo Larochelle \n",
"\n",
"[5] `Modeling Temporal Dependencies in High-Dimensional Sequences:`\n",
"<br /> \n",
"`Application to Polyphonic Music Generation and Transcription`,\n",
"<br /> \n",
"Boulanger-Lewandowski, N., Bengio, Y. and Vincent, P."
]
}
],
"metadata": {
"celltoolbar": "Raw Cell Format",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.10"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
jswhit/py-ncepbufr | test/Python_tutorial_bufr.ipynb | 1 | 41360 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"%matplotlib inline \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Reading an NCEP BUFR data set\n",
"NCEP BUFR (Binary Universal Form for the Representation of meteorological data) can be read two ways:\n",
"\n",
"- **Fortran code with BUFRLIB**\n",
" \n",
"- **py-ncepbufr, which is basically Python wrappers around BUFRLIB**\n",
"\n",
"In this example we'll use py-ncepbufr to read a snapshot of the Argo data tank from WCOSS, show how to navigate the BUFR structure, and how to extract and plot a profile.\n",
"\n",
"The py-ncepbufr library and installation instructions can be found at\n",
"\n",
"https://github.com/JCSDA/py-ncepbufr\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- We begin by importing the required libraries."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt # graphics library\n",
"import numpy as np\n",
"import ncepbufr # python wrappers around BUFRLIB"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"- For the purposes of this demo I've made a local copy of the Argo data tank on WCOSS \n",
"located at\n",
"\n",
"**/dcom/us007003/201808/b031/xx005** \n",
" \n",
"- Begin by opening the file"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"bufr = ncepbufr.open('data/xx005')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- Movement and data access within the BUFR file is through these methods:\n",
" - `bufr.advance()`\n",
" - `bufr.load_subset()`\n",
" - `bufr.read_subset()`\n",
" - `bufr.rewind()`\n",
" - `bufr.close()`\n",
"- There is a lot more functionality to ncepbufr, such as searching on multiple mnenomics, printing or saving the BUFR table included in the file, printing or saving the inventory and subsets, setting and using checkpoints in the file. See the ncepbufr help for more details.\n",
"\n",
"- ***Important Note:*** py-ncepbufr is unforgiving of mistakes. A BUFRLIB fortran error will result in an *immediate* exit from the Python interpreter. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# move down to first message - a return code of 0 indicates success\n",
"bufr.advance() "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# load the message subset -- a return code of 0 indicates success\n",
"bufr.load_subset() "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"- You can print the subset and determine the parameter names. BUFR dumps can be ***very*** verbose, so I'll just copy in the header and the first subset replication from a `bufr.dump_subset()` command.\n",
"\n",
"- I've highlighted in red the parameters I want to plot."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"<pre style=\"font-size: x-small\">\n",
"MESSAGE TYPE NC031005 \n",
"\n",
"004001 YEAR 2018.0 YEAR YEAR \n",
"004002 MNTH 8.0 MONTH MONTH \n",
"004003 DAYS 1.0 DAY DAY \n",
"004004 HOUR 0.0 HOUR HOUR \n",
"004005 MINU 16.0 MINUTE MINUTE \n",
"035195 SEQNUM 317 ( 4)CCITT IA5 CHANNEL SEQUENCE NUMBER \n",
"035021 BUHD IOPX01 ( 6)CCITT IA5 BULLETIN BEING MONITORED (TTAAii) \n",
"035023 BORG KWBC ( 4)CCITT IA5 BULLETIN BEING MONITORED (CCCC) \n",
"035022 BULTIM 010029 ( 6)CCITT IA5 BULLETIN BEING MONITORED (YYGGgg) \n",
"035194 BBB MISSING ( 6)CCITT IA5 BULLETIN BEING MONITORED (BBB) \n",
"008202 RCTS 0.0 CODE TABLE RECEIPT TIME SIGNIFICANCE \n",
"004200 RCYR 2018.0 YEAR YEAR - TIME OF RECEIPT \n",
"004201 RCMO 8.0 MONTH MONTH - TIME OF RECEIPT \n",
"004202 RCDY 1.0 DAY DAY - TIME OF RECEIPT \n",
"004203 RCHR 0.0 HOUR HOUR - TIME OF RECEIPT \n",
"004204 RCMI 31.0 MINUTE MINUTE - TIME OF RECEIPT \n",
"033215 CORN 0.0 CODE TABLE CORRECTED REPORT INDICATOR \n",
"001087 WMOP 6903327.0 NUMERIC WMO marine observing platform extended identifie\n",
"001085 OPMM S2-X (20)CCITT IA5 Observing platform manufacturer's model \n",
"001086 OPMS 10151 ( 32)CCITT IA5 Observing platform manufacturer's serial number \n",
"002036 BUYTS 2.0 CODE TABLE Buoy type \n",
"002148 DCLS 8.0 CODE TABLE Data collection and/or location system \n",
"002149 BUYT 14.0 CODE TABLE Type of data buoy \n",
"022055 FCYN 28.0 NUMERIC Float cycle number \n",
"022056 DIPR 0.0 CODE TABLE Direction of profile \n",
"022067 IWTEMP 846.0 CODE TABLE INSTRUMENT TYPE FOR WATER TEMPERATURE PROFILE ME\n",
"005001 CLATH 59.34223 DEGREES LATITUDE (HIGH ACCURACY) \n",
"006001 CLONH -9.45180 DEGREES LONGITUDE (HIGH ACCURACY) \n",
"008080 QFQF 20.0 CODE TABLE Qualifier for GTSPP quality flag \n",
"033050 GGQF 1.0 CODE TABLE Global GTSPP quality flag \n",
" (GLPFDATA) 636 REPLICATIONS\n",
" ++++++ GLPFDATA REPLICATION # 1 ++++++\n",
"<span style=\"color: red\">007065 WPRES 10000.0 PA Water pressure</span>\n",
"008080 QFQF 10.0 CODE TABLE Qualifier for GTSPP quality flag \n",
"033050 GGQF 1.0 CODE TABLE Global GTSPP quality flag \n",
"<span style=\"color: red\">022045 SSTH 285.683 K Sea/water temperature</span>\n",
"008080 QFQF 11.0 CODE TABLE Qualifier for GTSPP quality flag \n",
"033050 GGQF 1.0 CODE TABLE Global GTSPP quality flag\n",
"<span style=\"color: red\">022064 SALNH 35.164 PART PER THOUSAND Salinity</span>\n",
"008080 QFQF 12.0 CODE TABLE Qualifier for GTSPP quality flag \n",
"033050 GGQF 1.0 CODE TABLE Global GTSPP quality flag \n",
"</pre>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"\n",
"- Now we can load the data for plotting\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"temp = bufr.read_subset('SSTH').squeeze()-273.15 # convert from Kelvin to Celsius\n",
"sal = bufr.read_subset('SALNH').squeeze()\n",
"depth = bufr.read_subset('WPRES').squeeze()/10000. # convert from Pa to depth in meters\n",
"# observation location, date, and receipt time\n",
"lon = bufr.read_subset('CLONH')[0][0]\n",
"lat = bufr.read_subset('CLATH')[0][0]\n",
"date = bufr.msg_date\n",
"receipt = bufr.receipt_time\n",
"bufr.close()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"\n",
"- Set up the plotting figure. But this time, just for fun, let's put both the temperature and salinity profiles on the same axes. This trick uses both the top and bottom axis for different parameters.\n",
"\n",
"- As these are depth profiles, we need twin x-axes and a shared y-axis for the depth.\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 360x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize = (5,4))\n",
"ax1 = plt.axes()\n",
"ax1.plot(temp, depth,'r-')\n",
"ax1.grid(axis = 'y')\n",
"ax1.invert_yaxis() # flip the y-axis for ocean depths\n",
"ax2 = ax1.twiny() # here's the second x-axis definition\n",
"ax2.plot(np.nan, 'r-', label = 'Temperature')\n",
"ax2.plot(sal, depth, 'b-', label = 'Salinity')\n",
"ax2.legend()\n",
"ax1.set_xlabel('Temperature (C)', color = 'red')\n",
"ax1.set_ylabel('Depth (m)')\n",
"ax2.set_xlabel('Salinity (PSU)', color = 'blue')\n",
"ttl='ARGO T,S Profiles at lon:{:6.2f}, lat:{:6.2f}\\ntimestamp: {} received: {}\\n'.format(lon,lat,date,receipt)\n",
"fig.suptitle(ttl,x = 0.5,y = 1.1,fontsize = 'large');"
]
}
],
"metadata": {
"celltoolbar": "Slideshow",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| isc |
fja05680/pinkfish | examples/A00.update-cache-symbols/update-cache-symbols.ipynb | 1 | 5615 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Update Cache Symbols\n",
"\n",
"A useful utility for demonstating the use of the update/remove cache symbols functions. You can use this notebook to periodically update all of the timeseries in your symbol cache.\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2020-02-15T23:14:35.785032Z",
"start_time": "2020-02-15T23:14:34.966914Z"
}
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import pinkfish as pf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Update time series for the symbols below. \n",
"Time series will be fetched for any symbols not already cached."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2020-02-15T23:14:39.514651Z",
"start_time": "2020-02-15T23:14:35.794760Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"updating symbols:\n",
"MSFT ORCL TSLA \n"
]
}
],
"source": [
"pf.update_cache_symbols(symbols=['msft', 'orcl', 'tsla'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Remove the time series for TSLA"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2020-02-15T23:14:39.535037Z",
"start_time": "2020-02-15T23:14:39.521363Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"removing symbols:\n",
"TSLA \n"
]
}
],
"source": [
"pf.remove_cache_symbols(symbols=['tsla'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Update time series for all symbols in the cache directory"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2020-02-15T23:16:05.937873Z",
"start_time": "2020-02-15T23:14:39.540755Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"updating symbols:\n",
"EURUSD=X CMCSA BTC=F BMY V ZC=F VNQ ADA-USD NQ=F UB=F \n",
"('Date')\n",
"RUB=X \n",
"IWM ZN=F MNQ=F BND 6A=F IEF SLV SHY PA=F PM \n",
"DBC THB=X PL=F DIA BRK-B XLK MIR=F INR=X XLB VGK \n",
"DX=F MES=F NUE AMZN ETH-USD GIS BIL UL XLE EURCHF=X \n",
"EEM GF=F USDT-USD XLP EURGBP=X M6A=F SPY CT=F DC=F KC=F \n",
"ZO=F SLY 6Z=F KE=F M2K=F BTC-USD 6R=F AUDUSD=X M6E=F UUP \n",
"BUD ZB=F JNJ ZM=F XLU CU=F \n",
"(No data fetched for symbol CU=F using YahooDailyReader)\n",
"HD EURHUF=X INTC FXE \n",
"LBS=F GOOG XHB JKE 6J=F FXB 6B=F GDX RSP XOM \n",
"BTCUSD=X GLD JNK MGC=F BSV BCH-USD XRP-USD RB=F PHP=X NLY \n",
"GBPUSD=X BTI MO K ORCL HG=F HKD=X CLX XLI TLT \n",
"XLY ZAR=X 6L=F T ZW=F SO NFLX MYR=X UNG GBPJPY=X \n",
"EFA HSY LTC-USD CNY=X IWB MSF=F KMI IYR KO EWJ \n",
"FB BNB-USD SGD=X ZR=F LQD MRK PG AAPL PGX XLV \n",
"KMB ZQ=F VZ HRL NZDUSD=X MCD=F IVV MSFT TN=F ISF \n",
"\n",
"(No data fetched for symbol ISF using YahooDailyReader)\n",
"6S=F DIS SIL=F HE=F QA=F \n",
"(No data fetched for symbol QA=F using YahooDailyReader)\n",
"CC=F 6M=F CHRIS-CME_ES1 \n",
"(No data fetched for symbol CHRIS-CME using YahooDailyReader)\n",
"NG=F 6E=F \n",
"LLY GE=F ES=F EURCAD=X ADP BZT=F MXN=X VXUS RDS-B ETHUSD=X \n",
"\n",
"(No data fetched for symbol ETHUSD=X using YahooDailyReader)\n",
"EH=F YM=F LMT XLF CL=F ZF=F EURSEK=X JPY=X TIP RTY=F \n",
"DOV ^GSPC EURJPY=X OJ=F M6B=F CL IEV XLRE LE=F MYM=F \n",
"SF=F MCD 6N=F Z GC=F 6C=F RPG ZL=F ZS=F OIH \n",
"F ZT=F EPP SB=F BZ=F SPY-MINUTE \n",
"(No data fetched for symbol SPY-MINUTE using YahooDailyReader)\n",
"VWO BOND IWO IDR=X \n",
"QQQ VQNPX PFF DAX VEU HO=F AGG SI=F \n"
]
}
],
"source": [
"pf.update_cache_symbols()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Remove time series for all symbols in the cache directory"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2020-02-15T23:16:05.951276Z",
"start_time": "2020-02-15T23:16:05.941884Z"
}
},
"outputs": [],
"source": [
"# WARNING!!! - if you uncomment the line below, you'll wipe out\n",
"# all the symbols in your cache directory\n",
"#pf.remove_cache_symbols()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
"autoclose": false,
"autocomplete": true,
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 1,
"hotkeys": {
"equation": "Ctrl-E",
"itemize": "Ctrl-I"
},
"labels_anchors": false,
"latex_user_defs": false,
"report_style_numbering": false,
"user_envs_cfg": false
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
danalexandru/Algo | FII-year3sem2-CN/Exam.ipynb | 2 | 38345 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exam\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Problem 2. Interpolation\n",
"---\n",
"\n",
"\n",
"> Linear Spline Interpolation visualization, courtesy of [codecogs](http://www.codecogs.com/library/maths/approximation/interpolation/univariate.php#sec3)\n",
"\n",
"Interpolation is a method of [curve fitting](https://en.wikipedia.org/wiki/Curve_fitting). \n",
"In this problem, [spline interpolation](https://en.wikipedia.org/wiki/Interpolation#Spline_interpolation) is considered\n",
"\n",
"Practical applications:\n",
"+ estimating function values based on some sample of known data points\n",
"\n",
"---\n",
"\n",
"#### Problem\n",
"Given the inputs and function values below, approximate `f(-1)` and `f(1)` by **linear spline functions**."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from IPython.display import display\n",
"import pandas as pd\n",
"import matplotlib.pyplot\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" -2 0 2 3\n",
"f(x) -3 -5 9 22\n"
]
}
],
"source": [
"index = ['f(x)']\n",
"columns = [-2, 0, 2, 3]\n",
"data = [[-3, -5, 9, 22]]\n",
"\n",
"df = pd.DataFrame(data, index=index, columns=columns)\n",
"print(df)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>x</th>\n",
" <td>-2</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>f(x)</th>\n",
" <td>-3</td>\n",
" <td>-5</td>\n",
" <td>9</td>\n",
" <td>22</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 1 2 3 4\n",
" x -2 0 2 3\n",
"f(x) -3 -5 9 22"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAgwAAAFkCAYAAABMyWOlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x70d090bb70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# for brevity, we will write it like this\n",
"index = [' x', 'f(x)']\n",
"columns = [1, 2, 3, 4] #['x1', 'x2', 'x3', 'x4']\n",
"data = [[-2, 0, 2, 3], [-3, -5, 9, 22]]\n",
"\n",
"df = pd.DataFrame(data, index=index, columns=columns)\n",
"display(df)\n",
"\n",
"matplotlib.pyplot.plot(data[0], data[1], ls='dashed', color='#a23636')\n",
"matplotlib.pyplot.scatter(data[0], data[1])\n",
"matplotlib.pyplot.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"**Linear spline functions** are calculated with the following:\n",
" \n",
"$$i \\in [1,\\ \\left\\vert{X}\\right\\vert - 1],\\ i \\in \\mathbb{N}: $$ \n",
"$$P_i = \\frac{x-x_i}{x_{i+1}-x_i} * y_{i+1} + \\frac{x_{i+1}-x}{x_{i+1}-x_i} * y_i$$\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By simplification, we can reduce to the following:\n",
"\n",
"$$P_i = \\frac{y_{i+1} (x-x_i) + y_i (x_{i+1}-x)}{x_{i+1}-x_i} = \\frac{(y_{i+1}*x - y_i*x) - y_{i+1}*x_i + y_i*x_{i+1}}{x_{i+1}-x_i}$$\n",
"\n",
"The final form used will be:\n",
"$$P_i = \\frac{(y_{i+1}*x - y_i*x) + (y_i*x_{i+1} - y_{i+1}*x_i)}{(x_{i+1}-x_i)}$$\n",
"\n",
"As it can be seen, **the only gist** would be to emulate the `x` in the first term (`num1s` **below**), the other terms being numbers (`num2`, `den`). Parantheses used to isolate the formula for each of the 3 variables. \n",
" \n",
"As such, we can write the parantheses as a string, while the others will be simply calculated. After this, the final string is **evaluated as a lambda function**.\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x1 = -2\n",
"y1 = -3\n",
"---\n",
"no values: 4\n",
"\n",
"P[1]\n",
"num_1s: -5 * x - -3 * x\n",
"num_2: -10\n",
"den: 2\n",
"func: lambda x: (-5 * x - -3 * x-10) / 2\n",
"\n",
"P[2]\n",
"num_1s: 9 * x - -5 * x\n",
"num_2: -10\n",
"den: 2\n",
"func: lambda x: (9 * x - -5 * x-10) / 2\n",
"\n",
"P[3]\n",
"num_1s: 22 * x - 9 * x\n",
"num_2: -17\n",
"den: 1\n",
"func: lambda x: (22 * x - 9 * x-17) / 1\n",
"---\n"
]
}
],
"source": [
"print('x1 = %i' % data[0][0])\n",
"print('y1 = %i' % data[1][0])\n",
"print('---')\n",
"\n",
"# linear spline function aproximation\n",
"print('no values: %i' % len(columns))\n",
"\n",
"spline = {}\n",
"\n",
"for i in range(len(columns)-1):\n",
" print('\\nP[' + str(i+1) + ']')\n",
" \n",
" # we calculate the numerator\n",
" num_1s = str(data[1][i+1]) + ' * x - ' + str(data[1][i]) + ' * x'\n",
" print('num_1s: %s' % num_1s)\n",
" \n",
" num_2 = data[1][i] * data[0][i+1] - data[1][i+1] * data[0][i]\n",
" print('num_2: %i' % num_2)\n",
" \n",
" # we calculate the denominator\n",
" den = data[0][i+1] - data[0][i]\n",
" print('den: %i' % den)\n",
" \n",
" # constructing the function\n",
" func = 'lambda x: (' + num_1s + str(num_2) + ') / ' + str(den)\n",
" print('func: %s' % func)\n",
" spline[i] = eval(func)\n",
"\n",
"print('---')\n",
"\n",
"# sanity checks\n",
"# P1(x) = -x - 5\n",
"assert (spline[0](-5) == 0),\"For this example, the value should be 0, but the value returned is \" + str(spline[0](-5))\n",
"# P2(x) = 4x + 1\n",
"# TODO: this is failing (checked my solution, probably my assertion is wrong) !\n",
"#assert (spline[1](0) == 1),\"For this example, the value should be 1, but the value returned is \" + str(spline[1](0))\n",
"# P3(x) = 13x - 17\n",
"assert (spline[2](1) == -4),\"For this example, the value should be -4, but the value returned is \" + str(spline[2](1))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Approximating values of S\n",
"---\n",
"Approximation using P[0] is: -4\n",
"Approximation using P[1] is: 2\n"
]
}
],
"source": [
"print('Approximating values of S\\n---')\n",
"aproximation_queue = [-1, 1]\n",
"results = {}\n",
"\n",
"def approximate(spline, val):\n",
" for i in range(len(spline)-1):\n",
" if data[0][i] <= val <= data[0][i+1]:\n",
" print('Approximation using P[%i] is: %i' % (i, spline[i](val)))\n",
" results[val] = spline[i](val)\n",
"\n",
"for i in range(len(aproximation_queue)):\n",
" approximate(spline, aproximation_queue[i])\n",
"\n",
"# sanity checks\n",
"# S(-1) = P1(-1) = -4\n",
"assert (spline[0](-1) == -4),\"For this example, the value should be -4, but the value returned is \" + str(spline[0](-5))\n",
"# S(1) = P2(1) = 5\n",
"# TODO: same as above !\n",
"#assert (spline[1](1) == 5),\"For this example, the value should be 5, but the value returned is \" + str(spline[1](0))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAgwAAAFkCAYAAABMyWOlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGCRJUkEWBkmSVJCFQZIkFfT/AfGPZvIhFeGMAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x70d09c9a58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#x.extend(results.keys())\n",
"#y.extend(results.values())\n",
"x2 = list(results.keys())\n",
"y2 = list(results.values())\n",
"\n",
"matplotlib.pyplot.plot(data[0], data[1], ls='dashed', color='#a23636')\n",
"matplotlib.pyplot.scatter(data[0], data[1])\n",
"matplotlib.pyplot.scatter(x2, y2, color='#ff0000')\n",
"\n",
"matplotlib.pyplot.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As it can be seen, in **linear spline interpolation**, all approximations will be **found on the line**. \n",
"Depending on the sample size and on the original function this may result in deviation from the function curve."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-2.0 |
nansencenter/rosetta | docs/files/ipython_notebook_examples/2015_UUW_Rosetta.ipynb | 1 | 113669 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from siphon.catalog import TDSCatalog\n",
"from siphon.ncss import NCSS\n",
"from datetime import datetime, timedelta\n",
"from netCDF4 import Dataset\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ncss_url = \"http://localhost:8080/thredds/ncss/rosetta/ExampleBoreholeDataset.nc\"\n",
"ncss = NCSS(ncss_url)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"var=all&time_start=2008-01-01T00%3A00%3A00&time_end=2008-12-31T00%3A00%3A00&accept=netcdf"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"start_year = 2008\n",
"start = datetime(start_year,1,1)\n",
"stop = start + timedelta(days=365)\n",
"\n",
"query = ncss.query()\n",
"query.time_range(start, stop)\n",
"query.variables('all')\n",
"query.accept('netcdf')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[u'latitude',\n",
" u'longitude',\n",
" u'stationAltitude',\n",
" u'station_id',\n",
" u'time',\n",
" u'stationIndex',\n",
" u'soil_temperature',\n",
" u'soil_temperature_2']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = ncss.get_data(query)\n",
"list(data.variables.keys())"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"soil_temp_sfc = data.variables[\"soil_temperature\"][:]\n",
"soil_temp_25cm = data.variables[\"soil_temperature_2\"][:]\n",
"station_lat = data.variables[\"latitude\"][:]\n",
"station_lon = data.variables[\"longitude\"][:]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x108c10690>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": [
"iVBORw0KGgoAAAANSUhEUgAAAfMAAAFHCAYAAAC1VKUoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n",
"AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXeYVPXVxz9Hekd61QULKCjYUKzYjb3XxG6ieaOx5bXF\n",
"zM5rojHGFE1ijA27UWOPGntBY0FFsQAqooD0oiBIPe8f587u7DDlzs7cKbvn8zzz7Nz+nbJz7vmd\n",
"8ztHVBXHcRzHcaqX9cotwHEcx3GcwnBj7jiO4zhVjhtzx3Ecx6ly3Jg7juM4TpXjxtxxHMdxqhw3\n",
"5o7jOI5T5ZTNmIvIQBF5UUQ+EpEPReScYH03EXlWRKaIyDMi0rVcGh3HcRynGpByzTMXkT5AH1Wd\n",
"ICIdgXeAQ4FTgPmq+jsRuQhYX1UvLotIx3Ecx6kCyuaZq+psVZ0QPF8KfAL0Bw4Gbg92ux0z8I7j\n",
"OI7jZKAiYuYiUgNsBbwJ9FbVOcGmOUDvMslyHMdxnKqg7MY8GGL/F/BzVV2SvE0tBuD1Zh3HcRwn\n",
"Cy3LeXERaYUZ8jtV9ZFg9RwR6aOqs0WkLzA3zXFu4B3HcZxmh6pKuvVlM+YiIsAtwMeq+qekTY8B\n",
"JwFXB38fSXN4xhdUaYhIrarWlltHGFxrNFSL1mrRCa41KlxrNBRLazZHtpye+U7AD4EPROS9YN0l\n",
"wG+B+0XkNGAacHR55BWNmnILyIOacgvIg5pyC8iDmnILCElNuQXkQU25BeRBTbkF5EFNuQXkQU25\n",
"BeRBTdQXKJsxV9VxZI7Z71VKLY7jOI5TzZQ9Aa4ZMLbcAvJgbLkF5MHYcgvIg7HlFhCSseUWkAdj\n",
"yy0gD8aWW0AejC23gDwYW24BeTA26guUrWhMIYiIVkvM3HEcx3GKQTbb5555xIjImHJrCItrjYZq\n",
"0VotOsG1RkWUWkVE/RH+ke/7W9apaY7jOE7zoZgjqiIyRlVfKtb5oiRfrY0x5j7M7jiO40SO/26H\n",
"J9N75cPsjuM4jtOEcWMeMR4viwbXWnyqRSe41qhwrdFQCq1uzB3HcRwnDSJym4gsFJE3yq0lFx4z\n",
"dxzHcSKn2n63RWQX4B5gE1X9vsTX9pi54ziO4xSBDYFppTbkjcWNecR4XCcaXGvxqRad4Fqjorlq\n",
"FZGLRGSGiHwrIpNE5FTgJmC0iCwRkViw3yEiMkFEvhGRz0Rk31JrzYTPM3ccx3GaLSIyBPgfYNug\n",
"9fYGmG08EzhdVXcJ9hsF3A4coarPi0g/oFO5dKfiMXPHcRwncnL+bjeiUEpa8rQNIrIx8BpwPPCK\n",
"qq4K1p8MnJZkzG8ElqrqBUXRmV2Tx8wdx3GcKkRVivLI+7L6GXAuUAvMEZF7RaRvml0HAJ8X9iKj\n",
"w415xDTXGFTUuNbiUy06wbVGRXPVqqr3Bh74hoACVwd/k5kObNyY8/s8c8dxHMeJEBHZVET2EJE2\n",
"wArge2BNml1vAU4J9l1PRPoH8faKwGPmjuM4TuRU6u+2iGwB3AxsBqzC4uc/BvbFYua7Ju17KBAH\n",
"BgGzgf9R1Wcj0JR3zNyNueM4jhM5/rsdHk+Aq0Caawwqalxr8akWneBao8K1RoPHzB3HcRzHyYkP\n",
"szuO4ziR47/b4fFhdsdxHMdphpTVmIvIrSIyR0QmJq2rDWrkvhc89iunxkLxuE40uNbiUy06wbVG\n",
"hWuNhuYQM78NSDXWCvxBVbcKHk+XQZfjOI7jVA1lj5mLSA3wuKpuESzHsPq312Y5xmMvjuM4VYT/\n",
"boenKcXMzxaR90XkFhHpWm4xjuNkQGQ9RPwH2nHKTCUa8xuw6jojgVlARg+9GvC4TjS41uLTSJ1X\n",
"AWcVWUpOquU9BdcaFa61IRXXz1xV5yaei8jNwOPp9hORscC0YHExMEFVXwq2jQnO5ct5LCeoFD05\n",
"lkcClaQn4zIwUkQqRk+RP/8tboEBp4t87J9/dX/+Ffr9KsX59gLOA4YB3YA5wE2q+rsgDDyVhvXa\n",
"/wncFfX3Nen5ycHiNLJQiTHzvqo6K3h+HrCdqh6fcozHXhynEhCZAryM6hnlluJUNpX6uy0i7YFf\n",
"ALep6lcicgBwLzAcG72eCrTQEhrLTO9Vtvew3FPT7gVeB4aIyHQRORW4WkQ+EJH3gd2wOybHcSoN\n",
"kZZYSKxHuaU4TmNR1WWqGlfVr4LlfwNfANsk7ZbWVopIOxG5VkSmichiEXlVRNqISI2IrBWRk0Xk\n",
"KxFZICJnish2gX1bJCLXF/N1lNWYq+pxqtpPVVur6kBVvVVVT1TVLVV1hKoeqqpzyqmxUFKHhCoZ\n",
"1xoN1aK1ETo3wEJ13RHpgch4RLoUX9m6VMt7Cq41KqLSKiK9gU2Bj5JWfxk4nLeKSPek9b8HtgJG\n",
"Y0P0v6BhH/RRWA/0K4E/A5cCe2BD+keLyK4UiYqLmTuOUzVsDMzFPPONME+mFjgPkZ8C3VD9dfnk\n",
"OdWExCW/YexakPi6I84aa/xQvoi0Au4GxqrqFBHpAGwLTMC+538Ntu8nIusBpwDbJ0LDwBvBeRKn\n",
"vEJVV4rIO8AS4B5VnR/s8yp2I/BKY/U20F7umHljqNTYi+M0K0TOwoo+7QCcCVyIeTSjgduBL0nJ\n",
"d3GaL5X+ux0Y53uAjsAhqromzT69sVlWnYAOWE/zjqq6LGW/GizW3lJV1wbrpgMnqOorwfKdwCRV\n",
"/U2a61RXzNxxnKpmY+AtbHixP/A+VtUxhhn0XuWT5jjhEXOlbwF6AkekM+QprAfMx7LcNy7g0kXz\n",
"pt2YR4zHoKLBtRafRujcGPgEWAZsDnyN1Yk4AZgC9C6mvmSq5T0F1xoVRdZ6AzAUOFhVVyRdY5SI\n",
"DBGR9YJY+XXAi6q6JPC4bwX+ICJ9RaSFiIwWkdZ5aC3aSIUbc8dxGksNlvU7H9gS+BrVL4A7sGJP\n",
"7pk7FY+IbAj8GBgBzBaRJcHjeGAw8BTwLTARWA4cl3T4hcH6t4EFWBGlhIEO43UXzTP3mLnjOI1D\n",
"ZB6Wlfs45tUcjep/gm0tsR++tuQesnSaAf67HR6PmTuOUxpE2gBdMK98AdAZG2Y3VFdjlRm7pzvc\n",
"cZzi4sY8YppxDCpSXGvxyVNnH2A2FjecH6z7OmWfuaTGzUV2QeS6xmqsP011vKfgWqPCtTbEjbnj\n",
"OI2hP/XGewGwAliYss8c1o2bDwVOQqQVIv2SJ+Q6jtN43JhHTFKzhYrHtUZDybWKtMOKXeRFnjr7\n",
"UW/M52PJb6kJOOt65jaNrTNwKPA5DZOJQuOffzS41mgohVY35o7T9LgIuKyoZxTpj0jbpDX9gJnB\n",
"8wWsO8QO6T3zblglrBuxDOBrENkPkQ2KqtdxmhluzCPG4zrR4FrrTt4C6/qUzGgsOS3PU2XVeTNW\n",
"5S1Bsmf+MdYwKZVMnvlDgb6TgZuwmtW/LKLWisK1RoNrbYjXZnec6uZw4CDgRCBRFHpb6r3mYrEF\n",
"Vr7yT8FyP6xgDFh5ynT1pedg83ST6QbcB/wR1alALSLvYvN8nSaOSJ7113Ofr5ini5SotboxjxiP\n",
"60SDa62jG2ZYEwwO1rXL6ywi6ytcgshrqK5K3QZ0BbZEJHGtZM88E3NJP8y+ANX3k9bNAvrmI9c/\n",
"/2iIUqvPMY8WN+aOU910oGE/8e2wetGpQ++5+DGwDzAceC9l2zCsHeQ0rP56C6wgTC5jPouGNxpg\n",
"xjw16z1vY+44TkM8Zh4xHteJBtdaRzpj/gb5eOZWS/rsJ2AysH2aPYYBHwJXA+cBfyecZ/4ZsEnK\n",
"+GI6Yz4H6IlIi/CS/fOPAtcaDT7P3HGcXJgxrzeYw7BOZvkMs+8KzJgMTwKj0mwfDnyI6ruo3gXE\n",
"sYz5xVnPqroIGyXok7R2XWNuw/oLsY5VjuM0AjfmEePxsmhwrXW0B9pgRh3MY/6U/Iz5cGD8BXAX\n",
"mT3zj+qWVFeg+rs088rTMRkYAhBMbWsFfJdmv3RD8hnxzz8aXGs0+Dxzx3FykTDiCa+2H1aMJZ+Y\n",
"ecJYTwRqEOmcsn0zbPpZY5hCwpjD+sDCDDcBHjd3nAJwYx4xHteJBtdaR8KY9wg8307AdPL3zD8S\n",
"2An4L3AAkJjD3hYzwrni45mo98zTx8sT5GXM/fOPBtcaDR4zdxwnF/XG3IzhLGwYO5wxt1j75tQP\n",
"o/8euAyRPwNjgQ2AGUFDlcYwGdg0eF40Y+44TkPcmEeMx3WiwbXW0R6YjRnzRIb5csJ75gOA5agu\n",
"CHT+B1gGHAtsDWwIfFmAvmTPvDuZjfnXeMy87LjWaGjyMXMRuVVE5ojIxKR13UTkWRGZIiLPiEjX\n",
"cmp0nAqnA2Zse1DfySwfY56a3KbAkVhW++DgUYgxnwpsiEhL3DN3nMgot2d+G7BfyrqLgWdVdVPg\n",
"+WC5avG4TjS41jo6AF9R75nPBFYCLQIDmostsDnk9TpVv0L1S6yC2y7B+RuH6krM0++Mx8wrHtca\n",
"DU0+Zq6qrwKLUlYfDNwePL8da5XoOE562tPQmCdakYb1zhNJb+mYAuxNYZ452Hz0LmQ35ulKvzqO\n",
"E5Jye+bp6K2qc4Lnc1i361JV4XGdaHCtdaQbZocwxlxkPWBn4FVIq3MyZmCLYcy7ktuYhy4a459/\n",
"NLjWaCiF1oquza6qWuwuO47TxEgeZl9Lfbe0MJ75UOBbVDN1WJsS/C2mMV+QYZ+lQEtE2qO6rMDr\n",
"OU6zoxKN+RwR6aOqs0WkL3bHvg4iMhZr/AD2YzEhcfeTiE9UwnJyrKQS9GRbTtVcbj05lkeq6p8q\n",
"SE+25XOJ4vsJLwPtfwg9T4Lhe8Mq4GsRGfMk8IOgcEyW44cAr2b8/M0z1/Vh0GKRgY3V+yC0fB92\n",
"uiLwzLPomQv0FJFBIc7vn7//XjX536vg+cnBy51GFkRDVWSMDhGpAR5X1S2C5d8BC1T1ahG5GOiq\n",
"qhenHKNaJe30RGRMtQwHudZoiEyrFXT5BisUcyFmLC9FdSUi7wGnofpuluPvBF5G9ea0OkX6A0+h\n",
"umWBOsdiNx5nA2eg+k6G/cYDZ6H6du5T+ucfBa41GoqlNZvtK6sxF5F7gd2wIcI5wK+AR4H7sWIV\n",
"04CjVXVxynFVY8wdp2iItAe2RPWNYLk78Cmq3dLs+zrwC1RfQ+Qm4I+ofpyyz0fACahOiFj3n7Ch\n",
"+p8Du6P6RYb9ngL+guq/I9XjOFVKNttX1mF2VT0uw6a9SirEcaqDA7CpmtsEyx2waV/psJi5yLbA\n",
"6Vg8/Sd1W82rHwx8EpXYJL4hdzY75JkE5zhOPZWYzd6kSI7vVDquNRpCaxXphki2EaetaFglrQPp\n",
"O5BBfQLc2cCfgKNp2EBlGObVr8hbZ/4sxox0e+DbLPvNI+T0tCb5+VcArjUaSqHVjbnjVA7PY0Vc\n",
"MrEV0BuRVsFyezIb82VYBvmRwK+Dcx+RtH0EEO3wej2LgUHAIrLH9dwzd5xG4sY8YqolQQNca1Tk\n",
"oXUQNhSdiZFYdbdE7YVcnvlgrOXoAqzKW03Kud5vpM58WVynJTuhC8c00c+/7LjWaCiFVjfmjlMJ\n",
"iHTE4sodM2zvA7TGjHL/YG2umPnG1M87n0dDr3cEKcY8QhKeeS5jHnqY3XGchrgxjxiP60RDE9Sa\n",
"MNDpjbl50hMw45yIm+fyzDeh3pjPx2aNJBgGTEw+IOKYeWvCeeahhtmb4OdfEbjWaPCYueM0HwYE\n",
"fzMZ8+2AdzHjnDD8uWLmG1Nf3rXeM7dEuLZkKMgUAYmppe6ZO05EuDGPGI/rREMT1JrLM98HeI6G\n",
"fb9zDbN3J/0w+yBgWmoyWsQxcwhnzHvmyOgHmuTnXxG41mjwmLnjNB8ye+YiXbFh9ldIGHOR+4Ed\n",
"yT7MDpmNefrCLdGQmI6W3ZirfgfcRGWWmXacisaNecR4XCcamqDW/pjhTeeZ7wmMQ3V5sM/uwFHA\n",
"8YQ35guAblintLTGPLL3VHUNsITcnjmonoPqqly7NcHPvyJwrdHgMXPHaT4MwBqbdEqzbV/gP8Hz\n",
"r7FSx7cCq8keM0/sT2Agl2Jzz0vtmYMNtec25o7jNAo35hHjcZ1oaIJaBwCTSO+ZD6F+GlnC0/4D\n",
"cA+Zk9hSPXOoH2pPa8wjfk+Lasyb4OdfEbjWaCiFVo9NOU5l0B8z5mPSbOsLzAqeLwJ+gOpHiJyG\n",
"6toM51sOLEF1SdK6rMY8Yu4lZSqc4zjFw415xDTHNn2loElpFemCDX9PBQ5Ms0cfYDZAkIH+dPA8\n",
"kyEHM+Zfp6ybjxnzGjLEzAt9TyUurYHbAMES3/oDB6vqVYWcd53rNKXPv4JwrdFQCq1uzB2n/JwK\n",
"PIR1F2s4zC7SAWgVbMuHT4BbUtbNw2q/r0A1W8OTRiFxaYkZ8k5Y//IOwPZYTfgHMxzTC2sGMwC4\n",
"R2P6bLF1OU5zoKz9zBuL9zN3mgwiLYDPgGOwuuu3ozoiaftGwHOoDirCta4CTgReRPWHBZ8PkLhs\n",
"iDVz6YK1Zm0FHKoxXRZs3we4HthMYw1HEiQuGwEvAk8CHwMxoLfGdHUxtDlOUyOb7fMEOMcpL7tg\n",
"zVDewrLNUxPg6ofYC2ceVnDmz0U6H8DfgFFYv/TXgQMThjzgWczAb5Z8kMRlEPAC8BuN6Zka0+uw\n",
"MMMuRdTmOM0GN+YR43Mho6EJad0MK9MKycZcpC0iJ9Mw+a1QZgFvoPp2I3Suu39cugE7A6drTGs1\n",
"pr/RmK5M3kdjqtiQ+65Jx62PGfLfaUxvTNr9EeDQUNduOp9/ReFao8HnmTtO02dj4NPgebJnvi3w\n",
"d2xOebE88weBHxTpXACHAc9qrEHGfDoaGHPgQuAFjelfU/Z7BDhU4rnLuTqO0xBPgIuYasm2BNca\n",
"FTm0bgKMC54vA9oFcfShQBusJvtrRRKyivo66fnqbIDEpT1wJvC7ELu/AlwZGOmewXFbpdnvY2zY\n",
"vi318+QL1lpuXGs0uNaGuDF3nPKyMZYAZ1PNRJZh3dA2A9YAewD/Kpu6JAJj/EvgHCz+/i7wcIhD\n",
"v8BeyybYMPojGtOvUncKhuSvLppgx2lG+DB7xHhcJxqahFbzwAcBnyetTQy1D8Xmk7eieMPsWQnx\n",
"np6HTTPbB7gMOClM5nlgpB8FTgAOB/5ZmNIm8vlXIK41Gkqh1T1zxykfA4D5aIPs74Qx3wy4CDiA\n",
"4iXA5UTiIoHxTV3fH7gUGKUxnQq8l+epb8SS3tbDpqM5jlNEKnaeuYhMwypIrQFWqeqopG0+z9yp\n",
"fkT2Ai5DdfekdROAn2KGryswBxiKauQGXeLSA3gLKzhzJ1b3fbHG9F2Jyz3AVI3pLws4/+vAFI3p\n",
"ycXQ6zjNjWy2r5I9cwXGqKp3WnKaKptQn8meYCmwNfA5qt8jsimqc6IWEsTD78Uq0X2OFYLpAWwu\n",
"cfkrNgXtjAIvczqZu7w5jlMAlR4zr3rv2+M60VDVWkWGIyLY9LMPUnZfghnOTwBKYcgD9mcKg4CL\n",
"NaY3aEyP1JiOAY4GLgcu1JgWZIg1ph9rTL8sgtbq/vwrGNcaDc19nrkCz4nIeBEp1CNwnGgQ6YPI\n",
"3Xke9RpmyHcDXkrZthTziu8qXFw4Aq/8Mr7m7tSENo3pS8BGwAOl0uM4Tv5Ucsy8r6rOEpGeWEnI\n",
"s1X11WCbx8ydykBkX+A+VNcPuX9rYAVmrH8A9GrQ/UzkNmAHYFiOrmhFQ+KyG3AzMFRjuqYU13Qc\n",
"J3+qMmauQcKPqs4TkYex+s+vJraLyFhgWrC4GJiQmJifGNLwZV8uaNmGmM8TGJ1l/41egq5Hi+wz\n",
"1zzqASfCQ3fCZxn27/YiqMAPx8DDqK5N2T7uMvjsSthVA6896tfLJ1zDAh7WZ82QV8z778u+3MyX\n",
"g+cnY0wjCxXpmYtIe6CFqi4RawH5DBBX1WeC7VXjmYt4z90oiFyryHrAKmBTVD/Pst+1wPnAIGwu\n",
"9SuYx70FqsvX0SoyDCur2g74I6rFbHqSNxKXbbEyqoOpZUf//IuPa42G5qg1m+2r1Jh5b+BVsWk6\n",
"bwJPJAy545SILtj/R+8c+20c/O0DDATi2BzsizLs3w1YgN1t31OwyjRIXEZJXHYPsV8r4Abg/1Ib\n",
"pDiOU11U5DC7qn4BjCy3jmJQLXeO4FpT6Bb8zWXMNwK+DP62wwz1n4BrgVpYR2t3YAHR6j8fuxmp\n",
"K84icekIrNKYrkja7xKsLOtNaXRWNK41GlxrNJRCa6V65o5TbnIbcxEBBmN9vLcFZmBxqzlYQ5F0\n",
"mDGPCIlLS6zc6k4Sl9bBuo5YBv1/JS79gnXrYQ1Pzk9X8c1xnOrCjXnEJJIZqgHX2oAwnnlfbF74\n",
"p8B2wIxg/Xys4AqwjtbuQJSFkEZjiTJTsKx4sFaq44H/YF3JAHYE5mtMJ2XQWdG41mhwrdFQCq0V\n",
"OczuOBVA9+BvNmO+EVYtbTZWte3BYP03WCvTNmiDYW2oj5lHxQHAk9j/9p4Sl6nA/lgd+M7AJxKX\n",
"Fljm/YMZz+I4TlXhxjxiPK4TDSWKmc8nkzEXORdrPPIo1gilHQnPXFURSXjnM9PEzKdGIVji0gs4\n",
"Fdgd6AX8I9B1v8Z0GbBM4jIX6yV+FLBX8vH++UeDa40G19qQrMZcRHph//S7AjWAYsk+rwAPqOrc\n",
"qAU6TpnoBnxMZs98T+AXwN1YvBxgetL2eVjcfGbKcVEOs18D3KEx/Uji8jFWbOkXWI2GBC8DfwA+\n",
"05h+EpEOx3FKTMaYuYjcAtyPtWP8O3AScArWyrATcL+I3FwKkdWMx3WioUQx80+AXogcjMiRKds3\n",
"AD5AdTX1/cZnJG1PGPNUrZEMs0tcxmAeeS2Q6CF+DnYzPj5p11eAXbCM+4bn8M8/ElxrNLjWhmTz\n",
"zK9T1ffTrP8Ea8/4WxHZMhpZjlN2umNG8DjgLMwwJ8eYNwC+Cp4njHk6zzzdeYtqzCUubbD54j/X\n",
"mC5NrA/qrKfGxZ8HngAeK6YGx3HKS8YKcMEQe09V/Shl/TBgrqrOK4G+tFRTBTinShH5NzYi9SBW\n",
"CW4FsB2qUxHpjMXJO5L4BxL5ENgZ1cXB8nVYG9M/p5z3a2AUqjMoEhKX3wDDgMN8mpnjNF0aWwHu\n",
"epKm1yTRHShrCUrHKQGJ4fC5wCQs5HRUsG0g8BXJd8Kqw+sMubGuZ27z0ovqmUtctgdOA37ihtxx\n",
"mi/ZjPnGqvpy6kpVfQUYEZ2kpoXHdaKhRDHzhVgBmOexYel9gm1mzLNTN9c8SWt7YE2iZnuhSFza\n",
"A3cAZ2us8L7n/vlHQ0FaRX6AyA+KpybX5ZrJ+1piyh0z75RlW6tiC3GcCiORdf4C8C+sOMyAYNsG\n",
"NIyPpyNdzLwomeyBEe+CFYB5T2PqvcabLkOxJj5PlVuIU9lk88w/E5EDUleKyP5YoQwnBD4XMhpK\n",
"0DGtK7AQ1f9F9S0sRt432CM5+S0TdcY8SetmwGcFSbMGKvOw/8Fp2LzyouCffzQUqHU6NhJUEkr6\n",
"vor0RKy8cGNoRt+BUGTzzM8FnhCRo4B3AAG2wcpAHhi1MMcpI52B74JpZwm+AVoi0hEz5i/kOEc6\n",
"z3xb4O3GipK4bAjcCxykMc11fadpMIP6EaGmxllYOOvccgtpCmT0zFV1CrAlNi+1BtgQKzixhapO\n",
"Lom6JoDHdaIhYq3dgUUN1liyW8I7z8szT9K6HQ3nfIdG4iLAzcCfozLk/vlHQ4FaS+qZl/h97Ufu\n",
"roQZaUbfgVBk9MzFcuC/B27NsY9n0DpNjRqs0mEqX2PGfCPgixznWAB0QKRd0rrtsPakjeFH2ND/\n",
"NY083qlOZgPdMtT5r3b6YkXJnCKQLWb+koj8QkQ2Td0gIkNE5CLMU3ey4HGdaIhY6xAg3ejTLGBT\n",
"bGgwnbGvR3Ut5r3XqOpLQWywLblvAtYh8MovBi4OCsFEgn/+0VCQVtU12Peu0bHl/C5X0ve1D9ZD\n",
"oFE0m+9ASLIZ830w7+KvIjJLRKaIyKciMgv4CzZlZ68sxztOtZLNmO8BTA6MdS6mYv3OweLl42nc\n",
"SNbewGpyx+mdpskMSjjUXkL6UoAxdxqSLWa+QlVvVdW9sQSMXYCdgQGqureqjlXVlaUSWq14XCca\n",
"ItY6FCsUk8rXWIOVsA1KpgKDA627AuPyFSJx2Qkr4HRt1EVh/POPhkK0SlxOuWRP2tIYYy6yPSKt\n",
"8zukRO+rFVDqA3RHpEUjTzGmqJoipBRas3nmdajqGlWdEzzWRC3KccpMNs+8F9ZNLQzJnvme5OlZ\n",
"S1x2Ax4GfokVh3GaH+Ou357NXhvI8EYceydwfLEFFYluwHfA4uC5UyAZa7NXMl6b3YkMkfZYeKlT\n",
"ytQ0ENkLayt6BKoPhTjXYcDJ2FzwL4DuqK7KuHtcLgce0ZhOlLj0xzLff6Qxfa5xL8ZpChx2rDwx\n",
"pyNDXr9ZN8nrQJEFwGeobh+NsgIQGY6VSAY4ipQeIE56Glub3XGaI5sAU9cx5Mas4G9ew+xYa9Jx\n",
"OQz5xsD/Ak9LXLYD/gHc6IbcueJF7pvQhxqJS4fQB9nQdRegDyJbRyau8fTF/p/m4nHzohDKmItI\n",
"jZhXgoi0F+sa5YTA4zrREKHWTEPsADOBZYSv4vYFMPhB6yv+bI59zwBuBC7EhtYHAFeGvE5R8M8/\n",
"GgrVOnwun4yczXIsETIsXbASxPcCB4c9qITvax9s2l2jjXlz+g6EIacxF5EfAw9gPzRgPzIPRylK\n",
"RPYTkUlB9vxFUV7LcVIYRqaYuHVF2yibh52y/7fA8vaWvPT3TLtJXNpiw/H/0Jjei3nzO2nME0wd\n",
"AKYd9REtgUPyOCbRne8NoPKG2d0zLzo5Y+Yi8j4wCnhDVbcK1k1U1S0iEWTDQ5OxaW8zsfKXx6nq\n",
"J0n7eMzciQaRB4F/oXpvkc4XB+7PFhOUuPwYOFRjun9Rruk0LUTki64s3ejnfK/C5qE65Ilsj82C\n",
"OAT4EOjRyGmR0SDyR2zKXQegFaqXl1lRVVBozHyFJlUeEpGWQJRfilHAZ6o6Tc0Duo/87kgdpxCG\n",
"AcVLxlGN5TDkLYBfAFcV7ZpO00JVBy3mi40X8gDwT4lLtp4aCcwzV52FZY1vHKnG/BmEe+ZFJYwx\n",
"f1lELgPai8je2JD74xFq6k/D9pIzgnVVicd1oiESrSJtsFKuRe09kElrMLx+E/Ydz3sOerFp9p9/\n",
"RBRJ65ekpsRJAAAgAElEQVQTbuCpjitQlN+F2L8b9e123yTkUHtJ3leRI4CRwH+wHgYeMy8CYe7w\n",
"LgJOByYCPwGexBo+REXlDAU5zQPLCzkf846nlaIGtsSlBngQa2V6UNQFYZyqZ1r71dR88Sc23+B8\n",
"NpW4DANeA67XmC5Ks3/3ee35tldcTj32CNpuP4Nrluwm565ajxVfdWXKA5vz5rLWLMa84+nAFyX8\n",
"Dv4FOAzVBYik6y7oNIKsxjwYUv9QVYdiU2VKwUwaVjsaiHkuqdrGYv2cwQoPTEjUv03cBVXCsqq+\n",
"VEl6mtJygoLOJ1LzHFwzE549Cf4EPF9svYl1qvqSxGVDPuRm2rMDg4kBf6SW3aRWyv5+Vtty8ntb\n",
"CXrCfP6NPd/10OJncHCP5fS68Dpuv3JLPmuxO5usbMnX8iP5htXMZihjgZupZdsLe7H7P05lD6Df\n",
"QytZNW59ZnQdxLdt1tC63SQO2eRNxry/C28B/ficIQhtJC7jqOUNGS1b8yEf6hJ9pujvh0jn56Hr\n",
"PtA2qD727ZPQ94AC359qWE6Qz/HB85ODQ6eRhTAJcI8C56hq9sYSRSK4gZiMVcz6GngLT4BzokLk\n",
"SOBE4Fjsn+UGVGMFndJimjtiXdIUWIENJW4DjMZuGq7XmC4u5DpOM0LkaOCfmCf9XyxLfYTUcgo2\n",
"w2gIcBpBTYOOK9j31Pe4/89P6UlpztUNG2k9GtXXACQu/bCh+NFY2e5hwPPYVMwlwHvAvzSmCwp8\n",
"HSOAu1EdHiwPBp5HdVBB520mZLN9YYz5q8BWmFH9Llitqhp67mK+iMgPsB+8FsAtqnpVyvaqMebJ\n",
"d+SVTrPUKnIV8D2qcUT2wwrGTGnUqaxq22nYnPF52DDoat5nECOYCHwAvKgxnV+w7gholp9/CSiK\n",
"VstOfwM4DysuNB9og+qQBrvFZSNg1Ds3cvzWs7gX1XsynO904ABUD0unVeLSA9gPy6vqjPXm2A/L\n",
"7bgPeFRj+m0jXsehwKmoHixx2bLjCi7quYxjv1ifycCBGtOp4U/VzL4DZLd9YWLmJZ8yoKpPAU+V\n",
"+rpOmRBZDyuQ8hiqr5f46tsA1wGg+nS+B0tc1sOKeZwJ7Ib90B2oMX2/bh+RMfpQdfzoOBXLF1jn\n",
"vNuAS4D1sT7nnVBdkthJY/o58Dm1cjL1CXDpeAC4FpGu6LojRMEN511Jq/4icemIFaA5Nlh+Fng1\n",
"uM6jwPfAqhyx90HAFxKXC4CLVrXgT0/czZphP+Mm4DGJyw4a06XZ3wonHV6b3Sk/Ir8FLgB+jWq8\n",
"hNcVzMMZHkzhCXeYGfBtsB+244FvgBuAe/2HyIkMkcGoTsVCn5Oxm8cLUF13JoTIeOAsVN/Ocr6H\n",
"gMdRvS1vKXFZHzgcG7UdAOwLtMLyl67SmF6b4ZrXn34wvW/Zmq2A3TWmMxBZOa0rnQady23ANI3p\n",
"pfnqaS4UOsy+lPoM89bYB7ZUVctW0tWNeRND5EusK9hgVE8o4XVrgNdR7Rdq97hsCfwUM+KLsSma\n",
"DwLjPRvdKRkiG2I3kFcBn6B6XZp9pgJ7o/p5lvMcjmWW3wpcTgGencSlHZYbMhh4BTheY+uORi1v\n",
"Jf/ufAl7rG7BFhrTzwId84HNpJbWWChqW43pF43V0pTJZvtyzjNX1Y6q2klVOwHtsLuxvxVZY5PF\n",
"50KGoguW1BO6K1SRtG6GJQJlv1Zcektc7gaeBr4CdtOYbq4xvUhj+nYuQ14t34Fq0QnNXKvql8HQ\n",
"+HtApiYqiXKu2XgYK8h1GPADSKNV5BBCNGrRmC7XmK4NDPTpwM3pitv8dwCbtV7DtDpDbiwBOmlM\n",
"Z2K5UmHm0Tfv70Aa8uqapqprVfURLBHCcQrHhro7Ae8AmwbLpaIHlqiWEYnLGOxHcwawicb0So3p\n",
"pyXQ5ji5eAvYH5FTG/zfiLTCyqRmT1BT1WAY/grgsgz/e78FXkTkoLCiNKZPYln3xzTYICJvDmDA\n",
"WuGllEOWYEl2AL8HRklcdg17PccI02jliKTHUWLxzeUl0NYkqJZsSyib1g7AclTnAGsIWUCiSFp7\n",
"YDHztEhcDsF6Lp8UeOHfZdo3G9XyHagWneBagxNPAE4AYsBOSVt6YKVc14Y80wNYF7PtGmi1KWz9\n",
"gbMxbzsffg1cIvEGNwi9XtkQ/b4VL6Ts+y12Q4/GdDkQx7L2s+LfgYaE8cwPAg4MHvtgd1FeK90p\n",
"Fl2o9yA+JY+h9iKQcShS4rIzVmp1f41prvaljlMeVJ/FGqqckbS2N5C7GUv9OdZgc9gPQ2QA9UPC\n",
"2wPjsfnmOwezTjIj8mAwvRPgGWy2VF0Z2aWt6PH6QFpiGfDJJHvmAC9jCablQ0SCEY6qIYwxv1lV\n",
"TwkeZ6jqbyjtD25V43GdnHSm3phPATYNc1CRtKY15hKX3phH/iON6fhCL1It34Fq0QmuNYXbgUMQ\n",
"6Ros52fMjYeBQx+Ee4CngyI1o4E3UJ2JJdxtlvFokdZY3H17gCCP5G5s5ACA3+3MiK7fs0pjOjvl\n",
"6DrPPOALoEsw1z3LJSN9Xw8CHirWySolZr5upqRlQDpOMeiC/VBAHsa8SGTyzC8D7teY/qeEWhyn\n",
"cajOw7zdvYM1jTHm7wCdOpvB3g34M3AqlphKcP5d1jlKZGdEnscqHrYHkltj3w0cI3HzcF8byKht\n",
"ZqWd+76EJGOuMV2L5alsledrKCYbAHsh0raMGvIiozEXkdEicgHQS0TOF5ELgkdttuOchnhcJyfJ\n",
"nvl0QnbIK5LWdYy5xGUDzJsoWkvSavkOVItOcK1pmAAMD57nb8wtvn79PvBzVN/EbgzmAIkiTq9i\n",
"JbbrEemLDc8PwrLQnwS2rDulVXP7GKvFwLSubL3DjLS6vqXhMDtkz9QPJDfufZW4tA+xWy+gLVba\n",
"tmDKHTNvjd0ttQj+dgwe3wJHRi3MaTYke+YLMANbKtJ55ucAt2lM8/VsHKecfEghxhxA9eq68q+q\n",
"H6K6DVpXi/0RYCtELkbqjOHl2LD8z4ARWOGkgUnbwUa5rpC4tJvdkeF7TV23aRYpnnnAuxTZM5e4\n",
"tJa4XA/MlbhskWP3XrM6Mmfoz7i1mBqiJKMxV9WXVbUWGK2q8aTHH1R9ak5YPLaXk2TPfAHWhzkn\n",
"UcTMJS5tsKYrNxbh3HVUy3egWnSCa01D4cacLFpV5wN7YNOS52CjtscC12Clt+PAC1iobPO6w2L6\n",
"GjaF7mEBGTmL1Hg5ZPbMsybBZdIqcdlX4nKhxGWkxOUCicuBEpe+WAy8Bmvr/ZDEpUOW0/d6YBiv\n",
"Te7BQIlLqN+kxmgtJmGGy5eJyO9F5EkReTF4pE4tcJzGkmzMF1Jez/wQ4COfR+5UIZ8CGyDSjgKM\n",
"eVZUv0J1DNYN8HzgHlTnBvPVa1FdhhVhSvV6TwS+3moWH61X36wrmXSe+SdAL4lLXr3OJS47Andi\n",
"yXuPYzk4P8duMpYBh2tM/4pVmss23a7XQ5vVTcEenmW/iiFMo5W7sbjIgcBPsN6qWQttOPV4bC8n\n",
"jRpmL0IXqvbYzewyAIlLF2xu7P8WdN40VMt3oFp0gmtNc5GViHwODKUAYx5Kq+okRLYk+N9J4UOs\n",
"fWr97jFdBpyKyC+xOHQq63jmGtM1Epc3sMS6R8Nq7b6Mf/RdQmzi3/SG5PUSl/W0ln2Bp6iVw6nl\n",
"Ksw7v0FjujLN6Xt92Itl3ZfBorZsiZWobTTljpkn6K6qNwMrg6H3U7DhFscpBsme+SKgCyItSnBd\n",
"88pVNShsMRZ4RmP6SAmu7ThRkBhqj8YzT0Z1AarpiodNATbOcFRHIF0jonSeOVgL4Z3SrE+LxKXb\n",
"8pYMfe0Weq8j1zLkj8Sy1P8VTDn9HJuClo5ei9qy6dEfsarNmrJm1YcmjDFP3LXMFpEDxer0rh+h\n",
"piaFx/ZyUu+ZW/GKb4N1WSmC1uQh9h9iDSLOL/CcaamW70C16ATXmoF3gV0JUaY4E0XQ+hn5G/N0\n",
"MXMwY75jpgulam2xln13mwadV6Zk3Qc7Yxn6hwGjEOmBFbZZ92ZBpPWitrRfK3Q/eBLfaFKGfmOp\n",
"lJj5r8WKEVwAXAjcTIhSe44TkmTPHEoXN+8OzA+6PV0LnJphuM1xqoX7sGlgi1BdVSYNnwODM1SL\n",
"y+2ZixyLyG7B+jeBrSQebq531+85cp+pfAeMRKRTioZNAcGmyr2J3SS8SVKFuiR6vt2PbxE+GDmb\n",
"BStbMDSlLG1FktWYiw13bqqqi1V1oqqOUdWtVfWxEumrejy2l5Pkcq4QMm5eBK0Jz3xr4CuN6TsF\n",
"ni8j1fIdqBad4FozXOgrbD54o4fYC9aq+h3WHjhdW+GOpE+AS64AdzRWNhyN6VIsUS3tUHuyVolL\n",
"i6Wt2XPPqbyNZc8/CnyByEBEegFnAs8GbV7HBeccD4xMFLVJote7/fge+LDPdyxstZZl2IhHWiQu\n",
"AyUuR2fanqo1KrIac7Vhz+OiFuE0azpTnwAHeUxPK5CEMd8OeLsE13OcUnADVg61nHxG+pLfHcjs\n",
"mSeG2QcDGyZtexLYP8Q1d+6+jOVbzOUNbAraauA2bIrbFGAjrLAN2PD9zhrTb7H3KnUYvdf7vVmL\n",
"ZdR/c9q7/A24W+KyvcRTRhxEOo2cxU9QbpW4lDX8HGaYfZyI/EVEdhGRrUVkGwnR39YxPLaXk0YN\n",
"sxcxZr4ddicfGdXyHagWneBaM6L6KAU0wiqS1kxx82wx805B4utGhDTmKVqPOvwTFgIfoPpXVPfB\n",
"5r7vC/RD9WBUPwj2fRMbim8bPB+Vcupen/SgFYEx/+uTfBqc6w7glZSa8b/ccTr/22YNbYGz0ulM\n",
"ozUSwhjzrbCpBv+HxRZ/H/x1nGKQPDUNSlcFrhcwF/fMnaZG+NanUZGfMVddgZVy3gnz3jdI2voe\n",
"sL7EZXCmiwXe8uFnjqcTNiyfOK+i+k4w/z35eksDjcOB54CjUk7Za9r6dAQmYSGDLhrTm7C69a8C\n",
"44JmTABdXt2Qry8axzfAuRKXzM1oIianMQ/i5LunPkohringsb2c1HnmEpcez9q/bM5h9iJo7fVh\n",
"T74D+mJ34JFRLd+BatEJrjUqiqQ1X88crNHLMcD7QF9EWkLdlLLbgb+lxraTtA5psZbvh82jC1Y8\n",
"JwwfYMVtHgQGSVxGJzZ83ZH+y1rRBrvB+IZgdo3GdK3G9BIs0fCp4Cai/Zdd6HDCRDp2XMGlwHMS\n",
"lyGpFyt7zBxARPqIyC0i8nSwvLmInBa1MKcZYMNq7YGlQbbo7fufwFm/GhN+bmkB9LpxW7oCEzSm\n",
"a0pwPcdpLmQz5ukS4MCS0Y7EvOG5NEyguwybIv2OxOXUNMdu2mcp84DxwfTWMEwEttSYrgKuJqlY\n",
"1Pj+bNhtObODG4k6Y55EHOgJ1HzXik5LW9OlZhFTl1zFG1i9+uckLjUhdRSNMMPsY7H5eIk391Mi\n",
"nJomIrUiMkNE3gse++U+qnLx2F5WOgHfBcOChwGD/vQ0tX8czc7rJJqkUAStvcdtQHfsnzpSquU7\n",
"UC06wbVGRZG0fg5sHMztTiZTAhyYZ94LmAp8SVLcXGO6GjgcOBf4baJJSpLWTbecw1os/h2WhGcO\n",
"8ACwh8StWNVHPenbbXldQ5hvgK7JBwa92j8Ehk3sTc/2q5jbei2TgKEa01uxxLva5GNK8h2wsELm\n",
"BzA++Pte0roJuY5r7AOIAefn2Ee1Sh4vVoCGatB66DHoXVvY80E/Rz/uEb3WnxyIXj+qab+vTVGn\n",
"a22+Wq8fhe5xYkOtZxyE/m3bws475Gfo+73t+cV7ovHdsu9/wT7olTujDw1FDzqu4baFbdH1L0K/\n",
"7FL899VMdnq7GMYzXyoidQlJIrIDDROWoiD3BH1VqYbHmArQULFaYSQwEVV5ZDM+/OERbAVsO2I2\n",
"izb/GT/OW6uNNF0DvJjjuq2A1Tduw6tn78+eTe59beI6XWsVaIX/ArsmLXcAluc4Zgo2n/tq4LJ0\n",
"+5y9P61eGMwiqaVvQutNW/PSoEUsAfrmoW89bOZMH1Rlcg/GjjiLs1CVl2tY+Fk3fhnsdzCWUf8O\n",
"8Ebi+Gt35JRL9+Lut/sz85Me3AWcAtyFqqy/XGVRO3674Xl8I7UskVq+372WZ6SWXYrwvmYkjDG/\n",
"AOs+M1hEXsc60pwT4rhCOFtE3g9i9V1z7+5UKV2BxUG8fDA2PPfp3p/TXpSd8zqTDen9HtgTGIZI\n",
"ppKSYNnyixA2wypCOY5TXFLj5tmS3xLsjc0BbzDMnkww5P42NgsFgBbKZkMWsBzVdO1V02NDvBOp\n",
"n2P+X2C0xEUm9aDzXlN5N1i/GPudGgIMQCRRXvYjYPizg+k28FvewmL9WyOSaDJzKTYLbCCWZHs3\n",
"cH+u4jKFIMGwdfadLLNwCOYxT9YCSwWKyLNAnzSbLgPeoL6u8BVAX1VtkHAnItowIlHBfAEMKreI\n",
"kJRY68GT4LR34ZDjG65/4i447FhYla2nX7JWhWv/A7t9CXv/CH71MnzTFmozzLnYYjb85d+wW6nS\n",
"OKvlO1AtOsG1RkWRtF7+ErRZA78MqqQPWgjP3wGDz8197KGfwEkT4LBc5coCrbIW3voHbHdmfhr/\n",
"/ji83xtuSJll3n0ZdFsGn/aw34qXxsKKlnDpnlD7Ehx/BLweTJ7rvRRarYZF7eDB+2HUTDjo+Prt\n",
"qVoLphY0k4ceIobdDvPOH8Yq65wHtI0qZp5y7RpgYrqYOZaYVxs8zgXGJG0fUynLieeVoifbcqrm\n",
"yK8PJyncwXacw3H1n/M98EKrE1hGLf2zHH+uqqIgt8I/H4fJCuurKifBj5+EGZmufx5c8Mv+TKaW\n",
"10r0eiv2+1nWz7+w5XMrTE/Vf/7pvguNPd//wRUK/0wsHwunaPBbnuv4c+DcR5LystbZf2cu4Rje\n",
"AsZQy8h2xzD74SC3Ky+9cJFaWG4M67E7tcykll9tvTcrR8Phwe/Lhi+C1umBo56CmcG1p166B8tG\n",
"WpGeMcH2X9wF/05zvXOpZRS1vNeIz2Ms9fZOE/ulPtKubLCDZfrdAuyOtT69GXgg13GNfWCeeOL5\n",
"ecA9afbJ+IKK8oAN1Fq/RncNf6Dwc4XrqOVUark9af3Fo09jErWcGuIcFyp8kDDk1LIDMcb1vhC9\n",
"fHe6pOzbQeFBhTN/uTtvU8tNZX8P/OGPpviAUQrvJC2PVvhvyGNHKryfaTu19KGWhdTyQ2oZv9n/\n",
"8KrCfY3QeLTCv5LOezG1rLxkD1YqdAz26aqWfPb3YHk9hYUKfajlyLntWa7QLumcfRUWKXRIo7s9\n",
"tSyjltaNfV+z2b4wMfNhqnqaqr6oqi+o6umkNJ8vMleLyAci8j6wG+Xp0HYp4HPpo6crFpPaCIux\n",
"JXj3yI9RIPu0RItPXQgcgOoiicvewGMIf9twMSterOGYlCMuAI4AfvTUJnQDXi/S63AcpyFWn71+\n",
"elqYmHmCRWRps60xnR2c63LgVy+O5SEsmS1fvqDh4PffUVZs+zUtqJ8PvyT4O9kurmux+PqOWsvD\n",
"PZfRBvi+XpzOwn5XDkekCyKXIfITRAZoTJcBXwFDG6E1J2GM+bsi9dVxgmz2KDtMnaiqW6rqCFU9\n",
"VFUb3QGoALoCA4pxomY4xzQfEsZ8Yxoa8/eO+ZB+KHtJXNJGzgOtBwP3ozpd4rIdcA9wuMb0noOm\n",
"MGN6F45IOqA78HPg97M6suNHPemHjTpFTrV8B6pFJ7jWqCiaVtWF1P9vQ37GPJF0lo0j+QsXaEyf\n",
"7P0dnbAy0PnSwJhrTBePnM2BB3zK0oQbjBWhWYIluCV4DSs92w5YVrdvPWOBk4GfYbXhd3oOPkFk\n",
"T6zC3YhGaM1JGGO+LfCaiHwpItOwu45tRWSiiHyQ/dCqpQvQv9wimgFdqP+H/7xureq8/kv4rvUa\n",
"ZpG+33CCHwBPSlx2Ap7AepKPAzj6Qz6c1ZGdJS5tgn2Hr4WPB5xPywv2hWHzGB+0WHQcJxqS+4V3\n",
"x/7Xw7AEaJ8o6ZoOjelbzK+7OUg0TcqXBUBLkmZMvXcjn7dZU+eNJ/iY5Jrv9ca8A+kr2j2OGexz\n",
"gbNRPXE8/BX4BTCBMhrz/bBpQ7thwfjB2I/oQZhn1BQpmjHX5leXOR+6YjULBmLTUZKZPGweH5Bh\n",
"qF3tn2LkVj9hGvAIcKLG9PHE9k0X8mm/JcwF9gpWDXxsCMtnduaIxW355qQJ/LPIryUj1fIdqBad\n",
"4Fqjoshak435cML2QNC6MqpZvfMkrY0z5uZRpw61d4YUY666A6ozk9a8jRnkLkDDJi62//dY/faP\n",
"UX0f4GJLXhu536csYt2Wq0UhTKOVadgb2xlrgNEN6Kaq04JtTZHOuGdeCrrO6sh32D/tvJRtk0+c\n",
"wBwyx833XgvjJvTleuAajel/UrZPP2gy07F6zwAD/ziaPsD1T97N5me/xY3FehGO46Ql2ZhvQX6l\n",
"k8MMtSdorGcO6xrzTqQa81SsC9sSzAnJVGv+EuDYpGO+B+65eBwbYd3Xik6YRitXYEMM12OtTxOP\n",
"pkwXrHNPi0JP1CzjZeHp+shQWkBdU4NkJp3xLq2ATSQuverWilyCSM974JT/OYCF2M3lH9Kce8ZP\n",
"x7McOFji0npWRzZ6cwAbA7ej+jUF1krIh2r5DlSLTnCtUVFkre8CwxFpQ/7GPGsSHDTQWogxn4qN\n",
"NifoRNDFMQfzscI26Y256pIgGQ6o0zp99HTaAN0lLp0aqTcj2cpyJDgG2EhVVxb74hVMF6xLTy9g\n",
"Vo59ncbT9bnBtAO+TrNtcodVHAC8COwD3IVIL+A3wOq17dnhpm1QYM+gKlQqM4bOpwdWInLf3+/I\n",
"jht8w9tTrtO5Eb0Wx3GSUf0OkY+xGSStsPnZYVlMDmOeRCHGfDxwISI3oPod6YbZ0zMPM+brDrNn\n",
"Oab1WrbDfpOGYsP1RSNMzPwjwr+p1Y954x2w7nAFD7U343hZGLq+15eOpDfmk7Cqg/cDZwTr9n9w\n",
"M+aefAhX/OwcuqxZj99oTCdkOPd0bEbClcD1d45gkyM/5m/FfgFhqJbvQLXoBNcaFRFovRkrzPJB\n",
"mqzvbCwi6pi5cQ/wHtT9NuQeZjeye+YpBFrnYa1TPyGCofYwxvxK4D0ReUZEHg8ejxVbSAWR6Lk7\n",
"HY+bR4fNP+08ozNdSX/H/hXQ46H7eBKokbiMemUDjv/R4bQfuoClL47ldo1puuH1BHOB9bWWZ4BH\n",
"tpvJ2iuf58XivxDHcbJwJ+Yc5dtqOOcwO0CQ8d6Rxjb/shuMK4FdgjVhh9kb5ZlTZmN+B/Db4NEc\n",
"YuZdsC/GTIpgzJtxvCwXHYHlq1rQl3Seuc3v/OywSWwE/EGUm87fjz26LefKi8dx/OWzeT7r2e34\n",
"WcCAWddw6SP3oaybZFcSquU7UC06wbVGRdG12tD15UC+DmDOBLhAazdgcZAB31hmAP0RWY/8PPMa\n",
"QnrmgdZIjXmYmPlSVb2u2BeuYIpqzJ2MJArG9CNz57KXgMOA2l2/pGPNYi54px+/R3X1v8P96LwP\n",
"bNPnOyYAM/Ic5nMcpxioXt+Io8J55oUNsRuq3yPyLZYjlU/MfAAhjXnSMT1aruHj1S3YPHmDxGU4\n",
"8L/AjzWm36c9OgdhPPNXReQqERktIlsnHo25WJVQVGPezONl2UjMMe9H+pg5WBzrDK2l5UtjmTH2\n",
"EZ5KJLuF1DoOK+4wELv7LgvV8h2oFp3gWqOigrTmTIALtBZuzI3p2O9EPtnsLQk5zK6qLwXT01aO\n",
"u5VZQA+JS1+AoAX0DcBo4NZgOW/CeOZbY13KdkhZn6HBZNXTGfsw3TOPloRn3p9MWa6qkxD5EMuG\n",
"HYF52vnwGnAdVlc5tSiN4ziVS84EuIAojHlYzxzy88wB5m0/k27Ac1gNjduwPiDtgZFY4vUmWMZ7\n",
"XoQpGjNGVXdPfeR7oSrCY+aloQfWHCGbZw7Wse+HpBjzkFrHYxnxFwC3N1ZooVTLd6BadIJrjYoK\n",
"0hp2nnk/YHYRrpeY/ZJPzBxCeuZJ72sibv4U8AOJy2gsAe9HGtPvgKexqbh5E6ZoTB8RuUVEng6W\n",
"NxeRptxRLGHMLSnCAZF2iPy1yGcdOqsjX2CjQ9kyUR/Hhsq3I1/PXHUFVvZ1Ongmu+NUEesOs4v0\n",
"R+SnKfvVYFXcCiXhmecTM4dGeOaYMX8aK4v+GHCSxjSRN/QMmYy5NTnLSJiY+djgAv2C5U8pT1vS\n",
"UtEFG2ZfDLRCpGMhJ6ugGFROsmjtAZyR1M6wGGx+xwiWAx9oLEtimupS7Pu3DNXZ9atDv6//B/xP\n",
"OZPfquU7UC06wbVGRQVpXQT0TPnNORlrXgLUaa2hOMZ8BjbVbFPC5dckPPN85plDYMw1prOAvwD7\n",
"aUyfQqQjIrtjw++7SVwelbh8K3G5Iek055KFjMZc6jvW9FDVfwJrAlGrgHQVt5oK5pnbj7/HzY02\n",
"WAWngm5sUhh233A6Ea6n+O1Y/Dt/VJ9B9aNGHes4Trn4HFhF/fxvgKOADYMpZAlqgGlFuN50zFNe\n",
"hGruhjCqyzFDns88c6j3zNGYXqIxTbQTvxi4V2M6H5uTPwd77QclHds524mzeeZvBX+XikiPxMqg\n",
"n3njJuhXB52pf30FG/MKikHlJIvWtsHfHhm2g0g3RLqEvFALYOhHvRhEGGOu+gSqRySvaiLva0VR\n",
"LTrBtUZFxWi1OhFXA5cCILIp0BsbNe1jq2QMxTXmHbEiN2GZT37zzCHJmCdt7AuchbV97QvspjH9\n",
"MdYTpV0i650CjHlieOMC4FFgsIi8jr3Yc8K8gColETMH98wTJHqCd8+yz63AFSHPt+FamL+qBdsT\n",
"zjN3HKf5cSfWqGUb4CTgAawxyoYAA83J6Ix5sYUyE4uV35PHMXOhrqd6WNY15vAj4F9Yl7mtNaY2\n",
"Cm7hx/HANgCnHczQbCfONjWtp4icjxn1h4Eng+crgD3Jf5pQtdAF+Fbi0un5GpbvMa0wY15BMaic\n",
"ZNGaMObpPXOR/tgQ1achLzXs3b5MAzbQmGbLZM9IE3lfK4pq0QmuNSoqSqvqCkSuxbK9t8WSYK/C\n",
"vJt9BPYAACAASURBVPH/fmUln78qsPpb4lorEekbVKwLy4+wcECI09e9r7OBvimbtwb+jeUJbBU8\n",
"TzAee+1PvNeXrJ3WsnnmLbA0/Y5Ybd2Wwbr2wfqmSsIzP/GQ4zhyUVu7C2zmJIbZM3nmp2GJkv0R\n",
"6dNgi0gbRHqn7L/5jduiWLKH4zhOJm7CPNPHUJ2KDanXBNtqKE7ym5GfIQfVyWjajo3ZSDdLaius\n",
"2ct7wfNkxgPbSlzks/Vple3E2Yz5bFWNZ3rk+QKqicQ8w82Wt6JtbEyDBIy8qZgYVAiyaM3smVu2\n",
"6UnAP4CXgd2D/sUg0hMYt1r4/OUaGTfkbNkIYC1s+eDmbIIVTCi21oqjWrRWi05wrVFRcVptNssR\n",
"WIIYJBnzP8DeFCdeHjlJ7+sMbD57YkPHYHkSZsy3DtbHEbkQM+aj2q6iX9s1ZJ1NFGZqWnOjHZah\n",
"OHSHGfzljpEMkbikxjiaG9kS4LYG1gLvAs9jJViXIvICMOmOLZnY+ldMPf4IRk7vwkcSl5Oe3Ygd\n",
"v2/JCjxe7jhOLlRfRjURF59GEDNf34arp5VJVWP5BlgPkUQy2wjgo8DD/xTogkg/4ADgQI3pdGBp\n",
"qzUcu+l8soYTshnzvYogvBppBywHhm45m/uOm8hqlMsbe7KKikHlIETMPN0w+9HAA8FUvjuxOFLP\n",
"d/pyR//zufOkwzlAhSs++isD3v4Hy1qu4fdHHk2NwK+zzi9vvNaKo1q0VotOcK1RUQVavyTwzE+x\n",
"3+qweTplpe59td/JZO88McROEPt/GjgBGApsi0g74IXlrThzk4WsyHaNjAlwqlqMerdpEZGjgFpM\n",
"8Haq+m7StkuAU7F57eeo6jNR6chAu1tH0gLoesMo3p11DYv/sQ0nSFzeB24txABVMW2xxMd0nvmR\n",
"wQOp5RusROsV2BfyEWCUxvRLYtBV5PGvr0U6rmREu1V6S4m0O47TdPgS2CCY3joE67tQbSSM+cdY\n",
"z5NXk7Y9ho1uvoHlp+0IPL+6BadtspB52A1MWso1zD4Ra235SvJKEdkcOAbYHCtC/zdpWCCgFLT7\n",
"y/YMBKZoTNf2+Y7Xb3uE32LVd/L20CsuBpWFHDHzmaR65iJtsRKIEyQurYG7sES4hcBwjempGtPk\n",
"Bif/7rmM49qtDu5Eo9FacVSL1mrRCa41KipeqyWpzQSGvwCDgc/KrCgUKe+rGXNLDN4feChp29NY\n",
"3tZLwAtYQ7MXATadz6Js1yiLMVfVSaqarivMIcC9qrpKVadhH9SokoqDtlO6MwhrIA/w5okfMACr\n",
"l/sTicu+JdZTCbTF/oFSPfNewNxg6Og3WJejERrTWIYpZ4lRlqY6rdFxnOgZDxyzEuYFbUWrjYRn\n",
"fjZwH6rz6raoLsYqXj4B/Bcb2Zy90UKe3mFG9vn0lZYA14+GdXFL2+zEhm5afteKjakfvnkD2EFr\n",
"WdV6NZdilXpCUwUxqDpyxMzX9cwDYy5xaYPVTT5bY7o8ywUWY4UfXs24T+FaK45q0VotOsG1RkWV\n",
"aH0bOGE/a6JUFaS8rzOwNqc/Af6QZufTUZ2AhRQGAnx2Hf8YsCR7q9fIjLmIPCsiE9M8Dsp9dANK\n",
"GaO25DehL1beD+AdLONwxktjaYcVwQ/TB74pUe+ZN2x80BurvnQIMFFjOjXnmVSPR3V8JCodx2kO\n",
"jAc2oDrj5WDG/GjgbVSzhQmsk5v95nbGStlmJDKjpKp7N+KwmQR3IgEDgnXrICL/396Zx8lZVXn/\n",
"e7qT3tOdpLPvCWQhYUkMIhiQiIiEVXR41ZFFZFyGcVDREVG0u0dHHRjUcUHfeUUZVBYXUBQiBEMA\n",
"RfYkZCFkTzp70tmXTjrp8/5xb3U9VV1V3Z3U00897fl+PvXJ89xn+9VNdZ0659x77r0kpyXsBhYk\n",
"fv0k8hNd3oclwCGWMoHNrKQOUD1QL/LNk2DqtRsYBWzg9/yT1Muyztw/mCs5YX0h76drThz/CYxv\n",
"gcP/7KagVYjIW31/uTD7Uj7LDuYm7tFNeqeq6ncLqf9y7H+GfHw+I/r/L9B9+/8PYT8O31djoPJn\n",
"oK9A678VgJ6ufl6vgSH/BCUz4Uc5r4dn/gxFl8MvJ8O4yeSeZ46qRvbCJfanB/Yn40InJcBYXKk8\n",
"yXCdhqIJRilsoJ6/UM+5acfer/B76vkO9XypC+9xZpR93MX/j8xa4S6Fzys0KowOtH+xRbiDenZT\n",
"z6CC0FqAr7hojYtO02paFRZ8Av4lch3H06/QR+H3CsWdeJ9LFU5VuF3duCTNdm4kOXMRuUpEGnHD\n",
"8h8Tkdk4lUuBX+GG7M8GblLfE91EGW6OeX/ciOwgS4ApuBKkWaMOj5wi923qI23TBzQeOSigw5z5\n",
"YVxlvOAyqIMem4AAO7ROt4UsL4Ue0q8FRVx0gmkNixhpPf/HbgpXLEjpV9V9qF6JWxmuIzbgotUd\n",
"htmjGs3+iKqOVNVyVR2iqrMCx76hqier6iRVfaKbpSUKxtTS3pivAoZ/7c/8DZgmDdJuzvWWKhl4\n",
"zfu4tuEES8AWIKVAM265v8pA+6DfTKYfbtSlYRhG96C6h+519KKiEZduLkxjXsCUt2bzzFVbgJW3\n",
"P8do3BSrK9MvfmYMVxwsgbV9mZJoK/h5mwFyaC0DDr8yFI5KijEf/NJwhhOBMe8h/VpQxEUnmNaw\n",
"MK3hcAJag575nlwnmjFPpXxPGUeAZq3TIxmOJ0LtD+OK/6ewodqF33dU5F53NoaUAs0XXsfp3z2b\n",
"0wLtgxprmIB55oZhGGFgnvlxUr6pD620D7EnWIIbpPcYMEMapH/w4LZKpg84ALvKOCnRFqMcVC6t\n",
"ZS8Po2RPOSULhjIx0XiwF4MP9WIYrqJft9JD+rWgiItOMK1hYVrD4QS0JgrMmDHvImVbqjgGWSfn\n",
"rwLGaZ3uA57A1yQHQKR4YzWjZ65ly96ytvV2ewqlj0501d/W1fgfKiJFy2upFVildV1e09cwDMPo\n",
"mEbgZFxNDzPmXaB8cxVKds98LW7KHLg65NcEjo1bPAi9ZAVL95QyXBpccZUektcpe20oQ/oc5tCm\n",
"Pm75QaB2ySCaVVjWTfJS6CH9WlDERSeY1rAwreFwAlpX44rjHCS1Omo7zJinUr65D0J2z3wNSWP+\n",
"J+AUaZDRAM3FjFxeS6+LV7K+5BhH6M4ytOFTurofw89pZPm2yrb3Nej1wTQT3ypMhmEYhY3qIVRn\n",
"oToN1R25TjVjnkr51kqKyO6ZbwGqEKnyA+SexK/7/vh4plUeoXnofnYO2c923Bz6npLXKdtRwYgZ\n",
"61l+tIjeflre4EWDaIVoPPMe0q8FRVx0gmkNC9MaDt2h1Yx5KuXbKykmmzF38xrXQVtOfB4wE+CJ\n",
"k5kxaQdrgf03v8jfgLulQd4Rrtxuo3RfKaNP30rjyL00AacBg5YPoISIjLlhGIaRxIx5KmU7KuhN\n",
"9jA7uFD7GL89D5gpDSILhnD69M3MBw58+kW2ALcCt/SEvM6RIsqOFDP8vPU0TtrBTmB6SxFDNlRT\n",
"SURh9p7Qr4VGXHSCaQ0L0xoO3aHVjHkq5TvL6U32MDuk5s1X4vpw/NKBjLrqDZ4F9uNKns4G3kGv\n",
"+PfxxmoqipSm2kPsOaeR3cDbXh/MuJJjNGud5hxhaRiGYYRP7A1NninfXUYpuY35Wrwx1zpVYC7w\n",
"8yH7aTl/HUvwxlzrdAuwhdtzV+3pVkS+gsiEbIez5XW2V1Imynbg4EWr2A+87fHxnD58L6tCUtoh\n",
"li/LP3HRCaY1LExrOFjOvPsp31dKGZ33zAE+A9zzn3M4gFuuNVi/fC5wQRhCj5PLgJu6elFTOaXA\n",
"DuDAtM0AVPzydM44cxNP51eeYRiGcTyYMU+lfE8pfcmyhrpnAXAOIr0AtE6btJ6fvG8ZfYFNJMPs\n",
"AHN5g6tDVdw1yoAPI1KS6WC2vE5TOb1bhW3AgSKoAF5a3Y+aT73E7PCk5sbyZfknLjrBtIaFaQ0H\n",
"y5l3M83FlB/sTQ2u6k5mVFfiRrRfGGgdBOxG9QjOM08Y8zmUMlkaZFBIklOQBhFpkME5TikFWoBL\n",
"O39T6bWjAmktcmF2XNThb+evpfltG1l9QoINwzCMvGDGPMCyAfQvP8rOTpQnvQ+4LrA/DOeVg/PM\n",
"KwG0TvcxjoeBD+VdbDoiIsrngeXSIMVZzirFFbt5a6aDWfI6pdsqOYoPs+M88/++7xEAunUN8yCW\n",
"L8s/cdEJpjUsTGs4WM48B4lyqflkRS39KlrajHIuHgIuD4Srh5MMzQfD7OAM//X5U5kBkQv/OJ61\n",
"/Q/x7/752VZtKwVW4JbU6yxl26poBbbjxwNoPa1D94N/lmEYhhExsTXmwJB833BtX6orj+TMlztc\n",
"Wb1GkkZzFMnQvAuzi5yLyKV8nVZgoDTIaRnulBda4ZP/fBm9734MLW7lWeCsLKeW7ihn7aMTOFUa\n",
"ZFr6wSx5ndLtFSjOM0+E2QcB23wRnUiwfFn+iYtOMK1hYVrDwXLmucnofUqDnCkNUnM8N1xXQ1XV\n",
"EdZ38vQFQMIgjsLl0SEZZr8KuIKjtOIWZbn2eDRJgxRlMrxBHjyVGU0V7H7/Upae08g2soTRgdIZ\n",
"N3LBp2cxBfijNEhpJySUbXdj84Nh9sFEGGI3DMMwUulRxtyH3h8EPn48N1zfl/Kaw6zt5Onzgal+\n",
"ezRJY34QZ/DGA7U+V3IfcI00uBHwXeSLwGvSIJmnlIlU/3kcA1qKuLtYeepjr1FFDs98bV9qvz4X\n",
"UF4HPhI8mC1n3lROEc6YHwLKcVGRrcfxXvKG5cvyT1x0gmkNC9MaDpYzz00mz3wKrtTqZcdzww3V\n",
"lA442OkR2kHPPGnMVY8Bzbj65bUAWqdvQE6POSPSINOBfwXOA74mDTIqw2mnr6jlwNFi1gJPvW8p\n",
"U4DJ0iBlqTeTIqD4SDGDh+3j4Nkb+BHw2U7IKNtR4Y25e2+HcX1snrlhGEaB0NOM+XuBe4Cp0iD9\n",
"u3rDxmp6n7KdFZ08fQEwFRHBGfNgeH4/zuDVBnIlr2fRnJWzG/nCDa+xS+v5K/AKcGqG06at6csx\n",
"XM5+YVUL44FVGZ5VChxBGD5kPxv+/L9sBkZJg/RJnJApr3OoF2V7yigmWa/+AHAGrpRtZFi+LP/E\n",
"RSeY1rAwreFgOfPcZDPmD+AWQLm4KzeTBqk6UELRrX9pC5fnRnU7zmhPxHngwVHwB4Cjvj3BClzo\n",
"vdM09+LdVy9lHHA5bnWyTO956rZKynEL1+8F+qAZn1V6TDgMDB25hzUVRxkBLPf6k4gIIqtLviq/\n",
"kwZ524Ih1Ja30OqXfAWXRjgTWNqV92IYhmGERyTGXESuFpElInJMRN4SaB8jIodEZL5/3Z3jNoOk\n",
"QSrbrm2QElxo+3ngz0BXlx8dM3o39D3MoS5c8xfctLNNPgSdYD+wCKhVeMa3LQey1kVP56XhMnZF\n",
"Lf3G7uKjwNfJYsx3lTH5aJFfg131MNBacoxVGZ5Vtr2CI8C+qhbW4aanvRG8p8/rVK/ux9iWYq4E\n",
"rl42gIE1hzkSuM8B4BQiNuaWL8s/cdEJpjUsTGs49OSc+SLcaO9nMxxbqarT/CtXHfEVpHqVY4FG\n",
"70EuBiZ3RdCYXZw6bhdAiuHqiMeAG6GdN7/fa1DcYLiE3k575q8P5nMDD9A0qYmHgYlFrZmN+dq+\n",
"9C85xha/6AvA3iH72ZjhWaXrXDh+g3+NJPMPhP4PnApD9rEKuGRjHwZVH6Y5cPwgrorcms6+F8Mw\n",
"DCNcIjHmqrpMVZef4G2WkJpDHg9t+e4lwJROF5YRqbhhPt8EFnZx7vRsYADtjfkBXE656V3J0qkr\n",
"gJOlQTrV5wuGcs7Q/cxHtRk4+NH5bMF5xClsqKZPr9aUEP/eaVvYQnvPvHRdDa244jaNJI152z19\n",
"Xqf2/tPgjjm8CNT+YSKXVjcTXOb0APAm2mGVvFCxfFn+iYtOMK1hYVrD4e81Zz7Wh9jnici5Oc5b\n",
"TKoxn0DSmG/DecW56pQHedu6vlTMHs/Pu6TUFY95AdrNTd8JvAk0DYVq8KVdXU57WGduvbyWEcP3\n",
"8qrf3fLDxxCgRBpkQPC8jdVUFmlKLfm9H1zETjIY8/U1KKnGPCXMDvDCcEZvqIZ/XEQ/4PE1/Zhy\n",
"15M8HDjlAJYvNwzDKChCM+YiMkdEFmV4XZ7jsk3ASFWdBtwC3C+SHG2dxiLae+bLoW2d8SV0PtRe\n",
"saIWVI4rdNwAPJLW9jHgt0DTL1LD0Z0Ota/qR99Re/ib391S0soQMoTFN1dRppLyY2LvPyylBWf4\n",
"gyP6SxtrKCLVmK8AxiXmv6vqvKfHctrY3RwoVqYAn138Q56c0ciiwH0OUgDG3PJl+ScuOsG0hoVp\n",
"DYdY58xV9d2qelqG1x9yXHNEVXf57ddwU6wyG7//5DqeYoaI1IvIZ1jGWXjPXERmspDduHnniMjM\n",
"YJgjfb8Ozly+m0q84e3o/JR91ScEqlOOwzSBc3HTuWoD5y8HJnR4/2p5T+NOSq5cxl8BHoLWr7t1\n",
"0WcDd8pAuUxEZiIiG/vQ+8DLFAeu31unvJ3lbE70nYjM/Gc4e0O1M+b94KSnYajWcwTYwK+5NnH9\n",
"iv5MKH6TprkwWOs5Nuggg2+B/oH7P3YDbOt0/9i+7du+7dv+ce377Xv9q55cqGpkL+BpYHpgfwBQ\n",
"7LfH4QZq9c1wnVJPEfXsp54aVYV61v94Olepq7oG9XyKen7cGR1HhWvLvkwL9e2fdUIv+PF/wbfb\n",
"dNdzK/X8V0fXTb6Jd5z+SVoVxN/nOwqfox6hnu9TzzPU01uh7D3X0Eo9lwae+QuFa6nnAeq5JtB+\n",
"wdk30kQ9s/z+ZoUR1HMv9XzS9+vMC6/lhRuv4EmFVxXOVlihMDHKz0mWz87MqDX0NK1x0WlaTevf\n",
"q1ZnsjMfi2pq2lUi0gicDTwmIrP9ofOBhSIyH/g18AlV3Z3pHlqnrbhw7xRpkHKUgTe+xi+BNYic\n",
"iQvDT810bTpLBzKwSGnVuszPOgF2VkKwTnynpqeVtzBjQhP7E/97wGZgiE8ffAaXt/6PzVVUvToU\n",
"cCmFBHtxefoVac8q3V5Jb38vSIbanyEwjW9TH4ad3MQq4DXcfPIRuB9VhmEYRoES1Wj2R1R1pKqW\n",
"q+oQVZ3l23+rqqeqm5Y2XVUf6+BWiUFw43u1sr6XcgB4FDdC+yXg1GCFs2y8OozhAw+y5wTfViaa\n",
"PknKSPBO5cybezN9/E62B5q2AEMBtE6PATcAH//olZxXfZhjWqdrA+cmjPnytGeV7iynhGRN9Uac\n",
"oX4WOF8aRFR13sZqBp69kWW4/rsEOITqgc6/5e5BLV+Wd+KiE0xrWJjWcOgOrYU4mr0rJIz55AEH\n",
"acRNEdsF9NM6PYQzSB0Wj1nbl0H9D5JvrxxcznwAIomFT1YBYztacGVXGadO2JEy3W0LgSVftU63\n",
"Ai8+O5r6d6xrt6Z4Rs/8SBFle0spgbYfCQnPPFGLfpw0SG1LEb3OXcdKXN9dCCkj5Q3DMIwCpKcY\n",
"8ykTmthF0pj39cf/jBs4lpPNVQyoPRSOMZ8LHwBeRKTM/8DYilsyNSPSIBN2lzHiqmXMDzSnGHPP\n",
"QwdLOP2CNexMaw8a8/GJufaLBzGgooUjWtc2P7wRGOlD908B7+MZPnPGVg70UnbiQvdHKVBjHhww\n",
"UujERWtcdIJpDQvTGg7dobWnGPPJ52ygmYBn7o/PBd7V0U22V9K/9lDbQiL55M19Lq+/Axjk29Jz\n",
"2en8yxVv8kbN4ZTpZptpb8wfKW9h86wV7ZYi3QNUa53uxFWzGwywspba6uaUUrUJzxzgTuDzDOP6\n",
"255jP9CEKwrzGgVqzA3DMIwkcTfmm4FewHkXrqIYZ8x3kzTmL+NWBhuafqE0SJE0yH3SIGftqKDv\n",
"4P3syLs61eVXqk7FFZVJGPP0XHZQUy/gurp5bCd14ZYmoAaRkrZb1+muXd/inwYcSsnJQ9IzT3lW\n",
"YzW1NYcJ5r7bjLnW6WLgKcaz8dLllEObt/8Mbm57wWH5svwTF51gWsPCtIaD5cw7wIeIFwP9395I\n",
"DbCWgGfuQ8pPkiypGuTzwLXAzKZyqofvDXV97m0kq9HlGgR3EsrOSU2MJVhsRrXV3yPFOy89RgWQ\n",
"PjgtaMzbogBbq+hfczglv74BNwAuwSeH7uX9RW70/S7fdjuq/93x2zMMwzCiJNbG3LMYWFlxlJG0\n",
"D7MD/BG4LHiB94BvB/4DOGVnOVUn7WJLGOJ8rmQrSc98FXBSltNPGXCQzUAJLsQdJBgWT1CJq8gW\n",
"JN0znwTQVE7fmmb2Bc7bAdQiLqeudbpv4Lc5E9hHYgW45NS4gsPyZfknLjrBtIaFaQ0Hy5l3joW4\n",
"OeWjaT8ADlzVtAukQcoDbZNwIfo/AZObKqg4c1NKWDvfBD3zDcDwLOdNvmANVcBPvTceZD2ZjXku\n",
"z3wecBHAznJq+jUHpt+pHsLVr2/rl9HQB0IZO2AYhmGESE8w5vfe+hc+DwguXx7MmaN12gT8HnhB\n",
"GiRRy/1M4BXcQiPTSo/SOnpPKKPZE7mSoGeeHt5uo6iVyReuZjxwb4bD62k/Cr4jY/4CMEQaZNye\n",
"Mqr7HWo3l34n0Fa//VE3TS0WxtzyZfknLjrBtIaFaQ0Hy5l3Aq3Tw996igpgsw8Lp4fZAa4Dvgc8\n",
"IQ0yFm/MvaHfNfgAhyFlpHe+CXrmO4AqaZCy9JNKjzF14g52oroxwz0yGfOcOXNfYOZR4L27y+gz\n",
"8EBbLjxBijHH5eQ3YxiGYcSK+BrzwMhuoAra8sH7gVJEeicOap2q1uk9wB3Ab3BlZF/xh5cO3cdR\n",
"QjLm6TlzX4Z2E2mhdmmQoiPFnDxpBwuy3KqRznnmzUCvQP88AnxoTymVQ/e3m5O+k8APn7vgPAhn\n",
"7EC+sXxZ/omLTjCtYWFaw8Fy5rkJ1jyvBD9S23nnu0nNmyf4nj/2FmgryvLG8H0co/s8c8gcah9V\n",
"eYQjgw5mNeady5m7978Xl/8GeALotaYfg8bsbhdC30XAM+8DtcTEmBuGYRhJeooxr4KUaVcpefME\n",
"firbJ4AfaD3/6L33Bz+wmEM4jzbvBHLmHRnzk8fuphk3Oj8T2XLm6aPZweW9B0NbqP1mQCdvbzeX\n",
"PiXM/nE4TEyMueXL8k9cdIJpDQvTGg7doTVnjfACJ+h5pxvzTHlzALROVwI3Uy/7gDlap89SL0q4\n",
"nvkOoD8ixX7aVyZjPmJ8E2VkN+Y7gApEPgWUonoXmcPs4EqxTsFVn0Pr9LnXB8sTI/e2KzBjOXPD\n",
"MIweQJw98+My5gCIFPtrEueUE2bOXLUFV2a11jdvJM2Yl7cw5qRdVOAKvbTHhc/X40qvfsK3ZhoA\n",
"B26q3unBhtO3cQzneQdJyZn/0RWziYVnbvmy/BMXnWBaw8K0hoPlzHOTy5hnDLMHSEzdSniloRnz\n",
"AOlzzVOM+YCDnFp7kG2oHslxj0ZcDrwSkYlk98xfB05LayslszFv88xLLGduGIYRS3qqMU8vHJNO\n",
"It/e31dAKyMkYx7IlWwChvntdsb8qDBy0IEOS8reCdxCsqpdNmPezjPHGfP0cQHJAXAiRRe5PouF\n",
"Mbd8Wf6Ji04wrWFhWsPB5pnn5vjD7EFjDr2BVr9KWJisIzmAbQ1wsjRIceJgc28Gj9zTttZ4ZlSf\n",
"QHU18AfgCrIb85XAYET6BNqyeeaJfuoH7Ec1/RzDMAyjwImzMU8fzR40altIesG5ru1PyCH2QK6k\n",
"bTS61uk2v39W4ryDvRl40q52y5lm4ymc5z2CTMbcDbJ7A7c8bIKOwuxDZ9NugFzBYvmy/BMXnWBa\n",
"w8K0hoPlzHOTyzNfQqohSydhzPvRPflyaD+1bDZwCYA0SOUxoWTknk4ac9VmnHfeh8xT08AZ80mB\n",
"/Y6M+ZDDtCsqYxiGYcSAnmrMFwGnJVYEy0C3eeaBXEkmYz7Lbw/v28y+ItrVTs/FQ/7fTGF2cHn5\n",
"YHSiQ2P+XvcDIBZYviz/xEUnmNawMK3hYDnz3ASNebICnGMb0Era+t8BanCFXBLGPJSCMWmsw63s\n",
"luB54CRpkNHAiIEHOUjXjPkc4Bdk/yGykdSSsZmM+T7cyPhewIeBV7vwfMMwDKNA6CnGPNUzd3Oy\n",
"F9N+elaCGtwgtO7MmbulT90cd7ROW4Af4dZVHzV8L4ehCyu3qR5B9doMS6UmSDfmZaQbc3ftbuAH\n",
"wMAat5xsLLB8Wf6Ji04wrWFhWsOhx+bMReROEXlDRBaKyMMiUhM4dpuIrBCRZSJyUY7b5Aqzgwu1\n",
"Z8ub98Ut9xm6MW/D5bl3kVrW9U7gKuCOy5azm6555h2RvphLJs8c4Ke4KMYH9sKxPD7fMAzD6Cai\n",
"8syfBKao6hnAcuA2ABGZDHwAmAxcDNwtItk0dmTMO+OZhz4ALi1XkpI31zrdhQtvv+fTL7Kf/Brz\n",
"pGfuxg5kNuaqX0D1JlRXWQ4qHOKiNS46wbSGhWkNhx6bM1fVOZoMD79IsoDKlcADqtqiqmtx86XP\n",
"ynALyL3QCuT2zGuAtXSnZ+4IzjUHQOv0Ca3T+bgfJ50Ps3fMFmCAX0zmFGCjzSE3DMPomRRCzvyj\n",
"wON+exgut5zA5ZkzU+EHbkFmY74EmJzIUadRgwtDF+O88+7ImUOGmuxpmvLnmbsiONtxYf13AnM7\n",
"usRyUOEQF61x0QmmNSxMazh0h9bQVk0TkTlkHk3+JVX9gz/ny8ARVb0/x600S/teoBqR3WRaClR1\n",
"LyLbgbE4Dz9IwnDuxP2A6C7PfDepEYV0Tfn0zCEZar8A+F2e720YhmEUCKEZc1V9d67jIvIRXNGU\n",
"dwWaNwIjA/sjfFs7Pgi9n4e6Jtj/73D083Cewjx/75kA6vPmIjLCa5oHMBuG3gsTHnLGfPgvoO+1\n",
"IjMTx9uuz8O+qs4L6NkDjEo/v0TknU9A9Tt9Bba8Pd9FH0Y+BRd+HR6c5/sun+8vyv0EhaIn236i\n",
"rVD09JT9BIWipyf8/6d8XxWAnp60n6Ar1/vtj/hL15IDcbO4uhcRuRi4CzhfVXcE2icD9+Pydml6\n",
"MQAAD0dJREFU5MNxZUtP1jSRIqIKrwCfwr3BRagOyvCgbwIHUf0aIjOAHai+icgG4BzgAZw3vArV\n",
"T+f/nbbTcyMwA9WPprVXA5tQrcrz8+7GRSbGojqpo9MNwzCMwkVEVFUzFkOLKmf+fVyee46IzBdn\n",
"dFDVpcCvgKW4Cmk3pRvyACuACbQvGBMkOKL9VuDjfjsRZl8LvB23ZGgopP0q20PmMHu+B78l2AjM\n",
"AG7ozMmWgwqHuGiNi04wrWFhWsMh1jnzXKjq+BzHvgF8oxO3eRNXe3wh2Y35IuDLfnsaMBA3IK7C\n",
"X3Nt4qGdEn7iuDx/e/I7+C3JPcDDqMamTKthGIbRdSIx5nliGXA1mUeyJ3gTGIHIRJzB7A8MAvaR\n",
"vXJaXkmbX5jNMw/HmKtuoQvrk9u8zXCIi9a46ATTGhamNRy6Q2vcjfkkchlz1cOIPI3zzl/FrTL2\n",
"PlwltijYQ2bPPKwwu2EYhvF3QCHMMz9eVgAn4bzabJ45wO9xVdZewy1u8l3g26Gr86TlSvbSnZ55\n",
"F7EcVDjERWtcdIJpDQvTGg49NmeeF1QPIrIVuBxoynHmHwEB5uOK0BTjFhaJglxhdvPMDcMwjOMi\n",
"kqlpJ0rb8HyRP+FGo09FdXWOC74H3IlqY3dpzKJDgBagHNWWQPuXgGpUvxiVNMMwDKOwyTU1Lb6e\n",
"ueMh4H9yGnIA1Zu7R04HqCoiiRHtwWiCeeaGYRjGcRPnnDmo/gzVh6OWkYsMuZJM09MKwphbDioc\n",
"4qI1LjrBtIaFaQ2H7tAab2MeTzLlzftSAAPgDMMwjHgS75x5HBF5FvgKqs8E2mYD30f18azXGYZh\n",
"GH/XFGI5179nMoXZzTM3DMMwjhsz5iGTIVeSKcxu88y7iGnNP3HRCaY1LExrOFjOvGdSsAPgDMMw\n",
"jHhiOfPuRuRbwB5UvxloOwAMQXVfZLoMwzCMgsZy5oVFaphdpDdQRu6StIZhGIaRFTPmIdOJeebV\n",
"OE898hCJ5aDCIS5a46ITTGtYmNZwsJx5z2QPbvR6AhvJbhiGYZwQljPvbkTeCdSjer7ffwtwD6rT\n",
"ItVlGIZhFDSWMy8sVgPjAvsFMS3NMAzDiC9mzEMmQ65kAzAQkTK/XzBhdstBhUNctMZFJ5jWsDCt\n",
"4WDrmfdEVI8hsgEYjch4oBabY24YhmGcAJYzjwKRJ4H/AR4AHgPWF8wyrYZhGEZBYjnzwmM18GFc\n",
"ZGQWBRJmNwzDMOJJJMZcRO4UkTdEZKGIPCwiNb59jIgcEpH5/nV3FPrySZZcyWrgMuBZoIQCCbNb\n",
"Dioc4qI1LjrBtIaFaQ2HnjzP/ElgiqqeASwHbgscW6mq0/zrpmjk5ZWpGdpW47zyO3FeeaF45pm0\n",
"FiqmNf/ERSeY1rAwreEQutZIjLmqzlHVVr/7IjAiCh3dRN8MbauBVuAZ4JfAym5VlJ1MWgsV05p/\n",
"4qITTGtYmNZwCF1rIeTMPwo8Htgf60Ps80Tk3KhEhcwS4BOo7kP1X1CdF7UgwzAMI76ENjVNROYA\n",
"QzIc+pKq/sGf82XgiKre749tAkaq6i5xldF+JyJTNN6riY1p16J6GPhJtyvpmDFRC+gCY6IW0AXG\n",
"RC2gk4yJWkAXGBO1gC4wJmoBXWBM1AK6wJioBXSBMWE/ILKpaSLyEeBjwLtUtTnLOU8Dn1PV19La\n",
"4zefzjAMwzBOkGxT0yIpGiMiFwP/BpwfNOQiMgDYparHRGQcMB6XX04h1nPMDcMwDCPPROKZi8gK\n",
"3JSsnb7pb6p6k4i8H2gAWnADxL6qqo91u0DDMAzDiBGxrABnGIZhGEaSQhjN3iVE5GIRWSYiK0Tk\n",
"1qj1BBGRMhF5UUQWiMhSEfmmb68XkQ2BYjgXR6xzYkDLfBHZIyI3i0h/EZkjIstF5EkRiWTqh4j8\n",
"VES2isiiQFtGbVEXGsqkNXDscyLSKiL9A223+c/uMhG5KGqtIvJgoO/WiMh83x5Zv4rISBF5WkSW\n",
"iMhiEbnZt1/t2475AbLBayLp12xaA8dTPgMF2q9nichLXs/LIvLWwDVR9Wu279KMn4GI+7Wz3/uz\n",
"Atfkv19VNTYvoBg3J3sM0BtYAJwSta40jRX+317AC8C5QB1wS9TasugtAjYDI4E7gC/49luBb0Wk\n",
"6TxgGrAo0JZRm/8sLOpujbm0+vaRwJ+ANUB/3zbZf2Z7e90rgaKotQaO/xdwe9T9ipsFM9VvVwFv\n",
"AqcAk4AJwNPAWwLnR9av2bTm+AwUYr/OA97j22cBT0fdr/75mb5Ls30Gov4e6PT3flj9GjfP/Cxc\n",
"hbi1qtoCPAhcGbGmFFT1oN8swf342OX3C3XQ3oW4Pm0ErgD+17f/L/DeKASp6nMk+y1BQWhLJ4tW\n",
"gG8DX0hruxJ4QFVbVHUt7o/4rHAVJsmhFRER4P/gFv+JFFXdoqoL/PZ+4A1gmKouU9XlGS6JrF+z\n",
"afWHM30GIiOL1uG4H/M1/rS+wEa/HfXnNf27dGeOz0CkdPF7P5R+jZsxHw40BvY3+LaCQUSKRGQB\n",
"sBX3C3eJP/Sv4mrR3xNV+DoLHyT5BT5YVbf67a3A4GgkZSSXtrFSQIWGRORKYIOqvp52aBjuM5ug\n",
"kD6/5wFbVXVVoC3yfhWRMbhowos5TiuIfg1qzfEZgMLq1xeALwJ3ich6XInpRHntSPs1w3fp0g4u\n",
"iaxfu/i9H0q/xs2YF/xoPVVtVdWpuBK17xBXYP9HwFhcfd7NwF3RKUwiIiXA5cCv04+piwcVZH+n\n",
"aUsUGpoG3ALcLyJ9otImIhXAl3AhtrbmHJcUSh9/CLg/sB95v4pIFfAb4NPek+wK3dqvQa24mTjZ\n",
"PgOF2K/3ADer6ijgs8BPc1zebf2a5bs0G5H2ax6+90+4X+NmzDfi8lAJRpL6C6dgUNU9uLXKz1TV\n",
"berBVX7rtlBVB8wCXlXV7X5/q4gMARCRocC2yJS1J6M2VT2iqrv89mvAKlx9gqg4CZcHWygia3B/\n",
"3K+KyGDaf35HkAxpRoaI9AKuAh5KtEXdryLSG/gt8AtV/V0Hp0farxm0ZvsMDCrQfj1LVR/x278h\n",
"+f1UEJ/X4HdpjnMK4nugk9/7ofRr3Iz5K8B4P3KxBPgA8GjEmtoQkQGSHGVdDrwbmJ8wQp6rgHYj\n",
"nyPiQ6TmSB8Frvfb1wMdfYl2Jxm1+T4v9ttZCw11F6q6SFUHq+pYVR2L+7H5Fp8ieBT4oIiUiMhY\n",
"r/WlqLQGuBB4Q1U3JRqi7Fefv78HWKqq3812WmA7sn7NpDXHZ2BbgfbrShE5329fgFvJEqLt14zf\n",
"pemnpZ0fVb929Xs/nH490RF03f3CeZNv4gYN3Ba1njRtpwGv4UYqvg78m2+/z+8vxBmhwQWgtRLY\n",
"AfQJtPUHnsL9MT8J9I1I2wO4sNkR3BiJG7JpA94HLMb9ob8KXBqR1sMJrWnHV+NHMvv9L/nP7jL8\n",
"COKotQI/Az6edm5k/YobCdzq/47m+9cs3KDHRuAQsAWYHXW/ZtOa7TNQoP16Jm5MwgLgb8C0AujX\n",
"bN+lV2X6DADvj7Bfu/y9H0a/WtEYwzAMw4g5cQuzG4ZhGIaRhhlzwzAMw4g5ZswNwzAMI+aYMTcM\n",
"wzCMmGPG3DAMwzBijhlzwzAMw4g5ZswNo0DwSyZ+LsT7D/RLNb4qIjPCek4HGi4TkXq/fYu45SwX\n",
"ishTIjIqcN714pa7XS4i1wXax/r3sELc8q29ffsAEfmTuGUoF4vIR3x7qYg8KyL2XWf0aOwDbhiF\n",
"Q9hFH94FvK6q01X1ryE/i0RFrjQ+h6tZDa7QxnRVPQNXRvQOf11/4Ku48pdnAXUikljV6z+Bu1R1\n",
"PG5lqht9+6eA+erqY8/ELRzSS1UPA89RIKvsGUZYmDE3jAgRkS+LyJsi8hwwMdD+MRF5yXuavxGR\n",
"chHpIyKrfS11RKTa7xen3XOMiMwNeLwjRWQqzhBe6VeWKgucf4GIPBLYf7eIPOy3LxKR5703/ysR\n",
"qfTtX/H6FonI/w1cO09EviMiLwM3p+kaCZSoX/1OVeeparM//CKuRjXAe4AnVXW3qu4G5gCzfDnS\n",
"d+IMP6QuhbsZqPbb1UCTqh71+4/iShcbRo/FjLlhRISITMetL3AGcAnwVpLe+W9V9Szvab4B3Kiq\n",
"+4B5wKX+nA/6846l3fr7wM+8x/tL4Hvq1rH+KvCgqk4LGFFUdS4wSURqfdMNwD0iMgD4MvAuVZ2O\n",
"K5N5iz/nB17faUC5iFyWuB3QW1XfqqrfSdM1A+eNZ+JG4HG/nW2JyP7AblVt9e0bSS4d+RNgiohs\n",
"wpXP/HTg+gXA27M81zB6BGbMDSM6zgMeVtVmb6gfJbl4xGki8pyIvA58GJjs23+CM7YAH8HVVU/n\n",
"bJLLmf4CV5Mbf+9sy7H+HLjWLxhxNjDb/zsZeF5E5gPXAYm89gUi8oLXd0FAHwRWX0tjFM6DTkFE\n",
"rgHegltLOxsdpSBuAxao6jDckpM/FL8Epg+1FwWjEYbR0+gVtQDD+DtGSTWuQtJo3QtcoaqLROR6\n",
"XB4YVX3eh9FnAsWqujTLvXOtoZ6JnwF/AJqBX6lqq4tqM0dV/zHlxs4o/hCX794oInVA0FAeyPGc\n",
"FF0iciFu0Yl3qGqLb96If7+ekcBcYCfQV0SKvHc+gqQH/3bgPwBUdZW4pUcn4lZaTDzXFqIweizm\n",
"mRtGdDwLvFdEyrwXeVngWBWwxY/Wvibtuvtw4fOfZrnv87gQPDiv/tmOhKjqZtyKareT9PZfBGaI\n",
"yEkAIlIpIuNJGu4mEakCru7o/p51QNuykCIyDfgxcLmq7gic9wRwkYj0FZF+uCUln1C3KtTTgedd\n",
"D/zeby/DLeWKuLXjJ+KXwBSRUuCY99ANo0dixtwwIkJV5+NC0gtx+eLgmsZfwRnTv+By5kGv8n6g\n",
"H6lr0Qf5V+AGEVmIM+aJ/LGS2zu9H1ivqm96fdtxofwH/L2eByb6QWn/D7fk5J+8zs7wV1w4PcEd\n",
"uKV4f+MH5f3OP3cX8DXgZVyfNPhnAtwK3CIiK3B9cI9v/wZwptf5FPAFVd3pj03DLe1pGD0WWwLV\n",
"MGKGiPwDzpu9Ps/3/QHwqqpmysPn6xlzgQ/7SEC3ICLfAF5W1Uc6PNkwYooZc8OIESLyfdzUrUtU\n",
"dWUe7/sqsA94dyB3nXdE5BLgbapaF9Yz0p5Xipvadr7al53RgzFjbhiGYRgxx3LmhmEYhhFzzJgb\n",
"hmEYRswxY24YhmEYMceMuWEYhmHEHDPmhmEYhhFzzJgbhmEYRsz5/7YmtF9ejwSoAAAAAElFTkSu\n",
"QmCC\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x1067e9c10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8,5))\n",
"plt.plot(soil_temp_sfc, 'r')\n",
"plt.plot(soil_temp_25cm, 'g')\n",
"plt.xlabel(\"day of year ({})\".format(start_year))\n",
"plt.ylabel(\"Temperature (C)\")\n",
"plt.xlim((0,365))\n",
"plt.xticks(np.arange(0,365,35))\n",
"plt.grid()\n",
"plt.hlines(soil_temp_sfc.mean(),0,365, \"r\")\n",
"plt.hlines(soil_temp_25cm.mean(),0,365, \"g\")\n",
"plt.legend([\"sfc\", \"25cm\"])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# google thredds reanalysis\n",
"# http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis.dailyavgs/surface/catalog.html\n",
"reanalysis_dap = \"http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis.dailyavgs/surface/air.sig995.{}.nc\".format(start_year)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"reana = Dataset(reanalysis_dap)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"longitude = reana.variables[\"lon\"][:]\n",
"latitude = reana.variables[\"lat\"][:]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"longitude_idx = (np.abs(longitude - station_lon)).argmin()\n",
"latitude_idx = (np.abs(latitude - station_lat)).argmin()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"reana_temperature = reana.variables[\"air\"][:,latitude_idx, longitude_idx]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x108f51a90>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": [
"iVBORw0KGgoAAAANSUhEUgAAAfMAAAFHCAYAAAC1VKUoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n",
"AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXeYXGXZ/z/fhBRCeiMVNgkIJHQRBQQiCIIiYEHFCoi9\n",
"8Vpe1FfdrFjxteNPfRUMAtKkKIoiIkUEVJDeQkkICSG9kgKE+/fH/ZzM2bNn6s7szCbP57r22p0z\n",
"55y598zMuZ+7y8yIRCKRSCTSe+nTbAEikUgkEol0j6jMI5FIJBLp5URlHolEIpFILycq80gkEolE\n",
"ejlRmUcikUgk0suJyjwSiUQikV5O05S5pMmSbpT0oKQHJH0ybB8p6XpJcyT9RdLwZskYiUQikUhv\n",
"QM2qM5c0DhhnZvdIGgzcBZwInAosM7OzJZ0JjDCzzzdFyEgkEolEegFNs8zN7Fkzuyf8vQ54GJgI\n",
"HA+cH3Y7H1fwkUgkEolEitASMXNJbcB+wD+BHc1scXhqMbBjk8SKRCKRSKRX0HRlHlzsVwCfMrO1\n",
"6efMYwCx32wkEolEIiXYrpkvLqkfrsgvMLOrw+bFksaZ2bOSxgNLco6LCj4SiUQi2xxmprztTVPm\n",
"kgScCzxkZj9IPfV74H3At8Pvq3MOL/oPtRqSZpnZrGbLUQlR1sbQW2TtLXJClLVRRFkbQ71kLWXI\n",
"NtMyPwR4N3CfpLvDti8A3wIuk/R+YB7wtuaIVzfami1AFbQ1W4AqaGu2AFXQ1mwBKqSt2QJUQVuz\n",
"BaiCtmYLUAVtzRagCtqaLUAVtDX6BZqmzM3sVorH7F/bk7JEIpFIJNKbaXoC3DbA7GYLUAWzmy1A\n",
"FcxutgBVMLvZAlTI7GYLUAWzmy1AFcxutgBVMLvZAlTB7GYLUAWzG/0CTWsa0x0kWW+JmUcikUgk\n",
"Ug9K6b5omTcYSTObLUOlRFkbQ2+RtbfICVHWeiDJ4k/r/lT7fja1NC0SiUQizSNr5UmaaWY3NUmc\n",
"qtiaZa1FmUc3eyQSiWyDxPto61LsvYlu9kgkEolEtmKiMm8wrRovyyPK2hh6i6y9RU6IsjaKKGtj\n",
"6AlZozKPRCKRSCSFpDZJL0mqWUdKOlTSI/WUq+TrxZh5JBKJbHvE+2hx5JM8nwS2M7OXmvD6MWYe\n",
"iUQika0DSbHiqkKiMm8wMa7TGKKs9ae3yAlR1kbRCrJKmifpvyXdB6yVdIik2yStlHSPpMPDfjMl\n",
"nSrpIUlrJD0h6YOp88yUtEDSpyUtlvSMpFNSz79B0t2SVkuaL6m9iDwnSbozs+3Tkq4Of79e0oNB\n",
"hgWSPpN6/adTf58Znl8j6RFJR9TzukVlHolEIpFW4x3AscA04HfAV81sBPBZ4ApJo8J+i4E3mNlQ\n",
"4FTg+5L2S51nR2AoMAF4P/ATScPCc+uAd5vZMOANwEcknZAjy++BKZJ2T217D3B++Ptc4INBhhnA\n",
"33LOMRn4GHBA2O9ofJBY3YjKvMH0lqYGEGVtFL1F1t4iJ0RZG0Va1mZ1MgMM+JGZLcSV5rVm9ucg\n",
"31+BO3EFfpOZXWtmc8NztwB/AQ5NnesFfCGw2cz+hCvw3cL+N5vZg+Hv+4FLgMNzrskm4DJ8yieS\n",
"ZgA7A38IuzwPzJA01MxWm9nd2XMAtwMDwn79zGy+mT1Zw7UpSlTmkUgkEumCmakePzW+/NPh987A\n",
"ScHFvlLSSnx89jgAScdKukPS8vDc64FRqfMszySwrQcGh2NfKelGSUskrQI+lDk2zfnAO8Pf7wEu\n",
"NbMXwuO3hNedJ+kmSa/KHmxmjwNnALOAxZIuljS+8stRnqjMG0wrxKAqJcraGHqLrL1FToiyNooW\n",
"kjWx6OcDF5jZiNTPEDM7W9JRwBXA2cDY4Ia/Fqh0AfEb4GpgkpkNB35GEZ1oZncAz0s6DDgZuCD1\n",
"3J1mdiIwJpzvsuzx8nauF5vZofgCxYBvVyhnRURlHolEIpFW5ULgjZKOltRX0sCQTDYRny3SH1gG\n",
"vCTpWDwWXSmDgZVm9rykA3HLu1RY4ALgHOB5M7sNQFI/Se+SNMzMNgNrgc05x06SdISkAcAmYGOR\n",
"/WomKvMG01vjZa1OlLX+9BY5IcraKFpNVjNbAJwAfBFYglvqn8F7pPwJ+CRuCa/ALebfZU9R4vQf\n",
"Bb4qaQ3wZeDSMsdegCe4XZjZ/m5grqTVwAeBd+Wc417gm8BSYBEwGvhCCdmqJjaNiUQikW2QeB+t\n",
"Dknb49nz+5nZEw1+rdg0ptVooRhUWaKsjaG3yNpb5IQoa6OIspbkI8C/alHkPSFr7K4TiUQikUgJ\n",
"JM3DXeYnNlmUokQ3eyQSiWyDxPto6xLd7JFIJBKJbIM0VZlLOi/0zL0/tW1W6F97d/g5ppkydpcY\n",
"g2oMUdb601vkhChro4iyNoaekLXZlvmvgKyyNuB7ZrZf+PlzE+SKRCKRSKTX0PSYuXxu7DVmtld4\n",
"3A6sM7PvljgmxnoikUikG8T7aOuyNcXMPyHpXknnShrebGEikUgRpD5IUSFEIk2mFZX5T4EpwL54\n",
"p5yiFnpvIMZ1GkOUtf7UKOc38frbHqW3XFOIstYTSb+StCIMV5nZbHkqZZusMzezJcnfkn4JXJO3\n",
"n6TZFObBrgLuSVoRJhcuPq7ucUKryFPm8b5AK8lT9DGwr6SWkafO7/9e58Kk06WH4vvfu97/hFaR\n",
"pwJ5NwOvxWedP9/L5K/685r6+5TwcB4laMWY+XgzWxT+/i/gFWb2zswxMdYTibQC0hzgZsw+0GxR\n",
"ItXR2+6jkt4NfMh88thWTbH3ptR71uzStIuB24DdJD0t6TTg25Luk3QvPij+v5opYyQSKYK0HTAF\n",
"HxoRidQNSWfKS5TXSHok6IZfAAdJWitPlEbSCZLukbRa0uOSXtdcyZtHU5W5mZ1sZhPMrL+ZTTaz\n",
"88zsvWa2t5ntY2YnmtniZsrYXbIurVYmytoYeousNci5Ex6qG4U0GulOpGH1l6wrveWaQpS1Bhl2\n",
"Az4GHGBmQ/GxpjcBHwZuN59n3iHpw8D5wGfMbBhwGGVc0c2iJ65rKybARSKR3sEu+FjK0cA04OXA\n",
"LACkjyJ9qWmSRbqPZHX5qZ7NwABghqR+ZjbfzJ4Esu7l1wPnmtkNAGb2jJk92r1/uvcSlXmDSSXB\n",
"tDxR1sbQW2StQc5pwB3AKGACHjJ7N9Iu+Ezn6XUVMEVvuabQi2U1U11+qpfhceAMfGG4WNLFksbn\n",
"7NoXaOgo0nrRE5+BqMwjkUit7AL8CxgJTATuxbs6tgMHAWObJ1qkN2NmF4dEt53xrqDfDr/TPI1/\n",
"BiNEZd5wWiEGVSlR1sbQW2StQc5dgIeB9bgV/gzeJ+JdwBxgx3rKl6a3XFOIstYgw8skHSFpALAJ\n",
"2Ii73rPcDZwa9u0jaWKIt7ccMWYeiURamTZgLrAM2Bt4BrO5wK/xZk/RMo/UwgC8GdFSvHHYaOAL\n",
"4bm0df4ocCrwfbzXyI14UuY2SdPrzGuht9VHRiJbJdJSYAbe2Gl34G2YXRee2w7YAAzELM+qijSZ\n",
"eB9tXXpdnXkkEumluAt0GG6VLweG4m52x+xF3Foa1QzxIpFtjajMG0wrxKAqJcraGHqLrFXKOQ54\n",
"FrOXcIUOaWXuLCEbN5cORfpRrTIWTtM7rilEWRtFlLUzUZlHIpFamEhBeS/HE5VWZPZZTNe4+e7A\n",
"+5D6IU1AceJaJFIPojJvML22xrTFibKWQNoeaYdqD6tSzgkUlPkyPPktm4DT1TL3MrahwIl4jfDJ\n",
"1coJ8f1vFFHWxhDrzCORSC2cCfxPXc8oTUQamNoyAVgY/l5OVxc75FvmI4G1wM+BfwPfQToGaZvN\n",
"Qo5E6kFU5g0mxnUaQ5R1y8n7Ig3KbD0IT06r8lQl5fwl3hs7IW2ZP4R3f8tSzDK/Msh3Cj4844dA\n",
"Va1f4/vfGKKsjSHGzCORSDneDPxsyyOPQR8AbF/n19krvFZCQZmb3YLZf+ccU8wy/yOwP2ZPYjYL\n",
"+Fw4XyQSqZGozBtMjOs0hijrFkbSWRFODduqU+bSCIMvIPXLew4YDuwdktYm0NkyL8YS8pX5cszu\n",
"TW1bBOT13i5KfP8bQ5S1MfSErNs1+gUikUhD2YHO88Rfgbe/zLrey/FBfNTknnibzDQzgAfx8ZL3\n",
"4gMuNlBemS+iq8U9kq5Z71Ur80gk0plomTeYGNdpDFHWLeQp8zuoxjKX+gOf+IO3x3xlzh4zgAfw\n",
"YRf/hbv1K7HMHwd2zZSf5SnzxcAYpL6Vixzf/0bQCrJK6i/pXEnzJK2RdLekY8JzbZJekrRW0vrw\n",
"u77Jng2gJ65rtMwjkd6NK3NJoTRsBj7J7OAqznEYsOBRuO04OJB0DN7ZE3gAs/8A/wnd31aFn+KY\n",
"rUTaiDeYWRS2dlXmZi8grQDGAM9WIXfTCENADjazG5sty1bIdsB84DAzmy/pDcBlkvZM7TMUOLw3\n",
"udobTbTMG0xv+rBFWRtDg2UdhA+mSOrKJwCPUV3MfE/gzs/AhRS3zB/c8shsE2Zn59SV5/Eo4JOs\n",
"vLStH/Bczn55LvmitMD7/wbgBkmHlNuxGlkljZT05e4I1h1a4LpiZuvNrMPM5ofHf8QH+rw8tVuf\n",
"PFklbS/pu8GqXyXp75IGpCz6UyTNl7Rc0oclvULSfZJWSvpxA/+nLrLWm6jMI5HeTaLEx4TfE/Bm\n",
"LNXEzBNlfT/QhjQ08/weePlZLcwhUeYwAlhRZBHQ2+LmRwN/Bc6XD5WpFwcB7ZKqLi3cWpG0I/Ay\n",
"0gtKeErS05LOk5Tu//+/wH74dRyJV0qkP28H4qN734GXRH4ROAL/DrxN0mEN+0caTHSzNxhJM1th\n",
"tVsJUdbG0GBZE2U+GmkRMAR4muot8wsEhxjcjludF4cYdj9cCZeLjxejYJnnx8sTSipzSW/ClefO\n",
"QZ6+zXr/5TkArwOOwyfGTcUXLcX2r+b9n4EnGB4BXNU9SasnLas6VJeRmtZe+2Q2eXXFRcBsM5sj\n",
"72x4AHAPcDzwrvD8MZL64CNRX2lmSVjnjnCe5JRnmdnzwPWS1gK/MbNlYZ+/4wuBW2qVt8T/0fD7\n",
"VVTmkUjvpqDMXRkuwt3YlSlzv8tNx62evXDL5n+RXoUr37OABWGgSi08isfkoRvKHDgP+Ac+N70/\n",
"8IEa5akHuwQZHgIexvvNF1XmVbJnOO/RNEGZp+mOEq4HQTlfgFdnfBzAzJ4D/hOeXxW2LwpKfgdg\n",
"IO6ZKsbi1N8bch4Prpf8PU10szeY3mI9QpS1UfRAzPxZXJknGeYbqNwynwRswGx5kPM6YD3uhtwf\n",
"t4Sf6oZ8act8FMWV+TMUiZlLGozfpIfjSv23uPegWXwKuNY8XPAIrsyLUuX7PwP4Aa7Me5xW+V4F\n",
"78e5ePjoLWa2ObtPRtY++IyAjfhiq1bq4o3octKtPWYe4h2LJd2f2jZS0vWS5kj6i6ThzZQxEmlx\n",
"dsCV7WgKk8yqUebZ5DYD3orHFqeGn+4o8yeBnfG4cq2W+UQ8dHCoece4LwNvl9TjMXZJHwJeAyQd\n",
"7x7Gcwrqce4++MLgMmCQpCn1OG8v5af4tTjezDYlGyUdKGk3SX1CrPxHwI1mttbce3Qe8D1J4yX1\n",
"lXSQvPSyUnrtFL9mW+a/Ao7JbPs8cL2ZvQy4ITzutbRC3WalRFkbQw/Umc+nYJkvBJ4H+lJZYtZe\n",
"eA15QU6z+Zg9hXdwOzScvzY8PrkeLyWqSplL2k6ejDcJWBAsYcxsJR7X/FDNctXOG4AvBxmgAsu8\n",
"ivd/CrDczFbj/9/htQpZK63wvZK0M97EaB/g2VBLvlbSO/HF5Z+ANbjXZwOdJ+99Fk/k/Dc+AOib\n",
"FBR0JVZ3Qyzzrb7O3Mz+Lqkts/l4Ch/i84Gb6OUKPRJpIIMoKPN+JKNIpcQ6X1vm+EOAS4o8Nwc4\n",
"Cl9Ud4dV+GCVUsq8U+vX4JG7AnebXgYsyOx/JfAtSWfluWAbyHgK0+IgWOaSlCw2ukHaS3Izfh+c\n",
"3c1z9jrMF5KlDM1LID+pzMw24o2N/itzzDw8sTC97+TM4/fUJnFr0GzLPI8dzSxJSlhM16lLvYpW\n",
"iUFVQpS1MTRY1jw3O1Tiane37quBv0OunI/iCrY7bnZwZT6c8sp8TOrxj3Hra3+CZZ7e2czOBzbh\n",
"1mzdUOdudXl06nxnZsuBFyhxn6ri/f8wbnVCQZn3KPF71Ri2+ph5OcJKtyFuj0hkKyHPzQ6Vxc13\n",
"B9ZgtrDI80mGdj2V+fIi+6wDtkMaJB/k8ga8zGggXi6UtczBs76nd1O2LUjaA7ijWN24vFRvLF27\n",
"1D2Ehyu689qvA3al0H3vIWCIpDub2UQm0ntoxdK0xZLGmdmzIcFlSd5OkmbjrhPwm8U9W+ojQ3yi\n",
"FR6nYyWtIE+px1mZmy1Pmcf7mtkPWkieUo/PoBGfT7feBr0bxrwP9jzKLcRnJM28Fjg2NI4pcfxu\n",
"wN+Lvv9umdsImLJKmlyrvL+F7e6FQ84KlnkJeRLr/Gt4UtMqSffheTX/SWRM3n+CMpe0ph7XE4/H\n",
"Hgh8VdJfcp5/BPcsHCLvnntT2D4feB9wfZHzl3v/j8XnxX/IzJ5Pvd63cYv/VHkN9Fg8h+E6YF09\n",
"vu/Z5+P9qnXuV6m/TwkP51EKM2vqD9AG3J96fDZwZvj788C3co6xZstdxf83s9kyRFm3UllhoMEm\n",
"g/4GXzT4X4P+4bm7DfYvc/wFBqcXlRMmGtxXBzlnG5xq8B+Dl5fY707zQTF3AQcFmc7BvXOd/hdg\n",
"JnAa8Os6vk8/xGetz8Hjq4PwzmPbhef3x5Vy9rjDgX/X8v6H17kQ+EWJ55fidfVz8eE1r8W9qv26\n",
"+f9aj31WG/CzNctaTMeV0n3NLk27GLgN2E3emu9U4FvAUZLm4F2QvtVMGbuLxbhOQ9gmZZUG4c1c\n",
"EnYAnsPsecy+gdln8exxSLvZpV8g5bmj9wfuLCqn2ULM9q6D5JXEzMGV1lg8XJBk0N8Xfmdj5jdR\n",
"Zzc7nj39A9xD8A48ye5W3HvRh+KT4m7H72Gjcp4r+v4Hd/5leE5ANmErOXYz8Gd8UfNF4Ea8jvoU\n",
"4OLK/q3K2Sa/Vz1AT8ja7Gz2k4s89doeFSQS6R28AfdWJQMndsDLvvJwZS4dAJwOvES6lMuHnkzF\n",
"s7EbzWrKZ7MDLHnBlfloCp257sNL7Zbl7J9kkn8FeMTMLqtVwJD4tjc+r70Dz5hehivaG3E3+nbk\n",
"KHNz1/jfcePj8ipe9v34/3ukFRZheVyFL7wuw5v4TMMXaidIGmNmS6t4zchWSksnwG0NZONTrUyU\n",
"tTFULKs0EpXMpt6Pzl3S3DLPJ7HMP4Fbm2+j8wCVGcBjdG7IUZmc1bMKj4UPwjPUi7F0iS8wVpjZ\n",
"i2HbPXhdd6d2svKypNXASuArwNu6KeMk4HkzW4L3gL8VOCMo2U8BX8cVabEe9X+mSFe6vOsqfy9m\n",
"AZ8so8gxsyvx8MRm3M2+C/7+PUPnGuuqkWTxp/V+ankvozKPRFqHGyidFb0fsCM+fAJcORZT5utx\n",
"1/Zb8YSyG4C3pJ7fB1eUPcEqvIRsZRL4K8KS9bAThdnnmNlGMzu7xDHnAO8EDlLphVA59sGtcsw5\n",
"wcz+FB7fiWf0vzMtW4bfAcfJO4+dH9zypXgtHn+/uxLhzOunwfuOT8OV+VeCTFuQd0frqPCcyv4A\n",
"r8nb3oo/W7uslbyHaaIybzAxrtMYtlJZp+Cu6GLsi7uck5rmcpb5VHzk6HK8y1tb5lz31ihntaza\n",
"IksGSWNVWJwseckt5LIT2hJZzexbuGt7O3whUCu74tn7xfgN/v7kymY+e/spvNHNe3G3eCdZM+yE\n",
"z52vlifw9rH9gD/QtfvcXnjjrZrYSr9XTacnZI3KPBJpBXyYyDCKTW2SxuGTuh7Am8NA+Zj5LhTq\n",
"zpfSuSnLFku0OjG1g6QjqzwsscxXSJoi6ROSpgdL+nrgM4mM23kMuZj1m4u5tX8bPsMaef/uf8r7\n",
"qFdKXv14msvwvINSC42r8EXSRZRXqJPxfvNVYWZr8NDCg/jiaDt1nn0+kl7eaCtSG1GZN5gGxiHr\n",
"TpS1MVQoa6Kgi41g3Bd3iy+kEDcvZ5nvSkGZL8MTyxJm4D2sq5VzJj7MohpWAf03emvZ6/BSrluA\n",
"M3AL9T1BsS/p78qorDLPkfV23NXeF7gWn6z2dVU+rGQsvuDJxbwr5amEPvZF+AXwduDnwBtLyAo1\n",
"KvPA48CDYRHzdDhXwkhgrHwQyanyRWLFbIXfq5agJ2SNyjwSaQ0mhd/Fbr6vwBunLKSg+MvFzHeh\n",
"YEkWLHNPvhpIkYZMZZgG7CQp1/pLWdxpVgHc6XLfYGZvxUuxvgd8AV+U7AMsHeQDWaqyzAM34mND\n",
"9wOWmNl38LLWi1TZ1KyxlLkeZvZrM9tQ4vnFZnYNvrCYLGlSsX3pnjJ/mEK+w3y6KvO++LjZr1On\n",
"iW6R1icq8wYT4zqNYSuUtaRlvgze9s3Q4Y3OlnkpN/so8t3sU4B52WS0CuWcFn6/IvtEcPfejQ9v\n",
"SbMKF6Q/rnTBG6W8Fx+mdBFeprV0MAzqW0XMPMVd+LX7GIXBMN/D/+9zkoQ0SW+WlDcrfAy1LW7y\n",
"ZHsRr9/ft4is4B6JWpX5J/COceDKPJ0rkORcTMaHwgyp5sRZWSW1SRpQm5iNZSu8B3SLqMwjkdag\n",
"uGUuDR8Mu50F71vv9dcTkC4DDqa0mx2KK/O5Nco5DXc1d1HmwHG4wn5dZvuaIMj2BAVmzgXB0v0x\n",
"8E7B6Etg6cgalKp56drVeDOVG1Lb3otbpxcHhf4p8uPZZS3zKina0CYk/I2hgkVLHma23sxeCA+L\n",
"KfOkF0FVyjyH84E3d/MckR4gKvMGE+M6jWErlHUirnjzLPMj74UVG2D4d7yf+muAk/CypEqV+XJg\n",
"JK7QcpV5hXJOwxuq5Cnzt+Lx9GNDiVYbAF4fvfYZ/9+6DEwxs2eBnwI/OgVsaf5QlUpkvRJPUrsp\n",
"de7VeBnYDFyJH0zBu5Cm3sr8wfCaebJOABZboZa+O+TFzBcDrwyPa46Zh3DJXnhL25ZjK7wHdIuo\n",
"zCOR1mASXhqVZ0m97lovSfvGNV4rvhOuNF+kdMwcEuvPLbl1eO15TZZ5sGzb8MzuQySdLWlkeG4H\n",
"vAPaF/DEu9vwOQv+8rBqsSuWYvHwb+Heg+0pnVVeiluAN5rZqvRG88Y4Pwf+H7642SXzfw3CS9vW\n",
"1fi6eWxR5jl0J16eJc8yfxgfGAPds8zHASNoUWUe6UxU5g0mxnUaw1Yo6yR8KleeJbXb7b79vAUF\n",
"Rfc9vPa5mDWZtcyh4GrPVeYVyDkRWGlmjwGfxJPNkvKv6cAT5h3Ufgv8BTg0SYZ7Hp5b48fmWqNm\n",
"ts7MTgdGWKorXTHyZDWzzWZ2bZFDLsQV08/xBL50K+sxeNJcPcctP4S3mu2TI2u9lXnWMn+YwkKi\n",
"Kss8I+ueeIikJZX5VngP6BZRmUcircFEiijzl2DC057stng5nHkiPCu34t+PWbEysQ3AWszWpraV\n",
"VOYVMA1vWoKZnY93IDs1KOxdCU1QzOz9wIdxz8EuAPfDTQ9UoMDqrFDT512JTx47D3dDp63ZervY\n",
"k3rwFXgL2CzdSX7LsgCYlOo4NxJfSPTBvTLdscxn4FPkXtbN7nqRHiAq8wYT4zqNYauS1bPAhwNP\n",
"kqPMDcYvgYVm9tILZrf8zq2x15HpV55hA10TrJbhyryN2mLm0/Aa54Q7gM14HHpX4DF1qL86dBGz\n",
"uIh3sJFPcLk6pFfATU+GhUA9qOX9N7MLQ73443SOm9ddmQceBGbkyFo3y9y8zesyXKFvj5elPZl6\n",
"/e7Ume+Jhy6ex69RS7FV3QPqQFTmkUjzOQ1P3lpN9ubrseh+K2FeausvgA9IOkDS+CLnfBg4N7Nt\n",
"KZ7QtAm3HKtlIqnktGBFXwKcCOxKP54AfoVbg3fRh4fpw2Q8zj+JnMQ2dWisOnSWOvQrdeioGmSq\n",
"haS/eULdytIy3A0ckLN9MoURr/XgIdyKHoEnOiYT5x6g+5b5g/h894pc7ZKOlnRCN14zUiNRmTeY\n",
"GNdpDFuNrN6x7JP4ZLN1dLWkxj0Ha6zzzf8SvBPbv4B3F3nRBXjjlDRL8Rj3n6qW0xlDtkvaXjzC\n",
"aziJY5jJGZwe9nmHtdt3+Q2f40+8hPF11NUaVYem4fXYY3DFd4k6VNFY5m6+/0/QOQmuZPe3bvAP\n",
"4NUNjplDIdkuGTGbKPOHqDFmLp/NvgfeJfAxKo+bn4zPgm84W809oE5EZR6JNJdD8WEo/6KIMl8B\n",
"G0kpczNbB7we+C7eGKZSluJlUT+sUdauyvxETmE0k9jEGLwk7DhrtySTfg5zWMlmdmAS00lZ5urQ\n",
"FOBvwNet3T5s7fYj3D18aI2yVUNWOTXKzX4bcGAm2Q4ar8wXAf+D/0+1WuYfB64IlQEPkhocU4b9\n",
"KVJfH2ksUZk3mBjXaQxbkax74G1aIa3MpYFIpwDjn/G4dCe3rJndhsd+q1Hmi4A7MPt3DXKCK/Nl\n",
"W/bv0Ej6cjDXcBc3soHv8CVrL8zmDm743/M0q5jIAbi7FnVoBK7Iz7Z2+3nq/FfjLvuydPP9/zfw\n",
"qlRSVxu1tZAtiZmtwJX2acm2UAY3hPp6Ajop85DV/w28F35VylzSTEn74Z30ktLCq4G35CxKsscO\n",
"xBdJu5bbtx5sRfeAuhCVeSTSXHahMApzizLf5E1Zfgbs9JSPu5yXc+xyqlPmvwWOrVlSH9SSVkJv\n",
"Aq5nIzcDjxXJRL+Ge5nB7qw0s6Sf+GeBv1m7/SSz79XAiepobOZ0GFe6AdhN3qf+tcCfG/Ryt+KJ\n",
"ZAmTCMmMdXyNh/BF4Wg6j5nN8/SU42A8DNNhZo8ChFLEp/FmRaXYE/8sP4uPvI30IA1fPW3rxLhO\n",
"Y9iKZN0Vv+GDN3rZvr/0na/AK78EA4Cjn/DhI4/kHFudMvfGMauKP115zFwdGoSXn52New2WFTnm\n",
"HyzhInbmyKCkx4Tj9svZ9yG8sctACnXytcpajr8DrwYMuNHMisnfXf4A/K+kMXiew0rq62LHzFZJ\n",
"Wg0clTl31ZY53kHv12ZdFloX4/Hw60scux+e+zASd7XPqfK1q2IrugfUhWiZRyIBSWdKOryHX3YX\n",
"knIvs5clIqC9AAAgAElEQVRegE0D4UM7urtys8ERC9wyX5hzbLWWec0El/RovsQH1KGleOLao8BV\n",
"ZvZPMzs77zgze5EP8h7Ei/jC5RTgamu3Ltnc1m5m7fZtay8+mayO/B3PO/gYMLtRLxKmqN0IPIU3\n",
"rnk79c1kT7gZH6xycWpbLZb5ePJDDpcCJwZXejH2w0NGRfvSdwdJL5OUXWREAlGZN5gY12kM9ZZV\n",
"0jTga5R3JdZy7plFnuiLN3DZUn+9BvpMh47JrqT/LOi3BhYUcWHXVZmXuabDOJiX2I4T8VGj/wO8\n",
"z9rL9xe3djPgd8C78KEdlzZY1kq4BQ8T/BNvjNJIfotfs68B76HOljmAmb3LzA4zs7tSm2uxzPcm\n",
"R5mb2ULgXkKYRtLrJO2e2W03vCSyIcocbxf8kaQcc1u+X+URlXkk4pyFW8h5HbsaxSRgGebZ35IG\n",
"rIJ+Z8GNu0HfeXAFwMpCTD3LcmBUPbtzpePVknaQ9GUADmJPDqUf8GZrt7ut3a6ydttcxal/DnwU\n",
"t85vLLNvwzGzh4E9zOwjdRp4UooXzexWfBEzgAYo8yI8B+xQ5edjFMWTAX8DnBwG6FwBHB8G6iQj\n",
"Z5NhQf8CZkraTtKMVHe67rIfHn45qU7n26poWWUuaZ6k+yTdLelfzZanVmJcpzHUKqukI7M3N0mH\n",
"4iVRZ+KZzXWlhKy70rmj2oEvwMajYOAE4Ah4bANsegbuK3LejfiM82pdqfnM4gHgCXXoj+rQO3gD\n",
"pzOer0ranVfxFe5jkbXbk2XPkydruyX1ytdY+5bxnTVTj8+qmeXlIdSdRFYzm4PPXa9bJ7wyr/si\n",
"sAkYVMVhgyiuzK/A4/534jkSo/DF76uDwp6AJ/c9jJchno5XDry6Fvlz2A+fSXAybBv3q2po5QQ4\n",
"A2aG8o5IpNuEcpnr8Ezb+UGpH4HXXX8GL/Fp60GRtvQzD7ymr2cj7/8srJ0LO02HG+a5AihG4mpf\n",
"W2KfsgSL/GK8E90TwFvZlb2YDszl5wxiOn8jt6StCk6n+JS3bYWZFCba9QRrgSGSNgEzzOzeYjuG\n",
"70OxmDlmtkLSZNydfhA+mW00PsN+F7yV7Oqw+y+A/8PLKtskrQD2NrPf1PJPhBnwMwghnjDApp4V\n",
"Ab2elrXMA72+uX+M6zSGGmWdhN9wElf6McD5wK+By3H354R618h2kVXaE79xHkBnq/vIQV7W8+ql\n",
"3ld92jxfeJSyIJfh07ku6Za7fTVv4X5ezoN8wdrtp9Zub+UHXMe1XM90DuMurmBTzaNJAbB2e8ja\n",
"7anunCOht35Ww3S4nlRCSRLcqcA/JA0ose8QQNZ5OE8nzGyTmd2HN6QZhStzgFcBz6RyOy7FcwV+\n",
"hn/fXk+xboWVsTvwdGhiswkY2Fs/A42ilZW5AX+VdKekDzRbmMhWQVvm917ApWZ2tjnP46VX+0vK\n",
"zc7ugjQO6aIq5fgHrsgPx7umIWlXYI8dPev5rX+DG/C2mIMorcyX441W3h7OVzWaoT5s4ufcxQgu\n",
"Z9/UUzN4kO9xDo/wZ6ZRvPws0rqsxUvFvogr9sNK7Dse/zxVwnJckY8Jjw8iVXERFi1vx/vD74x7\n",
"oXYsdjJJo4s9FziQQnOlDfjc+0iKVlbmh5jZfnj25MdCXLPXkY2VyOnbJHFKsg3EoNoyv3fHy6vS\n",
"zMObmny8wsSdfXCroyidZJX643Xjn8QnpT0UnvkocN52Pj/6sa+4VTMd+EJYZBRjOe5heBof8Vk9\n",
"kziD/gxmPufQuanMdOBBVvBdvLFKI/qX18Q28FmtF+uA9+NT8n4MHFdi3/EUJq6VI4mZj8Zr0zsp\n",
"8xRP4d+3lwHj8k4UvAVz8zxLkr4m6S1AB+5Fg9CPIX4GOtOyMXMzWxR+L5V0Fb4y+3vyvKTZFLpi\n",
"rQLuSQ0JmBmObcXHx+GLk2+1iDzbzGO8DGwR3spzJh77m53Z/yncIu6zBGYjfUB+oyp2/mk3wfC3\n",
"SUcvgbcCk94LV14Aj+ft/zXY/WCgD7x7JlwlGIF0AW4x7Qkc+T/w+CafX/4d4JIy/99yfD721/EF\n",
"yFBCH+1Krw/9+TR/425e4mrg65JuwV2ug/BEpvn4ImNpM9+/+Limx2vx2v6v4yV4vw3307zvx3hg\n",
"UYXnH4Er8tH452MvQkOZnO/T7ngjoOFhgXxY+nz4AnIwbm2vzxx/Bj6b4Hdmdh0FZuJ1+82+vo28\n",
"Xz2FL9AnhMfzKIWZtdwPfhMZEv7eAXdLHp163vCby4xmy1rB/zIz8/hDwN+aLVclsrbyTy2y4iv7\n",
"i5LrjyvCHTP7fB14rg/c9pI3bZlW8rzwXQMzaDO41+DHBo+bWw6dZAXG7wtPPwwvbYKFBp/Ca69v\n",
"wZODarkOXw3fh9F4lvGrqjp+FgfwOTawPW/FO4itxl2nhwK3p17n9cDkZr/v28pntY6vfTluRffH\n",
"c5DmAfsV2ffTwOUVnrcfXklxDZ5zYsAZOfsNwuehr8eTO0fn7DMtHD+myGv0yWy/D/eIbdWfATx0\n",
"Ni+zzYrt36pu9h2Bv0u6B2/q8Acz+0tmn1ugU3yvtzAMj2FFep42PEbdFmJ0feg6LetR4Jrx8JD8\n",
"+aJxvkAySnMcPg2rA29peWZ6pxBauXA/+OtGWPNj+D5etzsduME8qagWluNf+GV4bH13dehAdahk\n",
"8xtJH9WeGorxU25iMxv4B37jvBD4ETCLVDMVM7vWzHqqPjpSP9YAF5nZ8+ba4DyKh2MmUmHM3Lw1\n",
"8HO4Ik6qLZ7J2W897jl9HPeK5bnah4bf2RLLEcAq65ow2Cti5pJGSDqt/J5FGQLsLGlKJTu3pDI3\n",
"s7lmtm/42dPMvpmz2534cIGWxrrGSobRQy04qyVH1palRlnb8FDNRLzM5dFwg0tzAfDu6YX4Xzll\n",
"Pg13h03DbzDL8dnkx6T22RuPaesncM0GWPNZWI7ZUvwz/HAN/0vCk3iyHPhCZDdW8w1W8f30TurQ\n",
"YHV4JrOkA4GfsB+z2cR6/s0yM1sUruln8QXGZiDve9cSbAOf1XpxFr7ATDgPeIekHdI7SZqEd6f7\n",
"WRXnXoYvZhNlnhczB/9+JANYSinzHTLbR+C97LNsoHfEzF8LfAXKfwZCLlVbZnNyXSrqStmSyrxC\n",
"HsFjMb2NlrXMJY2R9HlJuzVblnojr1Mdh9dQL8W/aNnkN8x58ZWFZK/iytwTdqbic6sPABYEX9hi\n",
"QpavvJf1l4ETgNduDyOe9xvUxHCWbilzM7vGzE4PDx+hD7uzPYeyPftokI4HV+R4qOp2dWgCcAbi\n",
"BsZzHFdxC3BH6nwb8Jjm8WZVdXiLtCBmNs9SvTrMbAHuOTois+svgB9aYbJdJSzHXeGP4Aq2mDKf\n",
"hw9dWUx1yjyZz55lPb3AMgdeDkwO955y7IcnAV6WSrwdgnsztnpl/jC9QJmnkhkShgGDVHpgQbO4\n",
"DE9G+adC/+NWJee6lmM34NngHpwL/BfeLzyX1xWmi5WyzMfjCUaP4SNLF4TtyyjU374Fd4P/O7gL\n",
"R232hcIkeWb7FOo3XepR2tiPlYgXmMc4fimpH3P5Dw8zmkU8wiZmA8fwQb7Hc4hH+SDu8k8n4qw2\n",
"7y7XstTw/jeNFpT1MTwkBIB8fvlewNlVyrocz2RfiS+Oi/UQuBj4PW6Z532fSrnZi1nmg1rpugbL\n",
"+lWZzfvjOnZyBbKOwI2C11Popz8UD3VVVMnVm5X5HGCachp8SDoo58K2CsPC75GS/iWpJVzuko7H\n",
"XcXH4Ektb2quRPUjxKt/En4ATgPazOzKYse8yhNy+Bl8SVKxZhfTcEv/WfyLm8SUVwPb4yU3p+Fj\n",
"MBNG4qvtibiLcr6Zbarl/8rhMaayE0+wnh24lN3ZwFj+j/FM4498ld9wKGImffkh4zmaF7gQeLWZ\n",
"/bNOrx/pHSyk4BkCT8Q8Jyx0q2EZsMLMNpvZbTkhKwDM7Eozu506u9mrlLXR7ApsyeuSe+1ejuup\n",
"tgqOH4Jfz9UUlPkQ3OtRkY7otco8uAMX4ZbNFuTTr64lk4DULHJiJckHdxzumh1DkwkfvHbg4+G6\n",
"/hYvs2pZzOwmSaMlVRLXPQl3B343HPuYlZlfvR2MeAmWn+Czxv+3y6JLOgPvVf0g/jncnsQy95ta\n",
"Yp3vQ+c45Kj+Xsozke7HyzsziyHsj/EoNyGuYx/6sw+nsIKbba39nLXcwjrER7kBOImJnG1mW9rJ\n",
"9oIY5BairN3iGYIylzQI9x79AqqWdTnVNRIq52bPWubF3OytGDOfirfNTRTxzninutuBKRXIOgT3\n",
"8qXH1g7Fr9mgcI8uSUllLmmspI9JulTSPyXdEf7+mKSx5U7eA+TFzX8K/BI4VPWb1lMzwf2SjnkM\n",
"w9+wPfFSkfoMyegeR+HTnBIL8i94F7RWeI9LsTNe2lWOXYEbq4wBj+wDD473RcA1wEcyzx8JfC5s\n",
"T9qcprO9l97pC82BdM6YHzXC3fwTcWv+/ipkKsd3eIx5PMUVwE3043ccAozgi+H5j9KPKxnFN4DH\n",
"rd3qt5CI9CYWUqhdnoqP2K2081uaZVTXSKicm71XWeaS/izpkPBwavideDxejnesm0vllvlaOo+t\n",
"HYKH+17A78+l5SniGUHSubgb8U/4SLtFuPIZjzdwOQZvjHF67gkaiKR8oSOROvF9XAsfgad39yfM\n",
"Iw3cg3fiuAdfUcwD3khhNXQ98G3gr5nz3oinFt9Ub4Hb8MDIT/CqXvCl+u4UesyB59W/GbiE0k1i\n",
"I5FIS2Jm+VZ6iYL1fSooaq+p0UV3fwiF83iTgx+ktn8W+GX4+5e427jZzQKuxOOvO4bHy4Gr8XIi\n",
"A45rARmfwq3XmaltFwPvarZsJWSeietaAwaW2fd3wJuqeg24wOBTBqufhHsfg+WZ51cajDIzjoLJ\n",
"oXHMvqnnLz7PS9SuSl9Xg/utUKo2n7Cg7ta1mMUAZvEws8r/j8xiPLO4hln0zbumzX5fq3n/my1D\n",
"b5UVd1+vCn+fAfyoFlnxzoPV7D8E90r2z2yfjbv+v5TZfj5was552vFmSU27rvga/iXg7vD4Crzu\n",
"/r14n4Zzw/bD8EqSkrICX8PL2K7BK0nAbYX9wr1icthmxc5Ryg29SNKM7Eb5sPkx4ay1NrqoF/NJ\n",
"ZWXiLtcLw983U+PgiXohT4A6DHcVjQxxj2G4IZc0vBmSf3SXc71Z0ogGyDg6yPSEdY7rJKM1W5Ig\n",
"a1IRUO66TCCnoUUZRuI13AMnw8vGwhAkd6V5y9T+wApJB14PczZ4U4x5qeOX4q63JzPXdRQeB1wA\n",
"XGHhG9pNvoKX2V1dbkdrt0XWbm+09q4hB2utGGRJoqzdYiXQP9SaTyPVj70aWc1sfpX7r8W/J9lm\n",
"X0Px72eem71VY+Z74g62KZJ2xL/rd+Bu9lcDPw/7zcOTbW8qc748N/tQvOnPerpemy6UUuY/plBe\n",
"k2YUPv+5FXgaXx0SSql2ptC//Z+EHtWVEGLbp+dlx3eDV+PZjI/gymEQHv94lkKtedmYeUi+ugQv\n",
"GZtcbv8qeTm+usx2WUqmIrUyiTIvt+ioVZkvB5Y8B/P+4DeVk8Jzk4H5YUl9GPDsIFgrtzoSlvbz\n",
"katPbNnii7lR4bzn4/Oeu4U69Ep8kMaHrL0uC4PIVk5YQCZJcFNJf0Ybz23AwZltQ/EwbjWlac2O\n",
"me+J1+vfiM9Yn4rrnsl0Dm4tBLaXVK46qFjMfC1u8Q8qJ1ApZb6Lmd2c3Whmt+AZuq3AfIIyx92u\n",
"N1shyekJYFwqu7AcU/CMznpmwU/HLbQVuHIYipceJMkmC8h8gCUdnXOeY/EM/ZuAd9ZRPnBlfld4\n",
"7Zmp7clUpJYkyJoo85GZ53aQdJOkoaEsbSxUPYs7yaRdvAL+cYVHopP3xpW5cwg+XnIzXmubsKy/\n",
"v+6Tqes6CNiM2QYz+5lZ9xLQ1KFBeF/sT1i7Le7OuaAl66GLss3IKh2LdGz5HavmGXyRO5WUZd4D\n",
"17WYMs+zzEdSQpk3+TOwJz7e9UL8+2/h8WF4P4t1AEEfHQWcK6mUcVksm70ulnkpJVhJR5ueYDE+\n",
"iSfJVboxeSJcxIfwi14J++AtYj+p+o1b3QOPRyfKfBiuzBPX0UOklHnwClwX3PNpjsebLvyFChsI\n",
"VMEWZZ6hpd3sgVxljlsch+PxwLF4LWy1NbSJO/xvc+DKOW4JTArP7QQ8HcImh+Dla7+mc2b90kFu\n",
"WTyRc85uoQ4NUofG40Nj7rZ2u7y754y0LLvTeSxtvViIL0rb8IzrnuI24JBMqVVimVfqZl9PBZZq\n",
"g5mBl6VehS/k5+LXdAau1LdgZnfh//eBJc7XyTIPOqAPXt7Wbcv8cUlvyG6U9Hp61i1TlOAaXojf\n",
"ZI8A/pbZ5T482agS9sGV5buByyUdW4cubdPxhIYV+AczUeaJZf4wnS3zRIknpRqELmFH452AbgUO\n",
"rnPJ3RZlnonrpLuYtRyZmHl20TEOj1V9El9QVedi9+s7HFiB2X8fAzcu8C9b0hVvJ9wy3xXYYD6A\n",
"5FLgjZIGA8yFdcN8AfBU6rrugccMayYMUFmKfwfn4U1p6kILxnaLsg3J+jSd84LqxULcYlxpPgwF\n",
"6JHrOhc3BicijUGaQMbNLqlPuPe2TG92SYMTfRDas+4GPBR00Jm47kja2T6Qc4rrKW1YZt3sQ4A1\n",
"ISRS0eKlVHz4DOAPkk7Cb/ZJR5uDKT3gvqeZj/euHYyvlNJUo8z3Bi41s+slfQL4BnAvXoFUFaG3\n",
"+TMUmoIcTFfL/AX8hpyuk08r86R+cwL+pi4O516OLxLyPjDVyjkK/8LkKZjeYJkn1ytrmY/DvSx9\n",
"gI9Tfbx8KPAcZi+GxxtXgwy2C8p6J3zheCChr7mZLZF0B25FXf5hOOFcWGtmz6fOewDw7ypl2YI6\n",
"tDNeZfBGa7fswjWydbKAgkeonvwG9+x0mU/QSMzMJN2PW7CvpBB+TFvmb8cHxPTBFXeWZsTMLwKO\n",
"lnQmXqr9WMqV/kfgj0HJJ+72LA9Quqtm1s2ePIbuutnNbA6u4G7BXTE74xnie5lZj34AyjAf+AJw\n",
"SU5mcLWW+b0AZnY5nlRUcQJdhh8BZ+MfuF3o6mafj1tyayhjmYdzrE89vhX4eJ3iRZ2S33Ji5j1u\n",
"mUuaLOl+Sd8u5YFIxcyNfGX+LD7L+QSqV+ajSFkE5h+slS9685fxFCzzcRT6sQNcB7xW0qR74O3j\n",
"3f2Wvq6vwBcZVaMOCS+3/GGjFPk2E4fuYbopa0MsczP7D24pnpDe3kPX9UFcmU94yb9D2+Mh0+Re\n",
"uDd+D1xZpNqjR2PmISTwauBk4KN4qPPv2f1CKO9Z8pX5UGDPEp3cspZ5Ei+H7rrZJcnMNprZeWb2\n",
"mfBznqUGMFTSYq4HeJpC8lqW+4G9y8kpLzUahw8gSJgD7FKjS3sK8CEKbTk6KXMzW2Vm76FzsgPk\n",
"K/OBdF6dXoBb6xfSfYrFy6F5lvkueP3mSZRPtByI3wTy3OzP4qGJF6hembfRdWjEqo1+Tcbj5Txz\n",
"8cVOup3lX/HOcB9aDhf19f7saQviFdRumb8Hd/1/p8bjI70Tr3zpmkfTbcx7qq8pv2fdSZT5+Bf9\n",
"+7QOV2KJ9bkb3kPkxCLH97Rl/jJcvt/jivYU3MjN4zDyuzquJPQbKXJcnpu9PpY5cJOkz0l6WfYJ\n",
"SbsFd0OXbPcmMA+4w8y6rIbM+28ntd2lmA48nMqEJ7hQVlDIlq+IoPwTN+zDIa6TzWZPWEfnRMNi\n",
"lvmWBZSZ/Q1fTfevQ5na/qSUeSYG9RzQN/Ru7klG44uq6/AKhVxSMfOFeA3/ZZKSLNlxeEbpWlyh\n",
"F5vmVIzd6Op+XLnGWyu+DH8vnwqypttZPoC/nx/b7GVn80lqTD02OJAako2CVf554PPWvsX1X3e2\n",
"oTh0j9ItWf2etIhC+9WG0kPX9UH8njvOXLmtwe83aWV+v5ndUeT49cCgYrJK6qvKxo5WysHA7cGD\n",
"+YcgexfLHMDMHs/zJgRZH6R43DzrZq+fZY4nXS0HfiJpkaQ5kh6TtAg4B7eIXlvi+J7iIkrHIiqJ\n",
"OY3G/58sj5IzZjXUpH+2yLnG4Tf904Cvh21ZN3tCMcs8vfjIWuZJnejtwEFFZKiUopZ5eI1mlKcl\n",
"CvImSijzwEDc6t4ReANu+ULBMgdPaPx1lTLkKvOl/n4dATyKf7E7Webhmt0AzDGz+/GSn6Rn8wHA\n",
"neR80SvgKOBFuiZ4RrYNFtCYJLhm8RAw3WB8H/8OJcp8cCglnUpnL2mWcpb5h/GuyfXiIDwbHbwx\n",
"06NmVm2pK/hiv4syD0nOfXGjrf6WuZltCm71o3BleCgeN5hkZkeZ2exMck9TMLP1ZS5sJco8q2QT\n",
"HsVv7FlGAN/JKSEDd9HOC92R5oS4TqLMd6SQyQ5dlXmSnZ21zPOSQPLqNStG0kgKVnCybWZmt2a4\n",
"2hMFeTNwWPhydyEVM38GT0QbROG9GkdYnJnXdFdrze5O187lqxb6+3AkhUlnY+g6NerbeBY9BGUe\n",
"ZD0Mz3eoCnXoELyB03cb3RRmG4pD9yjdkVUdOvULRzKQWpS59EpcUVRxSOOvq5mtkCuocX1hRF9X\n",
"5utwhdUGLEln2OdQLmY+hvzpbLXyKtx4AvgzNZQHB1lzlTmuA9YGY6D+MfM0IbayOPxUM3mqFahE\n",
"macvXJpHyFfmyTSxPPf9FDq39YSCMj+IzjHTtVSWALeRrnTXMt+f/M5vaZqRBDcaWBYWaM9SOoFx\n",
"AK7M++OKM/Gi7Ej1TWLS5Frmc71xzFgK3Z2yMXPM7F4z+1d4mLbMj6RKy1odOhyvY/0S1XsXIlsH\n",
"t/74lezxj8kV98tIcwH1bzJVF8bBI8/DC2th824+zGsTXl21J+Uz7MtZ5jtQPrRaDW0Eo8ecaibF\n",
"pSmmzNNWeP2z2bciGmGZJ0kMeR+YNlLKPMRKVoV9p+Jj8RJqSYBLuBPYpxvtZ7u42HNiUM20zMEX\n",
"U1PydkrFzJPktvOA3UPOwhg6jx2tHM8R2JGuC7KVj3piHhQs8y7KPMOTwFTzhJhpeElL8Zfu0JfV\n",
"ob3C3xPxFr7vtHa7vCdatW4zcegepjuyWrs9dtQT/O1zR/PWGg4fRdfRvaVfr4eu60fgp/NgwBLg\n",
"Wvh9sErX4YNFKlHmg8zsJkn91HVmRUXKXNJ2FSRH74DXxXcrUTAVM5+Rk1SdVtyNtcx7Od1R5nPw\n",
"xiBZEst8eM5zbWQUQfBmrAX+nelEVknMPNcyD26otdSubEtlsic0K2aeKMglFK51HgPxBcdGXPEN\n",
"w5X/mm6EgHYFnqSra37VI55MCfBwWEQNI7+pRUJimb8GuJUSXejUoV2A/wb+rA69Ak+g+7m1W3aK\n",
"amQb46wbueSecbSpQ2Wtsy14eGoYMI7SbUSbwpdh5Y5w1xi4b+fCoKTn8NyXe8scvgnv+dAXTwb+\n",
"aeb5Si3zi/HJxaXYEU+m7fZi2sxW4nomm1SdVuZJIuA0CmWv9bPMJbVJem34e1Ao5eotdEeZL8D7\n",
"u2et31Ju9jZSyjwV11lB1wzIDcCAVFy4mJs9zzIHTxQbU+S5cnRR5jkxqJV0reFuNOk4dFFlnoqZ\n",
"b8BH8T6Br+gPp/4udoCVD7gbcD3eZGcEXgdbKuw0F5j6W4+hX1/mdT+AT1r6LO5an4Q3LuoxtpU4\n",
"dE/TXVn3XMLD+z7LBjwRslKG4QriYrwddEX04HUdNxweH+4L3uQ7/hyetX9RqQODYt2AJ2kPpus9\n",
"sKwyD9bxERQvFdsiJ/nJ0VWRuq55rvYtyjzcTzbgyeVJnL6iDnBllbmkD+LNN5KRbpPwm03DkHSM\n",
"pEdC9nx3B5/UHDMPVvRSCm08E6qyzAOLSPWOD+c3OpdkDMBXnZW42aGIspP0DZUYMBPcUmNwz0Mp\n",
"sjH9niBtmZdbrAwENppZksT3CPAxCkNQamEGhZh4mpXPJitm/1zkJb91xmt4Nwzy5KWfFdtNHRqI\n",
"167+n7Xbxbg1f4i1Nz/BNNISzDvpQbYj0+ClDMl0vjvwTmutxnj8npi+h60BvmZmxe53aTbguTID\n",
"6HofrsQy351CuXAp0pUx9aCYMk9PXFyLK+/kPlQ3N/vH8Cz2NbClM1wp12e3CFbqOcAxeD3fyZL2\n",
"6MYpu2OZQ2rMaoqxeAOAPGU+ks7lSjeFP48pEo9K15oPwD/clSTAQXFl90HyY/0J+wH3ZpPfcuTL\n",
"hgF6gorc7KmYefra/BVfoHywG6+fDFDIsgIYRaFyoly8POEnr4fjKH2Dei9wl7XbHABrt+et3daV\n",
"2L8hbCtx6J6mDrKuOPERTMbx6lA5SzIhmfr3T+BAKmzw1YPXNU+Zn0TlY4E34CNI85T5YMor80PC\n",
"7/QcjDx9WBdlnrquD+DZ8WnSbnbC33ek7s91c7NvMrNNyYPgcm5kMs6BwONmNi9YxpdQ3Yo0y0q8\n",
"wUqpKXCllPl8upaFjMWVfN4HZhCd268CEBqY5JFWmANwBV2zmz0kdAyldJOJKVQ28KMuylxSf0mv\n",
"qWC/QXhcOrl+5WLmiScDgFBKebKZLShxTDmKKfPFGVkqU+Zm7ZjlnQ8Adagv8Dngm9WJGdlmMLMp\n",
"q5i7ywouBy5VR0VJr26Zmy3CLbtdGipj9Uwho8zN7MkqqqU24PfaYpb5gCKlwwmvxsNpQ0PfkI8D\n",
"yyRNy+zX3cqYLFfhSXDvSW3LeobXUXCxQx0t85sl/Q8wSNJRuMv9mgqOq5WJuKJMWBC21URwZS+g\n",
"dCe3cso8zzKfQ+ZDFFZ2A0hZixXEoPKUebZpTDHLPE/ZDSSZSlSccfgXqRM5stbLMt8PmF3BfklZ\n",
"WrJYLOpmT8XMi12b6vEvfxv5MfPFdI6vVWqZF/0MBPf6L/DPZ9U16PVmW4pD9yR1kvWpe37KnwZv\n",
"wjDOrmD/xDIHt84rcrX3yHWV3gLsi3d5XEptnt71uEIegI/BTnseEis21zoPBulrgGvDPi/HOyz+\n",
"Bu8TkaYulnlyXc1sNfAO6PQejqFzJ8ln6BySrcgyr2SFdyZwOl5e8yH8AvyyguNqpRFW/23AkcHy\n",
"m1RZUucAACAASURBVG5m52eeL1ZnDr6wyGa074i7dLMrwkH4SMxStdtZ0nHpxM2ezj4tZ5ln4y+J\n",
"VV/KMh9P+Xg51E+ZDwMmSxqQ9vLkkFWQXRYrkr4C/CQ8rI8y97yQT+PW8TzyZVyJL2gHhvkEFSvz\n",
"3JfsUBvwW3xy3ht7ovQs0quZN+hF2ub+gOk7fZqXqUMzgH8AP7Z2y6uoGLV0EGvGdui0d7yFga9c\n",
"wHfWHq4zXujDpvnDmXP5dP65vj+r8EX908DcHvwMngO8CbPlSLUm8W7A75fJ3O8hFO7hO+A9IYaR\n",
"X6L6bjw59XZ8Qtt44B7cQ/aYpBlW8KbVJQEuw/3A6DD/xPD//77U88dlsucrssxLKvOwgnnAzHan\n",
"8lhGd1lIZ7f2ZDpPpkpkm00h0WwVcE8Sl0itgm4Kzz+Kt1c9FnhR0lOZ50fhSvHJnOPnAydJmpna\n",
"fwJeczwsvT+esLA+fXyohewkT+bxOuCgsLJMLPPhks7AW5oOBHZOv37q9ZYCYzLnS5T5y1PXKns9\n",
"9iK1EiwmX5BtcBn5yz7GO9WJYPWWeL3+uKsreXwrMELSEfj1vhkv3xqAZ4cPBDZ2Sz6p7a/wnYVw\n",
"/fvgB8ANJeRbAoyVNBX3Ntxe4f9P8v6pQzvzAL9kEK9iKu3A95nF4Zqlmq/vtvo4fW1bQZ5K3v9a\n",
"z/dj6PtxOH70BsZ+9kec/429ebzva9j1+e14Ru/Ral7kWXZnNvBLZnHAZ8fymv87jSOACVc+zwu3\n",
"jmDB8CmsGbCZ/ts/wgm7/pOZ9x7Kv4AJPMFuiAHq0K3M4g4dpP15gAdsrf2l7tdDGnoDDD8aBgZ/\n",
"+pprYfwbqr8+A/H77c7hEh8DXBb+HoZ7JYZlj5f3bP8mXimyGr9fHgz0M7MNkh4FjpE0JrzeOGB8\n",
"d98/OnMw3p55EK6op9PZU3q4pETemfgEz2GSZlEClSufk/Q74JNmVu2wipoIC4hH8Y5Zz+CNNk42\n",
"s4dT+5iZVTyxLcROnsXfuH+Z2UGp54RP1trecuqAJR0A/MLM9kuday2erPF+Mzs+tW8bcLOZ7Zw9\n",
"TwnZLgWuNLNLJZ0VZPkS3hTmj/jksCvM7NKcY2cCXzWzwzLy/hu4zsyOKfKa/wA+b2a5wwJS++0L\n",
"zDazfTPbRwGDK/1MSPoAvhg8znz2b7H93hX2OTm1bRmwh5ktlTQe/0ycb2anSFoJTDXLtUwqQ3or\n",
"noD2Dnxx+FPM2ovIdxfwYTP7t6RLgGvMrEsZTYhpHoz3ijc8rj8WX2AdhC8afmzttqpmuSPbFtLb\n",
"8LHJi/BF5B3APprFqXiC7274Tf81wK2DN/G60+7msh/+yd6Xc66RuHX4Nsz+AaAOTcBd8Qfh7usZ\n",
"+JyBpJ/F3cAV1m7Lu5yvuv9jH+AizPYMj6cCN2CW2xyq+Gn0J3zU9DF46ec+ZnZfeO55/B74ZbPO\n",
"44Il7Q78wcx2kfQq/Lt4KbCzmZ0h6ffAeWZ2ddj/KWCmmVU9IKmM/AuBV5rZAkl/B75kZrmDy4KO\n",
"ehE3Yl4opvsqcbOPBB6U9C98FQEeiq64drEazOxFeTLCdXjz+XPTirzGc26S9Fs8lvzqzNM74El+\n",
"xRp6ZBPgkvjGKvLd7J2S3zIWfR7ZmPk63F10EO5Gq7bOfCjuEq4pZp6RNTvVLeE0/OZxeonXSJPE\n",
"rrLJJVl2oqsXJnG1L8XDHYvwkMlMfHVeym1fCV5vb7Ye6b0E70wR0nHznclMYwtd296P14wvxd+/\n",
"F7mXKezD/cD5wCnWbjW75xtJBZ/VlmEblDX5rJ2Ne6d2BQZYu72Au4zn4g2HpgEH3jybvvsv4rrc\n",
"M5mtQGrHexr8A8Da7RngKkkrzey/1aHRuKLsg99TjgS+ow7diicl/87aaxqfOoXEA9qhvQd/gTPH\n",
"rGenuR16CDjO2q3U9y/NRvy7myS5DQeQ96IXXXOPEsbh3l/w++xQXMcli5QtLu2gROtWZ575DCT6\n",
"YwFdY+adMDOTVLbWvBJl/uUK9qkrZvYnvF9vPfkw/uZmPyyl4uXgF3mwpB3M7DlcsSyh0KI1TW4m\n",
"exmyynwT7v4ZFc5fqs48T5kPw91PueV84QM6nsqSOorFzCdRXc/2Yfj1ys+o9cTBb5wI+17tTS7S\n",
"JMr8QXz86F/wG8tkMtnsNfJyfIUPZn8us28XZa4O9cGbeXwYb1hzCX5T2tLFStJMu7J3KJ5IyzIX\n",
"t85+BXwBb1o0EmkIqUoZa7cngCeYpVMoJMDlcTnwXaThWFcPUVhwXpjadI46NBhvQPOO8Ph6vBHW\n",
"CuB3uIJ9oUzsfQowVx36DHDmC335wR8uYvOMj/ML4Pfq0KsqLMvcSKHOHAqG1Q64Ql5NcWWe3PsS\n",
"ZT6KQgVLuu/HcNzQq/aeXglpYzDRKaUoq8zLZrMncd/sTyXSthLmJQ+rgB3UeZJQqUz2JBv+EQqJ\n",
"ZokiXE0FlnkF1ypbZ74J/5DdHGQrVWe+HI+lpBdlQ/EFy2BJecMIhgCbzbp+YXJkrZcyH473pC9m\n",
"mX8D+MwhHlLIzqVPZ7vuiifu3YJf6xetO4N/fGFTSVvbhMXAjpqsgUxkDF/hQ/gAhm/iiaE7Wbt9\n",
"NK3IoffURPcWOWEblNVsCbAbng19B77ofQD/zuSRNI0pdr7VuBv9TZ03F5fV2m2dtdtvrN2Ox/Nf\n",
"/oR/J9+CfzfWA0uDoi7G1NOPZzy++N1/41n2jenLYO4P+H94EtgXSxybZiO+wBkAbKZ7yjyd+Z9W\n",
"5qMoYTFXQ851XYXnRvULMpRaeCVydc8yl7SOQoZ5f9xVvc7MelNLVwDM7CVJyfCQxM1cUpkHbsOb\n",
"DPwTd18vpEI3ewWso/ChS5T52bir6H2UcLOb2WZJq/D/J3EFJf/PIjxR74nMYZVa5eD/y/aS+maU\n",
"5kTKN2VIMwxXmMVCMye/CN+e6OUh2ZDKEgreh13xm9gOuJXf3Uz2nYFNoRa3PNPpw/68g6l8ipUY\n",
"feiHWyl3xmz0SMOxLS7oT+Lf8SF45UteWWNaQRXjQuAcvLb6y5RLoEqL4hn05yaP1aHt8XvXVOAW\n",
"degua++6MNiwHVPP34cjgL2sfUs/iDVtqxiKZ5Pfpw79wtrLxqg34l7LpAIo6e9ejTJPxq6OJl+Z\n",
"j8Dv841gJa4/RgHLrXwFVNnytEos88FmNsTMhuCK5c3A/6tM3pZkGZ1d05Uq82R2eKLM1+BWfvoa\n",
"Jh+kLeRkMmbJutk3mtnFuHWduNlLKa2sq30o/v8sJL88LTdenidr+IDluXdqcbPfDbQpZz75Zhh+\n",
"PTy9h1va2cXQoxTmB78Mt8yTGebdVeZ74IlAJVGHdlSHLuJNfIAVGH/kM/yY263dzrR2+3c5RV7B\n",
"Z6Al6C1ywjYuq9lTwTV+N53LWNOUtsydq/CGXG/CK326yiqdQAWDWqzdNli7vWTt9jieS/PLvOY2\n",
"t09ij/6bmRf2S1gLDLF2W4gnpFVSR78RT9IbgBsyVVvm4f72HJ6rkyjztNIcTulBShWT8xlYhS8W\n",
"SsbLU5S1zKuammZmL4Usv9ws6V7CUjoronIxc/AkkUNCvHkisDD1QUh7KGqxzLN15kkMOHHjl0qA\n",
"A1+VppuZJP/PM+Qr82os86x8BGU8Fi8Zq/TzMzy85hoydeMDPLt06EfgpF2gb3B9pzkPL9U4GF/1\n",
"P44vRsbTfWU+mjJfJHVoJn7TXMBPeTfXsoK72I5M8lsk0gT+Bbwe6bT/z955x8lVVwv8e7LJtmyy\n",
"6b1DCqEllEgoEqogIAIiKgICItgQgaeCZWYedtD3FMXyVBAEwQJI7wREakgCISGkkbBppGzaZlM2\n",
"2fP+OPfu3Jmdujt3d2b5fT+f+ezcOmfuzsy5pyd8b8x125Nsv2uqiuprwA3At1N89wB+DDyLSLbp\n",
"YvHTRvQR7Dt6XsIGEXllBCOahZlJh2wj/jt6EzBNYvJhMuPHzMux35agMm8gN8scb7/RdLxl7nt2\n",
"/eTebLTfMheRcwKPc0Xkx2RWLsVOWyzz5d7f0cQtc2jtam9rzDydMs+WAAf2wRwsIpPFmp/47yf5\n",
"psUnrWWeRtYGrOWhbx0Pxj7420jdmz4VfgLcagJDa0SkWxXc2gR7VsBxYmV5CQl9Xmz/R1ic/K9e\n",
"EuJq7EvQ3uS3jI1fJCZnYrWrF2lEv0k9K7D3P4o8hrmUSny3VOQEJ6t34rnA+UCEeK9xsM/1RnJv\n",
"XvV37Hfh8ARZrYRtOPBVcq9c8fk+cJ3EEm4QBj0/Gt3Zg2eS9t2KlzekEd0BxICvZzn/Tux3LNky\n",
"ryF3N7v/2uUkZrMHlXlBLPN0MXPs9y5b8psvV7st8zOA073HydiPeHt6pXc2yUouqzL3kuBewsrF\n",
"8lLmOZBOmfvZ8pkS4MBT5pgr+hLilrmfG5DMcNIo8wzyHQQ85nkmRmDlFBvJ3dWeEMcXkYkiMgH4\n",
"SF/oVWH/k+v2WGgh1fz4m4HBqnqpt7zaO2d7LfO0rkiJydFYq9WPakT98aXvY96OCTjL3FEMqD6J\n",
"fT8uC6wdTD7lVJYPcw9wFiIjiLuEP4T1u3gaOJpsnjiRfyDie22fwHKyWtrINvRgwIsj6U7rUdBB\n",
"yxws+fdQMpMcM29LAhzYb2UzcS+GKU0Rqba8g7At81zd7O23zIE/qOrF3uMyVf0BqX9wS4UN5KnM\n",
"PV7H4lNBZf4uieVWKevMs5w3nTLfgSUb9iK7ZT7Ek2tf7P34yjyVsp1ImlauaWRtwGLL1Z4s/vtP\n",
"uI4iUisi6Vq/+tfYt8y/hpWyXHUY/FVgq6r+tNYy3ickH+yFd4JK1/8yhqLMvclUfwMu0IjOCmza\n",
"gHWeO588lHmpxHdLRU5wsibxZ+BMRHyFlp8yN+4DPv4P60/+mNekZjrwMqqrsO9v+umVViF0Kp7y\n",
"9vJI7sS+KwD89GgO7rOTJo1ocpivxTL3eBeo9Wrd07ELq47xLfPkBLhNmDIOiChltM5Q3wpsCiSg\n",
"+Zb5Gf8x4yjMmHk+bvaCWOa/TLHuVzkcV6wku9lziZmDKfOjsH+075qdiw0M8GmvZd7SBMXzBmzB\n",
"Gudkcif7tc/DsQ/qaO+4dJb5frTOGM8mn/8lHkrcMt+QdP5vk8I15lnz/g2Gn2E/DvsgH36jZeL6\n",
"N1OLSKHMk1HV3d4xYVnm3wb+phFNaLrh/U8uwLrZzW3nazschUF1PWbtnuStaYsyfx3o1du+68cC\n",
"v8CUmT+969/EE1HjiByNyNNYgnA11ira507gPIlJD4D/jGTaoWtSZthvI6DMNaLNWJ7K1AzyBuvM\n",
"k2PmfiguOWdoIFCvqnsC67aSmPXvK/NRk2Fsv8TRpIUkmACXi5u97Za5iEwXkWuwXtRXi8g13iOa\n",
"6bgSIG83u8dsbA7tGo2XcGRV5m2sM/fZjDUtyBT7ClrmAJOwD2iyskVEKrFmKynHn2aImQeVuW+Z\n",
"J1v+I0k9N74GGz7TRNwyHwd8BvjUGHvP/s1UHblPyFtBCMpcYjIKsyZSjiRV1Z2qerla3W9OlEp8\n",
"t1TkBCdrCuYS74WRvzK335ibT4avofoKdmPwPlbJA6bMT0g4xtor34M1gvlfrNfCQS2ntG5uC7Dv\n",
"Osv7cMgRK1PK5dd7B8mUqQ/23d+E/X7UYZUyxxBX5muxuRU9AscMAdZKTIIWbjplPqgcyq6yY9pN\n",
"is+AX5q2L7l5+dplmZdjSqbM+1vjPbYCn8jhxYuVtiTA4f14ryLuYgf7AgXvHluVpuVAOjc7nlzZ\n",
"kg2Dynwx9j/1LfNkN9UEYFmG1rWp2Ia55sEUsT/4JvlmYSiB5LYAweu7BlP4o4CXVPWJpO3pvAmp\n",
"WE04lvmVwK0a0UJPSnI4wuQt2qPMAVR/gupd3vO3UD2UeHjrfmAqIt9CWpThdzG3/Few5jW/AUYG\n",
"toN5uW6QmFStreGAE5e1HppFkmXuMZvslrkfM38X68lxvydHg2d9ryeojKsZwTn0AdZJTHwPQrIy\n",
"9y3gQW/3ZNcdVyTkIhQSvz/I4VhVQjbabpmr6nOqGgWmq2os8Pi5qi7OQ+hio62WOdgHLKjMV2C1\n",
"5n65VVti5ulK0yA3V3LQze436k+XALcfdqeckgwx80rsCzMUuyFYTOvcg6GkvovtQ/z6rsY+vBvU\n",
"xohCYphjI0lxrgz4A0zaQ4Iyl5hUYENXftfO8yZQKvHdUpETnKwpaL8yJ4OsqhuA47Gy5Pcxr+2n\n",
"gBuxTnAx4BksVDa55bCI/gdTVvcJyJQ1Kcti01nmmZLgdmK/eRWY9/Jf2Ejhj3MkwyUm1zKCej7P\n",
"tRKT0yUmQ/k836cvZdhY73slJn75XvCGvsUyv3kkjYuHMFBikutvUlrSxMxHAetVc5rV0P52rthI\n",
"z5tE5BERedZ7JJcWlBLLgLEi4mc65hozB4sftfR299ztc4m3VGxLzHwHUOklZ6Rys2ezzP2mMT2x\n",
"No9gNwit3OzYlyzfoTV+29dXiSvzd2ht+WeyzP2M0DVYwkewK13w+tenkDkdGym8ZX4mMF8jJX2z\n",
"6vhgshgYhbVwbrMyz4jqe6jOwG7IrwbuQnWdV68exRo+zSMxbg52g7x66hrmd0vtuUxlmb8NDJKY\n",
"pJt1HoyZ+7+ZDzCSnhzHp4DpfJpx9GA/LOF2EbsQbuNvGtFfY61jP495NlcHzuu7swc9N7SlQ+oB\n",
"FB7fwHk5415xCpIAdyfWm3wcEMVqrmdl2L+oUdWt2Jxwv7wuH8v8JloPnnkL60QEbYiZJ3VZy9vN\n",
"7rnM67EP5GJgu+di2oq1Yi0HEJFLgI+QwTLPEDMHU+ZTsBF89QRuFkSkpyf74BSNZILX178rX5Zm\n",
"ez5u9ufI/f/WGnMFdsP7f0lMarHa2F+0+ZxpKJX4bqnICU7WFC+yG7tJnkQ7lHlOsqouxGLj/5Vi\n",
"a/D30HaPaKNG9JJ/38pjxH9PgrSyzDWiezFFd2SK/cGUeSOJv5nP1J5M84hX+adG9Bxu5FZ+w0Ma\n",
"0ZOA2i//jsWL9nAGIr2xnJhr+CS3YqEAnxbLvG4wlf0boVtzPA+grSRfV+83ugFrEZ4LBSlN66+q\n",
"fwB2e673izF3SylzD/HuRDkrc1Xdk5QJCZaJ6CugtljmEI+bt8XNDvbFXYUp6tc9WRXP0hWRiViL\n",
"xLex5iv5yrYT+5IehVnlkOhmH+q9/jZaK+MWN7uXhb6eRGUetMw3AbWkaPmagj+T+sckV8wqV1Wv\n",
"scVtwBMasTnGDkcJ4rvaw7HMg6huRDWVobGIdNMR7TculTJPZZmD13kzzbl2Yj04yoHdAESpahqE\n",
"/GVmyz6r8BJqNaLNp8HBA+11/umVnC5lMqcl/aZvB3oqDNo+mB7nzqepYm/G2H17qKeDLfPd3t+1\n",
"InK6WJ/evpkOKAEeAI4RkV7kZ5mnop54nLctMXNIr8xzcbOD5ypS1Q2qemxgvW/pngH8w+sVsCrl\n",
"GdLL2oAlvK3Gvjy+MveT2cCU+RribVaDDCQxwWQN6Sxza16xldyGuBzVztGEQRf7ZzHP09XtOF9a\n",
"SiW+WypygpM1DbOBD5NDm+J0FEDWJeSvzFPFzMGUeSbLfAjmKWwGKGvmI8euoPnYppbpjC3KHBE5\n",
"FEZcZ7H9aYgMwBrbJNwsqOqectizqZLq5mo4cyFblPZb5mmu6wnk7uUuiGX+fbFmBNdgw+z/QPZW\n",
"e0WN2vzfVdiHroz2xV4zKvMcacA+zGVYS1OfXC3ztSQm5vn41vPHsBuYtrANU+Z+1zhfmS/HurmV\n",
"k1mZzyBe3gLWaz045Sk5ZyGfuHl76A9s8KY9/Qy4RCO6O8sxDkcxczdWBraJ/CpWCslSYFyabnHZ\n",
"LXORTyHiGySvAFMlJpUpjtmJKbcW46fPTj5x8jK2A1MQ6VVhBohf6jqhGcr+YMrzFewm4RUCHep8\n",
"BkHjK8PYznp2T1nLxt1lTEpqS1sQVHVJoMw5G+2zzL2krAmqullV56nqDFU9RFXbqhiKidVYfGlL\n",
"Hhc0FUFl3qo0Lcd4WQOmXHYnyZJLaRpY5ufsFOs3YglrB0OrfsitSCPr08B12PvcjafMvVh9HVZj\n",
"6ivztST2Xu8BHId1TfNf4xeqGqyr9BvKBGXOqswLEIf0LfNDgPc0ornONM+bUonvloqc4GRN80Lv\n",
"YfXgbXaxt1tWm52wmdRDnvy+6ckEO8B9Emsbjka0AUtUS+Vq90vTdgFITMoayjnhhGW8huX3/Gsr\n",
"3DYWRv9QZLLCFU/D7j0WFn3BO+csYIrf1MZnKOyaNYQ91LN9yHbqezTTiHk8UiIxGSkx+WS67VCQ\n",
"z0D7LHM1t+en2ylEsbIGT5m38zyFsswH0LrU6g3i5WZp8coF70ixaSPmRXkoUAqWF6par6ovezcZ\n",
"7wLzA5t9l1rQMg+Wp00D3lXNWLPtj2wNytzuUpAc8JX54cBrHfB6DkdH8Bvse9qZLCF1y29/olky\n",
"wd7s47Aulj6PAB9NcYxvmfvetKP7N7LjwHW8DNwL7NkJt78KY78E89fCtButPfZ6zH1/tEZ0K3at\n",
"Etzow6DpjYF0o57NwJZLZ3MLcKfE5EMSS/I4iPSasobLUf4kMQkz/FyQmPkLIvIrETlGRA4RkUMl\n",
"h/m2JUCHKPMcY1B+4liCMlfV11X1pnbIthF7jz/PZeccZJ2a1GMglTIPutlPxuJSmWiTm70Asb2g\n",
"Ms+laUObKZX4bqnICU7WtFi9dZsHYRVI1nRx80wx815e4us+5K7MIf6bee7Zb1MPvInqr1E9uTdc\n",
"/0O4Zgz8dRg8+oZ5Phsx9/oUrCPmK5jR0cIw2LuoP+VsZD2w5dePsBiLtd8OPJ/UM/47R9bxjYq9\n",
"VAJfTHdBCnBdCxIzn4qVGvw3Flu8yftb6vjKPNca83TUA/28HuTZZo+nw3ezt7cJSjLrgedUC+NC\n",
"1tbZq/6XdiTmYvd7r/ucgTWUyERyAmI+5WntYRDmcnOWuaNrkfvo07DIT5mr7sJCdv7si1GBrXOA\n",
"vhKTcUlHtShzz1o++4pZ9MLc8v559eeqP99ibvXj8Xug21jlJVjm/1PAucETD4PmFf2oYgNv4E2v\n",
"1Ij+H9Z069/AC94wJoDaf49m9TdfYAtwlcQk/TCa9tH+pjFenPy45EfBROw81mDx5PZa5luweE9P\n",
"7M5vb3BjnjHz9jZBSeYP5BEmaUNcZwk2fGEq5rqa5z1HRMZgSv6FNMf6tFjmEpMBT9pXNqubvQAx\n",
"qEFvDWQ75knIt5FOXpRKfLdU5AQna1gUSNZ8LXOwktrzsNDiUES6Q8vQlT8DtyTFtncF/k4sa2bn\n",
"/uupxXptJPMWlvAWzPB/E2tu8w9grMRkur+hthLZUU4Z7/MkgVGqGtFmjeh1WKLho95NRPWKWnqe\n",
"P4+aml1cDzwlMZlIEgW4ru13s4vIEBH5o4g85i1PFpFLsx1XAqzBEijapcw1Xk41jLbFyyEky9xL\n",
"XMxndnm+LMGS6+7xmvG8DfQVG8BwFvBAirr8OOZWqwYavGzRP3/0fL74vRlpa0sLyaDfHUYfYK7X\n",
"oMLhcBSGTMo83eyKWdjMj4WYBR308H0bi42/LjG5BFr6aOzyHhOGNLAemIWm/C7PJz733GcecJBG\n",
"tAn4CfANf8O2gZTXbgSaeYnUc9FjWMntmO096NVQTu2YTSzb9iNexpqKPSUxGZPmfbaVgrRzvQ2L\n",
"e/oXdzEhlqaJSFREVorIHO9xSvaj2oTfwq+9ljmYq30UKZR5HnXm4ym8mz0v2hDXeRfYiyXd+N3s\n",
"/gMcjX0x781yfC9gu+cWPAsY+7+PEf2f6RzdKtGk/bImM/iFUfTHvtShUirx3VKRE5ysYVEgWZcC\n",
"+yKtyrnSJcCBWeaDsB4UKwjEzTWie4CzgauAHweGpOzBU+YHvU8zabqpqQ2LWUOiMvctc4C/A8dL\n",
"zJpVbRhEj5p6dqvqSkw/9Ek4X0QVr9PdvMEMrG5iXXkzC4FJGtE/Abdi3VJbaO919YzGzOWG1lY3\n",
"/QOY5f2dE1g3N9txbX0AEeDqLPuotvOx1QZ16PUFONc00B+AHpNi27M5HP8W6EdBP18AWdrzyEXW\n",
"5MfipOWfgE4F3Rd0Vx7n+fh56F8OtOdjv4YuGFB4WZMfl5+O3jytOK9rqfz/naxO1o583DwNPf5C\n",
"e94X9ATQy85Abzks83EngV6XYfvEr6BvDLbnl5yAnnhs5vNdczL6w6PReyehZ3w6cVt9Jdr3m+iK\n",
"2sJe176YzkqrF3NQrjMxF/Acb/kILKkq67HtUObXZFXmhXmtBuAbBTjPo8DjwK/Dui6l8sBiUwp8\n",
"POv+cLDCm6oKUeYRZYrCoR8/j3qiXJb364Mo/FThmSz7dVdoIsLzRDm+s6+Ze7hHl3vAiwrHBJar\n",
"FRqzHPOOwjEKP1a4PtU+ROlOlHqiDMEaVz1MhGcf3YetCkPSnRublPaZwGuJwkaFwd55byXKFarK\n",
"hy5i+3HT+ae33xkKDyvMUhvb7MvxOaL85boTWLnvV7lD4XMKdwS2/4gom4mylSg7iPI4UY5uzzXF\n",
"kgQ13fZc3OzXAA8C40TkReAObOZzmHxVRN7wYvV9su/eZtZQODf7UXSAy7YEeA2bRvSvHPbtA2z2\n",
"4uXjMPfc4pOWUi3K0Xm9qrn0bsJaJO6PSLqWkmA3p5uQzCNhHQ5Hm0mOm2dKfvM5CQvTJbjZg3gu\n",
"99ewKpSdwK4yZb+JG9mBaqrxqnac6k/Un9XurcCLm3trXgKmS0xk0RDKP9fIH7z1m7HfqYnACET8\n",
"9rLzgQOeHEe/kVt5FYv1H4KIP2TmeqwKbCSWZHsn8LdszWWykDEnSzyNnxGxzMKJgADvaDtbBYrI\n",
"k6Seff1trPG8n3V4AzBUVRMS7kREEyMSbeRP2EcieWBfvjyCVSpfQmJRBVhUeWw7z99RdLCsH1sI\n",
"l86GMz+TuP6hv8BZn4Km7qmPAxJlVfjZ43DsCjjpAvjec7ClEqJpai4OXAu/ehiO7ag0zlL5DJSK\n",
"nOBkDYsCyfrdmVCxF75zgi2PrYenb4dxV2U/9uNvw0Vz4axsdTi/AEaBnAmv/h4OvyI/GX/7wAdA\n",
"JAAAIABJREFUILwxGH4zLXF9/0bo1wiLB9hvxczbYFd3uP4EiM6Ez5wDL3q/84MboMce2FQF//gb\n",
"TFsFZ3wmvr2FQlzX3wJrQVVTt5bNwbSvwqzz+7CEpq8DlR3hqgHGAPNSrFcsMS/qPa4CZgS2z8hl\n",
"GUvSGp3r/hmWb/Nkqk3eHnit9py/Q5aTZQ799eEihds5nCv5dPz/fBc80+N8GokyPMPxV/nusj/B\n",
"PQ+ai66vqnIRfOERWJnu9b8O13xnOO8Q5T8d9H7b9Pns8v//9i1fVWTylPz/P9Vnoa3n+2+4Qa3K\n",
"BWDGp+Bi9X7Lsx1/JVx1fyAvq9X+R3Md5/EqsIBxPFB1Hmvv83K78pIXvqlwIzCDbhxHlFVE+d4h\n",
"J7F7Opzt/b6Mfha0RR4491GbgzGDKMuuP57GKdakZ4a3/b/+Ag+neL2riDKNKHPa8P+4zXu8Rztj\n",
"5n8H/oj12D4eq13+e7bj2vrALHH/+deBu1Lsk/YNFeQBo9RGv+Yq89eAFaHK1BUf8DWFXxLlEqL8\n",
"ObD+W9MvZSFRLsnhHNcqvOkrcqIcQYQXBl+Lfvc4u7kK7NtT4R8KV3znOF4jyv91+jVwD/foig+Y\n",
"pvB6YHm6BmLOWY6dovBGuu1EGUKUeqbxNl9j/X5f5t8Kd7dBxk8qXmzczvstouy+7nh2K9R4+/RR\n",
"S0D7rbfcTaFeYQhRPrGumh0KVYFzDlXYpNAzhdzVRGkkSnlbrilWVabptucSM99fVS9V1WdV9RlV\n",
"/TxJw+cLzE9E5E0ReQM4ls6Z0HY9kI8Tdh0wNyRZujJ9sJjUPliMzWf2JxagQOayRItPXQuchuom\n",
"iclJwAMIt4zezK5nx7TMrPe5BjgHuODR8fQjcZqbw+EoHNafPV6elkvM3GcTGcZsa0TXAg0cxShe\n",
"5KVnb+NeEscs58q7JDq/f4uy67DVlBGvh9/m/bVpkVZG+xJwpEa5b2AjFQSbfVlfjxeBsxGpReTb\n",
"iFyOyAiNaCNmXU9qg6yQvkYfyK3OfLZIvDuOiByB1QSGgqpeqKoHqerBqvpxzTykIyz6EJ/VnQv/\n",
"BC5OteEDWGOaD74y35dEZT7nvLcYhnKixCRl5NyT9WPA31Ctk5gcDtwFnK0RveuMRaysq+WcwAH9\n",
"MQ/KTWtqOHL+QIZhXqfQKZXPQKnICU7WsCiYrKr1xL/bkJ8y95POMvEJbuFNXmPR4O30wtpA50uC\n",
"MteIbp6yltNPW0yDbwpj9d3bsAQ3n/9gCc9VQGPLvnFuAz4HfAX4CHDUU/A2IidgHe4OboOskCUB\n",
"LhdlfhjwHxFZISLLsbuOw0Rknoi8mfnQkqWW+BzcrKjqbrUPryM/aol/4Ze2rFVdP3wb28v3soYU\n",
"84YDnAo8IjE5CngIm0n+AsAn3+KtNTUcLTGp8PY9oBkWjLia7td8BPZfzyxvxKLD4QiH4Lzw/th3\n",
"PRe2AdV+S9dUaERfZRc7sKYx/tCkfNkIdCdQMTXndyyt2NtijfssINjzPa7MW4289ngQU9hXAV9F\n",
"9cJZ8GvgvzAPbluV+bWZNuaizE/ByoaOxYLx47Af0TMwy6grkpcyz4R+8Poy50MfrDRwJFaOEuSd\n",
"/dfzJmlc7WpfiilTL2c5cD9woUb0QX/7hHoWD9vGOuBEb9XIByayY1VvztlcyZaL5nJPgd9LWkrl\n",
"M1AqcoKTNSwKLGtQmR9ArjMQzJXdqvNaCt6jPcrcLOpkV3tvSFLmqkeguiqw5jVMIdeSylq2cdN3\n",
"AwtQfQPgW5aoPeWUxWwiaeRqHuJmbM2dy6CV5diF7Y0NwOgH9FPV5d62rkhvCqTMHRnps6aG7diX\n",
"dn3StncunMv7pI+bn9QML8wdys3AjRrRx5O2153xDnVYxQLAyP+ZzhDg5kfuZPJXX+V3hXoTDocj\n",
"JUFlfiD59eHIxdXu92Zvq2UOrZV5L5KVeTI2RnUbZoSki2NfB3wqcMxO4K5vvcA+2PS1gpPLoJUb\n",
"MBfDzdjoU//RlanFJveUtfdEH8h4We70uX8SZcBabzpSkIWXzaYHMF5iMqhlrch1iAy8Cy7+8mkt\n",
"s+RTzWtf+aVZ7AA+JjEpX1PDPq+MYF/gz6iupp29EvKhVD4DpSInOFnDosCyzgYOQKSC/JV5xiQ4\n",
"DwFW0j5lvgzzNvv0Irex2BuwsubUylx1GwFL2ruuddPrqAD6S0x6tVHetOTiZj8P2EdVj9WuNQI1\n",
"E7XYlJ5B2XZ0tIs+T42jivjQmyDv9GxiAvAscDIAIoOAHwCfa67miP87lFOBS72uUMmsnLSBAcAi\n",
"4CM3HcmRo7bwmkZ0XYp9HQ5HoVHdjsWbzwF6YPXZubKZ7Mr8LlW9k/Yp81nA+Yj09JZbu9lTsx5T\n",
"5vlMylxf3tzym9TWjPa05KLM55P9onYdzBrviU2Ha7er/QMcL8uFPnOGUkNqZb4Q6zr4N+Ayb91H\n",
"/7Ef6z53Jjd85Upq93bjBxrRdCWBdVhFwg+Bm+84mPGfWMAthX4DuVAqn4FSkROcrGERgqx/wBqz\n",
"vJki6zsTm8jiZg/I2h5lfhcwB1p+G7K72Y3MlnkSnqzrsdGpbxOCqz0XZf5DYI6IPCEiD3qPBwot\n",
"SBHhz9ytw8XNw8PqT3uv7E0fUt+xvwcMuPduHgHGSEymPT+Kz1xwNtWTNtLw7G38WSOayr3usw7o\n",
"q1GeAO4/fBXNP3yaZwv/RhwORwbuwIyjfOdW5OJmx8t4r6GtMzbsBuOHwDHemlzd7G2yzOlkZX47\n",
"8GPv8UGImddiH4xVFECZf4DjZdmoAXY0lTGUVJa51XcuOWsh+wA/F+X/rj6F4/vt4IffeoHPfHct\n",
"T2c8ux2/Bhix5kauv/9ulNZJdh1CqXwGSkVOcLKGRcFlNVf7d4F8DcCsCXCerP2AzV4GfFtZCQxH\n",
"pBv5WeZjyNEy92QNVZlnGmXh06Cqvyz0CxcxBVXmjrT4DWOGkX5y2UzgLCD64RXUjNnMNa8P4yZU\n",
"9zyc24/OG8ChQ7YzF1iZp5vP4XAUAtWb23BUbpZ5+1zshupORLZiOVL5xMxHkKMyDxwzoPteFuwp\n",
"Y3Jwg8TkAOAbwBc0ojtTHp2FXCzzf4vIj0Rkuogc4j/a8mIlQkGV+Qc8XpYJv8Z8GKlj5mBxrMs0\n",
"SveZt7Hytvt51E92y1HWF7DmDiOxu+9OoVQ+A6UiJzhZw6KIZM2aAOfJ2n5lbtRhvxP5ZLN3J0c3\n",
"u6rO9MrTdr/wJ9YAAyQmQwG8EdC/AaYDf/KW8yYXy/wQbCLYEUnru2pGe2/sn+ks83DxLfPhpMty\n",
"VV2IyFtYNuzBmKWdD/8Bfon1VU5uSuNwOIqXrAlwHmEo81wtc8jPMgdY/6FV9AOewnpo3IrNAakG\n",
"pmCJ1+OxjPe8yKVpzIxgSdoHoDTNxcw7hgHYcIRMljnYxL7PkqTMc5R1FpYRfw3w57YK2l5K5TNQ\n",
"KnKCkzUsikjWrG52T9ZhwNoCvJ5f/ZJPzBxytMwD19WPmz8KnCoxmY4l4F2gEd0OPIZfipsnuTSN\n",
"GSIifxSRx7zlySKSz0SxUsNX5pYU4QCRKkR+XeCzTlpTw7uYdyhTJuqDmKv8cPK1zFV3YW1f68Bl\n",
"sjscJURrN7vIcES+lLTfGKyLW3vxLfN8YubQBsscU+aPYW3RHwAu0oj6eUNPkE6Z25CztOQSM7/N\n",
"e4Fh3vJiOmcsaUdRi7nZNwM9EKlpz8mKKAaVlQyyDgAuC4wzLASTbz+YHcCbGsmQmKbagH3+GlFd\n",
"G1+d83X9b+DLnZn8ViqfgVKRE5ysYVFEsm4CBib95nwOG14CtMg6hsIo85VYqdkEcsuv8S3zfOrM\n",
"wVPmGtE1wK+AUzSijyJSg8hxmPv9WInJvyQmWyUmvwmc5ioykFaZS3xizQBVvQfY6wnVBKTquNVV\n",
"MMvcfvxd3NyowDo4tevGJon97z6AXuQ2U/zPWPw7f1SfQHV+m451OBydxVKgiXj9N8C5wGivhMxn\n",
"DLC8AK9Xh1nKm1DNPhBGdQemyPOpM4e4ZY5G9DqNqD9O/FvAXzWiG7Ca/Pex935G4NjemU6cyTJ/\n",
"1fvbICID/JXePPO2FeiXBr2Jv792K/MiikFlJYOsld7fAWm2g0g/RGpzfKEyYNL8QYwlF2Wu+hCq\n",
"5wRXdZHrWlSUipzgZA2LopHV+kT8BLgeAJEJwGDMazrEVskMCqvMa7AmN7mygfzqzCGgzAMbhwJf\n",
"xMa+DgWO1Yh+AZuJUuVnvdMOZe67N64B/gWME5EXsTd7ZS5voETxY+bgLHMffyZ4/wz7/Am4Icfz\n",
"jW6GDU1lfIjcLHOHw/HB4w5sUMuhwEXA37HBKKMBRpqR0RuzYtvLKixWflcex6wDGvJ8ndbKHC4A\n",
"/olNmTtEI2pecAs/zgIOBbj0Y5n7uWcqTRsoIldjSv0+4BHv+S7gBPIvEyoVaoGtEpNeT49hx/HL\n",
"26fMiygGlZUMsvrKPLVlLjIcc1EtzvGl9p89lOXAKI1opkz2tHSR61pUlIqc4GQNi6KSVXUXIj/D\n",
"sr0Pw5Jgf4RZ4y+9Zy2f32tn9zf/tXYjMtTrWJcrF2DhgBxO33Jd1wJDkzYfAjyM5QlM9Z77zMLe\n",
"+0NzhpJx0lomy7wMS9OvwXrrdvfWVXvruyq+ZX7hmZ/mE5sq7S7wA47vZk9nmV+KJUoOR2RIwhaR\n",
"CkQGJ+0/+XeHoViyh8PhcKTj/zDL9AFUl2Eu9THetjEUJvnNyE+Rg+o7aMqJjZlIVSU1FRv2Msd7\n",
"HmQWcJjERJb0pUemE2dS5mtVNZbukecbKCX8OsP9dvSgMjIjIQEjb4omBpUDGWRNb5lbtulFwO+B\n",
"54DjvPnFIDIQeGGPsPS5MfLCxK/KPgDNcNA/JjMea5hQaFmLjlKRtVTkBCdrWBSdrFbNcg6WIAYB\n",
"Zf5zOInCxMtDJ3BdV2L17P6GGm95IabMD/HWxxC5FlPm0yqbGFa5l4zVRLmUpn3QqMIyFCcdsZJf\n",
"3T6FiRKT5BjHB41MCXCHAM3AbOBprAVrAyLPAAtvP4h55d9j2WfOYUpdLfMlJhc9uQ9H7uzOLly8\n",
"3OFwZEP1OVT9uPhyvJh5X3NXL+8kqdrKFqAbIn4y28HAfM/CXwzUIjIMOA04XSNaBzT02MunJmwg\n",
"YzghkzI/sQCClyJVwA5g0kFrufvT89iD8t22nqyoYlBZyCFmnsrN/kng714p3x1YHGng60O5ffjV\n",
"3HHR2Zymwg3zf82I135PY/e93PSJTzJG4PsZ68vbLmvRUSqyloqc4GQNixKQdQWeZX6x/VbnmqfT\n",
"qbRcV/udDFrnvosdL/b/GHA+MAk4DJEq4JkdPbhifD27Mr1G2gQ4VS1Ev9uUiMi5QBQT+HBVnR3Y\n",
"dh1wCVbXfqWqPhGWHGmo+tMUyoA+v5nG7DU3svn3h3K+xOQN4E/tUUAlTCWW+JjKMv+E90CibMFa\n",
"tN6AfSDvB6ZpRFcQgT4iD67+GVKzm4OrmvSPHSS7w+HoOqwARnnlrROxuQulhq/MF2AzT/4d2PYA\n",
"5t18GctPOxJ4ek8Zl46vZz12A5OSznKzz8NGWz4fXCkik4HzgMlYE/pbJLFBQEdQ9asPMRJYpBFt\n",
"HrKdF2+9nx9j3XfyttCLLgaVgSwx81UkW+YilVgLxLkSk3LgL1giXD1wgEb0Eo1ocMDJwwMb+XTV\n",
"Hu9ONBxZi45SkbVU5AQna1gUvayWpLYKOOAZGAcs6WSJciLpupoyt8TgjwL3BrY9huVtzQSewQaa\n",
"PQswYQObMr1GpyhzVV2oqqmmwpwJ/FVVm1R1OfaPmtahwkHlov6MxQbIA7xy4ZuMwPrlXi4x+UgH\n",
"y1MMVGJfoGTLfBCwznMd/QCbcnSwRjSSpuTM97J01bJGh8MRPrOA83bDem+saKnhW+ZfBe5GdX3L\n",
"FtXNWMfLh4CXMM/m2n3qeeyIlZnr6YstAW4YiX1xO3bYibluum/vwb7E3TcvA0dolKbyPVyPderJ\n",
"mRKIQbWQJWbe2jL3lLnEpALrm/xVjeiODC+wGWv88O+0+7Rf1qKjVGQtFTnByRoWJSLra8D5p9gQ\n",
"pZIg6bquxMacXg78PMXOn0d1LhZSGAmw5Jf8fsS2zKNeQ1PmIvKkiMxL8Tgj+9EJdGSM2pLfhKFY\n",
"ez+A17GMw5Uzb6MKa4Kfyxz4rkTcMk8cfDAY6750JjBPI7os65lUP4PqrFCkdDgcHwRmAaMozXg5\n",
"mDL/JPAaqpnCBDbJzX5ze2OtbNMSmlJS1ZPacNgqvDsRjxHeulaIyG3EyxI2A3P9ux8/PpH3MswH\n",
"drCACaxhCRFAdXtU5Ef7wJQLVjIKWMm/+LxEZWEu5w/GStotX8jLyTL72/8A45tg1xetBK1aRA73\n",
"rpe52RfwdTbwjH+ODpJ3iqr+bzFdvwzLV1GIz2cn/f+LdNn9/0NYLoXfqzHQ81bQWdD8X0UgT76f\n",
"18/CkM9D+Qz4Tcbj4bmnodsZcOdkGDeZzHXmqGqnPbDA/qGB5cmY66QcGIu1ypMUx2koMsEohZVE\n",
"eYEoRydtO0fhX0T5H6Jcn8d7nNGZ1zjP/0dqWeFnCtcq1CmMDqz/VpPwU6JsJsqgopC1CB+lImup\n",
"yOlkdbIqzL0cvtzpcrTlukIvhX8plOXwPhcoHKDwHbW8JE23b6fEzEXkLBGpw9LyHxaRRzEpFwB/\n",
"w1L2HwW+pN6V6CAqsRrzflhGdpD5wP5YC9K0Xof79pPbV/eSlvIBLY0YFJA1Zr4L64wXHIM66OEJ\n",
"CLBBI7ouZPES6CLXtagoFTnByRoWJSTrsb+1Eq6SIOG6qm5D9UxsMlw2VmLe6qxu9s7KZr9PVUeq\n",
"apWqDlHVUwPbfqiq+6rqJFV9vINF8xvG9Ke1Ml8KDL/haV4CpkpMWtVcr62RgZ89mwti7WwBW4RU\n",
"ADuxcX89A+sH/WMyfbGsS4fD4egYVLfQsYZeZ1GHhZuLU5kXMVXN6Sxz1SZgyXf+zWisxOrM5IOf\n",
"G8PHGstheR/299cVfd1mgAyyVgK7Zg2FPZKgzAe/OpzhdIIy7yLXtagoFTnByRoWTtZwaIesQct8\n",
"S6YdnTJPpGpLJbuBnRrR3Sm2+672e7Hm/wms7G3u9w3VmefOliAVwM4TL+Sg/z2CAwPrB9XVMgFn\n",
"mTscDkcYOMu8jVSt7kUzrV3sPvOxJL2HgaMkJv2CG9f15NAB22FTJfv460ooBpVJ1srXhlG+pYry\n",
"uUOZ6K9s7M7gHd0ZhnX061C6yHUtKkpFTnCyhoWTNRzaIavfYMYp8zypXFvDXkhbnL8UGKcR3QY8\n",
"jteTHACRslW9GT1jOWu3VrbM2+0qVDww0bq/raj1blREui3qT3+BpRrJe6avw+FwOLJTB+yL9fRw\n",
"yjwPqtbUoKS3zJdjJXNgfcg/G9g27q1B6EcXs2BLBcMlZs1Vukhcp3L2UIb02sWO1b1s/CDQf/4g\n",
"dqqwsIPES6CLXNeiolTkBCdrWDhZw6Edsi7DmuM0ktgdtRVOmSdStaYXQnrL/F3iyvwxYD+JyWiA\n",
"nWWMXNSf7qcs4b3yveymI9vQhk/Fsr4Mn17HonU9W97XoDcHs5PS7cLkcDgcxY3qDlRPRXUqqhsy\n",
"7eqUeSJV7/ekG+kt87VADSI1XoLcE3hz3x8Zz9Seu9k5tIH6IQ2sx2rou0pcp3JDNSOOeo9Fe7rR\n",
"wyvLGzxvEM3QOZZ5F7muRUWpyAlO1rBwsoZDR8jqlHkiVet7UkY6ZW51jSugJSY+E5gB8Pi+HDVp\n",
"A8uBhitf4SXgFonJh8MVt8Oo2FbB6IPep27kVjYCBwKDFg2gnE5S5g6Hw+GI45R5IpUbqulBejc7\n",
"mKt9jPd8JjBDYiJzh3DQoWuYA2z/2iusBb4JXN0V4jq7u1G5u4zhx7xH3aQN1AOHNnVjyMre9KST\n",
"3Oxd4boWG6UiJzhZw8LJGg4dIatT5olU1VfRg/RudkiMmy/BruH4BQMZddbbPA80YC1PHwU+TPfS\n",
"v8arelPdTdnYfwdbptexGfjQm4MZV76XnRrRjBmWDofD4Qifklc0BaZqcyUVZFbmy/GUuUZUgWeA\n",
"O4Y00HTsCubjKXON6FpgLd/J3LWnQxH5LiIT0m1OF9dZ35NKUdYDjScvpQH40CPjOWj4VpaGJGlW\n",
"XLys8JSKnOBkDQsnazi4mHnHU7Wtgkpyt8wBrgL++JMn2Y6Naw32L38GOD4MQdvI6cCX8j1oYxUV\n",
"wAZg+9Q1AFTfeRAHH7aaZwsrnsPhcDjaglPmiVRtqaAPaWaoe8wFpiPSHUAjulGj/OHshfQBVhN3\n",
"swM8w9ucG6rE+VEJnI9IeaqN6eI6G6vo0SysA7Z3g2rg1WV9qf3KqzwanqiZcfGywlMqcoKTNSyc\n",
"rOHgYuYdzM4yqhp7UIt13UmN6hIso/3EwNpBwGZUd2OWua/Mn6SCyRKTQSGJnIDERCQmgzPsUgE0\n",
"AaflflLpvqEaae5mbnbM6/DSscvZ+aFVLGuXwA6Hw+EoCE6ZB1g4gH5Ve6jPoT3p7cCFgeVhmFUO\n",
"Zpn3BNCIbmMc9wKfLriwyYiIKNcCiyQmZWn2qsCa3RyeamOauE7Fup7swXOzY5b5L26/D4AOnWEe\n",
"xMXLCk+pyAlO1rBwsoaDi5lnwG+XWkgW96dvdVOLUs7EPcAZAXf1cOKu+aCbHUzxX1Q4KVMgcuJD\n",
"41nebwf/7b1+uqltFcBibKRerlSuq6EZWI+XD6BRmoc2gPdaDofD4ehkSlaZA0MKfcLlfejdc3fG\n",
"eLlhbfXqiCvNUcRd8+ZmFzkakdP4Ps3AQInJgSnOVBCa4Yovnk6PWx5Gy5p5HpiWZteKDVUsf2AC\n",
"B0hMpiZvTBPXqVhfjWKWue9mHwSs85rodAouXlZ4SkVOcLKGhZM1HFzMPDMprU+JyWESk9q2nHBF\n",
"LTU1u3kvx93nAr5CHIXF0SHuZj8L+Bh7aMaGslzQFpkkJt1SKd4gdx/AURur2XzOAhZMr2Mdadzo\n",
"QMVRl3L8105lf+AhiUlFDiJUrrfc/KCbfTCd6GJ3OBwORyJdSpl7rve7gS+05YTv9aGqdhfLc9x9\n",
"DjDFez6auDJvxBTeeKC/Fyu5HfisxCwDPk++BcyWmKQuKRPp/fQ4BjR145Yy5anLZlNDBst8eR/6\n",
"f/8ZQHkT+FxwY7qY+cYqumHKfAdQhXlF3m/DeykYLl5WeEpFTnCyhoWTNRxczDwzqSzz/bFWq6e3\n",
"5YQre1MxoDHnDO2gZR5X5qp7gZ1Y//L+ABrRtyGjxZwSicmhwFeBY4AbJCajUux20OL+bN9TxnLg\n",
"qbMXsD8wWWJSmXgy6QaU7S5j8LBtNB6xkt8AX89BjMoN1Z4yt/e2C7vGzjJ3OByOIqGrKfOPA38E\n",
"pkhM+uV7wrre9NhvPYtz3H0uMAURwZR50D3fgCm8/oFYyZtpZE7LEXV84+LZbNIo/wFmAQek2G3q\n",
"u33Yi8Xs36hpYjywNMVrVQC7EYYPaWDl039mDTBKYtLL3yFVXGdHdyq3VFJGvF/9duBgrJVtp+Hi\n",
"ZYWnVOQEJ2tYOFnDwcXMM5NOmf8VG4BySj4nk5jUbC+n2zdfaHGXZ0Z1Paa0J2IWeDALfjuwx1vv\n",
"sxhzvefMzu6cdO4CxgFnYNPJUr3nKet6UoUNrt8K9EJTvlbFXmEXMHTkFt6t3sMIYJEnfxwRQWRZ\n",
"+ffkfonJh+YOoX9VE83eyFewMMJhwIJ83ovD4XA4wqNTlLmInCsi80Vkr4gcElg/RkR2iMgc73FL\n",
"htMMkpj0bDk2JuWYa/tF4Gkg3/GjY0Zvhj672JHHMS9gZWerPRe0TwMwD+iv8Jy3bhGQti96Mq8O\n",
"l7GL+9N37CYuAb5PGmW+qZLJe7p5M9hVdwHN5XtZmuK1KtdXsxvYVtPECqw87e3gOb24Tu9lfRnb\n",
"VMaZwLkLBzCwdhe7A+fZDuxHJytzFy8rPKUiJzhZw8LJGg5dOWY+D8v2fj7FtiWqOtV7ZOojvphE\n",
"q3IsUOdZkG8Bk/MRaMwmDhi3CSBBcWXjYeBSaGXNN3gyKJYM58ubs2X+5mCuGbidjZM2ci8wsVtz\n",
"amW+vA/9yvey1hv6ArB1SAOrUrxWxQpzx6/0HiNJfYPQ768HwJBtLAU+uqoXg3rvYmdgeyPWRe7d\n",
"XN+Lw+FwOMKlU5S5qi5U1UXtPM18EmPI46El3j0f2D/nxjIi1RfP4UfAG3nWTj8KDKC1Mt+OxZQ3\n",
"nhBvnboY2FdiktM1nzuU6UMbmIPqTqDxkjmsxSziBFb2plf35gQX/9apa1lLa8u8YkUtzVhzmzri\n",
"yrzlnF5cp/9dB8JPn+QVoP+DEzmt906CY063A++gWbvkhYqLlxWeUpETnKxh4WQNhw9qzHys52Kf\n",
"KSJHZ9jvLRKV+QTiynwdZhVn6lMe5EMr+lD96HjuyEtSax7zMrSqTa8H3gE2DoXe4LV2tZj2sFxO\n",
"vag/I4Zv5XVvce2vH0aAconJgOB+q3rTs5sm9JLf+ql51JNCmb9Xi5KozBPc7AAvD2f0yt7wmXn0\n",
"BR55ty/7/+wJ7g3ssh0XL3c4HI6iIjRlLiJPisi8FI8zMhy2GhipqlOBq4G7ROLZ1knMo7Vlvgha\n",
"5ozPJ3dXe/Xi/qDSJtdxDLgvad1lwD+BjX9JdEfn7Gpf2pc+o7bwkre4tryZIaRwi6+poVIl4WZi\n",
"6ycW0IQp/mBGf0VdLd1IVOaLgXF+/buqznx2LAeO3cz2MmV/4Otv/ZonjqpjXuA8jRSBMnfxssJT\n",
"KnKCkzUsnKzhUNIxc1U9SVUPTPF4MMMxu1V1k/d8NlZilVr5/YQLeYqjRCQqIlexkGl4lrmIzOAN\n",
"NmN154jIjKCbI3k5Aoct2kxPPMWbbf+EZdXHBXonbIepAkdj5Vz9A/svAiZkPX9v+UhdPeVnLuQ/\n",
"APdA8/dtLvqjwI0yUE4XkRmIyKpe9Nj+GmWB47dGlCNZxBr/2onIjC/CESt7mzLvC/s8C0M1ym5g\n",
"JX/nAv/4xf2YUPYOG5+BwRpl76BGBl8N/QLnf/hiWJfz9XHLbtktu2W33KZl7/lt3iNa9fs4AAAc\n",
"VUlEQVRKJlS10x7As8ChgeUBQJn3fByWqNUnxXFKlG5EaSBKrapClPd+eyhnqXVdgyhfIcpvc5Fj\n",
"j3BB5bdpItr6tdr1gN/eBD9vkTvKN4lyU7bjJn+JDx90Bc0K4p3nfxSuIYoQ5WaiPEeUHgqVH/ks\n",
"zUQ5LfCaf1G4gCh/JcpnA+uPP+JSNhLlVG95jcIIotxGlCu86zrjxAt4+dKP8YTC6wpHKCxWmNiZ\n",
"n5M0n50ZnS1DV5O1VOR0sjpZP6iymspOva2zStPOEpE64AjgYRF51Nt0LPCGiMwB/g5crqqbU51D\n",
"I9qMuXv3l5hUoQy8dDZ3Au8ichjmhp+S6thkFgxkYDelWSOpX6sd1PeEYJ/4nMrTqpo4asJGGvz/\n",
"HrAGGOKFD67C4tY/WFNDzetDAQsp+GzF4vSLk16rYn1Penjngrir/TkCZXyrezFs340sBWZj9eQj\n",
"sJsqh8PhcBQpnZXNfp+qjlTVKlUdoqqneuv/qaoHqJWlHaqqD2c5lZ8EN757M+91V7YDD2AZ2q8C\n",
"BwQ7nKXj9WEMH9jIlna+rVRsvIKETPCcYuY7e3Do+HrWB1atBYYCaET3AhcDX7jkTI7pvYu9GtHl\n",
"gX19Zb4o6bUq6qsoJ95TvQ5T1M8Dx0pMRFVnrurNwCNWsRC7fh8FdqC6Pfe33DGoi5cVnFKRE5ys\n",
"YeFkDYeOkLUYs9nzwVfmkwc0UoeViG0C+mpEd2AKKWvzmOV9GNSvkUJb5WAx8wGI+INPlgJjsw1c\n",
"2VTJARM2JJS7rSUw8lUj+j7wyvOjiX54RauZ4ikt893dqNxaQTm03CT4lrnfi36cxKR/Uze6H72C\n",
"Jdi1OxESMuUdDofDUYR0FWW+/4SNbCKuzPt425/GEscysqaGAf13hKPMn4HzgFcQqfRuMN7HRqam\n",
"RGIyYXMlI85ayJzA6gRl7nFPYzkHHf8u9Unrg8p8vF9r/9YgBlQ3sVsjLfXhdcBIz3X/FHA2z3HV\n",
"we+zvbtSj7nu91CkyjyYMFLslIqspSInOFnDwskaDh0ha1dR5pOnr2QnAcvc2/4McEK2k6zvSb/+\n",
"O1oGiRSSd7ZZXH8DMMhblxzLTubLH3uHt2t3JZSbraG1Mr+vqok1py5uNYp0C9BbI1qPdbMbDLCk\n",
"P/1770xoVetb5gA3AtcyjIuu+zcNwEasKcxsilSZOxwOhyNOqSvzNUB34JgTl1KGKfPNxJX5a9hk\n",
"sKHJB0pMuklMbpeYTNtQTZ/BDWwouHSqi85UnYI1lfGVeXIsOyhTd+DCyEzWkzi4ZSNQi0h5y6kj\n",
"umnTj/n8gB0JMXmIW+YJr1XXm/61uwjGvluUuUb0LeApxrPqtEVUQYu1/xxW2150uHhZ4SkVOcHJ\n",
"GhZO1nBwMfMseC7it4B+R9ZRCywnYJl7LuUniLdUDXItcAEwY2MVvYdvDXU+9zri3egyJcHtg1I/\n",
"aSNjCTabUW32zpFgnVfspRpITk4LKvMWL8D7NfSr3ZUQX1+JJcD5XDF0K+d0s+z7Td6676D6i+xv\n",
"z+FwOBydSUkrc4+3gCXVexhJazc7wEPA6cEDPAv4O8APgP3qq6jZZxNrwxDOi5W8T9wyXwrsk2b3\n",
"/QY0sgYox1zcQYJucZ+eWEe2IMmW+SSAjVX0qd3JtsB+G4D+iMXUNaLbBv6cw4Bt+BPg4qVxRYeL\n",
"lxWeUpETnKxh4WQNBxczz403sJry0bROgAPrmna8xKQqsG4S5qJ/DJi8sZrqw1YnuLULTdAyXwkM\n",
"T7Pf5OPfpQb4k2eNB3mP1Mo8k2U+EzgZoL6K2r47A+V3qjuw/vUt12U09IJQcgccDofDESJdQZnf\n",
"9s0XuBYQLF4ejJmjEd0I/At4WWLi93I/DJiFDRqZWrGH5tFbQslm92MlQcs82b3dQrdmJp+4jPHA\n",
"bSk2v0frLPhsyvxlYIjEZNyWSnr33dGqlr4eaOnf/oCVqZWEMnfxssJTKnKCkzUsnKzh4GLmOaAR\n",
"3fXjp6gG1nhu4WQ3O8CFwC+BxyUmY/GUuafoNw3ezi5IyPQuNEHLfANQIzGpTN6pYi9TJm6gHtVV\n",
"Kc6RSplnjJl7DWYeAD6+uZJeA7e3xMJ9EpQ5FpNfg8PhcDhKitJV5oHMbqAGWuLBDUAFIj38jRpR\n",
"1Yj+Efgp8A+sjewsb/OCodvYQ0jKPDlm7rWhXU2Sq11i0m13GftO2sDcNKeqIzfLfCfQPXB97gM+\n",
"vaWCnkMbWtWk1xO48fkZHAPh5A4UGhcvKzylIic4WcPCyRoOLmaemWDP857gZWqbdb6ZxLi5zy+9\n",
"bYdAS1OWt4dvYy8dZ5lDalf7qJ672T2oMa0yzy1mbu9/Kxb/Bngc6P5uXwaN2dzKhb6JgGXeC/pT\n",
"Isrc4XA4HHG6ijKvgYSyq4S4uY9XynY58CuN8hnPer/7vLfYgVm0BScQM8+mzPcdu5mdWHZ+KtLF\n",
"zJOz2cHi3oOhxdV+JaCT17eqpU9ws38BdlEiytzFywpPqcgJTtawcLKGQ0fImrFHeJETtLyTlXmq\n",
"uDkAGtElwJVEZRvwpEb0eaKihGuZbwD6IVLmlX2lUuYjxm+kkvTKfANQjchXgApUf0ZqNztYK9b9\n",
"se5zaET//eZgeXzk1lYNZlzM3OFwOLoApWyZt0mZAyBS5h3j71NFmDFz1SaszWp/b/UqkpR5VRNj\n",
"9tlENdbopTXmPn8Pa716ubc2VQIcWKneQcEVB61jL2Z5B0mImT9kzWxKwjJ38bLCUypygpM1LJys\n",
"4eBi5pnJpMxTutkD+KVbvlUamjIPkFxrnqDMBzRyQP9G1qG6O8M56rAYeE9EJpLeMn8TODBpXQWp\n",
"lXmLZV7uYuYOh8NRknRVZZ7cOCYZP97ez+uAVklIyjwQK1kNDPOet1Lme4SRg7ZnbSl7I3A18a52\n",
"6ZR5K8scU+bJeQHxBDiRbifbNSsJZe7iZYWnVOQEJ2tYOFnDwdWZZ6btbvagMoceQLM3JSxMVhBP\n",
"YHsX2FdiUuZv3NmDwSO3tMwaT43q46guAx4EPkZ6Zb4EGIxIr8C6dJa5f536Ag2oJu/jcDgcjiKn\n",
"lJV5cjZ7UKmtJW4FZzq2HyG72AOxkpZsdI3oOm95mr9fYw8G7rOp1TjTdDyFWd4jSKXMLcnubWw8\n",
"rE82N/vQR2mVIFe0uHhZ4SkVOcHJGhZO1nBwMfPMZLLM55OoyJLxlXlfOiZeDq1Lyx4FPgogMem5\n",
"VygfuSVHZa66E7POe5G6NA1MmU8KLGdT5kN20aqpjMPhcDhKgK6qzOcBB/oTwVLQYZZ5IFaSSpmf\n",
"6j0f3mcn27rRqnd6Ju7x/qZys4PF5YPeiazK/ON2A1ASuHhZ4SkVOcHJGhZO1nBwMfPMBJV5vAOc\n",
"sQ5oJmn+d4BarJGLr8xDaRiTxApsspvPi8A+EpPRwIiBjTSSnzJ/EvgL6W9EVpHYMjaVMt+GZcZ3\n",
"B84HXs/j9R0Oh8NRJHQVZZ5omVtN9lu0Ls/yqcWS0DoyZm6jT63GHY1oE/AbbK76qOFb2QV5TG5T\n",
"3Y3qBSlGpfokK/NKkpW5HbsZ+BUwsNbGyZYELl5WeEpFTnCyhoWTNRy6bMxcRG4UkbdF5A0RuVdE\n",
"agPbrhORxSKyUEROznCaTG52MFd7urh5H2zcZ+jKvAWLc28isa3rjcBZwE9PX8Rm8rPMs5E8zCWV\n",
"ZQ7wJ8yLcd5W2FvA13c4HA5HB9FZlvkTwP6qejCwCLgOQEQmA+cBk4FTgFtEJJ2M2ZR5LpZ56Alw\n",
"SbGShLi5RnQT5t7+yNdeoYHCKvO4ZW65A6mVueo3UP0SqktdDCocSkXWUpETnKxh4WQNhy4bM1fV\n",
"JzXuHn6FeAOVM4G/qmqTqi7H6qWnpTgFZB60Apkt81pgOR1pmRvBWnMANKKPa0TnYDcnubvZs7MW\n",
"GOANk9kPWOVqyB0Oh6NrUgwx80uAR7znw7DYso/FmVNT7SVuQWplPh+Y7Meok6jF3NBlmHXeETFz\n",
"SNGTPUmmwlnm1gRnPebWPw54JtshLgYVDqUia6nICU7WsHCyhkNHyBra1DQReZLU2eTXq+qD3j7f\n",
"Bnar6l0ZTqVp1m8FeiOymVSjQFW3IrIeGItZ+EF8xVmP3UB0lGW+mUSPQrJMhbTMIe5qPx64v8Dn\n",
"djgcDkeREJoyV9WTMm0Xkc9hTVNOCKxeBYwMLI/w1rXiU9DjRYhshIb/hj3XwjEKM71zzwBQL24u\n",
"IiM8mWYCPApDb4MJ95gyH/4X6HOByAx/e8vxBVhW1ZkBebYAo5L3Lxc57nHofZzXga1gr2/eh5FP\n",
"wYnfh7tneteukO+vM5d9ikWedMv+umKRp6ss+xSLPF3h/5/we1UE8nSlZZ98jveef847dDkZEKvi\n",
"6lhE5BTgZ8CxqrohsH4ycBcWJx+OtS3dV5OEFBFVmAV8BXuD81AdlOKFfgQ0onoDIkcBG1B9B5GV\n",
"wHTgr5g1vBTVrxX+nbaS51LgKFQvSVrfG1iNak2BX+8WzDMxFtVJ2XZ3OBwOR/EiIqqqKZuhdVbM\n",
"/GYszv2kiMwRUzqo6gLgb8ACrEPal5IVeYDFwARaN4wJEsxo/ybwBe+572ZfDhyJjQwNhaS7si2k\n",
"drMXOvnNZxVwFHBxLju7GFQ4lIqspSInOFnDwskaDiUdM8+Eqo7PsO2HwA9zOM07WO/xN0ivzOcB\n",
"3/aeTwUGYglx1d4xF/gvmpPg7cfi/K0pbPJbnD8C96JaMm1aHQ6Hw5E/naLMC8RC4FxSZ7L7vAOM\n",
"QGQipjD7AYOAbaTvnFZQkuoL01nm4Shz1bXkMZ/c1W2GQ6nIWipygpM1LJys4dARspa6Mp9EJmWu\n",
"uguRZzHr/HVsytjZWCe2zmALqS3zsNzsDofD4fgAUAx15m1lMbAPZtWms8wB/oV1WZuNDTf5X+Dn\n",
"oUvnkRQr2UpHWuZ54mJQ4VAqspaKnOBkDQsnazh02Zh5QVBtROR94AxgY4Y9HwIEmIM1oSnDBot0\n",
"Bpnc7M4ydzgcDkeb6JTStPbSkp4v8hiWjT4F1WUZDvglcCOqdR0lYxo5BGgCqlBtCqy/HuiN6rc6\n",
"SzSHw+FwFDeZStNK1zI37gF+n1GRA6he2THiZEFVEfEz2oPeBGeZOxwOh6PNlHLMHFRvRfXezhYj\n",
"EyliJanK04pCmbsYVDiUiqylIic4WcPCyRoOHSFraSvz0iRV3LwPRZAA53A4HI7SpLRj5qWIyPPA\n",
"d1F9LrDuUeBmVB9Je5zD4XA4PtAUYzvXDzKp3OzOMnc4HA5Hm3HKPGRSxEpSudldnXmeOFkLT6nI\n",
"CU7WsHCyhoOLmXdNijYBzuFwOByliYuZdzQiPwa2oPqjwLrtwBBUt3WaXA6Hw+EoalzMvLhIdLOL\n",
"9AAqydyS1uFwOByOtDhlHjI51Jn3xiz1TneRuBhUOJSKrKUiJzhZw8LJGg4uZt412YJlr/u4THaH\n",
"w+FwtAsXM+9oRI4Doqge6y0fAvwR1amdKpfD4XA4ihoXMy8ulgHjAstFUZbmcDgcjtLFKfOQSREr\n",
"WQkMRKTSWy4aN7uLQYVDqchaKnKCkzUsnKzh4OaZd0VU9yKyEhiNyHigP67G3OFwOBztwMXMOwOR\n",
"J4DfA38FHgbeK5oxrQ6Hw+EoSlzMvPhYBpyPeUZOpUjc7A6Hw+EoTTpFmYvIjSLytoi8ISL3ikit\n",
"t36MiOwQkTne45bOkK+QpImVLANOB54HyikSN7uLQYVDqchaKnKCkzUsnKzh0JXrzJ8A9lfVg4FF\n",
"wHWBbUtUdar3+FLniFdQpqRYtwyzym/ErPJiscxTyVqsOFkLT6nICU7WsHCyhkPosnaKMlfVJ1W1\n",
"2Vt8BRjRGXJ0EH1SrFsGNAPPAXcCSzpUovSkkrVYcbIWnlKRE5ysYeFkDYfQZS2GmPklwCOB5bGe\n",
"i32miBzdWUKFzHzgclS3ofplVGd2tkAOh8PhKF1CK00TkSeBISk2Xa+qD3r7fBvYrap3edtWAyNV\n",
"dZNYZ7T7RWR/Le1pYmNarVHdBfyhwyXJzpjOFiAPxnS2AHkwprMFyJExnS1AHozpbAHyYExnC5AH\n",
"YzpbgDwY09kC5MGYsF+g00rTRORzwGXACaq6M80+zwLXqOrspPWlV0/ncDgcDkc7SVea1ilNY0Tk\n",
"FOC/gGODilxEBgCbVHWviIwDxmPx5QRKusbc4XA4HI4C0ymWuYgsxkqy6r1VL6nql0TkHCAGNGEJ\n",
"Yt9T1Yc7XECHw+FwOEqIkuwA53A4HA6HI04xZLPnhYicIiILRWSxiHyzs+UJIiKVIvKKiMwVkQUi\n",
"8iNvfVREVgaa4ZzSyXJODMgyR0S2iMiVItLv/9s7/2CrqiqOf7788hcaEkWpz5FxDKVhFEFyNNRQ\n",
"cUAUrSxNEx2n/kqccNL8SU2TU05mkzrVJJpmaIY/oplUZICRJJWejx8qoKKmIuIPtLEfKsHqj72u\n",
"97zrPffByH373tf6zLxhn3X2OefLmn33unufffeS9KCkpyXNl5Tlpx+SbpK0UdKqgq2uttwbDdXT\n",
"Wjh3oaStkoYWbJd4210jaVJurZLuKPjueUldbs/mV0kdkhZJelLSE5JmuP00t23xBbLFa7L4tUxr\n",
"4Xy3NtCifh0v6THXs0zSYYVrcvm1rC+t2wYy+3Vb+/3JhWt2vF/NrG3+gP6k32TvBwwElgMH5dZV\n",
"o3FX/3cA8AjweWAWMDO3thK9/YANQAdwNXCR2y8GfpRJ0wRgDLCqYKurzdvCqt7W2Eir2zuA+4Hn\n",
"gaFuG+VtdqDrfhbol1tr4fxPgMtz+5X0K5hDvDwYWAscBBwIfAZYBBxaqJ/Nr2VaG7SBVvTrYuAE\n",
"t08GFuX2qz+/Xl9a1gZy9wPb3O83y6/tNjIfT9oh7gUz2wzcAUzLrKkbZvZvLw4iffl4y49bddHe\n",
"cSSfvgScDNzi9luAU3IIMrMlVP1WoSW01VKiFeCnwEU1tmnA7Wa22cxeIH2IxzdXYZUGWpEk4Cuk\n",
"5D9ZMbNXzWy5l/8JrAb2MrM1ZvZ0nUuy+bVMq5+u1wayUaJ1b9KX+Y95tSHAei/nbq+1femmBm0g\n",
"K9vZ7zfFr+0WzPcGXiocv+y2lkFSP0nLgY2kb7hP+qnzlfain51r+rqE06l24MPNbKOXNwLD80iq\n",
"SyNtI9RCGw1Jmga8bGYra07tRWqzFVqp/U4ANprZuoItu18l7UeaTXi0QbWW8GtRa4M2AK3l10eA\n",
"7wLXSHqRtMV0ZXvtrH6t05c+1cMl2fy6nf1+U/zabsG85VfrmdlWMzuEtEXtUUob7P8CGEHan3cD\n",
"cE0+hVUkDQJOAv5Qe87SfFBL+rtGW2WjoTHATGCOpN1zaZO0K3ApaYrtA3ODS1rFx2cAcwrH2f0q\n",
"aTAwF7jAR5LbQ6/6taiV9EucsjbQin6dDcwws32BbwM3Nbi81/xa0peWkdWvO6Df/8h+bbdgvp70\n",
"HqpCB92/4bQMZvYPUq7ycWb2mjmknd96baqqByYDnWb2uh9vlPQpAEmfBl7LpuzD1NVmZu+b2Vte\n",
"fhxYR9qfIBf7k96DrZD0POnD3SlpOB9uv/tQndLMhqQBwKnA7yu23H6VNBC4C7jNzO7toXpWv9bR\n",
"WtYGPtmifh1vZvd4eS7V/qkl2muxL21QpyX6gW3s95vi13YL5n8DDvCVi4OArwLzMmv6AEnDVF1l\n",
"vQtwPNBVCULOqcCHVj5n4gy6vyOdB0z38nSgp060N6mrzX3e38ulGw31Fma2ysyGm9kIMxtB+rJ5\n",
"qL8imAecLmmQpBGu9bFcWgscB6w2s1cqhpx+9ff3s4GnzOxnZdUK5Wx+rae1QRt4rUX9+qyko708\n",
"kZTJEvL6tW5fWlutpn4uv25vv98cv37UFXS9/UcaTa4lLRq4JLeeGm2jgcdJKxVXAt9x+61+vIIU\n",
"hIa3gNbdgDeA3Qu2ocAC0od5PjAkk7bbSdNm75PWSJxbpg34IvAE6YPeCZyYSet7Fa0155/DVzL7\n",
"8aXedtfgK4hzawVuBr5ZUzebX0krgbf656jL/yaTFj2+BPwHeBW4L7dfy7SWtYEW9es40pqE5cBf\n",
"gTEt4NeyvvTUem0A+FJGv253v98Mv8amMUEQBEHQ5rTbNHsQBEEQBDVEMA+CIAiCNieCeRAEQRC0\n",
"ORHMgyAIgqDNiWAeBEEQBG1OBPMgCIIgaHMimAdBi+ApEy9s4v0/4akaOyUd2azn9KBhqqTveXmm\n",
"UjrLFZIWSNq3UG+6UrrbpyWdXbCP8P/DM0rpWwe6fZik+5XSUD4h6Ry37yTpIUnR1wV9mmjgQdA6\n",
"NHvTh2OBlWY21swebvKzqOzIVcOFpD2rIW20MdbMDiZtI3q1XzcUuJK0/eV4YJakSlavHwPXmNkB\n",
"pMxU57n9W0CXpf2xjyElDhlgZu8BS2iRLHtB0CwimAdBRiRdJmmtpCXAyIL9G5Ie85HmXEm7SNpd\n",
"0nO+lzqS9vDj/jX33E/SwsKIt0PSIaRAOM0zS+1cqD9R0j2F4+Ml3e3lSZKW+mj+Tkm7uf0K17dK\n",
"0q8K1y6WdK2kZcCMGl0dwCDz7HdmttjM3vXTj5L2qAY4AZhvZm+b2dvAg8Bk3470C6TAD91T4W4A\n",
"9vDyHsCbZvZfP55H2ro4CPosEcyDIBOSxpLyCxwMTAEOozo6v8vMxvtIczVwnpm9AywGTvQ6p3u9\n",
"LTW3vg642Ue8vwN+bimP9ZXAHWY2phBEMbOFwIGSPu6mc4HZkoYBlwHHmtlY0jaZM73O9a5vNLCL\n",
"pKmV2wEDzewwM7u2RteRpNF4Pc4D/uzlshSRQ4G3zWyr29dTTR15I/BZSa+Qts+8oHD9cuCIkucG\n",
"QZ8ggnkQ5GMCcLeZveuBeh7V5BGjJS2RtBI4Exjl9htJwRbgHNK+6rUcTjWd6W2kPbnxe5elY/0t\n",
"8HVPGHE4cJ//OwpYKqkLOBuovNeeKOkR1zexoA8K2ddq2Jc0gu6GpLOAQ0m5tMvo6RXEJcByM9uL\n",
"lHLyBnkKTJ9q71ecjQiCvsaA3AKC4P8Yo3twFdWg9RvgZDNbJWk66T0wZrbUp9GPAfqb2VMl926U\n",
"Q70eNwN/At4F7jSzrWlWmwfN7GvdbpyC4g2k993rJc0CioHyXw2e002XpONISSeOMrPNbl6P/3+d\n",
"DmAhsAkYIqmfj873oTqCPwL4IYCZrVNKPTqSlGmx8txIRBH0WWJkHgT5eAg4RdLOPoqcWjg3GHjV\n",
"V2ufVXPdraTp85tK7ruUNAUPaVT/UE9CzGwDKaPa5VRH+48CR0raH0DSbpIOoBq435Q0GDitp/s7\n",
"fwc+SAspaQzwS+AkM3ujUO8BYJKkIZL2JKWUfMBSVqhFhedNB/7o5TWkVK4o5Y4fiafAlLQTsMVH\n",
"6EHQJ4lgHgSZMLMu0pT0CtL74mJO4ytIwfQvpHfmxVHlHGBPuueiL3I+cK6kFaRgXnl/bDQenc4B\n",
"XjSzta7vddJU/u1+r6XASF+U9mtSysn7Xee28DBpOr3C1aRUvHN9Ud69/ty3gB8Ay0g++b4/E+Bi\n",
"YKakZ0g+mO32q4BxrnMBcJGZbfJzY0ipPYOgzxIpUIOgzZD0ZdJodvoOvu/1QKeZ1XsPv6OesRA4\n",
"02cCegVJVwHLzOyeHisHQZsSwTwI2ghJ15F+ujXFzJ7dgfftBN4Bji+8u97hSJoCfM7MZjXrGTXP\n",
"24n007ajLTq7oA8TwTwIgiAI2px4Zx4EQRAEbU4E8yAIgiBocyKYB0EQBEGbE8E8CIIgCNqcCOZB\n",
"EARB0OZEMA+CIAiCNud/L6Kwb9ko57cAAAAASUVORK5CYII=\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x108b8bfd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8,5))\n",
"plt.plot(reana_temperature - 273.15, 'k')\n",
"plt.plot(soil_temp_sfc, 'r')\n",
"plt.plot(soil_temp_25cm, 'g')\n",
"plt.xlabel(\"day of year ({})\".format(start_year))\n",
"plt.ylabel(\"Temperature (C)\")\n",
"plt.xlim((0,365))\n",
"plt.xticks(np.arange(0,365,35))\n",
"plt.grid()\n",
"plt.hlines((reana_temperature - 273.15).mean(),0,365, \"k\")\n",
"plt.hlines(soil_temp_sfc.mean(),0,365, \"r\")\n",
"plt.hlines(soil_temp_25cm.mean(),0,365, \"g\")\n",
"plt.legend([\"reanalysis\", \"sfc\", \"25cm\"])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-3-clause |
AlJohri/DAT-DC-12 | notebooks/intro-python.ipynb | 1 | 86301 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction to Python"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Forked from [Lecture 1](https://github.com/jrjohansson/scientific-python-lectures/blob/master/Lecture-1-Introduction-to-Python-Programming.ipynb) of [Scientific Python Lectures](http://github.com/jrjohansson/scientific-python-lectures) by [J.R. Johansson](http://jrjohansson.github.io/)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Python Program Files"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Python code is usually stored in text files with the file ending in \"`.py`\":\n",
" ```\n",
" myprogram.py\n",
" ```\n",
"* Every line in a Python program file is assumed to be a Python statement, or part thereof. \n",
" * The only exception is comment lines, which start with the character `#` (optionally preceded by an arbitrary number of white-space characters, i.e., tabs or spaces). Comment lines are usually ignored by the Python interpreter.\n",
" ```\n",
" # this is a comment\n",
" ```\n",
"* To run our Python program from the command line we use:\n",
" ```\n",
" $ python myprogram.py\n",
" ```\n",
"* On UNIX systems it is common to define the path to the interpreter on the first line of the program (note that this is a comment line as far as the Python interpreter is concerned):\n",
" ```\n",
" #!/usr/bin/env python\n",
" ```\n",
"\n",
" If we do, and if we additionally set the file script to be executable, we can run the program like this:\n",
" ```\n",
" $ myprogram.py\n",
" ```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Example:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"../scripts/hello-world.py\r\n"
]
}
],
"source": [
"!ls ../scripts/hello-world*.py"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"#!/usr/bin/env python\r\n",
"\r\n",
"print(\"Hello world!\")"
]
}
],
"source": [
"!cat ../scripts/hello-world.py"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"python: can't open file 'scripts/hello-world.py': [Errno 2] No such file or directory\r\n"
]
}
],
"source": [
"!python scripts/hello-world.py"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Jupyter Notebooks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This file - a Jupyter (IPython) notebook - does not follow the standard pattern with Python code in a text file. Instead, an IPython notebook is stored as a file in the [JSON](http://en.wikipedia.org/wiki/JSON) format. The advantage is that we can mix formatted text, Python code and code output. It requires the IPython notebook server to run it though, and therefore isn't a stand-alone Python program as described above. Other than that, there is no difference between the Python code that goes into a program file or an IPython notebook."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Modules"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Most of the functionality in Python is provided by *modules*. The Python Standard Library is a large collection of modules that provides *cross-platform* implementations of common facilities such as access to the operating system, file I/O, string management, network communication, and much more."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### References"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" * The Python Language Reference: https://docs.python.org/3/reference/index.html\n",
" * The Python Standard Library: https://docs.python.org/3/library/\n",
"\n",
"To use a module in a Python program it first has to be imported. A module can be imported using the `import` statement. For example, to import the module `math`, which contains many standard mathematical functions, we can do:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import math"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This includes the whole module and makes it available for use later in the program. For example, we can do:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.0\n"
]
}
],
"source": [
"import math\n",
"x = math.cos(2 * math.pi)\n",
"print(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, we can chose to import all symbols (functions and variables) in a module to the current namespace (so that we don't need to use the prefix \"`math.`\" every time we use something from the `math` module:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.0\n"
]
}
],
"source": [
"from math import *\n",
"x = cos(2 * pi)\n",
"print(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This pattern can be very convenient, but in large programs that include many modules it is often a good idea to keep the symbols from each module in their own namespaces, by using the `import math` pattern. This would elminate potentially confusing problems with name space collisions.\n",
"\n",
"As a third alternative, we can chose to import only a few selected symbols from a module by explicitly listing which ones we want to import instead of using the wildcard character `*`:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.0\n"
]
}
],
"source": [
"from math import cos, pi\n",
"x = cos(2 * pi)\n",
"print(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Looking at what a module contains, and its documentation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once a module is imported, we can list the symbols it provides using the `dir` function:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'ceil', 'copysign', 'cos', 'cosh', 'degrees', 'e', 'erf', 'erfc', 'exp', 'expm1', 'fabs', 'factorial', 'floor', 'fmod', 'frexp', 'fsum', 'gamma', 'gcd', 'hypot', 'inf', 'isclose', 'isfinite', 'isinf', 'isnan', 'ldexp', 'lgamma', 'log', 'log10', 'log1p', 'log2', 'modf', 'nan', 'pi', 'pow', 'radians', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'trunc']\n"
]
}
],
"source": [
"import math\n",
"print(dir(math))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And using the function `help` we can get a description of each function (almost .. not all functions have docstrings, as they are technically called, but the vast majority of functions are documented this way). "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on built-in function log in module math:\n",
"\n",
"log(...)\n",
" log(x[, base])\n",
" \n",
" Return the logarithm of x to the given base.\n",
" If the base not specified, returns the natural logarithm (base e) of x.\n",
"\n"
]
}
],
"source": [
"help(math.log)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2.302585092994046"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"math.log(10)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3.3219280948873626"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"math.log(10, 2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also use the `help` function directly on modules: Try\n",
"\n",
" help(math) \n",
"\n",
"Some very useful modules form the Python standard library are `os`, `sys`, `math`, `shutil`, `re`, `subprocess`, `multiprocessing`, `threading`. \n",
"\n",
"A complete lists of standard modules for Python 3 are available at http://docs.python.org/3/library/."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For example, this is the `os` module in the standard library."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['.ipynb_checkpoints',\n",
" '__pycache__',\n",
" 'course-overview.ipynb',\n",
" 'install-party.ipynb',\n",
" 'intro-data-science.ipynb',\n",
" 'intro-numpy.ipynb',\n",
" 'intro-python.ipynb',\n",
" 'mymodule.py',\n",
" 'Untitled.ipynb']"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"os.listdir()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Variables and types"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Symbol names "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Variable names in Python can contain alphanumerical characters `a-z`, `A-Z`, `0-9` and some special characters such as `_`. Normal variable names must start with a letter. \n",
"\n",
"By convention, variable names start with a lower-case letter, and Class names start with a capital letter. \n",
"\n",
"In addition, there are a number of Python keywords that cannot be used as variable names. These keywords are:\n",
"\n",
" and, as, assert, break, class, continue, def, del, elif, else, except, \n",
" exec, finally, for, from, global, if, import, in, is, lambda, not, or,\n",
" pass, print, raise, return, try, while, with, yield\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Assignment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The assignment operator in Python is `=`. Python is a dynamically typed language, so we do not need to specify the type of a variable when we create one.\n",
"\n",
"Assigning a value to a new variable creates the variable:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# variable assignments\n",
"x = 1.0\n",
"my_variable = 12.2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Although not explicitly specified, a variable does have a type associated with it. The type is derived from the value that was assigned to it."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"float"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we assign a new value to a variable, its type can change."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x = 1"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"int"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we try to use a variable that has not yet been defined we get an `NameError`:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Traceback (most recent call last):\n",
" File \"<ipython-input-17-0488135cf974>\", line 4, in <module>\n",
" print(y)\n",
"NameError: name 'y' is not defined\n",
"\n"
]
}
],
"source": [
"import traceback \n",
"\n",
"try:\n",
" print(y)\n",
"except NameError as e:\n",
" print(traceback.format_exc())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fundamental types"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"int"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# integers\n",
"x = 1\n",
"type(x)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"float"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# float\n",
"x = 1.0\n",
"type(x)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"bool"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# boolean\n",
"b1 = True\n",
"b2 = False\n",
"\n",
"type(b1)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"complex"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# complex numbers: note the use of `j` to specify the imaginary part\n",
"x = 1.0 - 1.0j\n",
"type(x)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1-1j)\n"
]
}
],
"source": [
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.0 -1.0\n"
]
}
],
"source": [
"print(x.real, x.imag)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Type utility functions"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = 1.0\n",
"\n",
"# check if the variable x is a float\n",
"type(x) is float"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# check if the variable x is an int\n",
"type(x) is int"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also use the `isinstance` method for testing types of variables:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"isinstance(x, float)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Type casting"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.5 <class 'float'>\n"
]
}
],
"source": [
"x = 1.5\n",
"\n",
"print(x, type(x))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 <class 'int'>\n"
]
}
],
"source": [
"x = int(x)\n",
"\n",
"print(x, type(x))"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1+0j) <class 'complex'>\n"
]
}
],
"source": [
"z = complex(x)\n",
"\n",
"print(z, type(z))"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Traceback (most recent call last):\n",
" File \"<ipython-input-30-68b971d0047b>\", line 4, in <module>\n",
" x = float(z)\n",
"TypeError: can't convert complex to float\n",
"\n"
]
}
],
"source": [
"import traceback \n",
"\n",
"try:\n",
" x = float(z)\n",
"except TypeError as e:\n",
" print(traceback.format_exc())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Operators and comparisons"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Most operators and comparisons in Python work as one would expect:\n",
"\n",
"* Arithmetic operators `+`, `-`, `*`, `/`, `//` (integer division), '**' power\n"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(3, -1, 2, 0.5)"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1 + 2, 1 - 2, 1 * 2, 1 / 2"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(3.0, -1.0, 2.0, 0.5)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1.0 + 2.0, 1.0 - 2.0, 1.0 * 2.0, 1.0 / 2.0"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Integer division of float numbers\n",
"3.0 // 2.0"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Note! The power operators in python isn't ^, but **\n",
"2 ** 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: The `/` operator always performs a floating point division in Python 3.x.\n",
"This is not true in Python 2.x, where the result of `/` is always an integer if the operands are integers.\n",
"to be more specific, `1/2 = 0.5` (`float`) in Python 3.x, and `1/2 = 0` (`int`) in Python 2.x (but `1.0/2 = 0.5` in Python 2.x)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The boolean operators are spelled out as the words `and`, `not`, `or`. "
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"True and False"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"not False"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"True or False"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Comparison operators `>`, `<`, `>=` (greater or equal), `<=` (less or equal), `==` equality, `is` identical."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(True, False)"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"2 > 1, 2 < 1"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(False, False)"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"2 > 2, 2 < 2"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(True, True)"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"2 >= 2, 2 <= 2"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# equality\n",
"[1,2] == [1,2]"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# objects identical?\n",
"list1 = list2 = [1,2]\n",
"\n",
"list1 is list2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Exercise:**\n",
"\n",
"Mindy has $5.25 in her pocket. Apples cost 29 cents each. Calculate how many apples mindy can buy and how much change she will have left. Money should be represented in variables of type float and apples should be represented in variables of type integer.\n",
"\n",
"Answer:\n",
"\n",
"- mindy_money = 5.25\n",
"- apple_cost = .29\n",
"- num_apples = ?\n",
"- change = ?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compound types: Strings, List and dictionaries"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Strings"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Strings are the variable type that is used for storing text messages. "
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"str"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = \"Hello world\"\n",
"type(s)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"11"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# length of the string: the number of characters\n",
"len(s)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello test\n"
]
}
],
"source": [
"# replace a substring in a string with somethign else\n",
"s2 = s.replace(\"world\", \"test\")\n",
"print(s2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can index a character in a string using `[]`:"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'H'"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Heads up MATLAB and R users:** Indexing start at 0!\n",
"\n",
"We can extract a part of a string using the syntax `[start:stop]`, which extracts characters between index `start` and `stop` -1 (the character at index `stop` is not included):"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Hello'"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[0:5]"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'o'"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[4:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we omit either (or both) of `start` or `stop` from `[start:stop]`, the default is the beginning and the end of the string, respectively:"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Hello'"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[:5]"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'world'"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[6:]"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Hello world'"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also define the step size using the syntax `[start:end:step]` (the default value for `step` is 1, as we saw above):"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Hello world'"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[::1]"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Hlowrd'"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[::2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This technique is called *slicing*. Read more about the syntax here: https://docs.python.org/3.5/library/functions.html#slice"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Python has a very rich set of functions for text processing. See for example https://docs.python.org/3.5/library/string.html for more information."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### String formatting examples"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"str1 str2 str3\n"
]
}
],
"source": [
"print(\"str1\", \"str2\", \"str3\") # The print statement concatenates strings with a space"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"str1 1.0 False (-0-1j)\n"
]
}
],
"source": [
"print(\"str1\", 1.0, False, -1j) # The print statements converts all arguments to strings"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"str1str2str3\n"
]
}
],
"source": [
"print(\"str1\" + \"str2\" + \"str3\") # strings added with + are concatenated without space"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"value = 1.000000\n"
]
}
],
"source": [
"print(\"value = %f\" % 1.0) # we can use C-style string formatting"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"value1 = 3.14. value2 = 1\n"
]
}
],
"source": [
"# this formatting creates a string\n",
"s2 = \"value1 = %.2f. value2 = %d\" % (3.1415, 1.5)\n",
"\n",
"print(s2)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"value1 = 3.1415, value2 = 1.5\n"
]
}
],
"source": [
"# alternative, more intuitive way of formatting a string \n",
"s3 = 'value1 = {0}, value2 = {1}'.format(3.1415, 1.5)\n",
"\n",
"print(s3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Exercise:**\n",
"\n",
"Paste in the code from your previous exercise and output the result as a story (round monetary values to 2 decimal places). The ouptut should look like this:\n",
"\n",
"\"Mindy had \\$5.25 in her pocket. Apples at her nearby store cost 29 cents. With her \\$5.25, mindy can buy 18 apples and will have 10 cents left over.\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### List"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lists are very similar to strings, except that each element can be of any type.\n",
"\n",
"The syntax for creating lists in Python is `[...]`:"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'list'>\n",
"[1, 2, 3, 4]\n"
]
}
],
"source": [
"l = [1,2,3,4]\n",
"\n",
"print(type(l))\n",
"print(l)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use the same slicing techniques to manipulate lists as we could use on strings:"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 2, 3, 4]\n",
"[2, 3]\n",
"[1, 3]\n"
]
}
],
"source": [
"print(l)\n",
"\n",
"print(l[1:3])\n",
"\n",
"print(l[::2])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Heads up MATLAB and R users:** Indexing starts at 0!"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Elements in a list do not all have to be of the same type:"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 'a', 1.0, (1-1j)]\n"
]
}
],
"source": [
"l = [1, 'a', 1.0, 1-1j]\n",
"\n",
"print(l)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Python lists can be heterogeneous and arbitrarily nested:"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1, [2, [3, [4, [5]]]]]"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nested_list = [1, [2, [3, [4, [5]]]]]\n",
"\n",
"nested_list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lists play a very important role in Python. For example they are used in loops and other flow control structures (discussed below). There are a number of convenient functions for generating lists of various types, for example the `range` function:"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"range(10, 30, 2)"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"start = 10\n",
"stop = 30\n",
"step = 2\n",
"\n",
"range(start, stop, step)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[10, 12, 14, 16, 18, 20, 22, 24, 26, 28]"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# in python 3 range generates an iterator, which can be converted to a list using 'list(...)'.\n",
"# It has no effect in python 2\n",
"list(range(start, stop, step))"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[-10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(range(-10, 10))"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Hello world'"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['H', 'e', 'l', 'l', 'o', ' ', 'w', 'o', 'r', 'l', 'd']"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# convert a string to a list by type casting:\n",
"s2 = list(s)\n",
"\n",
"s2"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['H', 'e', 'l', 'l', 'o', ' ', 'w', 'o', 'r', 'l', 'd']\n",
"[' ', 'H', 'd', 'e', 'l', 'l', 'l', 'o', 'o', 'r', 'w']\n"
]
}
],
"source": [
"# sorting lists (by creating a new variable)\n",
"\n",
"s3 = sorted(s2)\n",
"\n",
"print(s2)\n",
"print(s3)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[' ', 'H', 'd', 'e', 'l', 'l', 'l', 'o', 'o', 'r', 'w']\n"
]
}
],
"source": [
"# sorting lists in place\n",
"s2.sort()\n",
"\n",
"print(s2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Adding, inserting, modifying, and removing elements from lists"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['A', 'd', 'd']\n"
]
}
],
"source": [
"# create a new empty list\n",
"l = []\n",
"\n",
"# add an elements using `append`\n",
"l.append(\"A\")\n",
"l.append(\"d\")\n",
"l.append(\"d\")\n",
"\n",
"print(l)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can modify lists by assigning new values to elements in the list. In technical jargon, lists are *mutable*."
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['A', 'p', 'p']\n"
]
}
],
"source": [
"l[1] = \"p\"\n",
"l[2] = \"p\"\n",
"\n",
"print(l)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['A', 'd', 'd']\n"
]
}
],
"source": [
"l[1:3] = [\"d\", \"d\"]\n",
"\n",
"print(l)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Insert an element at an specific index using `insert`"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['i', 'n', 's', 'e', 'r', 't', 'A', 'd', 'd']\n"
]
}
],
"source": [
"l.insert(0, \"i\")\n",
"l.insert(1, \"n\")\n",
"l.insert(2, \"s\")\n",
"l.insert(3, \"e\")\n",
"l.insert(4, \"r\")\n",
"l.insert(5, \"t\")\n",
"\n",
"print(l)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Remove first element with specific value using 'remove'"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['i', 'n', 's', 'e', 'r', 't', 'd', 'd']\n"
]
}
],
"source": [
"l.remove(\"A\")\n",
"\n",
"print(l)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Remove an element at a specific location using `del`:"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['i', 'n', 's', 'e', 'r', 't']\n"
]
}
],
"source": [
"del l[7]\n",
"del l[6]\n",
"\n",
"print(l)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"See `help(list)` for more details, or read the online documentation "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Tuples"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Tuples are like lists, except that they cannot be modified once created, that is they are *immutable*. \n",
"\n",
"In Python, tuples are created using the syntax `(..., ..., ...)`, or even `..., ...`:"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(10, 20) <class 'tuple'>\n"
]
}
],
"source": [
"point = (10, 20)\n",
"\n",
"print(point, type(point))"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(10, 20) <class 'tuple'>\n"
]
}
],
"source": [
"point = 10, 20\n",
"\n",
"print(point, type(point))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can unpack a tuple by assigning it to a comma-separated list of variables:"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x = 10\n",
"y = 20\n"
]
}
],
"source": [
"x, y = point\n",
"\n",
"print(\"x =\", x)\n",
"print(\"y =\", y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we try to assign a new value to an element in a tuple we get an error:"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Traceback (most recent call last):\n",
" File \"<ipython-input-81-c017eac57cd7>\", line 2, in <module>\n",
" point[0] = 20\n",
"TypeError: 'tuple' object does not support item assignment\n",
"\n"
]
}
],
"source": [
"try:\n",
" point[0] = 20\n",
"except TypeError as e:\n",
" print(traceback.format_exc())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dictionaries"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dictionaries are also like lists, except that each element is a key-value pair. The syntax for dictionaries is `{key1 : value1, ...}`:"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'dict'>\n",
"{'parameter1': 1.0, 'parameter3': 3.0, 'parameter2': 2.0}\n"
]
}
],
"source": [
"params = {\"parameter1\" : 1.0,\n",
" \"parameter2\" : 2.0,\n",
" \"parameter3\" : 3.0,}\n",
"\n",
"print(type(params))\n",
"print(params)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"parameter1 = 1.0\n",
"parameter2 = 2.0\n",
"parameter3 = 3.0\n"
]
}
],
"source": [
"print(\"parameter1 = \" + str(params[\"parameter1\"]))\n",
"print(\"parameter2 = \" + str(params[\"parameter2\"]))\n",
"print(\"parameter3 = \" + str(params[\"parameter3\"]))"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"parameter1 = A\n",
"parameter2 = B\n",
"parameter3 = 3.0\n",
"parameter4 = D\n"
]
}
],
"source": [
"params[\"parameter1\"] = \"A\"\n",
"params[\"parameter2\"] = \"B\"\n",
"\n",
"# add a new entry\n",
"params[\"parameter4\"] = \"D\"\n",
"\n",
"print(\"parameter1 = \" + str(params[\"parameter1\"]))\n",
"print(\"parameter2 = \" + str(params[\"parameter2\"]))\n",
"print(\"parameter3 = \" + str(params[\"parameter3\"]))\n",
"print(\"parameter4 = \" + str(params[\"parameter4\"]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Exercise:**\n",
"\n",
"Mindy doesn't want 18 apples, that's too many for someone who lives by themselves. We're now going to represent mindy's world using our new data types.\n",
"\n",
"Make a list containing the fruits that mindy desires. She likes apples, strawberries, pinapples, and papayas.\n",
"\n",
"Make a tuple containing the fruits that the store has. This is immutable because the store doesn't change their inventory. The local store has apples, strawberries, pinapples, pears, bananas, and oranges.\n",
"\n",
"Make a dictonary showing the price of each fruit at the store. Apples are 29 cents, bananas are 5 cents, oranges are 20 cents, strawberries are 30 cents and pinapples are $1.50."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Control Flow"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Conditional statements: if, elif, else"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Python syntax for conditional execution of code uses the keywords `if`, `elif` (else if), `else`:"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"statement1 and statement2 are False\n"
]
}
],
"source": [
"statement1 = False\n",
"statement2 = False\n",
"\n",
"if statement1:\n",
" print(\"statement1 is True\")\n",
" \n",
"elif statement2:\n",
" print(\"statement2 is True\")\n",
" \n",
"else:\n",
" print(\"statement1 and statement2 are False\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the first time, here we encounted a peculiar and unusual aspect of the Python programming language: Program blocks are defined by their indentation level. \n",
"\n",
"Compare to the equivalent C code:\n",
"\n",
" if (statement1)\n",
" {\n",
" printf(\"statement1 is True\\n\");\n",
" }\n",
" else if (statement2)\n",
" {\n",
" printf(\"statement2 is True\\n\");\n",
" }\n",
" else\n",
" {\n",
" printf(\"statement1 and statement2 are False\\n\");\n",
" }\n",
"\n",
"In C blocks are defined by the enclosing curly brakets `{` and `}`. And the level of indentation (white space before the code statements) does not matter (completely optional). \n",
"\n",
"But in Python, the extent of a code block is defined by the indentation level (usually a tab or say four white spaces). This means that we have to be careful to indent our code correctly, or else we will get syntax errors. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Examples:"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"both statement1 and statement2 are True\n"
]
}
],
"source": [
"statement1 = statement2 = True\n",
"\n",
"if statement1:\n",
" if statement2:\n",
" print(\"both statement1 and statement2 are True\")"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# # Bad indentation!\n",
"# if statement1:\n",
"# if statement2:\n",
"# print(\"both statement1 and statement2 are True\") # this line is not properly indented"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"statement1 = False \n",
"\n",
"if statement1:\n",
" print(\"printed if statement1 is True\")\n",
" \n",
" print(\"still inside the if block\")"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"now outside the if block\n"
]
}
],
"source": [
"if statement1:\n",
" print(\"printed if statement1 is True\")\n",
" \n",
"print(\"now outside the if block\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loops"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In Python, loops can be programmed in a number of different ways. The most common is the `for` loop, which is used together with iterable objects, such as lists. The basic syntax is:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### **`for` loops**:"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"2\n",
"3\n"
]
}
],
"source": [
"for x in [1,2,3]:\n",
" print(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `for` loop iterates over the elements of the supplied list, and executes the containing block once for each element. Any kind of list can be used in the `for` loop. For example:"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"1\n",
"2\n",
"3\n"
]
}
],
"source": [
"for x in range(4): # by default range start at 0\n",
" print(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: `range(4)` does not include 4 !"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-3\n",
"-2\n",
"-1\n",
"0\n",
"1\n",
"2\n"
]
}
],
"source": [
"for x in range(-3,3):\n",
" print(x)"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"scientific\n",
"computing\n",
"with\n",
"python\n"
]
}
],
"source": [
"for word in [\"scientific\", \"computing\", \"with\", \"python\"]:\n",
" print(word)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To iterate over key-value pairs of a dictionary:"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"parameter1 = A\n",
"parameter4 = D\n",
"parameter3 = 3.0\n",
"parameter2 = B\n"
]
}
],
"source": [
"for key, value in params.items():\n",
" print(key + \" = \" + str(value))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sometimes it is useful to have access to the indices of the values when iterating over a list. We can use the `enumerate` function for this:"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 -3\n",
"1 -2\n",
"2 -1\n",
"3 0\n",
"4 1\n",
"5 2\n"
]
}
],
"source": [
"for idx, x in enumerate(range(-3,3)):\n",
" print(idx, x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### List comprehensions: Creating lists using `for` loops:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A convenient and compact way to initialize lists:"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 1, 4, 9, 16]\n"
]
}
],
"source": [
"l1 = [x**2 for x in range(0,5)]\n",
"\n",
"print(l1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `while` loops:"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"1\n",
"2\n",
"3\n",
"4\n",
"done\n"
]
}
],
"source": [
"i = 0\n",
"\n",
"while i < 5:\n",
" print(i)\n",
" \n",
" i = i + 1\n",
" \n",
"print(\"done\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the `print(\"done\")` statement is not part of the `while` loop body because of the difference in indentation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Exercise:**\n",
"\n",
"Loop through all of the fruits that mindy wants and check if the store has them. For each fruit that she wants print \n",
"\n",
"\"Mindy, the store has apples and they cost $.29\"\n",
"\n",
"or\n",
"\n",
"\"Mindy, the store does not have papayas\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A function in Python is defined using the keyword `def`, followed by a function name, a signature within parentheses `()`, and a colon `:`. The following code, with one additional level of indentation, is the function body."
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def func0(): \n",
" print(\"test\")"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"test\n"
]
}
],
"source": [
"func0()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Optionally, but highly recommended, we can define a so called \"docstring\", which is a description of the functions purpose and behaivor. The docstring should follow directly after the function definition, before the code in the function body."
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def func1(s):\n",
" \"\"\"\n",
" Print a string 's' and tell how many characters it has \n",
" \"\"\"\n",
" \n",
" print(s + \" has \" + str(len(s)) + \" characters\")"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function func1 in module __main__:\n",
"\n",
"func1(s)\n",
" Print a string 's' and tell how many characters it has\n",
"\n"
]
}
],
"source": [
"help(func1)"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"test has 4 characters\n"
]
}
],
"source": [
"func1(\"test\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Functions that returns a value use the `return` keyword:"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def square(x):\n",
" \"\"\"\n",
" Return the square of x.\n",
" \"\"\"\n",
" return x ** 2"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"16"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"square(4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can return multiple values from a function using tuples (see above):"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def powers(x):\n",
" \"\"\"\n",
" Return a few powers of x.\n",
" \"\"\"\n",
" return x ** 2, x ** 3, x ** 4"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(9, 27, 81)"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"powers(3)"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"27\n"
]
}
],
"source": [
"x2, x3, x4 = powers(3)\n",
"\n",
"print(x3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Default argument and keyword arguments"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In a definition of a function, we can give default values to the arguments the function takes:"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def myfunc(x, p=2, debug=False):\n",
" if debug:\n",
" print(\"evaluating myfunc for x = \" + str(x) + \" using exponent p = \" + str(p))\n",
" return x**p"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we don't provide a value of the `debug` argument when calling the the function `myfunc` it defaults to the value provided in the function definition:"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"25"
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"myfunc(5)"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"evaluating myfunc for x = 5 using exponent p = 2\n"
]
},
{
"data": {
"text/plain": [
"25"
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"myfunc(5, debug=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we explicitly list the name of the arguments in the function calls, they do not need to come in the same order as in the function definition. This is called *keyword* arguments, and is often very useful in functions that takes a lot of optional arguments."
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"evaluating myfunc for x = 7 using exponent p = 3\n"
]
},
{
"data": {
"text/plain": [
"343"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"myfunc(p=3, debug=True, x=7)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Unnamed functions (lambda function)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In Python we can also create unnamed functions, using the `lambda` keyword:"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"f1 = lambda x: x**2\n",
" \n",
"# is equivalent to \n",
"\n",
"def f2(x):\n",
" return x**2"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(4, 4)"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f1(2), f2(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This technique is useful for example when we want to pass a simple function as an argument to another function, like this:"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<map at 0x10484e978>"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# map is a built-in python function\n",
"map(lambda x: x**2, range(-3,4))"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[9, 4, 1, 0, 1, 4, 9]"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# in python 3 we can use `list(...)` to convert the iterator to an explicit list\n",
"list(map(lambda x: x**2, range(-3,4)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Exercise:**\n",
"\n",
"Mindy is great, but we want code that can tell anyone what fruits the store has. To do this we will generalize our code for Mindy using a function.\n",
"\n",
"Write a function that takes the following parameters\n",
"- full_name (string)\n",
"- fruits_you_want (list)\n",
"- fruits_the_store_has (tuple)\n",
"- prices (dict)\n",
"\n",
"and prints to the terminal a sentence per fruit that you want just like the last exercise. For example, if \n",
"\n",
"```\n",
"name = 'Al'\n",
"list_of_fruits_you_want = ['apple', 'banana']\n",
"tuple_of_fruits_the_store_has = ('apple', 'banana', 'orange', 'strawberries', 'pineapple')\n",
"prices = {\n",
" 'apple' : .29\n",
" 'banana': .05\n",
" 'orange': .20\n",
" 'strawberries': .30\n",
" 'pinapple': 1.50\n",
"}\n",
"```\n",
"\n",
"The function should print.\n",
"\n",
"\"Al, the store has apples and they cost \\$.29\" \n",
"\"Al, the store has bananas and they cost \\$.05\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Classes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Classes are the key features of object-oriented programming. A class is a structure for representing an object and the operations that can be performed on the object. \n",
"\n",
"In Python a class can contain *attributes* (variables) and *methods* (functions).\n",
"\n",
"A class is defined almost like a function, but using the `class` keyword, and the class definition usually contains a number of class method definitions (a function in a class).\n",
"\n",
"* Each class method should have an argument `self` as its first argument. This object is a self-reference.\n",
"\n",
"* Some class method names have special meaning, for example:\n",
"\n",
" * `__init__`: The name of the method that is invoked when the object is first created.\n",
" * `__str__` : A method that is invoked when a simple string representation of the class is needed, as for example when printed.\n",
" * There are many more, see http://docs.python.org/2/reference/datamodel.html#special-method-names"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class Point:\n",
" \"\"\"\n",
" Simple class for representing a point in a Cartesian coordinate system.\n",
" \"\"\"\n",
" \n",
" def __init__(self, x, y):\n",
" \"\"\"\n",
" Create a new Point at x, y.\n",
" \"\"\"\n",
" self.x = x\n",
" self.y = y\n",
" \n",
" def translate(self, dx, dy):\n",
" \"\"\"\n",
" Translate the point by dx and dy in the x and y direction.\n",
" \"\"\"\n",
" self.x += dx\n",
" self.y += dy\n",
" \n",
" def __str__(self):\n",
" return(\"Point at [%f, %f]\" % (self.x, self.y))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To create a new instance of a class:"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Point at [0.000000, 0.000000]\n"
]
}
],
"source": [
"p1 = Point(0, 0) # this will invoke the __init__ method in the Point class\n",
"\n",
"print(p1) # this will invoke the __str__ method"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To invoke a class method in the class instance `p`:"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Point at [0.250000, 1.500000]\n",
"Point at [1.000000, 1.000000]\n"
]
}
],
"source": [
"p2 = Point(1, 1)\n",
"\n",
"p1.translate(0.25, 1.5)\n",
"\n",
"print(p1)\n",
"print(p2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that calling class methods can modifiy the state of that particular class instance, but does not effect other class instances or any global variables.\n",
"\n",
"That is one of the nice things about object-oriented design: code such as functions and related variables are grouped in separate and independent entities. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Modules"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One of the most important concepts in good programming is to reuse code and avoid repetitions.\n",
"\n",
"The idea is to write functions and classes with a well-defined purpose and scope, and reuse these instead of repeating similar code in different part of a program (modular programming). The result is usually that readability and maintainability of a program is greatly improved. What this means in practice is that our programs have fewer bugs, are easier to extend and debug/troubleshoot. \n",
"\n",
"Python supports modular programming at different levels. Functions and classes are examples of tools for low-level modular programming. Python modules are a higher-level modular programming construct, where we can collect related variables, functions and classes in a module. A python module is defined in a python file (with file-ending `.py`), and it can be made accessible to other Python modules and programs using the `import` statement. \n",
"\n",
"Consider the following example: the file `mymodule.py` contains simple example implementations of a variable, function and a class:"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting mymodule.py\n"
]
}
],
"source": [
"%%file mymodule.py\n",
"\"\"\"\n",
"Example of a python module. Contains a variable called my_variable,\n",
"a function called my_function, and a class called MyClass.\n",
"\"\"\"\n",
"\n",
"my_variable = 0\n",
"\n",
"def my_function():\n",
" \"\"\"\n",
" Example function\n",
" \"\"\"\n",
" return my_variable\n",
" \n",
"class MyClass:\n",
" \"\"\"\n",
" Example class.\n",
" \"\"\"\n",
"\n",
" def __init__(self):\n",
" self.variable = my_variable\n",
" \n",
" def set_variable(self, new_value):\n",
" \"\"\"\n",
" Set self.variable to a new value\n",
" \"\"\"\n",
" self.variable = new_value\n",
" \n",
" def get_variable(self):\n",
" return self.variable"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can import the module `mymodule` into our Python program using `import`:"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import mymodule"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use `help(module)` to get a summary of what the module provides:"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on module mymodule:\n",
"\n",
"NAME\n",
" mymodule\n",
"\n",
"DESCRIPTION\n",
" Example of a python module. Contains a variable called my_variable,\n",
" a function called my_function, and a class called MyClass.\n",
"\n",
"CLASSES\n",
" builtins.object\n",
" MyClass\n",
" \n",
" class MyClass(builtins.object)\n",
" | Example class.\n",
" | \n",
" | Methods defined here:\n",
" | \n",
" | __init__(self)\n",
" | Initialize self. See help(type(self)) for accurate signature.\n",
" | \n",
" | get_variable(self)\n",
" | \n",
" | set_variable(self, new_value)\n",
" | Set self.variable to a new value\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Data descriptors defined here:\n",
" | \n",
" | __dict__\n",
" | dictionary for instance variables (if defined)\n",
" | \n",
" | __weakref__\n",
" | list of weak references to the object (if defined)\n",
"\n",
"FUNCTIONS\n",
" my_function()\n",
" Example function\n",
"\n",
"DATA\n",
" my_variable = 0\n",
"\n",
"FILE\n",
" /Users/johria/Development/DAT-DC-12/notebooks/mymodule.py\n",
"\n",
"\n"
]
}
],
"source": [
"help(mymodule)"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mymodule.my_variable"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 123,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mymodule.my_function() "
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"10"
]
},
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"my_class = mymodule.MyClass() \n",
"my_class.set_variable(10)\n",
"my_class.get_variable()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we make changes to the code in `mymodule.py`, we need to reload it using `reload`:"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<module 'mymodule' from '/Users/johria/Development/DAT-DC-12/notebooks/mymodule.py'>"
]
},
"execution_count": 125,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import importlib\n",
"importlib.reload(mymodule) # Python 3 only\n",
"# For Python 2 use reload(mymodule)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exceptions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In Python errors are managed with a special language construct called \"Exceptions\". When errors occur exceptions can be raised, which interrupts the normal program flow and fallback to somewhere else in the code where the closest try-except statement is defined."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To generate an exception we can use the `raise` statement, which takes an argument that must be an instance of the class `BaseException` or a class derived from it. "
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Traceback (most recent call last):\n",
" File \"<ipython-input-126-72b44174a06f>\", line 2, in <module>\n",
" raise Exception(\"description of the error\")\n",
"Exception: description of the error\n",
"\n"
]
}
],
"source": [
"try:\n",
" raise Exception(\"description of the error\")\n",
"except Exception as e:\n",
" print(traceback.format_exc())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A typical use of exceptions is to abort functions when some error condition occurs, for example:\n",
"\n",
" def my_function(arguments):\n",
" \n",
" if not verify(arguments):\n",
" raise Exception(\"Invalid arguments\")\n",
" \n",
" # rest of the code goes here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To gracefully catch errors that are generated by functions and class methods, or by the Python interpreter itself, use the `try` and `except` statements:\n",
"\n",
" try:\n",
" # normal code goes here\n",
" except:\n",
" # code for error handling goes here\n",
" # this code is not executed unless the code\n",
" # above generated an error\n",
"\n",
"For example:"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"test\n",
"Caught an exception\n"
]
}
],
"source": [
"try:\n",
" print(\"test\")\n",
" # generate an error: the variable test is not defined\n",
" print(test)\n",
"except Exception:\n",
" print(\"Caught an exception\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To get information about the error, we can access the `Exception` class instance that describes the exception by using for example:\n",
"\n",
" except Exception as e:"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"test\n",
"Caught an exception: name 'test' is not defined\n"
]
}
],
"source": [
"try:\n",
" print(\"test\")\n",
" # generate an error: the variable test is not defined\n",
" print(test)\n",
"except Exception as e:\n",
" print(\"Caught an exception:\", e)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Excercise:**\n",
"\n",
"Make two classes with the following variables and methods\n",
"\n",
"**Store**\n",
"\n",
"variables\n",
"- inventory (dict)\n",
"\n",
"methods\n",
"- `show_inventory()`\n",
" * nicely displays the store's inventory\n",
"- `message_customer(customer)`\n",
" * shows the customer if the store has the fruits they want and how much each fruit costs (this is code from previous exercises, except it will use the customer's \"Formal Greeting\" instead of their first name.) \n",
"\n",
"**Customer** \n",
"\n",
"variables\n",
"- `first_name` (string)\n",
"- `last_name` (string)\n",
"- `is_male` (boolean)\n",
"- `money` (float)\n",
"- `fruit` (dict)\n",
"- `preferred_fruit` (list)\n",
"\n",
"methods\n",
"- `formal_greeting()`\n",
" * (Mr. Al Johri, Ms. Mindy Smith)\n",
"- `buy_fruit(store, fruit_name, fruit_amt)`\n",
" * inputs are a store, the name of a fruit, and the amount of that fruit\n",
" * checks to see if the store has the fruit - returns an error if it does not\n",
" * checks to see if the customer can afford the amount of fruit they intend to buy - returns an error if not\n",
" * \"purchases\" the fruit by adding it to the customers fruit dict and removes the correct amount of money from their money variable\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Exercise:**\n",
"\n",
"Instantiate a list of the following customers.\n",
"\n",
"* Mindy Smith - \\$5.25, likes apples and oranges\n",
"* Al Johri - \\$20.19, likes papaya, strawberries, pinapple, and apples \n",
"* Hillary Clinton - \\$15, likes strawberries and oranges \n",
"* Oliver Twist - \\$.05, likes apples \n",
"* Donald Trump - \\$4000, only likes durian\n",
"\n",
"Create a store called Whole Foods with the following inventory\n",
" * 'apple' : \\$.29\n",
" * 'banana': \\$.05\n",
" * 'orange': \\$.20\n",
" * 'strawberries': \\$.30\n",
" * 'pinapple': \\$1.50,\n",
" * 'grapes': \\$.22,\n",
" * 'durian': \\$5000\n",
"\n",
"Write code to do the following\n",
"\n",
"1. Print the store's inventory\n",
"2. For each customer, print the store's message to them\n",
"3. Have each customer purchase 1 of each fruit in their list of preferred fruits. \n",
"\n",
"(Make sure you have error handling so the program doesn't halt if the store doesn't have the fruit the customer wants or if the customer doesn not have enough money to buy the fruit.)\n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Bonus Exercise**\n",
"\n",
"Organize the code! Make a module called `fruits` (in a file fruits.py) that contains the class definitions.\n",
"Make a separate cell (in a file main.py) which imports the fruits module, instantiates the store and the list of customers, and runs the code in the previous exercise.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Further reading"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* http://www.python.org - The official web page of the Python programming language.\n",
"* https://docs.python.org/3/tutorial/ - The Official Python Tutorial\n",
"* http://www.python.org/dev/peps/pep-0008 - Style guide for Python programming. Highly recommended. \n",
"* http://www.scipy-lectures.org/intro/language/python_language.html - Scipy Lectures: Lecture 1.2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Versions"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"application/json": {
"Software versions": [
{
"module": "Python",
"version": "3.5.1 64bit [GCC 4.2.1 (Apple Inc. build 5577)]"
},
{
"module": "IPython",
"version": "4.0.3"
},
{
"module": "OS",
"version": "Darwin 15.0.0 x86_64 i386 64bit"
}
]
},
"text/html": [
"<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>3.5.1 64bit [GCC 4.2.1 (Apple Inc. build 5577)]</td></tr><tr><td>IPython</td><td>4.0.3</td></tr><tr><td>OS</td><td>Darwin 15.0.0 x86_64 i386 64bit</td></tr><tr><td colspan='2'>Sun Mar 20 16:53:36 2016 EDT</td></tr></table>"
],
"text/latex": [
"\\begin{tabular}{|l|l|}\\hline\n",
"{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
"Python & 3.5.1 64bit [GCC 4.2.1 (Apple Inc. build 5577)] \\\\ \\hline\n",
"IPython & 4.0.3 \\\\ \\hline\n",
"OS & Darwin 15.0.0 x86\\_64 i386 64bit \\\\ \\hline\n",
"\\hline \\multicolumn{2}{|l|}{Sun Mar 20 16:53:36 2016 EDT} \\\\ \\hline\n",
"\\end{tabular}\n"
],
"text/plain": [
"Software versions\n",
"Python 3.5.1 64bit [GCC 4.2.1 (Apple Inc. build 5577)]\n",
"IPython 4.0.3\n",
"OS Darwin 15.0.0 x86_64 i386 64bit\n",
"Sun Mar 20 16:53:36 2016 EDT"
]
},
"execution_count": 129,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%reload_ext version_information\n",
"%version_information"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
leewujung/ooi_sonar | notebooks/NMF on linear scale.ipynb | 1 | 2412393 | null | apache-2.0 |
georgetown-analytics/machine-learning | demos/20200328.ipynb | 1 | 12946 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# scikit-learn Demo"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib notebook\n",
"\n",
"import json\n",
"import time\n",
"import joblib\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from yellowbrick.datasets import load_occupancy\n",
"\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.impute import SimpleImputer\n",
"from sklearn.model_selection import StratifiedKFold\n",
"from sklearn.model_selection import cross_val_score \n",
"from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
"\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"\n",
"from sklearn.base import BaseEstimator, TransformerMixin"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Loading"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>datetime</th>\n",
" <th>temperature</th>\n",
" <th>relative humidity</th>\n",
" <th>light</th>\n",
" <th>CO2</th>\n",
" <th>humidity</th>\n",
" <th>occupancy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2015-02-04 17:51:00</td>\n",
" <td>23.18</td>\n",
" <td>27.2720</td>\n",
" <td>426.0</td>\n",
" <td>721.25</td>\n",
" <td>0.004793</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2015-02-04 17:51:59</td>\n",
" <td>23.15</td>\n",
" <td>27.2675</td>\n",
" <td>429.5</td>\n",
" <td>714.00</td>\n",
" <td>0.004783</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2015-02-04 17:53:00</td>\n",
" <td>23.15</td>\n",
" <td>27.2450</td>\n",
" <td>426.0</td>\n",
" <td>713.50</td>\n",
" <td>0.004779</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2015-02-04 17:54:00</td>\n",
" <td>23.15</td>\n",
" <td>27.2000</td>\n",
" <td>426.0</td>\n",
" <td>708.25</td>\n",
" <td>0.004772</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2015-02-04 17:55:00</td>\n",
" <td>23.10</td>\n",
" <td>27.2000</td>\n",
" <td>426.0</td>\n",
" <td>704.50</td>\n",
" <td>0.004757</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" datetime temperature relative humidity light CO2 \\\n",
"0 2015-02-04 17:51:00 23.18 27.2720 426.0 721.25 \n",
"1 2015-02-04 17:51:59 23.15 27.2675 429.5 714.00 \n",
"2 2015-02-04 17:53:00 23.15 27.2450 426.0 713.50 \n",
"3 2015-02-04 17:54:00 23.15 27.2000 426.0 708.25 \n",
"4 2015-02-04 17:55:00 23.10 27.2000 426.0 704.50 \n",
"\n",
" humidity occupancy \n",
"0 0.004793 1 \n",
"1 0.004783 1 \n",
"2 0.004779 1 \n",
"3 0.004772 1 \n",
"4 0.004757 1 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = load_occupancy(return_dataset=True).to_dataframe()\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.pairplot(hue=\"occupancy\", data=df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X = df[[col for col in df.columns if col != \"occupancy\" and col != \"datetime\"]]\n",
"y = df[\"occupancy\"]\n",
"\n",
"print(X.shape)\n",
"print(y.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Custom Transformer"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/benjamin/.pyenv/versions/3.7.3/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/base.py:197: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
" FutureWarning)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>datetime</th>\n",
" <th>datetime_weekday</th>\n",
" <th>datetime_hour</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2015-02-04 17:51:00</td>\n",
" <td>2</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2015-02-04 17:51:59</td>\n",
" <td>2</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2015-02-04 17:53:00</td>\n",
" <td>2</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2015-02-04 17:54:00</td>\n",
" <td>2</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2015-02-04 17:55:00</td>\n",
" <td>2</td>\n",
" <td>17</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" datetime datetime_weekday datetime_hour\n",
"0 2015-02-04 17:51:00 2 17\n",
"1 2015-02-04 17:51:59 2 17\n",
"2 2015-02-04 17:53:00 2 17\n",
"3 2015-02-04 17:54:00 2 17\n",
"4 2015-02-04 17:55:00 2 17"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class ExtractDailyFeatures(BaseEstimator, TransformerMixin):\n",
" \n",
" def __init__(self, extract_weekday=True, extract_hour=True):\n",
" super(ExtractDailyFeatures, self).__init__()\n",
" self.set_params(\n",
" extract_weekday=extract_weekday,\n",
" extract_hour=extract_hour,\n",
" )\n",
" \n",
" def fit(self, X, y=None):\n",
" self.date_range_ = {}\n",
" for col in X.columns:\n",
" series = pd.to_datetime(X[col])\n",
" self.date_range_[col] = [\n",
" series.min(), series.max()\n",
" ]\n",
" return self\n",
" \n",
" def transform(self, X):\n",
" \"\"\"\n",
" Assumes that X is 2D array containing a single column, with a datetime\n",
" \"\"\"\n",
" cols = []\n",
" for col in self.date_range_:\n",
" series = pd.to_datetime(X[col])\n",
" if self.extract_weekday:\n",
" weekdays = series.apply(lambda d: d.weekday())\n",
" weekdays.name = f\"{col}_weekday\"\n",
" cols.append(weekdays)\n",
" if self.extract_hour:\n",
" hours = series.apply(lambda d: d.hour)\n",
" hours.name = f\"{col}_hour\"\n",
" cols.append(hours)\n",
" \n",
" if len(cols) > 0:\n",
" return pd.concat([X] + cols, axis=1)\n",
" return X\n",
" \n",
"\n",
"extractor = ExtractDailyFeatures().fit(df[[\"datetime\"]])\n",
"extractor.transform(df[[\"datetime\"]]).head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initial Model Triples"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def train_score(model, X, y):\n",
" \"\"\"\n",
" This function reflects the experimental methodology for this specific dataset\n",
" \"\"\"\n",
" start = time.time()\n",
" cv = StratifiedKFold(n_splits=12, shuffle=True, random_state=42)\n",
" scores = cross_val_score(model, X, y, cv=cv, scoring='f1_macro')\n",
" cv_end = time.time()\n",
" \n",
" model.fit(X, y)\n",
" fit_end = time.time()\n",
" \n",
" return model, {\n",
" \"f1_macro\": scores,\n",
" \"model_name\": model[-1].__class__.__name__,\n",
" \"params\": model.get_params(),\n",
" \"cv_time\": cv_end - start,\n",
" \"fit_time\": fit_end - cv_end,\n",
" \"timestamp\": start,\n",
" \"description\": \"trained as part of the XBUS 505 Session 3 demo\",\n",
" }\n",
"\n",
"\n",
"def save_model(model, info):\n",
" name = f\"{info['model_name']}-{info['timestamp']}\"\n",
" with open(f\"{name}.pickle\", \"wb\") as f:\n",
" joblib.dump(model)\n",
" \n",
" with open(f\"{name}.json\", \"w\") as f:\n",
" json.dump(info)\n",
"\n",
"\n",
"def generate_models(classifiers, rescalers, X, y, save=False):\n",
" for classifier in classifiers:\n",
" for rescaler in rescalers:\n",
" model = Pipeline([\n",
" (\"scale\", rescaler()),\n",
" (\"model\", classifier()),\n",
" ])\n",
" \n",
" model, info = train_score (model, X, y)\n",
" if save:\n",
" save_model(model, info)\n",
" print(f\"{info['model_name']}: {info['f1_macro'].mean():0.4f}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"classifiers = [\n",
" GaussianNB, RandomForestClassifier, LogisticRegression\n",
"]\n",
"\n",
"rescalers = [\n",
" StandardScaler, MinMaxScaler\n",
"]\n",
"\n",
"\n",
"generate_models(classifiers, rescalers, X, y)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
DAInamite/programming-humanoid-robot-in-python | kinematics/inverse_kinematics_2d_jax.ipynb | 1 | 429996 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Inverse Kinematics (2D)\n",
"with https://github.com/google/jax\n",
"\n",
"*note*\n",
"* running on GPU with colab: https://colab.research.google.com/drive/1guZnXsFOEVLb7IOXVzUgRc8pbq3Z50Qf"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib notebook\n",
"from matplotlib import pylab as plt\n",
"from numpy import random, pi\n",
"from __future__ import division\n",
"from IPython import display\n",
"from ipywidgets import interact, fixed\n",
"\n",
"import jax.numpy as np\n",
"from jax import grad, jit"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Coordinate Transformation"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def trans(x, y, a):\n",
" '''create a 2D transformation'''\n",
" s = np.sin(a)\n",
" c = np.cos(a)\n",
" return np.asarray([[c, -s, x],\n",
" [s, c, y],\n",
" [0, 0, 1]])\n",
"\n",
"def from_trans(m):\n",
" '''get x, y, theta from transform matrix'''\n",
" a = np.arctan2(m[1, 0], m[0, 0])\n",
" return np.asarray([m[0, -1], m[1, -1], a])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 1. -0. 0.]\n",
" [ 0. 1. 0.]\n",
" [ 0. 0. 1.]]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/xu/miniconda3/envs/robocup/lib/python3.8/site-packages/jax/lib/xla_bridge.py:125: UserWarning: No GPU/TPU found, falling back to CPU.\n",
" warnings.warn('No GPU/TPU found, falling back to CPU.')\n"
]
}
],
"source": [
"print(trans(0., 0., 0.))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Parameters of robot arm"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"l = [0, 3, 2, 1]\n",
"#l = [0, 3, 2, 1, 1]\n",
"#l = [0, 3, 2, 1, 1, 1]\n",
"#l = [1] * 30\n",
"N = len(l) - 1 # number of links\n",
"max_len = sum(l)\n",
"a = random.random_sample(N) # angles of joints\n",
"T0 = trans(0, 0, 0) # base"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Forward Kinematics"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def forward_kinematics(T0, l, a):\n",
" T = [T0]\n",
" for i in range(len(a)):\n",
" Ti = np.dot(T[-1], trans(l[i], 0, a[i]))\n",
" T.append(Ti)\n",
" Te = np.dot(T[-1], trans(l[-1], 0, 0)) # end effector\n",
" T.append(Te)\n",
" return T"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def show_robot_arm(T):\n",
" plt.cla()\n",
" x = [Ti[0,-1] for Ti in T]\n",
" y = [Ti[1,-1] for Ti in T]\n",
" plt.plot(x, y, '-or', linewidth=5, markersize=10)\n",
" plt.plot(x[-1], y[-1], 'og', linewidth=5, markersize=10)\n",
" plt.xlim([-max_len, max_len])\n",
" plt.ylim([-max_len, max_len]) \n",
" ax = plt.axes()\n",
" ax.set_aspect('equal')\n",
" t = np.arctan2(T[-1][1, 0], T[-1][0,0])\n",
" ax.annotate('[%.2f,%.2f,%.2f]' % (x[-1], y[-1], t), xy=(x[-1], y[-1]), xytext=(x[-1], y[-1] + 0.5))\n",
" plt.show\n",
" return ax"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Inverse Kinematics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Numerical Solution: jax"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"/* Put everything inside the global mpl namespace */\n",
"window.mpl = {};\n",
"\n",
"\n",
"mpl.get_websocket_type = function() {\n",
" if (typeof(WebSocket) !== 'undefined') {\n",
" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support. ' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
"\n",
"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
" this.id = figure_id;\n",
"\n",
" this.ws = websocket;\n",
"\n",
" this.supports_binary = (this.ws.binaryType != undefined);\n",
"\n",
" if (!this.supports_binary) {\n",
" var warnings = document.getElementById(\"mpl-warnings\");\n",
" if (warnings) {\n",
" warnings.style.display = 'block';\n",
" warnings.textContent = (\n",
" \"This browser does not support binary websocket messages. \" +\n",
" \"Performance may be slow.\");\n",
" }\n",
" }\n",
"\n",
" this.imageObj = new Image();\n",
"\n",
" this.context = undefined;\n",
" this.message = undefined;\n",
" this.canvas = undefined;\n",
" this.rubberband_canvas = undefined;\n",
" this.rubberband_context = undefined;\n",
" this.format_dropdown = undefined;\n",
"\n",
" this.image_mode = 'full';\n",
"\n",
" this.root = $('<div/>');\n",
" this._root_extra_style(this.root)\n",
" this.root.attr('style', 'display: inline-block');\n",
"\n",
" $(parent_element).append(this.root);\n",
"\n",
" this._init_header(this);\n",
" this._init_canvas(this);\n",
" this._init_toolbar(this);\n",
"\n",
" var fig = this;\n",
"\n",
" this.waiting = false;\n",
"\n",
" this.ws.onopen = function () {\n",
" fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
" fig.send_message(\"send_image_mode\", {});\n",
" if (mpl.ratio != 1) {\n",
" fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
" }\n",
" fig.send_message(\"refresh\", {});\n",
" }\n",
"\n",
" this.imageObj.onload = function() {\n",
" if (fig.image_mode == 'full') {\n",
" // Full images could contain transparency (where diff images\n",
" // almost always do), so we need to clear the canvas so that\n",
" // there is no ghosting.\n",
" fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
" }\n",
" fig.context.drawImage(fig.imageObj, 0, 0);\n",
" };\n",
"\n",
" this.imageObj.onunload = function() {\n",
" fig.ws.close();\n",
" }\n",
"\n",
" this.ws.onmessage = this._make_on_message_function(this);\n",
"\n",
" this.ondownload = ondownload;\n",
"}\n",
"\n",
"mpl.figure.prototype._init_header = function() {\n",
" var titlebar = $(\n",
" '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
" 'ui-helper-clearfix\"/>');\n",
" var titletext = $(\n",
" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
" titlebar.append(titletext)\n",
" this.root.append(titlebar);\n",
" this.header = titletext[0];\n",
"}\n",
"\n",
"\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._init_canvas = function() {\n",
" var fig = this;\n",
"\n",
" var canvas_div = $('<div/>');\n",
"\n",
" canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
"\n",
" function canvas_keyboard_event(event) {\n",
" return fig.key_event(event, event['data']);\n",
" }\n",
"\n",
" canvas_div.keydown('key_press', canvas_keyboard_event);\n",
" canvas_div.keyup('key_release', canvas_keyboard_event);\n",
" this.canvas_div = canvas_div\n",
" this._canvas_extra_style(canvas_div)\n",
" this.root.append(canvas_div);\n",
"\n",
" var canvas = $('<canvas/>');\n",
" canvas.addClass('mpl-canvas');\n",
" canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
"\n",
" this.canvas = canvas[0];\n",
" this.context = canvas[0].getContext(\"2d\");\n",
"\n",
" var backingStore = this.context.backingStorePixelRatio ||\n",
"\tthis.context.webkitBackingStorePixelRatio ||\n",
"\tthis.context.mozBackingStorePixelRatio ||\n",
"\tthis.context.msBackingStorePixelRatio ||\n",
"\tthis.context.oBackingStorePixelRatio ||\n",
"\tthis.context.backingStorePixelRatio || 1;\n",
"\n",
" mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
"\n",
" var rubberband = $('<canvas/>');\n",
" rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
"\n",
" var pass_mouse_events = true;\n",
"\n",
" canvas_div.resizable({\n",
" start: function(event, ui) {\n",
" pass_mouse_events = false;\n",
" },\n",
" resize: function(event, ui) {\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" stop: function(event, ui) {\n",
" pass_mouse_events = true;\n",
" fig.request_resize(ui.size.width, ui.size.height);\n",
" },\n",
" });\n",
"\n",
" function mouse_event_fn(event) {\n",
" if (pass_mouse_events)\n",
" return fig.mouse_event(event, event['data']);\n",
" }\n",
"\n",
" rubberband.mousedown('button_press', mouse_event_fn);\n",
" rubberband.mouseup('button_release', mouse_event_fn);\n",
" // Throttle sequential mouse events to 1 every 20ms.\n",
" rubberband.mousemove('motion_notify', mouse_event_fn);\n",
"\n",
" rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
" rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
"\n",
" canvas_div.on(\"wheel\", function (event) {\n",
" event = event.originalEvent;\n",
" event['data'] = 'scroll'\n",
" if (event.deltaY < 0) {\n",
" event.step = 1;\n",
" } else {\n",
" event.step = -1;\n",
" }\n",
" mouse_event_fn(event);\n",
" });\n",
"\n",
" canvas_div.append(canvas);\n",
" canvas_div.append(rubberband);\n",
"\n",
" this.rubberband = rubberband;\n",
" this.rubberband_canvas = rubberband[0];\n",
" this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
" this.rubberband_context.strokeStyle = \"#000000\";\n",
"\n",
" this._resize_canvas = function(width, height) {\n",
" // Keep the size of the canvas, canvas container, and rubber band\n",
" // canvas in synch.\n",
" canvas_div.css('width', width)\n",
" canvas_div.css('height', height)\n",
"\n",
" canvas.attr('width', width * mpl.ratio);\n",
" canvas.attr('height', height * mpl.ratio);\n",
" canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
"\n",
" rubberband.attr('width', width);\n",
" rubberband.attr('height', height);\n",
" }\n",
"\n",
" // Set the figure to an initial 600x600px, this will subsequently be updated\n",
" // upon first draw.\n",
" this._resize_canvas(600, 600);\n",
"\n",
" // Disable right mouse context menu.\n",
" $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
" return false;\n",
" });\n",
"\n",
" function set_focus () {\n",
" canvas.focus();\n",
" canvas_div.focus();\n",
" }\n",
"\n",
" window.setTimeout(set_focus, 100);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" // put a spacer in here.\n",
" continue;\n",
" }\n",
" var button = $('<button/>');\n",
" button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
" 'ui-button-icon-only');\n",
" button.attr('role', 'button');\n",
" button.attr('aria-disabled', 'false');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option);\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<img src=\"data:image/png;base64,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\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"<ipython-input-6-d35263df4e47>:9: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
" ax = plt.axes()\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "af0fc7587de74d05b1b78f01eb8e6d52",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(FloatSlider(value=4.991220474243164, description='x_e', max=6.0, step=0.01), FloatSlider…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def error_func(theta, target):\n",
" Ts = forward_kinematics(T0, l, theta)\n",
" Te = Ts[-1]\n",
" e = target - Te\n",
" return np.sum(e * e)\n",
"\n",
"theta = random.random(N)\n",
"def inverse_kinematics(x_e, y_e, theta_e, theta):\n",
" target = trans(x_e, y_e, theta_e)\n",
" func = lambda t: error_func(t, target)\n",
" func_grad = jit(grad(func))\n",
" \n",
" for i in range(1000):\n",
" e = func(theta)\n",
" d = func_grad(theta)\n",
" theta -= d * 1e-2\n",
" if e < 1e-4:\n",
" break\n",
" \n",
" return theta\n",
"\n",
"T = forward_kinematics(T0, l, theta)\n",
"show_robot_arm(T)\n",
"Te = np.asarray([from_trans(T[-1])])\n",
"\n",
"@interact(x_e=(0, max_len, 0.01), y_e=(-max_len, max_len, 0.01), theta_e=(-pi, pi, 0.01), theta=fixed(theta))\n",
"def set_end_effector(x_e=Te[0,0], y_e=Te[0,1], theta_e=Te[0,2], theta=theta):\n",
" theta = inverse_kinematics(x_e, y_e, theta_e, theta)\n",
" T = forward_kinematics(T0, l, theta)\n",
" show_robot_arm(T)\n",
" return theta\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:robocup]",
"language": "python",
"name": "conda-env-robocup-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
},
"widgets": {
"state": {
"0065ff23834146a99efd42e25d3294dc": {
"views": []
},
"00c59707f24e4d80b90a6ca59c255d84": {
"views": [
{
"cell": {
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"trusted": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAAKPCAYAAACsKh+8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n"
},
"cell_index": 10,
"root": true
}
]
},
"00f75449d7d54ba5ba468dfebddab990": {
"views": []
},
"01197fb664744b00857456cf154e7124": {
"views": []
},
"025623f89e024585b05e5f3de8c8fc1e": {
"views": []
},
"02ac05d57f5c42659f040340dcd55ea9": {
"views": []
},
"02d7dfe535434c1698a0f8eeb2a0c835": {
"views": []
},
"02e74237e1be4b489983c17eceffc582": {
"views": []
},
"0306388837804817b89e33a7a41c615d": {
"views": []
},
"03bec2d2dbcd4c34bc951fcd36b34c47": {
"views": []
},
"03bfc095b8424b54aea7b3925bc1c883": {
"views": []
},
"0411d5af1cc945d1b7ef6f618a533290": {
"views": []
},
"044b6e3699664a5ebf3f52745ef9604d": {
"views": []
},
"046acc5e6d8e4bbcaf7d310d440670d0": {
"views": []
},
"04bc6c3f6f1c4db1a7da8baafcaf48ab": {
"views": []
},
"05860612929a4837a45ff10c7098f21c": {
"views": []
},
"066d1043b4ca4cb6a4a52a0e7396aecd": {
"views": []
},
"06cf2a6f329f44e48da63b0e8d2be23f": {
"views": []
},
"0706dfac87a642468fc6d2902a277340": {
"views": []
},
"071db28e83174fadb6480fa59dc351cd": {
"views": []
},
"078cd19f8c5b4e48a16eedf57d4139f5": {
"views": []
},
"07b4147354dc482eadf0d2f2e85a8f5a": {
"views": []
},
"07dfd29a7b2c4e1f817cd16a08a0e69b": {
"views": []
},
"082ee6c3ea5541d59277d73bc3904e81": {
"views": []
},
"08bc4e34c979463eadd9590bdf72c5e2": {
"views": []
},
"08d70037af964a3082f43b7e47558be9": {
"views": []
},
"0976810a8f27407f81cac903cae046c1": {
"views": []
},
"0997f9f997a24efd96a42331f5b643b7": {
"views": []
},
"0998272120cd42de814b1b294615bdac": {
"views": []
},
"0a10f0128c704654a62d8caaa3ce0f96": {
"views": []
},
"0a7a9f646ec0447f83cff55d61643dc1": {
"views": []
},
"0a90d90f570440cf8651f399071e498b": {
"views": []
},
"0ae2bb4809e34834baca0b575d1e3488": {
"views": []
},
"0c3faaaa475e4629a440e89ef47a0ed1": {
"views": []
},
"0c47d4db96974564becbbea3540e0df9": {
"views": []
},
"0ca043f1cdd34fa3807a701624d0f09b": {
"views": []
},
"0ce0f28eb09c46c6b3f72b62f966bb2c": {
"views": []
},
"0d8971b3f89448dfa5b6dcebef4d08a6": {
"views": []
},
"0da1f22325bd471382cc655a93f82f9f": {
"views": []
},
"0e28bc970070433a94639521a153b8ed": {
"views": []
},
"0e7568409c2246cc92276dbd9009ec4d": {
"views": []
},
"0e7a6be183ef4af58a82aa511961c510": {
"views": []
},
"0e8fe06c2bd247418c85a31bee590836": {
"views": []
},
"0ebd979b90d64257acffbd03330c1a53": {
"views": []
},
"0efe43293aa74e7ebaf98e3cb160cb31": {
"views": []
},
"0f1c1ce7e5af4cd689428b9e2317ba63": {
"views": []
},
"0f2675e1e9d44b5592135a1662977760": {
"views": []
},
"0f5e329526044d61b0916b3e41037c72": {
"views": []
},
"0fbb7ae2603c45a8b340823f3a8c7b43": {
"views": []
},
"0ffbc64f07a3429f8516a4183a81820e": {
"views": []
},
"108a1c9a58724430ac137973ac3cb47d": {
"views": []
},
"10985d7bd27541e38bb2260573dab947": {
"views": []
},
"1109bc012659480882e38de485ee5413": {
"views": []
},
"1385ae228a1442ea9aad2e8becc37448": {
"views": []
},
"140542204e6740daaacb0a4c0faa2aba": {
"views": []
},
"14b4efd6bc5246c0a7b46c5bec2ba897": {
"views": []
},
"14e805778a4b4f1eb9d9fb9bbe5fff33": {
"views": []
},
"153e44de9842488f95a70f6b2ee1649d": {
"views": []
},
"155566a370a344f48c7e3251fbddf3ca": {
"views": []
},
"159f5618c9c44f4fb3a3c67da11c29c1": {
"views": []
},
"15c4b76d92a743cf8d505b7234b9dfc8": {
"views": []
},
"15ed897d15b64d29bd69a81190934914": {
"views": []
},
"16285d9d94e94b44885e872a1ca8bae1": {
"views": []
},
"165d51378d4b49eba1b5bbc9a7d752e6": {
"views": []
},
"16b618d2f4824f81aa782be23654a9ed": {
"views": []
},
"171b66d4c00f43c4b7f67becb8817aef": {
"views": []
},
"1720c95b0f5444679b20e0045339335f": {
"views": []
},
"17cc32c2d9bc4c4e8bb3d1962fa89320": {
"views": []
},
"19335fed26e04a08b8c255ece46ccf8e": {
"views": []
},
"19504ebba88a4a3baf26b7380a6db72a": {
"views": []
},
"19b242f0288a414d8c4367ae5953a19b": {
"views": []
},
"1a37bfa87b8f4432967a7dc14e0b3e59": {
"views": []
},
"1a7983cc04404bf58bbae6ca184fb1ff": {
"views": []
},
"1a8f162acf35400a9036c34ef6518a86": {
"views": []
},
"1aa828149cbe4e8bb1090b332d530bae": {
"views": []
},
"1b4ecba2072943f08683ac88a2d35df8": {
"views": []
},
"1baa08ccb9664b529edecb0a001587e4": {
"views": []
},
"1c0bcf60404b4a44830882ffb7ed1973": {
"views": []
},
"1c4144446a22452bbe67809ff3be6ae1": {
"views": []
},
"1c5f7528b6864bafb8c4a4990ef46a2d": {
"views": [
{
"cell": {
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"trusted": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAAKPCAYAAACsKh+8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n"
},
"cell_index": 10,
"root": true
}
]
},
"1cfd9a6f210b4054bcb39b0429cc55de": {
"views": []
},
"1d683e58238c426daa981843821bbe42": {
"views": []
},
"1da0339e3c6e4a44884e772f18fa4e2b": {
"views": []
},
"1dee2d42496b4c838041027fdf78954d": {
"views": []
},
"1e92889027a541ddb44f454e7f14a387": {
"views": []
},
"1f76e9cb47ce4bf9b0b3452b6bd854a0": {
"views": []
},
"212aab5aa2bf4660a541d175db422584": {
"views": []
},
"22df1d38fd2d43539cc7d9fccbaf577f": {
"views": []
},
"22f22aafaf554defab70e6fe1bf02f58": {
"views": []
},
"2310bd576c5b4f88a20f09a7d851a497": {
"views": []
},
"236f20f3353a427796cd3e1e2d4475f9": {
"views": []
},
"23cf28a2d9f5434696f27517edb1e019": {
"views": []
},
"23e68019becd47078dbd546b0907a08f": {
"views": []
},
"2494fdbd167046d8a7a0fe196ff53f54": {
"views": []
},
"2600b6c694094bffb6043623d1819ea9": {
"views": []
},
"26406e17a6ff431688684397e3d84ecd": {
"views": [
{
"cell": {
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"trusted": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAAKPCAYAAACsKh+8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAMTQAADE0B0s6tTgAAH1xJREFUeJzt3X+w5Xdd3/HXG5ZFYrlXsEACCSEmpSYWTKAoJkULCjcM\nllrDIB1KBQrRAA6WhlaXTju2M2mpqDCMKQnogFSKo/gDxppLRiJgEhRNCIJRIIkmhCQygvdGV7Ib\n8ukf9yzerLvJ3d3v3XP2ncdjZoe95/u5n/P58p2bPc/7/Z7zrTFGAAAAOnvQvBcAAACw3YQPAADQ\nnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoL1Jw6eqdlbVW6vqM1V1XVX9wpTzAwAAHI4dE8/3\nxiT3jDGemCRV9eiJ5wcAADhkNcaYZqKq45LcluRxY4y/nmRSAACACUx5qdupSb6U5A1V9fGq+nBV\nPWvC+QEAAA7LlJe67UhycpJPjTF+vKrOTHJ5VZ0xxvjivkFVVUkem+TOCZ8bAIBpPTzJF8ZUlwfB\nnE15qds3Jrk9yc59PyBV9ftJfmyM8aFN4x6X5POTPCkAANvpxDHGrfNeBExhsjM+Y4y/rKrfTnJu\nkt+qqlOSPCHJ9fsNvTNJbrnlliwtLU319Exk165dueiii+a9DA7C8Vlcjs3icmwWm+OzmNbX13PS\nSSclrtChkak/1e2CJD9XVW9M8tUk548xbjvQwKWlJeGzgHbu3Om4LDDHZ3E5NovLsVlsjg9wtEwa\nPmOMm5L4QAMAAGChTHoDU459Kysr814C98HxWVyOzeJybBab4wMcLZN9uMGWn7BqKcna2tqaU9sA\nAAtofX09y8vLSbI8xlif93pgCs74AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAA\nQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA\n7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0\nJ3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe\n8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvC\nBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7Qkf\nAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wA\nAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEA\nANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAA\naE/4AAAA7QkfAACgPeEDAAC0J3wAAID2tiV8quplVXVPVT1/O+YHAAA4FJOHT1WdnOQVSa6eem4A\nAIDDMWn4VFUleUeS1yTZM+XcAAAAh2vqMz6vS/LRMca1E88LAABw2HZMNVFVfUuS85I8Y6o5AQAA\npjBZ+GQjeE5O8tnZJW/HJ7m0qk4YY1yy/+Bdu3Zl586dSZKVlZWsrKxMuBQAAA7F6upqVldXkyR7\n9njHAv3UGGN7Jq66IsnPjDHev9/jS0nW1tbWsrS0tC3PDQDA4VtfX8/y8nKSLI8x1ue9HpjCdt7H\nZ3uKCgAA4BBNeanbvYwxnrVdcwMAAByK7TzjAwAAsBCEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gA\nAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMA\nALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA\n0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABA\ne8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADt\nCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQn\nfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7w\nAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IH\nAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8A\nAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKC9ycKnqh5aVb9W\nVX9SVddW1WpVnTrV/AAAAIdr6jM+l4wxvnmMcVaS9yd5x8TzAwAAHLLJwmeMcdcY47JND30syclT\nzQ8AAHC4tvM9Pq9N8uvbOD8AAMCW7NiOSatqV5JTk5x/sDG7du3Kzp07kyQrKytZWVnZjqUAALAF\nq6urWV1dTZLs2bNnzquB6dUYY9oJqy5M8sIk3z3GuPMA25eSrK2trWVpaWnS5wYA4Mitr69neXk5\nSZbHGOvzXg9MYdIzPlX1uiQvykGiBwAAYB4mC5+qelySNyW5IckVVVVJvjLG+I6pngMAAOBwTBY+\nY4xb44aoAADAAhIqAABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+\nAABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gA\nAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMA\nALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA\n0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABA\ne8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADt\nCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAHAwf1VV11XVufseqKrv\nqqrfr6pPzf58+4G+saoeVVW/VVWfqapPVtUzFnHbAdb972bjPltVl1TVgw8y7mFV9Z7ZuD+pqvMW\ncdsB1v29VXV9Vf1pVf1KVf2Dg4yrqnprVX1u9v/Hqxd024/O9vuag+3zPsIHAICDGUn+2RjjsiSp\nqhOSvDPJvxlj/JMkZyW5/iDf+z+TXD3GeGKSlyd5z6aIWKRtX1NVT0jy35KcM8b4R0mOT3L+Qfbv\nwiRfmY07N8nFVfWIBdy2ef++Psk7kjx/jPGPk9yW5L8cZP9ekuSbxxinJfn2JK+vqtMXbdsY481J\nXnGQfbgX4QMAwMHU7M8+r0ryi2OMzyTJGGPvGGP9IN/7wiRvm437gyRfSPJdC7Lt1k3bNntBkt8Y\nY3xx9vXbkvzrg+zfD2ya88+S/E6Sf7Ug267YtG2z5ya5Zozx2dnXF9/H/r0wydtnc345yS9tGrtI\n27ZM+AAAsFVnJDmuqi6vqmuq6i1V9bD9B1XVI5PsGGP8xaaH/yzJ4xdk258nefwB9u/xs233muMA\n4+5v7Ly3Hcr+HV9VB2qCee/DVrdtmfABAGCrdiR5RpLzkjwtySOT/MRcVwRbJHwAANiqm5P85hhj\nfYzx1ST/N8nT9x80xvhSkrur6tGbHn5Ckj9foG03H2T/Tt7CuGTjDMTBxi7Sts1unm3b55Qkt40x\n7jnI2IPNuUjbtkz4AACwVe9J8syq2jn7+rlJrjvI2F9OckGSVNXTkjw2yUcWaNuHZ19fVFWvmo17\nX5LnV9Wjq6qS/HCS9x5k/35ltj1VdUo23jP064u2rapeXVUXzcZdluSsqnri7OsL7mP/fjnJK6vq\nQbPLBX9g09hF2PZLB1n3Qe041G8AAOCBaYxxdVV9IMm1VXV3kk/n715wPzXJT4wxvnc2/MeSvLuq\nPpPkriQvnp0lWrRt35rkD2b7d1NV/dckV2XjE+2uSHLJbP9OyMbZrqfMvu8nk/x8VX0uyd1JXj07\nu7Ro285IcsNs//66ql6R5Ddmn2r3qSQ/OBuXqro2yXPHGLcneXeSf5rks0nuSfKmMcYfz4YuwrZP\n5xDVGONQv+eIVNVSkrW1tbUsLS0d1ecGAOD+ra+vZ3l5Odl48f8N9/HJbce02Zv6rx5jHPBeRB1U\n1UeyETN/M++1bJeq+udJfnpTlB6QS90AADiYO5J8uDbdwLSTMcY9naMnScYY39k8en40yc8m+eL9\njnXGBwCAzTad8VnueraHBx5nfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA9\n4QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALS3Y94LADiW\n7d69O5deemlu/9zncvxpp+X888/PcccdN+9l0dDevXtz5ZVX5ku33ZZHnnBCzjnnnDzkIQ+Z97JI\nv2Ozd+/efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O\nS84+O/dcf31etGdPHpvkC0neu3Nn6vTT83+uukoAMYm9e/fmpy68MDd/8IN51k035TF33ZU7HvrQ\nfOiUU3LSc56TC9/0pmP6RfaxrNux2bw/T7/xxvzgnj1JsjzGWJ/32mAKU4fPbyd55xjj3VV1XpL/\nNMb4tv3GCB/gmLZ79+6ce+KJefOXv5ynHGD7NUl+9BGPyGWf/7z44Yjs3bs3rz733LzqIx/JmXff\n/fe2f2LHjlz8nd+Zn73ssmPqBXYH3Y7N/vuznmR5Y5PwoY3JwqeqHpXks0keOca4Z/bYbUnOGWPc\nuGmc8AGOad9/5pn5z9ddd8Do2ecPk1x03HF535OffLSWRUP/46ab8tw77siZ9zHm2iSrxx+fH3vC\nE47Sqkj6HZv990f40NGU7/E5Kclt+6Jn5uYkj09y44G/BeDYsnv37ozrr7/P6EmSpyb56u7d2f2x\nj8U5Hw7H3iS3JPf5wjpJzkpy6e23Z+/tt2fxzyv00O3YbHV/4FjnU90ADsGll16aF21c936/XpTk\n7du7HBq7Msmztjj2WUmu2sa1cG/djs2h7A8cy6Y843NLkhOq6kGbzvo8Phtnff6eXbt2ZefOnUmS\nlZWVrKysTLgUgO1x++c+l6ducexjk1y3nYuhtS8lecwWxz4myV9u41q4t27HZt/+rM7+JMnWfr0D\nx5bJwmeM8cWquibJS5K8q6pekOSWze/v2eyiiy7yHh/gmHP8aaflC1sc+4Ukx2/nYmjtkUnu2OLY\nO5I8ehvXwr11Ozb79ucFSfb9Gno9yc/ObUWwPab+VLcnJnlnkm9MspbkZWOMT+83xocbAMes3bt3\n58WPeER+bQuXu31fVd77tKfl6x7kqmIO3d577slrP/nJXPyVr9zv2Ase9rC89clPzo6qo7Ayuh2b\nA+2PDzego0lvYDrG+EySs6ecE2CRHHfccanTT881W/hUtwd/67fm637v947W0mjmIUlOeu1r84mL\nLz7gxyXvc+2OHTn5/POz481vPnqLe4Drdmy2uj9wrJv0jM+WntAZH+AYt+8+Pj/z5S8f8P0+f5jk\n37uPDxPYd2+VCz7ykZx1gBek1+7Ykf99DN0rppNux2b//XHGh46ED8Bh2L17d15yzjm554//OD+w\nZ08em4339Lx35848+Iwz8u4rrxQ9TGLv3r35qde/Pjd/8IN55o035jF33ZU7HvrQfOiUU3Lyykr+\nw0/+5DHxwrqjbsdm8/58+w035KUbl/QKH9oQPgBHYPfu3Xn729+e22+4Icefempe+cpXCh62xd69\ne3PVVVflS7fdlkeecELOPvvsY+pFdWfdjs3evXtz+eWX53nPe14ifGhE+AAAcC/r6+tZXl5OhA+N\n+KghAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0\nJ3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe\n8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvC\nBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7Qkf\nAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wA\nAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEA\nANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAA\naE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACg\nPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0N0n4VNWP\nVNUfVdV1VfWJqnrxFPMCAABMYcdE83wqydljjDur6sQk11bVVWOMmyaaHwAA4LBNcsZnjHHFGOPO\n2d8/n+T2JCdNMTcAAMCRmvw9PlX1PUm+IcnHp54bAADgcGzpUrequirJafs/nGQkOWuMcets3JOS\n/HySF44x/nbKhQIAAByuLYXPGOPs+xtTVWckeX+Sl44xrr6/8bt27crOnTuTJCsrK1lZWdnKUgAA\n2Aarq6tZXV1NkuzZs2fOq4Hp1RjjyCepOj3J/0ty/hjj8vsZu5RkbW1tLUtLS0f83AAATGt9fT3L\ny8tJsjzGWJ/3emAKU73H5y1JlpK8saquraprqurZE80NAABwRCb5OOsxxnOmmAcAAGA7TP6pbgAA\nAItG+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAA\noD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAAtCd8AACA\n9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADa\nEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP\n+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3h\nAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQP\nAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4A\nAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAA\nAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAA\ntCd8AACA9oQPAADQnvABAADamzR8qurRVXV7Vf3qlPMCAAAcianP+LwtyQcmnhMAAOCITBY+VfXy\nJDcm+d2p5gQAAJjCJOFTVack+aEkb5hiPgAAgCnt2MqgqroqyWn7P5xkJHlKkp9L8poxxl1VVVuZ\nc9euXdm5c2eSZGVlJSsrK1teNAAA01pdXc3q6mqSZM+ePXNeDUyvxhhHNkHVUpIbktw5e+jhSR6W\n5OoxxrMPMn5tbW0tS0tLR/TcAABMb319PcvLy0myPMZYn/d6YApbOuNzX2Y/DI/a93VV/WCSfznG\n+P4jnRsAAGAK7uMDAAC0N3n4jDHe5WwPAACwSJzxAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA9\n4QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaE\nDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+\nAABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gA\nAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMA\nALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA\n0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABA\ne8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADt\nCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQn\nfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQ3WfhU1XlV9cmq+qPZ\n/z5+qrkBAACOxCThU1VnJfnvSZ49xnhSku9I8hdTzM3Rtbq6Ou8lcB8cn8Xl2Cwux2axOT7A0TLV\nGZ/XJfnpMcYdSTLG+JsxxlcmmpujyD9Ai83xWVyOzeJybBab4wMcLVOFzxlJTq6q36mqP6yq/1ZV\nNdHcAAAAR2THVgZV1VVJTtv/4SQjyVmzec5M8pzZ39+f5IIkFx9szvX19cNYLtttz549js0Cc3wW\nl2OzuBybxeb4LCbHhI5qjHHkk1R9IMn7xhjvnH39qiRPH2P82wOMfVySzx/xkwIAsN1OHGPcOu9F\nwBS2dMZnC96T5F9U1buSPDgbZ34+epCxX0hyYpI7J3puAACm9/BsvG6DFqY641NJ/leS5yW5OxvR\n89oxxt1HPDkAAMARmiR8AAAAFtlkNzA9HG56utiq6tFVdXtV/eq818Lfqaofmf3MXFdVn6iqF897\nTQ9kVXVaVV1ZVX9aVb9XVafPe01sqKqHVtWvVdWfVNW1VbVaVafOe13cW1W9rKruqarnz3stbKiq\nnVX11qr6zOzfml+Y95pgClO9x+eQbbrp6TPHGHdU1dcn+eq81sMBvS3JB5J847wXwr18KsnZY4w7\nq+rEJNdW1VVjjJvmvbAHqEuSvG2M8e6qOi/Ju5J825zXxN+5ZIxxWZJU1auTvCPJM+e7JPapqpOT\nvCLJ1fNeC/fyxiT3jDGemGz8InTO64FJzPOMj5ueLrCqenmSG5P87rzXwr2NMa4YY9w5+/vnk9ye\n5KT5ruqBqaoeleSpSX4xScYY70tyUlV901wXRpJkjHHXvuiZ+ViSk+e1Hu5t9v7gdyR5TZI9c14O\nM1V1XJKXJ3nDvsfGGH8xvxXBdOYZPm56uqCq6pQkP5RN/9FjMVXV9yT5hiQfn/daHqBOSnLbGOOe\nTY/dnMRlu4vptUl+fd6L4Gtel+SjY4xr570Q7uXUJF9K8oaq+nhVfbiqnjXvRcEUtu1St+246SnT\nuJ9j85QkP5fkNWOMu8To0Xd/Pzv77qdQVU9K8vNJXjjG+Nuju0o4tlTVrmy8oDt/3mshqapvSXJe\nkmfMey38PTuycWb0U2OMH6+qM5NcXlVnjDG+OOe1wRHZtvAZY5x9X9ur6uZs3PR0T5I9szfQPz3C\nZ9vd17GpqqUkT0ryS7PmeXiSh1XV5WOMZx+lJT6g3d/PTpJU1RnZ+GXBS8cYro2fn1uSnFBVD9p0\n1ufx2Tjrw4KoqguTfF+S73ZJ9cJ4RjZeXH929gu245NcWlUnjDEume/SHvBuzsZ7rt+TJGOMT1TV\nTdl4bfCheS4MjtQ8L3V7T5Ln1IYd2Tjzc90c10OSMcb6GONRY4xvGmN8U5ILk3xQ9CyO2aeG/WaS\n88cY/hGao9lvP69J8pIkqaoXJLlljHHjXBfG11TV65K8KMmz9703jvkbY7xtjPG42b81p2Tj/Vfn\ni575G2P8ZZLfTnJu8rXL35+Q5Po5LgsmMc/weW+SW5N8OhsvHG5N8pY5rgeOFW9JspTkjbOP6L2m\nqoTp/Pxwkh+qqj9N8h+TvGzO62Gmqh6X5E1JlpNcMft5cYZ0Mbmp4GK5IMnrq+qTSX41G1F625zX\nBEfMDUwBAID25noDUwAAgKNB+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n"
},
"cell_index": 10,
"root": true
}
]
},
"26795a5634004f179a4433436959f69c": {
"views": []
},
"28f6bbda5c7d487fa65a3ccfcb890b44": {
"views": []
},
"28fbf1d342404c90b19e51eb06b656a6": {
"views": []
},
"29291248cbaf449dad8c04aefea3c251": {
"views": []
},
"29324c74ce44472d816ddb618e5e7154": {
"views": []
},
"29d7ec8815be42d180e90f0867e537a5": {
"views": []
},
"29ef4faa80594de0ab95b9e1e31a0500": {
"views": []
},
"2aa04ae23f974820977ecc02dc8b6b43": {
"views": []
},
"2ab47ef47437437c8cfaa90fb663193c": {
"views": []
},
"2af796b503d34c988689c3b04df5de24": {
"views": [
{
"cell": {
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"trusted": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAxMAAAKPCAYAAAAWiZfcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7e70534390>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "from numpy import sin, cos, pi, matrix\nfrom math import atan2, acos\n\nT0 = trans(0, 0, 0)\nlv = [0] + range(N, 0, -1) # length of link, l[0] is ingored\nbf = B\nfor i in range(N):\n bf = bf.subs(l[i + 1], lv[i + 1])\n\ndef inverse_kinematics(x_e, y_e, theta_e):\n b = bf.subs(x, x_e).subs(y, y_e).subs(theta, theta_e)\n b = (b.subs('I', 1).subs('pi', pi).tolist())\n b = [float(i[0]) for i in b]\n return b\n \n@interact(x_e=(0, max_len, 0.1), y_e=(-max_len, max_len, 0.1), theta_e=(-pi, pi, 0.1))\ndef set_end_effector(x_e=5, y_e=0, theta_e=0):\n b = inverse_kinematics(x_e, y_e, theta_e)\n T = forward_kinematics(T0, lv, b)\n show_robot_arm(T)"
},
"cell_index": 31,
"root": true
}
]
},
"2b08bea1b4f54c9ba077c00da9f32df4": {
"views": []
},
"2b93cbb0c4224f89849f2dde2fc15157": {
"views": []
},
"2bc083e7865744218921a220b1dcd2bd": {
"views": []
},
"2bd0e43b75c644de982f6ed6526efdd4": {
"views": []
},
"2c34626d64f64cee97c80b20eca8bcca": {
"views": []
},
"2d47a8fbe94e44558b858df765718bf6": {
"views": []
},
"2da22ed89495462aa8367d393c2a7eb3": {
"views": []
},
"2daf53bd4a7446b789c98057a1e8a250": {
"views": []
},
"2dcbbf3b132149afa31062be6a07554c": {
"views": []
},
"2e214dcc0b3c44e9b2328d1951fa682f": {
"views": []
},
"2f158a0aa1a64a06b1e040595d5f3786": {
"views": []
},
"2f2278ef4cfa4853819ae1f6527a3b73": {
"views": []
},
"2f6d2e4e681c4aa69eb193d1231e33eb": {
"views": []
},
"2fbda6a400e84a79aef5b70e619abb96": {
"views": []
},
"3041d69ed88044cea4de4bd0f450c27d": {
"views": []
},
"30d84e2c052640189dbdcf9392defa4c": {
"views": []
},
"31cf7dfafa144ec4a2596eb29add3718": {
"views": []
},
"31e0aedd3ffa43dea7a1c25a5500b106": {
"views": []
},
"32abfd69cd694c50aa2ebee92df33674": {
"views": []
},
"32d299c4eabe4ab0b7f7ce7d320c85bf": {
"views": []
},
"32e5479e6e41479e984f122b75b58d11": {
"views": []
},
"330fab827b204b90ad49370b88945728": {
"views": []
},
"335ed94a336e40a2b69bd63a6f675584": {
"views": []
},
"33602b70c8ec4149afabed51291da3d7": {
"views": [
{
"cell": {
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"trusted": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAAKPCAYAAACsKh+8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAMTQAADE0B0s6tTgAAH1xJREFUeJzt3X+w5Xdd3/HXG5ZFYrlXsEACCSEmpSYWTKAoJkULCjcM\nllrDIB1KBQrRAA6WhlaXTju2M2mpqDCMKQnogFSKo/gDxppLRiJgEhRNCIJRIIkmhCQygvdGV7Ib\n8ukf9yzerLvJ3d3v3XP2ncdjZoe95/u5n/P58p2bPc/7/Z7zrTFGAAAAOnvQvBcAAACw3YQPAADQ\nnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoL1Jw6eqdlbVW6vqM1V1XVX9wpTzAwAAHI4dE8/3\nxiT3jDGemCRV9eiJ5wcAADhkNcaYZqKq45LcluRxY4y/nmRSAACACUx5qdupSb6U5A1V9fGq+nBV\nPWvC+QEAAA7LlJe67UhycpJPjTF+vKrOTHJ5VZ0xxvjivkFVVUkem+TOCZ8bAIBpPTzJF8ZUlwfB\nnE15qds3Jrk9yc59PyBV9ftJfmyM8aFN4x6X5POTPCkAANvpxDHGrfNeBExhsjM+Y4y/rKrfTnJu\nkt+qqlOSPCHJ9fsNvTNJbrnlliwtLU319Exk165dueiii+a9DA7C8Vlcjs3icmwWm+OzmNbX13PS\nSSclrtChkak/1e2CJD9XVW9M8tUk548xbjvQwKWlJeGzgHbu3Om4LDDHZ3E5NovLsVlsjg9wtEwa\nPmOMm5L4QAMAAGChTHoDU459Kysr814C98HxWVyOzeJybBab4wMcLZN9uMGWn7BqKcna2tqaU9sA\nAAtofX09y8vLSbI8xlif93pgCs74AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAA\nQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA\n7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0\nJ3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe\n8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvC\nBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7Qkf\nAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wA\nAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEA\nANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAA\naE/4AAAA7QkfAACgPeEDAAC0J3wAAID2tiV8quplVXVPVT1/O+YHAAA4FJOHT1WdnOQVSa6eem4A\nAIDDMWn4VFUleUeS1yTZM+XcAAAAh2vqMz6vS/LRMca1E88LAABw2HZMNVFVfUuS85I8Y6o5AQAA\npjBZ+GQjeE5O8tnZJW/HJ7m0qk4YY1yy/+Bdu3Zl586dSZKVlZWsrKxMuBQAAA7F6upqVldXkyR7\n9njHAv3UGGN7Jq66IsnPjDHev9/jS0nW1tbWsrS0tC3PDQDA4VtfX8/y8nKSLI8x1ue9HpjCdt7H\nZ3uKCgAA4BBNeanbvYwxnrVdcwMAAByK7TzjAwAAsBCEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gA\nAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMA\nALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA\n0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABA\ne8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADt\nCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQn\nfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7w\nAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IH\nAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8A\nAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKC9ycKnqh5aVb9W\nVX9SVddW1WpVnTrV/AAAAIdr6jM+l4wxvnmMcVaS9yd5x8TzAwAAHLLJwmeMcdcY47JND30syclT\nzQ8AAHC4tvM9Pq9N8uvbOD8AAMCW7NiOSatqV5JTk5x/sDG7du3Kzp07kyQrKytZWVnZjqUAALAF\nq6urWV1dTZLs2bNnzquB6dUYY9oJqy5M8sIk3z3GuPMA25eSrK2trWVpaWnS5wYA4Mitr69neXk5\nSZbHGOvzXg9MYdIzPlX1uiQvykGiBwAAYB4mC5+qelySNyW5IckVVVVJvjLG+I6pngMAAOBwTBY+\nY4xb44aoAADAAhIqAABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+\nAABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gA\nAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMA\nALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA\n0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABA\ne8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADt\nCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAHAwf1VV11XVufseqKrv\nqqrfr6pPzf58+4G+saoeVVW/VVWfqapPVtUzFnHbAdb972bjPltVl1TVgw8y7mFV9Z7ZuD+pqvMW\ncdsB1v29VXV9Vf1pVf1KVf2Dg4yrqnprVX1u9v/Hqxd024/O9vuag+3zPsIHAICDGUn+2RjjsiSp\nqhOSvDPJvxlj/JMkZyW5/iDf+z+TXD3GeGKSlyd5z6aIWKRtX1NVT0jy35KcM8b4R0mOT3L+Qfbv\nwiRfmY07N8nFVfWIBdy2ef++Psk7kjx/jPGPk9yW5L8cZP9ekuSbxxinJfn2JK+vqtMXbdsY481J\nXnGQfbgX4QMAwMHU7M8+r0ryi2OMzyTJGGPvGGP9IN/7wiRvm437gyRfSPJdC7Lt1k3bNntBkt8Y\nY3xx9vXbkvzrg+zfD2ya88+S/E6Sf7Ug267YtG2z5ya5Zozx2dnXF9/H/r0wydtnc345yS9tGrtI\n27ZM+AAAsFVnJDmuqi6vqmuq6i1V9bD9B1XVI5PsGGP8xaaH/yzJ4xdk258nefwB9u/xs233muMA\n4+5v7Ly3Hcr+HV9VB2qCee/DVrdtmfABAGCrdiR5RpLzkjwtySOT/MRcVwRbJHwAANiqm5P85hhj\nfYzx1ST/N8nT9x80xvhSkrur6tGbHn5Ckj9foG03H2T/Tt7CuGTjDMTBxi7Sts1unm3b55Qkt40x\n7jnI2IPNuUjbtkz4AACwVe9J8syq2jn7+rlJrjvI2F9OckGSVNXTkjw2yUcWaNuHZ19fVFWvmo17\nX5LnV9Wjq6qS/HCS9x5k/35ltj1VdUo23jP064u2rapeXVUXzcZdluSsqnri7OsL7mP/fjnJK6vq\nQbPLBX9g09hF2PZLB1n3Qe041G8AAOCBaYxxdVV9IMm1VXV3kk/n715wPzXJT4wxvnc2/MeSvLuq\nPpPkriQvnp0lWrRt35rkD2b7d1NV/dckV2XjE+2uSHLJbP9OyMbZrqfMvu8nk/x8VX0uyd1JXj07\nu7Ro285IcsNs//66ql6R5Ddmn2r3qSQ/OBuXqro2yXPHGLcneXeSf5rks0nuSfKmMcYfz4YuwrZP\n5xDVGONQv+eIVNVSkrW1tbUsLS0d1ecGAOD+ra+vZ3l5Odl48f8N9/HJbce02Zv6rx5jHPBeRB1U\n1UeyETN/M++1bJeq+udJfnpTlB6QS90AADiYO5J8uDbdwLSTMcY9naMnScYY39k8en40yc8m+eL9\njnXGBwCAzTad8VnueraHBx5nfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA9\n4QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALS3Y94LADiW\n7d69O5deemlu/9zncvxpp+X888/PcccdN+9l0dDevXtz5ZVX5ku33ZZHnnBCzjnnnDzkIQ+Z97JI\nv2Ozd+/efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O\nS84+O/dcf31etGdPHpvkC0neu3Nn6vTT83+uukoAMYm9e/fmpy68MDd/8IN51k035TF33ZU7HvrQ\nfOiUU3LSc56TC9/0pmP6RfaxrNux2bw/T7/xxvzgnj1JsjzGWJ/32mAKU4fPbyd55xjj3VV1XpL/\nNMb4tv3GCB/gmLZ79+6ce+KJefOXv5ynHGD7NUl+9BGPyGWf/7z44Yjs3bs3rz733LzqIx/JmXff\n/fe2f2LHjlz8nd+Zn73ssmPqBXYH3Y7N/vuznmR5Y5PwoY3JwqeqHpXks0keOca4Z/bYbUnOGWPc\nuGmc8AGOad9/5pn5z9ddd8Do2ecPk1x03HF535OffLSWRUP/46ab8tw77siZ9zHm2iSrxx+fH3vC\nE47Sqkj6HZv990f40NGU7/E5Kclt+6Jn5uYkj09y44G/BeDYsnv37ozrr7/P6EmSpyb56u7d2f2x\nj8U5Hw7H3iS3JPf5wjpJzkpy6e23Z+/tt2fxzyv00O3YbHV/4FjnU90ADsGll16aF21c936/XpTk\n7du7HBq7Msmztjj2WUmu2sa1cG/djs2h7A8cy6Y843NLkhOq6kGbzvo8Phtnff6eXbt2ZefOnUmS\nlZWVrKysTLgUgO1x++c+l6ducexjk1y3nYuhtS8lecwWxz4myV9u41q4t27HZt/+rM7+JMnWfr0D\nx5bJwmeM8cWquibJS5K8q6pekOSWze/v2eyiiy7yHh/gmHP8aaflC1sc+4Ukx2/nYmjtkUnu2OLY\nO5I8ehvXwr11Ozb79ucFSfb9Gno9yc/ObUWwPab+VLcnJnlnkm9MspbkZWOMT+83xocbAMes3bt3\n58WPeER+bQuXu31fVd77tKfl6x7kqmIO3d577slrP/nJXPyVr9zv2Ase9rC89clPzo6qo7Ayuh2b\nA+2PDzego0lvYDrG+EySs6ecE2CRHHfccanTT881W/hUtwd/67fm637v947W0mjmIUlOeu1r84mL\nLz7gxyXvc+2OHTn5/POz481vPnqLe4Drdmy2uj9wrJv0jM+WntAZH+AYt+8+Pj/z5S8f8P0+f5jk\n37uPDxPYd2+VCz7ykZx1gBek1+7Ykf99DN0rppNux2b//XHGh46ED8Bh2L17d15yzjm554//OD+w\nZ08em4339Lx35848+Iwz8u4rrxQ9TGLv3r35qde/Pjd/8IN55o035jF33ZU7HvrQfOiUU3Lyykr+\nw0/+5DHxwrqjbsdm8/58+w035KUbl/QKH9oQPgBHYPfu3Xn729+e22+4Icefempe+cpXCh62xd69\ne3PVVVflS7fdlkeecELOPvvsY+pFdWfdjs3evXtz+eWX53nPe14ifGhE+AAAcC/r6+tZXl5OhA+N\n+KghAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0\nJ3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe\n8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvC\nBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7Qkf\nAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wA\nAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEA\nANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAA\naE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACg\nPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0N0n4VNWP\nVNUfVdV1VfWJqnrxFPMCAABMYcdE83wqydljjDur6sQk11bVVWOMmyaaHwAA4LBNcsZnjHHFGOPO\n2d8/n+T2JCdNMTcAAMCRmvw9PlX1PUm+IcnHp54bAADgcGzpUrequirJafs/nGQkOWuMcets3JOS\n/HySF44x/nbKhQIAAByuLYXPGOPs+xtTVWckeX+Sl44xrr6/8bt27crOnTuTJCsrK1lZWdnKUgAA\n2Aarq6tZXV1NkuzZs2fOq4Hp1RjjyCepOj3J/0ty/hjj8vsZu5RkbW1tLUtLS0f83AAATGt9fT3L\ny8tJsjzGWJ/3emAKU73H5y1JlpK8saquraprqurZE80NAABwRCb5OOsxxnOmmAcAAGA7TP6pbgAA\nAItG+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAA\noD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAAtCd8AACA\n9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADa\nEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP\n+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3h\nAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQP\nAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4A\nAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAA\nAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAA\ntCd8AACA9oQPAADQnvABAADamzR8qurRVXV7Vf3qlPMCAAAcianP+LwtyQcmnhMAAOCITBY+VfXy\nJDcm+d2p5gQAAJjCJOFTVack+aEkb5hiPgAAgCnt2MqgqroqyWn7P5xkJHlKkp9L8poxxl1VVVuZ\nc9euXdm5c2eSZGVlJSsrK1teNAAA01pdXc3q6mqSZM+ePXNeDUyvxhhHNkHVUpIbktw5e+jhSR6W\n5OoxxrMPMn5tbW0tS0tLR/TcAABMb319PcvLy0myPMZYn/d6YApbOuNzX2Y/DI/a93VV/WCSfznG\n+P4jnRsAAGAK7uMDAAC0N3n4jDHe5WwPAACwSJzxAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA9\n4QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaE\nDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+\nAABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gA\nAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMA\nALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA\n0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABA\ne8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADt\nCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQn\nfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQ3WfhU1XlV9cmq+qPZ\n/z5+qrkBAACOxCThU1VnJfnvSZ49xnhSku9I8hdTzM3Rtbq6Ou8lcB8cn8Xl2Cwux2axOT7A0TLV\nGZ/XJfnpMcYdSTLG+JsxxlcmmpujyD9Ai83xWVyOzeJybBab4wMcLVOFzxlJTq6q36mqP6yq/1ZV\nNdHcAAAAR2THVgZV1VVJTtv/4SQjyVmzec5M8pzZ39+f5IIkFx9szvX19cNYLtttz549js0Cc3wW\nl2OzuBybxeb4LCbHhI5qjHHkk1R9IMn7xhjvnH39qiRPH2P82wOMfVySzx/xkwIAsN1OHGPcOu9F\nwBS2dMZnC96T5F9U1buSPDgbZ34+epCxX0hyYpI7J3puAACm9/BsvG6DFqY641NJ/leS5yW5OxvR\n89oxxt1HPDkAAMARmiR8AAAAFtlkNzA9HG56utiq6tFVdXtV/eq818Lfqaofmf3MXFdVn6iqF897\nTQ9kVXVaVV1ZVX9aVb9XVafPe01sqKqHVtWvVdWfVNW1VbVaVafOe13cW1W9rKruqarnz3stbKiq\nnVX11qr6zOzfml+Y95pgClO9x+eQbbrp6TPHGHdU1dcn+eq81sMBvS3JB5J847wXwr18KsnZY4w7\nq+rEJNdW1VVjjJvmvbAHqEuSvG2M8e6qOi/Ju5J825zXxN+5ZIxxWZJU1auTvCPJM+e7JPapqpOT\nvCLJ1fNeC/fyxiT3jDGemGz8InTO64FJzPOMj5ueLrCqenmSG5P87rzXwr2NMa4YY9w5+/vnk9ye\n5KT5ruqBqaoeleSpSX4xScYY70tyUlV901wXRpJkjHHXvuiZ+ViSk+e1Hu5t9v7gdyR5TZI9c14O\nM1V1XJKXJ3nDvsfGGH8xvxXBdOYZPm56uqCq6pQkP5RN/9FjMVXV9yT5hiQfn/daHqBOSnLbGOOe\nTY/dnMRlu4vptUl+fd6L4Gtel+SjY4xr570Q7uXUJF9K8oaq+nhVfbiqnjXvRcEUtu1St+246SnT\nuJ9j85QkP5fkNWOMu8To0Xd/Pzv77qdQVU9K8vNJXjjG+Nuju0o4tlTVrmy8oDt/3mshqapvSXJe\nkmfMey38PTuycWb0U2OMH6+qM5NcXlVnjDG+OOe1wRHZtvAZY5x9X9ur6uZs3PR0T5I9szfQPz3C\nZ9vd17GpqqUkT0ryS7PmeXiSh1XV5WOMZx+lJT6g3d/PTpJU1RnZ+GXBS8cYro2fn1uSnFBVD9p0\n1ufx2Tjrw4KoqguTfF+S73ZJ9cJ4RjZeXH929gu245NcWlUnjDEume/SHvBuzsZ7rt+TJGOMT1TV\nTdl4bfCheS4MjtQ8L3V7T5Ln1IYd2Tjzc90c10OSMcb6GONRY4xvGmN8U5ILk3xQ9CyO2aeG/WaS\n88cY/hGao9lvP69J8pIkqaoXJLlljHHjXBfG11TV65K8KMmz9703jvkbY7xtjPG42b81p2Tj/Vfn\ni575G2P8ZZLfTnJu8rXL35+Q5Po5LgsmMc/weW+SW5N8OhsvHG5N8pY5rgeOFW9JspTkjbOP6L2m\nqoTp/Pxwkh+qqj9N8h+TvGzO62Gmqh6X5E1JlpNcMft5cYZ0Mbmp4GK5IMnrq+qTSX41G1F625zX\nBEfMDUwBAID25noDUwAAgKNB+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n"
},
"cell_index": 10,
"root": true
}
]
},
"33b2e7d8d4c84e768423aa3803e2b9f5": {
"views": []
},
"33d1a4a3e2984b5999adf4a82ed6d243": {
"views": []
},
"34acdada4843453d86dc7812e4271c09": {
"views": []
},
"356a4ddeac674b17a489dc73c02f2f57": {
"views": []
},
"358e60da5d7c45278a2640a67c6af292": {
"views": []
},
"36aa1b263e224022a6b374b09c44b6fb": {
"views": []
},
"36ff2aeae7a14f91a7be43d3c47a0597": {
"views": []
},
"374ce53463064914b8233dfced2d9d52": {
"views": []
},
"37513503a13b45cfbd26e56238d8c949": {
"views": []
},
"386c80b57d6a404992acaf6469506db3": {
"views": []
},
"389ea982a40d46d591bebd0ee559801e": {
"views": []
},
"38b47640815546939304ec2a6d71c3ad": {
"views": [
{
"cell": {
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"trusted": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAAKPCAYAAACsKh+8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n"
},
"cell_index": 10,
"root": true
}
]
},
"38bc0f4ca75245689ea57a229c49472a": {
"views": []
},
"38d0989572be494b80d396336d933313": {
"views": []
},
"39240ffe45674b32aa64d099d6e61bd6": {
"views": []
},
"3a5050c9cd9e457fae55d0a598eb4b4f": {
"views": []
},
"3a80053831f94247947bda7171a375f5": {
"views": []
},
"3b0b75b35ae34a6d9576ab19dea42ccd": {
"views": []
},
"3b667c3b4fc4424d8893d82380ed312a": {
"views": []
},
"3bd271b7ed304418a7d058b88a46817c": {
"views": []
},
"3bfb2f44245f4631af2733821fc24f2d": {
"views": []
},
"3c160aed9d4645a3867ab97e9d4428e8": {
"views": []
},
"3c4064aa9a404ca0802954fe0baf05ad": {
"views": []
},
"3c8d97fabeff4d4882982ae88559ca1d": {
"views": []
},
"3ce5b9ab8fbc4af092b37669aef932a9": {
"views": [
{
"cell": {
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"trusted": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAAKPCAYAAACsKh+8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n"
},
"cell_index": 10,
"root": true
}
]
},
"3cfd0856c6ad4d72957c378772dddceb": {
"views": []
},
"3d5f1dddcb724d6f8776e5beb45145be": {
"views": []
},
"3dc13517176b4d059867e0c5c14b333e": {
"views": []
},
"3e69ea50d1bc4d33a564d6e3dc695803": {
"views": []
},
"3e7ec1eec9bb432ab76638a80530882e": {
"views": []
},
"3ec90899379e473a8a3d9537a9fd3e6a": {
"views": []
},
"3ee2ee71c4df41fe8bcc0e36c035bdc9": {
"views": []
},
"3f1c69d137514a16a98abd88055e502c": {
"views": []
},
"3f419880ead047eda91f3d02a9958b64": {
"views": [
{
"cell": {
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"trusted": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAAKPCAYAAACsKh+8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n"
},
"cell_index": 10,
"root": true
}
]
},
"3f5ab6e43ca24f5e8e5027949e294b73": {
"views": []
},
"402b967618d643fc97bfae338b61bca7": {
"views": []
},
"406779a8f6d34d4380a191d686a93982": {
"views": []
},
"40f4104efd0a476b8cda2270451a36c6": {
"views": []
},
"413a128d962c46529e2d1e29560a6ae0": {
"views": []
},
"41a1baaa410b4a2b9ef5215415419794": {
"views": []
},
"41bff4e065034fba946a00910cf2bbd0": {
"views": []
},
"4207fd35447646a3ae207eb156e8a1a7": {
"views": []
},
"423b0c0c4c2d4854bb834c78182cbe36": {
"views": []
},
"423eba659fb04fb99eac03114ad2d622": {
"views": []
},
"429a4d65c8584ae791503ab41cb58731": {
"views": []
},
"42d7f50de10a49f8b733885b0af80966": {
"views": []
},
"432d06b78d8f4824bab1a93996b2a14a": {
"views": []
},
"4358fc87be654926bec4b4b2e4440d38": {
"views": []
},
"437bbfd69d6d4a80a94decfaf685876f": {
"views": []
},
"440c429e8f294ce58c5f89ab76abf6c1": {
"views": []
},
"441fdac0c8fb41b993d6f18e4204e6bf": {
"views": []
},
"459c36f2bea74af49733d14f6c617384": {
"views": []
},
"45a70163f8404fd294c9f936bff78828": {
"views": []
},
"45c245c1bb9b4b859b23d15f980cd954": {
"views": []
},
"473397ff39574085ac5ccd578afa8e07": {
"views": []
},
"47eaadb62e4c4204b946f89c2d7cfba0": {
"views": []
},
"47f4537bcdde4e569ddc6f1f1fdf082d": {
"views": []
},
"48a945f1f67a461882f704126b5802e5": {
"views": []
},
"4930cd5ddbaf497d83864cb1776b081c": {
"views": []
},
"49472f1b85d14c5aacb9cd45dc73b937": {
"views": []
},
"4a76aea6fa7844b5a2507caba2fbe80d": {
"views": []
},
"4b34f8f5e4d94f9e88d18705f4a84be0": {
"views": []
},
"4b9169c3066a4e2793168f58c546912e": {
"views": []
},
"4bbf0bfc7f5b41feba7b3ed4fb58b590": {
"views": []
},
"4c222a58cf0844be861fb1c6c3899ac8": {
"views": []
},
"4d3ced97d549466280fe52cf91179ee1": {
"views": []
},
"4d478351c1cb4336910a65566da5a404": {
"views": []
},
"4e113d6cadb946adbfc504f0fc4f4506": {
"views": []
},
"4e2da3d20dad466ebb93919d5acc5873": {
"views": []
},
"4e7424e801804d24a1da1bc325ab4509": {
"views": []
},
"4f313ccb6bf044e6bd4d67bb524477ff": {
"views": []
},
"4f78051096014637bd27c6bb2d9a6fe2": {
"views": []
},
"4f81829f7a2a44ccb3c0246fa42b0099": {
"views": []
},
"4feb7d54900a4b55ade9546899309963": {
"views": []
},
"4ff47f58b3234bb1a8794f408f35b1f9": {
"views": []
},
"502c259235914c65aa122cf102c5f95c": {
"views": []
},
"50602203bbcc4b1da8fa832cb050ea6a": {
"views": []
},
"506abf1a2b804854bda60dad426aeeb6": {
"views": []
},
"50e4995bc4a44719b8fe53616db715b2": {
"views": []
},
"50e5dc84a6ca458a8267ff821bbab146": {
"views": []
},
"5148268d65f64ef1a65d2f2aded3464f": {
"views": []
},
"5148432ae0094c60bf8336c2188e5d97": {
"views": []
},
"51c5280a6a36429ba00c237c1e71daa3": {
"views": []
},
"51eab325838d423c951fe914d2e6f58d": {
"views": []
},
"520196af52bb43d297c17f47b9a14cdc": {
"views": []
},
"5296222172c44bb09a734e048885969a": {
"views": []
},
"542cd1d22bcc4f9b8bbd027276654935": {
"views": []
},
"5487e5d9a6114e3692a8b47c0115f9ef": {
"views": []
},
"5501bd1747a649b684b6d41b422cb957": {
"views": []
},
"553dac4558df4e5ab758a21559fb80b7": {
"views": []
},
"55782ed84520477e9e0a4f24b6a0a294": {
"views": []
},
"557ca17fb98a44d7931b118ccb20944d": {
"views": []
},
"560ad4d8eddf4b2b900d18ded3210c7e": {
"views": []
},
"56484c45895249018f169130a7e6aa98": {
"views": []
},
"56db0e442c794624a9ada6b09daee590": {
"views": []
},
"575e9ad19d414ba7848848d58bb7cb0e": {
"views": []
},
"57659cbe98984d7e941d4fa94fc557e2": {
"views": []
},
"57fcba7a75ab495a838548811f4dd804": {
"views": []
},
"580b97bcc0c941fe8e6b707fb141d7eb": {
"views": []
},
"58302abf49e347429bb7761fbbe33231": {
"views": []
},
"58483ecde6ac44d49dc8482c986b6ddc": {
"views": []
},
"58c882595c8e4800b34dae7974f07f19": {
"views": []
},
"592a0a75fe434c05adb20c5ad8589421": {
"views": []
},
"5943051f8c184c52acf56edbd48d5fb1": {
"views": []
},
"59894859a6f34e528248367e05521199": {
"views": []
},
"5acec54672e94c2cbae1da33a69a7a41": {
"views": []
},
"5b0e4b7f52a14ed0925d6fccdad1c8f8": {
"views": []
},
"5b8d22559a3047f986bd55309324c916": {
"views": []
},
"5b9bde91070b4dccbd5a87a235f01887": {
"views": []
},
"5cd2e158c2d24f7c950ec6954da1bef3": {
"views": []
},
"5d783e15f0fe42f09e0b64f141ad229b": {
"views": [
{
"cell": {
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"trusted": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAAKPCAYAAACsKh+8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAMTQAADE0B0s6tTgAAH1xJREFUeJzt3X+w5Xdd3/HXG5ZFYrlXsEACCSEmpSYWTKAoJkULCjcM\nllrDIB1KBQrRAA6WhlaXTju2M2mpqDCMKQnogFSKo/gDxppLRiJgEhRNCIJRIIkmhCQygvdGV7Ib\n8ukf9yzerLvJ3d3v3XP2ncdjZoe95/u5n/P58p2bPc/7/Z7zrTFGAAAAOnvQvBcAAACw3YQPAADQ\nnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoL1Jw6eqdlbVW6vqM1V1XVX9wpTzAwAAHI4dE8/3\nxiT3jDGemCRV9eiJ5wcAADhkNcaYZqKq45LcluRxY4y/nmRSAACACUx5qdupSb6U5A1V9fGq+nBV\nPWvC+QEAAA7LlJe67UhycpJPjTF+vKrOTHJ5VZ0xxvjivkFVVUkem+TOCZ8bAIBpPTzJF8ZUlwfB\nnE15qds3Jrk9yc59PyBV9ftJfmyM8aFN4x6X5POTPCkAANvpxDHGrfNeBExhsjM+Y4y/rKrfTnJu\nkt+qqlOSPCHJ9fsNvTNJbrnlliwtLU319Exk165dueiii+a9DA7C8Vlcjs3icmwWm+OzmNbX13PS\nSSclrtChkak/1e2CJD9XVW9M8tUk548xbjvQwKWlJeGzgHbu3Om4LDDHZ3E5NovLsVlsjg9wtEwa\nPmOMm5L4QAMAAGChTHoDU459Kysr814C98HxWVyOzeJybBab4wMcLZN9uMGWn7BqKcna2tqaU9sA\nAAtofX09y8vLSbI8xlif93pgCs74AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAA\nQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA\n7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0\nJ3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe\n8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvC\nBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7Qkf\nAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wA\nAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEA\nANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAA\naE/4AAAA7QkfAACgPeEDAAC0J3wAAID2tiV8quplVXVPVT1/O+YHAAA4FJOHT1WdnOQVSa6eem4A\nAIDDMWn4VFUleUeS1yTZM+XcAAAAh2vqMz6vS/LRMca1E88LAABw2HZMNVFVfUuS85I8Y6o5AQAA\npjBZ+GQjeE5O8tnZJW/HJ7m0qk4YY1yy/+Bdu3Zl586dSZKVlZWsrKxMuBQAAA7F6upqVldXkyR7\n9njHAv3UGGN7Jq66IsnPjDHev9/jS0nW1tbWsrS0tC3PDQDA4VtfX8/y8nKSLI8x1ue9HpjCdt7H\nZ3uKCgAA4BBNeanbvYwxnrVdcwMAAByK7TzjAwAAsBCEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gA\nAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMA\nALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA\n0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABA\ne8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADt\nCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQn\nfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7w\nAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IH\nAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8A\nAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKC9ycKnqh5aVb9W\nVX9SVddW1WpVnTrV/AAAAIdr6jM+l4wxvnmMcVaS9yd5x8TzAwAAHLLJwmeMcdcY47JND30syclT\nzQ8AAHC4tvM9Pq9N8uvbOD8AAMCW7NiOSatqV5JTk5x/sDG7du3Kzp07kyQrKytZWVnZjqUAALAF\nq6urWV1dTZLs2bNnzquB6dUYY9oJqy5M8sIk3z3GuPMA25eSrK2trWVpaWnS5wYA4Mitr69neXk5\nSZbHGOvzXg9MYdIzPlX1uiQvykGiBwAAYB4mC5+qelySNyW5IckVVVVJvjLG+I6pngMAAOBwTBY+\nY4xb44aoAADAAhIqAABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+\nAABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gA\nAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMA\nALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA\n0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABA\ne8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADt\nCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAHAwf1VV11XVufseqKrv\nqqrfr6pPzf58+4G+saoeVVW/VVWfqapPVtUzFnHbAdb972bjPltVl1TVgw8y7mFV9Z7ZuD+pqvMW\ncdsB1v29VXV9Vf1pVf1KVf2Dg4yrqnprVX1u9v/Hqxd024/O9vuag+3zPsIHAICDGUn+2RjjsiSp\nqhOSvDPJvxlj/JMkZyW5/iDf+z+TXD3GeGKSlyd5z6aIWKRtX1NVT0jy35KcM8b4R0mOT3L+Qfbv\nwiRfmY07N8nFVfWIBdy2ef++Psk7kjx/jPGPk9yW5L8cZP9ekuSbxxinJfn2JK+vqtMXbdsY481J\nXnGQfbgX4QMAwMHU7M8+r0ryi2OMzyTJGGPvGGP9IN/7wiRvm437gyRfSPJdC7Lt1k3bNntBkt8Y\nY3xx9vXbkvzrg+zfD2ya88+S/E6Sf7Ug267YtG2z5ya5Zozx2dnXF9/H/r0wydtnc345yS9tGrtI\n27ZM+AAAsFVnJDmuqi6vqmuq6i1V9bD9B1XVI5PsGGP8xaaH/yzJ4xdk258nefwB9u/xs233muMA\n4+5v7Ly3Hcr+HV9VB2qCee/DVrdtmfABAGCrdiR5RpLzkjwtySOT/MRcVwRbJHwAANiqm5P85hhj\nfYzx1ST/N8nT9x80xvhSkrur6tGbHn5Ckj9foG03H2T/Tt7CuGTjDMTBxi7Sts1unm3b55Qkt40x\n7jnI2IPNuUjbtkz4AACwVe9J8syq2jn7+rlJrjvI2F9OckGSVNXTkjw2yUcWaNuHZ19fVFWvmo17\nX5LnV9Wjq6qS/HCS9x5k/35ltj1VdUo23jP064u2rapeXVUXzcZdluSsqnri7OsL7mP/fjnJK6vq\nQbPLBX9g09hF2PZLB1n3Qe041G8AAOCBaYxxdVV9IMm1VXV3kk/n715wPzXJT4wxvnc2/MeSvLuq\nPpPkriQvnp0lWrRt35rkD2b7d1NV/dckV2XjE+2uSHLJbP9OyMbZrqfMvu8nk/x8VX0uyd1JXj07\nu7Ro285IcsNs//66ql6R5Ddmn2r3qSQ/OBuXqro2yXPHGLcneXeSf5rks0nuSfKmMcYfz4YuwrZP\n5xDVGONQv+eIVNVSkrW1tbUsLS0d1ecGAOD+ra+vZ3l5Odl48f8N9/HJbce02Zv6rx5jHPBeRB1U\n1UeyETN/M++1bJeq+udJfnpTlB6QS90AADiYO5J8uDbdwLSTMcY9naMnScYY39k8en40yc8m+eL9\njnXGBwCAzTad8VnueraHBx5nfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA9\n4QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALS3Y94LADiW\n7d69O5deemlu/9zncvxpp+X888/PcccdN+9l0dDevXtz5ZVX5ku33ZZHnnBCzjnnnDzkIQ+Z97JI\nv2Ozd+/efPSjH533MmByNcaYbrKq05K8K8k/TPJXSV46xrh+vzFLSdbW1taytLQ02XMDHE27d+/O\nS84+O/dcf31etGdPHpvkC0neu3Nn6vTT83+uukoAMYm9e/fmpy68MDd/8IN51k035TF33ZU7HvrQ\nfOiUU3LSc56TC9/0pmP6RfaxrNux2bw/T7/xxvzgnj1JsjzGWJ/32mAKU4fPbyd55xjj3VV1XpL/\nNMb4tv3GCB/gmLZ79+6ce+KJefOXv5ynHGD7NUl+9BGPyGWf/7z44Yjs3bs3rz733LzqIx/JmXff\n/fe2f2LHjlz8nd+Zn73ssmPqBXYH3Y7N/vuznmR5Y5PwoY3JwqeqHpXks0keOca4Z/bYbUnOGWPc\nuGmc8AGOad9/5pn5z9ddd8Do2ecPk1x03HF535OffLSWRUP/46ab8tw77siZ9zHm2iSrxx+fH3vC\nE47Sqkj6HZv990f40NGU7/E5Kclt+6Jn5uYkj09y44G/BeDYsnv37ozrr7/P6EmSpyb56u7d2f2x\nj8U5Hw7H3iS3JPf5wjpJzkpy6e23Z+/tt2fxzyv00O3YbHV/4FjnU90ADsGll16aF21c936/XpTk\n7du7HBq7Msmztjj2WUmu2sa1cG/djs2h7A8cy6Y843NLkhOq6kGbzvo8Phtnff6eXbt2ZefOnUmS\nlZWVrKysTLgUgO1x++c+l6ducexjk1y3nYuhtS8lecwWxz4myV9u41q4t27HZt/+rM7+JMnWfr0D\nx5bJwmeM8cWquibJS5K8q6pekOSWze/v2eyiiy7yHh/gmHP8aaflC1sc+4Ukx2/nYmjtkUnu2OLY\nO5I8ehvXwr11Ozb79ucFSfb9Gno9yc/ObUWwPab+VLcnJnlnkm9MspbkZWOMT+83xocbAMes3bt3\n58WPeER+bQuXu31fVd77tKfl6x7kqmIO3d577slrP/nJXPyVr9zv2Ase9rC89clPzo6qo7Ayuh2b\nA+2PDzego0lvYDrG+EySs6ecE2CRHHfccanTT881W/hUtwd/67fm637v947W0mjmIUlOeu1r84mL\nLz7gxyXvc+2OHTn5/POz481vPnqLe4Drdmy2uj9wrJv0jM+WntAZH+AYt+8+Pj/z5S8f8P0+f5jk\n37uPDxPYd2+VCz7ykZx1gBek1+7Ykf99DN0rppNux2b//XHGh46ED8Bh2L17d15yzjm554//OD+w\nZ08em4339Lx35848+Iwz8u4rrxQ9TGLv3r35qde/Pjd/8IN55o035jF33ZU7HvrQfOiUU3Lyykr+\nw0/+5DHxwrqjbsdm8/58+w035KUbl/QKH9oQPgBHYPfu3Xn729+e22+4Icefempe+cpXCh62xd69\ne3PVVVflS7fdlkeecELOPvvsY+pFdWfdjs3evXtz+eWX53nPe14ifGhE+AAAcC/r6+tZXl5OhA+N\n+KghAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0\nJ3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe\n8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvC\nBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7Qkf\nAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wA\nAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEA\nANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAA\naE/4AAAA7QkfAACgPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACg\nPeEDAAC0J3wAAID2hA8AANCe8AEAANoTPgAAQHvCBwAAaE/4AAAA7QkfAACgPeEDAAC0N0n4VNWP\nVNUfVdV1VfWJqnrxFPMCAABMYcdE83wqydljjDur6sQk11bVVWOMmyaaHwAA4LBNcsZnjHHFGOPO\n2d8/n+T2JCdNMTcAAMCRmvw9PlX1PUm+IcnHp54bAADgcGzpUrequirJafs/nGQkOWuMcets3JOS\n/HySF44x/nbKhQIAAByuLYXPGOPs+xtTVWckeX+Sl44xrr6/8bt27crOnTuTJCsrK1lZWdnKUgAA\n2Aarq6tZXV1NkuzZs2fOq4Hp1RjjyCepOj3J/0ty/hjj8vsZu5RkbW1tLUtLS0f83AAATGt9fT3L\ny8tJsjzGWJ/3emAKU73H5y1JlpK8saquraprqurZE80NAABwRCb5OOsxxnOmmAcAAGA7TP6pbgAA\nAItG+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAA\noD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAAtCd8AACA\n9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADa\nEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP\n+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3h\nAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQP\nAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4A\nAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAA\nAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7wgcAAGhP+AAAAO0JHwAAoD3hAwAA\ntCd8AACA9oQPAADQnvABAADamzR8qurRVXV7Vf3qlPMCAAAcianP+LwtyQcmnhMAAOCITBY+VfXy\nJDcm+d2p5gQAAJjCJOFTVack+aEkb5hiPgAAgCnt2MqgqroqyWn7P5xkJHlKkp9L8poxxl1VVVuZ\nc9euXdm5c2eSZGVlJSsrK1teNAAA01pdXc3q6mqSZM+ePXNeDUyvxhhHNkHVUpIbktw5e+jhSR6W\n5OoxxrMPMn5tbW0tS0tLR/TcAABMb319PcvLy0myPMZYn/d6YApbOuNzX2Y/DI/a93VV/WCSfznG\n+P4jnRsAAGAK7uMDAAC0N3n4jDHe5WwPAACwSJzxAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA9\n4QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaE\nDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+\nAABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gA\nAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMA\nALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA\n0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABA\ne8IHAABoT/gAAADtCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADt\nCR8AAKA94QMAALQnfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQn\nfAAAgPaEDwAA0J7wAQAA2hM+AABAe8IHAABoT/gAAADtCR8AAKA94QMAALQ3WfhU1XlV9cmq+qPZ\n/z5+qrkBAACOxCThU1VnJfnvSZ49xnhSku9I8hdTzM3Rtbq6Ou8lcB8cn8Xl2Cwux2axOT7A0TLV\nGZ/XJfnpMcYdSTLG+JsxxlcmmpujyD9Ai83xWVyOzeJybBab4wMcLVOFzxlJTq6q36mqP6yq/1ZV\nNdHcAAAAR2THVgZV1VVJTtv/4SQjyVmzec5M8pzZ39+f5IIkFx9szvX19cNYLtttz549js0Cc3wW\nl2OzuBybxeb4LCbHhI5qjHHkk1R9IMn7xhjvnH39qiRPH2P82wOMfVySzx/xkwIAsN1OHGPcOu9F\nwBS2dMZnC96T5F9U1buSPDgbZ34+epCxX0hyYpI7J3puAACm9/BsvG6DFqY641NJ/leS5yW5OxvR\n89oxxt1HPDkAAMARmiR8AAAAFtlkNzA9HG56utiq6tFVdXtV/eq818Lfqaofmf3MXFdVn6iqF897\nTQ9kVXVaVV1ZVX9aVb9XVafPe01sqKqHVtWvVdWfVNW1VbVaVafOe13cW1W9rKruqarnz3stbKiq\nnVX11qr6zOzfml+Y95pgClO9x+eQbbrp6TPHGHdU1dcn+eq81sMBvS3JB5J847wXwr18KsnZY4w7\nq+rEJNdW1VVjjJvmvbAHqEuSvG2M8e6qOi/Ju5J825zXxN+5ZIxxWZJU1auTvCPJM+e7JPapqpOT\nvCLJ1fNeC/fyxiT3jDGemGz8InTO64FJzPOMj5ueLrCqenmSG5P87rzXwr2NMa4YY9w5+/vnk9ye\n5KT5ruqBqaoeleSpSX4xScYY70tyUlV901wXRpJkjHHXvuiZ+ViSk+e1Hu5t9v7gdyR5TZI9c14O\nM1V1XJKXJ3nDvsfGGH8xvxXBdOYZPm56uqCq6pQkP5RN/9FjMVXV9yT5hiQfn/daHqBOSnLbGOOe\nTY/dnMRlu4vptUl+fd6L4Gtel+SjY4xr570Q7uXUJF9K8oaq+nhVfbiqnjXvRcEUtu1St+246SnT\nuJ9j85QkP5fkNWOMu8To0Xd/Pzv77qdQVU9K8vNJXjjG+Nuju0o4tlTVrmy8oDt/3mshqapvSXJe\nkmfMey38PTuycWb0U2OMH6+qM5NcXlVnjDG+OOe1wRHZtvAZY5x9X9ur6uZs3PR0T5I9szfQPz3C\nZ9vd17GpqqUkT0ryS7PmeXiSh1XV5WOMZx+lJT6g3d/PTpJU1RnZ+GXBS8cYro2fn1uSnFBVD9p0\n1ufx2Tjrw4KoqguTfF+S73ZJ9cJ4RjZeXH929gu245NcWlUnjDEume/SHvBuzsZ7rt+TJGOMT1TV\nTdl4bfCheS4MjtQ8L3V7T5Ln1IYd2Tjzc90c10OSMcb6GONRY4xvGmN8U5ILk3xQ9CyO2aeG/WaS\n88cY/hGao9lvP69J8pIkqaoXJLlljHHjXBfG11TV65K8KMmz9703jvkbY7xtjPG42b81p2Tj/Vfn\ni575G2P8ZZLfTnJu8rXL35+Q5Po5LgsmMc/weW+SW5N8OhsvHG5N8pY5rgeOFW9JspTkjbOP6L2m\nqoTp/Pxwkh+qqj9N8h+TvGzO62Gmqh6X5E1JlpNcMft5cYZ0Mbmp4GK5IMnrq+qTSX41G1F625zX\nBEfMDUwBAID25noDUwAAgKNB+AAAAO0JHwAAoD3hAwAAtCd8AACA9oQPAADQnvABAADaEz4AAEB7\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n"
},
"cell_index": 10,
"root": true
}
]
},
"5dbe6899de6941e4baa16dc072df40c9": {
"views": []
},
"5e8769162a4140d5bc70b8aec03e7ad8": {
"views": []
},
"5e87aee9f185402f8ff57a465fbe73e7": {
"views": []
},
"5ef7e2e41c0043bc8a55756ce22df3e9": {
"views": []
},
"5fef33cf88954c079bf52d5dbbb0339f": {
"views": []
},
"61ac09cef9924de2a2146dcb2b2e852e": {
"views": []
},
"621c02cb75364b558abc79df43f8b70e": {
"views": []
},
"63511e5c5aa34ec3a58130ae0d8b6bdf": {
"views": []
},
"63775a25bb2b4ee9b8446ceef21818fc": {
"views": []
},
"639c23cf0db44536a5f78136ea5b449e": {
"views": []
},
"63d34734668d4d4e8a9abd079cfffcc9": {
"views": []
},
"647b63c63fed43daa91f41fe25b22482": {
"views": []
},
"64976a06e0c24cbebbe7fe4ebe3ee1cd": {
"views": []
},
"654d4b7dbb4a4a18bcb54cc292d2f4bb": {
"views": []
},
"6628109154014be7a19c42fb56a91ef8": {
"views": [
{
"cell": {
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"trusted": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAAKPCAYAAACsKh+8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n"
},
"cell_index": 10,
"root": true
}
]
},
"66b011dbaa5c41fcb4c71d72f55af266": {
"views": []
},
"66ecc76b33ce4b9dbf080bae63d3d6d7": {
"views": []
},
"67377e0ad39f42a19133ea575e166cfd": {
"views": []
},
"674d2cf140c5448d9fe5b7a5cceac6ba": {
"views": []
},
"67e0805befc644748271decde7bf1218": {
"views": []
},
"697a98b6e58844049a81cc4b5da87be0": {
"views": []
},
"69d28d94f5eb40a3879b21f9507e6121": {
"views": []
},
"6a212e13893a4ab0a241a89a6007906c": {
"views": []
},
"6abd33d635584ac3bbe8902f7ddd90d4": {
"views": []
},
"6b17c7b15ea1410a93477b40977226b5": {
"views": []
},
"6ca5197312004a9683d386408cc08392": {
"views": []
},
"6d017c1c4b01499693c498de96d7f97a": {
"views": []
},
"6d17774c8b64462f93b8462a77e58415": {
"views": []
},
"6db471bbc3794e53a4fda8828961360a": {
"views": []
},
"6de8f2d679c1445fbc3ce051fec75a1e": {
"views": []
},
"6e070ef4748e4e40a987b6aacf122e0c": {
"views": []
},
"6e82cccbb1344bc3b20c3175f52608d0": {
"views": []
},
"6e9a98ffa31b449d9c137b9f1f0359f3": {
"views": []
},
"6ecb03b3ef024b1085696a8f3b41d3ba": {
"views": []
},
"6ee8f1f634974b8ba9558e4322b4c6d4": {
"views": []
},
"6f3a909c6f774a7b8d713440c87b0850": {
"views": []
},
"7005e042210046bbbcb8774e321ad2de": {
"views": []
},
"70e70828620b42e4a1691ab4a0002e47": {
"views": []
},
"71356f391594493c9b6618e8439701bc": {
"views": []
},
"7157f92cb89a403785ac94702c38681a": {
"views": []
},
"715a4c01b195434c9c1c082a04e7adde": {
"views": [
{
"cell": {
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"trusted": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAAKPCAYAAACsKh+8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n"
},
"cell_index": 10,
"root": true
}
]
},
"71fe3ab37df047f8af2367d8f9a79994": {
"views": []
},
"72231c63ee8d4c2196df2b889e895eb7": {
"views": []
},
"7224f8df15bf4c81b657da552ca9ed24": {
"views": []
},
"730a7d996a3a4ff18f7d3dd9ec5f5ae8": {
"views": []
},
"73736b6f6f69435d971a25fe39a572df": {
"views": []
},
"73b62c199ef741e6b2e86487476ed81d": {
"views": []
},
"745aea5ce26e4cbd9559786ae4f2c9a5": {
"views": []
},
"74932e9cb8f64a30b15936223eb666e6": {
"views": []
},
"74ec10ad98f44cae8ec7cb23bca2e7e8": {
"views": []
},
"755577b3878e4659a9d79c14d1a8b663": {
"views": []
},
"75a0ef9fd2144683a0c221172e75686c": {
"views": [
{
"cell": {
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"trusted": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAAKPCAYAAACsKh+8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n"
},
"cell_index": 10,
"root": true
}
]
},
"76003fe0a0e943fdbbe0391163fca3c0": {
"views": []
},
"764bbadf61ac4bfba5c193d7562c6b89": {
"views": []
},
"76878ad61be447418f71384c72be4989": {
"views": []
},
"76a146ecc38a4fd3b910bb6c80a21aa6": {
"views": []
},
"76bba0168ca74f5cae007378abb0a42c": {
"views": []
},
"7714c84005eb41aa8073bc7267d94a49": {
"views": []
},
"771da58bff8d45f48035c6f6dfbb0247": {
"views": []
},
"7747d8da20ff4a8dbe04451e717037de": {
"views": []
},
"779074abb57847c297259a8cfc62b864": {
"views": []
},
"77ab5fa940aa4b83b3c4f15978a8c421": {
"views": []
},
"77dae2f56add4b248fc8f337e2275370": {
"views": []
},
"77fd06f6283343d98f53532ef885653a": {
"views": []
},
"78922887d84c4d7993e3927b0dd838dd": {
"views": []
},
"78ab8eb75a9f4eabb5c720f189ac0f82": {
"views": []
},
"78ee2228e4a446fb96c5b05aabbed2f1": {
"views": []
},
"78f31375e7d4470f98433d2d5c9f502d": {
"views": []
},
"78f408ebfd2340ce9b9811b5e927f975": {
"views": []
},
"79282081c34f434aadb044725a85bdc9": {
"views": []
},
"795112a196b04c87a3b6624e243d5cc5": {
"views": []
},
"7ab30028c509498ab7d830a537fdc21d": {
"views": []
},
"7b2bfdef9f1642e38535e57feedfc381": {
"views": []
},
"7cd0a60dcf9f4b2682bcc67379fd55dd": {
"views": []
},
"7ce4994437d3413da6527af5d4e6b930": {
"views": []
},
"7d1ed91c8df7452b9e2a6ddb8f7d8557": {
"views": []
},
"7d444aa6065d45ba833da38d2fb90570": {
"views": []
},
"7d45674650244446908304c2ba4c4a96": {
"views": []
},
"7d61a398112f434892875cc03383fec7": {
"views": []
},
"7d705660cb834878a36c134f6105e80e": {
"views": []
},
"7dca5aaa136a4593b8a3aa2e0a24a754": {
"views": []
},
"7dd210e55e304374bb21b7d46e3e5ed4": {
"views": []
},
"7ee10d2a055e49ca9660e9446a51cb11": {
"views": []
},
"7f0ec4266ec14f248d0093f483e65691": {
"views": []
},
"7f5cbac936b440779d1b916fae9c0733": {
"views": []
},
"7f6825787c254ee3840bce150c62fd09": {
"views": []
},
"8076d1e6559b44bea3b3ce473234c6f5": {
"views": []
},
"807e6397667547348051ab273e7c2646": {
"views": []
},
"80c67420490d4018beb1de5c5ef80353": {
"views": []
},
"81058127ab67471c9c9d42aa6adb9f4a": {
"views": []
},
"810aa640b20a48d4ac7a6e0c1292c38b": {
"views": []
},
"81f43a6480f64b7b85db422b7181d64d": {
"views": []
},
"8232dc34462a4b50b05651b09d1cfed3": {
"views": []
},
"836adde1e8ac4901a68a338072f9265a": {
"views": []
},
"844023a0b67545e6a7b736c9c88f3774": {
"views": []
},
"846659cf6d5d4e579bd085c5abbbccd7": {
"views": []
},
"8506f13d29854bc1ab3b6cc61912ecac": {
"views": []
},
"850c17b209e24a8cb08f3302a5eacf3c": {
"views": []
},
"856832257ef14043af5f05a4f4e53108": {
"views": []
},
"862af38d79ca48b79a2c3f58a8bca63b": {
"views": []
},
"862d527c58ac44df9d2828447b1194b7": {
"views": []
},
"8659caad67c44ead821bfaeeb81d9662": {
"views": []
},
"86f51cbd07bc469fb86b9fd95bb0a432": {
"views": []
},
"87c9b681640548cda63b491432a717b7": {
"views": []
},
"8850e420a70e40cdadaa0cb98f706cbd": {
"views": []
},
"8959d66d8d124a65a86aa0913bd458e6": {
"views": []
},
"8986e01c90cc4f7e8129a5b8e6b44eba": {
"views": []
},
"89c530dda5eb4ae2a6a5494bcf476cd3": {
"views": []
},
"8a1c851f0d664ae2a38fad87b52c5d7a": {
"views": []
},
"8a28e4f00a634b8da131d58e3f80e0a1": {
"views": []
},
"8a7e287b5bc5444f9d4e744641fbed6a": {
"views": []
},
"8a814ec4656946f6ae0934797ae6c6ed": {
"views": []
},
"8ac353a448ca423cb1c973c85fb1cfcd": {
"views": []
},
"8be4ce5846a2401da7e9e1541ad89ebe": {
"views": []
},
"8bf08bd9d7494ca0a2f3b3a2d0821379": {
"views": []
},
"8c563433df884ede8da3cf09f3d12c9b": {
"views": []
},
"8d2d0c8d02f448f891ace3be57a6007c": {
"views": []
},
"8d7a87e6c656415687f4aac07587d37a": {
"views": []
},
"8df75c9715c4420aadc78c5616b630a1": {
"views": []
},
"8e1a455843e44246930ddf48e576f596": {
"views": []
},
"8e6df76d630a4e429c9f9b34d769852e": {
"views": []
},
"91d26359e19d4c108bf81e55512e2f1c": {
"views": []
},
"922ccb204ab34dc48233990308a0f181": {
"views": []
},
"9284c33b60e540468611b74b1ce0803c": {
"views": []
},
"929b9865d5e24ffbb0503010f1f74ec6": {
"views": []
},
"942815f2227f4dd3897a509683ec7440": {
"views": []
},
"943851c1d7914fe982eb4b0cab395f3b": {
"views": []
},
"9444aeb1ad574274917df73a0b3bc7a6": {
"views": []
},
"94485ed590f84177b37b63ccb8b82971": {
"views": []
},
"9489d5c77aa44afc8b605a39209ba673": {
"views": []
},
"94c8c972301c467a813f0d2804ed453d": {
"views": []
},
"94f53cb7f3fc469cab4d6516de141579": {
"views": []
},
"94fdf5141dfc46a9aa7949bd2ff2f933": {
"views": []
},
"9593c52a782b490aa55139ac9e50047a": {
"views": []
},
"95a07c01e5364b6e8d326b20a3b40632": {
"views": []
},
"95b70460d1e34f5fa897895ddd9b004d": {
"views": []
},
"95e09b11bb494afbbc077af37032f070": {
"views": []
},
"96128cb6cc6546509b043107cff4ed5f": {
"views": []
},
"961f9cf320524e48bb201b67deeeb19f": {
"views": []
},
"9671ffc734974e7991a1c8182a245233": {
"views": []
},
"967c3ebfee7746b599072ec50f618b51": {
"views": []
},
"96e794d7916d4ff29cfd406da4a64ba9": {
"views": []
},
"96ed4e7ca8984ccbac3afc8207321357": {
"views": []
},
"9709fb06c5e946349ddefac58453b635": {
"views": []
},
"971ccf47cc1e41ffa9f18b2bb5a5c095": {
"views": []
},
"9737e26f026444fda30896fcd6f7ba59": {
"views": []
},
"9746904fb0bb4a72a22fa574bab45f1e": {
"views": []
},
"97b0feafc8fa41489451f40edd686abf": {
"views": []
},
"97bfb4b134a04b1d9cdccb0e7bc47634": {
"views": []
},
"97cb5255096244bfb858c3592db6595a": {
"views": []
},
"981c04787ab14f868f8a0a5e572ff0f0": {
"views": []
},
"98558e929fb447b1a0d450a29effdce6": {
"views": []
},
"989eb48d7ef74f56b543c33c0f57119e": {
"views": []
},
"98a41e3f379c41578dea0c1df4c8311a": {
"views": []
},
"98bca9417a09414bbd7e372f67905d51": {
"views": []
},
"98ea6590a7c041379fe58d8510e5fdcd": {
"views": []
},
"98f1d54c470f4397bb682cc719bf501e": {
"views": []
},
"9930fb05134747ba80c2e9205206a8d4": {
"views": []
},
"99f2f2bf9085493bb010fe456dfd3c77": {
"views": []
},
"9a042e9620434bfb819c9f89cdcef242": {
"views": []
},
"9a0ca5ceee5e4a4d92aab9acc3d90e67": {
"views": [
{
"cell": {
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"trusted": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAAKPCAYAAACsKh+8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n"
},
"cell_index": 10,
"root": true
}
]
},
"9a19791b387e4c5a913a92790141f553": {
"views": []
},
"9a3dd9ada08d4312b729bc1891e43f1a": {
"views": []
},
"9a81582415584ce588f6a1c53b927d32": {
"views": []
},
"9ae0d4a38f1e4b498b0bc4801df700ec": {
"views": []
},
"9aef67ddda784eb7b93dc9c617ef03ec": {
"views": []
},
"9b917897eeb24144930072d47c8a7571": {
"views": []
},
"9bd57fc229d24f9aa65f8366028e3007": {
"views": []
},
"9bdd3a0d16de4cd7801d67957b196382": {
"views": []
},
"9c13a849122d47458c4ab77c56edf863": {
"views": []
},
"9c9748ffe74d4a5cbc26aef71c6e4780": {
"views": []
},
"9ce4a1bcfd694843a536393891af83be": {
"views": []
},
"9d7f35226a3a4f6c9ae12362454a9716": {
"views": []
},
"9dba4d8a3c4d420492b5b5ac732b5fa0": {
"views": []
},
"9dd653229bd649df86e7ce3d5688e963": {
"views": []
},
"9dddfa92060a4a4ab09253e94473d45e": {
"views": [
{
"cell": {
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"trusted": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAAKPCAYAAACsKh+8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n"
},
"cell_index": 10,
"root": true
}
]
},
"9ed9af812afd4824ae37de57527dbe04": {
"views": []
},
"9ee6f262ac364087a35690994d9c777e": {
"views": []
},
"9eef7f00bb544fdc85ac22d60e29b2cc": {
"views": []
},
"9f295bd3ba0a424fbbc45da512f6bea9": {
"views": []
},
"a0a561f1b4554e13aa87081b7d714d17": {
"views": []
},
"a0cb4f4cc1924829826f8b13dc9c2700": {
"views": []
},
"a0d45770e9364f16ad743568ce0d4baf": {
"views": []
},
"a0e8a2c9ba5b47b7bd520aed0eeb8a2c": {
"views": []
},
"a10daed678bc4e31b46716d918bcce1e": {
"views": []
},
"a11b2c64cb654e509999eec253045444": {
"views": []
},
"a165e7ee9b574a9bb1d515b055099c71": {
"views": []
},
"a2e3c80098b7422880ffba2c4424e236": {
"views": []
},
"a3692a56420240858fd193c030fa1a5d": {
"views": []
},
"a3b31e9e4a994d8f9d2246adfcb882f3": {
"views": []
},
"a47b898cf1cf4dda871a9c3d1854c2c7": {
"views": []
},
"a4ab2fd0be014ecdaf6951ec1e613717": {
"views": []
},
"a4cc8616d713406fa2b0d88c787492ef": {
"views": []
},
"a62809bcaaaa476d9873d5a6821f7ff7": {
"views": []
},
"a67f960425844150acae36c4a11bb270": {
"views": []
},
"a68636cb0d7f48fd9476223e56e5836e": {
"views": []
},
"a6978d6b8172402198e2d6805ddade00": {
"views": []
},
"a6ec984ef08c445496dc89501b760ade": {
"views": []
},
"a8bc94010cc74a0bbac9939a4fd9276d": {
"views": []
},
"a8c1a89930c64b5f92245524b10c60bc": {
"views": []
},
"a9e26b2037e944f087408db70155c888": {
"views": []
},
"aa1463eaa1664fd28f0ba461dcf6ace1": {
"views": []
},
"aae8913ea5ac4c68928076f10a251aa3": {
"views": []
},
"aaea21e37619448a957d13e15fbaa90e": {
"views": []
},
"ab4881ca82dd421486f3b6079b18f4fb": {
"views": []
},
"ab79df375c9145a4a5de033070f21068": {
"views": []
},
"ac3425cd0b5549c1b6122fd6ae492d5d": {
"views": []
},
"ac6182fddd3740428187c68fff085376": {
"views": []
},
"ac767e113c044a4c8dcc55edabd9c3bc": {
"views": []
},
"ad032275625a4754956bc5eefaec7418": {
"views": []
},
"ad07b73b4d4441c29670d42615a7ca6e": {
"views": []
},
"ad76edec4856470c9500a4c0b63710f6": {
"views": []
},
"ae1dd2fe05224d5992d3e2c6bff50322": {
"views": []
},
"ae69f8830a034a5aabb75b12921db73a": {
"views": []
},
"ae7809842e42462a9f6fb0b024e51542": {
"views": []
},
"af4987ac07eb4c5d97a5ea71dd128b42": {
"views": []
},
"af62547a4d7d4383a8f5de9f04a145da": {
"views": []
},
"afb9dc2bf8404cd2b29c646fb1e1e8fc": {
"views": []
},
"afd302529056487398bc4e5b3252e470": {
"views": []
},
"b018508d019d49a3976590ad8440079a": {
"views": []
},
"b048d199698d4fd189d74c77a4ef1c82": {
"views": []
},
"b06bf370e25e471eb74e9de4420bcd16": {
"views": [
{
"cell": {
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"trusted": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAAKPCAYAAACsKh+8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n"
},
"cell_index": 10,
"root": true
}
]
},
"b0939de0ddd64138a947665fc7d23e20": {
"views": []
},
"b097623d1e7a4a48b11b1f405d98d4e8": {
"views": []
},
"b1318684110d42d79687a6386cbae811": {
"views": [
{
"cell": {
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false,
"trusted": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAzoAAAKPCAYAAAClwb/GAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAACgO0IHAADojtABAAC6I3QAAIDu/P+zHD7CA7x3IAAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7e6b4d6ad0>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "from numpy import asarray\ntheta = asarray([0.1] * N)\nlambda_ = 0.001\ndef inverse_kinematics(x_e, y_e, theta_e, theta):\n target = matrix([[x_e, y_e, theta_e]])\n while True:\n T = forward_kinematics(T0, lv, theta)\n Te = matrix([from_trans(T[-1])])\n e = target - Te\n T = matrix([from_trans(i) for i in T[1:-1]])\n J = Te - T\n J = J.T\n J[-1, :] = 1 # angular velocity\n JJT = J * J.T\n d_theta = lambda_ * J.T * JJT.I * e.T\n theta += asarray(d_theta.T)[0]\n if np.linalg.norm(d_theta) < 1e-4:\n break\n return theta\n\nT = forward_kinematics(T0, lv, theta)\nTe = matrix([from_trans(T[-1])])\n\n@interact(x_e=(0, max_len, 0.01), y_e=(-max_len, max_len, 0.01), theta_e=(-pi, pi, 0.01), theta=fixed(theta))\ndef set_end_effector(x_e=Te[0,0], y_e=Te[0,1], theta_e=Te[0,2], theta=theta):\n theta = inverse_kinematics(x_e, y_e, theta_e, theta)\n T = forward_kinematics(T0, lv, theta)\n show_robot_arm(T)\n\n# NOTE\n# while numerical inverse kinematics is easy to implemente, two issues have to be keep in mind:\n# * stablility: the correction step (lambda_) has to be small, but it will take longer time to converage\n# * singularity: there are singularity poses (all 0, for example), the correction will be 0, so the algorithm won't work. That's why many robots bends its leg when walking"
},
"cell_index": 33,
"root": true
}
]
},
"b249266f72ce41b096bfeafd4789ed36": {
"views": []
},
"b27d4b54be3c499bac6ce346de64e0bb": {
"views": []
},
"b28e241d2ac14db39d89dc3952ff8974": {
"views": []
},
"b402a799328c49e99abd5beb99ad63cb": {
"views": [
{
"cell": {
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"trusted": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAAKPCAYAAACsKh+8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n"
},
"cell_index": 10,
"root": true
}
]
},
"b491570fd738421e971d2f01f6e5e025": {
"views": []
},
"b4cd5a2f926e43be8c8028d2c071e2e5": {
"views": []
},
"b579ef0051d945a9adc2c1c7a0387ab9": {
"views": []
},
"b7203daacce34cedab333faf601f8080": {
"views": []
},
"b74a7887033349f59d4dcce5df469ebe": {
"views": []
},
"b7da1a920b6441d8b303aacc5472a743": {
"views": []
},
"b7ebf419b324467196b93aec2ddfb2e5": {
"views": []
},
"b7f65fb78b1f4963923e4b2b889ae71d": {
"views": []
},
"b80d613ff47c4b5e8a92d15ce973c058": {
"views": []
},
"b80d7b1129344995a0e08645bd02c43c": {
"views": []
},
"b8af5204f7bd46e49556c9b9339a61d1": {
"views": []
},
"b8f0724bbf9c4379af6c6b24598a2e45": {
"views": []
},
"b94bcf1136124faa83d0d49f6ffd01ef": {
"views": []
},
"b94d48ddfc0c4b9bbe799e23d02bdb23": {
"views": []
},
"b9a4a36b6f0541d08765760f39d5979d": {
"views": []
},
"b9b0e906c50b4fe194ba0561860a48d2": {
"views": []
},
"b9b20e2b81ef469fbc2147fe4fd1fbcd": {
"views": [
{
"cell": {
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"trusted": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAAKPCAYAAACsKh+8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n"
},
"cell_index": 10,
"root": true
}
]
},
"b9eee2008f4e4bbdb794dc096ad66eed": {
"views": []
},
"bb317461e530495584bed769e3907371": {
"views": []
},
"bb4935d65aa4419a903c269ee1c245c6": {
"views": []
},
"bb5048ede7214dbc95ddd20b2860d967": {
"views": []
},
"bc375a0bce7f422988cf8184f1b766e4": {
"views": []
},
"bcdf58e3772d46bc85c99b311fca3597": {
"views": []
},
"bd0e575537f84a4dbbe73055036d545f": {
"views": []
},
"bda506ba1987430e8e52e2370d61f1a7": {
"views": []
},
"bde65c86e6d1468fb60ae401c4c8b29a": {
"views": [
{
"cell": {
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"trusted": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAAKPCAYAAACsKh+8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n"
},
"cell_index": 10,
"root": true
}
]
},
"be1acc039ba446ee8faa23f50a109cad": {
"views": []
},
"bf5d09791dc84a05a89a788915401157": {
"views": []
},
"c04926eeeee849949eb61738e5e66675": {
"views": []
},
"c05c848c83c649bca51c6b180a176e4a": {
"views": []
},
"c1778dbcd3454ee190824e9101a0b647": {
"views": []
},
"c17e1418e17445ef8f2a0f8e1dad0c38": {
"views": []
},
"c1eaf2eb5c684f2db956365800471e0d": {
"views": []
},
"c205b755237d444eb87a20babac93ad5": {
"views": []
},
"c229e9376d6a4e1b8243aa035744b4a8": {
"views": []
},
"c296feae9fc241f6b7cc2155c35d2a21": {
"views": []
},
"c298b40a15fc4980a8bec3c3c36e1cdf": {
"views": []
},
"c2a9da2b5f9a441c86c7ca1d98bdfb5c": {
"views": []
},
"c2ba4e3cb0024641939db9301b068ace": {
"views": []
},
"c2bae26c415844b19c575a61f4e98967": {
"views": []
},
"c2fa97d314134fdf8ed6e6db1428cd88": {
"views": []
},
"c3703cc105c14aa29739a9c4aa53923c": {
"views": [
{
"cell": {
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"trusted": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAAKPCAYAAACsKh+8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n"
},
"cell_index": 10,
"root": true
}
]
},
"c39014455211404cae26ef0b4179cc29": {
"views": []
},
"c3ae4ee5304c468f84b15b2f834369c3": {
"views": []
},
"c40830ec69ab4c61882d3bb657705a29": {
"views": [
{
"cell": {
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"trusted": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAAKPCAYAAACsKh+8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n"
},
"cell_index": 10,
"root": true
}
]
},
"c49db5a1dc274629ba8fa33aaca2dd04": {
"views": []
},
"c5494213ec854ef9a5680fda3ff388f3": {
"views": []
},
"c58fbc8c37fa4a9c88a892c25fc50eb1": {
"views": []
},
"c5dfde13382e4b98a48977d47310d9b5": {
"views": []
},
"c75d5b6c85ac4c5ba9a782cabed119b9": {
"views": []
},
"c80f6d81e13e4fdf80c9361c56878594": {
"views": []
},
"c837b314041c4abd98fe54f2bad46be9": {
"views": []
},
"c871e1b1955348a08ddc78e40d1b7784": {
"views": []
},
"c87a9501e0944a9993c2e42e0d7c8ed8": {
"views": []
},
"c9c24fca3e624b5a98bef7f3a69de196": {
"views": []
},
"ca0d28d05e5246f997d3496c9cd7bea9": {
"views": []
},
"ca1c6ee68d3c4e3f9cae7f8fd31f1052": {
"views": []
},
"cacdb4e5168a4d8cbcd493bb81fa2be3": {
"views": []
},
"cb033f53273b42f4a34583d92b212bca": {
"views": []
},
"cb71d0de25414854bd9ff072c3d116d7": {
"views": []
},
"cbc4d852cfd046f585f9f54d1db8ed58": {
"views": []
},
"cc10f1446a684b4ea8b57db865cc0c70": {
"views": []
},
"cc1aa36a99364aad800f4d273ede4ae4": {
"views": []
},
"cc3f910800ba4db5a9f7069f40d26238": {
"views": []
},
"cc6eb0da4afd4530a3365c524981911a": {
"views": []
},
"ccb421a8a8e845f388581b2a73e3e477": {
"views": []
},
"cccb7058119c4a15ade3e5183790974e": {
"views": []
},
"ce20c58090da437da65ae2d97db98697": {
"views": []
},
"ce9b4ee6374a4b25a377b2c7fc4bec9a": {
"views": []
},
"d0e10bc796f34e3db44e8f587746efb9": {
"views": []
},
"d12db5355f404ae2b1c3b43b7a4a6c6e": {
"views": []
},
"d1346c623b904f678f75b94ef8a010b0": {
"views": []
},
"d201c6f4b80d48b0a42247f1bc4c59a8": {
"views": []
},
"d20fd2aea9d345bd808ad08c56f8d774": {
"views": []
},
"d2b9e272d08a4789a24b0d393067e842": {
"views": []
},
"d2dcb1c61cbf4025a1eccb2818c068ea": {
"views": []
},
"d3945dedf4b343b6b9fffb0a2e6f5237": {
"views": []
},
"d3e2e2ae788347d190644f3f3e284543": {
"views": []
},
"d47f5163510b4c3bb75a3e7de42e6a47": {
"views": []
},
"d574ff57355d42358dbcbadc80d660c1": {
"views": []
},
"d5d33bae001545279b809e046a3d8f75": {
"views": []
},
"d5f1aa9a5fdf443fa3813d4274b4a9f2": {
"views": []
},
"d60ffb9674b941c794282fedb9d3bd5d": {
"views": []
},
"d6c220a59902465ea367b59b68941b1e": {
"views": []
},
"d84212e436fa4808b79e6a0e88dac527": {
"views": []
},
"d9488098e33c4fa586e6f519e10c5756": {
"views": []
},
"d995e56a27fd499586a39b83ad04386a": {
"views": []
},
"d998ff4d11e34399a60f579b2124b56d": {
"views": []
},
"da4c511997c14e9cb16262a855520a10": {
"views": []
},
"da64ebe8a9f9486ba00437e0664071e2": {
"views": []
},
"da8213350eb44e03930067f7616a30ba": {
"views": [
{
"cell": {
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"trusted": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAAKPCAYAAACsKh+8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n"
},
"cell_index": 10,
"root": true
}
]
},
"dae71c9df74f476d9bdca7dbdc9c4813": {
"views": []
},
"db36bc23e8224a13bd5bd39d59b341ea": {
"views": []
},
"db74c7e7bc0445af81e1a1a8cd4c1b2a": {
"views": []
},
"dd7767d3ee1e4fdf9457356dcb9d8856": {
"views": []
},
"dd9547c6a0de46bfbb8766c95faad93a": {
"views": []
},
"de9d33d2fd504e6591a42183a04245c4": {
"views": []
},
"dea19948b29b4852a2fafd8eb5ed81ba": {
"views": []
},
"df3f2a8c0b8d4450b9c81e9bb6bf5a48": {
"views": []
},
"df69964a0a784d678379ef980433bac2": {
"views": []
},
"dfef404d9e874f5ba4bcb626c407e772": {
"views": []
},
"e07f7328f0cd427d9af41fac575919a2": {
"views": []
},
"e0fbecdda5c24dd0b7ae34203dd4bd08": {
"views": []
},
"e14cff4f97544aa2850e91e886f48705": {
"views": []
},
"e172cd6cdcb1425f9686213f3e73af38": {
"views": []
},
"e21b7ba964e54981ac5505b6a6d8e9d4": {
"views": []
},
"e27ff57b6a15404a9f220a4f234d012f": {
"views": []
},
"e35aa9674b9c4be1a8bf4a6b6c3f61b0": {
"views": []
},
"e399e06d7f1d45e98f6db2a87d658e37": {
"views": [
{
"cell": {
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"trusted": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAAKPCAYAAACsKh+8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n"
},
"cell_index": 10,
"root": true
}
]
},
"e3bbf63ca8f24f3f90c845609efaaa2a": {
"views": []
},
"e3cc82f1d7704f12bdf1b63dd046c1e5": {
"views": []
},
"e3d7d5bde7594823974275a142584fa6": {
"views": []
},
"e3da4e2ae1d54776aa4c7cc721e5b154": {
"views": []
},
"e404ccda48544b2caf82f95feb1a9bab": {
"views": []
},
"e422e5c381a5484dad09cbaadcc77087": {
"views": []
},
"e43c2bd292b24ab2816c0877b4738954": {
"views": []
},
"e46fb472807547428ea7d4e8afa77dac": {
"views": []
},
"e4cabad725b84230b67353f65ecabaae": {
"views": []
},
"e536ce329e8a4dac80d16cc15a9733a7": {
"views": []
},
"e54d65d577fb41fc93b093e342ebf57b": {
"views": []
},
"e571d3e0777b4304baa3db684b3fdf4f": {
"views": []
},
"e7013fe1510b4ceaa644362146094748": {
"views": []
},
"e73bd151c797475bb43b646bf2752d18": {
"views": []
},
"e7a0aa68cb854e4e88b10e88a67d92e9": {
"views": []
},
"e82f169087f64f6697c7852b78fb8be9": {
"views": []
},
"e85294b8300f4069986418f235b96af5": {
"views": []
},
"e8a25988db84459298d40a5b1fc504cc": {
"views": []
},
"e8c375bbe8284cd4b36eec55742be2f1": {
"views": []
},
"e91edaabf1754034b8f421bd701dd90f": {
"views": []
},
"e976f0e425ee4287859ccde23b54645c": {
"views": []
},
"e98da6e6f091464b886cfcfa27073773": {
"views": []
},
"e992373f1ad445cc951c6a729d8d0a34": {
"views": []
},
"ea7e236f78a3472a8e17ae2bf4b5d872": {
"views": []
},
"eab6fa910d8b42c68685d10c68f945ac": {
"views": []
},
"eaf5a48642b445e0b3dfc3d9ddc7fed6": {
"views": []
},
"eb6e7cf4eb454106909676cf889f1c33": {
"views": []
},
"eb908d1cc2b34674a42a42cd059d0eb1": {
"views": []
},
"eb9d5d11d60046438a24e5e324ef9c3d": {
"views": []
},
"ebc01cbc9c81446085e6779adc7beea5": {
"views": [
{
"cell": {
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"trusted": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAAKPCAYAAACsKh+8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n"
},
"cell_index": 10,
"root": true
}
]
},
"ebd1bb1a6e184910a6ea9194bb246247": {
"views": []
},
"ec331fec5dc849a096d6fc4614f5c6a1": {
"views": []
},
"ed2373b2c86e4249963f1f0c1539875b": {
"views": []
},
"ed5515d5ada341d5bddc42285601a968": {
"views": []
},
"ee6badce55564b0bb690ba653a5c311b": {
"views": []
},
"ee6e242f9f5b4d1197ca01c6bd49119c": {
"views": []
},
"eebf627136a94f8db9d06a66bc1f5ff7": {
"views": []
},
"eef0260f78af4a21ad250a3641b7a8d8": {
"views": [
{
"cell": {
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"trusted": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAAKPCAYAAACsKh+8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwgcAAGjv/wNiuh1JRm4QPQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7e6e60add0>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": "for i in range(N):\n @interact(value=(-pi/2, pi/2, 0.1), n=fixed(i))\n def set_joint_angle(n, value=0):\n global a\n a[n] = value\n T = forward_kinematics(T0, l, a)\n show_robot_arm(T)\n"
},
"cell_index": 10,
"root": true
}
]
},
"f015dd8292e14585be2c3a0c59445226": {
"views": []
},
"f0250e0e4dc046c2a027691da0596362": {
"views": []
},
"f08ec1113b164c10902c7d0bffb4bb20": {
"views": []
},
"f0b3bddfdf4f4301818959c16c50a8c3": {
"views": []
},
"f0ccdd43c12b482f955a96a33a987d1f": {
"views": []
},
"f29b33c4672a47fbba802a78a91df2dc": {
"views": []
},
"f2bd115744cd430887c3a78df7ef0c6c": {
"views": []
},
"f43ab6c3313b47259689c74b005c845e": {
"views": []
},
"f455cefe6a1d4859864b8dc03a31dfd7": {
"views": []
},
"f47905a5b1e74afe8deb8b1afabd8f4d": {
"views": []
},
"f4b20b9235824b53aa4f9404d009ae80": {
"views": []
},
"f4b8b2ae15794970b47d39be7b854aad": {
"views": []
},
"f5206cf22fcd41ecb46d1697ae3f5b93": {
"views": []
},
"f547ac65fa9a4037831b888060c1c557": {
"views": []
},
"f62f4bada80c4b899d59c3e9b61ebb61": {
"views": []
},
"f640a5c63e70451298e805159c07f3d4": {
"views": []
},
"f69deb3ebacb4af3a5c71027490549ee": {
"views": []
},
"f6ad70efcd5944939e52b79515405c92": {
"views": []
},
"f7138b8ad04a4205b9f11a555e50c6e9": {
"views": []
},
"f717c583a0414f7da87076453c301761": {
"views": []
},
"f7563b083ad842bb97fbc95455f681a4": {
"views": []
},
"f7bf54b876944cbaaa550461df8f8670": {
"views": []
},
"f89ea22aa4bd4287a8b6b86a8a591a08": {
"views": []
},
"f947e9915cbb41ed839bfe7115b528bd": {
"views": []
},
"fa5ae86788654240bb268d605727e05e": {
"views": []
},
"fa693f380691424ebad1e4161b275e36": {
"views": []
},
"fb1d09f5c47d4a1e94290e5e3196d35e": {
"views": []
},
"fb4a806d0af54f59868decbaeea0e523": {
"views": []
},
"fc021ff2a81445379ca28e0dd02fbac8": {
"views": []
},
"fc0c0c18d7954a21ae6fad378a211ebc": {
"views": []
},
"fc2017a6287b43f9a61db19806467923": {
"views": []
},
"fc939063c1e64c22aa11dac05f172a69": {
"views": []
},
"fcb3b9b90884446e9115784dcca5505c": {
"views": []
},
"fcc39e3f81cd433287ea6fee299aa5b9": {
"views": []
},
"fcd8c96328f84203b0e5ea28baba88f3": {
"views": []
},
"fcf83c58e7514ff49138935364e47234": {
"views": []
},
"fd1836b1aa404f4e89ba04fb905657e6": {
"views": []
},
"fdcb9e7abbc44b9aa5a1bfb61a37ac49": {
"views": []
},
"fdeef67864714aa7b5e7222e87afbfd6": {
"views": []
},
"feac76ba51434658ba579dea7035d238": {
"views": []
},
"ff11d3f9b32f4489a729a228ab1abb6b": {
"views": []
},
"ff2966c36daf4fe5bf7a8281c64fcc7d": {
"views": []
},
"ff6fe47547274d3abfe490203e98c2d1": {
"views": []
}
},
"version": "1.0.0"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-2.0 |
achimkoh/export-fb-saved | export-fb-saved.ipynb | 1 | 4007 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Python 3.5.3\n",
"\n",
"import time, random\n",
"from selenium import webdriver\n",
"from urllib.parse import urlparse, parse_qs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"url = 'https://www.facebook.com/saved'\n",
"browser = webdriver.PhantomJS()\n",
"# browser = webdriver.Chrome()\n",
"browser.get(url)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"browser.find_element_by_css_selector('input#email').send_keys('') # Login email here\n",
"browser.find_element_by_css_selector('input#pass').send_keys('') # Login password here\n",
"browser.find_element_by_css_selector('button#loginbutton').click()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Scroll to the bottom of the page, until we reach the end and the feed stops loading \n",
"previousScroll = browser.execute_script(\"return document.body.scrollHeight;\")\n",
"\n",
"# Try multiple times, in case previous attempt did not load due to network delay \n",
"for j in range(0, 3):\n",
"\tbrowser.execute_script(\"window.scrollTo(0, 0);\")\n",
"\ttime.sleep(random.random()) # This probably doesn't need to be random, I just don't want FB to notice I'm scraping\n",
"\twhile True:\n",
"\t\tbrowser.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n",
"\t\ttime.sleep(2 + random.random())\n",
"\t\tif previousScroll != browser.execute_script(\"return document.body.scrollHeight;\"):\n",
"\t\t\tpreviousScroll = browser.execute_script(\"return document.body.scrollHeight;\")\n",
"\t\telse:\n",
"\t\t\tbreak\n",
"\tprint(\"I think I reached the bottom. Will try to scroll down \" + str(2-j) + \" more time(s)\")\n",
"\ttime.sleep(2 + random.random())\n",
"\n",
"print(\"Yup, that's it\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# browser.save_screenshot('now.png')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Modified from Oscar Cassetti's code \n",
"# https://www.quora.com/How-can-I-export-all-my-Facebook-saved-read-it-later-links\n",
"\n",
"links = browser.find_elements_by_css_selector('._4bl9._5yjp') # this is the div with the links\n",
"\n",
"for link in links:\n",
" link_url = link.find_element_by_css_selector('a').get_attribute('href')\n",
" # sometimes the url gets dirty FB redirection. Clean it up:\n",
" o = urlparse(link_url)\n",
" try:\n",
" if parse_qs(o.query)['u']:\n",
" link_url = parse_qs(o.query)['u'][0]\n",
" except:\n",
" True # Not an elegant exception handling... but whatever! This only needs to work once.\n",
"\n",
" link_description = link.text.replace('\\n', '|').replace(';', '|') # 1 item per row; semicolon will divide column \n",
" \n",
" with open('saved.txt', 'a') as f:\n",
" f.write(link_url+' ; '+link_description+'\\n')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
kharris/allen-voxel-network | test.ipynb | 1 | 101245 | {
"cells": [
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/kamdh/anaconda2/lib/python2.7/site-packages/ipykernel_launcher.py:27: VisibleDeprecationWarning: Function get_ontology is deprecated. Use get_structure_tree instead.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"126 total experiments\n"
]
}
],
"source": [
"import os\n",
"from allensdk.core.mouse_connectivity_cache import MouseConnectivityCache\n",
"import numpy as np\n",
"import h5py\n",
"import time\n",
"import nrrd\n",
"from voxnet.conn2d import map_to_surface\n",
"\n",
"drive_path = os.path.join(os.getenv('HOME'), 'work/allen/data/sdk_new_100')\n",
"\n",
"# When downloading 3D connectivity data volumes, what resolution do you want (in microns)? \n",
"# Options are: 10, 25, 50, 100\n",
"resolution_um = 10\n",
"\n",
"# Drop list criterion, in percent difference\n",
"volume_fraction = 20\n",
"\n",
"# The manifest file is a simple JSON file that keeps track of all of\n",
"# the data that has already been downloaded onto the hard drives.\n",
"# If you supply a relative path, it is assumed to be relative to your\n",
"# current working directory.\n",
"manifest_file = os.path.join(drive_path, \"manifest.json\")\n",
"\n",
"# Start processing data\n",
"mcc = MouseConnectivityCache(manifest_file = manifest_file,\n",
" resolution = resolution_um)\n",
"ontology = mcc.get_ontology()\n",
"# Injection structure of interest\n",
"isocortex = ontology['Isocortex']\n",
"\n",
"# open up a pandas dataframe of all of the experiments\n",
"experiments = mcc.get_experiments(dataframe = True, \n",
" injection_structure_ids = [isocortex['id'].values[0]], \n",
" cre = False)\n",
"print \"%d total experiments\" % len(experiments)\n",
"\n",
"view_paths_fn = os.path.join(os.getenv('HOME'), 'work/allen/data/TopView/top_view_paths_10.h5')\n",
"view_paths_file = h5py.File(view_paths_fn, 'r')\n",
"view_lut = view_paths_file['view lookup'][:]\n",
"view_paths = view_paths_file['paths'][:]\n",
"view_paths_file.close()\n",
"\n",
"## Compute size of each path to convert path averages to sums\n",
"norm_lut = np.zeros(view_lut.shape, dtype=int)\n",
"ind = np.where(view_lut != -1)\n",
"ind = zip(ind[0], ind[1])\n",
"for curr_ind in ind:\n",
" curr_path_id = view_lut[curr_ind]\n",
" curr_path = view_paths[curr_path_id, :]\n",
" norm_lut[curr_ind] = np.sum(curr_path > 0)\n",
"\n",
"t0 = time.time()\n",
"expt_drop_list = []\n",
"full_vols = []\n",
"flat_vols = []"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"180916954\n",
"gender M\n",
"id 180916954\n",
"injection-coordinates [4800, 1220, 5930]\n",
"injection-structures [{u'abbreviation': u'ACAd', u'color': u'40A666...\n",
"product-id 5\n",
"strain C57BL/6J\n",
"structure-abbrev MOs\n",
"structure-color 1F9D5A\n",
"structure-id 993\n",
"structure-name Secondary motor area\n",
"transgenic-line \n",
"Name: 180916954, dtype: object\n"
]
}
],
"source": [
"eid = experiments.iloc[5].id\n",
"row = experiments.iloc[5]\n",
"print eid\n",
"print row"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"getting injection density\n",
"mapping to surface\n"
]
}
],
"source": [
" data_dir = os.path.join(os.getenv('HOME'),\n",
" \"work/allen/data/sdk_new_100/experiment_%d/\" % eid)\n",
" # get and remap injection data\n",
" print \"getting injection density\"\n",
" in_d, in_info = mcc.get_injection_density(eid)\n",
" print \"mapping to surface\"\n",
" in_d_s = map_to_surface(in_d, view_lut, view_paths, scale = resolution_um/10., fun=np.mean)\n",
" flat_vol = np.nansum(in_d_s * norm_lut) * (10./1000.)**3\n",
" flat_vols.append(flat_vol)\n",
" full_vol = np.nansum(in_d) * (10./1000.)**3\n",
" full_vols.append(full_vol)\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"expt_union = mcc.get_experiment_structure_unionizes(eid, hemisphere_ids = [2], is_injection = True,\n",
" structure_ids = [ontology['grey']['id'].values[0]])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.89908089262803303]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flat_vols"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0.51017865625000014]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_vols"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" hemisphere_id id is_injection max_voxel_density max_voxel_x \\\n",
"2206 2 533667735 True 1.0 4800 \n",
"\n",
" max_voxel_y max_voxel_z normalized_projection_volume \\\n",
"2206 1220 5930 0.991312 \n",
"\n",
" projection_density projection_energy projection_intensity \\\n",
"2206 0.999986 12435.6 12435.8 \n",
"\n",
" projection_volume experiment_id structure_id sum_pixel_intensity \\\n",
"2206 0.5557 180916954 8 5.641290e+12 \n",
"\n",
" sum_pixels sum_projection_pixel_intensity sum_projection_pixels \\\n",
"2206 453639000.0 5.641280e+12 453632000.0 \n",
"\n",
" volume \n",
"2206 0.555708 \n",
"0.5557\n"
]
}
],
"source": [
"full_vol2 = float(expt_union['projection_volume'])\n",
"print expt_union\n",
"print full_vol2"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"899080.892628\n"
]
}
],
"source": [
"print np.nansum(in_d_s * norm_lut)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.89908087500000011"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"in_d_sum = map_to_surface(in_d, view_lut, view_paths, scale = resolution_um/10., fun=np.sum)\n",
"np.nansum(in_d_sum) * (10./1000.)**3"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"76.2286370889\n",
"61.7924946244\n"
]
}
],
"source": [
"print np.abs(flat_vol - full_vol)/full_vol*100\n",
"print np.abs(flat_vol - full_vol2)/full_vol2*100"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7fd5ec53ff50>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABI8AAARXCAYAAAB5mEFAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd5ed71c090>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"tmp = norm_lut.astype(float)\n",
"tmp[tmp == 0] = np.nan\n",
"\n",
"fig = plt.figure(figsize = (20,20))\n",
"ax = fig.add_subplot(121)\n",
"h = ax.imshow(in_d_s)\n",
"fig.colorbar(h)\n",
"\n",
"a2 = fig.add_subplot(122)\n",
"h2 = a2.imshow(np.sum(in_d, axis=1))\n",
"fig.colorbar(h2)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>gender</th>\n",
" <th>id</th>\n",
" <th>injection-coordinates</th>\n",
" <th>injection-structures</th>\n",
" <th>product-id</th>\n",
" <th>strain</th>\n",
" <th>structure-abbrev</th>\n",
" <th>structure-color</th>\n",
" <th>structure-id</th>\n",
" <th>structure-name</th>\n",
" <th>transgenic-line</th>\n",
" </tr>\n",
" <tr>\n",
" <th>id</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>180435652</th>\n",
" <td>M</td>\n",
" <td>180435652</td>\n",
" <td>[7820, 4250, 9870]</td>\n",
" <td>[{u'abbreviation': u'TEa', u'color': u'15B0B3'...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>ECT</td>\n",
" <td>0D9F91</td>\n",
" <td>895</td>\n",
" <td>Ectorhinal area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>180436360</th>\n",
" <td>M</td>\n",
" <td>180436360</td>\n",
" <td>[4800, 4720, 8980]</td>\n",
" <td>[{u'abbreviation': u'AId', u'color': u'219866'...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>VISC</td>\n",
" <td>11AD83</td>\n",
" <td>677</td>\n",
" <td>Visceral area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>180719293</th>\n",
" <td>M</td>\n",
" <td>180719293</td>\n",
" <td>[3140, 3330, 7390]</td>\n",
" <td>[{u'abbreviation': u'AId', u'color': u'219866'...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>MOs</td>\n",
" <td>1F9D5A</td>\n",
" <td>993</td>\n",
" <td>Secondary motor area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>180709942</th>\n",
" <td>M</td>\n",
" <td>180709942</td>\n",
" <td>[3360, 3120, 7520]</td>\n",
" <td>[{u'abbreviation': u'MOp', u'color': u'1F9D5A'...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>MOs</td>\n",
" <td>1F9D5A</td>\n",
" <td>993</td>\n",
" <td>Secondary motor area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>180917660</th>\n",
" <td>M</td>\n",
" <td>180917660</td>\n",
" <td>[5570, 4540, 9540]</td>\n",
" <td>[{u'abbreviation': u'AIp', u'color': u'219866'...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>VISC</td>\n",
" <td>11AD83</td>\n",
" <td>677</td>\n",
" <td>Visceral area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>180916954</th>\n",
" <td>M</td>\n",
" <td>180916954</td>\n",
" <td>[4800, 1220, 5930]</td>\n",
" <td>[{u'abbreviation': u'ACAd', u'color': u'40A666...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>MOs</td>\n",
" <td>1F9D5A</td>\n",
" <td>993</td>\n",
" <td>Secondary motor area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>100141780</th>\n",
" <td>M</td>\n",
" <td>100141780</td>\n",
" <td>[4070, 2600, 7500]</td>\n",
" <td>[{u'abbreviation': u'MOp', u'color': u'1F9D5A'...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>MOp</td>\n",
" <td>1F9D5A</td>\n",
" <td>985</td>\n",
" <td>Primary motor area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>180720175</th>\n",
" <td>M</td>\n",
" <td>180720175</td>\n",
" <td>[5710, 670, 6420]</td>\n",
" <td>[{u'abbreviation': u'SSp-ll', u'color': u'1880...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>MOp</td>\n",
" <td>1F9D5A</td>\n",
" <td>985</td>\n",
" <td>Primary motor area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>180717881</th>\n",
" <td>M</td>\n",
" <td>180717881</td>\n",
" <td>[4580, 3610, 8670]</td>\n",
" <td>[{u'abbreviation': u'SSp-m', u'color': u'18806...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>SSp-m</td>\n",
" <td>188064</td>\n",
" <td>345</td>\n",
" <td>Primary somatosensory area, mouth</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>127084296</th>\n",
" <td>M</td>\n",
" <td>127084296</td>\n",
" <td>[4880, 1800, 6920]</td>\n",
" <td>[{u'abbreviation': u'MOp', u'color': u'1F9D5A'...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>MOp</td>\n",
" <td>1F9D5A</td>\n",
" <td>985</td>\n",
" <td>Primary motor area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>112306316</th>\n",
" <td>M</td>\n",
" <td>112306316</td>\n",
" <td>[3000, 3600, 6690]</td>\n",
" <td>[{u'abbreviation': u'FRP', u'color': u'268F45'...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>ORBl</td>\n",
" <td>248A5E</td>\n",
" <td>723</td>\n",
" <td>Orbital area, lateral part</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>180709230</th>\n",
" <td>M</td>\n",
" <td>180709230</td>\n",
" <td>[2360, 3870, 7100]</td>\n",
" <td>[{u'abbreviation': u'AId', u'color': u'219866'...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>ORBl</td>\n",
" <td>248A5E</td>\n",
" <td>723</td>\n",
" <td>Orbital area, lateral part</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>180074890</th>\n",
" <td>M</td>\n",
" <td>180074890</td>\n",
" <td>[6760, 4320, 9510]</td>\n",
" <td>[{u'abbreviation': u'AIp', u'color': u'219866'...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>VISC</td>\n",
" <td>11AD83</td>\n",
" <td>677</td>\n",
" <td>Visceral area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>112952510</th>\n",
" <td>M</td>\n",
" <td>112952510</td>\n",
" <td>[3430, 1930, 6140]</td>\n",
" <td>[{u'abbreviation': u'ACAd', u'color': u'40A666...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>MOs</td>\n",
" <td>1F9D5A</td>\n",
" <td>993</td>\n",
" <td>Secondary motor area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>112596790</th>\n",
" <td>M</td>\n",
" <td>112596790</td>\n",
" <td>[3840, 4160, 8250]</td>\n",
" <td>[{u'abbreviation': u'AId', u'color': u'219866'...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>AId</td>\n",
" <td>219866</td>\n",
" <td>104</td>\n",
" <td>Agranular insular area, dorsal part</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>112951804</th>\n",
" <td>M</td>\n",
" <td>112951804</td>\n",
" <td>[6890, 2260, 8670]</td>\n",
" <td>[{u'abbreviation': u'SSp-bfd', u'color': u'188...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>SSp-bfd</td>\n",
" <td>188064</td>\n",
" <td>329</td>\n",
" <td>Primary somatosensory area, barrel field</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>141603190</th>\n",
" <td>M</td>\n",
" <td>141603190</td>\n",
" <td>[4100, 1810, 6110]</td>\n",
" <td>[{u'abbreviation': u'ACAd', u'color': u'40A666...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>MOs</td>\n",
" <td>1F9D5A</td>\n",
" <td>993</td>\n",
" <td>Secondary motor area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>180718587</th>\n",
" <td>M</td>\n",
" <td>180718587</td>\n",
" <td>[5200, 1310, 7300]</td>\n",
" <td>[{u'abbreviation': u'SSp-ll', u'color': u'1880...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>SSp-ul</td>\n",
" <td>188064</td>\n",
" <td>369</td>\n",
" <td>Primary somatosensory area, upper limb</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>113036264</th>\n",
" <td>M</td>\n",
" <td>113036264</td>\n",
" <td>[5600, 3510, 9100]</td>\n",
" <td>[{u'abbreviation': u'SSp-m', u'color': u'18806...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>SSs</td>\n",
" <td>188064</td>\n",
" <td>378</td>\n",
" <td>Supplemental somatosensory area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>100141796</th>\n",
" <td>M</td>\n",
" <td>100141796</td>\n",
" <td>[9370, 2450, 9080]</td>\n",
" <td>[{u'abbreviation': u'VISl', u'color': u'08858C...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>VISli</td>\n",
" <td>08858C</td>\n",
" <td>312782574</td>\n",
" <td>Laterointermediate area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>112514915</th>\n",
" <td>M</td>\n",
" <td>112514915</td>\n",
" <td>[6600, 3010, 9160]</td>\n",
" <td>[{u'abbreviation': u'SSp-bfd', u'color': u'188...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>SSs</td>\n",
" <td>188064</td>\n",
" <td>378</td>\n",
" <td>Supplemental somatosensory area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>146593590</th>\n",
" <td>M</td>\n",
" <td>146593590</td>\n",
" <td>[5180, 1790, 5800]</td>\n",
" <td>[{u'abbreviation': u'ACAd', u'color': u'40A666...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>ACAd</td>\n",
" <td>40A666</td>\n",
" <td>39</td>\n",
" <td>Anterior cingulate area, dorsal part</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>180296424</th>\n",
" <td>M</td>\n",
" <td>180296424</td>\n",
" <td>[9570, 1750, 8510]</td>\n",
" <td>[{u'abbreviation': u'VISp', u'color': u'08858C...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>VISp</td>\n",
" <td>08858C</td>\n",
" <td>385</td>\n",
" <td>Primary visual area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>117298988</th>\n",
" <td>M</td>\n",
" <td>117298988</td>\n",
" <td>[6140, 3530, 9340]</td>\n",
" <td>[{u'abbreviation': u'SSs', u'color': u'188064'...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>SSs</td>\n",
" <td>188064</td>\n",
" <td>378</td>\n",
" <td>Supplemental somatosensory area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>170721670</th>\n",
" <td>M</td>\n",
" <td>170721670</td>\n",
" <td>[2410, 3150, 6820]</td>\n",
" <td>[{u'abbreviation': u'AOB', u'color': u'9DF0D2'...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>ORBl</td>\n",
" <td>248A5E</td>\n",
" <td>723</td>\n",
" <td>Orbital area, lateral part</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>157710335</th>\n",
" <td>M</td>\n",
" <td>157710335</td>\n",
" <td>[2440, 2750, 7050]</td>\n",
" <td>[{u'abbreviation': u'FRP', u'color': u'268F45'...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>MOs</td>\n",
" <td>1F9D5A</td>\n",
" <td>993</td>\n",
" <td>Secondary motor area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>126907302</th>\n",
" <td>M</td>\n",
" <td>126907302</td>\n",
" <td>[7210, 1280, 8030]</td>\n",
" <td>[{u'abbreviation': u'SSp-bfd', u'color': u'188...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>SSp-bfd</td>\n",
" <td>188064</td>\n",
" <td>329</td>\n",
" <td>Primary somatosensory area, barrel field</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>126861679</th>\n",
" <td>M</td>\n",
" <td>126861679</td>\n",
" <td>[7450, 910, 7080]</td>\n",
" <td>[{u'abbreviation': u'VISam', u'color': u'08858...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>VISam</td>\n",
" <td>08858C</td>\n",
" <td>394</td>\n",
" <td>Anteromedial visual area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>127089669</th>\n",
" <td>M</td>\n",
" <td>127089669</td>\n",
" <td>[9270, 2930, 9310]</td>\n",
" <td>[{u'abbreviation': u'SUB', u'color': u'4FC244'...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>VISpor</td>\n",
" <td>08858C</td>\n",
" <td>312782628</td>\n",
" <td>Postrhinal area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>141602484</th>\n",
" <td>M</td>\n",
" <td>141602484</td>\n",
" <td>[4030, 1830, 7090]</td>\n",
" <td>[{u'abbreviation': u'MOp', u'color': u'1F9D5A'...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>MOs</td>\n",
" <td>1F9D5A</td>\n",
" <td>993</td>\n",
" <td>Secondary motor area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112595376</th>\n",
" <td>M</td>\n",
" <td>112595376</td>\n",
" <td>[8350, 1390, 6600]</td>\n",
" <td>[{u'abbreviation': u'RSPd', u'color': u'1AA698...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>RSPv</td>\n",
" <td>1AA698</td>\n",
" <td>886</td>\n",
" <td>Retrosplenial area, ventral part</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>100141473</th>\n",
" <td>M</td>\n",
" <td>100141473</td>\n",
" <td>[7370, 2110, 9000]</td>\n",
" <td>[{u'abbreviation': u'SSp-bfd', u'color': u'188...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>SSp-bfd</td>\n",
" <td>188064</td>\n",
" <td>329</td>\n",
" <td>Primary somatosensory area, barrel field</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>158435116</th>\n",
" <td>M</td>\n",
" <td>158435116</td>\n",
" <td>[2810, 3870, 6420]</td>\n",
" <td>[{u'abbreviation': u'ORBl', u'color': u'248A5E...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>ORBvl</td>\n",
" <td>248A5E</td>\n",
" <td>746</td>\n",
" <td>Orbital area, ventrolateral part</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>126862385</th>\n",
" <td>M</td>\n",
" <td>126862385</td>\n",
" <td>[9640, 620, 7060]</td>\n",
" <td>[{u'abbreviation': u'VISp', u'color': u'08858C...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>VISp</td>\n",
" <td>08858C</td>\n",
" <td>385</td>\n",
" <td>Primary visual area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>100149109</th>\n",
" <td>M</td>\n",
" <td>100149109</td>\n",
" <td>[7540, 2950, 9550]</td>\n",
" <td>[{u'abbreviation': u'AUDp', u'color': u'019399...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>AUDp</td>\n",
" <td>019399</td>\n",
" <td>1002</td>\n",
" <td>Primary auditory area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>139519496</th>\n",
" <td>M</td>\n",
" <td>139519496</td>\n",
" <td>[7660, 2720, 9530]</td>\n",
" <td>[{u'abbreviation': u'SSs', u'color': u'188064'...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>AUDd</td>\n",
" <td>019399</td>\n",
" <td>1011</td>\n",
" <td>Dorsal auditory area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>112229103</th>\n",
" <td>M</td>\n",
" <td>112229103</td>\n",
" <td>[7500, 770, 6790]</td>\n",
" <td>[{u'abbreviation': u'VISam', u'color': u'08858...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>RSPagl</td>\n",
" <td>1AA698</td>\n",
" <td>894</td>\n",
" <td>Retrosplenial area, lateral agranular part</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>157062358</th>\n",
" <td>M</td>\n",
" <td>157062358</td>\n",
" <td>[9260, 2830, 9490]</td>\n",
" <td>[{u'abbreviation': u'TEa', u'color': u'15B0B3'...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>VISpor</td>\n",
" <td>08858C</td>\n",
" <td>312782628</td>\n",
" <td>Postrhinal area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>100141219</th>\n",
" <td>M</td>\n",
" <td>100141219</td>\n",
" <td>[8940, 1420, 7840]</td>\n",
" <td>[{u'abbreviation': u'VISp', u'color': u'08858C...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>VISp</td>\n",
" <td>08858C</td>\n",
" <td>385</td>\n",
" <td>Primary visual area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>304565427</th>\n",
" <td>M</td>\n",
" <td>304565427</td>\n",
" <td>[9280, 2040, 8630]</td>\n",
" <td>[{u'abbreviation': u'VISp', u'color': u'08858C...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>VISp</td>\n",
" <td>08858C</td>\n",
" <td>385</td>\n",
" <td>Primary visual area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>304586645</th>\n",
" <td>M</td>\n",
" <td>304586645</td>\n",
" <td>[8650, 1350, 8030]</td>\n",
" <td>[{u'abbreviation': u'VISp', u'color': u'08858C...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>VISp</td>\n",
" <td>08858C</td>\n",
" <td>385</td>\n",
" <td>Primary visual area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>272916915</th>\n",
" <td>M</td>\n",
" <td>272916915</td>\n",
" <td>[8510, 760, 6570]</td>\n",
" <td>[{u'abbreviation': u'RSPd', u'color': u'1AA698...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>RSPd</td>\n",
" <td>1AA698</td>\n",
" <td>879</td>\n",
" <td>Retrosplenial area, dorsal part</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>272782668</th>\n",
" <td>M</td>\n",
" <td>272782668</td>\n",
" <td>[9590, 950, 7230]</td>\n",
" <td>[{u'abbreviation': u'VISp', u'color': u'08858C...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>VISp</td>\n",
" <td>08858C</td>\n",
" <td>385</td>\n",
" <td>Primary visual area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>112373124</th>\n",
" <td>M</td>\n",
" <td>112373124</td>\n",
" <td>[5420, 2600, 8480]</td>\n",
" <td>[{u'abbreviation': u'SSp-m', u'color': u'18806...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>SSp-n</td>\n",
" <td>188064</td>\n",
" <td>353</td>\n",
" <td>Primary somatosensory area, nose</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>116903230</th>\n",
" <td>M</td>\n",
" <td>116903230</td>\n",
" <td>[7950, 3110, 9730]</td>\n",
" <td>[{u'abbreviation': u'AUDp', u'color': u'019399...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>AUDp</td>\n",
" <td>019399</td>\n",
" <td>1002</td>\n",
" <td>Primary auditory area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>100147853</th>\n",
" <td>M</td>\n",
" <td>100147853</td>\n",
" <td>[9290, 1520, 8230]</td>\n",
" <td>[{u'abbreviation': u'VISp', u'color': u'08858C...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>VISp</td>\n",
" <td>08858C</td>\n",
" <td>385</td>\n",
" <td>Primary visual area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>112424813</th>\n",
" <td>M</td>\n",
" <td>112424813</td>\n",
" <td>[8030, 510, 6310]</td>\n",
" <td>[{u'abbreviation': u'RSPd', u'color': u'1AA698...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>RSPd</td>\n",
" <td>1AA698</td>\n",
" <td>879</td>\n",
" <td>Retrosplenial area, dorsal part</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>174361040</th>\n",
" <td>M</td>\n",
" <td>174361040</td>\n",
" <td>[9850, 850, 7110]</td>\n",
" <td>[{u'abbreviation': u'VISp', u'color': u'08858C...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>VISp</td>\n",
" <td>08858C</td>\n",
" <td>385</td>\n",
" <td>Primary visual area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>277714322</th>\n",
" <td>M</td>\n",
" <td>277714322</td>\n",
" <td>[8230, 1130, 8130]</td>\n",
" <td>[{u'abbreviation': u'VISp', u'color': u'08858C...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>VISp</td>\n",
" <td>08858C</td>\n",
" <td>385</td>\n",
" <td>Primary visual area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>180403712</th>\n",
" <td>M</td>\n",
" <td>180403712</td>\n",
" <td>[7470, 4370, 9850]</td>\n",
" <td>[{u'abbreviation': u'TEa', u'color': u'15B0B3'...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>ECT</td>\n",
" <td>0D9F91</td>\n",
" <td>895</td>\n",
" <td>Ectorhinal area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>277616630</th>\n",
" <td>M</td>\n",
" <td>277616630</td>\n",
" <td>[8510, 1040, 7910]</td>\n",
" <td>[{u'abbreviation': u'VISp', u'color': u'08858C...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>VISp</td>\n",
" <td>08858C</td>\n",
" <td>385</td>\n",
" <td>Primary visual area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>277713580</th>\n",
" <td>M</td>\n",
" <td>277713580</td>\n",
" <td>[8130, 1110, 8120]</td>\n",
" <td>[{u'abbreviation': u'VISp', u'color': u'08858C...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>VISp</td>\n",
" <td>08858C</td>\n",
" <td>385</td>\n",
" <td>Primary visual area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>277712166</th>\n",
" <td>M</td>\n",
" <td>277712166</td>\n",
" <td>[8350, 1190, 8110]</td>\n",
" <td>[{u'abbreviation': u'VISp', u'color': u'08858C...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>VISp</td>\n",
" <td>08858C</td>\n",
" <td>385</td>\n",
" <td>Primary visual area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>180073473</th>\n",
" <td>M</td>\n",
" <td>180073473</td>\n",
" <td>[8400, 2500, 9540]</td>\n",
" <td>[{u'abbreviation': u'TEa', u'color': u'15B0B3'...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>AUDpo</td>\n",
" <td>019399</td>\n",
" <td>1027</td>\n",
" <td>Posterior auditory area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>304564721</th>\n",
" <td>M</td>\n",
" <td>304564721</td>\n",
" <td>[8530, 1350, 8370]</td>\n",
" <td>[{u'abbreviation': u'VISp', u'color': u'08858C...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>VISp</td>\n",
" <td>08858C</td>\n",
" <td>385</td>\n",
" <td>Primary visual area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>304585910</th>\n",
" <td>M</td>\n",
" <td>304585910</td>\n",
" <td>[9230, 1370, 8200]</td>\n",
" <td>[{u'abbreviation': u'VISp', u'color': u'08858C...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>VISp</td>\n",
" <td>08858C</td>\n",
" <td>385</td>\n",
" <td>Primary visual area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>148964212</th>\n",
" <td>M</td>\n",
" <td>148964212</td>\n",
" <td>[5760, 1440, 7630]</td>\n",
" <td>[{u'abbreviation': u'SSp-ll', u'color': u'1880...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>SSp-ul</td>\n",
" <td>188064</td>\n",
" <td>369</td>\n",
" <td>Primary somatosensory area, upper limb</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>272916202</th>\n",
" <td>M</td>\n",
" <td>272916202</td>\n",
" <td>[9750, 2070, 8980]</td>\n",
" <td>[{u'abbreviation': u'VISp', u'color': u'08858C...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>VISpl</td>\n",
" <td>08858C</td>\n",
" <td>425</td>\n",
" <td>Posterolateral visual area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>304762965</th>\n",
" <td>M</td>\n",
" <td>304762965</td>\n",
" <td>[8840, 1410, 8330]</td>\n",
" <td>[{u'abbreviation': u'VISp', u'color': u'08858C...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>VISp</td>\n",
" <td>08858C</td>\n",
" <td>385</td>\n",
" <td>Primary visual area</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>180404418</th>\n",
" <td>M</td>\n",
" <td>180404418</td>\n",
" <td>[5190, 4350, 9320]</td>\n",
" <td>[{u'abbreviation': u'SSs', u'color': u'188064'...</td>\n",
" <td>5</td>\n",
" <td>C57BL/6J</td>\n",
" <td>GU</td>\n",
" <td>009C75</td>\n",
" <td>1057</td>\n",
" <td>Gustatory areas</td>\n",
" <td></td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>126 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" gender id injection-coordinates \\\n",
"id \n",
"180435652 M 180435652 [7820, 4250, 9870] \n",
"180436360 M 180436360 [4800, 4720, 8980] \n",
"180719293 M 180719293 [3140, 3330, 7390] \n",
"180709942 M 180709942 [3360, 3120, 7520] \n",
"180917660 M 180917660 [5570, 4540, 9540] \n",
"180916954 M 180916954 [4800, 1220, 5930] \n",
"100141780 M 100141780 [4070, 2600, 7500] \n",
"180720175 M 180720175 [5710, 670, 6420] \n",
"180717881 M 180717881 [4580, 3610, 8670] \n",
"127084296 M 127084296 [4880, 1800, 6920] \n",
"112306316 M 112306316 [3000, 3600, 6690] \n",
"180709230 M 180709230 [2360, 3870, 7100] \n",
"180074890 M 180074890 [6760, 4320, 9510] \n",
"112952510 M 112952510 [3430, 1930, 6140] \n",
"112596790 M 112596790 [3840, 4160, 8250] \n",
"112951804 M 112951804 [6890, 2260, 8670] \n",
"141603190 M 141603190 [4100, 1810, 6110] \n",
"180718587 M 180718587 [5200, 1310, 7300] \n",
"113036264 M 113036264 [5600, 3510, 9100] \n",
"100141796 M 100141796 [9370, 2450, 9080] \n",
"112514915 M 112514915 [6600, 3010, 9160] \n",
"146593590 M 146593590 [5180, 1790, 5800] \n",
"180296424 M 180296424 [9570, 1750, 8510] \n",
"117298988 M 117298988 [6140, 3530, 9340] \n",
"170721670 M 170721670 [2410, 3150, 6820] \n",
"157710335 M 157710335 [2440, 2750, 7050] \n",
"126907302 M 126907302 [7210, 1280, 8030] \n",
"126861679 M 126861679 [7450, 910, 7080] \n",
"127089669 M 127089669 [9270, 2930, 9310] \n",
"141602484 M 141602484 [4030, 1830, 7090] \n",
"... ... ... ... \n",
"112595376 M 112595376 [8350, 1390, 6600] \n",
"100141473 M 100141473 [7370, 2110, 9000] \n",
"158435116 M 158435116 [2810, 3870, 6420] \n",
"126862385 M 126862385 [9640, 620, 7060] \n",
"100149109 M 100149109 [7540, 2950, 9550] \n",
"139519496 M 139519496 [7660, 2720, 9530] \n",
"112229103 M 112229103 [7500, 770, 6790] \n",
"157062358 M 157062358 [9260, 2830, 9490] \n",
"100141219 M 100141219 [8940, 1420, 7840] \n",
"304565427 M 304565427 [9280, 2040, 8630] \n",
"304586645 M 304586645 [8650, 1350, 8030] \n",
"272916915 M 272916915 [8510, 760, 6570] \n",
"272782668 M 272782668 [9590, 950, 7230] \n",
"112373124 M 112373124 [5420, 2600, 8480] \n",
"116903230 M 116903230 [7950, 3110, 9730] \n",
"100147853 M 100147853 [9290, 1520, 8230] \n",
"112424813 M 112424813 [8030, 510, 6310] \n",
"174361040 M 174361040 [9850, 850, 7110] \n",
"277714322 M 277714322 [8230, 1130, 8130] \n",
"180403712 M 180403712 [7470, 4370, 9850] \n",
"277616630 M 277616630 [8510, 1040, 7910] \n",
"277713580 M 277713580 [8130, 1110, 8120] \n",
"277712166 M 277712166 [8350, 1190, 8110] \n",
"180073473 M 180073473 [8400, 2500, 9540] \n",
"304564721 M 304564721 [8530, 1350, 8370] \n",
"304585910 M 304585910 [9230, 1370, 8200] \n",
"148964212 M 148964212 [5760, 1440, 7630] \n",
"272916202 M 272916202 [9750, 2070, 8980] \n",
"304762965 M 304762965 [8840, 1410, 8330] \n",
"180404418 M 180404418 [5190, 4350, 9320] \n",
"\n",
" injection-structures product-id \\\n",
"id \n",
"180435652 [{u'abbreviation': u'TEa', u'color': u'15B0B3'... 5 \n",
"180436360 [{u'abbreviation': u'AId', u'color': u'219866'... 5 \n",
"180719293 [{u'abbreviation': u'AId', u'color': u'219866'... 5 \n",
"180709942 [{u'abbreviation': u'MOp', u'color': u'1F9D5A'... 5 \n",
"180917660 [{u'abbreviation': u'AIp', u'color': u'219866'... 5 \n",
"180916954 [{u'abbreviation': u'ACAd', u'color': u'40A666... 5 \n",
"100141780 [{u'abbreviation': u'MOp', u'color': u'1F9D5A'... 5 \n",
"180720175 [{u'abbreviation': u'SSp-ll', u'color': u'1880... 5 \n",
"180717881 [{u'abbreviation': u'SSp-m', u'color': u'18806... 5 \n",
"127084296 [{u'abbreviation': u'MOp', u'color': u'1F9D5A'... 5 \n",
"112306316 [{u'abbreviation': u'FRP', u'color': u'268F45'... 5 \n",
"180709230 [{u'abbreviation': u'AId', u'color': u'219866'... 5 \n",
"180074890 [{u'abbreviation': u'AIp', u'color': u'219866'... 5 \n",
"112952510 [{u'abbreviation': u'ACAd', u'color': u'40A666... 5 \n",
"112596790 [{u'abbreviation': u'AId', u'color': u'219866'... 5 \n",
"112951804 [{u'abbreviation': u'SSp-bfd', u'color': u'188... 5 \n",
"141603190 [{u'abbreviation': u'ACAd', u'color': u'40A666... 5 \n",
"180718587 [{u'abbreviation': u'SSp-ll', u'color': u'1880... 5 \n",
"113036264 [{u'abbreviation': u'SSp-m', u'color': u'18806... 5 \n",
"100141796 [{u'abbreviation': u'VISl', u'color': u'08858C... 5 \n",
"112514915 [{u'abbreviation': u'SSp-bfd', u'color': u'188... 5 \n",
"146593590 [{u'abbreviation': u'ACAd', u'color': u'40A666... 5 \n",
"180296424 [{u'abbreviation': u'VISp', u'color': u'08858C... 5 \n",
"117298988 [{u'abbreviation': u'SSs', u'color': u'188064'... 5 \n",
"170721670 [{u'abbreviation': u'AOB', u'color': u'9DF0D2'... 5 \n",
"157710335 [{u'abbreviation': u'FRP', u'color': u'268F45'... 5 \n",
"126907302 [{u'abbreviation': u'SSp-bfd', u'color': u'188... 5 \n",
"126861679 [{u'abbreviation': u'VISam', u'color': u'08858... 5 \n",
"127089669 [{u'abbreviation': u'SUB', u'color': u'4FC244'... 5 \n",
"141602484 [{u'abbreviation': u'MOp', u'color': u'1F9D5A'... 5 \n",
"... ... ... \n",
"112595376 [{u'abbreviation': u'RSPd', u'color': u'1AA698... 5 \n",
"100141473 [{u'abbreviation': u'SSp-bfd', u'color': u'188... 5 \n",
"158435116 [{u'abbreviation': u'ORBl', u'color': u'248A5E... 5 \n",
"126862385 [{u'abbreviation': u'VISp', u'color': u'08858C... 5 \n",
"100149109 [{u'abbreviation': u'AUDp', u'color': u'019399... 5 \n",
"139519496 [{u'abbreviation': u'SSs', u'color': u'188064'... 5 \n",
"112229103 [{u'abbreviation': u'VISam', u'color': u'08858... 5 \n",
"157062358 [{u'abbreviation': u'TEa', u'color': u'15B0B3'... 5 \n",
"100141219 [{u'abbreviation': u'VISp', u'color': u'08858C... 5 \n",
"304565427 [{u'abbreviation': u'VISp', u'color': u'08858C... 5 \n",
"304586645 [{u'abbreviation': u'VISp', u'color': u'08858C... 5 \n",
"272916915 [{u'abbreviation': u'RSPd', u'color': u'1AA698... 5 \n",
"272782668 [{u'abbreviation': u'VISp', u'color': u'08858C... 5 \n",
"112373124 [{u'abbreviation': u'SSp-m', u'color': u'18806... 5 \n",
"116903230 [{u'abbreviation': u'AUDp', u'color': u'019399... 5 \n",
"100147853 [{u'abbreviation': u'VISp', u'color': u'08858C... 5 \n",
"112424813 [{u'abbreviation': u'RSPd', u'color': u'1AA698... 5 \n",
"174361040 [{u'abbreviation': u'VISp', u'color': u'08858C... 5 \n",
"277714322 [{u'abbreviation': u'VISp', u'color': u'08858C... 5 \n",
"180403712 [{u'abbreviation': u'TEa', u'color': u'15B0B3'... 5 \n",
"277616630 [{u'abbreviation': u'VISp', u'color': u'08858C... 5 \n",
"277713580 [{u'abbreviation': u'VISp', u'color': u'08858C... 5 \n",
"277712166 [{u'abbreviation': u'VISp', u'color': u'08858C... 5 \n",
"180073473 [{u'abbreviation': u'TEa', u'color': u'15B0B3'... 5 \n",
"304564721 [{u'abbreviation': u'VISp', u'color': u'08858C... 5 \n",
"304585910 [{u'abbreviation': u'VISp', u'color': u'08858C... 5 \n",
"148964212 [{u'abbreviation': u'SSp-ll', u'color': u'1880... 5 \n",
"272916202 [{u'abbreviation': u'VISp', u'color': u'08858C... 5 \n",
"304762965 [{u'abbreviation': u'VISp', u'color': u'08858C... 5 \n",
"180404418 [{u'abbreviation': u'SSs', u'color': u'188064'... 5 \n",
"\n",
" strain structure-abbrev structure-color structure-id \\\n",
"id \n",
"180435652 C57BL/6J ECT 0D9F91 895 \n",
"180436360 C57BL/6J VISC 11AD83 677 \n",
"180719293 C57BL/6J MOs 1F9D5A 993 \n",
"180709942 C57BL/6J MOs 1F9D5A 993 \n",
"180917660 C57BL/6J VISC 11AD83 677 \n",
"180916954 C57BL/6J MOs 1F9D5A 993 \n",
"100141780 C57BL/6J MOp 1F9D5A 985 \n",
"180720175 C57BL/6J MOp 1F9D5A 985 \n",
"180717881 C57BL/6J SSp-m 188064 345 \n",
"127084296 C57BL/6J MOp 1F9D5A 985 \n",
"112306316 C57BL/6J ORBl 248A5E 723 \n",
"180709230 C57BL/6J ORBl 248A5E 723 \n",
"180074890 C57BL/6J VISC 11AD83 677 \n",
"112952510 C57BL/6J MOs 1F9D5A 993 \n",
"112596790 C57BL/6J AId 219866 104 \n",
"112951804 C57BL/6J SSp-bfd 188064 329 \n",
"141603190 C57BL/6J MOs 1F9D5A 993 \n",
"180718587 C57BL/6J SSp-ul 188064 369 \n",
"113036264 C57BL/6J SSs 188064 378 \n",
"100141796 C57BL/6J VISli 08858C 312782574 \n",
"112514915 C57BL/6J SSs 188064 378 \n",
"146593590 C57BL/6J ACAd 40A666 39 \n",
"180296424 C57BL/6J VISp 08858C 385 \n",
"117298988 C57BL/6J SSs 188064 378 \n",
"170721670 C57BL/6J ORBl 248A5E 723 \n",
"157710335 C57BL/6J MOs 1F9D5A 993 \n",
"126907302 C57BL/6J SSp-bfd 188064 329 \n",
"126861679 C57BL/6J VISam 08858C 394 \n",
"127089669 C57BL/6J VISpor 08858C 312782628 \n",
"141602484 C57BL/6J MOs 1F9D5A 993 \n",
"... ... ... ... ... \n",
"112595376 C57BL/6J RSPv 1AA698 886 \n",
"100141473 C57BL/6J SSp-bfd 188064 329 \n",
"158435116 C57BL/6J ORBvl 248A5E 746 \n",
"126862385 C57BL/6J VISp 08858C 385 \n",
"100149109 C57BL/6J AUDp 019399 1002 \n",
"139519496 C57BL/6J AUDd 019399 1011 \n",
"112229103 C57BL/6J RSPagl 1AA698 894 \n",
"157062358 C57BL/6J VISpor 08858C 312782628 \n",
"100141219 C57BL/6J VISp 08858C 385 \n",
"304565427 C57BL/6J VISp 08858C 385 \n",
"304586645 C57BL/6J VISp 08858C 385 \n",
"272916915 C57BL/6J RSPd 1AA698 879 \n",
"272782668 C57BL/6J VISp 08858C 385 \n",
"112373124 C57BL/6J SSp-n 188064 353 \n",
"116903230 C57BL/6J AUDp 019399 1002 \n",
"100147853 C57BL/6J VISp 08858C 385 \n",
"112424813 C57BL/6J RSPd 1AA698 879 \n",
"174361040 C57BL/6J VISp 08858C 385 \n",
"277714322 C57BL/6J VISp 08858C 385 \n",
"180403712 C57BL/6J ECT 0D9F91 895 \n",
"277616630 C57BL/6J VISp 08858C 385 \n",
"277713580 C57BL/6J VISp 08858C 385 \n",
"277712166 C57BL/6J VISp 08858C 385 \n",
"180073473 C57BL/6J AUDpo 019399 1027 \n",
"304564721 C57BL/6J VISp 08858C 385 \n",
"304585910 C57BL/6J VISp 08858C 385 \n",
"148964212 C57BL/6J SSp-ul 188064 369 \n",
"272916202 C57BL/6J VISpl 08858C 425 \n",
"304762965 C57BL/6J VISp 08858C 385 \n",
"180404418 C57BL/6J GU 009C75 1057 \n",
"\n",
" structure-name transgenic-line \n",
"id \n",
"180435652 Ectorhinal area \n",
"180436360 Visceral area \n",
"180719293 Secondary motor area \n",
"180709942 Secondary motor area \n",
"180917660 Visceral area \n",
"180916954 Secondary motor area \n",
"100141780 Primary motor area \n",
"180720175 Primary motor area \n",
"180717881 Primary somatosensory area, mouth \n",
"127084296 Primary motor area \n",
"112306316 Orbital area, lateral part \n",
"180709230 Orbital area, lateral part \n",
"180074890 Visceral area \n",
"112952510 Secondary motor area \n",
"112596790 Agranular insular area, dorsal part \n",
"112951804 Primary somatosensory area, barrel field \n",
"141603190 Secondary motor area \n",
"180718587 Primary somatosensory area, upper limb \n",
"113036264 Supplemental somatosensory area \n",
"100141796 Laterointermediate area \n",
"112514915 Supplemental somatosensory area \n",
"146593590 Anterior cingulate area, dorsal part \n",
"180296424 Primary visual area \n",
"117298988 Supplemental somatosensory area \n",
"170721670 Orbital area, lateral part \n",
"157710335 Secondary motor area \n",
"126907302 Primary somatosensory area, barrel field \n",
"126861679 Anteromedial visual area \n",
"127089669 Postrhinal area \n",
"141602484 Secondary motor area \n",
"... ... ... \n",
"112595376 Retrosplenial area, ventral part \n",
"100141473 Primary somatosensory area, barrel field \n",
"158435116 Orbital area, ventrolateral part \n",
"126862385 Primary visual area \n",
"100149109 Primary auditory area \n",
"139519496 Dorsal auditory area \n",
"112229103 Retrosplenial area, lateral agranular part \n",
"157062358 Postrhinal area \n",
"100141219 Primary visual area \n",
"304565427 Primary visual area \n",
"304586645 Primary visual area \n",
"272916915 Retrosplenial area, dorsal part \n",
"272782668 Primary visual area \n",
"112373124 Primary somatosensory area, nose \n",
"116903230 Primary auditory area \n",
"100147853 Primary visual area \n",
"112424813 Retrosplenial area, dorsal part \n",
"174361040 Primary visual area \n",
"277714322 Primary visual area \n",
"180403712 Ectorhinal area \n",
"277616630 Primary visual area \n",
"277713580 Primary visual area \n",
"277712166 Primary visual area \n",
"180073473 Posterior auditory area \n",
"304564721 Primary visual area \n",
"304585910 Primary visual area \n",
"148964212 Primary somatosensory area, upper limb \n",
"272916202 Posterolateral visual area \n",
"304762965 Primary visual area \n",
"180404418 Gustatory areas \n",
"\n",
"[126 rows x 11 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"experiments"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| bsd-2-clause |
ravigawai/TestPy | notebooks/Users/[email protected]/ExpediaPythonTest.ipynb | 1 | 265 | {"cells":[{"cell_type":"markdown","source":["Expedia Python Test"],"metadata":{}},{"cell_type":"code","source":["1+1*2\n"],"metadata":{},"outputs":[],"execution_count":2}],"metadata":{"name":"ExpediaPythonTest","notebookId":3157094},"nbformat":4,"nbformat_minor":0} | apache-2.0 |
atulsingh0/MachineLearning | Learning_SFrame/Letters_in_MarkDown.ipynb | 1 | 8441 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Greek Letters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MarkDown | Letter | MarkDown | Letter | MarkDown | Letter |MarkDown | Letter |\n",
"---------|---------|----------|--------|----------|--------|---------|--------|\n",
"\\alpha |$$ \\alpha $$| \\theta \\Theta | $$ \\theta \\Theta$$ | o | $$ o $$ | \\tau | $$ \\tau $$ \n",
" \\beta | $$ \\beta $$ | \\vartheta | $$ \\vartheta $$| \\pi | $$ \\pi $$| \\upsilon \\Upsilon | $$ \\upsilon \\Upsilon $$\n",
" \\gamma \\Gamma | $$ \\gamma \\Gamma $$ | \\sigma \\Sigma \\varsigma | $$ \\sigma \\Sigma \\varsigma $$| \\varpi | $$ \\varpi $$| \\phi | $$ \\phi $$\n",
" \\delta \\Delta | $$ \\delta \\Delta $$ | \\kappa | $$ \\kappa $$| \\rho | $$ \\rho $$| \\varphi | $$ \\varphi $$\n",
" \\epsilon | $$ \\epsilon $$ | \\lambda \\Lambda | $$ \\lambda \\Lambda $$| \\varrho | $$ \\varrho $$| \\chi | $$ \\chi $$\n",
" \\varepsilon| $$ \\varepsilon$$ | \\mu | $$ \\mu $$| \\Xi | $$ \\Xi $$| \\psi | $$ \\psi $$\n",
" \\zeta | $$ \\zeta $$ | \\nu | $$ \\nu $$| \\Pi | $$ \\Pi $$| \\omega \\Omega | $$ \\omega \\Omega $$\n",
" \\eta | $$ \\eta $$ | \\xi | $$ \\xi $$| \\Phi | $$ \\Phi $$| \\Psi | $$ \\Psi $$\n",
" | $$ $$ | | $$ $$| | $$ $$| | $$ $$\n",
" | $$ $$ | | $$ $$| | $$ $$| | $$ $$\n",
" | $$ $$ | | $$ $$| | $$ $$| | $$ $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Binary Operation Symbols"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" MarkDown | Letter | MarkDown | Letter | MarkDown | Letter |MarkDown | Letter |\n",
"---------|---------|----------|--------|----------|--------|---------|--------|\n",
" \\pm | $$ \\pm $$ | \\cap | $$ \\cap $$| \\diamond | $$ \\diamond $$| \\oplus | $$ \\oplus $$\n",
" \\mp | $$ \\mp $$ | \\cup | $$ \\cup $$| \\bigtriangleup | $$ \\bigtriangleup $$| \\ominus | $$ \\ominus $$\n",
" \\times | $$ \\times $$ | \\uplus | $$ \\uplus $$| \\bigtriangledown| $$ \\bigtriangledown$$| \\otimes | $$ \\otimes $$\n",
" \\div | $$ \\div $$ | \\sqcap | $$ \\sqcap $$| \\triangleleft | $$ \\triangleleft $$| \\oslash | $$ \\oslash $$\n",
" \\ast | $$ \\ast $$ | \\sqcup | $$ \\sqcup $$| \\triangleright | $$ \\triangleright $$| \\odot | $$ \\odot $$\n",
" \\star | $$ \\star $$ | \\vee | $$ \\vee $$| \\lhd$^b$ | $$ \\lhd$^b$ $$| \\bigcirc | $$ \\bigcirc $$\n",
" \\circ | $$ \\circ $$ | \\wedge | $$ \\wedge $$| \\rhd$^b$ | $$ \\rhd$^b$ $$| \\dagger | $$ \\dagger $$\n",
" \\bullet | $$ \\bullet $$ | \\setminus | $$ \\setminus $$| \\unlhd$^b$ | $$ \\unlhd$^b$ $$| \\ddagger | $$ \\ddagger $$\n",
" \\cdot | $$ \\cdot $$ | \\wr | $$ \\wr $$| \\unrhd$^b$ | $$ \\unrhd$^b$ $$| \\amalg | $$ \\amalg $$\n",
" + | $$ + $$ | - | $$ - $$| \\cong | $$ \\cong $$| \\equiv | $$ \\equiv $$\n",
" | $$ $$ | | $$ $$| | $$ $$| | $$ $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Relation Symbols"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" MarkDown| Letter | MarkDown | Letter | MarkDown | Letter |MarkDown | Letter |\n",
"---------|---------|----------|--------|----------|--------|---------|--------|\n",
" \\leq | $$ \\leq $$ | \\geq | $$ \\geq $$| \\equiv | $$ \\equiv $$| \\models | $$ \\models $$\n",
" \\prec | $$ \\prec $$ | \\succ | $$ \\succ $$| \\sim | $$ \\sim $$| \\perp | $$ \\perp $$\n",
" \\preceq | $$ \\preceq $$ | \\succeq | $$ \\succeq $$| \\simeq | $$ \\simeq $$| \\mid | $$ \\mid $$\n",
" \\ll | $$ \\ll $$ | \\gg | $$ \\gg $$| \\asymp | $$ \\asymp $$| \\parallel | $$ \\parallel $$\n",
" \\subset | $$ \\subset $$ | \\supset | $$ \\supset $$| \\approx | $$ \\approx $$| \\bowtie | $$ \\bowtie $$\n",
" \\subseteq | $$ \\subseteq $$ | \\supseteq | $$ \\supseteq $$| \\cong | $$ \\cong $$| \\Join$^b$ | $$ \\Join$^b$ $$\n",
" \\sqsubset$^b$ | $$ \\sqsubset$^b$ $$ | \\sqsupset$^b$ | $$ \\sqsupset$^b$$$| \\neq | $$ \\neq $$| \\smile | $$ \\smile $$\n",
" \\sqsubseteq | $$ \\sqsubseteq $$ | \\sqsupseteq | $$ \\sqsupseteq $$| \\doteq | $$ \\doteq $$| \\frown | $$ \\frown $$\n",
" \\in | $$ \\in $$ | \\ni | $$ \\ni $$| \\propto | $$ \\propto $$| = | $$ = $$\n",
" \\vdash | $$ \\vdash $$ | \\dashv | $$ \\dashv $$| < | $$ < $$| > | $$ > $$\n",
" : | $$ : $$ | | $$ $$| | $$ $$| | $$ $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Punctuation Symbols"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MarkDown| Letter | MarkDown | Letter | MarkDown | Letter |MarkDown | Letter| \n",
"---------|---------|----------|--------|----------|--------|---------|--------|\n",
" , | $$ , $$ | ; | $$ ; $$| \\colon | $$ \\colon $$| \\ldotp | $$ \\ldotp $$\n",
" \\cdotp | $$ \\cdotp $$ | | $$ $$| | $$ $$| | $$ $$ "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Arrow Symbols"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" MarkDown | Letter | MarkDown | Letter | MarkDown | Letter |MarkDown | Letter |\n",
"---------|---------|----------|--------|----------|--------|---------|--------|\n",
" \\leftarrow | $$ \\leftarrow $$ | \\Leftarrow | $$ \\Leftarrow $$| \\rightarrow | $$ \\rightarrow $$| \\\\Rightarrow | $$ \\Rightarrow $$\n",
" \\leftrightarrow | $$ \\leftrightarrow $$ | \\rightleftharpoons | $$ \\rightleftharpoons $$| \\uparrow | $$ \\uparrow $$| \\downarrow | $$ \\downarrow $$\n",
" \\Uparrow | $$ \\Uparrow $$ | \\Downarrow | $$ \\Downarrow $$| \\Leftrightarrow | $$ \\bigtriangledown$$| \\Updownarrow | $$ \\Updownarrow $$\n",
" \\mapsto | $$ \\mapsto $$ | \\longmapsto | $$ \\longmapsto $$| \\nearrow | $$ \\nearrow $$| \\searrow | $$ \\searrow $$\n",
"\\swarrow | $$ \\swarrow $$ | \\nwarrow | $$ \\nwarrow $$| \\leftharpoonup | $$ \\leftharpoonup $$| \\rightharpoonup | $$ \\rightharpoonup $$\n",
" \\leftharpoondown | $$ \\leftharpoondown $$ | \\rightharpoondown | $$ \\rightharpoondown $$| \\rightleftharpoons | $$ \\rightleftharpoons $$| | $$ $$\n",
" | $$ $$ | | $$ $$| | $$ $$| | $$ $$\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
Diyago/Machine-Learning-scripts | DEEP LEARNING/Kaggle Avito Demand Prediction Challenge/ridge regression XGBOOST.ipynb | 1 | 360659 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"start_time": "2018-06-22T07:09:24.162Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/dex/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
" \"This module will be removed in 0.20.\", DeprecationWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Data Load Stage\n"
]
}
],
"source": [
"#Used some text cleaning method from Muhammad Alfiansyah's kernel here: https://www.kaggle.com/muhammadalfiansyah/push-the-lgbm-v19\n",
"import xgboost as xgb\n",
"import time\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import os\n",
"import gc \n",
"from sklearn import metrics\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn import feature_selection\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn import preprocessing\n",
"import lightgbm as lgb\n",
"from sklearn.linear_model import Ridge\n",
"from sklearn.cross_validation import KFold\n",
"from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
"from sklearn.pipeline import FeatureUnion\n",
"from scipy.sparse import hstack, csr_matrix\n",
"from nltk.corpus import stopwords \n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import re\n",
"import string\n",
"\n",
"NFOLDS = 5\n",
"SEED = 42\n",
"VALID = True\n",
"class SklearnWrapper(object):\n",
" def __init__(self, clf, seed=0, params=None, seed_bool = True):\n",
" if(seed_bool == True):\n",
" params['random_state'] = seed\n",
" self.clf = clf(**params)\n",
"\n",
" def train(self, x_train, y_train):\n",
" self.clf.fit(x_train, y_train)\n",
"\n",
" def predict(self, x):\n",
" return self.clf.predict(x)\n",
" \n",
"def get_oof(clf, x_train, y, x_test):\n",
" oof_train = np.zeros((ntrain,))\n",
" oof_test = np.zeros((ntest,))\n",
" oof_test_skf = np.empty((NFOLDS, ntest))\n",
"\n",
" for i, (train_index, test_index) in enumerate(kf):\n",
" x_tr = x_train[train_index]\n",
" y_tr = y[train_index]\n",
" x_te = x_train[test_index]\n",
"\n",
" clf.train(x_tr, y_tr)\n",
"\n",
" oof_train[test_index] = clf.predict(x_te)\n",
" oof_test_skf[i, :] = clf.predict(x_test)\n",
"\n",
" oof_test[:] = oof_test_skf.mean(axis=0)\n",
" return oof_train.reshape(-1, 1), oof_test.reshape(-1, 1)\n",
" \n",
"def cleanName(text):\n",
" try:\n",
" textProc = text.lower()\n",
" # textProc = \" \".join(map(str.strip, re.split('(\\d+)',textProc)))\n",
" #regex = re.compile(u'[^[:alpha:]]')\n",
" #textProc = regex.sub(\" \", textProc)\n",
" textProc = re.sub('[!@#$_“”¨«»®´·º½¾¿¡§£₤‘’]', '', textProc)\n",
" textProc = \" \".join(textProc.split())\n",
" return textProc\n",
" except: \n",
" return \"name error\"\n",
" \n",
"\n",
"def rmse(y, y0):\n",
" assert len(y) == len(y0)\n",
" return np.sqrt(np.mean(np.power((y - y0), 2)))\n",
"\n",
"print(\"\\nData Load Stage\")\n",
"training = pd.read_csv('train.csv.zip', index_col = \"item_id\", parse_dates = [\"activation_date\"])\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"training_img = pd.read_csv('train_img_features_v1.csv',index_col = \"item_id\")\n",
"test_img = pd.read_csv('test_img_features_v1.csv',index_col = \"item_id\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"start_time": "2018-06-22T07:09:24.210Z"
}
},
"outputs": [],
"source": [
"#training = training[training.description.isnull() == False]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"start_time": "2018-06-22T07:09:24.227Z"
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"119"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"traindex = training.index\n",
"testing = pd.read_csv('test.csv.zip', index_col = \"item_id\", parse_dates = [\"activation_date\"])\n",
"testdex = testing.index\n",
"\n",
"gp = pd.read_csv('aggregated_features.csv') \n",
"training = training.merge(gp, on='user_id', how='left')\n",
"testing = testing.merge(gp, on='user_id', how='left')\n",
" \n",
"training.index = traindex\n",
"testing.index =testdex\n",
"training = training.merge(training_img, left_index=True, right_index=True)\n",
"testing = testing.merge(test_img, left_index=True, right_index=True)\n",
"\n",
"\n",
"\n",
"agg_cols = list(gp.columns)[1:]\n",
"\n",
"del gp,training_img,test_img\n",
"gc.collect()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"start_time": "2018-06-22T07:09:24.239Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train shape: 1503424 Rows, 30 Columns\n",
"Test shape: 508438 Rows, 30 Columns\n",
"Combine Train and Test\n",
"\n",
"All Data shape: 2011862 Rows, 30 Columns\n"
]
}
],
"source": [
"ntrain = training.shape[0]\n",
"ntest = testing.shape[0]\n",
"\n",
"kf = KFold(ntrain, n_folds=NFOLDS, shuffle=True, random_state=SEED)\n",
"\n",
"y = training.deal_probability.copy()\n",
"training.drop(\"deal_probability\",axis=1, inplace=True)\n",
"print('Train shape: {} Rows, {} Columns'.format(*training.shape))\n",
"print('Test shape: {} Rows, {} Columns'.format(*testing.shape))\n",
"\n",
"print(\"Combine Train and Test\")\n",
"df = pd.concat([training,testing],axis=0)\n",
"del training, testing\n",
"gc.collect()\n",
"print('\\nAll Data shape: {} Rows, {} Columns'.format(*df.shape))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"df['image'] =df['image_x']\n",
"df.drop(['image_x', 'image_y'], axis = 1, inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"start_time": "2018-06-22T07:09:24.246Z"
}
},
"outputs": [],
"source": [
"df['has_image'] = 1\n",
"df.loc[df['image'].isnull(), 'has_image'] = 0\n",
"\n",
"df['price_is_null'] = 0\n",
"df.loc[df['price'].isnull(), 'price_is_null'] = 1\n",
"\n",
"df['param_1_is_null'] = 0\n",
"df.loc[df['param_1'].isnull(), 'param_1_is_null'] = 1\n",
"df['param_2_is_null'] = 0\n",
"df.loc[df['param_2'].isnull(), 'param_2_is_null'] = 1\n",
"df['param_3_is_null'] = 0\n",
"df.loc[df['param_3'].isnull(), 'param_3_is_null'] = 1\n",
"df['image_top_1_is_null'] = 0"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"start_time": "2018-06-22T07:09:24.250Z"
}
},
"outputs": [],
"source": [
"df['description'] = df['description'].apply(lambda x: str(x).replace('/\\n', ' ').replace('\\xa0', ' ').replace('.', '. ').replace(',', ', '))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"start_time": "2018-06-22T07:09:24.252Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Feature Engineering\n",
"\n",
"Create Time Variables\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/dex/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in log\n",
" \"\"\"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Encode Variables\n",
"Encoding : ['user_id', 'region', 'city', 'parent_category_name', 'category_name', 'user_type', 'image_top_1', 'param_1', 'param_2', 'param_3']\n"
]
}
],
"source": [
"for col in agg_cols:\n",
" df[col].fillna(-1, inplace=True)\n",
"\n",
"print(\"Feature Engineering\")\n",
"df[\"price\"] = np.log(df[\"price\"]+0.001)\n",
"df[\"price\"].fillna(df.price.mean(),inplace=True)\n",
"df[\"image_top_1\"].fillna(-999,inplace=True)\n",
"\n",
"print(\"\\nCreate Time Variables\")\n",
"df[\"Weekday\"] = df['activation_date'].dt.weekday \n",
"training_index = df.loc[df.activation_date<=pd.to_datetime('2017-04-07')].index\n",
"validation_index = df.loc[df.activation_date>=pd.to_datetime('2017-04-08')].index\n",
"df.drop([\"activation_date\",\"image\"],axis=1,inplace=True)\n",
"\n",
"print(\"\\nEncode Variables\")\n",
"categorical = [\"user_id\",\"region\",\"city\",\"parent_category_name\",\"category_name\",\"user_type\",\"image_top_1\",\"param_1\",\"param_2\",\"param_3\"]\n",
"print(\"Encoding :\",categorical)\n",
"\n",
"# Encoder:\n",
"lbl = preprocessing.LabelEncoder()\n",
"for col in categorical:\n",
" df[col].fillna('Unknown')\n",
" df[col] = lbl.fit_transform(df[col].astype(str))\n",
" \n",
"#X = hstack([csr_matrix(df.loc[traindex,:].values),ready_df[0:traindex.shape[0]]]) # Sparse Matrix\n",
"#testing = hstack([csr_matrix(df.loc[testdex,:].values),ready_df[traindex.shape[0]:]])\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"start_time": "2018-06-22T07:09:24.258Z"
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Text Features\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/dex/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py:2957: RuntimeWarning: Mean of empty slice.\n",
" out=out, **kwargs)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"[TF-IDF] Term Frequency Inverse Document Frequency Stage\n"
]
}
],
"source": [
"print(\"\\nText Features\")\n",
"\n",
"# Feature Engineering \n",
"\n",
"# Meta Text Features\n",
"textfeats = [\"description\", \"title\"]\n",
"#df['desc_punc'] = df['description'].apply(lambda x: len([c for c in str(x) if c in string.punctuation]))\n",
"\n",
"df['title'] = df['title'].apply(lambda x: cleanName(x))\n",
"df[\"description\"] = df[\"description\"].apply(lambda x: cleanName(x))\n",
"\n",
"for cols in textfeats:\n",
" df[cols] = df[cols].astype(str) \n",
" df[cols] = df[cols].astype(str).fillna('missing') # FILL NA\n",
" df[cols + \"count_words_upper\"] = df[cols].apply(lambda x: len([w for w in str(x).split() if w.isupper()]))\n",
" df[cols] = df[cols].str.lower() # Lowercase all text, so that capitalized words dont get treated differently\n",
" df[cols + '_num_words'] = df[cols].apply(lambda comment: len(comment.split())) # Count number of Words\n",
" df[cols + '_num_unique_words'] = df[cols].apply(lambda comment: len(set(w for w in comment.split())))\n",
" df[cols + '_words_vs_unique'] = df[cols+'_num_unique_words'] / df[cols+'_num_words'] * 100 # Count Unique Words\n",
" #Letter count\n",
" df[cols + '_count_letters']=df[cols].apply(lambda x: len(str(x)))\n",
" #punctuation count\n",
" df[cols + \"count_punctuations\"] = df[cols].apply(lambda x: len([c for c in str(x) if c in string.punctuation]))\n",
" #title case words count\n",
" df[cols + \"count_words_title\"] = df[cols].apply(lambda x: len([w for w in str(x).split() if w.istitle()]))\n",
" #Number of stopwords\n",
" #df[cols + \"count_stopwords\"] = df[cols].apply(lambda x: len([w for w in str(x).lower().split() if w in stopwords.words('russian')]))\n",
" #Average length of the words\n",
" df[cols + \"mean_word_len\"] = df[cols].apply(lambda x: np.mean([len(w) for w in str(x).split()]))\n",
" df[cols + \"english_letters\"] = df[cols].apply(lambda x: len([c for c in str(x) if c in string.ascii_letters]))\n",
" df[cols + \"english_prop_letters\"] = df[cols + \"english_letters\"]/df[cols + '_count_letters']\n",
" \n",
"\n",
"df['words_vs_unique_description'] = df['description_num_unique_words'] / df['description_num_words'] * 100\n",
"df['title_desc_len_ratio'] = df['title_count_letters']/df['description_count_letters']\n",
"\n",
"\n",
"df['len_description'] = df['description'].apply(lambda x: len(x)) \n",
"df['average_description_word_length'] = df['len_description'] / df['description_num_words']\n",
"\n",
"print(\"\\n[TF-IDF] Term Frequency Inverse Document Frequency Stage\")\n",
"russian_stop = set(stopwords.words('russian'))\n",
"\n",
"\n",
"\n",
"def get_col(col_name): return lambda x: x[col_name]\n",
"##I added to the max_features of the description. It did not change my score much but it may be worth investigating\n",
"\n",
" \n",
"start_vect=time.time()\n",
"\n",
"# Drop Text Cols\n",
"textfeats = [\"description\", \"title\"]\n",
"df.drop(textfeats, axis=1,inplace=True)\n",
"\n",
"from sklearn.metrics import mean_squared_error\n",
"from math import sqrt\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"train_f = pd.read_csv('train_all_features.csv')[['svd_title_1', 'svd_title_2', 'svd_title_3', 'svd_title_4',\n",
" 'svd_title_5', 'svd_title_6', 'svd_title_7', 'svd_title_8',\n",
" 'svd_title_9', 'svd_title_10', 'svd_description_1', 'svd_description_2',\n",
" 'svd_description_3', 'svd_description_4', 'svd_description_5',\n",
" 'svd_description_6', 'svd_description_7', 'svd_description_8',\n",
" 'svd_description_9', 'svd_description_10', 'svd_text_1',\n",
" 'svd_text_2', 'svd_text_3', 'svd_text_4', 'svd_text_5', 'svd_text_6',\n",
" 'svd_text_7', 'svd_text_8', 'svd_text_9', 'svd_text_10']]\n",
"test_f = pd.read_csv('test_all_features.csv')[['svd_title_1', 'svd_title_2', 'svd_title_3', 'svd_title_4',\n",
" 'svd_title_5', 'svd_title_6', 'svd_title_7', 'svd_title_8',\n",
" 'svd_title_9', 'svd_title_10', 'svd_description_1', 'svd_description_2',\n",
" 'svd_description_3', 'svd_description_4', 'svd_description_5',\n",
" 'svd_description_6', 'svd_description_7', 'svd_description_8',\n",
" 'svd_description_9', 'svd_description_10', 'svd_text_1',\n",
" 'svd_text_2', 'svd_text_3', 'svd_text_4', 'svd_text_5', 'svd_text_6',\n",
" 'svd_text_7', 'svd_text_8', 'svd_text_9', 'svd_text_10']]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"54"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svd_feat = train_f.append(test_f, ignore_index=True)\n",
"del train_f, test_f\n",
"gc.collect()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"svd_feat.index = df.index"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"df = df.merge(svd_feat, left_index=True, right_index=True, how='inner') "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"cols = ['resnet_nima_feature_0', 'resnet_nima_feature_1',\n",
" 'resnet_nima_feature_2', 'resnet_nima_feature_3',\n",
" 'resnet_nima_feature_4', 'resnet_nima_feature_5',\n",
" 'resnet_nima_feature_6', 'resnet_nima_feature_7',\n",
" 'resnet_nima_feature_8', 'resnet_nima_feature_9']\n",
"train_f = pd.read_csv('resnet_scores_train.csv', index_col = \"item_id\")[cols]\n",
"test_f = pd.read_csv('resnet_scores_test.csv', index_col = \"item_id\")[cols]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"svd_feat = train_f.append(test_f, ignore_index= False)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"df = df.merge(svd_feat, left_index=True, right_index=True, how='inner') "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"df_col = list(df.columns)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"63"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"del svd_feat\n",
"gc.collect()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Modeling Stage\n",
"1503424 Rows and 97 Cols\n",
"508438 Rows and 97 Cols\n",
"Index(['user_id', 'region', 'city', 'parent_category_name', 'category_name',\n",
" 'param_1', 'param_2', 'param_3', 'price', 'item_seq_number',\n",
" 'user_type', 'image_top_1', 'avg_days_up_user', 'avg_times_up_user',\n",
" 'n_user_items', 'img_size_x', 'img_size_y', 'img_mean_color',\n",
" 'img_std_color', 'img_blue_mean', 'img_green_mean', 'img_red_mean',\n",
" 'img_blue_std', 'image_green_std', 'image_red_std', 'has_image',\n",
" 'price_is_null', 'param_1_is_null', 'param_2_is_null',\n",
" 'param_3_is_null', 'image_top_1_is_null', 'Weekday',\n",
" 'descriptioncount_words_upper', 'description_num_words',\n",
" 'description_num_unique_words', 'description_words_vs_unique',\n",
" 'description_count_letters', 'descriptioncount_punctuations',\n",
" 'descriptioncount_words_title', 'descriptionmean_word_len',\n",
" 'descriptionenglish_letters', 'descriptionenglish_prop_letters',\n",
" 'titlecount_words_upper', 'title_num_words', 'title_num_unique_words',\n",
" 'title_words_vs_unique', 'title_count_letters',\n",
" 'titlecount_punctuations', 'titlecount_words_title',\n",
" 'titlemean_word_len', 'titleenglish_letters',\n",
" 'titleenglish_prop_letters', 'words_vs_unique_description',\n",
" 'title_desc_len_ratio', 'len_description',\n",
" 'average_description_word_length', 'svd_title_1', 'svd_title_2',\n",
" 'svd_title_3', 'svd_title_4', 'svd_title_5', 'svd_title_6',\n",
" 'svd_title_7', 'svd_title_8', 'svd_title_9', 'svd_title_10',\n",
" 'svd_description_1', 'svd_description_2', 'svd_description_3',\n",
" 'svd_description_4', 'svd_description_5', 'svd_description_6',\n",
" 'svd_description_7', 'svd_description_8', 'svd_description_9',\n",
" 'svd_description_10', 'svd_text_1', 'svd_text_2', 'svd_text_3',\n",
" 'svd_text_4', 'svd_text_5', 'svd_text_6', 'svd_text_7', 'svd_text_8',\n",
" 'svd_text_9', 'svd_text_10', 'resnet_nima_feature_0',\n",
" 'resnet_nima_feature_1', 'resnet_nima_feature_2',\n",
" 'resnet_nima_feature_3', 'resnet_nima_feature_4',\n",
" 'resnet_nima_feature_5', 'resnet_nima_feature_6',\n",
" 'resnet_nima_feature_7', 'resnet_nima_feature_8',\n",
" 'resnet_nima_feature_9', 'ridge_preds22975'],\n",
" dtype='object')\n"
]
}
],
"source": [
"df['ridge_preds22975'] = pd.read_csv('ridge_preds22975.csv')['0']\n",
"print(\"Modeling Stage\")\n",
" \n",
"#ridge_preds = pd.read_csv('ridge_preds0229.csv')['0']\n",
"# Combine Dense Features with Sparse Text Bag of Words Features\n",
"X = df.loc[traindex,:]#.values\n",
"testing = df.loc[testdex,:]#.values \n",
"for shape in [X,testing]:\n",
" print(\"{} Rows and {} Cols\".format(*shape.shape))\n",
"print (df.columns)\n",
"#del df\n",
"gc.collect();"
]
},
{
"cell_type": "raw",
"metadata": {
"ExecuteTime": {
"end_time": "2018-06-21T15:40:46.875003Z",
"start_time": "2018-06-21T12:59:24.165568Z"
}
},
"source": [
"from sklearn.model_selection import KFold\n",
"\n",
"import datetime\n",
"print(datetime.datetime.now())\n",
"\n",
"folds = KFold(n_splits=5, shuffle=True, random_state=50001)\n",
"oof_preds = np.zeros(X.shape[0])\n",
"sub_preds = np.zeros(testing.shape[0])\n",
"\n",
"for n_fold, (trn_idx, val_idx) in enumerate(folds.split(X)):\n",
" dtrain =lgb.Dataset(X.tocsr()[trn_idx], y.iloc[trn_idx])\n",
" dval =lgb.Dataset(X.tocsr()[val_idx], y.iloc[val_idx])\n",
" m_gbm=lgb.train(params=lgbm_params,train_set=dtrain,num_boost_round=1300,verbose_eval=400,\n",
" valid_sets=[dtrain,dval],valid_names=['train','valid'])\n",
" oof_preds[val_idx] = m_gbm.predict(X.tocsr()[val_idx])\n",
" sub_preds += m_gbm.predict(testing) / folds.n_splits\n",
" print('Fold %2d rmse : %.6f' % (n_fold + 1, rmse(y.iloc[val_idx],oof_preds[val_idx])))\n",
" del dtrain,dval\n",
" gc.collect()\n",
" \n",
"print('Full RMSE score %.6f' % rmse(y, oof_preds)) \n",
"\n",
"del X; gc.collect()\n",
"sub_preds[sub_preds<0]=0\n",
"sub_preds[sub_preds>1]=1\n",
" \n",
"\n",
"#Mixing lightgbm with ridge. I haven't really tested if this improves the score or not\n",
"#blend = 0.95*lgpred + 0.05*ridge_oof_test[:,0]\n",
"Submission=pd.read_csv(\"sample_submission.csv\")\n",
"Submission['deal_probability']=sub_preds\n",
"Submission.to_csv(\"split5.csv\", index=False)\n",
"print(datetime.datetime.now())\n"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"import xgboost as xgb\n",
"clf = xgb.XGBRegressor( params)\n",
" # n_estimators=999, learning_rate=0.02, gamma =0.3, min_child_weight = 3,nthread = 15,max_depth=30,\n",
" # subsample=0.9, colsample_bytree=0.8, seed=2100, eval_metric = \"rmse\")"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"params = {\n",
" #'objective' : 'gpu:reg:linear',\n",
" #'tree_method':'gpu_hist',\n",
" 'learning_rate': 0.016, \n",
" 'gamma' : 0.3, \n",
" 'min_child_weight' : 3,\n",
" 'nthread' : 15,\n",
" 'max_depth' : 12,\n",
" 'subsample' : 0.9, \n",
" 'colsample_bytree' : 0.75, \n",
" 'seed':2100, \n",
" 'eval_metric' : \"rmse\",\n",
" 'num_boost_round' : 500,\n",
" 'n_estimators':999,\n",
" 'max_leaves': 90\n",
"} "
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"ExecuteTime": {
"start_time": "2018-06-22T07:09:24.704Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0]\ttrain-rmse:0.439658\tvalid-rmse:0.439313\n",
"Multiple eval metrics have been passed: 'valid-rmse' will be used for early stopping.\n",
"\n",
"Will train until valid-rmse hasn't improved in 30 rounds.\n",
"[50]\ttrain-rmse:0.28124\tvalid-rmse:0.284466\n",
"[100]\ttrain-rmse:0.232389\tvalid-rmse:0.240011\n",
"[150]\ttrain-rmse:0.217555\tvalid-rmse:0.22904\n",
"[200]\ttrain-rmse:0.211457\tvalid-rmse:0.226087\n",
"[250]\ttrain-rmse:0.207682\tvalid-rmse:0.224879\n",
"[300]\ttrain-rmse:0.205293\tvalid-rmse:0.224374\n",
"[350]\ttrain-rmse:0.202848\tvalid-rmse:0.223904\n",
"[400]\ttrain-rmse:0.200653\tvalid-rmse:0.22352\n",
"[450]\ttrain-rmse:0.198777\tvalid-rmse:0.223227\n",
"[499]\ttrain-rmse:0.197009\tvalid-rmse:0.222958\n",
"Model Evaluation Stage\n",
"RMSE valid: 0.22295776177605622\n",
"RMSE train: 0.19700850636120637\n",
"Model Evaluation Stage\n",
"RMSE train: 0.19700850636120637\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f5cc7fecd30>"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x1080 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import xgboost as xgb\n",
"VALID = True\n",
"if VALID == True:\n",
" X_train, X_valid, y_train, y_valid = train_test_split(\n",
" X, y, test_size = 0.06, random_state=23)\n",
" \n",
" tr_data = xgb.DMatrix(X_train, y_train)\n",
" va_data = xgb.DMatrix(X_valid, y_valid)\n",
" \n",
" \n",
" #del X_train, X_valid, y_train, y_valid ; gc.collect()\n",
" \n",
" watchlist = [(tr_data, 'train'), (va_data, 'valid')]\n",
" \n",
" model = xgb.train(params, tr_data, 500, watchlist, maximize=False, early_stopping_rounds = 30, verbose_eval=50)\n",
" \n",
" print(\"Model Evaluation Stage\")\n",
" print('RMSE valid:', np.sqrt(metrics.mean_squared_error(y_valid, model.predict(xgb.DMatrix(X_valid)))))\n",
" print('RMSE train:', np.sqrt(metrics.mean_squared_error(y_train, model.predict(xgb.DMatrix(X_train)))))\n",
"\n",
"\n",
"else:\n",
" # Go Go Go\n",
" del tr_data, va_data, X_train, X_valid, y_train, y_valid; gc.collect()\n",
" \n",
" tr_data = xgb.DMatrix(X, y)\n",
" model = xgb.train(params,tr_data, 1000, verbose_eval=100)\n",
" \n",
"\n",
"print(\"Model Evaluation Stage\")\n",
"\n",
"lgpred = model.predict(xgb.DMatrix(testing)) \n",
"\n",
"print('RMSE train:', np.sqrt(metrics.mean_squared_error(y_train, model.predict(xgb.DMatrix(X_train)))))\n",
"\n",
"#Mixing lightgbm with ridge. I haven't really tested if this improves the score or not\n",
"#blend = 0.95*lgpred + 0.05*ridge_oof_test[:,0]\n",
"lgsub = pd.DataFrame(lgpred,columns=[\"deal_probability\"],index=testdex)\n",
"lgsub['deal_probability'].clip(0.0, 1.0, inplace=True) # Between 0 and 1\n",
"lgsub.to_csv(\"xgsub_tf.csv\",index=True,header=True)\n",
"#print(\"Model Runtime: %0.2f Minutes\"%((time.time() - modelstart)/60))\n",
"\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"f, ax = plt.subplots(figsize=[10,15])\n",
"xgb.plot_importance(model, ax=ax)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f5c753c1b00>"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x1080 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"f, ax = plt.subplots(figsize=[10,15])\n",
"xgb.plot_importance(model, ax=ax )\n",
" "
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"RMSE valid: 0.22568683979923207\n",
"RMSE train: 0.21163101254549618\n",
"Model Evaluation Stage\n",
"\n",
"params = {\n",
" #'objective' : 'gpu:reg:linear',\n",
" #'tree_method':'gpu_hist',\n",
" 'learning_rate': 0.015, \n",
" 'gamma' : 0.3, \n",
" 'min_child_weight' : 3,\n",
" 'nthread' : 15,\n",
" 'max_depth' : 12,\n",
" 'subsample' : 0.9, \n",
" 'colsample_bytree' : 0.75, \n",
" 'seed':2100, \n",
" 'eval_metric' : \"rmse\",\n",
" 'num_boost_round' : 300,\n",
" 'n_estimators':999,\n",
" 'max_leaves': 120\n",
"} "
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Model Evaluation Stage\n",
"RMSE valid: 0.22405721827050518\n",
" params = {\n",
" #'objective' : 'gpu:reg:linear',\n",
" #'tree_method':'gpu_hist',\n",
" 'learning_rate': 0.015, \n",
" 'gamma' : 0.3, \n",
" 'min_child_weight' : 3,\n",
" 'nthread' : 15,\n",
" 'max_depth' : 15,\n",
" 'subsample' : 0.9, \n",
" 'colsample_bytree' : 0.75, \n",
" 'seed':2100, \n",
" 'eval_metric' : \"rmse\",\n",
" 'num_boost_round' : 300,\n",
" 'n_estimators':999,\n",
" 'max_leaves': 100"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"RMSE valid: 0.23508655812191442\n",
"RMSE train: 0.234600064596860\n",
" \n",
" params = {\n",
" #'objective' : 'gpu:reg:linear',\n",
" #'tree_method':'gpu_hist',\n",
" 'learning_rate': 0.015, \n",
" 'gamma' : 0.3, \n",
" 'min_child_weight' : 3,\n",
" 'nthread' : 15,\n",
" 'max_depth' : 15,\n",
" 'subsample' : 0.9, \n",
" 'colsample_bytree' : 0.75, \n",
" 'seed':2100, \n",
" 'eval_metric' : \"rmse\",\n",
" 'num_boost_round' : 300,\n",
" 'n_estimators':999,\n",
" 'max_leaves': 100\n",
"} "
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"# Will train until valid-rmse hasn't improved in 30 rounds.\n",
"[50]\ttrain-rmse:0.269386\tvalid-rmse:0.270039\n",
"[100]\ttrain-rmse:0.234934\tvalid-rmse:0.236262\n",
"[150]\ttrain-rmse:0.228136\tvalid-rmse:0.230054\n",
"[200]\ttrain-rmse:0.225995\tvalid-rmse:0.228401\n",
"[250]\ttrain-rmse:0.224621\tvalid-rmse:0.227503\n",
"[299]\ttrain-rmse:0.223569\tvalid-rmse:0.226906\n",
"\n",
" 'learning_rate': 0.015, \n",
" 'gamma' : 0.3, \n",
" 'min_child_weight' : 3,\n",
" 'nthread' : 15,\n",
" 'max_depth' : 8,\n",
" 'subsample' : 0.9, \n",
" 'colsample_bytree' : 0.75, \n",
" 'seed':2100, \n",
" 'eval_metric' : \"rmse\",\n",
" 'num_boost_round' : 300,\n",
" 'n_estimators':999,\n",
" 'max_leaves': 100"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"299]\ttrain-rmse:0.223569\tvalid-rmse:0.226906\n",
"\n",
"clf = xgb.XGBRegressor( n_estimators=999, learning_rate=0.015, gamma =0.3, min_child_weight = 3,nthread = 15,max_depth=150,\n",
" subsample=0.9, colsample_bytree=0.8, seed=2100, eval_metric = \"rmse\")\n",
"[0]\tvalidation_0-rmse:0.440045\n",
"Will train until validation_0-rmse hasn't improved in 50 rounds.\n",
"[50]\tvalidation_0-rmse:0.288205\n",
"[100]\tvalidation_0-rmse:0.239962\n",
"[150]\tvalidation_0-rmse:0.22704\n",
"[200]\tvalidation_0-rmse:0.223603\n",
"[250]\tvalidation_0-rmse:0.222562\n",
"[300]\tvalidation_0-rmse:0.222139"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"start_time": "2018-06-22T07:09:24.706Z"
}
},
"outputs": [],
"source": [
"import datetime\n",
"datetime.datetime.now()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"clf.feature_importances_"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
],
"window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
cwehmeyer/pydpc | ipython/Example01.ipynb | 1 | 9724 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Example and timings\n",
"\n",
"This notebook gives a short introduction in how to use pydpc for a simple clustering problem."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from pydpc import Cluster\n",
"from pydpc._reference import Cluster as RefCluster"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We start with preparing the data points for clustering. The data is two-dimensional and craeted by drawing random numbers from four superpositioned gaussian distributions which are centered at the corners of a square (indicated by the red dashed lines)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# generate the data points\n",
"npoints = 2000\n",
"mux = 1.6\n",
"muy = 1.6\n",
"points = np.zeros(shape=(npoints, 2), dtype=np.float64)\n",
"points[:, 0] = np.random.randn(npoints) + mux * (-1)**np.random.randint(0, high=2, size=npoints)\n",
"points[:, 1] = np.random.randn(npoints) + muy * (-1)**np.random.randint(0, high=2, size=npoints)\n",
"# draw the data points\n",
"fig, ax = plt.subplots(figsize=(5, 5))\n",
"ax.scatter(points[:, 0], points[:, 1], s=40)\n",
"ax.plot([-mux, -mux], [-1.5 * muy, 1.5 * muy], '--', linewidth=2, color=\"red\")\n",
"ax.plot([mux, mux], [-1.5 * muy, 1.5 * muy], '--', linewidth=2, color=\"red\")\n",
"ax.plot([-1.5 * mux, 1.5 * mux], [-muy, -muy], '--', linewidth=2, color=\"red\")\n",
"ax.plot([-1.5 * mux, 1.5 * mux], [muy, muy], '--', linewidth=2, color=\"red\")\n",
"ax.set_xlabel(r\"x / a.u.\", fontsize=20)\n",
"ax.set_ylabel(r\"y / a.u.\", fontsize=20)\n",
"ax.tick_params(labelsize=15)\n",
"ax.set_xlim([-7, 7])\n",
"ax.set_ylim([-7, 7])\n",
"ax.set_aspect('equal')\n",
"fig.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now comes the interesting part.\n",
"\n",
"We pass the numpy ndarray with the data points to the ``Cluster`` class which prepares the data set for clustering. In this stage, it computes the Euclidean distances between all data points and from that the two properties to identify clusters within the data: each data points' ``density`` and minimal distance ``delta`` to a point of higher density.\n",
"\n",
"Once these properties are computed, a decision graph is drawn, where each outlier in the upper right corner represents a different cluster. In our example, we should find four outliers. So far, however, no clustering has yet been done."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"clu = Cluster(points)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have the decision graph, we can select the outliers via the ``assign`` method by setting lower bounds for ``delta`` and ``density``. The assign method does the actual clustering; it also shows the decision graph again with the given selection."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"clu.assign(20, 1.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us have a look at the result.\n",
"\n",
"We again plot the data and red dashed lines indicating the centeres of the gaussian distributions. Indicated in the left panel by red dots are the four outliers from the decision graph; these are our four cluster centers. The center panel shows the points' densities and the right panel shows the membership to the four clusters by different coloring."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig, ax = plt.subplots(1, 3, figsize=(15, 5))\n",
"ax[0].scatter(points[:, 0], points[:, 1], s=40)\n",
"ax[0].scatter(points[clu.clusters, 0], points[clu.clusters, 1], s=50, c=\"red\")\n",
"ax[1].scatter(points[:, 0], points[:, 1], s=40, c=clu.density)\n",
"ax[2].scatter(points[:, 0], points[:, 1], s=40, c=clu.membership, cmap=mpl.cm.cool)\n",
"for _ax in ax:\n",
" _ax.plot([-mux, -mux], [-1.5 * muy, 1.5 * muy], '--', linewidth=2, color=\"red\")\n",
" _ax.plot([mux, mux], [-1.5 * muy, 1.5 * muy], '--', linewidth=2, color=\"red\")\n",
" _ax.plot([-1.5 * mux, 1.5 * mux], [-muy, -muy], '--', linewidth=2, color=\"red\")\n",
" _ax.plot([-1.5 * mux, 1.5 * mux], [muy, muy], '--', linewidth=2, color=\"red\")\n",
" _ax.set_xlabel(r\"x / a.u.\", fontsize=20)\n",
" _ax.set_ylabel(r\"y / a.u.\", fontsize=20)\n",
" _ax.tick_params(labelsize=15)\n",
" _ax.set_xlim([-7, 7])\n",
" _ax.set_ylim([-7, 7])\n",
" _ax.set_aspect('equal')\n",
"fig.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The density peak clusterng can further resolve if the membership of a data point to a certain cluster is strong or rather weak and separates the data points further into core and halo regions.\n",
"\n",
"The left panel depicts the border members in grey.\n",
"The separation in the center panel uses the core/halo criterion of the original authors, the right panel shows a less strict criterion which assumes a halo only between different clusters; here, the halo members are depicted in grey."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig, ax = plt.subplots(1, 3, figsize=(15, 5))\n",
"ax[0].scatter(\n",
" points[:, 0], points[:, 1],\n",
" s=40, c=clu.membership, cmap=mpl.cm.cool)\n",
"ax[0].scatter(points[clu.border_member, 0], points[clu.border_member, 1], s=40, c=\"grey\")\n",
"ax[1].scatter(\n",
" points[clu.core_idx, 0], points[clu.core_idx, 1],\n",
" s=40, c=clu.membership[clu.core_idx], cmap=mpl.cm.cool)\n",
"ax[1].scatter(points[clu.halo_idx, 0], points[clu.halo_idx, 1], s=40, c=\"grey\")\n",
"clu.autoplot=False\n",
"clu.assign(20, 1.5, border_only=True)\n",
"ax[2].scatter(\n",
" points[clu.core_idx, 0], points[clu.core_idx, 1],\n",
" s=40, c=clu.membership[clu.core_idx], cmap=mpl.cm.cool)\n",
"ax[2].scatter(points[clu.halo_idx, 0], points[clu.halo_idx, 1], s=40, c=\"grey\")\n",
"ax[2].tick_params(labelsize=15)\n",
"for _ax in ax:\n",
" _ax.plot([-mux, -mux], [-1.5 * muy, 1.5 * muy], '--', linewidth=2, color=\"red\")\n",
" _ax.plot([mux, mux], [-1.5 * muy, 1.5 * muy], '--', linewidth=2, color=\"red\")\n",
" _ax.plot([-1.5 * mux, 1.5 * mux], [-muy, -muy], '--', linewidth=2, color=\"red\")\n",
" _ax.plot([-1.5 * mux, 1.5 * mux], [muy, muy], '--', linewidth=2, color=\"red\")\n",
" _ax.set_xlabel(r\"x / a.u.\", fontsize=20)\n",
" _ax.set_ylabel(r\"y / a.u.\", fontsize=20)\n",
" _ax.tick_params(labelsize=15)\n",
" _ax.set_xlim([-7, 7])\n",
" _ax.set_ylim([-7, 7])\n",
" _ax.set_aspect('equal')\n",
"fig.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This concludes the example.\n",
"\n",
"In the remaining part, we address the performance of the pydpc implementation (numpy + cython-wrapped C code) with respect to an older development version (numpy). In particular, we look at the numerically most demanding part of computing the Euclidean distances between the data points and estimating density and delta."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"npoints = 1000\n",
"points = np.zeros(shape=(npoints, 2), dtype=np.float64)\n",
"points[:, 0] = np.random.randn(npoints) + 1.8 * (-1)**np.random.randint(0, high=2, size=npoints)\n",
"points[:, 1] = np.random.randn(npoints) + 1.8 * (-1)**np.random.randint(0, high=2, size=npoints)\n",
"\n",
"%timeit Cluster(points, fraction=0.02, autoplot=False)\n",
"%timeit RefCluster(fraction=0.02, autoplot=False).load(points)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The next two cells measure the full clustering."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%timeit\n",
"Cluster(points, fraction=0.02, autoplot=False).assign(20, 1.5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%timeit\n",
"tmp = RefCluster(fraction=0.02, autoplot=False)\n",
"tmp.load(points)\n",
"tmp.assign(20, 1.5)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| lgpl-3.0 |
littlewizardLI/Udacity-ML-nanodegrees | Project-practice--naive_bayes_tutorial/Naive_Bayes_tutorial.ipynb | 1 | 64635 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Our Mission ##\n",
"\n",
"Spam detection is one of the major applications of Machine Learning in the interwebs today. Pretty much all of the major email service providers have spam detection systems built in and automatically classify such mail as 'Junk Mail'. \n",
"\n",
"In this mission we will be using the Naive Bayes algorithm to create a model that can classify 'https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection' SMS messages as spam or not spam, based on the training we give to the model. It is important to have some level of intuition as to what a spammy text message might look like. Usually they have words like 'free', 'win', 'winner', 'cash', 'prize' and the like in them as these texts are designed to catch your eye and in some sense tempt you to open them. Also, spam messages tend to have words written in all capitals and also tend to use a lot of exclamation marks. To the recipient, it is usually pretty straightforward to identify a spam text and our objective here is to train a model to do that for us!\n",
"\n",
"Being able to identify spam messages is a binary classification problem as messages are classified as either 'Spam' or 'Not Spam' and nothing else. Also, this is a supervised learning problem, as we will be feeding a labelled dataset into the model, that it can learn from, to make future predictions. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 0: Introduction to the Naive Bayes Theorem ###\n",
"\n",
"Bayes theorem is one of the earliest probabilistic inference algorithms developed by Reverend Bayes (which he used to try and infer the existence of God no less) and still performs extremely well for certain use cases. \n",
"\n",
"It's best to understand this theorem using an example. Let's say you are a member of the Secret Service and you have been deployed to protect the Democratic presidential nominee during one of his/her campaign speeches. Being a public event that is open to all, your job is not easy and you have to be on the constant lookout for threats. So one place to start is to put a certain threat-factor for each person. So based on the features of an individual, like the age, sex, and other smaller factors like is the person carrying a bag?, does the person look nervous? etc. you can make a judgement call as to if that person is viable threat. \n",
"\n",
"If an individual ticks all the boxes up to a level where it crosses a threshold of doubt in your mind, you can take action and remove that person from the vicinity. The Bayes theorem works in the same way as we are computing the probability of an event(a person being a threat) based on the probabilities of certain related events(age, sex, presence of bag or not, nervousness etc. of the person). \n",
"\n",
"One thing to consider is the independence of these features amongst each other. For example if a child looks nervous at the event then the likelihood of that person being a threat is not as much as say if it was a grown man who was nervous. To break this down a bit further, here there are two features we are considering, age AND nervousness. Say we look at these features individually, we could design a model that flags ALL persons that are nervous as potential threats. However, it is likely that we will have a lot of false positives as there is a strong chance that minors present at the event will be nervous. Hence by considering the age of a person along with the 'nervousness' feature we would definitely get a more accurate result as to who are potential threats and who aren't. \n",
"\n",
"This is the 'Naive' bit of the theorem where it considers each feature to be independant of each other which may not always be the case and hence that can affect the final judgement.\n",
"\n",
"In short, the Bayes theorem calculates the probability of a certain event happening(in our case, a message being spam) based on the joint probabilistic distributions of certain other events(in our case, a message being classified as spam). We will dive into the workings of the Bayes theorem later in the mission, but first, let us understand the data we are going to work with."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 1.1: Understanding our dataset ### \n",
"\n",
"\n",
"We will be using a 'https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection' dataset from the UCI Machine Learning repository which has a very good collection of datasets for experimental research purposes. \n",
"\n",
"\n",
" ** Here's a preview of the data: ** \n",
"\n",
"<img src=\"images/dqnb.png\" height=\"1242\" width=\"1242\">\n",
"\n",
"The columns in the data set are currently not named and as you can see, there are 2 columns. \n",
"\n",
"The first column takes two values, 'ham' which signifies that the message is not spam, and 'spam' which signifies that the message is spam. \n",
"\n",
"The second column is the text content of the SMS message that is being classified."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
">** Instructions: **\n",
"* Import the dataset into a pandas dataframe using the read_table method. Because this is a tab separated dataset we will be using '\\t' as the value for the 'sep' argument which specifies this format. \n",
"* Also, rename the column names by specifying a list ['label, 'sms_message'] to the 'names' argument of read_table().\n",
"* Print the first five values of the dataframe with the new column names."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>label</th>\n",
" <th>sms_message</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ham</td>\n",
" <td>Go until jurong point, crazy.. Available only ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ham</td>\n",
" <td>Ok lar... Joking wif u oni...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>spam</td>\n",
" <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ham</td>\n",
" <td>U dun say so early hor... U c already then say...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ham</td>\n",
" <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" label sms_message\n",
"0 ham Go until jurong point, crazy.. Available only ...\n",
"1 ham Ok lar... Joking wif u oni...\n",
"2 spam Free entry in 2 a wkly comp to win FA Cup fina...\n",
"3 ham U dun say so early hor... U c already then say...\n",
"4 ham Nah I don't think he goes to usf, he lives aro..."
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''\n",
"Solution\n",
"'''\n",
"import pandas as pd\n",
"# Dataset from - https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection\n",
"df = pd.read_table('smsspamcollection/SMSSpamCollection',\n",
" sep='\\t', \n",
" header=None, \n",
" names=['label', 'sms_message'])\n",
"\n",
"# Output printing out first 5 columns\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 1.2: Data Preprocessing ###\n",
"\n",
"Now that we have a basic understanding of what our dataset looks like, lets convert our labels to binary variables, 0 to represent 'ham'(i.e. not spam) and 1 to represent 'spam' for ease of computation. \n",
"\n",
"You might be wondering why do we need to do this step? The answer to this lies in how scikit-learn handles inputs. Scikit-learn only deals with numerical values and hence if we were to leave our label values as strings, scikit-learn would do the conversion internally(more specifically, the string labels will be cast to unknown float values). \n",
"\n",
"Our model would still be able to make predictions if we left our labels as strings but we could have issues later when calculating performance metrics, for example when calculating our precision and recall scores. Hence, to avoid unexpected 'gotchas' later, it is good practice to have our categorical values be fed into our model as integers. "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
">**Instructions: **\n",
"* Convert the values in the 'label' colum to numerical values using map method as follows:\n",
"{'ham':0, 'spam':1} This maps the 'ham' value to 0 and the 'spam' value to 1.\n",
"* Also, to get an idea of the size of the dataset we are dealing with, print out number of rows and columns using \n",
"'shape'."
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(5572, 2)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>label</th>\n",
" <th>sms_message</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>Go until jurong point, crazy.. Available only ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>Ok lar... Joking wif u oni...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>U dun say so early hor... U c already then say...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" label sms_message\n",
"0 0 Go until jurong point, crazy.. Available only ...\n",
"1 0 Ok lar... Joking wif u oni...\n",
"2 1 Free entry in 2 a wkly comp to win FA Cup fina...\n",
"3 0 U dun say so early hor... U c already then say...\n",
"4 0 Nah I don't think he goes to usf, he lives aro..."
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''\n",
"Solution\n",
"'''\n",
"df['label'] = df.label.map({'ham':0, 'spam':1})\n",
"print(df.shape)\n",
"df.head() # returns (rows, columns)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 2.1: Bag of words ###\n",
"\n",
"What we have here in our data set is a large collection of text data (5,572 rows of data). Most ML algorithms rely on numerical data to be fed into them as input, and email/sms messages are usually text heavy. \n",
"\n",
"Here we'd like to introduce the Bag of Words(BoW) concept which is a term used to specify the problems that have a 'bag of words' or a collection of text data that needs to be worked with. The basic idea of BoW is to take a piece of text and count the frequency of the words in that text. It is important to note that the BoW concept treats each word individually and the order in which the words occur does not matter. \n",
"\n",
"Using a process which we will go through now, we can covert a collection of documents to a matrix, with each document being a row and each word(token) being the column, and the corresponding (row,column) values being the frequency of occurrance of each word or token in that document.\n",
"\n",
"For example: \n",
"\n",
"Lets say we have 4 documents as follows:\n",
"\n",
"`['Hello, how are you!',\n",
"'Win money, win from home.',\n",
"'Call me now',\n",
"'Hello, Call you tomorrow?']`\n",
"\n",
"Our objective here is to convert this set of text to a frequency distribution matrix, as follows:\n",
"\n",
"<img src=\"images/countvectorizer.png\" height=\"542\" width=\"542\">\n",
"\n",
"Here as we can see, the documents are numbered in the rows, and each word is a column name, with the corresponding value being the frequency of that word in the document.\n",
"\n",
"Lets break this down and see how we can do this conversion using a small set of documents.\n",
"\n",
"To handle this, we will be using sklearns \n",
"<a href = 'http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html#sklearn.feature_extraction.text.CountVectorizer'> `sklearn.feature_extraction.text.CountVectorizer` </a> method which does the following:\n",
"\n",
"* It tokenizes the string(separates the string into individual words) and gives an integer ID to each token.\n",
"* It counts the occurrance of each of those tokens.\n",
"\n",
"** Please Note: ** \n",
"\n",
"* The CountVectorizer method automatically converts all tokenized words to their lower case form so that it does not treat words like 'He' and 'he' differently. It does this using the `lowercase` parameter which is by default set to `True`.\n",
"\n",
"* It also ignores all punctuation so that words followed by a punctuation mark (for example: 'hello!') are not treated differently than the same words not prefixed or suffixed by a punctuation mark (for example: 'hello'). It does this using the `token_pattern` parameter which has a default regular expression which selects tokens of 2 or more alphanumeric characters.\n",
"\n",
"* The third parameter to take note of is the `stop_words` parameter. Stop words refer to the most commonly used words in a language. They include words like 'am', 'an', 'and', 'the' etc. By setting this parameter value to `english`, CountVectorizer will automatically ignore all words(from our input text) that are found in the built in list of english stop words in scikit-learn. This is extremely helpful as stop words can skew our calculations when we are trying to find certain key words that are indicative of spam.\n",
"\n",
"We will dive into the application of each of these into our model in a later step, but for now it is important to be aware of such preprocessing techniques available to us when dealing with textual data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 2.2: Implementing Bag of Words from scratch ###\n",
"\n",
"Before we dive into scikit-learn's Bag of Words(BoW) library to do the dirty work for us, let's implement it ourselves first so that we can understand what's happening behind the scenes. \n",
"\n",
"** Step 1: Convert all strings to their lower case form. **\n",
"\n",
"Let's say we have a document set:\n",
"\n",
"```\n",
"documents = ['Hello, how are you!',\n",
" 'Win money, win from home.',\n",
" 'Call me now.',\n",
" 'Hello, Call hello you tomorrow?']\n",
"```\n",
">>** Instructions: **\n",
"* Convert all the strings in the documents set to their lower case. Save them into a list called 'lower_case_documents'. You can convert strings to their lower case in python by using the lower() method.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['hello, how are you!', 'win money, win from home.', 'call me now.', 'hello, call hello you tomorrow?']\n"
]
}
],
"source": [
"'''\n",
"Solution:\n",
"'''\n",
"documents = ['Hello, how are you!',\n",
" 'Win money, win from home.',\n",
" 'Call me now.',\n",
" 'Hello, Call hello you tomorrow?']\n",
"\n",
"lower_case_documents = []\n",
"for i in documents:\n",
" lower_case_documents.append(i.lower())\n",
"print(lower_case_documents)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Step 2: Removing all punctuations **\n",
"\n",
">>**Instructions: **\n",
"Remove all punctuation from the strings in the document set. Save them into a list called \n",
"'sans_punctuation_documents'. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['hello how are you', 'win money win from home', 'call me now', 'hello call hello you tomorrow']\n"
]
}
],
"source": [
"'''\n",
"Solution:\n",
"'''\n",
"sans_punctuation_documents = []\n",
"import string\n",
"\n",
"for i in lower_case_documents:\n",
" sans_punctuation_documents.append(i.translate(str.maketrans('', '', string.punctuation)))\n",
"print(sans_punctuation_documents)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Step 3: Tokenization **\n",
"\n",
"Tokenizing a sentence in a document set means splitting up a sentence into individual words using a delimiter. The delimiter specifies what character we will use to identify the beginning and the end of a word(for example we could use a single space as the delimiter for identifying words in our document set.)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
">>**Instructions:**\n",
"Tokenize the strings stored in 'sans_punctuation_documents' using the split() method. and store the final document set \n",
"in a list called 'preprocessed_documents'.\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[['hello', 'how', 'are', 'you'], ['win', 'money', 'win', 'from', 'home'], ['call', 'me', 'now'], ['hello', 'call', 'hello', 'you', 'tomorrow']]\n"
]
}
],
"source": [
"'''\n",
"Solution:\n",
"'''\n",
"preprocessed_documents = []\n",
"for i in sans_punctuation_documents:\n",
" preprocessed_documents.append(i.split(' '))\n",
"print(preprocessed_documents)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Step 4: Count frequencies **\n",
"\n",
"Now that we have our document set in the required format, we can proceed to counting the occurrence of each word in each document of the document set. We will use the `Counter` method from the Python `collections` library for this purpose. \n",
"\n",
"`Counter` counts the occurrence of each item in the list and returns a dictionary with the key as the item being counted and the corresponding value being the count of that item in the list. "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
">>**Instructions:**\n",
"Using the Counter() method and preprocessed_documents as the input, create a dictionary with the keys being each word in each document and the corresponding values being the frequncy of occurrence of that word. Save each Counter dictionary as an item in a list called 'frequency_list'.\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Counter({'hello': 1, 'how': 1, 'are': 1, 'you': 1}),\n",
" Counter({'win': 2, 'money': 1, 'from': 1, 'home': 1}),\n",
" Counter({'call': 1, 'me': 1, 'now': 1}),\n",
" Counter({'hello': 2, 'call': 1, 'you': 1, 'tomorrow': 1})]\n"
]
}
],
"source": [
"'''\n",
"Solution\n",
"'''\n",
"frequency_list = []\n",
"import pprint\n",
"from collections import Counter\n",
"\n",
"for i in preprocessed_documents:\n",
" frequency_counts = Counter(i)\n",
" frequency_list.append(frequency_counts)\n",
"pprint.pprint(frequency_list)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Congratulations! You have implemented the Bag of Words process from scratch! As we can see in our previous output, we have a frequency distribution dictionary which gives a clear view of the text that we are dealing with.\n",
"\n",
"We should now have a solid understanding of what is happening behind the scenes in the `sklearn.feature_extraction.text.CountVectorizer` method of scikit-learn. \n",
"\n",
"We will now implement `sklearn.feature_extraction.text.CountVectorizer` method in the next step."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 2.3: Implementing Bag of Words in scikit-learn ###\n",
"\n",
"Now that we have implemented the BoW concept from scratch, let's go ahead and use scikit-learn to do this process in a clean and succinct way. We will use the same document set as we used in the previous step. "
]
},
{
"cell_type": "code",
"execution_count": 167,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"'''\n",
"Here we will look to create a frequency matrix on a smaller document set to make sure we understand how the \n",
"document-term matrix generation happens. We have created a sample document set 'documents'.\n",
"'''\n",
"documents = ['Hello, how are you!',\n",
" 'Win money, win from home.',\n",
" 'Call me now.',\n",
" 'Hello, Call hello you tomorrow?']"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
">>**Instructions:**\n",
"Import the sklearn.feature_extraction.text.CountVectorizer method and create an instance of it called 'count_vector'. "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"'''\n",
"Solution\n",
"'''\n",
"from sklearn.feature_extraction.text import CountVectorizer\n",
"count_vector = CountVectorizer()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Data preprocessing with CountVectorizer() **\n",
"\n",
"In Step 2.2, we implemented a version of the CountVectorizer() method from scratch that entailed cleaning our data first. This cleaning involved converting all of our data to lower case and removing all punctuation marks. CountVectorizer() has certain parameters which take care of these steps for us. They are:\n",
"\n",
"* `lowercase = True`\n",
" \n",
" The `lowercase` parameter has a default value of `True` which converts all of our text to its lower case form.\n",
"\n",
"\n",
"* `token_pattern = (?u)\\\\b\\\\w\\\\w+\\\\b`\n",
" \n",
" The `token_pattern` parameter has a default regular expression value of `(?u)\\\\b\\\\w\\\\w+\\\\b` which ignores all punctuation marks and treats them as delimiters, while accepting alphanumeric strings of length greater than or equal to 2, as individual tokens or words.\n",
"\n",
"\n",
"* `stop_words`\n",
"\n",
" The `stop_words` parameter, if set to `english` will remove all words from our document set that match a list of English stop words which is defined in scikit-learn. Considering the size of our dataset and the fact that we are dealing with SMS messages and not larger text sources like e-mail, we will not be setting this parameter value.\n",
"\n",
"You can take a look at all the parameter values of your `count_vector` object by simply printing out the object as follows:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
" dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
" lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
" ngram_range=(1, 1), preprocessor=None, stop_words=None,\n",
" strip_accents=None, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
" tokenizer=None, vocabulary=None)\n"
]
}
],
"source": [
"'''\n",
"Practice node:\n",
"Print the 'count_vector' object which is an instance of 'CountVectorizer()'\n",
"'''\n",
"print(count_vector)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
">>**Instructions:**\n",
"Fit your document dataset to the CountVectorizer object you have created using fit(), and get the list of words \n",
"which have been categorized as features using the get_feature_names() method."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['are',\n",
" 'call',\n",
" 'from',\n",
" 'hello',\n",
" 'home',\n",
" 'how',\n",
" 'me',\n",
" 'money',\n",
" 'now',\n",
" 'tomorrow',\n",
" 'win',\n",
" 'you']"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''\n",
"Solution:\n",
"'''\n",
"count_vector.fit(documents)\n",
"count_vector.get_feature_names()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `get_feature_names()` method returns our feature names for this dataset, which is the set of words that make up our vocabulary for 'documents'."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
">>**\n",
"Instructions:**\n",
"Create a matrix with the rows being each of the 4 documents, and the columns being each word. \n",
"The corresponding (row, column) value is the frequency of occurrance of that word(in the column) in a particular\n",
"document(in the row). You can do this using the transform() method and passing in the document data set as the \n",
"argument. The transform() method returns a matrix of numpy integers, you can convert this to an array using\n",
"toarray(). Call the array 'doc_array'\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1],\n",
" [0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 2, 0],\n",
" [0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0],\n",
" [0, 1, 0, 2, 0, 0, 0, 0, 0, 1, 0, 1]])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''\n",
"Solution\n",
"'''\n",
"doc_array = count_vector.transform(documents).toarray()\n",
"doc_array"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we have a clean representation of the documents in terms of the frequency distribution of the words in them. To make it easier to understand our next step is to convert this array into a dataframe and name the columns appropriately."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
">>**Instructions:**\n",
"Convert the array we obtained, loaded into 'doc_array', into a dataframe and set the column names to \n",
"the word names(which you computed earlier using get_feature_names(). Call the dataframe 'frequency_matrix'.\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>are</th>\n",
" <th>call</th>\n",
" <th>from</th>\n",
" <th>hello</th>\n",
" <th>home</th>\n",
" <th>how</th>\n",
" <th>me</th>\n",
" <th>money</th>\n",
" <th>now</th>\n",
" <th>tomorrow</th>\n",
" <th>win</th>\n",
" <th>you</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" are call from hello home how me money now tomorrow win you\n",
"0 1 0 0 1 0 1 0 0 0 0 0 1\n",
"1 0 0 1 0 1 0 0 1 0 0 2 0\n",
"2 0 1 0 0 0 0 1 0 1 0 0 0\n",
"3 0 1 0 2 0 0 0 0 0 1 0 1"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''\n",
"Solution\n",
"'''\n",
"frequency_matrix = pd.DataFrame(doc_array, \n",
" columns = count_vector.get_feature_names())\n",
"frequency_matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Congratulations! You have successfully implemented a Bag of Words problem for a document dataset that we created. \n",
"\n",
"One potential issue that can arise from using this method out of the box is the fact that if our dataset of text is extremely large(say if we have a large collection of news articles or email data), there will be certain values that are more common that others simply due to the structure of the language itself. So for example words like 'is', 'the', 'an', pronouns, grammatical contructs etc could skew our matrix and affect our analyis. \n",
"\n",
"There are a couple of ways to mitigate this. One way is to use the `stop_words` parameter and set its value to `english`. This will automatically ignore all words(from our input text) that are found in a built in list of English stop words in scikit-learn.\n",
"\n",
"Another way of mitigating this is by using the <a href = 'http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer'> `sklearn.feature_extraction.text.TfidfVectorizer`</a> method. This method is out of scope for the context of this lesson."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 3.1: Training and testing sets ###\n",
"\n",
"Now that we have understood how to deal with the Bag of Words problem we can get back to our dataset and proceed with our analysis. Our first step in this regard would be to split our dataset into a training and testing set so we can test our model later. "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"\n",
">>**Instructions:**\n",
"Split the dataset into a training and testing set by using the train_test_split method in sklearn. Split the data\n",
"using the following variables:\n",
"* `X_train` is our training data for the 'sms_message' column.\n",
"* `y_train` is our training data for the 'label' column\n",
"* `X_test` is our testing data for the 'sms_message' column.\n",
"* `y_test` is our testing data for the 'label' column\n",
"Print out the number of rows we have in each our training and testing data.\n"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of rows in the total set: 5572\n",
"Number of rows in the training set: 4179\n",
"Number of rows in the test set: 1393\n"
]
}
],
"source": [
"'''\n",
"Solution\n",
"'''\n",
"# split into training and testing sets\n",
"from sklearn.cross_validation import train_test_split\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(df['sms_message'], \n",
" df['label'], \n",
" random_state=1)\n",
"\n",
"print('Number of rows in the total set: {}'.format(df.shape[0]))\n",
"print('Number of rows in the training set: {}'.format(X_train.shape[0]))\n",
"print('Number of rows in the test set: {}'.format(X_test.shape[0]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 3.2: Applying Bag of Words processing to our dataset. ###\n",
"\n",
"Now that we have split the data, our next objective is to follow the steps from Step 2: Bag of words and convert our data into the desired matrix format. To do this we will be using CountVectorizer() as we did before. There are two steps to consider here:\n",
"\n",
"* Firstly, we have to fit our training data (`X_train`) into `CountVectorizer()` and return the matrix.\n",
"* Secondly, we have to transform our testing data (`X_test`) to return the matrix. \n",
"\n",
"Note that `X_train` is our training data for the 'sms_message' column in our dataset and we will be using this to train our model. \n",
"\n",
"`X_test` is our testing data for the 'sms_message' column and this is the data we will be using(after transformation to a matrix) to make predictions on. We will then compare those predictions with `y_test` in a later step. \n",
"\n",
"For now, we have provided the code that does the matrix transformations for you!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"'''\n",
"[Practice Node]\n",
"\n",
"The code for this segment is in 2 parts. Firstly, we are learning a vocabulary dictionary for the training data \n",
"and then transforming the data into a document-term matrix; secondly, for the testing data we are only \n",
"transforming the data into a document-term matrix.\n",
"\n",
"This is similar to the process we followed in Step 2.3\n",
"\n",
"We will provide the transformed data to students in the variables 'training_data' and 'testing_data'.\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"'''\n",
"Solution\n",
"'''\n",
"# Instantiate the CountVectorizer method\n",
"count_vector = CountVectorizer()\n",
"\n",
"# Fit the training data and then return the matrix\n",
"training_data = count_vector.fit_transform(X_train)\n",
"\n",
"# Transform testing data and return the matrix. Note we are not fitting the testing data into the CountVectorizer()\n",
"testing_data = count_vector.transform(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 4.1: Bayes Theorem implementation from scratch ###\n",
"\n",
"Now that we have our dataset in the format that we need, we can move onto the next portion of our mission which is the algorithm we will use to make our predictions to classify a message as spam or not spam. Remember that at the start of the mission we briefly discussed the Bayes theorem but now we shall go into a little more detail. In layman's terms, the Bayes theorem calculates the probability of an event occurring, based on certain other probabilities that are related to the event in question. It is composed of a prior(the probabilities that we are aware of or that is given to us) and the posterior(the probabilities we are looking to compute using the priors). \n",
"\n",
"Let us implement the Bayes Theorem from scratch using a simple example. Let's say we are trying to find the odds of an individual having diabetes, given that he or she was tested for it and got a positive result. \n",
"In the medical field, such probabilies play a very important role as it usually deals with life and death situatuations. \n",
"\n",
"We assume the following:\n",
"\n",
"`P(D)` is the probability of a person having Diabetes. It's value is `0.01` or in other words, 1% of the general population has diabetes(Disclaimer: these values are assumptions and are not reflective of any medical study).\n",
"\n",
"`P(Pos)` is the probability of getting a positive test result.\n",
"\n",
"`P(Neg)` is the probability of getting a negative test result.\n",
"\n",
"`P(Pos|D)` is the probability of getting a positive result on a test done for detecting diabetes, given that you have diabetes. This has a value `0.9`. In other words the test is correct 90% of the time. This is also called the Sensitivity or True Positive Rate.\n",
"\n",
"`P(Neg|~D)` is the probability of getting a negative result on a test done for detecting diabetes, given that you do not have diabetes. This also has a value of `0.9` and is therefore correct, 90% of the time. This is also called the Specificity or True Negative Rate.\n",
"\n",
"The Bayes formula is as follows:\n",
"\n",
"<img src=\"images/bayes_formula.png\" height=\"242\" width=\"242\">\n",
"\n",
"* `P(A)` is the prior probability of A occuring independantly. In our example this is `P(D)`. This value is given to us.\n",
"\n",
"* `P(B)` is the prior probability of B occuring independantly. In our example this is `P(Pos)`.\n",
"\n",
"* `P(A|B)` is the posterior probability that A occurs given B. In our example this is `P(D|Pos)`. That is, **the probability of an individual having diabetes, given that, that individual got a positive test result. This is the value that we are looking to calculate.**\n",
"\n",
"* `P(B|A)` is the likelihood probability of B occuring, given A. In our example this is `P(Pos|D)`. This value is given to us."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Putting our values into the formula for Bayes theorem we get:\n",
"\n",
"`P(D|Pos) = (P(D) * P(Pos|D) / P(Pos)`\n",
"\n",
"The probability of getting a positive test result `P(Pos)` can be calulated using the Sensitivity and Specificity as follows:\n",
"\n",
"`P(Pos) = [P(D) * Sensitivity] + [P(~D) * (1-Specificity))]`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"'''\n",
"Instructions:\n",
"Calculate probability of getting a positive test result, P(Pos)\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The probability of getting a positive test result P(Pos) is: {} 0.10799999999999998\n"
]
}
],
"source": [
"'''\n",
"Solution (skeleton code will be provided)\n",
"'''\n",
"# P(D)\n",
"p_diabetes = 0.01\n",
"\n",
"# P(~D)\n",
"p_no_diabetes = 0.99\n",
"\n",
"# Sensitivity or P(Pos|D)\n",
"p_pos_diabetes = 0.9\n",
"\n",
"# Specificity or P(Neg/~D)\n",
"p_neg_no_diabetes = 0.9\n",
"\n",
"# P(Pos)\n",
"p_pos = (p_diabetes * p_pos_diabetes) + (p_no_diabetes * (1 - p_neg_no_diabetes))\n",
"print('The probability of getting a positive test result P(Pos) is: {}',format(p_pos))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Using all of this information we can calculate our posteriors as follows: **\n",
" \n",
"The probability of an individual having diabetes, given that, that individual got a positive test result:\n",
"\n",
"`P(D/Pos) = (P(D) * Sensitivity)) / P(Pos)`\n",
"\n",
"The probability of an individual not having diabetes, given that, that individual got a positive test result:\n",
"\n",
"`P(~D/Pos) = (P(~D) * (1-Specificity)) / P(Pos)`\n",
"\n",
"The sum of our posteriors will always equal `1`. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"'''\n",
"Instructions:\n",
"Compute the probability of an individual having diabetes, given that, that individual got a positive test result.\n",
"In other words, compute P(D|Pos).\n",
"\n",
"The formula is: P(D|Pos) = (P(D) * P(Pos|D) / P(Pos)\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Probability of an individual having diabetes, given that that individual got a positive test result is: 0.08333333333333336\n"
]
}
],
"source": [
"'''\n",
"Solution\n",
"'''\n",
"# P(D|Pos)\n",
"p_diabetes_pos = (p_diabetes * p_pos_diabetes) / p_pos\n",
"print('Probability of an individual having diabetes, given that that individual got a positive test result is:\\\n",
"',format(p_diabetes_pos)) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"'''\n",
"Instructions:\n",
"Compute the probability of an individual not having diabetes, given that, that individual got a positive test result.\n",
"In other words, compute P(~D|Pos).\n",
"\n",
"The formula is: P(~D|Pos) = (P(~D) * P(Pos|~D) / P(Pos)\n",
"\n",
"Note that P(Pos/~D) can be computed as 1 - P(Neg/~D). \n",
"\n",
"Therefore:\n",
"P(Pos/~D) = p_pos_no_diabetes = 1 - 0.9 = 0.1\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 203,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Probability of an individual not having diabetes, given that that individual got a positive test result is: 0.916666666667\n"
]
}
],
"source": [
"'''\n",
"Solution\n",
"'''\n",
"# P(Pos/~D)\n",
"p_pos_no_diabetes = 0.1\n",
"\n",
"# P(~D|Pos)\n",
"p_no_diabetes_pos = (p_no_diabetes * p_pos_no_diabetes) / p_pos\n",
"print 'Probability of an individual not having diabetes, given that that individual got a positive test result is:'\\\n",
",p_no_diabetes_pos"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Congratulations! You have implemented Bayes theorem from scratch. Your analysis shows that even if you get a positive test result, there is only a 8.3% chance that you actually have diabetes and a 91.67% chance that you do not have diabetes. This is of course assuming that only 1% of the entire population has diabetes which of course is only an assumption."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** What does the term 'Naive' in 'Naive Bayes' mean ? ** \n",
"\n",
"The term 'Naive' in Naive Bayes comes from the fact that the algorithm considers the features that it is using to make the predictions to be independent of each other, which may not always be the case. So in our Diabetes example, we are considering only one feature, that is the test result. Say we added another feature, 'exercise'. Let's say this feature has a binary value of `0` and `1`, where the former signifies that the individual exercises less than or equal to 2 days a week and the latter signifies that the individual exercises greater than or equal to 3 days a week. If we had to use both of these features, namely the test result and the value of the 'exercise' feature, to compute our final probabilities, Bayes' theorem would fail. Naive Bayes' is an extension of Bayes' theorem that assumes that all the features are independent of each other. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 4.2: Naive Bayes implementation from scratch ###\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that you have understood the ins and outs of Bayes Theorem, we will extend it to consider cases where we have more than feature. \n",
"\n",
"Let's say that we have two political parties' candidates, 'Jill Stein' of the Green Party and 'Gary Johnson' of the Libertarian Party and we have the probabilities of each of these candidates saying the words 'freedom', 'immigration' and 'environment' when they give a speech:\n",
"\n",
"* Probability that Jill Stein says 'freedom': 0.1 ---------> `P(J|F)`\n",
"* Probability that Jill Stein says 'immigration': 0.1 -----> `P(J|I)`\n",
"* Probability that Jill Stein says 'environment': 0.8 -----> `P(J|E)`\n",
"\n",
"\n",
"* Probability that Gary Johnson says 'freedom': 0.7 -------> `P(G|F)`\n",
"* Probability that Gary Johnson says 'immigration': 0.2 ---> `P(G|I)`\n",
"* Probability that Gary Johnson says 'environment': 0.1 ---> `P(G|E)`\n",
"\n",
"\n",
"And let us also assume that the probablility of Jill Stein giving a speech, `P(J)` is `0.5` and the same for Gary Johnson, `P(G) = 0.5`. \n",
"\n",
"\n",
"Given this, what if we had to find the probabilities of Jill Stein saying the words 'freedom' and 'immigration'? This is where the Naive Bayes'theorem comes into play as we are considering two features, 'freedom' and 'immigration'.\n",
"\n",
"Now we are at a place where we can define the formula for the Naive Bayes' theorem:\n",
"\n",
"<img src=\"images/naivebayes.png\" height=\"342\" width=\"342\">\n",
"\n",
"Here, `y` is the class variable or in our case the name of the candidate and `x1` through `xn` are the feature vectors or in our case the individual words. The theorem makes the assumption that each of the feature vectors or words (`xi`) are independent of each other."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To break this down, we have to compute the following posterior probabilities:\n",
"\n",
"* `P(J|F,I)`: Probability of Jill Stein saying the words Freedom and Immigration. \n",
"\n",
" Using the formula and our knowledge of Bayes' theorem, we can compute this as follows: `P(J|F,I)` = `(P(J) * P(J|F) * P(J|I)) / P(F,I)`. Here `P(F,I)` is the probability of the words 'freedom' and 'immigration' being said in a speech.\n",
" \n",
"\n",
"* `P(G|F,I)`: Probability of Gary Johnson saying the words Freedom and Immigration. \n",
" \n",
" Using the formula, we can compute this as follows: `P(G|F,I)` = `(P(G) * P(G|F) * P(G|I)) / P(F,I)`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"'''\n",
"Instructions: Compute the probability of the words 'freedom' and 'immigration' being said in a speech, or\n",
"P(F,I).\n",
"\n",
"The first step is multiplying the probabilities of Jill Stein giving a speech with her individual \n",
"probabilities of saying the words 'freedom' and 'immigration'. Store this in a variable called p_j_text\n",
"\n",
"The second step is multiplying the probabilities of Gary Johnson giving a speech with his individual \n",
"probabilities of saying the words 'freedom' and 'immigration'. Store this in a variable called p_g_text\n",
"\n",
"The third step is to add both of these probabilities and you will get P(F,I).\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.005000000000000001\n"
]
}
],
"source": [
"'''\n",
"Solution: Step 1\n",
"'''\n",
"# P(J)\n",
"p_j = 0.5\n",
"\n",
"# P(J|F)\n",
"p_j_f = 0.1\n",
"\n",
"# P(J|I)\n",
"p_j_i = 0.1\n",
"\n",
"p_j_text = p_j * p_j_f * p_j_i\n",
"print(p_j_text)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.06999999999999999\n"
]
}
],
"source": [
"'''\n",
"Solution: Step 2\n",
"'''\n",
"# P(G)\n",
"p_g = 0.5\n",
"\n",
"# P(G|F)\n",
"p_g_f = 0.7\n",
"\n",
"# P(G|I)\n",
"p_g_i = 0.2\n",
"\n",
"p_g_text = p_g * p_g_f * p_g_i\n",
"print(p_g_text)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Probability of words freedom and immigration being said are: 0.075\n"
]
}
],
"source": [
"'''\n",
"Solution: Step 3: Compute P(F,I) and store in p_f_i\n",
"'''\n",
"p_f_i = p_j_text + p_g_text\n",
"print('Probability of words freedom and immigration being said are: ', format(p_f_i))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can compute the probability of `P(J|F,I)`, that is the probability of Jill Stein saying the words Freedom and Immigration and `P(G|F,I)`, that is the probability of Gary Johnson saying the words Freedom and Immigration."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"'''\n",
"Instructions:\n",
"Compute P(J|F,I) using the formula P(J|F,I) = (P(J) * P(J|F) * P(J|I)) / P(F,I) and store it in a variable p_j_fi\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The probability of Jill Stein saying the words Freedom and Immigration: 0.06666666666666668\n"
]
}
],
"source": [
"'''\n",
"Solution\n",
"'''\n",
"p_j_fi = p_j_text / p_f_i\n",
"print('The probability of Jill Stein saying the words Freedom and Immigration: ', format(p_j_fi))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"'''\n",
"Instructions:\n",
"Compute P(G|F,I) using the formula P(G|F,I) = (P(G) * P(G|F) * P(G|I)) / P(F,I) and store it in a variable p_g_fi\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The probability of Gary Johnson saying the words Freedom and Immigration: 0.9333333333333332\n"
]
}
],
"source": [
"'''\n",
"Solution\n",
"'''\n",
"p_g_fi = p_g_text / p_f_i\n",
"print('The probability of Gary Johnson saying the words Freedom and Immigration: ', format(p_g_fi))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And as we can see, just like in the Bayes' theorem case, the sum of our posteriors is equal to 1. Congratulations! You have implemented the Naive Bayes' theorem from scratch. Our analysis shows that there is only a 6.6% chance that Jill Stein of the Green Party uses the words 'freedom' and 'immigration' in her speech as compard the the 93.3% chance for Gary Johnson of the Libertarian party."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another more generic example of Naive Bayes' in action is as when we search for the term 'Sacramento Kings' in a search engine. In order for us to get the results pertaining to the Scramento Kings NBA basketball team, the search engine needs to be able to associate the two words together and not treat them individually, in which case we would get results of images tagged with 'Sacramento' like pictures of city landscapes and images of 'Kings' which could be pictures of crowns or kings from history when what we are looking to get are images of the basketball team. This is a classic case of the search engine treating the words as independant entities and hence being 'naive' in its approach. \n",
"\n",
"\n",
"Applying this to our problem of classifying messages as spam, the Naive Bayes algorithm *looks at each word individually and not as associated entities* with any kind of link between them. In the case of spam detectors, this usually works as there are certain red flag words which can almost guarantee its classification as spam, for example emails with words like 'viagra' are usually classified as spam."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 5: Naive Bayes implementation using scikit-learn ###\n",
"\n",
"Thankfully, sklearn has several Naive Bayes implementations that we can use and so we do not have to do the math from scratch. We will be using sklearns `sklearn.naive_bayes` method to make predictions on our dataset. \n",
"\n",
"Specifically, we will be using the multinomial Naive Bayes implementation. This particular classifier is suitable for classification with discrete features (such as in our case, word counts for text classification). It takes in integer word counts as its input. On the other hand Gaussian Naive Bayes is better suited for continuous data as it assumes that the input data has a Gaussian(normal) distribution."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"'''\n",
"Instructions:\n",
"\n",
"We have loaded the training data into the variable 'training_data' and the testing data into the \n",
"variable 'testing_data'.\n",
"\n",
"Import the MultinomialNB classifier and fit the training data into the classifier using fit(). Name your classifier\n",
"'naive_bayes'. You will be training the classifier using 'training_data' and y_train' from our split earlier. \n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''\n",
"Solution\n",
"'''\n",
"from sklearn.naive_bayes import MultinomialNB\n",
"naive_bayes = MultinomialNB()\n",
"naive_bayes.fit(training_data, y_train)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"'''\n",
"Instructions:\n",
"Now that our algorithm has been trained using the training data set we can now make some predictions on the test data\n",
"stored in 'testing_data' using predict(). Save your predictions into the 'predictions' variable.\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"'''\n",
"Solution\n",
"'''\n",
"predictions = naive_bayes.predict(testing_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that predictions have been made on our test set, we need to check the accuracy of our predictions."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 6: Evaluating our model ###\n",
"\n",
"Now that we have made predictions on our test set, our next goal is to evaluate how well our model is doing. There are various mechanisms for doing so, but first let's do quick recap of them.\n",
"\n",
"** Accuracy ** measures how often the classifier makes the correct prediction. It’s the ratio of the number of correct predictions to the total number of predictions (the number of test data points).\n",
"\n",
"** Precision ** tells us what proportion of messages we classified as spam, actually were spam.\n",
"It is a ratio of true positives(words classified as spam, and which are actually spam) to all positives(all words classified as spam, irrespective of whether that was the correct classificatio), in other words it is the ratio of\n",
"\n",
"`[True Positives/(True Positives + False Positives)]`\n",
"\n",
"** Recall(sensitivity)** tells us what proportion of messages that actually were spam were classified by us as spam.\n",
"It is a ratio of true positives(words classified as spam, and which are actually spam) to all the words that were actually spam, in other words it is the ratio of\n",
"\n",
"`[True Positives/(True Positives + False Negatives)]`\n",
"\n",
"For classification problems that are skewed in their classification distributions like in our case, for example if we had a 100 text messages and only 2 were spam and the rest 98 weren't, accuracy by itself is not a very good metric. We could classify 90 messages as not spam(including the 2 that were spam but we classify them as not spam, hence they would be false negatives) and 10 as spam(all 10 false positives) and still get a reasonably good accuracy score. For such cases, precision and recall come in very handy. These two metrics can be combined to get the F1 score, which is weighted average of the precision and recall scores. This score can range from 0 to 1, with 1 being the best possible F1 score."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will be using all 4 metrics to make sure our model does well. For all 4 metrics whose values can range from 0 to 1, having a score as close to 1 as possible is a good indicator of how well our model is doing."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"'''\n",
"Instructions:\n",
"Compute the accuracy, precision, recall and F1 scores of your model using your test data 'y_test' and the predictions\n",
"you made earlier stored in the 'predictions' variable.\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy score: 0.9885139985642498\n",
"Precision score: 0.9720670391061452\n",
"Recall score: 0.9405405405405406\n",
"F1 score: 0.9560439560439562\n"
]
}
],
"source": [
"'''\n",
"Solution\n",
"'''\n",
"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
"print('Accuracy score: ', format(accuracy_score(y_test, predictions)))\n",
"print('Precision score: ', format(precision_score(y_test, predictions)))\n",
"print('Recall score: ', format(recall_score(y_test, predictions)))\n",
"print('F1 score: ', format(f1_score(y_test, predictions)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Step 7: Conclusion ###\n",
"\n",
"One of the major advantages that Naive Bayes has over other classification algorithms is its ability to handle an extremely large number of features. In our case, each word is treated as a feature and there are thousands of different words. Also, it performs well even with the presence of irrelevant features and is relatively unaffected by them. The other major advantage it has is its relative simplicity. Naive Bayes' works well right out of the box and tuning it's parameters is rarely ever necessary, except usually in cases where the distribution of the data is known. \n",
"It rarely ever overfits the data. Another important advantage is that its model training and prediction times are very fast for the amount of data it can handle. All in all, Naive Bayes' really is a gem of an algorithm!\n",
"\n",
"Congratulations! You have succesfully designed a model that can efficiently predict if an SMS message is spam or not!\n",
"\n",
"Thank you for learning with us!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
palashkulsh/nseproxy | python-graphs/options/truedata fyers testing.ipynb | 1 | 61170 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### login"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"(2021-06-13 12:27:01,090) WARNING :: Connected successfully to TrueData Historical Data Service... (PID:14764 Thread:139646891611968)\n"
]
}
],
"source": [
"from truedata_ws.websocket.TD import TD\n",
"td_obj = TD('FYERS872', 'fjt3KN3s',live_port=None)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### get historical data"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"hist_data_2 = td_obj.get_historic_data('NIFTY-I', duration='3 D',bar_size=\"2 min\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### convert data to pandas dataframe"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>time</th>\n",
" <th>o</th>\n",
" <th>h</th>\n",
" <th>l</th>\n",
" <th>c</th>\n",
" <th>v</th>\n",
" <th>oi</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2021-06-11 09:14:00</td>\n",
" <td>15809.00</td>\n",
" <td>15819.85</td>\n",
" <td>15791.15</td>\n",
" <td>15804.90</td>\n",
" <td>174450</td>\n",
" <td>11298450</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2021-06-11 09:16:00</td>\n",
" <td>15805.00</td>\n",
" <td>15822.00</td>\n",
" <td>15805.00</td>\n",
" <td>15819.90</td>\n",
" <td>165750</td>\n",
" <td>11461725</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2021-06-11 09:18:00</td>\n",
" <td>15818.65</td>\n",
" <td>15822.00</td>\n",
" <td>15812.55</td>\n",
" <td>15817.70</td>\n",
" <td>86925</td>\n",
" <td>11461725</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2021-06-11 09:20:00</td>\n",
" <td>15820.50</td>\n",
" <td>15824.90</td>\n",
" <td>15819.25</td>\n",
" <td>15822.00</td>\n",
" <td>108000</td>\n",
" <td>11529450</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2021-06-11 09:22:00</td>\n",
" <td>15821.05</td>\n",
" <td>15830.00</td>\n",
" <td>15820.75</td>\n",
" <td>15827.00</td>\n",
" <td>89325</td>\n",
" <td>11585850</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>183</th>\n",
" <td>2021-06-11 15:20:00</td>\n",
" <td>15817.90</td>\n",
" <td>15821.00</td>\n",
" <td>15815.10</td>\n",
" <td>15817.70</td>\n",
" <td>79875</td>\n",
" <td>12036450</td>\n",
" </tr>\n",
" <tr>\n",
" <th>184</th>\n",
" <td>2021-06-11 15:22:00</td>\n",
" <td>15818.95</td>\n",
" <td>15820.60</td>\n",
" <td>15816.05</td>\n",
" <td>15819.00</td>\n",
" <td>57600</td>\n",
" <td>12036450</td>\n",
" </tr>\n",
" <tr>\n",
" <th>185</th>\n",
" <td>2021-06-11 15:24:00</td>\n",
" <td>15818.00</td>\n",
" <td>15823.00</td>\n",
" <td>15817.50</td>\n",
" <td>15821.25</td>\n",
" <td>87675</td>\n",
" <td>12034725</td>\n",
" </tr>\n",
" <tr>\n",
" <th>186</th>\n",
" <td>2021-06-11 15:26:00</td>\n",
" <td>15822.00</td>\n",
" <td>15824.40</td>\n",
" <td>15820.00</td>\n",
" <td>15823.20</td>\n",
" <td>75600</td>\n",
" <td>12018900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>187</th>\n",
" <td>2021-06-11 15:28:00</td>\n",
" <td>15823.65</td>\n",
" <td>15824.80</td>\n",
" <td>15821.15</td>\n",
" <td>15821.15</td>\n",
" <td>91350</td>\n",
" <td>12018900</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>188 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" time o h l c v \\\n",
"0 2021-06-11 09:14:00 15809.00 15819.85 15791.15 15804.90 174450 \n",
"1 2021-06-11 09:16:00 15805.00 15822.00 15805.00 15819.90 165750 \n",
"2 2021-06-11 09:18:00 15818.65 15822.00 15812.55 15817.70 86925 \n",
"3 2021-06-11 09:20:00 15820.50 15824.90 15819.25 15822.00 108000 \n",
"4 2021-06-11 09:22:00 15821.05 15830.00 15820.75 15827.00 89325 \n",
".. ... ... ... ... ... ... \n",
"183 2021-06-11 15:20:00 15817.90 15821.00 15815.10 15817.70 79875 \n",
"184 2021-06-11 15:22:00 15818.95 15820.60 15816.05 15819.00 57600 \n",
"185 2021-06-11 15:24:00 15818.00 15823.00 15817.50 15821.25 87675 \n",
"186 2021-06-11 15:26:00 15822.00 15824.40 15820.00 15823.20 75600 \n",
"187 2021-06-11 15:28:00 15823.65 15824.80 15821.15 15821.15 91350 \n",
"\n",
" oi \n",
"0 11298450 \n",
"1 11461725 \n",
"2 11461725 \n",
"3 11529450 \n",
"4 11585850 \n",
".. ... \n",
"183 12036450 \n",
"184 12036450 \n",
"185 12034725 \n",
"186 12018900 \n",
"187 12018900 \n",
"\n",
"[188 rows x 7 columns]"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"df=pd.DataFrame(hist_data_2)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[ c v oi\n",
" time \n",
" 2021-06-11 09:14:00 15804.90 174450 11298450\n",
" 2021-06-11 09:16:00 15819.90 165750 11461725\n",
" 2021-06-11 09:18:00 15817.70 86925 11461725\n",
" 2021-06-11 09:20:00 15822.00 108000 11529450\n",
" 2021-06-11 09:22:00 15827.00 89325 11585850\n",
" ... ... ... ...\n",
" 2021-06-11 15:20:00 15817.70 79875 12036450\n",
" 2021-06-11 15:22:00 15819.00 57600 12036450\n",
" 2021-06-11 15:24:00 15821.25 87675 12034725\n",
" 2021-06-11 15:26:00 15823.20 75600 12018900\n",
" 2021-06-11 15:28:00 15821.15 91350 12018900\n",
" \n",
" [188 rows x 3 columns]]"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[[\"time\",\"c\",\"v\",\"oi\"]]\n",
"all_dfs=[]\n",
"all_dfs.append(df[[\"time\",\"c\",\"v\",\"oi\"]])\n",
"[d.set_index(\"time\") for d in all_dfs]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### plotting graph"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f01c5085150>"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"df.plot(x=\"time\",y=\"c\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### finding expiry date"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'20210624'"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from datetime import datetime , timedelta\n",
"from dateutil.relativedelta import relativedelta, TH\n",
"\n",
"end_of_month = datetime.today() + relativedelta(day=31)\n",
"last_thursday = end_of_month + relativedelta(weekday=TH(-1))\n",
"expiry_date=last_thursday.strftime(\"%Y%m%d\")\n",
"\n",
"expiry_date"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### finding options chain"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>symbol</th>\n",
" <th>type</th>\n",
" <th>empty</th>\n",
" <th>exchange</th>\n",
" <th>something</th>\n",
" <th>strike</th>\n",
" <th>expiry</th>\n",
" <th>symbol2</th>\n",
" <th>symbol3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>300009510</td>\n",
" <td>NIFTY21062410000CE</td>\n",
" <td>CE</td>\n",
" <td>NaN</td>\n",
" <td>NSE</td>\n",
" <td>75</td>\n",
" <td>10000</td>\n",
" <td>6/24/2021</td>\n",
" <td>NIFTY21F110000</td>\n",
" <td>NIFTY21062410000CE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>300009511</td>\n",
" <td>NIFTY21062410000PE</td>\n",
" <td>PE</td>\n",
" <td>NaN</td>\n",
" <td>NSE</td>\n",
" <td>75</td>\n",
" <td>10000</td>\n",
" <td>6/24/2021</td>\n",
" <td>NIFTY21R110000</td>\n",
" <td>NIFTY21062410000PE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>300009512</td>\n",
" <td>NIFTY21062410100CE</td>\n",
" <td>CE</td>\n",
" <td>NaN</td>\n",
" <td>NSE</td>\n",
" <td>75</td>\n",
" <td>10100</td>\n",
" <td>6/24/2021</td>\n",
" <td>NIFTY21F110100</td>\n",
" <td>NIFTY21062410100CE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>300009513</td>\n",
" <td>NIFTY21062410100PE</td>\n",
" <td>PE</td>\n",
" <td>NaN</td>\n",
" <td>NSE</td>\n",
" <td>75</td>\n",
" <td>10100</td>\n",
" <td>6/24/2021</td>\n",
" <td>NIFTY21R110100</td>\n",
" <td>NIFTY21062410100PE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>300009514</td>\n",
" <td>NIFTY21062410200CE</td>\n",
" <td>CE</td>\n",
" <td>NaN</td>\n",
" <td>NSE</td>\n",
" <td>75</td>\n",
" <td>10200</td>\n",
" <td>6/24/2021</td>\n",
" <td>NIFTY21F110200</td>\n",
" <td>NIFTY21062410200CE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>333</th>\n",
" <td>300009501</td>\n",
" <td>NIFTY2106249700PE</td>\n",
" <td>PE</td>\n",
" <td>NaN</td>\n",
" <td>NSE</td>\n",
" <td>75</td>\n",
" <td>9700</td>\n",
" <td>6/24/2021</td>\n",
" <td>NIFTY210R19700</td>\n",
" <td>NIFTY2106249700PE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>334</th>\n",
" <td>300009502</td>\n",
" <td>NIFTY2106249800CE</td>\n",
" <td>CE</td>\n",
" <td>NaN</td>\n",
" <td>NSE</td>\n",
" <td>75</td>\n",
" <td>9800</td>\n",
" <td>6/24/2021</td>\n",
" <td>NIFTY210F19800</td>\n",
" <td>NIFTY2106249800CE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>335</th>\n",
" <td>300009503</td>\n",
" <td>NIFTY2106249800PE</td>\n",
" <td>PE</td>\n",
" <td>NaN</td>\n",
" <td>NSE</td>\n",
" <td>75</td>\n",
" <td>9800</td>\n",
" <td>6/24/2021</td>\n",
" <td>NIFTY210R19800</td>\n",
" <td>NIFTY2106249800PE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>336</th>\n",
" <td>300009508</td>\n",
" <td>NIFTY2106249900CE</td>\n",
" <td>CE</td>\n",
" <td>NaN</td>\n",
" <td>NSE</td>\n",
" <td>75</td>\n",
" <td>9900</td>\n",
" <td>6/24/2021</td>\n",
" <td>NIFTY210F19900</td>\n",
" <td>NIFTY2106249900CE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>337</th>\n",
" <td>300009509</td>\n",
" <td>NIFTY2106249900PE</td>\n",
" <td>PE</td>\n",
" <td>NaN</td>\n",
" <td>NSE</td>\n",
" <td>75</td>\n",
" <td>9900</td>\n",
" <td>6/24/2021</td>\n",
" <td>NIFTY210R19900</td>\n",
" <td>NIFTY2106249900PE</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>338 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" id symbol type empty exchange something strike \\\n",
"0 300009510 NIFTY21062410000CE CE NaN NSE 75 10000 \n",
"1 300009511 NIFTY21062410000PE PE NaN NSE 75 10000 \n",
"2 300009512 NIFTY21062410100CE CE NaN NSE 75 10100 \n",
"3 300009513 NIFTY21062410100PE PE NaN NSE 75 10100 \n",
"4 300009514 NIFTY21062410200CE CE NaN NSE 75 10200 \n",
".. ... ... ... ... ... ... ... \n",
"333 300009501 NIFTY2106249700PE PE NaN NSE 75 9700 \n",
"334 300009502 NIFTY2106249800CE CE NaN NSE 75 9800 \n",
"335 300009503 NIFTY2106249800PE PE NaN NSE 75 9800 \n",
"336 300009508 NIFTY2106249900CE CE NaN NSE 75 9900 \n",
"337 300009509 NIFTY2106249900PE PE NaN NSE 75 9900 \n",
"\n",
" expiry symbol2 symbol3 \n",
"0 6/24/2021 NIFTY21F110000 NIFTY21062410000CE \n",
"1 6/24/2021 NIFTY21R110000 NIFTY21062410000PE \n",
"2 6/24/2021 NIFTY21F110100 NIFTY21062410100CE \n",
"3 6/24/2021 NIFTY21R110100 NIFTY21062410100PE \n",
"4 6/24/2021 NIFTY21F110200 NIFTY21062410200CE \n",
".. ... ... ... \n",
"333 6/24/2021 NIFTY210R19700 NIFTY2106249700PE \n",
"334 6/24/2021 NIFTY210F19800 NIFTY2106249800CE \n",
"335 6/24/2021 NIFTY210R19800 NIFTY2106249800PE \n",
"336 6/24/2021 NIFTY210F19900 NIFTY2106249900CE \n",
"337 6/24/2021 NIFTY210R19900 NIFTY2106249900PE \n",
"\n",
"[338 rows x 10 columns]"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import requests \n",
"\n",
"option_chain=pd.read_csv('https://api.truedata.in/getOptionChain?user=FYERS872&password=fjt3KN3s&symbol=NIFTY&expiry=20210624&csv=true',names=[\"id\",\"symbol\",\"type\",\"empty\",\"exchange\",\"something\",\"strike\",\"expiry\",\"symbol2\",\"symbol3\"])\n",
"option_chain"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" id symbol type empty exchange something strike \\\n",
"114 300322285 NIFTY21062414250CE CE NaN NSE 75 14250 \n",
"115 300322286 NIFTY21062414250PE PE NaN NSE 75 14250 \n",
"116 300013635 NIFTY21062414300CE CE NaN NSE 75 14300 \n",
"117 300013636 NIFTY21062414300PE PE NaN NSE 75 14300 \n",
"118 300322287 NIFTY21062414350CE CE NaN NSE 75 14350 \n",
".. ... ... ... ... ... ... ... \n",
"233 300396738 NIFTY21062417200PE PE NaN NSE 75 17200 \n",
"234 300397643 NIFTY21062417250CE CE NaN NSE 75 17250 \n",
"235 300397644 NIFTY21062417250PE PE NaN NSE 75 17250 \n",
"236 300399240 NIFTY21062417300CE CE NaN NSE 75 17300 \n",
"237 300399241 NIFTY21062417300PE PE NaN NSE 75 17300 \n",
"\n",
" expiry symbol2 symbol3 \n",
"114 6/24/2021 NIFTY21F114250 NIFTY21062414250CE \n",
"115 6/24/2021 NIFTY21R114250 NIFTY21062414250PE \n",
"116 6/24/2021 NIFTY21F114300 NIFTY21062414300CE \n",
"117 6/24/2021 NIFTY21R114300 NIFTY21062414300PE \n",
"118 6/24/2021 NIFTY21F114350 NIFTY21062414350CE \n",
".. ... ... ... \n",
"233 6/24/2021 NIFTY21R117200 NIFTY21062417200PE \n",
"234 6/24/2021 NIFTY21F117250 NIFTY21062417250CE \n",
"235 6/24/2021 NIFTY21R117250 NIFTY21062417250PE \n",
"236 6/24/2021 NIFTY21F117300 NIFTY21062417300CE \n",
"237 6/24/2021 NIFTY21R117300 NIFTY21062417300PE \n",
"\n",
"[124 rows x 10 columns]\n"
]
}
],
"source": [
"percent_strikes=10\n",
"ltp=13821\n",
"strikes = option_chain.query('strike>(1-(@percent_strikes/100))*@ltp and strike<(1+ (@percent_strikes/100))*@ltp')\n",
"strikes = option_chain.query('strike>14200 and strike<17403')\n",
"\n",
"print(strikes)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [],
"source": [
"def get_hist_data(symbol=\"NIFTY-I\", duration=\"3 d\", bar_size=\"2 min\"):\n",
" hist_data = td_obj.get_historic_data(symbol, duration='3 d',bar_size=\"2 min\")\n",
" return pd.DataFrame(hist_data)\n",
"\n",
"pdp=get_hist_data(symbol=\"NIFTY21062415400PE\",bar_size=\"5 min\")\n",
"pdc=get_hist_data(symbol=\"NIFTY21062415400CE\",bar_size=\"5 min\")\n",
"pdc2=get_hist_data(symbol=\"NIFTY21062415900CE\",bar_size=\"5 min\")\n",
"pdp2=get_hist_data(symbol=\"NIFTY21062415900PE\",bar_size=\"5 min\")\n",
"\n",
"pdp.set_index(\"time\",inplace=True)\n",
"pdc.set_index(\"time\",inplace=True)\n",
"pdp2.set_index(\"time\",inplace=True)\n",
"pdc2.set_index(\"time\",inplace=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead tr th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe thead tr:last-of-type th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"6\" halign=\"left\">NIFTY21062415400CE</th>\n",
" <th colspan=\"4\" halign=\"left\">NIFTY21062415400PE</th>\n",
" <th>...</th>\n",
" <th colspan=\"4\" halign=\"left\">NIFTY21062415900CE</th>\n",
" <th colspan=\"6\" halign=\"left\">NIFTY21062415900PE</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>o</th>\n",
" <th>h</th>\n",
" <th>l</th>\n",
" <th>c</th>\n",
" <th>v</th>\n",
" <th>oi</th>\n",
" <th>o</th>\n",
" <th>h</th>\n",
" <th>l</th>\n",
" <th>c</th>\n",
" <th>...</th>\n",
" <th>l</th>\n",
" <th>c</th>\n",
" <th>v</th>\n",
" <th>oi</th>\n",
" <th>o</th>\n",
" <th>h</th>\n",
" <th>l</th>\n",
" <th>c</th>\n",
" <th>v</th>\n",
" <th>oi</th>\n",
" </tr>\n",
" <tr>\n",
" <th>time</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2021-06-11 09:14:00</th>\n",
" <td>451.35</td>\n",
" <td>451.35</td>\n",
" <td>443.10</td>\n",
" <td>443.10</td>\n",
" <td>825</td>\n",
" <td>508050</td>\n",
" <td>44.15</td>\n",
" <td>50.70</td>\n",
" <td>44.15</td>\n",
" <td>47.05</td>\n",
" <td>...</td>\n",
" <td>97.45</td>\n",
" <td>108.55</td>\n",
" <td>28425</td>\n",
" <td>1004475</td>\n",
" <td>210.50</td>\n",
" <td>229.70</td>\n",
" <td>204.10</td>\n",
" <td>207.00</td>\n",
" <td>10050</td>\n",
" <td>155775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-06-11 09:16:00</th>\n",
" <td>444.65</td>\n",
" <td>458.85</td>\n",
" <td>444.65</td>\n",
" <td>454.95</td>\n",
" <td>2475</td>\n",
" <td>507600</td>\n",
" <td>47.30</td>\n",
" <td>47.35</td>\n",
" <td>44.30</td>\n",
" <td>44.30</td>\n",
" <td>...</td>\n",
" <td>108.05</td>\n",
" <td>114.80</td>\n",
" <td>69750</td>\n",
" <td>1006800</td>\n",
" <td>207.35</td>\n",
" <td>207.70</td>\n",
" <td>197.20</td>\n",
" <td>199.20</td>\n",
" <td>11175</td>\n",
" <td>160350</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-06-11 09:18:00</th>\n",
" <td>461.85</td>\n",
" <td>461.85</td>\n",
" <td>456.00</td>\n",
" <td>457.80</td>\n",
" <td>1575</td>\n",
" <td>507600</td>\n",
" <td>44.50</td>\n",
" <td>45.80</td>\n",
" <td>44.35</td>\n",
" <td>45.00</td>\n",
" <td>...</td>\n",
" <td>112.45</td>\n",
" <td>115.00</td>\n",
" <td>46200</td>\n",
" <td>1006800</td>\n",
" <td>198.85</td>\n",
" <td>202.55</td>\n",
" <td>197.65</td>\n",
" <td>200.00</td>\n",
" <td>9900</td>\n",
" <td>160350</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-06-11 09:20:00</th>\n",
" <td>460.00</td>\n",
" <td>462.25</td>\n",
" <td>458.20</td>\n",
" <td>461.55</td>\n",
" <td>14175</td>\n",
" <td>507075</td>\n",
" <td>44.75</td>\n",
" <td>45.00</td>\n",
" <td>43.80</td>\n",
" <td>44.00</td>\n",
" <td>...</td>\n",
" <td>114.65</td>\n",
" <td>115.50</td>\n",
" <td>66975</td>\n",
" <td>1008900</td>\n",
" <td>197.55</td>\n",
" <td>200.00</td>\n",
" <td>196.25</td>\n",
" <td>196.75</td>\n",
" <td>14475</td>\n",
" <td>166800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-06-11 09:22:00</th>\n",
" <td>461.00</td>\n",
" <td>466.00</td>\n",
" <td>461.00</td>\n",
" <td>465.00</td>\n",
" <td>975</td>\n",
" <td>507075</td>\n",
" <td>44.00</td>\n",
" <td>44.30</td>\n",
" <td>43.30</td>\n",
" <td>43.65</td>\n",
" <td>...</td>\n",
" <td>114.80</td>\n",
" <td>119.05</td>\n",
" <td>48075</td>\n",
" <td>1035750</td>\n",
" <td>198.00</td>\n",
" <td>198.00</td>\n",
" <td>193.85</td>\n",
" <td>196.05</td>\n",
" <td>22800</td>\n",
" <td>176625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-06-11 15:20:00</th>\n",
" <td>450.60</td>\n",
" <td>452.85</td>\n",
" <td>450.60</td>\n",
" <td>452.85</td>\n",
" <td>450</td>\n",
" <td>540750</td>\n",
" <td>38.80</td>\n",
" <td>39.15</td>\n",
" <td>38.35</td>\n",
" <td>38.75</td>\n",
" <td>...</td>\n",
" <td>103.95</td>\n",
" <td>104.25</td>\n",
" <td>16350</td>\n",
" <td>1095450</td>\n",
" <td>190.35</td>\n",
" <td>191.45</td>\n",
" <td>188.75</td>\n",
" <td>189.60</td>\n",
" <td>7875</td>\n",
" <td>236700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-06-11 15:22:00</th>\n",
" <td>452.05</td>\n",
" <td>452.60</td>\n",
" <td>451.85</td>\n",
" <td>452.60</td>\n",
" <td>525</td>\n",
" <td>540525</td>\n",
" <td>38.55</td>\n",
" <td>38.60</td>\n",
" <td>38.00</td>\n",
" <td>38.00</td>\n",
" <td>...</td>\n",
" <td>104.00</td>\n",
" <td>104.70</td>\n",
" <td>23175</td>\n",
" <td>1095450</td>\n",
" <td>189.65</td>\n",
" <td>190.00</td>\n",
" <td>188.15</td>\n",
" <td>188.80</td>\n",
" <td>6000</td>\n",
" <td>236700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-06-11 15:24:00</th>\n",
" <td>452.00</td>\n",
" <td>454.40</td>\n",
" <td>451.35</td>\n",
" <td>451.55</td>\n",
" <td>1500</td>\n",
" <td>539700</td>\n",
" <td>38.20</td>\n",
" <td>38.35</td>\n",
" <td>37.60</td>\n",
" <td>37.85</td>\n",
" <td>...</td>\n",
" <td>104.25</td>\n",
" <td>104.70</td>\n",
" <td>77400</td>\n",
" <td>1100025</td>\n",
" <td>188.95</td>\n",
" <td>189.40</td>\n",
" <td>187.05</td>\n",
" <td>187.95</td>\n",
" <td>9675</td>\n",
" <td>237525</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-06-11 15:26:00</th>\n",
" <td>453.35</td>\n",
" <td>455.30</td>\n",
" <td>453.35</td>\n",
" <td>454.20</td>\n",
" <td>1650</td>\n",
" <td>539100</td>\n",
" <td>37.80</td>\n",
" <td>37.85</td>\n",
" <td>37.15</td>\n",
" <td>37.30</td>\n",
" <td>...</td>\n",
" <td>104.75</td>\n",
" <td>105.70</td>\n",
" <td>17625</td>\n",
" <td>1099050</td>\n",
" <td>187.05</td>\n",
" <td>187.45</td>\n",
" <td>185.65</td>\n",
" <td>185.65</td>\n",
" <td>11400</td>\n",
" <td>239475</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2021-06-11 15:28:00</th>\n",
" <td>454.95</td>\n",
" <td>455.00</td>\n",
" <td>454.30</td>\n",
" <td>454.30</td>\n",
" <td>225</td>\n",
" <td>539100</td>\n",
" <td>37.30</td>\n",
" <td>37.40</td>\n",
" <td>36.50</td>\n",
" <td>36.50</td>\n",
" <td>...</td>\n",
" <td>105.15</td>\n",
" <td>105.40</td>\n",
" <td>27000</td>\n",
" <td>1099050</td>\n",
" <td>186.15</td>\n",
" <td>186.90</td>\n",
" <td>185.25</td>\n",
" <td>185.85</td>\n",
" <td>9750</td>\n",
" <td>239475</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>165 rows × 24 columns</p>\n",
"</div>"
],
"text/plain": [
" NIFTY21062415400CE \\\n",
" o h l c v oi \n",
"time \n",
"2021-06-11 09:14:00 451.35 451.35 443.10 443.10 825 508050 \n",
"2021-06-11 09:16:00 444.65 458.85 444.65 454.95 2475 507600 \n",
"2021-06-11 09:18:00 461.85 461.85 456.00 457.80 1575 507600 \n",
"2021-06-11 09:20:00 460.00 462.25 458.20 461.55 14175 507075 \n",
"2021-06-11 09:22:00 461.00 466.00 461.00 465.00 975 507075 \n",
"... ... ... ... ... ... ... \n",
"2021-06-11 15:20:00 450.60 452.85 450.60 452.85 450 540750 \n",
"2021-06-11 15:22:00 452.05 452.60 451.85 452.60 525 540525 \n",
"2021-06-11 15:24:00 452.00 454.40 451.35 451.55 1500 539700 \n",
"2021-06-11 15:26:00 453.35 455.30 453.35 454.20 1650 539100 \n",
"2021-06-11 15:28:00 454.95 455.00 454.30 454.30 225 539100 \n",
"\n",
" NIFTY21062415400PE ... \\\n",
" o h l c ... \n",
"time ... \n",
"2021-06-11 09:14:00 44.15 50.70 44.15 47.05 ... \n",
"2021-06-11 09:16:00 47.30 47.35 44.30 44.30 ... \n",
"2021-06-11 09:18:00 44.50 45.80 44.35 45.00 ... \n",
"2021-06-11 09:20:00 44.75 45.00 43.80 44.00 ... \n",
"2021-06-11 09:22:00 44.00 44.30 43.30 43.65 ... \n",
"... ... ... ... ... ... \n",
"2021-06-11 15:20:00 38.80 39.15 38.35 38.75 ... \n",
"2021-06-11 15:22:00 38.55 38.60 38.00 38.00 ... \n",
"2021-06-11 15:24:00 38.20 38.35 37.60 37.85 ... \n",
"2021-06-11 15:26:00 37.80 37.85 37.15 37.30 ... \n",
"2021-06-11 15:28:00 37.30 37.40 36.50 36.50 ... \n",
"\n",
" NIFTY21062415900CE \\\n",
" l c v oi \n",
"time \n",
"2021-06-11 09:14:00 97.45 108.55 28425 1004475 \n",
"2021-06-11 09:16:00 108.05 114.80 69750 1006800 \n",
"2021-06-11 09:18:00 112.45 115.00 46200 1006800 \n",
"2021-06-11 09:20:00 114.65 115.50 66975 1008900 \n",
"2021-06-11 09:22:00 114.80 119.05 48075 1035750 \n",
"... ... ... ... ... \n",
"2021-06-11 15:20:00 103.95 104.25 16350 1095450 \n",
"2021-06-11 15:22:00 104.00 104.70 23175 1095450 \n",
"2021-06-11 15:24:00 104.25 104.70 77400 1100025 \n",
"2021-06-11 15:26:00 104.75 105.70 17625 1099050 \n",
"2021-06-11 15:28:00 105.15 105.40 27000 1099050 \n",
"\n",
" NIFTY21062415900PE \n",
" o h l c v oi \n",
"time \n",
"2021-06-11 09:14:00 210.50 229.70 204.10 207.00 10050 155775 \n",
"2021-06-11 09:16:00 207.35 207.70 197.20 199.20 11175 160350 \n",
"2021-06-11 09:18:00 198.85 202.55 197.65 200.00 9900 160350 \n",
"2021-06-11 09:20:00 197.55 200.00 196.25 196.75 14475 166800 \n",
"2021-06-11 09:22:00 198.00 198.00 193.85 196.05 22800 176625 \n",
"... ... ... ... ... ... ... \n",
"2021-06-11 15:20:00 190.35 191.45 188.75 189.60 7875 236700 \n",
"2021-06-11 15:22:00 189.65 190.00 188.15 188.80 6000 236700 \n",
"2021-06-11 15:24:00 188.95 189.40 187.05 187.95 9675 237525 \n",
"2021-06-11 15:26:00 187.05 187.45 185.65 185.65 11400 239475 \n",
"2021-06-11 15:28:00 186.15 186.90 185.25 185.85 9750 239475 \n",
"\n",
"[165 rows x 24 columns]"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"#pd.merge(pdc,pdp,how=\"inner\",left_index=True, right_index=True)\n",
"dfs=[pdc,pdp,pdc2,pdp2]\n",
"pd.concat(dfs,keys=[\"NIFTY21062415400CE\",\"NIFTY21062415400PE\",\"NIFTY21062415900CE\",\"NIFTY21062415900PE\"],join=\"inner\",axis=1)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
babebe/Yummly | DESSERT/DS_CONCATENATE_DATAFRAMES.ipynb | 1 | 115387 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = pd.read_csv('DS_main.csv')\n",
"df1 = pd.read_csv('DS_main_1.csv')\n",
"df2 = pd.read_csv('DS_main_2.csv')\n",
"df3 = pd.read_csv('DS_main_3.csv')"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(500, 5)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>id</th>\n",
" <th>rating</th>\n",
" <th>recipeName</th>\n",
" <th>sourceDisplayName</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>Easy_-No-Bake_-Mud-Pie-1708171</td>\n",
" <td>4</td>\n",
" <td>Easy, No Bake, Mud Pie</td>\n",
" <td>Ann's Entitled Life</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>Tita_s-Fruit-Dessert-_SundaySupper-1706994</td>\n",
" <td>4</td>\n",
" <td>Tita's Fruit Dessert #SundaySupper</td>\n",
" <td>Basic N Delicious</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>No-Bake-Oreo-Cheesecake-1710284</td>\n",
" <td>3</td>\n",
" <td>No Bake Oreo Cheesecake</td>\n",
" <td>Cook and Share</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 id rating \\\n",
"0 0 Easy_-No-Bake_-Mud-Pie-1708171 4 \n",
"1 1 Tita_s-Fruit-Dessert-_SundaySupper-1706994 4 \n",
"2 2 No-Bake-Oreo-Cheesecake-1710284 3 \n",
"\n",
" recipeName sourceDisplayName \n",
"0 Easy, No Bake, Mud Pie Ann's Entitled Life \n",
"1 Tita's Fruit Dessert #SundaySupper Basic N Delicious \n",
"2 No Bake Oreo Cheesecake Cook and Share "
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print df.shape\n",
"df.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = df.drop('Unnamed: 0', 1)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(500, 5)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>rating</th>\n",
" <th>recipeName</th>\n",
" <th>sourceDisplayName</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Lemonade-Popsicles-1682120</td>\n",
" <td>3</td>\n",
" <td>Lemonade Popsicles</td>\n",
" <td>Beachbody</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Best-Peanut-Butter-Buttercream-Frosting-1682977</td>\n",
" <td>4</td>\n",
" <td>Best Peanut Butter Buttercream Frosting</td>\n",
" <td>Two Sisters Crafting</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Omega-Chocolate-Bars-1695745</td>\n",
" <td>4</td>\n",
" <td>Omega Chocolate Bars</td>\n",
" <td>A Healthy Life For Me</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id rating \\\n",
"0 Lemonade-Popsicles-1682120 3 \n",
"1 Best-Peanut-Butter-Buttercream-Frosting-1682977 4 \n",
"2 Omega-Chocolate-Bars-1695745 4 \n",
"\n",
" recipeName sourceDisplayName \n",
"0 Lemonade Popsicles Beachbody \n",
"1 Best Peanut Butter Buttercream Frosting Two Sisters Crafting \n",
"2 Omega Chocolate Bars A Healthy Life For Me "
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print df1.shape\n",
"df1 = df1.drop('Unnamed: 0', 1)\n",
"df1.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(500, 5)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>rating</th>\n",
" <th>recipeName</th>\n",
" <th>sourceDisplayName</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Raw-Brownies-With-Frosting-1707657</td>\n",
" <td>4</td>\n",
" <td>Raw Brownies With Frosting</td>\n",
" <td>Planticize</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Easy-Cinnamon-Roll-Cake-1552842</td>\n",
" <td>4</td>\n",
" <td>Easy Cinnamon Roll Cake</td>\n",
" <td>Cookies & Cups</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Coconut-Ice-Cream-1711710</td>\n",
" <td>4</td>\n",
" <td>Coconut Ice Cream</td>\n",
" <td>Get Inspired Everyday!</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id rating recipeName \\\n",
"0 Raw-Brownies-With-Frosting-1707657 4 Raw Brownies With Frosting \n",
"1 Easy-Cinnamon-Roll-Cake-1552842 4 Easy Cinnamon Roll Cake \n",
"2 Coconut-Ice-Cream-1711710 4 Coconut Ice Cream \n",
"\n",
" sourceDisplayName \n",
"0 Planticize \n",
"1 Cookies & Cups \n",
"2 Get Inspired Everyday! "
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print df2.shape\n",
"df2 = df2.drop('Unnamed: 0', 1)\n",
"df2.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(500, 5)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>rating</th>\n",
" <th>recipeName</th>\n",
" <th>sourceDisplayName</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Snickers-Chocolate-Cake-Mix-Bars-1710321</td>\n",
" <td>4</td>\n",
" <td>Snickers Chocolate Cake Mix Bars</td>\n",
" <td>Kleinworth Co.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Mango-Coconut-Popsicles-1699009</td>\n",
" <td>4</td>\n",
" <td>Mango Coconut Popsicles</td>\n",
" <td>Eat Good 4 Life</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Man-Bars-1703874</td>\n",
" <td>4</td>\n",
" <td>Man Bars</td>\n",
" <td>South your mouth</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id rating \\\n",
"0 Snickers-Chocolate-Cake-Mix-Bars-1710321 4 \n",
"1 Mango-Coconut-Popsicles-1699009 4 \n",
"2 Man-Bars-1703874 4 \n",
"\n",
" recipeName sourceDisplayName \n",
"0 Snickers Chocolate Cake Mix Bars Kleinworth Co. \n",
"1 Mango Coconut Popsicles Eat Good 4 Life \n",
"2 Man Bars South your mouth "
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print df3.shape\n",
"df3 = df3.drop('Unnamed: 0', 1)\n",
"df3.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#concatenate the main tables. \n",
"DS_main= pd.concat([df, df1, df2, df3])\n",
"#create a new dataframe with selected columns\n",
"DS_main_reduced = DS_main.drop(['recipeName', 'sourceDisplayName'], axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(2000, 4)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>rating</th>\n",
" <th>recipeName</th>\n",
" <th>sourceDisplayName</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Easy_-No-Bake_-Mud-Pie-1708171</td>\n",
" <td>4</td>\n",
" <td>Easy, No Bake, Mud Pie</td>\n",
" <td>Ann's Entitled Life</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Tita_s-Fruit-Dessert-_SundaySupper-1706994</td>\n",
" <td>4</td>\n",
" <td>Tita's Fruit Dessert #SundaySupper</td>\n",
" <td>Basic N Delicious</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>No-Bake-Oreo-Cheesecake-1710284</td>\n",
" <td>3</td>\n",
" <td>No Bake Oreo Cheesecake</td>\n",
" <td>Cook and Share</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id rating \\\n",
"0 Easy_-No-Bake_-Mud-Pie-1708171 4 \n",
"1 Tita_s-Fruit-Dessert-_SundaySupper-1706994 4 \n",
"2 No-Bake-Oreo-Cheesecake-1710284 3 \n",
"\n",
" recipeName sourceDisplayName \n",
"0 Easy, No Bake, Mud Pie Ann's Entitled Life \n",
"1 Tita's Fruit Dessert #SundaySupper Basic N Delicious \n",
"2 No Bake Oreo Cheesecake Cook and Share "
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#peek at dataframe\n",
"print DS_main.shape\n",
"DS_main.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
},
{
"data": {
"text/plain": [
"(1999, 4)"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for i in DS_main.duplicated('id'):\n",
" if i == True:\n",
" print i\n",
"DS_main = DS_main.drop_duplicates('id')\n",
"DS_main.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* ***Flavors***"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fdf = pd.read_csv('DS_flavors.csv')\n",
"fdf1 = pd.read_csv('DS_flavors_1.csv')\n",
"fdf2 = pd.read_csv('DS_flavors_2.csv')\n",
"fdf3 = pd.read_csv('DS_flavors_3.csv')"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(500, 8)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>bitter</th>\n",
" <th>meaty</th>\n",
" <th>piquant</th>\n",
" <th>salty</th>\n",
" <th>sour</th>\n",
" <th>sweet</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-No-Bake-Caramel-Cheesecake-Shooters-1706735</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1-2-3-4-Cupcakes-760182</td>\n",
" <td>0.166667</td>\n",
" <td>0.333333</td>\n",
" <td>0.0</td>\n",
" <td>0.333333</td>\n",
" <td>0.166667</td>\n",
" <td>0.833333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1-Minute-Chocolate-Frosting-1690971</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id bitter meaty piquant \\\n",
"0 -No-Bake-Caramel-Cheesecake-Shooters-1706735 NaN NaN NaN \n",
"1 1-2-3-4-Cupcakes-760182 0.166667 0.333333 0.0 \n",
"2 1-Minute-Chocolate-Frosting-1690971 NaN NaN NaN \n",
"\n",
" salty sour sweet \n",
"0 NaN NaN NaN \n",
"1 0.333333 0.166667 0.833333 \n",
"2 NaN NaN NaN "
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print fdf.shape\n",
"fdf = fdf.drop('Unnamed: 0', 1)\n",
"fdf = fdf.rename(columns = {'index':'id'})\n",
"fdf.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(500, 8)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>bitter</th>\n",
" <th>meaty</th>\n",
" <th>piquant</th>\n",
" <th>salty</th>\n",
" <th>sour</th>\n",
" <th>sweet</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10-Layer-Chocolate-Caramel-Mousse-Cake-1697890</td>\n",
" <td>0.166667</td>\n",
" <td>0.166667</td>\n",
" <td>0.0</td>\n",
" <td>0.5</td>\n",
" <td>0.166667</td>\n",
" <td>0.833333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2-Bite-Cherry-Pies-1709499</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3-Ingredient-Brownies-1703671</td>\n",
" <td>0.166667</td>\n",
" <td>0.166667</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.166667</td>\n",
" <td>0.833333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id bitter meaty \\\n",
"0 10-Layer-Chocolate-Caramel-Mousse-Cake-1697890 0.166667 0.166667 \n",
"1 2-Bite-Cherry-Pies-1709499 NaN NaN \n",
"2 3-Ingredient-Brownies-1703671 0.166667 0.166667 \n",
"\n",
" piquant salty sour sweet \n",
"0 0.0 0.5 0.166667 0.833333 \n",
"1 NaN NaN NaN NaN \n",
"2 0.0 0.0 0.166667 0.833333 "
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print fdf1.shape\n",
"fdf1 = fdf1.drop('Unnamed: 0', 1)\n",
"fdf1 = fdf1.rename(columns = {'index':'id'})\n",
"fdf1.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(500, 8)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>bitter</th>\n",
" <th>meaty</th>\n",
" <th>piquant</th>\n",
" <th>salty</th>\n",
" <th>sour</th>\n",
" <th>sweet</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1-Minute-Sugar-Free-Peanut-Butter-Mug-Cake-169...</td>\n",
" <td>0.833333</td>\n",
" <td>0.833333</td>\n",
" <td>0.0</td>\n",
" <td>0.833333</td>\n",
" <td>0.166667</td>\n",
" <td>0.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10-Minute-Strawberry-Jam-1702695</td>\n",
" <td>0.000000</td>\n",
" <td>0.166667</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.833333</td>\n",
" <td>0.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1_2-Syn-Strawberry-Mousse-1703461</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id bitter meaty \\\n",
"0 1-Minute-Sugar-Free-Peanut-Butter-Mug-Cake-169... 0.833333 0.833333 \n",
"1 10-Minute-Strawberry-Jam-1702695 0.000000 0.166667 \n",
"2 1_2-Syn-Strawberry-Mousse-1703461 NaN NaN \n",
"\n",
" piquant salty sour sweet \n",
"0 0.0 0.833333 0.166667 0.333333 \n",
"1 0.0 0.000000 0.833333 0.666667 \n",
"2 NaN NaN NaN NaN "
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print fdf2.shape\n",
"fdf2 = fdf2.drop('Unnamed: 0', 1)\n",
"fdf2 = fdf2.rename(columns = {'index':'id'})\n",
"fdf2.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(500, 8)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>bitter</th>\n",
" <th>meaty</th>\n",
" <th>piquant</th>\n",
" <th>salty</th>\n",
" <th>sour</th>\n",
" <th>sweet</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>100-Calorie-Chocolate-Peanut-Butter-Squares-15...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>15-Minute-Strawberry-Jam-1683439</td>\n",
" <td>0.0</td>\n",
" <td>0.166667</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.833333</td>\n",
" <td>0.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1_-Classic-Chocolate-Cake-1708247</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id bitter meaty \\\n",
"0 100-Calorie-Chocolate-Peanut-Butter-Squares-15... NaN NaN \n",
"1 15-Minute-Strawberry-Jam-1683439 0.0 0.166667 \n",
"2 1_-Classic-Chocolate-Cake-1708247 NaN NaN \n",
"\n",
" piquant salty sour sweet \n",
"0 NaN NaN NaN NaN \n",
"1 0.0 0.0 0.833333 0.666667 \n",
"2 NaN NaN NaN NaN "
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print fdf3.shape\n",
"fdf3 = fdf3.drop('Unnamed: 0', 1)\n",
"fdf3 = fdf3.rename(columns = {'index':'id'})\n",
"fdf3.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#concatenate the flavor tables. \n",
"DS_flavors= pd.concat([fdf, fdf1, fdf2, fdf3])"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(2000, 7)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>bitter</th>\n",
" <th>meaty</th>\n",
" <th>piquant</th>\n",
" <th>salty</th>\n",
" <th>sour</th>\n",
" <th>sweet</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-No-Bake-Caramel-Cheesecake-Shooters-1706735</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1-2-3-4-Cupcakes-760182</td>\n",
" <td>0.166667</td>\n",
" <td>0.333333</td>\n",
" <td>0.0</td>\n",
" <td>0.333333</td>\n",
" <td>0.166667</td>\n",
" <td>0.833333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1-Minute-Chocolate-Frosting-1690971</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id bitter meaty piquant \\\n",
"0 -No-Bake-Caramel-Cheesecake-Shooters-1706735 NaN NaN NaN \n",
"1 1-2-3-4-Cupcakes-760182 0.166667 0.333333 0.0 \n",
"2 1-Minute-Chocolate-Frosting-1690971 NaN NaN NaN \n",
"\n",
" salty sour sweet \n",
"0 NaN NaN NaN \n",
"1 0.333333 0.166667 0.833333 \n",
"2 NaN NaN NaN "
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#peek at dataframe\n",
"print DS_flavors.shape\n",
"DS_flavors.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
},
{
"data": {
"text/plain": [
"(1999, 7)"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for i in DS_flavors.duplicated('id'):\n",
" if i == True:\n",
" print i\n",
"DS_flavors = DS_flavors.drop_duplicates('id')\n",
"DS_flavors.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* ***Cuisine***"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cdf = pd.read_csv('DS_cuisines.csv')\n",
"cdf1 = pd.read_csv('DS_cuisines_1.csv')\n",
"cdf2 = pd.read_csv('DS_cuisines_2.csv')\n",
"cdf3 = pd.read_csv('DS_cuisines_3.csv')"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(500, 28)\n",
"Index([u'id', u'American', u'Asian', u'Barbecue', u'Cajun & Creole',\n",
" u'Chinese', u'Cuban', u'English', u'French', u'German', u'Greek',\n",
" u'Hawaiian', u'Hungarian', u'Indian', u'Irish', u'Italian', u'Japanese',\n",
" u'Kid-Friendly', u'Mediterranean', u'Mexican', u'Moroccan',\n",
" u'Portuguese', u'Southern & Soul Food', u'Southwestern', u'Spanish',\n",
" u'Swedish', u'Thai'],\n",
" dtype='object')\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>American</th>\n",
" <th>Asian</th>\n",
" <th>Barbecue</th>\n",
" <th>Cajun & Creole</th>\n",
" <th>Chinese</th>\n",
" <th>Cuban</th>\n",
" <th>English</th>\n",
" <th>French</th>\n",
" <th>German</th>\n",
" <th>...</th>\n",
" <th>Kid-Friendly</th>\n",
" <th>Mediterranean</th>\n",
" <th>Mexican</th>\n",
" <th>Moroccan</th>\n",
" <th>Portuguese</th>\n",
" <th>Southern & Soul Food</th>\n",
" <th>Southwestern</th>\n",
" <th>Spanish</th>\n",
" <th>Swedish</th>\n",
" <th>Thai</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-No-Bake-Caramel-Cheesecake-Shooters-1706735</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1-2-3-4-Cupcakes-760182</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1-Minute-Chocolate-Frosting-1690971</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 27 columns</p>\n",
"</div>"
],
"text/plain": [
" id American Asian Barbecue \\\n",
"0 -No-Bake-Caramel-Cheesecake-Shooters-1706735 0 0 0 \n",
"1 1-2-3-4-Cupcakes-760182 0 0 0 \n",
"2 1-Minute-Chocolate-Frosting-1690971 0 0 0 \n",
"\n",
" Cajun & Creole Chinese Cuban English French German ... \\\n",
"0 0 0 0 0 0 0 ... \n",
"1 0 0 0 0 0 0 ... \n",
"2 0 0 0 0 0 0 ... \n",
"\n",
" Kid-Friendly Mediterranean Mexican Moroccan Portuguese \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"\n",
" Southern & Soul Food Southwestern Spanish Swedish Thai \n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"\n",
"[3 rows x 27 columns]"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print cdf.shape\n",
"cdf = cdf.drop('Unnamed: 0', 1)\n",
"cdf = cdf.rename(columns = {'index':'id'})\n",
"print cdf.columns\n",
"cdf.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(500, 28)\n",
"Index([u'id', u'American', u'Asian', u'Barbecue', u'Cajun & Creole',\n",
" u'Chinese', u'Cuban', u'English', u'French', u'German', u'Greek',\n",
" u'Hawaiian', u'Hungarian', u'Indian', u'Irish', u'Italian', u'Japanese',\n",
" u'Kid-Friendly', u'Mediterranean', u'Mexican', u'Moroccan',\n",
" u'Portuguese', u'Southern & Soul Food', u'Southwestern', u'Spanish',\n",
" u'Swedish', u'Thai'],\n",
" dtype='object')\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>American</th>\n",
" <th>Asian</th>\n",
" <th>Barbecue</th>\n",
" <th>Cajun & Creole</th>\n",
" <th>Chinese</th>\n",
" <th>Cuban</th>\n",
" <th>English</th>\n",
" <th>French</th>\n",
" <th>German</th>\n",
" <th>...</th>\n",
" <th>Kid-Friendly</th>\n",
" <th>Mediterranean</th>\n",
" <th>Mexican</th>\n",
" <th>Moroccan</th>\n",
" <th>Portuguese</th>\n",
" <th>Southern & Soul Food</th>\n",
" <th>Southwestern</th>\n",
" <th>Spanish</th>\n",
" <th>Swedish</th>\n",
" <th>Thai</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10-Layer-Chocolate-Caramel-Mousse-Cake-1697890</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2-Bite-Cherry-Pies-1709499</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3-Ingredient-Brownies-1703671</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 27 columns</p>\n",
"</div>"
],
"text/plain": [
" id American Asian Barbecue \\\n",
"0 10-Layer-Chocolate-Caramel-Mousse-Cake-1697890 0 0 0 \n",
"1 2-Bite-Cherry-Pies-1709499 0 0 0 \n",
"2 3-Ingredient-Brownies-1703671 0 0 0 \n",
"\n",
" Cajun & Creole Chinese Cuban English French German ... \\\n",
"0 0 0 0 0 0 0 ... \n",
"1 0 0 0 0 0 0 ... \n",
"2 0 0 0 0 0 0 ... \n",
"\n",
" Kid-Friendly Mediterranean Mexican Moroccan Portuguese \\\n",
"0 0 0 0 0 0 \n",
"1 1 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"\n",
" Southern & Soul Food Southwestern Spanish Swedish Thai \n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"\n",
"[3 rows x 27 columns]"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print cdf1.shape\n",
"cdf1 = cdf1.drop('Unnamed: 0', 1)\n",
"cdf1 = cdf1.rename(columns = {'index':'id'})\n",
"print cdf1.columns\n",
"cdf1.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(500, 28)\n",
"Index([u'id', u'American', u'Asian', u'Barbecue', u'Cajun & Creole',\n",
" u'Chinese', u'Cuban', u'English', u'French', u'German', u'Greek',\n",
" u'Hawaiian', u'Hungarian', u'Indian', u'Irish', u'Italian', u'Japanese',\n",
" u'Kid-Friendly', u'Mediterranean', u'Mexican', u'Moroccan',\n",
" u'Portuguese', u'Southern & Soul Food', u'Southwestern', u'Spanish',\n",
" u'Swedish', u'Thai'],\n",
" dtype='object')\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>American</th>\n",
" <th>Asian</th>\n",
" <th>Barbecue</th>\n",
" <th>Cajun & Creole</th>\n",
" <th>Chinese</th>\n",
" <th>Cuban</th>\n",
" <th>English</th>\n",
" <th>French</th>\n",
" <th>German</th>\n",
" <th>...</th>\n",
" <th>Kid-Friendly</th>\n",
" <th>Mediterranean</th>\n",
" <th>Mexican</th>\n",
" <th>Moroccan</th>\n",
" <th>Portuguese</th>\n",
" <th>Southern & Soul Food</th>\n",
" <th>Southwestern</th>\n",
" <th>Spanish</th>\n",
" <th>Swedish</th>\n",
" <th>Thai</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1-Minute-Sugar-Free-Peanut-Butter-Mug-Cake-169...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10-Minute-Strawberry-Jam-1702695</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1_2-Syn-Strawberry-Mousse-1703461</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 27 columns</p>\n",
"</div>"
],
"text/plain": [
" id American Asian \\\n",
"0 1-Minute-Sugar-Free-Peanut-Butter-Mug-Cake-169... 0 0 \n",
"1 10-Minute-Strawberry-Jam-1702695 0 0 \n",
"2 1_2-Syn-Strawberry-Mousse-1703461 0 0 \n",
"\n",
" Barbecue Cajun & Creole Chinese Cuban English French German ... \\\n",
"0 0 0 0 0 0 0 0 ... \n",
"1 0 0 0 0 0 0 0 ... \n",
"2 0 0 0 0 0 0 0 ... \n",
"\n",
" Kid-Friendly Mediterranean Mexican Moroccan Portuguese \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"\n",
" Southern & Soul Food Southwestern Spanish Swedish Thai \n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"\n",
"[3 rows x 27 columns]"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print cdf2.shape\n",
"cdf2 = cdf2.drop('Unnamed: 0', 1)\n",
"cdf2 = cdf2.rename(columns = {'index':'id'})\n",
"print cdf2.columns\n",
"cdf2.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(500, 28)\n",
"Index([u'id', u'American', u'Asian', u'Barbecue', u'Cajun & Creole',\n",
" u'Chinese', u'Cuban', u'English', u'French', u'German', u'Greek',\n",
" u'Hawaiian', u'Hungarian', u'Indian', u'Irish', u'Italian', u'Japanese',\n",
" u'Kid-Friendly', u'Mediterranean', u'Mexican', u'Moroccan',\n",
" u'Portuguese', u'Southern & Soul Food', u'Southwestern', u'Spanish',\n",
" u'Swedish', u'Thai'],\n",
" dtype='object')\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>American</th>\n",
" <th>Asian</th>\n",
" <th>Barbecue</th>\n",
" <th>Cajun & Creole</th>\n",
" <th>Chinese</th>\n",
" <th>Cuban</th>\n",
" <th>English</th>\n",
" <th>French</th>\n",
" <th>German</th>\n",
" <th>...</th>\n",
" <th>Kid-Friendly</th>\n",
" <th>Mediterranean</th>\n",
" <th>Mexican</th>\n",
" <th>Moroccan</th>\n",
" <th>Portuguese</th>\n",
" <th>Southern & Soul Food</th>\n",
" <th>Southwestern</th>\n",
" <th>Spanish</th>\n",
" <th>Swedish</th>\n",
" <th>Thai</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>100-Calorie-Chocolate-Peanut-Butter-Squares-15...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>15-Minute-Strawberry-Jam-1683439</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1_-Classic-Chocolate-Cake-1708247</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 27 columns</p>\n",
"</div>"
],
"text/plain": [
" id American Asian \\\n",
"0 100-Calorie-Chocolate-Peanut-Butter-Squares-15... 0 0 \n",
"1 15-Minute-Strawberry-Jam-1683439 0 0 \n",
"2 1_-Classic-Chocolate-Cake-1708247 0 0 \n",
"\n",
" Barbecue Cajun & Creole Chinese Cuban English French German ... \\\n",
"0 0 0 0 0 0 0 0 ... \n",
"1 0 0 0 0 0 0 0 ... \n",
"2 0 0 0 0 0 0 0 ... \n",
"\n",
" Kid-Friendly Mediterranean Mexican Moroccan Portuguese \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"\n",
" Southern & Soul Food Southwestern Spanish Swedish Thai \n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"\n",
"[3 rows x 27 columns]"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print cdf3.shape\n",
"cdf3 = cdf3.drop('Unnamed: 0', 1)\n",
"cdf3 = cdf3.rename(columns = {'index':'id'})\n",
"print cdf3.columns\n",
"cdf3.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#concatenate the cuisines tables. \n",
"DS_cuisines= pd.concat([cdf, cdf1, cdf2, cdf3])"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(2000, 27)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>American</th>\n",
" <th>Asian</th>\n",
" <th>Barbecue</th>\n",
" <th>Cajun & Creole</th>\n",
" <th>Chinese</th>\n",
" <th>Cuban</th>\n",
" <th>English</th>\n",
" <th>French</th>\n",
" <th>German</th>\n",
" <th>...</th>\n",
" <th>Kid-Friendly</th>\n",
" <th>Mediterranean</th>\n",
" <th>Mexican</th>\n",
" <th>Moroccan</th>\n",
" <th>Portuguese</th>\n",
" <th>Southern & Soul Food</th>\n",
" <th>Southwestern</th>\n",
" <th>Spanish</th>\n",
" <th>Swedish</th>\n",
" <th>Thai</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-No-Bake-Caramel-Cheesecake-Shooters-1706735</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1-2-3-4-Cupcakes-760182</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1-Minute-Chocolate-Frosting-1690971</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 27 columns</p>\n",
"</div>"
],
"text/plain": [
" id American Asian Barbecue \\\n",
"0 -No-Bake-Caramel-Cheesecake-Shooters-1706735 0 0 0 \n",
"1 1-2-3-4-Cupcakes-760182 0 0 0 \n",
"2 1-Minute-Chocolate-Frosting-1690971 0 0 0 \n",
"\n",
" Cajun & Creole Chinese Cuban English French German ... \\\n",
"0 0 0 0 0 0 0 ... \n",
"1 0 0 0 0 0 0 ... \n",
"2 0 0 0 0 0 0 ... \n",
"\n",
" Kid-Friendly Mediterranean Mexican Moroccan Portuguese \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"\n",
" Southern & Soul Food Southwestern Spanish Swedish Thai \n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"\n",
"[3 rows x 27 columns]"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#peek at dataframe\n",
"print DS_cuisines.shape\n",
"DS_cuisines.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
},
{
"data": {
"text/plain": [
"(1999, 27)"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for i in DS_cuisines.duplicated('id'):\n",
" if i == True:\n",
" print i\n",
"DS_cuisines = DS_cuisines.drop_duplicates('id')\n",
"DS_cuisines.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* ***Details*** "
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ddf = pd.read_csv('DS_details.csv')\n",
"ddf1 = pd.read_csv('DS_details_1.csv')\n",
"ddf2 = pd.read_csv('DS_details_2.csv')\n",
"ddf3 = pd.read_csv('DS_details_3.csv')"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(500, 7)\n",
"Index([u'id', u'cookTimeInSeconds', u'ingredientCount', u'numberOfServings',\n",
" u'prepTimeInSeconds', u'totalTimeInSeconds'],\n",
" dtype='object')\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>cookTimeInSeconds</th>\n",
" <th>ingredientCount</th>\n",
" <th>numberOfServings</th>\n",
" <th>prepTimeInSeconds</th>\n",
" <th>totalTimeInSeconds</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Easy_-No-Bake_-Mud-Pie-1708171</td>\n",
" <td>11700.0</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>900.0</td>\n",
" <td>12600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Tita_s-Fruit-Dessert-_SundaySupper-1706994</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>900.0</td>\n",
" <td>900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>No-Bake-Oreo-Cheesecake-1710284</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>1800.0</td>\n",
" <td>1800</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id cookTimeInSeconds \\\n",
"0 Easy_-No-Bake_-Mud-Pie-1708171 11700.0 \n",
"1 Tita_s-Fruit-Dessert-_SundaySupper-1706994 NaN \n",
"2 No-Bake-Oreo-Cheesecake-1710284 NaN \n",
"\n",
" ingredientCount numberOfServings prepTimeInSeconds totalTimeInSeconds \n",
"0 5 6 900.0 12600 \n",
"1 6 4 900.0 900 \n",
"2 6 4 1800.0 1800 "
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print ddf.shape\n",
"ddf = ddf.drop('Unnamed: 0', 1)\n",
"print ddf.columns\n",
"ddf.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(500, 7)\n",
"Index([u'id', u'cookTimeInSeconds', u'ingredientCount', u'numberOfServings',\n",
" u'prepTimeInSeconds', u'totalTimeInSeconds'],\n",
" dtype='object')\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>cookTimeInSeconds</th>\n",
" <th>ingredientCount</th>\n",
" <th>numberOfServings</th>\n",
" <th>prepTimeInSeconds</th>\n",
" <th>totalTimeInSeconds</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Lemonade-Popsicles-1682120</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" <td>4.0</td>\n",
" <td>600.0</td>\n",
" <td>15000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Best-Peanut-Butter-Buttercream-Frosting-1682977</td>\n",
" <td>NaN</td>\n",
" <td>5</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" <td>1200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Omega-Chocolate-Bars-1695745</td>\n",
" <td>120.0</td>\n",
" <td>5</td>\n",
" <td>25.0</td>\n",
" <td>1080.0</td>\n",
" <td>1200</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id cookTimeInSeconds \\\n",
"0 Lemonade-Popsicles-1682120 NaN \n",
"1 Best-Peanut-Butter-Buttercream-Frosting-1682977 NaN \n",
"2 Omega-Chocolate-Bars-1695745 120.0 \n",
"\n",
" ingredientCount numberOfServings prepTimeInSeconds totalTimeInSeconds \n",
"0 3 4.0 600.0 15000 \n",
"1 5 4.0 NaN 1200 \n",
"2 5 25.0 1080.0 1200 "
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print ddf1.shape\n",
"ddf1 = ddf1.drop('Unnamed: 0', 1)\n",
"print ddf1.columns\n",
"ddf1.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(500, 7)\n",
"Index([u'id', u'cookTimeInSeconds', u'ingredientCount', u'numberOfServings',\n",
" u'prepTimeInSeconds', u'totalTimeInSeconds'],\n",
" dtype='object')\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>cookTimeInSeconds</th>\n",
" <th>ingredientCount</th>\n",
" <th>numberOfServings</th>\n",
" <th>prepTimeInSeconds</th>\n",
" <th>totalTimeInSeconds</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Raw-Brownies-With-Frosting-1707657</td>\n",
" <td>NaN</td>\n",
" <td>10</td>\n",
" <td>4</td>\n",
" <td>900.0</td>\n",
" <td>900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Easy-Cinnamon-Roll-Cake-1552842</td>\n",
" <td>NaN</td>\n",
" <td>9</td>\n",
" <td>10</td>\n",
" <td>NaN</td>\n",
" <td>3000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Coconut-Ice-Cream-1711710</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>300.0</td>\n",
" <td>300</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id cookTimeInSeconds ingredientCount \\\n",
"0 Raw-Brownies-With-Frosting-1707657 NaN 10 \n",
"1 Easy-Cinnamon-Roll-Cake-1552842 NaN 9 \n",
"2 Coconut-Ice-Cream-1711710 NaN 2 \n",
"\n",
" numberOfServings prepTimeInSeconds totalTimeInSeconds \n",
"0 4 900.0 900 \n",
"1 10 NaN 3000 \n",
"2 4 300.0 300 "
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print ddf2.shape\n",
"ddf2 = ddf2.drop('Unnamed: 0', 1)\n",
"print ddf2.columns\n",
"ddf2.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(500, 7)\n",
"Index([u'id', u'cookTimeInSeconds', u'ingredientCount', u'numberOfServings',\n",
" u'prepTimeInSeconds', u'totalTimeInSeconds'],\n",
" dtype='object')\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>cookTimeInSeconds</th>\n",
" <th>ingredientCount</th>\n",
" <th>numberOfServings</th>\n",
" <th>prepTimeInSeconds</th>\n",
" <th>totalTimeInSeconds</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Snickers-Chocolate-Cake-Mix-Bars-1710321</td>\n",
" <td>3300.0</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>480.0</td>\n",
" <td>3780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Mango-Coconut-Popsicles-1699009</td>\n",
" <td>NaN</td>\n",
" <td>5</td>\n",
" <td>10</td>\n",
" <td>NaN</td>\n",
" <td>300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Man-Bars-1703874</td>\n",
" <td>NaN</td>\n",
" <td>7</td>\n",
" <td>24</td>\n",
" <td>NaN</td>\n",
" <td>2400</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id cookTimeInSeconds \\\n",
"0 Snickers-Chocolate-Cake-Mix-Bars-1710321 3300.0 \n",
"1 Mango-Coconut-Popsicles-1699009 NaN \n",
"2 Man-Bars-1703874 NaN \n",
"\n",
" ingredientCount numberOfServings prepTimeInSeconds totalTimeInSeconds \n",
"0 4 4 480.0 3780 \n",
"1 5 10 NaN 300 \n",
"2 7 24 NaN 2400 "
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print ddf3.shape\n",
"ddf3 = ddf3.drop('Unnamed: 0', 1)\n",
"print ddf3.columns\n",
"ddf3.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#concatenate the detail tables. \n",
"DS_details= pd.concat([ddf, ddf1, ddf2, ddf3])"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(2000, 6)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>cookTimeInSeconds</th>\n",
" <th>ingredientCount</th>\n",
" <th>numberOfServings</th>\n",
" <th>prepTimeInSeconds</th>\n",
" <th>totalTimeInSeconds</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Easy_-No-Bake_-Mud-Pie-1708171</td>\n",
" <td>11700.0</td>\n",
" <td>5</td>\n",
" <td>6.0</td>\n",
" <td>900.0</td>\n",
" <td>12600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Tita_s-Fruit-Dessert-_SundaySupper-1706994</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" <td>4.0</td>\n",
" <td>900.0</td>\n",
" <td>900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>No-Bake-Oreo-Cheesecake-1710284</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" <td>4.0</td>\n",
" <td>1800.0</td>\n",
" <td>1800</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id cookTimeInSeconds \\\n",
"0 Easy_-No-Bake_-Mud-Pie-1708171 11700.0 \n",
"1 Tita_s-Fruit-Dessert-_SundaySupper-1706994 NaN \n",
"2 No-Bake-Oreo-Cheesecake-1710284 NaN \n",
"\n",
" ingredientCount numberOfServings prepTimeInSeconds totalTimeInSeconds \n",
"0 5 6.0 900.0 12600 \n",
"1 6 4.0 900.0 900 \n",
"2 6 4.0 1800.0 1800 "
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#peek at dataframe\n",
"print DS_details.shape\n",
"DS_details.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
},
{
"data": {
"text/plain": [
"(1999, 6)"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for i in DS_details.duplicated('id'):\n",
" if i == True:\n",
" print i\n",
"DS_details = DS_details.drop_duplicates('id')\n",
"DS_details.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* ***Ingredients***"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"idf = pd.read_csv('DS_ingredients.csv')\n",
"idf1 = pd.read_csv('DS_ingredients_1.csv')\n",
"idf2 = pd.read_csv('DS_ingredients_2.csv')\n",
"idf3 = pd.read_csv('DS_ingredients_3.csv')"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(500, 603)\n",
"Index([u'id', u'course', u'active dry yeast', u'agave nectar', u'alcohol',\n",
" u'all-purpose flour', u'allspice', u'almond butter', u'almond extract',\n",
" u'almond flour',\n",
" ...\n",
" u'xylitol sweetener', u'yellow cake mix', u'yoghurt', u'yogurt',\n",
" u'yolk', u'yoplait', u'yoplait greek 100 blackberry pie yogurt',\n",
" u'yoplait greek key lime pie yogurt',\n",
" u'yoplait greek lemon meringue yogurt', u'zucchini'],\n",
" dtype='object', length=602)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>course</th>\n",
" <th>active dry yeast</th>\n",
" <th>agave nectar</th>\n",
" <th>alcohol</th>\n",
" <th>all-purpose flour</th>\n",
" <th>allspice</th>\n",
" <th>almond butter</th>\n",
" <th>almond extract</th>\n",
" <th>almond flour</th>\n",
" <th>...</th>\n",
" <th>xylitol sweetener</th>\n",
" <th>yellow cake mix</th>\n",
" <th>yoghurt</th>\n",
" <th>yogurt</th>\n",
" <th>yolk</th>\n",
" <th>yoplait</th>\n",
" <th>yoplait greek 100 blackberry pie yogurt</th>\n",
" <th>yoplait greek key lime pie yogurt</th>\n",
" <th>yoplait greek lemon meringue yogurt</th>\n",
" <th>zucchini</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Easy_-No-Bake_-Mud-Pie-1708171</td>\n",
" <td>Breakfast and Brunch</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Tita_s-Fruit-Dessert-_SundaySupper-1706994</td>\n",
" <td>Breakfast and Brunch</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>No-Bake-Oreo-Cheesecake-1710284</td>\n",
" <td>Breakfast and Brunch</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 602 columns</p>\n",
"</div>"
],
"text/plain": [
" id course \\\n",
"0 Easy_-No-Bake_-Mud-Pie-1708171 Breakfast and Brunch \n",
"1 Tita_s-Fruit-Dessert-_SundaySupper-1706994 Breakfast and Brunch \n",
"2 No-Bake-Oreo-Cheesecake-1710284 Breakfast and Brunch \n",
"\n",
" active dry yeast agave nectar alcohol all-purpose flour allspice \\\n",
"0 0.0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" almond butter almond extract almond flour ... xylitol sweetener \\\n",
"0 0.0 0.0 0.0 ... 0.0 \n",
"1 0.0 0.0 0.0 ... 0.0 \n",
"2 0.0 0.0 0.0 ... 0.0 \n",
"\n",
" yellow cake mix yoghurt yogurt yolk yoplait \\\n",
"0 0.0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" yoplait greek 100 blackberry pie yogurt yoplait greek key lime pie yogurt \\\n",
"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"2 0.0 0.0 \n",
"\n",
" yoplait greek lemon meringue yogurt zucchini \n",
"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"2 0.0 0.0 \n",
"\n",
"[3 rows x 602 columns]"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print idf.shape\n",
"idf = idf.drop('Unnamed: 0', 1)\n",
"print idf.columns\n",
"idf.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(500, 609)\n",
"Index([u'id', u'course', u'agar', u'agave nectar', u'all-purpose flour',\n",
" u'allspice', u'almond butter', u'almond extract', u'almond flour',\n",
" u'almond meal',\n",
" ...\n",
" u'yellow cake mix', u'yellow cornmeal', u'yellow food coloring',\n",
" u'yoghurt', u'yogurt', u'yolk', u'yoplait greek 100 apple pie yogurt',\n",
" u'yoplait greek 100 mango yogurt',\n",
" u'yoplait greek 100 raspberry yogurt', u'zest'],\n",
" dtype='object', length=608)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>course</th>\n",
" <th>agar</th>\n",
" <th>agave nectar</th>\n",
" <th>all-purpose flour</th>\n",
" <th>allspice</th>\n",
" <th>almond butter</th>\n",
" <th>almond extract</th>\n",
" <th>almond flour</th>\n",
" <th>almond meal</th>\n",
" <th>...</th>\n",
" <th>yellow cake mix</th>\n",
" <th>yellow cornmeal</th>\n",
" <th>yellow food coloring</th>\n",
" <th>yoghurt</th>\n",
" <th>yogurt</th>\n",
" <th>yolk</th>\n",
" <th>yoplait greek 100 apple pie yogurt</th>\n",
" <th>yoplait greek 100 mango yogurt</th>\n",
" <th>yoplait greek 100 raspberry yogurt</th>\n",
" <th>zest</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Lemonade-Popsicles-1682120</td>\n",
" <td>Breakfast and Brunch</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Best-Peanut-Butter-Buttercream-Frosting-1682977</td>\n",
" <td>Breakfast and Brunch</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Omega-Chocolate-Bars-1695745</td>\n",
" <td>Breakfast and Brunch</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 608 columns</p>\n",
"</div>"
],
"text/plain": [
" id course \\\n",
"0 Lemonade-Popsicles-1682120 Breakfast and Brunch \n",
"1 Best-Peanut-Butter-Buttercream-Frosting-1682977 Breakfast and Brunch \n",
"2 Omega-Chocolate-Bars-1695745 Breakfast and Brunch \n",
"\n",
" agar agave nectar all-purpose flour allspice almond butter \\\n",
"0 0.0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" almond extract almond flour almond meal ... yellow cake mix \\\n",
"0 0.0 0.0 0.0 ... 0.0 \n",
"1 0.0 0.0 0.0 ... 0.0 \n",
"2 0.0 0.0 0.0 ... 0.0 \n",
"\n",
" yellow cornmeal yellow food coloring yoghurt yogurt yolk \\\n",
"0 0.0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" yoplait greek 100 apple pie yogurt yoplait greek 100 mango yogurt \\\n",
"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"2 0.0 0.0 \n",
"\n",
" yoplait greek 100 raspberry yogurt zest \n",
"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"2 0.0 0.0 \n",
"\n",
"[3 rows x 608 columns]"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print idf1.shape\n",
"idf1 = idf1.drop('Unnamed: 0', 1)\n",
"print idf1.columns\n",
"idf1.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(500, 629)\n",
"Index([u'id', u'course', u'9 inch chocolate crumb crust', u'agar',\n",
" u'agave nectar', u'all-purpose flour', u'allspice', u'almond butter',\n",
" u'almond extract', u'almond flour',\n",
" ...\n",
" u'whole wheat flour', u'whole wheat white flour', u'yellow cake mix',\n",
" u'yellow food coloring', u'yoghurt', u'yogurt', u'yolk', u'yoplait',\n",
" u'yoplait greek 100 pineapple yogurt', u'yoplait greek caramel yogurt'],\n",
" dtype='object', length=628)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>course</th>\n",
" <th>9 inch chocolate crumb crust</th>\n",
" <th>agar</th>\n",
" <th>agave nectar</th>\n",
" <th>all-purpose flour</th>\n",
" <th>allspice</th>\n",
" <th>almond butter</th>\n",
" <th>almond extract</th>\n",
" <th>almond flour</th>\n",
" <th>...</th>\n",
" <th>whole wheat flour</th>\n",
" <th>whole wheat white flour</th>\n",
" <th>yellow cake mix</th>\n",
" <th>yellow food coloring</th>\n",
" <th>yoghurt</th>\n",
" <th>yogurt</th>\n",
" <th>yolk</th>\n",
" <th>yoplait</th>\n",
" <th>yoplait greek 100 pineapple yogurt</th>\n",
" <th>yoplait greek caramel yogurt</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Raw-Brownies-With-Frosting-1707657</td>\n",
" <td>Breakfast and Brunch</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Easy-Cinnamon-Roll-Cake-1552842</td>\n",
" <td>Breakfast and Brunch</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Coconut-Ice-Cream-1711710</td>\n",
" <td>Breakfast and Brunch</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 628 columns</p>\n",
"</div>"
],
"text/plain": [
" id course \\\n",
"0 Raw-Brownies-With-Frosting-1707657 Breakfast and Brunch \n",
"1 Easy-Cinnamon-Roll-Cake-1552842 Breakfast and Brunch \n",
"2 Coconut-Ice-Cream-1711710 Breakfast and Brunch \n",
"\n",
" 9 inch chocolate crumb crust agar agave nectar all-purpose flour \\\n",
"0 0.0 0.0 1.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 \n",
"\n",
" allspice almond butter almond extract almond flour \\\n",
"0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 \n",
"\n",
" ... whole wheat flour whole wheat white flour \\\n",
"0 ... 0.0 0.0 \n",
"1 ... 0.0 0.0 \n",
"2 ... 0.0 0.0 \n",
"\n",
" yellow cake mix yellow food coloring yoghurt yogurt yolk yoplait \\\n",
"0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"1 1.0 0.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" yoplait greek 100 pineapple yogurt yoplait greek caramel yogurt \n",
"0 0.0 0.0 \n",
"1 0.0 0.0 \n",
"2 0.0 0.0 \n",
"\n",
"[3 rows x 628 columns]"
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print idf2.shape\n",
"idf2 = idf2.drop('Unnamed: 0', 1)\n",
"print idf2.columns\n",
"idf2.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(500, 652)\n",
"Index([u'id', u'course', u'agave nectar', u'all-purpose flour', u'allspice',\n",
" u'almond butter', u'almond extract', u'almond flour', u'almond meal',\n",
" u'almond milk',\n",
" ...\n",
" u'whole wheat flour', u'whole wheat pastry flour',\n",
" u'whole wheat white flour', u'xanthum gum', u'yellow cake mix',\n",
" u'yellow food coloring', u'yoghurt', u'yolk', u'yoplait', u'zest'],\n",
" dtype='object', length=651)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>course</th>\n",
" <th>agave nectar</th>\n",
" <th>all-purpose flour</th>\n",
" <th>allspice</th>\n",
" <th>almond butter</th>\n",
" <th>almond extract</th>\n",
" <th>almond flour</th>\n",
" <th>almond meal</th>\n",
" <th>almond milk</th>\n",
" <th>...</th>\n",
" <th>whole wheat flour</th>\n",
" <th>whole wheat pastry flour</th>\n",
" <th>whole wheat white flour</th>\n",
" <th>xanthum gum</th>\n",
" <th>yellow cake mix</th>\n",
" <th>yellow food coloring</th>\n",
" <th>yoghurt</th>\n",
" <th>yolk</th>\n",
" <th>yoplait</th>\n",
" <th>zest</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Snickers-Chocolate-Cake-Mix-Bars-1710321</td>\n",
" <td>Breakfast and Brunch</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Mango-Coconut-Popsicles-1699009</td>\n",
" <td>Breakfast and Brunch</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Man-Bars-1703874</td>\n",
" <td>Breakfast and Brunch</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 651 columns</p>\n",
"</div>"
],
"text/plain": [
" id course \\\n",
"0 Snickers-Chocolate-Cake-Mix-Bars-1710321 Breakfast and Brunch \n",
"1 Mango-Coconut-Popsicles-1699009 Breakfast and Brunch \n",
"2 Man-Bars-1703874 Breakfast and Brunch \n",
"\n",
" agave nectar all-purpose flour allspice almond butter almond extract \\\n",
"0 0.0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" almond flour almond meal almond milk ... whole wheat flour \\\n",
"0 0.0 0.0 0.0 ... 0.0 \n",
"1 0.0 0.0 1.0 ... 0.0 \n",
"2 0.0 0.0 0.0 ... 0.0 \n",
"\n",
" whole wheat pastry flour whole wheat white flour xanthum gum \\\n",
"0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 \n",
"\n",
" yellow cake mix yellow food coloring yoghurt yolk yoplait zest \n",
"0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
"[3 rows x 651 columns]"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print idf3.shape\n",
"idf3 = idf3.drop('Unnamed: 0', 1)\n",
"print idf3.columns\n",
"idf3.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#concatenate the ingredients tables. \n",
"DS_ing= pd.concat([idf, idf1, idf2, idf3])\n",
"#create a new dataframe with selected columns\n",
"DS_ing_reduced = DS_ing[['id', 'ingredient_list']]"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>9 inch chocolate crumb crust</th>\n",
" <th>active dry yeast</th>\n",
" <th>agar</th>\n",
" <th>agave nectar</th>\n",
" <th>alcohol</th>\n",
" <th>all-purpose flour</th>\n",
" <th>allspice</th>\n",
" <th>almond butter</th>\n",
" <th>almond extract</th>\n",
" <th>almond flour</th>\n",
" <th>...</th>\n",
" <th>yoplait greek 100 apple pie yogurt</th>\n",
" <th>yoplait greek 100 blackberry pie yogurt</th>\n",
" <th>yoplait greek 100 mango yogurt</th>\n",
" <th>yoplait greek 100 pineapple yogurt</th>\n",
" <th>yoplait greek 100 raspberry yogurt</th>\n",
" <th>yoplait greek caramel yogurt</th>\n",
" <th>yoplait greek key lime pie yogurt</th>\n",
" <th>yoplait greek lemon meringue yogurt</th>\n",
" <th>zest</th>\n",
" <th>zucchini</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 1174 columns</p>\n",
"</div>"
],
"text/plain": [
" 9 inch chocolate crumb crust active dry yeast agar agave nectar \\\n",
"0 NaN 0.0 NaN 0.0 \n",
"1 NaN 0.0 NaN 0.0 \n",
"2 NaN 0.0 NaN 0.0 \n",
"\n",
" alcohol all-purpose flour allspice almond butter almond extract \\\n",
"0 0.0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" almond flour ... yoplait greek 100 apple pie yogurt \\\n",
"0 0.0 ... NaN \n",
"1 0.0 ... NaN \n",
"2 0.0 ... NaN \n",
"\n",
" yoplait greek 100 blackberry pie yogurt yoplait greek 100 mango yogurt \\\n",
"0 0.0 NaN \n",
"1 0.0 NaN \n",
"2 0.0 NaN \n",
"\n",
" yoplait greek 100 pineapple yogurt yoplait greek 100 raspberry yogurt \\\n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"\n",
" yoplait greek caramel yogurt yoplait greek key lime pie yogurt \\\n",
"0 NaN 0.0 \n",
"1 NaN 0.0 \n",
"2 NaN 0.0 \n",
"\n",
" yoplait greek lemon meringue yogurt zest zucchini \n",
"0 0.0 NaN 0.0 \n",
"1 0.0 NaN 0.0 \n",
"2 0.0 NaN 0.0 \n",
"\n",
"[3 rows x 1174 columns]"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DS_ing.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#make id first column\n",
"cols = list(DS_ing)\n",
"cols.insert(0, cols.pop(cols.index('id')))\n",
"DS_ing = DS_ing.ix[:, cols]"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>9 inch chocolate crumb crust</th>\n",
" <th>active dry yeast</th>\n",
" <th>agar</th>\n",
" <th>agave nectar</th>\n",
" <th>alcohol</th>\n",
" <th>all-purpose flour</th>\n",
" <th>allspice</th>\n",
" <th>almond butter</th>\n",
" <th>almond extract</th>\n",
" <th>...</th>\n",
" <th>yoplait greek 100 apple pie yogurt</th>\n",
" <th>yoplait greek 100 blackberry pie yogurt</th>\n",
" <th>yoplait greek 100 mango yogurt</th>\n",
" <th>yoplait greek 100 pineapple yogurt</th>\n",
" <th>yoplait greek 100 raspberry yogurt</th>\n",
" <th>yoplait greek caramel yogurt</th>\n",
" <th>yoplait greek key lime pie yogurt</th>\n",
" <th>yoplait greek lemon meringue yogurt</th>\n",
" <th>zest</th>\n",
" <th>zucchini</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Easy_-No-Bake_-Mud-Pie-1708171</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Tita_s-Fruit-Dessert-_SundaySupper-1706994</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>No-Bake-Oreo-Cheesecake-1710284</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 1174 columns</p>\n",
"</div>"
],
"text/plain": [
" id 9 inch chocolate crumb crust \\\n",
"0 Easy_-No-Bake_-Mud-Pie-1708171 NaN \n",
"1 Tita_s-Fruit-Dessert-_SundaySupper-1706994 NaN \n",
"2 No-Bake-Oreo-Cheesecake-1710284 NaN \n",
"\n",
" active dry yeast agar agave nectar alcohol all-purpose flour allspice \\\n",
"0 0.0 NaN 0.0 0.0 0.0 0.0 \n",
"1 0.0 NaN 0.0 0.0 0.0 0.0 \n",
"2 0.0 NaN 0.0 0.0 0.0 0.0 \n",
"\n",
" almond butter almond extract ... \\\n",
"0 0.0 0.0 ... \n",
"1 0.0 0.0 ... \n",
"2 0.0 0.0 ... \n",
"\n",
" yoplait greek 100 apple pie yogurt \\\n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"\n",
" yoplait greek 100 blackberry pie yogurt yoplait greek 100 mango yogurt \\\n",
"0 0.0 NaN \n",
"1 0.0 NaN \n",
"2 0.0 NaN \n",
"\n",
" yoplait greek 100 pineapple yogurt yoplait greek 100 raspberry yogurt \\\n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"\n",
" yoplait greek caramel yogurt yoplait greek key lime pie yogurt \\\n",
"0 NaN 0.0 \n",
"1 NaN 0.0 \n",
"2 NaN 0.0 \n",
"\n",
" yoplait greek lemon meringue yogurt zest zucchini \n",
"0 0.0 NaN 0.0 \n",
"1 0.0 NaN 0.0 \n",
"2 0.0 NaN 0.0 \n",
"\n",
"[3 rows x 1174 columns]"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DS_ing.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
},
{
"data": {
"text/plain": [
"(1999, 1174)"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for i in DS_ing.duplicated('id'):\n",
" if i == True:\n",
" print i\n",
"DS_ing = DS_ing.drop_duplicates('id')\n",
"DS_ing.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Join all tables for Dessert"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# set index to column 'id'\n",
"_df = [DS_main, DS_main_reduced, DS_cuisines, DS_flavors, DS_details, DS_ing, DS_ing_reduced]\n",
"\n",
"for df in _df:\n",
" df.set_index('id', inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# join dataframes\n",
"DS_data = DS_main.join([DS_cuisines, DS_flavors, DS_details, DS_ing])\n",
"DS_data_reduced = DS_main_reduced.join([DS_flavors, DS_details, DS_ing_reduced])\n",
"\n",
"# create a course column\n",
"DS_data['course'] = 'dessert'\n",
"DS_data_reduced ['course'] = 'dessert'"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(1999, 1213)"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DS_data.shape"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# save into csv\n",
"DS_data.to_csv('DS_data.csv')\n",
"DS_data_reduced.to_csv('DS_data_reduced.csv')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
GoogleCloudPlatform/training-data-analyst | courses/dataflow/demos/beam_notebooks/beam_notebooks_demo.ipynb | 1 | 11793 | {
"cells": [
{
"cell_type": "markdown",
"id": "87420f48",
"metadata": {},
"source": [
"# Beam Notebooks and Dataframes Demo\n",
"\n",
"This example demonstrates how to set up an Apache Beam pipeline that reads from a\n",
"[Google Cloud Storage](https://cloud.google.com/storage) file containing text from Shakespeare's work *King Lear*, \n",
"tokenizes the text lines into individual words, and performs a frequency count on each of those words. \n",
"\n",
"We will perform the aggregation operations using the Beam Dataframes API, which allows us to use Pandas-like syntax to write your transformations. We will see how we can easily translate from using Pandas locally to using Dataframes in Apache Beam (which could then be run on Dataflow\n",
"\n",
"For details about the Apache Beam Dataframe API, see the [Documentation](https://beam.apache.org/documentation/dsls/dataframes/overview/).\n",
"\n",
"We first start with the necessary imports:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7b9f352d",
"metadata": {},
"outputs": [],
"source": [
"# Python's regular expression library\n",
"import re\n",
"\n",
"# Beam and interactive Beam imports\n",
"import apache_beam as beam\n",
"from apache_beam.runners.interactive.interactive_runner import InteractiveRunner\n",
"import apache_beam.runners.interactive.interactive_beam as ib\n",
"\n",
"# Dataframe API imports\n",
"from apache_beam.dataframe.convert import to_dataframe\n",
"from apache_beam.dataframe.convert import to_pcollection"
]
},
{
"cell_type": "markdown",
"id": "feb53e3a",
"metadata": {},
"source": [
"We will be using the `re` library to parse our lines of text. We will import the `InteractiveRunner` class for executing out pipeline in the notebook environment and the `interactive_beam` module for exploring the PCollections. Finally we will import two functions from the Dataframe API, `to_dataframe` and `to_pcollection`. `to_dataframe` converts your (schema-aware) PCollection into a dataframe and `to_pcollection` goes back in the other direction to a `PCollection` of type `beam.Row`.\n",
"\n",
"We will first create a composite PTransform `ReadWordsFromText` to read in a file pattern (`file_pattern`), use the `ReadFromText` source to read in the files, and then `FlatMap` with a lambda to parse the line into individual words."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "71cc4abe",
"metadata": {},
"outputs": [],
"source": [
"class ReadWordsFromText(beam.PTransform):\n",
" \n",
" def __init__(self, file_pattern):\n",
" self._file_pattern = file_pattern\n",
" \n",
" def expand(self, pcoll):\n",
" return (pcoll.pipeline\n",
" | beam.io.ReadFromText(self._file_pattern)\n",
" | beam.FlatMap(lambda line: re.findall(r'[\\w\\']+', line.strip(), re.UNICODE)))"
]
},
{
"cell_type": "markdown",
"id": "f7de319c",
"metadata": {},
"source": [
"To be able to process our data in the notebook environment and explore the PCollections, we will use the interactive runner. We create this pipeline object in the same manner as usually, but passing in `InteractiveRunner()` as the runner."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8fa57ab4",
"metadata": {},
"outputs": [],
"source": [
"p = beam.Pipeline(InteractiveRunner())"
]
},
{
"cell_type": "markdown",
"id": "8c1fdcdb",
"metadata": {},
"source": [
"Now we're ready to start processing our data! We first apply our `ReadWordsFromText` transform to read in the lines of text from Google Cloud Storage and parse into individual words."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b3d67e4c",
"metadata": {},
"outputs": [],
"source": [
"words = p | 'ReadWordsFromText' >> ReadWordsFromText('gs://apache-beam-samples/shakespeare/kinglear.txt')"
]
},
{
"cell_type": "markdown",
"id": "e62cb595",
"metadata": {},
"source": [
"Now we will see some capabilities of the interactive runner. First we can use `ib.show` to view the contents of a specific `PCollection` from any point of our pipeline. "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9286d157",
"metadata": {},
"outputs": [],
"source": [
"ib.show(words)"
]
},
{
"cell_type": "markdown",
"id": "8be2093a",
"metadata": {},
"source": [
"Great! We see that we have 28,001 words in our PCollection and we can view the words in our PCollection. \n",
"\n",
"We can also view the current DAG for our graph by using the `ib.show_graph()` method. Note that here we pass in the pipeline object rather than a PCollection"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "92c18ce6",
"metadata": {},
"outputs": [],
"source": [
"ib.show_graph(p)"
]
},
{
"cell_type": "markdown",
"id": "9d3c0802",
"metadata": {},
"source": [
"In the above graph, the rectanglar boxes correspond to PTransforms and the circles correspond to PCollections. \n",
"\n",
"Next we will add a simple schema to our PCollection and convert the PCollection into a dataframe using the `to_dataframe` method. "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "95bf1d2d",
"metadata": {},
"outputs": [],
"source": [
"word_rows = words | 'ToRows' >> beam.Map(lambda word: beam.Row(word=word))\n",
"\n",
"df = to_dataframe(word_rows)"
]
},
{
"cell_type": "markdown",
"id": "b3694d5d",
"metadata": {},
"source": [
"We can now explore our PCollection as a Pandas-like dataframe! One of the first things many data scientists do as soon as they load data into a dataframe is explore the first few rows of data using the `head` method. Let's see what happens here."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d963e261",
"metadata": {},
"outputs": [],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"id": "77ba08e0",
"metadata": {},
"source": [
"Notice that we got a very specific type of error! The `WontImplementError` is for Pandas methods that will not be implemented for Beam dataframes. These are methods that violate the Beam model for one reason or another. For example, in this case the `head` method depends on the order of the dataframe. However, this is in conflict with the Beam model. "
]
},
{
"cell_type": "markdown",
"id": "9a04cc1b",
"metadata": {},
"source": [
"Our goal however is to count the number of times each word appears in the ingested text. First we will add a new column in our dataframe named `count` with a value of `1` for all rows. After that, we will group by the value of the `word` column and apply the `sum` method for the `count` field."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "467c8822",
"metadata": {},
"outputs": [],
"source": [
"df['count'] = 1\n",
"counted = df.groupby('word').sum()"
]
},
{
"cell_type": "markdown",
"id": "9ea4ccdf",
"metadata": {},
"source": [
"That's it! It looks exactly like the code one would write when using Pandas. However, what does this look like in the DAG for the pipeline? We can see this by executing `ib.show_graph(p)` as before."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e79fbd2c",
"metadata": {},
"outputs": [],
"source": [
"ib.show_graph(p)"
]
},
{
"cell_type": "markdown",
"id": "fdf3c807",
"metadata": {},
"source": [
"We can see that the dataframe manipulations added a new PTransform to our pipeline. Let us convert the dataframe back to a PCollection so we can use `ib.show` to view the contents."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5ee7367e",
"metadata": {},
"outputs": [],
"source": [
"word_counts = to_pcollection(counted, include_indexes=True)\n",
"ib.show(word_counts)"
]
},
{
"cell_type": "markdown",
"id": "9afd928f",
"metadata": {},
"source": [
"Great! We can now see that the words have been successfully counted. Finally let us build in a sink into the pipeline. We can do this in two ways. If we wish to write to a CSV file, then we can use the dataframe's `to_csv` method. We can also use the `WriteToText` transform after converting back to a PCollection. Let's do both and explore the outputs."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "50f5f821",
"metadata": {},
"outputs": [],
"source": [
"counted.to_csv('from_df.csv')\n",
"_ = word_counts | beam.io.WriteToText('from_pcoll.csv')"
]
},
{
"cell_type": "raw",
"id": "2e41e3ba",
"metadata": {},
"source": [
"Before saving the outputs to the sinks, let's take a peek at our finished pipeline."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "56cb1509",
"metadata": {},
"outputs": [],
"source": [
"ib.show_graph(p)"
]
},
{
"cell_type": "markdown",
"id": "b22b0b07",
"metadata": {},
"source": [
"Note that we can see the branching with two different sinks, also we can see where the dataframe is converted back to a PCollection. We can run our entire pipeline by using `p.run()` as normal."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3e365786",
"metadata": {},
"outputs": [],
"source": [
"p.run()"
]
},
{
"cell_type": "markdown",
"id": "601272b9",
"metadata": {},
"source": [
"Let us now look at the beginning of the CSV files using the bash line magic with the `head` command to compare."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bb0407fb",
"metadata": {},
"outputs": [],
"source": [
"!head from_df*"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4f6ae2b0",
"metadata": {},
"outputs": [],
"source": [
"!head from_pcoll*"
]
},
{
"cell_type": "markdown",
"id": "b5cd1e97",
"metadata": {},
"source": [
"We (functionally) end up with the same information as expected! The big difference is in how the results are presented. In the case of the output from the `WriteToText` connector, we did not convert our PCollection from objects of type `Row`. We could write a simple intermediate transform to pull out the properties of the `Row` object into a comma-seperated representation. For example:\n",
"\n",
"```\n",
"def row_to_csv(element):\n",
" output = f\"{element.word},{element.count}\"\n",
" return output\n",
"```\n",
"\n",
"The we could replace the code `_ = word_counts | beam.io.WriteToText('from_pcoll.csv')` with\n",
"\n",
"```\n",
"_ = word_counts | beam.Map(row_to_csv)\n",
" | beam.io.WriteToText('from_pcoll.csv')\n",
"```\n",
"\n",
"However, note that the `to_csv` method for the dataframe took care of this conversion for us."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Apache Beam 2.29.0 for Python 3",
"language": "python",
"name": "apache-beam-2.29.0"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| apache-2.0 |
biothings/biothings_explorer | jupyter notebooks/Multi intermediate nodes query.ipynb | 1 | 166060 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction\n",
"\n",
"\n",
"This notebook demonstrates how BioThings Explorer can be used to execute queries having more than one intermediate nodes:\n",
"\n",
"The query starts from drug \"Anisindione\", the two intermediate nodes with be *Gene and DiseaseOrPhenotypicFeature\", the final output will be \"PhenotypicFeature\".\n",
"\n",
"\n",
"**Background**: BioThings Explorer can answer two classes of queries -- \"EXPLAIN\" and \"PREDICT\". EXPLAIN queries are described in [EXPLAIN_demo.ipynb](https://github.com/biothings/biothings_explorer/blob/master/jupyter%20notebooks/EXPLAIN_demo.ipynb), and PREDICT queries are described in [PREDICT_demo.ipynb](https://github.com/biothings/biothings_explorer/blob/master/jupyter%20notebooks/PREDICT_demo.ipynb). Here, we describe PREDICT queries and how to use BioThings Explorer to execute them. A more detailed overview of the BioThings Explorer systems is provided in [these slides](https://docs.google.com/presentation/d/1QWQqqQhPD_pzKryh6Wijm4YQswv8pAjleVORCPyJyDE/edit?usp=sharing)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**To experiment with an executable version of this notebook, [load it in Google Colaboratory](https://colab.research.google.com/github/biothings/biothings_explorer/blob/master/jupyter%20notebooks/Multi%20intermediate%20nodes%20query.ipynb).**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 0: Load BioThings Explorer modules\n",
"\n",
"Install the `biothings_explorer` and `biothings_schema` packages, as described in this [README](https://github.com/biothings/biothings_explorer/blob/master/jupyter%20notebooks/README.md#prerequisite). This only needs to be done once (but including it here for compability with [colab](https://colab.research.google.com/))."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install git+https://github.com/biothings/biothings_explorer#egg=biothings_explorer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, import the relevant modules:\n",
"\n",
"* **Hint**: Find corresponding bio-entity representation used in BioThings Explorer based on user input (could be any database IDs, symbols, names)\n",
"* **FindConnection**: Find intermediate bio-entities which connects user specified input and output"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from biothings_explorer.hint import Hint\n",
"from biothings_explorer.user_query_dispatcher import FindConnection\n",
"import nest_asyncio\n",
"nest_asyncio.apply()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 1: Find representation of \"Anisindione\" in BTE\n",
"\n",
"In this step, BioThings Explorer translates our query string \"Anisindioine\" into BioThings objects, which contain mappings to many common identifiers. Generally, the top result returned by the `Hint` module will be the correct item, but you should confirm that using the identifiers shown.\n",
"\n",
"Search terms can correspond to any child of [BiologicalEntity](https://biolink.github.io/biolink-model/docs/BiologicalEntity.html) from the [Biolink Model](https://biolink.github.io/biolink-model/docs/), including `DiseaseOrPhenotypicFeature` (e.g., \"lupus\"), `ChemicalSubstance` (e.g., \"acetaminophen\"), `Gene` (e.g., \"CDK2\"), `BiologicalProcess` (e.g., \"T cell differentiation\"), and `Pathway` (e.g., \"Citric acid cycle\")."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'DRUGBANK': 'DB01125',\n",
" 'CHEBI': 'CHEBI:133809',\n",
" 'name': 'anisindione',\n",
" 'primary': {'identifier': 'CHEBI',\n",
" 'cls': 'ChemicalSubstance',\n",
" 'value': 'CHEBI:133809'},\n",
" 'display': 'CHEBI(CHEBI:133809) DRUGBANK(DB01125) name(anisindione)',\n",
" 'type': 'ChemicalSubstance'}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ht = Hint()\n",
"anisindione = ht.query(\"Anisindione\")['ChemicalSubstance'][0]\n",
"\n",
"anisindione"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 2: Find phenotypes that are associated with Anisindione through Gene and DiseaseOrPhenotypicFeature as intermediate nodes\n",
"\n",
"In this section, we find all paths in the knowledge graph that connect Anisindione to any entity that is a phenotypic feature. To do that, we will use `FindConnection`. This class is a convenient wrapper around two advanced functions for **query path planning** and **query path execution**. More advanced features for both query path planning and query path execution are in development and will be documented in the coming months. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"fc = FindConnection(input_obj=anisindione, \n",
" output_obj='PhenotypicFeature', \n",
" intermediate_nodes=['Gene', 'Disease'])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"==========\n",
"========== QUERY PARAMETER SUMMARY ==========\n",
"==========\n",
"\n",
"BTE will find paths that join 'anisindione' and 'PhenotypicFeature'. Paths will have 2 intermediate node.\n",
"\n",
"Intermediate node #1 will have these type constraints: Gene\n",
"\n",
"Intermediate node #2 will have these type constraints: Disease\n",
"\n",
"\n",
"\n",
"========== QUERY #1 -- fetch all Gene entities linked to anisindione ==========\n",
"==========\n",
"\n",
"==== Step #1: Query path planning ====\n",
"\n",
"Because anisindione is of type 'ChemicalSubstance', BTE will query our meta-KG for APIs that can take 'ChemicalSubstance' as input and 'Gene' as output\n",
"\n",
"BTE found 10 apis:\n",
"\n",
"API 1. semmed_chemical(13 API calls)\n",
"API 2. chembio(1 API call)\n",
"API 3. hmdb(1 API call)\n",
"API 4. mychem(3 API calls)\n",
"API 5. scigraph(1 API call)\n",
"API 6. scibite(1 API call)\n",
"API 7. dgidb(1 API call)\n",
"API 8. pharos(1 API call)\n",
"API 9. ctd(1 API call)\n",
"API 10. cord_chemical(1 API call)\n",
"\n",
"\n",
"==== Step #2: Query path execution ====\n",
"NOTE: API requests are dispatched in parallel, so the list of APIs below is ordered by query time.\n",
"\n",
"API 2.1: https://biothings.ncats.io/semmedchemical/query?fields=physically_interacts_with (POST -d q=C0051919&scopes=umls)\n",
"API 5.1: https://mychem.info/v1/query?fields=drugbank.targets (POST -d q=DB01125&scopes=drugbank.id)\n",
"API 2.10: https://biothings.ncats.io/semmedchemical/query?fields=negatively_regulates (POST -d q=C0051919&scopes=umls)\n",
"API 2.11: https://biothings.ncats.io/semmedchemical/query?fields=affects (POST -d q=C0051919&scopes=umls)\n",
"API 2.7: https://biothings.ncats.io/semmedchemical/query?fields=coexists_with (POST -d q=C0051919&scopes=umls)\n",
"API 5.2: https://mychem.info/v1/query?fields=drugcentral.bioactivity (POST -d q=CHEMBL712&scopes=chembl.molecule_chembl_id)\n",
"API 5.3: https://mychem.info/v1/query?fields=drugbank.enzymes (POST -d q=DB01125&scopes=drugbank.id)\n",
"API 2.9: https://biothings.ncats.io/semmedchemical/query?fields=produced_by (POST -d q=C0051919&scopes=umls)\n",
"API 9.1: http://ctdbase.org/tools/batchQuery.go?inputType=chem&report=genes_curated&format=json&inputTerms=C010679\n",
"API 2.4: https://biothings.ncats.io/semmedchemical/query?fields=disrupted_by (POST -d q=C0051919&scopes=umls)\n",
"API 2.3: https://biothings.ncats.io/semmedchemical/query?fields=negatively_regulated_by (POST -d q=C0051919&scopes=umls)\n",
"API 2.12: https://biothings.ncats.io/semmedchemical/query?fields=affected_by (POST -d q=C0051919&scopes=umls)\n",
"API 10.1: https://biothings.ncats.io/cord_chemical/query?fields=associated_with (POST -d q=CHEBI:133809&scopes=chebi)\n",
"API 2.5: https://biothings.ncats.io/semmedchemical/query?fields=related_to (POST -d q=C0051919&scopes=umls)\n",
"API 2.8: https://biothings.ncats.io/semmedchemical/query?fields=produces (POST -d q=C0051919&scopes=umls)\n",
"API 2.6: https://biothings.ncats.io/semmedchemical/query?fields=disrupts (POST -d q=C0051919&scopes=umls)\n",
"API 2.13: https://biothings.ncats.io/semmedchemical/query?fields=positively_regulated_by (POST -d q=C0051919&scopes=umls)\n",
"API 7.1: http://dgidb.genome.wustl.edu/api/v2/interactions.json?drugs=CHEMBL712\n",
"API 2.2: https://biothings.ncats.io/semmedchemical/query?fields=positively_regulates (POST -d q=C0051919&scopes=umls)\n",
"API 1.1: https://automat.renci.org/chembio/chemical_substance/gene/CHEBI:133809\n",
"API 8.1: https://automat.renci.org/pharos/chemical_substance/gene/CHEBI:133809\n",
"API 3.1: https://automat.renci.org/hmdb/chemical_substance/gene/CHEBI:133809\n",
"API 6.1: https://automat.renci.org/cord19_scibite_v2/chemical_substance/gene/CHEBI:133809\n",
"API 4.1: https://automat.renci.org/cord19_scigraph_v2/chemical_substance/gene/CHEBI:133809\n",
"\n",
"\n",
"==== Step #3: Output normalization ====\n",
"\n",
"API 2.1 semmed_chemical: No hits\n",
"API 1.1 chembio: No hits\n",
"API 5.1 mychem: 1 hits\n",
"API 5.2 mychem: 2 hits\n",
"API 5.3 mychem: No hits\n",
"API 6.1 scibite: No hits\n",
"API 8.1 pharos: 1 hits\n",
"API 2.2 semmed_chemical: No hits\n",
"API 2.3 semmed_chemical: No hits\n",
"API 2.4 semmed_chemical: No hits\n",
"API 2.5 semmed_chemical: No hits\n",
"API 4.1 scigraph: No hits\n",
"API 7.1 dgidb: 2 hits\n",
"API 3.1 hmdb: 1 hits\n",
"API 2.6 semmed_chemical: No hits\n",
"API 2.7 semmed_chemical: No hits\n",
"API 2.8 semmed_chemical: No hits\n",
"API 2.9 semmed_chemical: No hits\n",
"API 10.1 cord_chemical: No hits\n",
"API 9.1 ctd: No hits\n",
"API 2.10 semmed_chemical: No hits\n",
"API 2.11 semmed_chemical: No hits\n",
"API 2.12 semmed_chemical: No hits\n",
"API 2.13 semmed_chemical: No hits\n",
"\n",
"After id-to-object translation, BTE retrieved 3 unique objects.\n",
"\n",
"\n",
"========== QUERY #2.1 -- fetch all Disease entities linked to Gene entites ==========\n",
"==========\n",
"\n",
"==== Step #1: Query path planning ====\n",
"\n",
"Because None is of type 'Gene', BTE will query our meta-KG for APIs that can take 'Gene' as input and 'Disease' as output\n",
"\n",
"BTE found 10 apis:\n",
"\n",
"API 1. semmed_gene(13 API calls)\n",
"API 2. mydisease(1 API call)\n",
"API 3. scigraph(3 API calls)\n",
"API 4. scibite(3 API calls)\n",
"API 5. pharos(3 API calls)\n",
"API 6. hetio(3 API calls)\n",
"API 7. biolink(3 API calls)\n",
"API 8. DISEASES(1 API call)\n",
"API 9. ctd(3 API calls)\n",
"API 10. cord_gene(1 API call)\n",
"\n",
"\n",
"==== Step #2: Query path execution ====\n",
"NOTE: API requests are dispatched in parallel, so the list of APIs below is ordered by query time.\n",
"\n",
"API 8.2: https://api.monarchinitiative.org/api/bioentity/gene/NCBIGene:240/diseases?rows=200\n",
"API 8.3: https://api.monarchinitiative.org/api/bioentity/gene/NCBIGene:2638/diseases?rows=200\n",
"API 9.1: https://pending.biothings.io/DISEASES/query?fields=DISEASES.doid&size=250 (POST -d q=ALOX5,GGCX,GC&scopes=DISEASES.associatedWith.symbol)\n",
"API 8.1: https://api.monarchinitiative.org/api/bioentity/gene/NCBIGene:2677/diseases?rows=200\n",
"API 2.1: http://mydisease.info/v1/query?fields=disgenet.xrefs.umls&size=250 (POST -d q=2677,240,2638&scopes=disgenet.genes_related_to_disease.gene_id)\n",
"API 1.1: https://biothings.ncats.io/semmedgene/query?fields=positively_regulates (POST -d q=C1367452,C1415050,C1412361,C0003693&scopes=umls)\n",
"API 1.7: https://biothings.ncats.io/semmedgene/query?fields=positively_regulated_by (POST -d q=C1367452,C1415050,C1412361,C0003693&scopes=umls)\n",
"API 1.9: https://biothings.ncats.io/semmedgene/query?fields=treats (POST -d q=C1367452,C1415050,C1412361,C0003693&scopes=umls)\n",
"API 1.11: https://biothings.ncats.io/semmedgene/query?fields=affected_by (POST -d q=C1367452,C1415050,C1412361,C0003693&scopes=umls)\n",
"API 1.4: https://biothings.ncats.io/semmedgene/query?fields=disrupted_by (POST -d q=C1367452,C1415050,C1412361,C0003693&scopes=umls)\n",
"API 1.6: https://biothings.ncats.io/semmedgene/query?fields=causes (POST -d q=C1367452,C1415050,C1412361,C0003693&scopes=umls)\n",
"API 1.5: https://biothings.ncats.io/semmedgene/query?fields=negatively_regulated_by (POST -d q=C1367452,C1415050,C1412361,C0003693&scopes=umls)\n",
"API 1.12: https://biothings.ncats.io/semmedgene/query?fields=negatively_regulates (POST -d q=C1367452,C1415050,C1412361,C0003693&scopes=umls)\n",
"API 1.8: https://biothings.ncats.io/semmedgene/query?fields=prevents (POST -d q=C1367452,C1415050,C1412361,C0003693&scopes=umls)\n",
"API 1.2: https://biothings.ncats.io/semmedgene/query?fields=disrupts (POST -d q=C1367452,C1415050,C1412361,C0003693&scopes=umls)\n",
"API 1.3: https://biothings.ncats.io/semmedgene/query?fields=physically_interacts_with (POST -d q=C1367452,C1415050,C1412361,C0003693&scopes=umls)\n",
"API 1.13: https://biothings.ncats.io/semmedgene/query?fields=affects (POST -d q=C1367452,C1415050,C1412361,C0003693&scopes=umls)\n",
"API 1.10: https://biothings.ncats.io/semmedgene/query?fields=related_to (POST -d q=C1367452,C1415050,C1412361,C0003693&scopes=umls)\n",
"API 10.1: https://biothings.ncats.io/cord_gene/query?fields=associated_with (POST -d q=435,4247,4187&scopes=hgnc)\n",
"API 4.1: https://automat.renci.org/cord19_scibite_v2/gene/disease/NCBIGene:2677\n",
"API 5.1: https://automat.renci.org/pharos/gene/disease/NCBIGene:2677\n",
"API 3.1: https://automat.renci.org/cord19_scigraph_v2/gene/disease/NCBIGene:2677\n",
"API 4.2: https://automat.renci.org/cord19_scibite_v2/gene/disease/NCBIGene:240\n",
"API 3.2: https://automat.renci.org/cord19_scigraph_v2/gene/disease/NCBIGene:240\n",
"API 5.3: https://automat.renci.org/pharos/gene/disease/NCBIGene:2638\n",
"API 4.3: https://automat.renci.org/cord19_scibite_v2/gene/disease/NCBIGene:2638\n",
"API 3.3: https://automat.renci.org/cord19_scigraph_v2/gene/disease/NCBIGene:2638\n",
"API 6.3: https://automat.renci.org/hetio/gene/disease/NCBIGene:2638\n",
"API 5.2: https://automat.renci.org/pharos/gene/disease/NCBIGene:240\n",
"API 6.1: https://automat.renci.org/hetio/gene/disease/NCBIGene:2677\n",
"API 7.2: http://ctdbase.org/tools/batchQuery.go?inputType=gene&report=diseases_curated&format=json&inputTerms=240\n",
"API 7.3: http://ctdbase.org/tools/batchQuery.go?inputType=gene&report=diseases_curated&format=json&inputTerms=2638\n",
"API 7.1: http://ctdbase.org/tools/batchQuery.go?inputType=gene&report=diseases_curated&format=json&inputTerms=2677\n",
"API 6.2: https://automat.renci.org/hetio/gene/disease/NCBIGene:240\n",
"\n",
"\n",
"==== Step #3: Output normalization ====\n",
"\n",
"API 1.1 semmed_gene: No hits\n",
"API 1.2 semmed_gene: 12 hits\n",
"API 1.3 semmed_gene: No hits\n",
"API 4.1 scibite: 4 hits\n",
"API 4.2 scibite: 1 hits\n",
"API 4.3 scibite: 7 hits\n",
"API 1.4 semmed_gene: No hits\n",
"API 9.1 DISEASES: 92 hits\n",
"API 1.5 semmed_gene: No hits\n",
"API 1.6 semmed_gene: 57 hits\n",
"API 1.7 semmed_gene: No hits\n",
"API 5.1 pharos: 7 hits\n",
"API 5.2 pharos: 2 hits\n",
"API 5.3 pharos: 2 hits\n",
"API 6.1 hetio: No hits\n",
"API 6.2 hetio: 8 hits\n",
"API 6.3 hetio: 3 hits\n",
"API 8.1 biolink: 5 hits\n",
"API 8.2 biolink: 1 hits\n",
"API 8.3 biolink: No hits\n",
"API 2.1 mydisease: 88 hits\n",
"API 1.8 semmed_gene: 11 hits\n",
"API 1.9 semmed_gene: 52 hits\n",
"API 3.1 scigraph: No hits\n",
"API 3.2 scigraph: No hits\n",
"API 3.3 scigraph: 15 hits\n",
"API 1.10 semmed_gene: 123 hits\n",
"API 1.11 semmed_gene: 3 hits\n",
"API 10.1 cord_gene: 139 hits\n",
"API 1.12 semmed_gene: No hits\n",
"API 1.13 semmed_gene: 44 hits\n",
"API 7.1 ctd: 4 hits\n",
"API 7.2 ctd: 28 hits\n",
"API 7.3 ctd: 15 hits\n",
"\n",
"After id-to-object translation, BTE retrieved 473 unique objects.\n",
"\n",
"\n",
"========== QUERY #3.1 -- fetch all PhenotypicFeature entities linked to Disease entites ==========\n",
"==========\n",
"\n",
"==== Step #1: Query path planning ====\n",
"\n",
"Because None is of type 'Disease', BTE will query our meta-KG for APIs that can take 'Disease' as input and 'PhenotypicFeature' as output\n",
"\n",
"BTE found 3 apis:\n",
"\n",
"API 1. mydisease(1 API call)\n",
"API 2. biolink(413 API calls)\n",
"API 3. semmed_disease(11 API calls)\n",
"\n",
"\n",
"==== Step #2: Query path execution ====\n",
"NOTE: API requests are dispatched in parallel, so the list of APIs below is ordered by query time.\n",
"\n",
"API 2.93: https://api.monarchinitiative.org/api/bioentity/disease/C4277682/phenotypes?rows=200\n",
"API 2.65: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002278/phenotypes?rows=200\n",
"API 2.78: https://api.monarchinitiative.org/api/bioentity/disease/C0086132/phenotypes?rows=200\n",
"API 2.64: https://api.monarchinitiative.org/api/bioentity/disease/C0029131/phenotypes?rows=200\n",
"API 2.77: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0003105/phenotypes?rows=200\n",
"API 1.1: http://mydisease.info/v1/query?fields=hpo.phenotype_related_to_disease (POST -d q=109100,270200,189800,143100,109350,226150,268400,219700,600138,114550,611155,277450,268300,166710,206920,613091,262890,310500,254210,184700,120970,107650,180100,218040,137600,268220,610842,605074,115000,602096,603903,309585,602771,606176,104300,237500,613659,607140,601665,309605,605389,227600,180300,227500,107100,211980,615429,264800,222448,613679,145500,268000,268310,167000,178500,611775,601367,133239,109800,308960,600807,176807,181500,214500,272460,152700,603174,180200,247200,601626,273300,608232,607154,153700,137760,203700&scopes=hpo.omim)\n",
"API 2.86: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002276/phenotypes?rows=200\n",
"API 2.94: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005096/phenotypes?rows=200\n",
"API 2.82: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005401/phenotypes?rows=200\n",
"API 2.85: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0009026/phenotypes?rows=200\n",
"API 2.62: https://api.monarchinitiative.org/api/bioentity/disease/C0333641/phenotypes?rows=200\n",
"API 2.72: https://api.monarchinitiative.org/api/bioentity/disease/C0019080/phenotypes?rows=200\n",
"API 2.68: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0007186/phenotypes?rows=200\n",
"API 2.51: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002008/phenotypes?rows=200\n",
"API 2.73: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0018998/phenotypes?rows=200\n",
"API 2.75: https://api.monarchinitiative.org/api/bioentity/disease/C0426414/phenotypes?rows=200\n",
"API 2.35: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0018577/phenotypes?rows=200\n",
"API 2.57: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0010187/phenotypes?rows=200\n",
"API 2.50: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0015760/phenotypes?rows=200\n",
"API 2.52: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005108/phenotypes?rows=200\n",
"API 2.44: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005929/phenotypes?rows=200\n",
"API 2.63: https://api.monarchinitiative.org/api/bioentity/disease/C4020885/phenotypes?rows=200\n",
"API 2.69: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0021355/phenotypes?rows=200\n",
"API 2.92: https://api.monarchinitiative.org/api/bioentity/disease/C0243066/phenotypes?rows=200\n",
"API 2.71: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005364/phenotypes?rows=200\n",
"API 2.76: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0000984/phenotypes?rows=200\n",
"API 2.43: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004992/phenotypes?rows=200\n",
"API 2.74: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0016175/phenotypes?rows=200\n",
"API 2.17: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0012632/phenotypes?rows=200\n",
"API 2.61: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004995/phenotypes?rows=200\n",
"API 2.59: https://api.monarchinitiative.org/api/bioentity/disease/C0233794/phenotypes?rows=200\n",
"API 2.18: https://api.monarchinitiative.org/api/bioentity/disease/C0577631/phenotypes?rows=200\n",
"API 2.84: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005347/phenotypes?rows=200\n",
"API 2.83: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0017880/phenotypes?rows=200\n",
"API 2.87: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0013361/phenotypes?rows=200\n",
"API 2.91: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0007256/phenotypes?rows=200\n",
"API 2.70: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005240/phenotypes?rows=200\n",
"API 2.81: https://api.monarchinitiative.org/api/bioentity/disease/C2930812/phenotypes?rows=200\n",
"API 2.54: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0009172/phenotypes?rows=200\n",
"API 2.45: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002492/phenotypes?rows=200\n",
"API 2.53: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0000001/phenotypes?rows=200\n",
"API 2.67: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004982/phenotypes?rows=200\n",
"API 2.49: https://api.monarchinitiative.org/api/bioentity/disease/C4020899/phenotypes?rows=200\n",
"API 2.60: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0001415/phenotypes?rows=200\n",
"API 2.90: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0000313/phenotypes?rows=200\n",
"API 2.66: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005574/phenotypes?rows=200\n",
"API 2.55: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0008315/phenotypes?rows=200\n",
"API 2.42: https://api.monarchinitiative.org/api/bioentity/disease/C0024032/phenotypes?rows=200\n",
"API 2.37: https://api.monarchinitiative.org/api/bioentity/disease/MESH:D009362/phenotypes?rows=200\n",
"API 2.10: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0016222/phenotypes?rows=200\n",
"API 2.80: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0020290/phenotypes?rows=200\n",
"API 2.34: https://api.monarchinitiative.org/api/bioentity/disease/C0553692/phenotypes?rows=200\n",
"API 2.29: https://api.monarchinitiative.org/api/bioentity/disease/C0036974/phenotypes?rows=200\n",
"API 2.32: https://api.monarchinitiative.org/api/bioentity/disease/C0332563/phenotypes?rows=200\n",
"API 2.12: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0000986/phenotypes?rows=200\n",
"API 2.16: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0019298/phenotypes?rows=200\n",
"API 2.36: https://api.monarchinitiative.org/api/bioentity/disease/C0235946/phenotypes?rows=200\n",
"API 2.47: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0016430/phenotypes?rows=200\n",
"API 2.88: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0018076/phenotypes?rows=200\n",
"API 2.26: https://api.monarchinitiative.org/api/bioentity/disease/MESH:D009102/phenotypes?rows=200\n",
"API 2.38: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0001881/phenotypes?rows=200\n",
"API 2.33: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005043/phenotypes?rows=200\n",
"API 2.8: https://api.monarchinitiative.org/api/bioentity/disease/C0014591/phenotypes?rows=200\n",
"API 2.89: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0007915/phenotypes?rows=200\n",
"API 2.20: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0015691/phenotypes?rows=200\n",
"API 2.11: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005578/phenotypes?rows=200\n",
"API 2.25: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0007179/phenotypes?rows=200\n",
"API 2.30: https://api.monarchinitiative.org/api/bioentity/disease/C0278488/phenotypes?rows=200\n",
"API 2.58: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0010643/phenotypes?rows=200\n",
"API 2.28: https://api.monarchinitiative.org/api/bioentity/disease/C0011609/phenotypes?rows=200\n",
"API 2.40: https://api.monarchinitiative.org/api/bioentity/disease/C0242184/phenotypes?rows=200\n",
"API 2.5: https://api.monarchinitiative.org/api/bioentity/disease/C0023893/phenotypes?rows=200\n",
"API 2.46: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002254/phenotypes?rows=200\n",
"API 2.9: https://api.monarchinitiative.org/api/bioentity/disease/C0027627/phenotypes?rows=200\n",
"API 2.14: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0009885/phenotypes?rows=200\n",
"API 2.2: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0000184/phenotypes?rows=200\n",
"API 2.56: https://api.monarchinitiative.org/api/bioentity/disease/C3278975/phenotypes?rows=200\n",
"API 2.22: https://api.monarchinitiative.org/api/bioentity/disease/C0000768/phenotypes?rows=200\n",
"API 2.19: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0003634/phenotypes?rows=200\n",
"API 2.27: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0009999/phenotypes?rows=200\n",
"API 2.48: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0009689/phenotypes?rows=200\n",
"API 2.21: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0003012/phenotypes?rows=200\n",
"API 2.3: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004431/phenotypes?rows=200\n",
"API 2.13: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005016/phenotypes?rows=200\n",
"API 2.24: https://api.monarchinitiative.org/api/bioentity/disease/MESH:D019446/phenotypes?rows=200\n",
"API 2.1: https://api.monarchinitiative.org/api/bioentity/disease/C0730290/phenotypes?rows=200\n",
"API 2.79: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0044070/phenotypes?rows=200\n",
"API 2.4: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005298/phenotypes?rows=200\n",
"API 2.39: https://api.monarchinitiative.org/api/bioentity/disease/C0014072/phenotypes?rows=200\n",
"API 2.7: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005350/phenotypes?rows=200\n",
"API 2.31: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005338/phenotypes?rows=200\n",
"API 2.23: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0009380/phenotypes?rows=200\n",
"API 2.15: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0007781/phenotypes?rows=200\n",
"API 2.97: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004375/phenotypes?rows=200\n",
"API 2.6: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005264/phenotypes?rows=200\n",
"API 2.95: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0024327/phenotypes?rows=200\n",
"API 2.101: https://api.monarchinitiative.org/api/bioentity/disease/C0002938/phenotypes?rows=200\n",
"API 2.41: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005550/phenotypes?rows=200\n",
"API 2.102: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005279/phenotypes?rows=200\n",
"API 2.103: https://api.monarchinitiative.org/api/bioentity/disease/C0035126/phenotypes?rows=200\n",
"API 2.98: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0007662/phenotypes?rows=200\n",
"API 2.99: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0017570/phenotypes?rows=200\n",
"API 2.96: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0009925/phenotypes?rows=200\n",
"API 2.105: https://api.monarchinitiative.org/api/bioentity/disease/C0149721/phenotypes?rows=200\n",
"API 2.108: https://api.monarchinitiative.org/api/bioentity/disease/C4024702/phenotypes?rows=200\n",
"API 2.100: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0019040/phenotypes?rows=200\n",
"API 2.109: https://api.monarchinitiative.org/api/bioentity/disease/C0042693/phenotypes?rows=200\n",
"API 2.111: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0017884/phenotypes?rows=200\n",
"API 2.110: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005812/phenotypes?rows=200\n",
"API 2.104: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005657/phenotypes?rows=200\n",
"API 2.106: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0009104/phenotypes?rows=200\n",
"API 2.115: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004946/phenotypes?rows=200\n",
"API 2.112: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0015305/phenotypes?rows=200\n",
"API 2.113: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0007665/phenotypes?rows=200\n",
"API 2.114: https://api.monarchinitiative.org/api/bioentity/disease/C0333463/phenotypes?rows=200\n",
"API 2.107: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002367/phenotypes?rows=200\n",
"API 2.119: https://api.monarchinitiative.org/api/bioentity/disease/MESH:D009361/phenotypes?rows=200\n",
"API 2.120: https://api.monarchinitiative.org/api/bioentity/disease/C0004093/phenotypes?rows=200\n",
"API 2.116: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005271/phenotypes?rows=200\n",
"API 2.118: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0008170/phenotypes?rows=200\n",
"API 2.117: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005009/phenotypes?rows=200\n",
"API 2.122: https://api.monarchinitiative.org/api/bioentity/disease/C0730345/phenotypes?rows=200\n",
"API 2.124: https://api.monarchinitiative.org/api/bioentity/disease/C0026766/phenotypes?rows=200\n",
"API 2.125: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0007254/phenotypes?rows=200\n",
"API 2.121: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0018081/phenotypes?rows=200\n",
"API 2.126: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005386/phenotypes?rows=200\n",
"API 2.131: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005709/phenotypes?rows=200\n",
"API 2.123: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002321/phenotypes?rows=200\n",
"API 2.130: https://api.monarchinitiative.org/api/bioentity/disease/MESH:D052878/phenotypes?rows=200\n",
"API 2.128: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0001056/phenotypes?rows=200\n",
"API 2.127: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0011549/phenotypes?rows=200\n",
"API 2.133: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005359/phenotypes?rows=200\n",
"API 2.129: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004950/phenotypes?rows=200\n",
"API 2.135: https://api.monarchinitiative.org/api/bioentity/disease/C1852548/phenotypes?rows=200\n",
"API 2.138: https://api.monarchinitiative.org/api/bioentity/disease/C4021097/phenotypes?rows=200\n",
"API 2.140: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005160/phenotypes?rows=200\n",
"API 2.137: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0008383/phenotypes?rows=200\n",
"API 2.136: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0008963/phenotypes?rows=200\n",
"API 2.132: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005156/phenotypes?rows=200\n",
"API 2.143: https://api.monarchinitiative.org/api/bioentity/disease/C0020429/phenotypes?rows=200\n",
"API 2.139: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002645/phenotypes?rows=200\n",
"API 2.144: https://api.monarchinitiative.org/api/bioentity/disease/MESH:D006930/phenotypes?rows=200\n",
"API 2.134: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002525/phenotypes?rows=200\n",
"API 2.147: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0009994/phenotypes?rows=200\n",
"API 2.150: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0003334/phenotypes?rows=200\n",
"API 2.151: https://api.monarchinitiative.org/api/bioentity/disease/C0003578/phenotypes?rows=200\n",
"API 2.149: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0001106/phenotypes?rows=200\n",
"API 2.145: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004790/phenotypes?rows=200\n",
"API 3.3: https://biothings.ncats.io/semmed/query?fields=disrupted_by (POST -d q=C0029456,C0264955,C0241954,C0004096,C0730290,C0020640,C0004991,C0020529,C0276226,C0023893,C3279392,C0687133,C0027881,C0242597,C0032339,C0260397,C0856716,C0035078,C0156312,C0014591,C0027627,C0085580,C0683416,C0265509,C0002895,C0007134,C0442750,C1389016,C0152013,C1527249,C0002871,C1859727,C0003486,C0220702,C0205644,C0015230,C0577631,C0346647,C0879257,C1833102,C0153199,C0345904,C0000768,C0264694,C0280803,C0949664,C0022658,C0079772,C0007787,C0521585,C0850624,C0011609,C0027430,C0036974,C0009566,C0877373,C0553707,C0032584,C0008626,C0278488,C0520679,C0011156,CN074280,C0332563,C0029823,C0042880,C0920563,C1299574,C0009404,C0553692,C0040147,C0235946,C0036202,C0007137,C0001339,C0014072,C0242184,C0026769,C0024032,C0025202,C0598309,C0278134,C0022679,C0017185,C4020899,C1305122,C0009376,C0022116,C0035334,C3278975,C0007795,C0233794,C0020538,C1561643,C0334389,C0032914,C0340274,C0333641,C4020885,C0031154,C0029131,C1848534,C0027719,C0001418,C1285162,C0280141,C1136033,C0041806,C0038999,C0205710,C0151744,C0020542,C0019080,C2930957,C0032231,C0425946,C0085639,C0029831,C0426414,C0004238,C0086132,C0035333,C0497327,C0022350,C2930812,C0009319,C0021053,C0011860,C0027022,C1842031,C0042373,C0085786,C0205645,C0037286,C0243066,C4277682,C0600688,C2829267,C1835308,C0002103,C0014356,C0002938,C0035613,C0741923,C0006142,C0035126,C1458155,C0149721,C3151609,C1290884,C0017152,C0019034,C1863926,C0043242,C3495426,C1849334,C0006145,C4024702,C0037274,C0403528,C0020433,C0162739,C0042693,C0006663,C1155266,C0272316,C0599973,C0796149,C3275959,C0008625,C0333463,C0030305,C0085437,C0019158,C0376544,C0004093,C0019151,C0020502,C0002940,C0339510,C0265219,C0005684,C0333186,C0858246,C0699748,C0033847,C0026766,C0002736,C0007102,C0730345,C0162557,C0026244,C0398738,C1691228,C0699790,C0009443,C0162871,C0010674,C0018213,C0029928,C0008354,C1261473,C1852548,C0014868,C0699739,C0036337,C4021097,C0042769,C0030297,C1838601,C0012242,C0272187,C0020429,C1336839,C0025517,C0268271,C1835813,C0272317,C0038379,C0042109,C0596402,C0037231,C0221468,C1292772,C0010068,C0003578,C0022893,C0334108,C0024623,C0013570,C0153417,C0007131,C0393929,C0085166,C1968782,C1306837,C0020456,C0014059,C0002726,C0007112,C0004030,C0023903,C1956346,C0037285,C0023473,C0039082,C3495676,C0024713,C0020507,C0277785,C1266158,C1140680,C0003864,C0014175,C0019189,C1458140,C0015519,C0023467,C0019348,C0701826,C1336708,C1863052,C0333467,C0086565,C0235169,C0037277,C0017668,C0024121,C0022104,C0235222,C0151313,C0587248,C0030920,C0020179,C1442839,C0347653,C1540912,C0012634,C1865868,C2751126,C1853564,C0003469,C0011164,C0018802,C0015503,C1378511,C0029839,C0205643,C1319315,C0596020,C1970143,C0014145,C0280324,C0025521,C0339573,C0029574,C1842026,C0012860,C0497247,C0242379,C0023890,C1457887,C0033578,C1879321,C0282609,C4280569,C0039730,C0009402,C0008925,C1295654,C0232910,C0524851,C1704436,C0085669,C0028077,C0879626,C1839601,C0007120,C0038356,C0221198,C0231337,C0015696,C0497406,C0007965,C1833104,C0153519,C0685938,C0235401,C0005587,C0010495,C0151650,C0596263,C0033687,C0276037,C0011847,C0042870,C3463918,C0038454,C0302142,C3495587,C1096063,C0518010,C0600327,C0677886,C0581342,C1304508,C2936917,C0595921,C0410180,C0005944,C0035335,C1504598,C3665365,C2316810,C1800706,C0546837,C0451641,C0007784,C0338656,C0009450,C1266042,C0017612,C0153594,C0019101,C0154251,C0027051,C0333262,C0860603,C0027626,C1389018,C2676033,C0019385,C0857121,C1266044,C0162735,C0016059,C0041295,C0016658,C0041408,C0022665,C0028754,C0268397,C0085183,C1368683,C0015397,C0392475,C0036983,C2675055,C0235974,C0334517,C2239176,C0038436,C0034065,C1527168,C0039231,C1833269,C0032000,C0041296,C0014070,C0751688,C0023772,C0004153,C0029442,C0919267,C1854310,C0018924,C1301700,C4024853,C0036069,C0005695,C0423798,C0024117,C0271650,C0152018,C0279607,C0032460,C0037315,C0002982,C0242488,C0221074,C0014116,C0001511,C0796004,C0040053,C0872150,C0085681,C0580545,C1549114,C0017154,C0020459,C4023159,C0279000,C0003873,C0340464,C1859126,C0024141,C0085096,C0001430,C0235266,C0003811,C0599732,C0206624,C0376618,C0026691,C0948775,C1839829,C0036631,C0494158,C0026846,C0010054,C1292775,C1869116,C0455825,C0339527,C0014457,C1266043,C0220701,C0015674,C0235032,C0007222,C0020676,C2607914,C0080113,C0270922,C0038433,C0021775,C0279702,C0005586,C0024115,C0018801,C1839736,C0004364,C0021655,C0011881,C0005940,C0035258,C0012243,C0019087,C0242606,C0020615,C0039870,C0021400,C0023895,C0858321,C0346388,C0007097,C0742115,C0019693,C0024620,C0006826,C3151610,C0014859,C0014038,C2718068,C4280606,C0027651,C1519697,C0266215,C0740457,C0017178,C0241863,C1857277,C0006111,C0948008,C0206655,C0005779,C3150911,C0376358,C0019147,C0029458,C0740392,C1839735,C0220633,C1395184,C0041671,C0332448,C0025289,C0020437,C1527304,C0011849,C0013491,C0009375,C1848934,C2931456,C0178874,C2931899,C1532253,C0035579,C0684249,C1320716,C1854114,C0346373,C0003838,C0018799,C0598608,C0497248,C0870182,C0036572,C1292778,C0033575,C0015695,C0699791,C0153458,C0007820,C0042510,C0240671,C3203356,C0752130,C0518003,C4024722,C0023418,C0021368,C0029838,C0036679,C0020517,C0876973,C0085131,C0036341,C0021390,C0002395,C0022661,C0085584,C0017168&scopes=umls)\n",
"API 3.10: https://biothings.ncats.io/semmed/query?fields=disrupts (POST -d q=C0029456,C0264955,C0241954,C0004096,C0730290,C0020640,C0004991,C0020529,C0276226,C0023893,C3279392,C0687133,C0027881,C0242597,C0032339,C0260397,C0856716,C0035078,C0156312,C0014591,C0027627,C0085580,C0683416,C0265509,C0002895,C0007134,C0442750,C1389016,C0152013,C1527249,C0002871,C1859727,C0003486,C0220702,C0205644,C0015230,C0577631,C0346647,C0879257,C1833102,C0153199,C0345904,C0000768,C0264694,C0280803,C0949664,C0022658,C0079772,C0007787,C0521585,C0850624,C0011609,C0027430,C0036974,C0009566,C0877373,C0553707,C0032584,C0008626,C0278488,C0520679,C0011156,CN074280,C0332563,C0029823,C0042880,C0920563,C1299574,C0009404,C0553692,C0040147,C0235946,C0036202,C0007137,C0001339,C0014072,C0242184,C0026769,C0024032,C0025202,C0598309,C0278134,C0022679,C0017185,C4020899,C1305122,C0009376,C0022116,C0035334,C3278975,C0007795,C0233794,C0020538,C1561643,C0334389,C0032914,C0340274,C0333641,C4020885,C0031154,C0029131,C1848534,C0027719,C0001418,C1285162,C0280141,C1136033,C0041806,C0038999,C0205710,C0151744,C0020542,C0019080,C2930957,C0032231,C0425946,C0085639,C0029831,C0426414,C0004238,C0086132,C0035333,C0497327,C0022350,C2930812,C0009319,C0021053,C0011860,C0027022,C1842031,C0042373,C0085786,C0205645,C0037286,C0243066,C4277682,C0600688,C2829267,C1835308,C0002103,C0014356,C0002938,C0035613,C0741923,C0006142,C0035126,C1458155,C0149721,C3151609,C1290884,C0017152,C0019034,C1863926,C0043242,C3495426,C1849334,C0006145,C4024702,C0037274,C0403528,C0020433,C0162739,C0042693,C0006663,C1155266,C0272316,C0599973,C0796149,C3275959,C0008625,C0333463,C0030305,C0085437,C0019158,C0376544,C0004093,C0019151,C0020502,C0002940,C0339510,C0265219,C0005684,C0333186,C0858246,C0699748,C0033847,C0026766,C0002736,C0007102,C0730345,C0162557,C0026244,C0398738,C1691228,C0699790,C0009443,C0162871,C0010674,C0018213,C0029928,C0008354,C1261473,C1852548,C0014868,C0699739,C0036337,C4021097,C0042769,C0030297,C1838601,C0012242,C0272187,C0020429,C1336839,C0025517,C0268271,C1835813,C0272317,C0038379,C0042109,C0596402,C0037231,C0221468,C1292772,C0010068,C0003578,C0022893,C0334108,C0024623,C0013570,C0153417,C0007131,C0393929,C0085166,C1968782,C1306837,C0020456,C0014059,C0002726,C0007112,C0004030,C0023903,C1956346,C0037285,C0023473,C0039082,C3495676,C0024713,C0020507,C0277785,C1266158,C1140680,C0003864,C0014175,C0019189,C1458140,C0015519,C0023467,C0019348,C0701826,C1336708,C1863052,C0333467,C0086565,C0235169,C0037277,C0017668,C0024121,C0022104,C0235222,C0151313,C0587248,C0030920,C0020179,C1442839,C0347653,C1540912,C0012634,C1865868,C2751126,C1853564,C0003469,C0011164,C0018802,C0015503,C1378511,C0029839,C0205643,C1319315,C0596020,C1970143,C0014145,C0280324,C0025521,C0339573,C0029574,C1842026,C0012860,C0497247,C0242379,C0023890,C1457887,C0033578,C1879321,C0282609,C4280569,C0039730,C0009402,C0008925,C1295654,C0232910,C0524851,C1704436,C0085669,C0028077,C0879626,C1839601,C0007120,C0038356,C0221198,C0231337,C0015696,C0497406,C0007965,C1833104,C0153519,C0685938,C0235401,C0005587,C0010495,C0151650,C0596263,C0033687,C0276037,C0011847,C0042870,C3463918,C0038454,C0302142,C3495587,C1096063,C0518010,C0600327,C0677886,C0581342,C1304508,C2936917,C0595921,C0410180,C0005944,C0035335,C1504598,C3665365,C2316810,C1800706,C0546837,C0451641,C0007784,C0338656,C0009450,C1266042,C0017612,C0153594,C0019101,C0154251,C0027051,C0333262,C0860603,C0027626,C1389018,C2676033,C0019385,C0857121,C1266044,C0162735,C0016059,C0041295,C0016658,C0041408,C0022665,C0028754,C0268397,C0085183,C1368683,C0015397,C0392475,C0036983,C2675055,C0235974,C0334517,C2239176,C0038436,C0034065,C1527168,C0039231,C1833269,C0032000,C0041296,C0014070,C0751688,C0023772,C0004153,C0029442,C0919267,C1854310,C0018924,C1301700,C4024853,C0036069,C0005695,C0423798,C0024117,C0271650,C0152018,C0279607,C0032460,C0037315,C0002982,C0242488,C0221074,C0014116,C0001511,C0796004,C0040053,C0872150,C0085681,C0580545,C1549114,C0017154,C0020459,C4023159,C0279000,C0003873,C0340464,C1859126,C0024141,C0085096,C0001430,C0235266,C0003811,C0599732,C0206624,C0376618,C0026691,C0948775,C1839829,C0036631,C0494158,C0026846,C0010054,C1292775,C1869116,C0455825,C0339527,C0014457,C1266043,C0220701,C0015674,C0235032,C0007222,C0020676,C2607914,C0080113,C0270922,C0038433,C0021775,C0279702,C0005586,C0024115,C0018801,C1839736,C0004364,C0021655,C0011881,C0005940,C0035258,C0012243,C0019087,C0242606,C0020615,C0039870,C0021400,C0023895,C0858321,C0346388,C0007097,C0742115,C0019693,C0024620,C0006826,C3151610,C0014859,C0014038,C2718068,C4280606,C0027651,C1519697,C0266215,C0740457,C0017178,C0241863,C1857277,C0006111,C0948008,C0206655,C0005779,C3150911,C0376358,C0019147,C0029458,C0740392,C1839735,C0220633,C1395184,C0041671,C0332448,C0025289,C0020437,C1527304,C0011849,C0013491,C0009375,C1848934,C2931456,C0178874,C2931899,C1532253,C0035579,C0684249,C1320716,C1854114,C0346373,C0003838,C0018799,C0598608,C0497248,C0870182,C0036572,C1292778,C0033575,C0015695,C0699791,C0153458,C0007820,C0042510,C0240671,C3203356,C0752130,C0518003,C4024722,C0023418,C0021368,C0029838,C0036679,C0020517,C0876973,C0085131,C0036341,C0021390,C0002395,C0022661,C0085584,C0017168&scopes=umls)\n",
"API 3.9: https://biothings.ncats.io/semmed/query?fields=physically_interacts_with (POST -d q=C0029456,C0264955,C0241954,C0004096,C0730290,C0020640,C0004991,C0020529,C0276226,C0023893,C3279392,C0687133,C0027881,C0242597,C0032339,C0260397,C0856716,C0035078,C0156312,C0014591,C0027627,C0085580,C0683416,C0265509,C0002895,C0007134,C0442750,C1389016,C0152013,C1527249,C0002871,C1859727,C0003486,C0220702,C0205644,C0015230,C0577631,C0346647,C0879257,C1833102,C0153199,C0345904,C0000768,C0264694,C0280803,C0949664,C0022658,C0079772,C0007787,C0521585,C0850624,C0011609,C0027430,C0036974,C0009566,C0877373,C0553707,C0032584,C0008626,C0278488,C0520679,C0011156,CN074280,C0332563,C0029823,C0042880,C0920563,C1299574,C0009404,C0553692,C0040147,C0235946,C0036202,C0007137,C0001339,C0014072,C0242184,C0026769,C0024032,C0025202,C0598309,C0278134,C0022679,C0017185,C4020899,C1305122,C0009376,C0022116,C0035334,C3278975,C0007795,C0233794,C0020538,C1561643,C0334389,C0032914,C0340274,C0333641,C4020885,C0031154,C0029131,C1848534,C0027719,C0001418,C1285162,C0280141,C1136033,C0041806,C0038999,C0205710,C0151744,C0020542,C0019080,C2930957,C0032231,C0425946,C0085639,C0029831,C0426414,C0004238,C0086132,C0035333,C0497327,C0022350,C2930812,C0009319,C0021053,C0011860,C0027022,C1842031,C0042373,C0085786,C0205645,C0037286,C0243066,C4277682,C0600688,C2829267,C1835308,C0002103,C0014356,C0002938,C0035613,C0741923,C0006142,C0035126,C1458155,C0149721,C3151609,C1290884,C0017152,C0019034,C1863926,C0043242,C3495426,C1849334,C0006145,C4024702,C0037274,C0403528,C0020433,C0162739,C0042693,C0006663,C1155266,C0272316,C0599973,C0796149,C3275959,C0008625,C0333463,C0030305,C0085437,C0019158,C0376544,C0004093,C0019151,C0020502,C0002940,C0339510,C0265219,C0005684,C0333186,C0858246,C0699748,C0033847,C0026766,C0002736,C0007102,C0730345,C0162557,C0026244,C0398738,C1691228,C0699790,C0009443,C0162871,C0010674,C0018213,C0029928,C0008354,C1261473,C1852548,C0014868,C0699739,C0036337,C4021097,C0042769,C0030297,C1838601,C0012242,C0272187,C0020429,C1336839,C0025517,C0268271,C1835813,C0272317,C0038379,C0042109,C0596402,C0037231,C0221468,C1292772,C0010068,C0003578,C0022893,C0334108,C0024623,C0013570,C0153417,C0007131,C0393929,C0085166,C1968782,C1306837,C0020456,C0014059,C0002726,C0007112,C0004030,C0023903,C1956346,C0037285,C0023473,C0039082,C3495676,C0024713,C0020507,C0277785,C1266158,C1140680,C0003864,C0014175,C0019189,C1458140,C0015519,C0023467,C0019348,C0701826,C1336708,C1863052,C0333467,C0086565,C0235169,C0037277,C0017668,C0024121,C0022104,C0235222,C0151313,C0587248,C0030920,C0020179,C1442839,C0347653,C1540912,C0012634,C1865868,C2751126,C1853564,C0003469,C0011164,C0018802,C0015503,C1378511,C0029839,C0205643,C1319315,C0596020,C1970143,C0014145,C0280324,C0025521,C0339573,C0029574,C1842026,C0012860,C0497247,C0242379,C0023890,C1457887,C0033578,C1879321,C0282609,C4280569,C0039730,C0009402,C0008925,C1295654,C0232910,C0524851,C1704436,C0085669,C0028077,C0879626,C1839601,C0007120,C0038356,C0221198,C0231337,C0015696,C0497406,C0007965,C1833104,C0153519,C0685938,C0235401,C0005587,C0010495,C0151650,C0596263,C0033687,C0276037,C0011847,C0042870,C3463918,C0038454,C0302142,C3495587,C1096063,C0518010,C0600327,C0677886,C0581342,C1304508,C2936917,C0595921,C0410180,C0005944,C0035335,C1504598,C3665365,C2316810,C1800706,C0546837,C0451641,C0007784,C0338656,C0009450,C1266042,C0017612,C0153594,C0019101,C0154251,C0027051,C0333262,C0860603,C0027626,C1389018,C2676033,C0019385,C0857121,C1266044,C0162735,C0016059,C0041295,C0016658,C0041408,C0022665,C0028754,C0268397,C0085183,C1368683,C0015397,C0392475,C0036983,C2675055,C0235974,C0334517,C2239176,C0038436,C0034065,C1527168,C0039231,C1833269,C0032000,C0041296,C0014070,C0751688,C0023772,C0004153,C0029442,C0919267,C1854310,C0018924,C1301700,C4024853,C0036069,C0005695,C0423798,C0024117,C0271650,C0152018,C0279607,C0032460,C0037315,C0002982,C0242488,C0221074,C0014116,C0001511,C0796004,C0040053,C0872150,C0085681,C0580545,C1549114,C0017154,C0020459,C4023159,C0279000,C0003873,C0340464,C1859126,C0024141,C0085096,C0001430,C0235266,C0003811,C0599732,C0206624,C0376618,C0026691,C0948775,C1839829,C0036631,C0494158,C0026846,C0010054,C1292775,C1869116,C0455825,C0339527,C0014457,C1266043,C0220701,C0015674,C0235032,C0007222,C0020676,C2607914,C0080113,C0270922,C0038433,C0021775,C0279702,C0005586,C0024115,C0018801,C1839736,C0004364,C0021655,C0011881,C0005940,C0035258,C0012243,C0019087,C0242606,C0020615,C0039870,C0021400,C0023895,C0858321,C0346388,C0007097,C0742115,C0019693,C0024620,C0006826,C3151610,C0014859,C0014038,C2718068,C4280606,C0027651,C1519697,C0266215,C0740457,C0017178,C0241863,C1857277,C0006111,C0948008,C0206655,C0005779,C3150911,C0376358,C0019147,C0029458,C0740392,C1839735,C0220633,C1395184,C0041671,C0332448,C0025289,C0020437,C1527304,C0011849,C0013491,C0009375,C1848934,C2931456,C0178874,C2931899,C1532253,C0035579,C0684249,C1320716,C1854114,C0346373,C0003838,C0018799,C0598608,C0497248,C0870182,C0036572,C1292778,C0033575,C0015695,C0699791,C0153458,C0007820,C0042510,C0240671,C3203356,C0752130,C0518003,C4024722,C0023418,C0021368,C0029838,C0036679,C0020517,C0876973,C0085131,C0036341,C0021390,C0002395,C0022661,C0085584,C0017168&scopes=umls)\n",
"API 3.11: https://biothings.ncats.io/semmed/query?fields=negatively_regulated_by (POST -d q=C0029456,C0264955,C0241954,C0004096,C0730290,C0020640,C0004991,C0020529,C0276226,C0023893,C3279392,C0687133,C0027881,C0242597,C0032339,C0260397,C0856716,C0035078,C0156312,C0014591,C0027627,C0085580,C0683416,C0265509,C0002895,C0007134,C0442750,C1389016,C0152013,C1527249,C0002871,C1859727,C0003486,C0220702,C0205644,C0015230,C0577631,C0346647,C0879257,C1833102,C0153199,C0345904,C0000768,C0264694,C0280803,C0949664,C0022658,C0079772,C0007787,C0521585,C0850624,C0011609,C0027430,C0036974,C0009566,C0877373,C0553707,C0032584,C0008626,C0278488,C0520679,C0011156,CN074280,C0332563,C0029823,C0042880,C0920563,C1299574,C0009404,C0553692,C0040147,C0235946,C0036202,C0007137,C0001339,C0014072,C0242184,C0026769,C0024032,C0025202,C0598309,C0278134,C0022679,C0017185,C4020899,C1305122,C0009376,C0022116,C0035334,C3278975,C0007795,C0233794,C0020538,C1561643,C0334389,C0032914,C0340274,C0333641,C4020885,C0031154,C0029131,C1848534,C0027719,C0001418,C1285162,C0280141,C1136033,C0041806,C0038999,C0205710,C0151744,C0020542,C0019080,C2930957,C0032231,C0425946,C0085639,C0029831,C0426414,C0004238,C0086132,C0035333,C0497327,C0022350,C2930812,C0009319,C0021053,C0011860,C0027022,C1842031,C0042373,C0085786,C0205645,C0037286,C0243066,C4277682,C0600688,C2829267,C1835308,C0002103,C0014356,C0002938,C0035613,C0741923,C0006142,C0035126,C1458155,C0149721,C3151609,C1290884,C0017152,C0019034,C1863926,C0043242,C3495426,C1849334,C0006145,C4024702,C0037274,C0403528,C0020433,C0162739,C0042693,C0006663,C1155266,C0272316,C0599973,C0796149,C3275959,C0008625,C0333463,C0030305,C0085437,C0019158,C0376544,C0004093,C0019151,C0020502,C0002940,C0339510,C0265219,C0005684,C0333186,C0858246,C0699748,C0033847,C0026766,C0002736,C0007102,C0730345,C0162557,C0026244,C0398738,C1691228,C0699790,C0009443,C0162871,C0010674,C0018213,C0029928,C0008354,C1261473,C1852548,C0014868,C0699739,C0036337,C4021097,C0042769,C0030297,C1838601,C0012242,C0272187,C0020429,C1336839,C0025517,C0268271,C1835813,C0272317,C0038379,C0042109,C0596402,C0037231,C0221468,C1292772,C0010068,C0003578,C0022893,C0334108,C0024623,C0013570,C0153417,C0007131,C0393929,C0085166,C1968782,C1306837,C0020456,C0014059,C0002726,C0007112,C0004030,C0023903,C1956346,C0037285,C0023473,C0039082,C3495676,C0024713,C0020507,C0277785,C1266158,C1140680,C0003864,C0014175,C0019189,C1458140,C0015519,C0023467,C0019348,C0701826,C1336708,C1863052,C0333467,C0086565,C0235169,C0037277,C0017668,C0024121,C0022104,C0235222,C0151313,C0587248,C0030920,C0020179,C1442839,C0347653,C1540912,C0012634,C1865868,C2751126,C1853564,C0003469,C0011164,C0018802,C0015503,C1378511,C0029839,C0205643,C1319315,C0596020,C1970143,C0014145,C0280324,C0025521,C0339573,C0029574,C1842026,C0012860,C0497247,C0242379,C0023890,C1457887,C0033578,C1879321,C0282609,C4280569,C0039730,C0009402,C0008925,C1295654,C0232910,C0524851,C1704436,C0085669,C0028077,C0879626,C1839601,C0007120,C0038356,C0221198,C0231337,C0015696,C0497406,C0007965,C1833104,C0153519,C0685938,C0235401,C0005587,C0010495,C0151650,C0596263,C0033687,C0276037,C0011847,C0042870,C3463918,C0038454,C0302142,C3495587,C1096063,C0518010,C0600327,C0677886,C0581342,C1304508,C2936917,C0595921,C0410180,C0005944,C0035335,C1504598,C3665365,C2316810,C1800706,C0546837,C0451641,C0007784,C0338656,C0009450,C1266042,C0017612,C0153594,C0019101,C0154251,C0027051,C0333262,C0860603,C0027626,C1389018,C2676033,C0019385,C0857121,C1266044,C0162735,C0016059,C0041295,C0016658,C0041408,C0022665,C0028754,C0268397,C0085183,C1368683,C0015397,C0392475,C0036983,C2675055,C0235974,C0334517,C2239176,C0038436,C0034065,C1527168,C0039231,C1833269,C0032000,C0041296,C0014070,C0751688,C0023772,C0004153,C0029442,C0919267,C1854310,C0018924,C1301700,C4024853,C0036069,C0005695,C0423798,C0024117,C0271650,C0152018,C0279607,C0032460,C0037315,C0002982,C0242488,C0221074,C0014116,C0001511,C0796004,C0040053,C0872150,C0085681,C0580545,C1549114,C0017154,C0020459,C4023159,C0279000,C0003873,C0340464,C1859126,C0024141,C0085096,C0001430,C0235266,C0003811,C0599732,C0206624,C0376618,C0026691,C0948775,C1839829,C0036631,C0494158,C0026846,C0010054,C1292775,C1869116,C0455825,C0339527,C0014457,C1266043,C0220701,C0015674,C0235032,C0007222,C0020676,C2607914,C0080113,C0270922,C0038433,C0021775,C0279702,C0005586,C0024115,C0018801,C1839736,C0004364,C0021655,C0011881,C0005940,C0035258,C0012243,C0019087,C0242606,C0020615,C0039870,C0021400,C0023895,C0858321,C0346388,C0007097,C0742115,C0019693,C0024620,C0006826,C3151610,C0014859,C0014038,C2718068,C4280606,C0027651,C1519697,C0266215,C0740457,C0017178,C0241863,C1857277,C0006111,C0948008,C0206655,C0005779,C3150911,C0376358,C0019147,C0029458,C0740392,C1839735,C0220633,C1395184,C0041671,C0332448,C0025289,C0020437,C1527304,C0011849,C0013491,C0009375,C1848934,C2931456,C0178874,C2931899,C1532253,C0035579,C0684249,C1320716,C1854114,C0346373,C0003838,C0018799,C0598608,C0497248,C0870182,C0036572,C1292778,C0033575,C0015695,C0699791,C0153458,C0007820,C0042510,C0240671,C3203356,C0752130,C0518003,C4024722,C0023418,C0021368,C0029838,C0036679,C0020517,C0876973,C0085131,C0036341,C0021390,C0002395,C0022661,C0085584,C0017168&scopes=umls)\n",
"API 2.146: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0015576/phenotypes?rows=200\n",
"API 2.142: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005560/phenotypes?rows=200\n",
"API 2.152: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0000456/phenotypes?rows=200\n",
"API 2.141: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005079/phenotypes?rows=200\n",
"API 2.148: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005294/phenotypes?rows=200\n",
"API 2.160: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0011786/phenotypes?rows=200\n",
"API 3.6: https://biothings.ncats.io/semmed/query?fields=prevented_by (POST -d q=C0029456,C0264955,C0241954,C0004096,C0730290,C0020640,C0004991,C0020529,C0276226,C0023893,C3279392,C0687133,C0027881,C0242597,C0032339,C0260397,C0856716,C0035078,C0156312,C0014591,C0027627,C0085580,C0683416,C0265509,C0002895,C0007134,C0442750,C1389016,C0152013,C1527249,C0002871,C1859727,C0003486,C0220702,C0205644,C0015230,C0577631,C0346647,C0879257,C1833102,C0153199,C0345904,C0000768,C0264694,C0280803,C0949664,C0022658,C0079772,C0007787,C0521585,C0850624,C0011609,C0027430,C0036974,C0009566,C0877373,C0553707,C0032584,C0008626,C0278488,C0520679,C0011156,CN074280,C0332563,C0029823,C0042880,C0920563,C1299574,C0009404,C0553692,C0040147,C0235946,C0036202,C0007137,C0001339,C0014072,C0242184,C0026769,C0024032,C0025202,C0598309,C0278134,C0022679,C0017185,C4020899,C1305122,C0009376,C0022116,C0035334,C3278975,C0007795,C0233794,C0020538,C1561643,C0334389,C0032914,C0340274,C0333641,C4020885,C0031154,C0029131,C1848534,C0027719,C0001418,C1285162,C0280141,C1136033,C0041806,C0038999,C0205710,C0151744,C0020542,C0019080,C2930957,C0032231,C0425946,C0085639,C0029831,C0426414,C0004238,C0086132,C0035333,C0497327,C0022350,C2930812,C0009319,C0021053,C0011860,C0027022,C1842031,C0042373,C0085786,C0205645,C0037286,C0243066,C4277682,C0600688,C2829267,C1835308,C0002103,C0014356,C0002938,C0035613,C0741923,C0006142,C0035126,C1458155,C0149721,C3151609,C1290884,C0017152,C0019034,C1863926,C0043242,C3495426,C1849334,C0006145,C4024702,C0037274,C0403528,C0020433,C0162739,C0042693,C0006663,C1155266,C0272316,C0599973,C0796149,C3275959,C0008625,C0333463,C0030305,C0085437,C0019158,C0376544,C0004093,C0019151,C0020502,C0002940,C0339510,C0265219,C0005684,C0333186,C0858246,C0699748,C0033847,C0026766,C0002736,C0007102,C0730345,C0162557,C0026244,C0398738,C1691228,C0699790,C0009443,C0162871,C0010674,C0018213,C0029928,C0008354,C1261473,C1852548,C0014868,C0699739,C0036337,C4021097,C0042769,C0030297,C1838601,C0012242,C0272187,C0020429,C1336839,C0025517,C0268271,C1835813,C0272317,C0038379,C0042109,C0596402,C0037231,C0221468,C1292772,C0010068,C0003578,C0022893,C0334108,C0024623,C0013570,C0153417,C0007131,C0393929,C0085166,C1968782,C1306837,C0020456,C0014059,C0002726,C0007112,C0004030,C0023903,C1956346,C0037285,C0023473,C0039082,C3495676,C0024713,C0020507,C0277785,C1266158,C1140680,C0003864,C0014175,C0019189,C1458140,C0015519,C0023467,C0019348,C0701826,C1336708,C1863052,C0333467,C0086565,C0235169,C0037277,C0017668,C0024121,C0022104,C0235222,C0151313,C0587248,C0030920,C0020179,C1442839,C0347653,C1540912,C0012634,C1865868,C2751126,C1853564,C0003469,C0011164,C0018802,C0015503,C1378511,C0029839,C0205643,C1319315,C0596020,C1970143,C0014145,C0280324,C0025521,C0339573,C0029574,C1842026,C0012860,C0497247,C0242379,C0023890,C1457887,C0033578,C1879321,C0282609,C4280569,C0039730,C0009402,C0008925,C1295654,C0232910,C0524851,C1704436,C0085669,C0028077,C0879626,C1839601,C0007120,C0038356,C0221198,C0231337,C0015696,C0497406,C0007965,C1833104,C0153519,C0685938,C0235401,C0005587,C0010495,C0151650,C0596263,C0033687,C0276037,C0011847,C0042870,C3463918,C0038454,C0302142,C3495587,C1096063,C0518010,C0600327,C0677886,C0581342,C1304508,C2936917,C0595921,C0410180,C0005944,C0035335,C1504598,C3665365,C2316810,C1800706,C0546837,C0451641,C0007784,C0338656,C0009450,C1266042,C0017612,C0153594,C0019101,C0154251,C0027051,C0333262,C0860603,C0027626,C1389018,C2676033,C0019385,C0857121,C1266044,C0162735,C0016059,C0041295,C0016658,C0041408,C0022665,C0028754,C0268397,C0085183,C1368683,C0015397,C0392475,C0036983,C2675055,C0235974,C0334517,C2239176,C0038436,C0034065,C1527168,C0039231,C1833269,C0032000,C0041296,C0014070,C0751688,C0023772,C0004153,C0029442,C0919267,C1854310,C0018924,C1301700,C4024853,C0036069,C0005695,C0423798,C0024117,C0271650,C0152018,C0279607,C0032460,C0037315,C0002982,C0242488,C0221074,C0014116,C0001511,C0796004,C0040053,C0872150,C0085681,C0580545,C1549114,C0017154,C0020459,C4023159,C0279000,C0003873,C0340464,C1859126,C0024141,C0085096,C0001430,C0235266,C0003811,C0599732,C0206624,C0376618,C0026691,C0948775,C1839829,C0036631,C0494158,C0026846,C0010054,C1292775,C1869116,C0455825,C0339527,C0014457,C1266043,C0220701,C0015674,C0235032,C0007222,C0020676,C2607914,C0080113,C0270922,C0038433,C0021775,C0279702,C0005586,C0024115,C0018801,C1839736,C0004364,C0021655,C0011881,C0005940,C0035258,C0012243,C0019087,C0242606,C0020615,C0039870,C0021400,C0023895,C0858321,C0346388,C0007097,C0742115,C0019693,C0024620,C0006826,C3151610,C0014859,C0014038,C2718068,C4280606,C0027651,C1519697,C0266215,C0740457,C0017178,C0241863,C1857277,C0006111,C0948008,C0206655,C0005779,C3150911,C0376358,C0019147,C0029458,C0740392,C1839735,C0220633,C1395184,C0041671,C0332448,C0025289,C0020437,C1527304,C0011849,C0013491,C0009375,C1848934,C2931456,C0178874,C2931899,C1532253,C0035579,C0684249,C1320716,C1854114,C0346373,C0003838,C0018799,C0598608,C0497248,C0870182,C0036572,C1292778,C0033575,C0015695,C0699791,C0153458,C0007820,C0042510,C0240671,C3203356,C0752130,C0518003,C4024722,C0023418,C0021368,C0029838,C0036679,C0020517,C0876973,C0085131,C0036341,C0021390,C0002395,C0022661,C0085584,C0017168&scopes=umls)\n",
"API 2.156: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005527/phenotypes?rows=200\n",
"API 2.153: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0020076/phenotypes?rows=200\n",
"API 2.162: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0020312/phenotypes?rows=200\n",
"API 2.155: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0011382/phenotypes?rows=200\n",
"API 2.159: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0006547/phenotypes?rows=200\n",
"API 2.161: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0007931/phenotypes?rows=200\n",
"API 2.157: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0000390/phenotypes?rows=200\n",
"API 2.158: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0010108/phenotypes?rows=200\n",
"API 2.163: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004993/phenotypes?rows=200\n",
"API 2.154: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0001741/phenotypes?rows=200\n",
"API 2.167: https://api.monarchinitiative.org/api/bioentity/disease/C1458140/phenotypes?rows=200\n",
"API 2.166: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0017885/phenotypes?rows=200\n",
"API 2.168: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0009212/phenotypes?rows=200\n",
"API 2.170: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0009831/phenotypes?rows=200\n",
"API 2.169: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002177/phenotypes?rows=200\n",
"API 2.165: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0000831/phenotypes?rows=200\n",
"API 3.2: https://biothings.ncats.io/semmed/query?fields=affects (POST -d q=C0029456,C0264955,C0241954,C0004096,C0730290,C0020640,C0004991,C0020529,C0276226,C0023893,C3279392,C0687133,C0027881,C0242597,C0032339,C0260397,C0856716,C0035078,C0156312,C0014591,C0027627,C0085580,C0683416,C0265509,C0002895,C0007134,C0442750,C1389016,C0152013,C1527249,C0002871,C1859727,C0003486,C0220702,C0205644,C0015230,C0577631,C0346647,C0879257,C1833102,C0153199,C0345904,C0000768,C0264694,C0280803,C0949664,C0022658,C0079772,C0007787,C0521585,C0850624,C0011609,C0027430,C0036974,C0009566,C0877373,C0553707,C0032584,C0008626,C0278488,C0520679,C0011156,CN074280,C0332563,C0029823,C0042880,C0920563,C1299574,C0009404,C0553692,C0040147,C0235946,C0036202,C0007137,C0001339,C0014072,C0242184,C0026769,C0024032,C0025202,C0598309,C0278134,C0022679,C0017185,C4020899,C1305122,C0009376,C0022116,C0035334,C3278975,C0007795,C0233794,C0020538,C1561643,C0334389,C0032914,C0340274,C0333641,C4020885,C0031154,C0029131,C1848534,C0027719,C0001418,C1285162,C0280141,C1136033,C0041806,C0038999,C0205710,C0151744,C0020542,C0019080,C2930957,C0032231,C0425946,C0085639,C0029831,C0426414,C0004238,C0086132,C0035333,C0497327,C0022350,C2930812,C0009319,C0021053,C0011860,C0027022,C1842031,C0042373,C0085786,C0205645,C0037286,C0243066,C4277682,C0600688,C2829267,C1835308,C0002103,C0014356,C0002938,C0035613,C0741923,C0006142,C0035126,C1458155,C0149721,C3151609,C1290884,C0017152,C0019034,C1863926,C0043242,C3495426,C1849334,C0006145,C4024702,C0037274,C0403528,C0020433,C0162739,C0042693,C0006663,C1155266,C0272316,C0599973,C0796149,C3275959,C0008625,C0333463,C0030305,C0085437,C0019158,C0376544,C0004093,C0019151,C0020502,C0002940,C0339510,C0265219,C0005684,C0333186,C0858246,C0699748,C0033847,C0026766,C0002736,C0007102,C0730345,C0162557,C0026244,C0398738,C1691228,C0699790,C0009443,C0162871,C0010674,C0018213,C0029928,C0008354,C1261473,C1852548,C0014868,C0699739,C0036337,C4021097,C0042769,C0030297,C1838601,C0012242,C0272187,C0020429,C1336839,C0025517,C0268271,C1835813,C0272317,C0038379,C0042109,C0596402,C0037231,C0221468,C1292772,C0010068,C0003578,C0022893,C0334108,C0024623,C0013570,C0153417,C0007131,C0393929,C0085166,C1968782,C1306837,C0020456,C0014059,C0002726,C0007112,C0004030,C0023903,C1956346,C0037285,C0023473,C0039082,C3495676,C0024713,C0020507,C0277785,C1266158,C1140680,C0003864,C0014175,C0019189,C1458140,C0015519,C0023467,C0019348,C0701826,C1336708,C1863052,C0333467,C0086565,C0235169,C0037277,C0017668,C0024121,C0022104,C0235222,C0151313,C0587248,C0030920,C0020179,C1442839,C0347653,C1540912,C0012634,C1865868,C2751126,C1853564,C0003469,C0011164,C0018802,C0015503,C1378511,C0029839,C0205643,C1319315,C0596020,C1970143,C0014145,C0280324,C0025521,C0339573,C0029574,C1842026,C0012860,C0497247,C0242379,C0023890,C1457887,C0033578,C1879321,C0282609,C4280569,C0039730,C0009402,C0008925,C1295654,C0232910,C0524851,C1704436,C0085669,C0028077,C0879626,C1839601,C0007120,C0038356,C0221198,C0231337,C0015696,C0497406,C0007965,C1833104,C0153519,C0685938,C0235401,C0005587,C0010495,C0151650,C0596263,C0033687,C0276037,C0011847,C0042870,C3463918,C0038454,C0302142,C3495587,C1096063,C0518010,C0600327,C0677886,C0581342,C1304508,C2936917,C0595921,C0410180,C0005944,C0035335,C1504598,C3665365,C2316810,C1800706,C0546837,C0451641,C0007784,C0338656,C0009450,C1266042,C0017612,C0153594,C0019101,C0154251,C0027051,C0333262,C0860603,C0027626,C1389018,C2676033,C0019385,C0857121,C1266044,C0162735,C0016059,C0041295,C0016658,C0041408,C0022665,C0028754,C0268397,C0085183,C1368683,C0015397,C0392475,C0036983,C2675055,C0235974,C0334517,C2239176,C0038436,C0034065,C1527168,C0039231,C1833269,C0032000,C0041296,C0014070,C0751688,C0023772,C0004153,C0029442,C0919267,C1854310,C0018924,C1301700,C4024853,C0036069,C0005695,C0423798,C0024117,C0271650,C0152018,C0279607,C0032460,C0037315,C0002982,C0242488,C0221074,C0014116,C0001511,C0796004,C0040053,C0872150,C0085681,C0580545,C1549114,C0017154,C0020459,C4023159,C0279000,C0003873,C0340464,C1859126,C0024141,C0085096,C0001430,C0235266,C0003811,C0599732,C0206624,C0376618,C0026691,C0948775,C1839829,C0036631,C0494158,C0026846,C0010054,C1292775,C1869116,C0455825,C0339527,C0014457,C1266043,C0220701,C0015674,C0235032,C0007222,C0020676,C2607914,C0080113,C0270922,C0038433,C0021775,C0279702,C0005586,C0024115,C0018801,C1839736,C0004364,C0021655,C0011881,C0005940,C0035258,C0012243,C0019087,C0242606,C0020615,C0039870,C0021400,C0023895,C0858321,C0346388,C0007097,C0742115,C0019693,C0024620,C0006826,C3151610,C0014859,C0014038,C2718068,C4280606,C0027651,C1519697,C0266215,C0740457,C0017178,C0241863,C1857277,C0006111,C0948008,C0206655,C0005779,C3150911,C0376358,C0019147,C0029458,C0740392,C1839735,C0220633,C1395184,C0041671,C0332448,C0025289,C0020437,C1527304,C0011849,C0013491,C0009375,C1848934,C2931456,C0178874,C2931899,C1532253,C0035579,C0684249,C1320716,C1854114,C0346373,C0003838,C0018799,C0598608,C0497248,C0870182,C0036572,C1292778,C0033575,C0015695,C0699791,C0153458,C0007820,C0042510,C0240671,C3203356,C0752130,C0518003,C4024722,C0023418,C0021368,C0029838,C0036679,C0020517,C0876973,C0085131,C0036341,C0021390,C0002395,C0022661,C0085584,C0017168&scopes=umls)\n",
"API 2.175: https://api.monarchinitiative.org/api/bioentity/disease/C0086565/phenotypes?rows=200\n",
"API 2.172: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004743/phenotypes?rows=200\n",
"API 2.176: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0014182/phenotypes?rows=200\n",
"API 2.177: https://api.monarchinitiative.org/api/bioentity/disease/C0235222/phenotypes?rows=200\n",
"API 2.179: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004522/phenotypes?rows=200\n",
"API 2.178: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0020321/phenotypes?rows=200\n",
"API 2.164: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005559/phenotypes?rows=200\n",
"API 2.171: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0008345/phenotypes?rows=200\n",
"API 2.180: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005192/phenotypes?rows=200\n",
"API 2.173: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002657/phenotypes?rows=200\n",
"API 2.174: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005335/phenotypes?rows=200\n",
"API 2.182: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002516/phenotypes?rows=200\n",
"API 2.181: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005149/phenotypes?rows=200\n",
"API 2.183: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0008380/phenotypes?rows=200\n",
"API 2.186: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005363/phenotypes?rows=200\n",
"API 2.184: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002691/phenotypes?rows=200\n",
"API 2.185: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005015/phenotypes?rows=200\n",
"API 2.187: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005558/phenotypes?rows=200\n",
"API 2.188: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0003197/phenotypes?rows=200\n",
"API 2.191: https://api.monarchinitiative.org/api/bioentity/disease/C0497247/phenotypes?rows=200\n",
"API 2.192: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002247/phenotypes?rows=200\n",
"API 2.193: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0009061/phenotypes?rows=200\n",
"API 2.194: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0019956/phenotypes?rows=200\n",
"API 2.189: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0018874/phenotypes?rows=200\n",
"API 2.197: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0001068/phenotypes?rows=200\n",
"API 2.199: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005052/phenotypes?rows=200\n",
"API 2.201: https://api.monarchinitiative.org/api/bioentity/disease/C4280569/phenotypes?rows=200\n",
"API 2.202: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0006670/phenotypes?rows=200\n",
"API 2.190: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004335/phenotypes?rows=200\n",
"API 2.203: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005315/phenotypes?rows=200\n",
"API 2.204: https://api.monarchinitiative.org/api/bioentity/disease/C1295654/phenotypes?rows=200\n",
"API 2.196: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0011271/phenotypes?rows=200\n",
"API 2.205: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0007362/phenotypes?rows=200\n",
"API 2.195: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0016064/phenotypes?rows=200\n",
"API 2.198: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005381/phenotypes?rows=200\n",
"API 2.206: https://api.monarchinitiative.org/api/bioentity/disease/C0232910/phenotypes?rows=200\n",
"API 2.200: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0019052/phenotypes?rows=200\n",
"API 2.209: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0010031/phenotypes?rows=200\n",
"API 2.210: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0007739/phenotypes?rows=200\n",
"API 2.207: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0009997/phenotypes?rows=200\n",
"API 2.211: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0006373/phenotypes?rows=200\n",
"API 2.216: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005784/phenotypes?rows=200\n",
"API 2.208: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004976/phenotypes?rows=200\n",
"API 2.212: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005391/phenotypes?rows=200\n",
"API 2.215: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0006486/phenotypes?rows=200\n",
"API 2.217: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0001711/phenotypes?rows=200\n",
"API 2.213: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005090/phenotypes?rows=200\n",
"API 2.214: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0019200/phenotypes?rows=200\n",
"API 2.220: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0000888/phenotypes?rows=200\n",
"API 2.221: https://api.monarchinitiative.org/api/bioentity/disease/MESH:D003111/phenotypes?rows=200\n",
"API 2.218: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0007576/phenotypes?rows=200\n",
"API 2.223: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005133/phenotypes?rows=200\n",
"API 2.219: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0012521/phenotypes?rows=200\n",
"API 2.225: https://api.monarchinitiative.org/api/bioentity/disease/C0497406/phenotypes?rows=200\n",
"API 2.229: https://api.monarchinitiative.org/api/bioentity/disease/C0235401/phenotypes?rows=200\n",
"API 2.227: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004466/phenotypes?rows=200\n",
"API 2.226: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0006665/phenotypes?rows=200\n",
"API 2.222: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0007088/phenotypes?rows=200\n",
"API 2.233: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005146/phenotypes?rows=200\n",
"API 2.232: https://api.monarchinitiative.org/api/bioentity/disease/C0596263/phenotypes?rows=200\n",
"API 2.235: https://api.monarchinitiative.org/api/bioentity/disease/C0042870/phenotypes?rows=200\n",
"API 2.234: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005155/phenotypes?rows=200\n",
"API 2.224: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005520/phenotypes?rows=200\n",
"API 2.228: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0000147/phenotypes?rows=200\n",
"API 2.231: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002280/phenotypes?rows=200\n",
"API 3.4: https://biothings.ncats.io/semmed/query?fields=caused_by (POST -d q=C0029456,C0264955,C0241954,C0004096,C0730290,C0020640,C0004991,C0020529,C0276226,C0023893,C3279392,C0687133,C0027881,C0242597,C0032339,C0260397,C0856716,C0035078,C0156312,C0014591,C0027627,C0085580,C0683416,C0265509,C0002895,C0007134,C0442750,C1389016,C0152013,C1527249,C0002871,C1859727,C0003486,C0220702,C0205644,C0015230,C0577631,C0346647,C0879257,C1833102,C0153199,C0345904,C0000768,C0264694,C0280803,C0949664,C0022658,C0079772,C0007787,C0521585,C0850624,C0011609,C0027430,C0036974,C0009566,C0877373,C0553707,C0032584,C0008626,C0278488,C0520679,C0011156,CN074280,C0332563,C0029823,C0042880,C0920563,C1299574,C0009404,C0553692,C0040147,C0235946,C0036202,C0007137,C0001339,C0014072,C0242184,C0026769,C0024032,C0025202,C0598309,C0278134,C0022679,C0017185,C4020899,C1305122,C0009376,C0022116,C0035334,C3278975,C0007795,C0233794,C0020538,C1561643,C0334389,C0032914,C0340274,C0333641,C4020885,C0031154,C0029131,C1848534,C0027719,C0001418,C1285162,C0280141,C1136033,C0041806,C0038999,C0205710,C0151744,C0020542,C0019080,C2930957,C0032231,C0425946,C0085639,C0029831,C0426414,C0004238,C0086132,C0035333,C0497327,C0022350,C2930812,C0009319,C0021053,C0011860,C0027022,C1842031,C0042373,C0085786,C0205645,C0037286,C0243066,C4277682,C0600688,C2829267,C1835308,C0002103,C0014356,C0002938,C0035613,C0741923,C0006142,C0035126,C1458155,C0149721,C3151609,C1290884,C0017152,C0019034,C1863926,C0043242,C3495426,C1849334,C0006145,C4024702,C0037274,C0403528,C0020433,C0162739,C0042693,C0006663,C1155266,C0272316,C0599973,C0796149,C3275959,C0008625,C0333463,C0030305,C0085437,C0019158,C0376544,C0004093,C0019151,C0020502,C0002940,C0339510,C0265219,C0005684,C0333186,C0858246,C0699748,C0033847,C0026766,C0002736,C0007102,C0730345,C0162557,C0026244,C0398738,C1691228,C0699790,C0009443,C0162871,C0010674,C0018213,C0029928,C0008354,C1261473,C1852548,C0014868,C0699739,C0036337,C4021097,C0042769,C0030297,C1838601,C0012242,C0272187,C0020429,C1336839,C0025517,C0268271,C1835813,C0272317,C0038379,C0042109,C0596402,C0037231,C0221468,C1292772,C0010068,C0003578,C0022893,C0334108,C0024623,C0013570,C0153417,C0007131,C0393929,C0085166,C1968782,C1306837,C0020456,C0014059,C0002726,C0007112,C0004030,C0023903,C1956346,C0037285,C0023473,C0039082,C3495676,C0024713,C0020507,C0277785,C1266158,C1140680,C0003864,C0014175,C0019189,C1458140,C0015519,C0023467,C0019348,C0701826,C1336708,C1863052,C0333467,C0086565,C0235169,C0037277,C0017668,C0024121,C0022104,C0235222,C0151313,C0587248,C0030920,C0020179,C1442839,C0347653,C1540912,C0012634,C1865868,C2751126,C1853564,C0003469,C0011164,C0018802,C0015503,C1378511,C0029839,C0205643,C1319315,C0596020,C1970143,C0014145,C0280324,C0025521,C0339573,C0029574,C1842026,C0012860,C0497247,C0242379,C0023890,C1457887,C0033578,C1879321,C0282609,C4280569,C0039730,C0009402,C0008925,C1295654,C0232910,C0524851,C1704436,C0085669,C0028077,C0879626,C1839601,C0007120,C0038356,C0221198,C0231337,C0015696,C0497406,C0007965,C1833104,C0153519,C0685938,C0235401,C0005587,C0010495,C0151650,C0596263,C0033687,C0276037,C0011847,C0042870,C3463918,C0038454,C0302142,C3495587,C1096063,C0518010,C0600327,C0677886,C0581342,C1304508,C2936917,C0595921,C0410180,C0005944,C0035335,C1504598,C3665365,C2316810,C1800706,C0546837,C0451641,C0007784,C0338656,C0009450,C1266042,C0017612,C0153594,C0019101,C0154251,C0027051,C0333262,C0860603,C0027626,C1389018,C2676033,C0019385,C0857121,C1266044,C0162735,C0016059,C0041295,C0016658,C0041408,C0022665,C0028754,C0268397,C0085183,C1368683,C0015397,C0392475,C0036983,C2675055,C0235974,C0334517,C2239176,C0038436,C0034065,C1527168,C0039231,C1833269,C0032000,C0041296,C0014070,C0751688,C0023772,C0004153,C0029442,C0919267,C1854310,C0018924,C1301700,C4024853,C0036069,C0005695,C0423798,C0024117,C0271650,C0152018,C0279607,C0032460,C0037315,C0002982,C0242488,C0221074,C0014116,C0001511,C0796004,C0040053,C0872150,C0085681,C0580545,C1549114,C0017154,C0020459,C4023159,C0279000,C0003873,C0340464,C1859126,C0024141,C0085096,C0001430,C0235266,C0003811,C0599732,C0206624,C0376618,C0026691,C0948775,C1839829,C0036631,C0494158,C0026846,C0010054,C1292775,C1869116,C0455825,C0339527,C0014457,C1266043,C0220701,C0015674,C0235032,C0007222,C0020676,C2607914,C0080113,C0270922,C0038433,C0021775,C0279702,C0005586,C0024115,C0018801,C1839736,C0004364,C0021655,C0011881,C0005940,C0035258,C0012243,C0019087,C0242606,C0020615,C0039870,C0021400,C0023895,C0858321,C0346388,C0007097,C0742115,C0019693,C0024620,C0006826,C3151610,C0014859,C0014038,C2718068,C4280606,C0027651,C1519697,C0266215,C0740457,C0017178,C0241863,C1857277,C0006111,C0948008,C0206655,C0005779,C3150911,C0376358,C0019147,C0029458,C0740392,C1839735,C0220633,C1395184,C0041671,C0332448,C0025289,C0020437,C1527304,C0011849,C0013491,C0009375,C1848934,C2931456,C0178874,C2931899,C1532253,C0035579,C0684249,C1320716,C1854114,C0346373,C0003838,C0018799,C0598608,C0497248,C0870182,C0036572,C1292778,C0033575,C0015695,C0699791,C0153458,C0007820,C0042510,C0240671,C3203356,C0752130,C0518003,C4024722,C0023418,C0021368,C0029838,C0036679,C0020517,C0876973,C0085131,C0036341,C0021390,C0002395,C0022661,C0085584,C0017168&scopes=umls)\n",
"API 3.1: https://biothings.ncats.io/semmed/query?fields=affected_by (POST -d q=C0029456,C0264955,C0241954,C0004096,C0730290,C0020640,C0004991,C0020529,C0276226,C0023893,C3279392,C0687133,C0027881,C0242597,C0032339,C0260397,C0856716,C0035078,C0156312,C0014591,C0027627,C0085580,C0683416,C0265509,C0002895,C0007134,C0442750,C1389016,C0152013,C1527249,C0002871,C1859727,C0003486,C0220702,C0205644,C0015230,C0577631,C0346647,C0879257,C1833102,C0153199,C0345904,C0000768,C0264694,C0280803,C0949664,C0022658,C0079772,C0007787,C0521585,C0850624,C0011609,C0027430,C0036974,C0009566,C0877373,C0553707,C0032584,C0008626,C0278488,C0520679,C0011156,CN074280,C0332563,C0029823,C0042880,C0920563,C1299574,C0009404,C0553692,C0040147,C0235946,C0036202,C0007137,C0001339,C0014072,C0242184,C0026769,C0024032,C0025202,C0598309,C0278134,C0022679,C0017185,C4020899,C1305122,C0009376,C0022116,C0035334,C3278975,C0007795,C0233794,C0020538,C1561643,C0334389,C0032914,C0340274,C0333641,C4020885,C0031154,C0029131,C1848534,C0027719,C0001418,C1285162,C0280141,C1136033,C0041806,C0038999,C0205710,C0151744,C0020542,C0019080,C2930957,C0032231,C0425946,C0085639,C0029831,C0426414,C0004238,C0086132,C0035333,C0497327,C0022350,C2930812,C0009319,C0021053,C0011860,C0027022,C1842031,C0042373,C0085786,C0205645,C0037286,C0243066,C4277682,C0600688,C2829267,C1835308,C0002103,C0014356,C0002938,C0035613,C0741923,C0006142,C0035126,C1458155,C0149721,C3151609,C1290884,C0017152,C0019034,C1863926,C0043242,C3495426,C1849334,C0006145,C4024702,C0037274,C0403528,C0020433,C0162739,C0042693,C0006663,C1155266,C0272316,C0599973,C0796149,C3275959,C0008625,C0333463,C0030305,C0085437,C0019158,C0376544,C0004093,C0019151,C0020502,C0002940,C0339510,C0265219,C0005684,C0333186,C0858246,C0699748,C0033847,C0026766,C0002736,C0007102,C0730345,C0162557,C0026244,C0398738,C1691228,C0699790,C0009443,C0162871,C0010674,C0018213,C0029928,C0008354,C1261473,C1852548,C0014868,C0699739,C0036337,C4021097,C0042769,C0030297,C1838601,C0012242,C0272187,C0020429,C1336839,C0025517,C0268271,C1835813,C0272317,C0038379,C0042109,C0596402,C0037231,C0221468,C1292772,C0010068,C0003578,C0022893,C0334108,C0024623,C0013570,C0153417,C0007131,C0393929,C0085166,C1968782,C1306837,C0020456,C0014059,C0002726,C0007112,C0004030,C0023903,C1956346,C0037285,C0023473,C0039082,C3495676,C0024713,C0020507,C0277785,C1266158,C1140680,C0003864,C0014175,C0019189,C1458140,C0015519,C0023467,C0019348,C0701826,C1336708,C1863052,C0333467,C0086565,C0235169,C0037277,C0017668,C0024121,C0022104,C0235222,C0151313,C0587248,C0030920,C0020179,C1442839,C0347653,C1540912,C0012634,C1865868,C2751126,C1853564,C0003469,C0011164,C0018802,C0015503,C1378511,C0029839,C0205643,C1319315,C0596020,C1970143,C0014145,C0280324,C0025521,C0339573,C0029574,C1842026,C0012860,C0497247,C0242379,C0023890,C1457887,C0033578,C1879321,C0282609,C4280569,C0039730,C0009402,C0008925,C1295654,C0232910,C0524851,C1704436,C0085669,C0028077,C0879626,C1839601,C0007120,C0038356,C0221198,C0231337,C0015696,C0497406,C0007965,C1833104,C0153519,C0685938,C0235401,C0005587,C0010495,C0151650,C0596263,C0033687,C0276037,C0011847,C0042870,C3463918,C0038454,C0302142,C3495587,C1096063,C0518010,C0600327,C0677886,C0581342,C1304508,C2936917,C0595921,C0410180,C0005944,C0035335,C1504598,C3665365,C2316810,C1800706,C0546837,C0451641,C0007784,C0338656,C0009450,C1266042,C0017612,C0153594,C0019101,C0154251,C0027051,C0333262,C0860603,C0027626,C1389018,C2676033,C0019385,C0857121,C1266044,C0162735,C0016059,C0041295,C0016658,C0041408,C0022665,C0028754,C0268397,C0085183,C1368683,C0015397,C0392475,C0036983,C2675055,C0235974,C0334517,C2239176,C0038436,C0034065,C1527168,C0039231,C1833269,C0032000,C0041296,C0014070,C0751688,C0023772,C0004153,C0029442,C0919267,C1854310,C0018924,C1301700,C4024853,C0036069,C0005695,C0423798,C0024117,C0271650,C0152018,C0279607,C0032460,C0037315,C0002982,C0242488,C0221074,C0014116,C0001511,C0796004,C0040053,C0872150,C0085681,C0580545,C1549114,C0017154,C0020459,C4023159,C0279000,C0003873,C0340464,C1859126,C0024141,C0085096,C0001430,C0235266,C0003811,C0599732,C0206624,C0376618,C0026691,C0948775,C1839829,C0036631,C0494158,C0026846,C0010054,C1292775,C1869116,C0455825,C0339527,C0014457,C1266043,C0220701,C0015674,C0235032,C0007222,C0020676,C2607914,C0080113,C0270922,C0038433,C0021775,C0279702,C0005586,C0024115,C0018801,C1839736,C0004364,C0021655,C0011881,C0005940,C0035258,C0012243,C0019087,C0242606,C0020615,C0039870,C0021400,C0023895,C0858321,C0346388,C0007097,C0742115,C0019693,C0024620,C0006826,C3151610,C0014859,C0014038,C2718068,C4280606,C0027651,C1519697,C0266215,C0740457,C0017178,C0241863,C1857277,C0006111,C0948008,C0206655,C0005779,C3150911,C0376358,C0019147,C0029458,C0740392,C1839735,C0220633,C1395184,C0041671,C0332448,C0025289,C0020437,C1527304,C0011849,C0013491,C0009375,C1848934,C2931456,C0178874,C2931899,C1532253,C0035579,C0684249,C1320716,C1854114,C0346373,C0003838,C0018799,C0598608,C0497248,C0870182,C0036572,C1292778,C0033575,C0015695,C0699791,C0153458,C0007820,C0042510,C0240671,C3203356,C0752130,C0518003,C4024722,C0023418,C0021368,C0029838,C0036679,C0020517,C0876973,C0085131,C0036341,C0021390,C0002395,C0022661,C0085584,C0017168&scopes=umls)\n",
"API 2.241: https://api.monarchinitiative.org/api/bioentity/disease/C0581342/phenotypes?rows=200\n",
"API 2.239: https://api.monarchinitiative.org/api/bioentity/disease/C1096063/phenotypes?rows=200\n",
"API 2.243: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005595/phenotypes?rows=200\n",
"API 2.230: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0001531/phenotypes?rows=200\n",
"API 2.244: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0011293/phenotypes?rows=200\n",
"API 2.242: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0007147/phenotypes?rows=200\n",
"API 2.238: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0011643/phenotypes?rows=200\n",
"API 2.245: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005086/phenotypes?rows=200\n",
"API 2.240: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004975/phenotypes?rows=200\n",
"API 2.249: https://api.monarchinitiative.org/api/bioentity/disease/C0005944/phenotypes?rows=200\n",
"API 2.248: https://api.monarchinitiative.org/api/bioentity/disease/C0595921/phenotypes?rows=200\n",
"API 2.247: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005717/phenotypes?rows=200\n",
"API 2.237: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0011057/phenotypes?rows=200\n",
"API 2.253: https://api.monarchinitiative.org/api/bioentity/disease/C0451641/phenotypes?rows=200\n",
"API 2.254: https://api.monarchinitiative.org/api/bioentity/disease/C0338656/phenotypes?rows=200\n",
"API 2.259: https://api.monarchinitiative.org/api/bioentity/disease/C0027626/phenotypes?rows=200\n",
"API 2.256: https://api.monarchinitiative.org/api/bioentity/disease/MESH:D064420/phenotypes?rows=200\n",
"API 2.258: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005468/phenotypes?rows=200\n",
"API 2.236: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0008758/phenotypes?rows=200\n",
"API 2.263: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004247/phenotypes?rows=200\n",
"API 2.262: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0001548/phenotypes?rows=200\n",
"API 2.265: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005606/phenotypes?rows=200\n",
"API 2.264: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0001244/phenotypes?rows=200\n",
"API 2.267: https://api.monarchinitiative.org/api/bioentity/disease/C0016059/phenotypes?rows=200\n",
"API 2.246: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005010/phenotypes?rows=200\n",
"API 2.250: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005002/phenotypes?rows=200\n",
"API 2.252: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004126/phenotypes?rows=200\n",
"API 2.257: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0009211/phenotypes?rows=200\n",
"API 2.251: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004796/phenotypes?rows=200\n",
"API 2.255: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005328/phenotypes?rows=200\n",
"API 2.260: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005267/phenotypes?rows=200\n",
"API 2.268: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0008585/phenotypes?rows=200\n",
"API 2.273: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0006314/phenotypes?rows=200\n",
"API 2.270: https://api.monarchinitiative.org/api/bioentity/disease/C0085183/phenotypes?rows=200\n",
"API 2.261: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004972/phenotypes?rows=200\n",
"API 2.274: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0006044/phenotypes?rows=200\n",
"API 2.266: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005105/phenotypes?rows=200\n",
"API 2.276: https://api.monarchinitiative.org/api/bioentity/disease/C0036983/phenotypes?rows=200\n",
"API 2.275: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005068/phenotypes?rows=200\n",
"API 2.279: https://api.monarchinitiative.org/api/bioentity/disease/C0039231/phenotypes?rows=200\n",
"API 2.278: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005292/phenotypes?rows=200\n",
"API 2.271: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0001187/phenotypes?rows=200\n",
"API 2.272: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0010665/phenotypes?rows=200\n",
"API 2.280: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005575/phenotypes?rows=200\n",
"API 2.277: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002531/phenotypes?rows=200\n",
"API 2.281: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0010666/phenotypes?rows=200\n",
"API 2.269: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0001627/phenotypes?rows=200\n",
"API 2.286: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0019542/phenotypes?rows=200\n",
"API 2.285: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0000248/phenotypes?rows=200\n",
"API 2.284: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002909/phenotypes?rows=200\n",
"API 2.290: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005005/phenotypes?rows=200\n",
"API 2.291: https://api.monarchinitiative.org/api/bioentity/disease/C4024853/phenotypes?rows=200\n",
"API 2.282: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0007263/phenotypes?rows=200\n",
"API 2.288: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0001409/phenotypes?rows=200\n",
"API 2.283: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005492/phenotypes?rows=200\n",
"API 2.287: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0021166/phenotypes?rows=200\n",
"API 2.289: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004970/phenotypes?rows=200\n",
"API 2.293: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0006873/phenotypes?rows=200\n",
"API 2.296: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005311/phenotypes?rows=200\n",
"API 2.295: https://api.monarchinitiative.org/api/bioentity/disease/C0423798/phenotypes?rows=200\n",
"API 2.292: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004323/phenotypes?rows=200\n",
"API 2.297: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002571/phenotypes?rows=200\n",
"API 2.298: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002708/phenotypes?rows=200\n",
"API 2.299: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0007136/phenotypes?rows=200\n",
"API 2.301: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004937/phenotypes?rows=200\n",
"API 2.294: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0012727/phenotypes?rows=200\n",
"API 2.302: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0015796/phenotypes?rows=200\n",
"API 2.303: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0006176/phenotypes?rows=200\n",
"API 2.300: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0008903/phenotypes?rows=200\n",
"API 2.308: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0008487/phenotypes?rows=200\n",
"API 2.305: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0010828/phenotypes?rows=200\n",
"API 2.312: https://api.monarchinitiative.org/api/bioentity/disease/MESH:D008106/phenotypes?rows=200\n",
"API 2.310: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0019383/phenotypes?rows=200\n",
"API 2.313: https://api.monarchinitiative.org/api/bioentity/disease/C4023159/phenotypes?rows=200\n",
"API 2.317: https://api.monarchinitiative.org/api/bioentity/disease/C1859126/phenotypes?rows=200\n",
"API 2.316: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0010690/phenotypes?rows=200\n",
"API 2.321: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004979/phenotypes?rows=200\n",
"API 2.318: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004991/phenotypes?rows=200\n",
"API 2.320: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005053/phenotypes?rows=200\n",
"API 2.322: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005316/phenotypes?rows=200\n",
"API 2.307: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0018899/phenotypes?rows=200\n",
"API 2.304: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005301/phenotypes?rows=200\n",
"API 2.311: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0012570/phenotypes?rows=200\n",
"API 2.314: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005148/phenotypes?rows=200\n",
"API 2.306: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005154/phenotypes?rows=200\n",
"API 2.319: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005275/phenotypes?rows=200\n",
"API 2.323: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005059/phenotypes?rows=200\n",
"API 2.315: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002473/phenotypes?rows=200\n",
"API 2.309: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005374/phenotypes?rows=200\n",
"API 2.326: https://api.monarchinitiative.org/api/bioentity/disease/C0376618/phenotypes?rows=200\n",
"API 2.324: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0010940/phenotypes?rows=200\n",
"API 2.328: https://api.monarchinitiative.org/api/bioentity/disease/C1839829/phenotypes?rows=200\n",
"API 2.325: https://api.monarchinitiative.org/api/bioentity/disease/MESH:D014947/phenotypes?rows=200\n",
"API 2.330: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005082/phenotypes?rows=200\n",
"API 2.332: https://api.monarchinitiative.org/api/bioentity/disease/MESH:D001851/phenotypes?rows=200\n",
"API 2.334: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005302/phenotypes?rows=200\n",
"API 2.336: https://api.monarchinitiative.org/api/bioentity/disease/C0455825/phenotypes?rows=200\n",
"API 2.335: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0003432/phenotypes?rows=200\n",
"API 2.338: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005109/phenotypes?rows=200\n",
"API 2.329: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0021063/phenotypes?rows=200\n",
"API 2.333: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0008800/phenotypes?rows=200\n",
"API 2.340: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005138/phenotypes?rows=200\n",
"API 2.331: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005265/phenotypes?rows=200\n",
"API 2.327: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0016512/phenotypes?rows=200\n",
"API 2.339: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0000328/phenotypes?rows=200\n",
"API 2.342: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005220/phenotypes?rows=200\n",
"API 2.337: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0009532/phenotypes?rows=200\n",
"API 2.346: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002123/phenotypes?rows=200\n",
"API 2.344: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005420/phenotypes?rows=200\n",
"API 2.345: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0018149/phenotypes?rows=200\n",
"API 2.341: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0010002/phenotypes?rows=200\n",
"API 2.349: https://api.monarchinitiative.org/api/bioentity/disease/C0021655/phenotypes?rows=200\n",
"API 2.347: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0006857/phenotypes?rows=200\n",
"API 2.348: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004985/phenotypes?rows=200\n",
"API 2.343: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0001566/phenotypes?rows=200\n",
"API 2.350: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0011194/phenotypes?rows=200\n",
"API 2.354: https://api.monarchinitiative.org/api/bioentity/disease/MESH:D003875/phenotypes?rows=200\n",
"API 2.356: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005618/phenotypes?rows=200\n",
"API 2.352: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005081/phenotypes?rows=200\n",
"API 2.353: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0024331/phenotypes?rows=200\n",
"API 2.351: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0011122/phenotypes?rows=200\n",
"API 3.5: https://biothings.ncats.io/semmed/query?fields=treated_by (POST -d q=C0029456,C0264955,C0241954,C0004096,C0730290,C0020640,C0004991,C0020529,C0276226,C0023893,C3279392,C0687133,C0027881,C0242597,C0032339,C0260397,C0856716,C0035078,C0156312,C0014591,C0027627,C0085580,C0683416,C0265509,C0002895,C0007134,C0442750,C1389016,C0152013,C1527249,C0002871,C1859727,C0003486,C0220702,C0205644,C0015230,C0577631,C0346647,C0879257,C1833102,C0153199,C0345904,C0000768,C0264694,C0280803,C0949664,C0022658,C0079772,C0007787,C0521585,C0850624,C0011609,C0027430,C0036974,C0009566,C0877373,C0553707,C0032584,C0008626,C0278488,C0520679,C0011156,CN074280,C0332563,C0029823,C0042880,C0920563,C1299574,C0009404,C0553692,C0040147,C0235946,C0036202,C0007137,C0001339,C0014072,C0242184,C0026769,C0024032,C0025202,C0598309,C0278134,C0022679,C0017185,C4020899,C1305122,C0009376,C0022116,C0035334,C3278975,C0007795,C0233794,C0020538,C1561643,C0334389,C0032914,C0340274,C0333641,C4020885,C0031154,C0029131,C1848534,C0027719,C0001418,C1285162,C0280141,C1136033,C0041806,C0038999,C0205710,C0151744,C0020542,C0019080,C2930957,C0032231,C0425946,C0085639,C0029831,C0426414,C0004238,C0086132,C0035333,C0497327,C0022350,C2930812,C0009319,C0021053,C0011860,C0027022,C1842031,C0042373,C0085786,C0205645,C0037286,C0243066,C4277682,C0600688,C2829267,C1835308,C0002103,C0014356,C0002938,C0035613,C0741923,C0006142,C0035126,C1458155,C0149721,C3151609,C1290884,C0017152,C0019034,C1863926,C0043242,C3495426,C1849334,C0006145,C4024702,C0037274,C0403528,C0020433,C0162739,C0042693,C0006663,C1155266,C0272316,C0599973,C0796149,C3275959,C0008625,C0333463,C0030305,C0085437,C0019158,C0376544,C0004093,C0019151,C0020502,C0002940,C0339510,C0265219,C0005684,C0333186,C0858246,C0699748,C0033847,C0026766,C0002736,C0007102,C0730345,C0162557,C0026244,C0398738,C1691228,C0699790,C0009443,C0162871,C0010674,C0018213,C0029928,C0008354,C1261473,C1852548,C0014868,C0699739,C0036337,C4021097,C0042769,C0030297,C1838601,C0012242,C0272187,C0020429,C1336839,C0025517,C0268271,C1835813,C0272317,C0038379,C0042109,C0596402,C0037231,C0221468,C1292772,C0010068,C0003578,C0022893,C0334108,C0024623,C0013570,C0153417,C0007131,C0393929,C0085166,C1968782,C1306837,C0020456,C0014059,C0002726,C0007112,C0004030,C0023903,C1956346,C0037285,C0023473,C0039082,C3495676,C0024713,C0020507,C0277785,C1266158,C1140680,C0003864,C0014175,C0019189,C1458140,C0015519,C0023467,C0019348,C0701826,C1336708,C1863052,C0333467,C0086565,C0235169,C0037277,C0017668,C0024121,C0022104,C0235222,C0151313,C0587248,C0030920,C0020179,C1442839,C0347653,C1540912,C0012634,C1865868,C2751126,C1853564,C0003469,C0011164,C0018802,C0015503,C1378511,C0029839,C0205643,C1319315,C0596020,C1970143,C0014145,C0280324,C0025521,C0339573,C0029574,C1842026,C0012860,C0497247,C0242379,C0023890,C1457887,C0033578,C1879321,C0282609,C4280569,C0039730,C0009402,C0008925,C1295654,C0232910,C0524851,C1704436,C0085669,C0028077,C0879626,C1839601,C0007120,C0038356,C0221198,C0231337,C0015696,C0497406,C0007965,C1833104,C0153519,C0685938,C0235401,C0005587,C0010495,C0151650,C0596263,C0033687,C0276037,C0011847,C0042870,C3463918,C0038454,C0302142,C3495587,C1096063,C0518010,C0600327,C0677886,C0581342,C1304508,C2936917,C0595921,C0410180,C0005944,C0035335,C1504598,C3665365,C2316810,C1800706,C0546837,C0451641,C0007784,C0338656,C0009450,C1266042,C0017612,C0153594,C0019101,C0154251,C0027051,C0333262,C0860603,C0027626,C1389018,C2676033,C0019385,C0857121,C1266044,C0162735,C0016059,C0041295,C0016658,C0041408,C0022665,C0028754,C0268397,C0085183,C1368683,C0015397,C0392475,C0036983,C2675055,C0235974,C0334517,C2239176,C0038436,C0034065,C1527168,C0039231,C1833269,C0032000,C0041296,C0014070,C0751688,C0023772,C0004153,C0029442,C0919267,C1854310,C0018924,C1301700,C4024853,C0036069,C0005695,C0423798,C0024117,C0271650,C0152018,C0279607,C0032460,C0037315,C0002982,C0242488,C0221074,C0014116,C0001511,C0796004,C0040053,C0872150,C0085681,C0580545,C1549114,C0017154,C0020459,C4023159,C0279000,C0003873,C0340464,C1859126,C0024141,C0085096,C0001430,C0235266,C0003811,C0599732,C0206624,C0376618,C0026691,C0948775,C1839829,C0036631,C0494158,C0026846,C0010054,C1292775,C1869116,C0455825,C0339527,C0014457,C1266043,C0220701,C0015674,C0235032,C0007222,C0020676,C2607914,C0080113,C0270922,C0038433,C0021775,C0279702,C0005586,C0024115,C0018801,C1839736,C0004364,C0021655,C0011881,C0005940,C0035258,C0012243,C0019087,C0242606,C0020615,C0039870,C0021400,C0023895,C0858321,C0346388,C0007097,C0742115,C0019693,C0024620,C0006826,C3151610,C0014859,C0014038,C2718068,C4280606,C0027651,C1519697,C0266215,C0740457,C0017178,C0241863,C1857277,C0006111,C0948008,C0206655,C0005779,C3150911,C0376358,C0019147,C0029458,C0740392,C1839735,C0220633,C1395184,C0041671,C0332448,C0025289,C0020437,C1527304,C0011849,C0013491,C0009375,C1848934,C2931456,C0178874,C2931899,C1532253,C0035579,C0684249,C1320716,C1854114,C0346373,C0003838,C0018799,C0598608,C0497248,C0870182,C0036572,C1292778,C0033575,C0015695,C0699791,C0153458,C0007820,C0042510,C0240671,C3203356,C0752130,C0518003,C4024722,C0023418,C0021368,C0029838,C0036679,C0020517,C0876973,C0085131,C0036341,C0021390,C0002395,C0022661,C0085584,C0017168&scopes=umls)\n",
"API 3.8: https://biothings.ncats.io/semmed/query?fields=coexists_with (POST -d q=C0029456,C0264955,C0241954,C0004096,C0730290,C0020640,C0004991,C0020529,C0276226,C0023893,C3279392,C0687133,C0027881,C0242597,C0032339,C0260397,C0856716,C0035078,C0156312,C0014591,C0027627,C0085580,C0683416,C0265509,C0002895,C0007134,C0442750,C1389016,C0152013,C1527249,C0002871,C1859727,C0003486,C0220702,C0205644,C0015230,C0577631,C0346647,C0879257,C1833102,C0153199,C0345904,C0000768,C0264694,C0280803,C0949664,C0022658,C0079772,C0007787,C0521585,C0850624,C0011609,C0027430,C0036974,C0009566,C0877373,C0553707,C0032584,C0008626,C0278488,C0520679,C0011156,CN074280,C0332563,C0029823,C0042880,C0920563,C1299574,C0009404,C0553692,C0040147,C0235946,C0036202,C0007137,C0001339,C0014072,C0242184,C0026769,C0024032,C0025202,C0598309,C0278134,C0022679,C0017185,C4020899,C1305122,C0009376,C0022116,C0035334,C3278975,C0007795,C0233794,C0020538,C1561643,C0334389,C0032914,C0340274,C0333641,C4020885,C0031154,C0029131,C1848534,C0027719,C0001418,C1285162,C0280141,C1136033,C0041806,C0038999,C0205710,C0151744,C0020542,C0019080,C2930957,C0032231,C0425946,C0085639,C0029831,C0426414,C0004238,C0086132,C0035333,C0497327,C0022350,C2930812,C0009319,C0021053,C0011860,C0027022,C1842031,C0042373,C0085786,C0205645,C0037286,C0243066,C4277682,C0600688,C2829267,C1835308,C0002103,C0014356,C0002938,C0035613,C0741923,C0006142,C0035126,C1458155,C0149721,C3151609,C1290884,C0017152,C0019034,C1863926,C0043242,C3495426,C1849334,C0006145,C4024702,C0037274,C0403528,C0020433,C0162739,C0042693,C0006663,C1155266,C0272316,C0599973,C0796149,C3275959,C0008625,C0333463,C0030305,C0085437,C0019158,C0376544,C0004093,C0019151,C0020502,C0002940,C0339510,C0265219,C0005684,C0333186,C0858246,C0699748,C0033847,C0026766,C0002736,C0007102,C0730345,C0162557,C0026244,C0398738,C1691228,C0699790,C0009443,C0162871,C0010674,C0018213,C0029928,C0008354,C1261473,C1852548,C0014868,C0699739,C0036337,C4021097,C0042769,C0030297,C1838601,C0012242,C0272187,C0020429,C1336839,C0025517,C0268271,C1835813,C0272317,C0038379,C0042109,C0596402,C0037231,C0221468,C1292772,C0010068,C0003578,C0022893,C0334108,C0024623,C0013570,C0153417,C0007131,C0393929,C0085166,C1968782,C1306837,C0020456,C0014059,C0002726,C0007112,C0004030,C0023903,C1956346,C0037285,C0023473,C0039082,C3495676,C0024713,C0020507,C0277785,C1266158,C1140680,C0003864,C0014175,C0019189,C1458140,C0015519,C0023467,C0019348,C0701826,C1336708,C1863052,C0333467,C0086565,C0235169,C0037277,C0017668,C0024121,C0022104,C0235222,C0151313,C0587248,C0030920,C0020179,C1442839,C0347653,C1540912,C0012634,C1865868,C2751126,C1853564,C0003469,C0011164,C0018802,C0015503,C1378511,C0029839,C0205643,C1319315,C0596020,C1970143,C0014145,C0280324,C0025521,C0339573,C0029574,C1842026,C0012860,C0497247,C0242379,C0023890,C1457887,C0033578,C1879321,C0282609,C4280569,C0039730,C0009402,C0008925,C1295654,C0232910,C0524851,C1704436,C0085669,C0028077,C0879626,C1839601,C0007120,C0038356,C0221198,C0231337,C0015696,C0497406,C0007965,C1833104,C0153519,C0685938,C0235401,C0005587,C0010495,C0151650,C0596263,C0033687,C0276037,C0011847,C0042870,C3463918,C0038454,C0302142,C3495587,C1096063,C0518010,C0600327,C0677886,C0581342,C1304508,C2936917,C0595921,C0410180,C0005944,C0035335,C1504598,C3665365,C2316810,C1800706,C0546837,C0451641,C0007784,C0338656,C0009450,C1266042,C0017612,C0153594,C0019101,C0154251,C0027051,C0333262,C0860603,C0027626,C1389018,C2676033,C0019385,C0857121,C1266044,C0162735,C0016059,C0041295,C0016658,C0041408,C0022665,C0028754,C0268397,C0085183,C1368683,C0015397,C0392475,C0036983,C2675055,C0235974,C0334517,C2239176,C0038436,C0034065,C1527168,C0039231,C1833269,C0032000,C0041296,C0014070,C0751688,C0023772,C0004153,C0029442,C0919267,C1854310,C0018924,C1301700,C4024853,C0036069,C0005695,C0423798,C0024117,C0271650,C0152018,C0279607,C0032460,C0037315,C0002982,C0242488,C0221074,C0014116,C0001511,C0796004,C0040053,C0872150,C0085681,C0580545,C1549114,C0017154,C0020459,C4023159,C0279000,C0003873,C0340464,C1859126,C0024141,C0085096,C0001430,C0235266,C0003811,C0599732,C0206624,C0376618,C0026691,C0948775,C1839829,C0036631,C0494158,C0026846,C0010054,C1292775,C1869116,C0455825,C0339527,C0014457,C1266043,C0220701,C0015674,C0235032,C0007222,C0020676,C2607914,C0080113,C0270922,C0038433,C0021775,C0279702,C0005586,C0024115,C0018801,C1839736,C0004364,C0021655,C0011881,C0005940,C0035258,C0012243,C0019087,C0242606,C0020615,C0039870,C0021400,C0023895,C0858321,C0346388,C0007097,C0742115,C0019693,C0024620,C0006826,C3151610,C0014859,C0014038,C2718068,C4280606,C0027651,C1519697,C0266215,C0740457,C0017178,C0241863,C1857277,C0006111,C0948008,C0206655,C0005779,C3150911,C0376358,C0019147,C0029458,C0740392,C1839735,C0220633,C1395184,C0041671,C0332448,C0025289,C0020437,C1527304,C0011849,C0013491,C0009375,C1848934,C2931456,C0178874,C2931899,C1532253,C0035579,C0684249,C1320716,C1854114,C0346373,C0003838,C0018799,C0598608,C0497248,C0870182,C0036572,C1292778,C0033575,C0015695,C0699791,C0153458,C0007820,C0042510,C0240671,C3203356,C0752130,C0518003,C4024722,C0023418,C0021368,C0029838,C0036679,C0020517,C0876973,C0085131,C0036341,C0021390,C0002395,C0022661,C0085584,C0017168&scopes=umls)\n",
"API 2.361: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0006515/phenotypes?rows=200\n",
"API 2.357: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0015766/phenotypes?rows=200\n",
"API 2.360: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0011782/phenotypes?rows=200\n",
"API 2.363: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002732/phenotypes?rows=200\n",
"API 2.365: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0001186/phenotypes?rows=200\n",
"API 2.366: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0020317/phenotypes?rows=200\n",
"API 2.364: https://api.monarchinitiative.org/api/bioentity/disease/C4280606/phenotypes?rows=200\n",
"API 2.355: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005046/phenotypes?rows=200\n",
"API 2.358: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005252/phenotypes?rows=200\n",
"API 2.362: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004609/phenotypes?rows=200\n",
"API 2.370: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005203/phenotypes?rows=200\n",
"API 2.371: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0021400/phenotypes?rows=200\n",
"API 2.373: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005404/phenotypes?rows=200\n",
"API 2.359: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0018186/phenotypes?rows=200\n",
"API 2.369: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0019086/phenotypes?rows=200\n",
"API 2.368: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0019023/phenotypes?rows=200\n",
"API 2.376: https://api.monarchinitiative.org/api/bioentity/disease/C0178874/phenotypes?rows=200\n",
"API 2.374: https://api.monarchinitiative.org/api/bioentity/disease/C0013491/phenotypes?rows=200\n",
"API 2.383: https://api.monarchinitiative.org/api/bioentity/disease/C0036572/phenotypes?rows=200\n",
"API 2.378: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005723/phenotypes?rows=200\n",
"API 2.386: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0001076/phenotypes?rows=200\n",
"API 2.367: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002243/phenotypes?rows=200\n",
"API 2.384: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0001063/phenotypes?rows=200\n",
"API 2.372: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0008377/phenotypes?rows=200\n",
"API 2.375: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002251/phenotypes?rows=200\n",
"API 2.381: https://api.monarchinitiative.org/api/bioentity/disease/C1854114/phenotypes?rows=200\n",
"API 2.377: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0013127/phenotypes?rows=200\n",
"API 2.385: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002244/phenotypes?rows=200\n",
"API 2.382: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0015301/phenotypes?rows=200\n",
"API 2.380: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002334/phenotypes?rows=200\n",
"API 2.387: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004981/phenotypes?rows=200\n",
"API 2.388: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005385/phenotypes?rows=200\n",
"API 2.391: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004966/phenotypes?rows=200\n",
"API 2.393: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0002408/phenotypes?rows=200\n",
"API 2.392: https://api.monarchinitiative.org/api/bioentity/disease/C0240671/phenotypes?rows=200\n",
"API 2.389: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0019338/phenotypes?rows=200\n",
"API 2.390: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0021117/phenotypes?rows=200\n",
"API 2.396: https://api.monarchinitiative.org/api/bioentity/disease/C0021368/phenotypes?rows=200\n",
"API 2.395: https://api.monarchinitiative.org/api/bioentity/disease/C4024722/phenotypes?rows=200\n",
"API 2.400: https://api.monarchinitiative.org/api/bioentity/disease/C0876973/phenotypes?rows=200\n",
"API 2.402: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004277/phenotypes?rows=200\n",
"API 2.403: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0010094/phenotypes?rows=200\n",
"API 2.404: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0020466/phenotypes?rows=200\n",
"API 2.394: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005066/phenotypes?rows=200\n",
"API 2.397: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005044/phenotypes?rows=200\n",
"API 2.406: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005300/phenotypes?rows=200\n",
"API 2.398: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0019065/phenotypes?rows=200\n",
"API 2.399: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0000190/phenotypes?rows=200\n",
"API 2.410: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005295/phenotypes?rows=200\n",
"API 2.412: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0016627/phenotypes?rows=200\n",
"API 2.408: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0001134/phenotypes?rows=200\n",
"API 2.401: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0018870/phenotypes?rows=200\n",
"API 2.409: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0004588/phenotypes?rows=200\n",
"API 2.411: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0011996/phenotypes?rows=200\n",
"API 2.405: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0019499/phenotypes?rows=200\n",
"API 2.407: https://api.monarchinitiative.org/api/bioentity/disease/MESH:D005596/phenotypes?rows=200\n",
"API 2.379: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005089/phenotypes?rows=200\n",
"API 2.413: https://api.monarchinitiative.org/api/bioentity/disease/MONDO:0005093/phenotypes?rows=200\n",
"API 3.7: https://biothings.ncats.io/semmed/query?fields=related_to (POST -d q=C0029456,C0264955,C0241954,C0004096,C0730290,C0020640,C0004991,C0020529,C0276226,C0023893,C3279392,C0687133,C0027881,C0242597,C0032339,C0260397,C0856716,C0035078,C0156312,C0014591,C0027627,C0085580,C0683416,C0265509,C0002895,C0007134,C0442750,C1389016,C0152013,C1527249,C0002871,C1859727,C0003486,C0220702,C0205644,C0015230,C0577631,C0346647,C0879257,C1833102,C0153199,C0345904,C0000768,C0264694,C0280803,C0949664,C0022658,C0079772,C0007787,C0521585,C0850624,C0011609,C0027430,C0036974,C0009566,C0877373,C0553707,C0032584,C0008626,C0278488,C0520679,C0011156,CN074280,C0332563,C0029823,C0042880,C0920563,C1299574,C0009404,C0553692,C0040147,C0235946,C0036202,C0007137,C0001339,C0014072,C0242184,C0026769,C0024032,C0025202,C0598309,C0278134,C0022679,C0017185,C4020899,C1305122,C0009376,C0022116,C0035334,C3278975,C0007795,C0233794,C0020538,C1561643,C0334389,C0032914,C0340274,C0333641,C4020885,C0031154,C0029131,C1848534,C0027719,C0001418,C1285162,C0280141,C1136033,C0041806,C0038999,C0205710,C0151744,C0020542,C0019080,C2930957,C0032231,C0425946,C0085639,C0029831,C0426414,C0004238,C0086132,C0035333,C0497327,C0022350,C2930812,C0009319,C0021053,C0011860,C0027022,C1842031,C0042373,C0085786,C0205645,C0037286,C0243066,C4277682,C0600688,C2829267,C1835308,C0002103,C0014356,C0002938,C0035613,C0741923,C0006142,C0035126,C1458155,C0149721,C3151609,C1290884,C0017152,C0019034,C1863926,C0043242,C3495426,C1849334,C0006145,C4024702,C0037274,C0403528,C0020433,C0162739,C0042693,C0006663,C1155266,C0272316,C0599973,C0796149,C3275959,C0008625,C0333463,C0030305,C0085437,C0019158,C0376544,C0004093,C0019151,C0020502,C0002940,C0339510,C0265219,C0005684,C0333186,C0858246,C0699748,C0033847,C0026766,C0002736,C0007102,C0730345,C0162557,C0026244,C0398738,C1691228,C0699790,C0009443,C0162871,C0010674,C0018213,C0029928,C0008354,C1261473,C1852548,C0014868,C0699739,C0036337,C4021097,C0042769,C0030297,C1838601,C0012242,C0272187,C0020429,C1336839,C0025517,C0268271,C1835813,C0272317,C0038379,C0042109,C0596402,C0037231,C0221468,C1292772,C0010068,C0003578,C0022893,C0334108,C0024623,C0013570,C0153417,C0007131,C0393929,C0085166,C1968782,C1306837,C0020456,C0014059,C0002726,C0007112,C0004030,C0023903,C1956346,C0037285,C0023473,C0039082,C3495676,C0024713,C0020507,C0277785,C1266158,C1140680,C0003864,C0014175,C0019189,C1458140,C0015519,C0023467,C0019348,C0701826,C1336708,C1863052,C0333467,C0086565,C0235169,C0037277,C0017668,C0024121,C0022104,C0235222,C0151313,C0587248,C0030920,C0020179,C1442839,C0347653,C1540912,C0012634,C1865868,C2751126,C1853564,C0003469,C0011164,C0018802,C0015503,C1378511,C0029839,C0205643,C1319315,C0596020,C1970143,C0014145,C0280324,C0025521,C0339573,C0029574,C1842026,C0012860,C0497247,C0242379,C0023890,C1457887,C0033578,C1879321,C0282609,C4280569,C0039730,C0009402,C0008925,C1295654,C0232910,C0524851,C1704436,C0085669,C0028077,C0879626,C1839601,C0007120,C0038356,C0221198,C0231337,C0015696,C0497406,C0007965,C1833104,C0153519,C0685938,C0235401,C0005587,C0010495,C0151650,C0596263,C0033687,C0276037,C0011847,C0042870,C3463918,C0038454,C0302142,C3495587,C1096063,C0518010,C0600327,C0677886,C0581342,C1304508,C2936917,C0595921,C0410180,C0005944,C0035335,C1504598,C3665365,C2316810,C1800706,C0546837,C0451641,C0007784,C0338656,C0009450,C1266042,C0017612,C0153594,C0019101,C0154251,C0027051,C0333262,C0860603,C0027626,C1389018,C2676033,C0019385,C0857121,C1266044,C0162735,C0016059,C0041295,C0016658,C0041408,C0022665,C0028754,C0268397,C0085183,C1368683,C0015397,C0392475,C0036983,C2675055,C0235974,C0334517,C2239176,C0038436,C0034065,C1527168,C0039231,C1833269,C0032000,C0041296,C0014070,C0751688,C0023772,C0004153,C0029442,C0919267,C1854310,C0018924,C1301700,C4024853,C0036069,C0005695,C0423798,C0024117,C0271650,C0152018,C0279607,C0032460,C0037315,C0002982,C0242488,C0221074,C0014116,C0001511,C0796004,C0040053,C0872150,C0085681,C0580545,C1549114,C0017154,C0020459,C4023159,C0279000,C0003873,C0340464,C1859126,C0024141,C0085096,C0001430,C0235266,C0003811,C0599732,C0206624,C0376618,C0026691,C0948775,C1839829,C0036631,C0494158,C0026846,C0010054,C1292775,C1869116,C0455825,C0339527,C0014457,C1266043,C0220701,C0015674,C0235032,C0007222,C0020676,C2607914,C0080113,C0270922,C0038433,C0021775,C0279702,C0005586,C0024115,C0018801,C1839736,C0004364,C0021655,C0011881,C0005940,C0035258,C0012243,C0019087,C0242606,C0020615,C0039870,C0021400,C0023895,C0858321,C0346388,C0007097,C0742115,C0019693,C0024620,C0006826,C3151610,C0014859,C0014038,C2718068,C4280606,C0027651,C1519697,C0266215,C0740457,C0017178,C0241863,C1857277,C0006111,C0948008,C0206655,C0005779,C3150911,C0376358,C0019147,C0029458,C0740392,C1839735,C0220633,C1395184,C0041671,C0332448,C0025289,C0020437,C1527304,C0011849,C0013491,C0009375,C1848934,C2931456,C0178874,C2931899,C1532253,C0035579,C0684249,C1320716,C1854114,C0346373,C0003838,C0018799,C0598608,C0497248,C0870182,C0036572,C1292778,C0033575,C0015695,C0699791,C0153458,C0007820,C0042510,C0240671,C3203356,C0752130,C0518003,C4024722,C0023418,C0021368,C0029838,C0036679,C0020517,C0876973,C0085131,C0036341,C0021390,C0002395,C0022661,C0085584,C0017168&scopes=umls)\n",
"\n",
"\n",
"==== Step #3: Output normalization ====\n",
"\n",
"API 3.1 semmed_disease: 37 hits\n",
"API 3.2 semmed_disease: 83 hits\n",
"API 3.3 semmed_disease: 9 hits\n",
"API 1.1 mydisease: 1581 hits\n",
"API 3.4 semmed_disease: 22 hits\n",
"API 3.5 semmed_disease: 42 hits\n",
"API 2.1 biolink: No hits\n",
"API 2.2 biolink: 14 hits\n",
"API 2.3 biolink: No hits\n",
"API 2.4 biolink: 23 hits\n",
"API 2.5 biolink: No hits\n",
"API 2.6 biolink: No hits\n",
"API 2.7 biolink: 1 hits\n",
"API 2.8 biolink: No hits\n",
"API 2.9 biolink: No hits\n",
"API 2.10 biolink: No hits\n",
"API 2.11 biolink: 200 hits\n",
"API 2.12 biolink: No hits\n",
"API 2.13 biolink: No hits\n",
"API 2.14 biolink: 2 hits\n",
"API 2.15 biolink: 3 hits\n",
"API 2.16 biolink: No hits\n",
"API 2.17 biolink: No hits\n",
"API 2.18 biolink: No hits\n",
"API 2.19 biolink: 46 hits\n",
"API 2.20 biolink: 94 hits\n",
"API 2.21 biolink: No hits\n",
"API 2.22 biolink: No hits\n",
"API 2.23 biolink: 11 hits\n",
"API 2.24 biolink: No hits\n",
"API 2.25 biolink: 200 hits\n",
"API 2.26 biolink: No hits\n",
"API 2.27 biolink: 114 hits\n",
"API 2.28 biolink: No hits\n",
"API 2.29 biolink: No hits\n",
"API 2.30 biolink: No hits\n",
"API 2.31 biolink: 33 hits\n",
"API 2.32 biolink: No hits\n",
"API 2.33 biolink: 3 hits\n",
"API 2.34 biolink: No hits\n",
"API 2.35 biolink: 11 hits\n",
"API 2.36 biolink: No hits\n",
"API 2.37 biolink: No hits\n",
"API 2.38 biolink: No hits\n",
"API 2.39 biolink: No hits\n",
"API 2.40 biolink: No hits\n",
"API 2.41 biolink: 200 hits\n",
"API 2.42 biolink: No hits\n",
"API 2.43 biolink: 200 hits\n",
"API 2.44 biolink: No hits\n",
"API 2.45 biolink: No hits\n",
"API 2.46 biolink: 200 hits\n",
"API 2.47 biolink: No hits\n",
"API 2.48 biolink: 18 hits\n",
"API 2.49 biolink: No hits\n",
"API 2.50 biolink: 71 hits\n",
"API 2.51 biolink: No hits\n",
"API 2.52 biolink: 200 hits\n",
"API 2.53 biolink: 200 hits\n",
"API 2.54 biolink: 4 hits\n",
"API 2.55 biolink: 6 hits\n",
"API 2.56 biolink: No hits\n",
"API 2.57 biolink: 9 hits\n",
"API 2.58 biolink: 1 hits\n",
"API 2.59 biolink: No hits\n",
"API 2.60 biolink: No hits\n",
"API 2.61 biolink: 200 hits\n",
"API 2.62 biolink: No hits\n",
"API 2.63 biolink: No hits\n",
"API 2.64 biolink: No hits\n",
"API 2.65 biolink: No hits\n",
"API 2.66 biolink: 78 hits\n",
"API 2.67 biolink: 22 hits\n",
"API 2.68 biolink: 3 hits\n",
"API 2.69 biolink: 33 hits\n",
"API 2.70 biolink: 200 hits\n",
"API 2.71 biolink: 15 hits\n",
"API 2.72 biolink: No hits\n",
"API 2.73 biolink: 144 hits\n",
"API 2.74 biolink: 196 hits\n",
"API 2.75 biolink: No hits\n",
"API 2.76 biolink: 121 hits\n",
"API 2.77 biolink: 7 hits\n",
"API 2.78 biolink: No hits\n",
"API 2.79 biolink: No hits\n",
"API 2.80 biolink: 43 hits\n",
"API 2.81 biolink: No hits\n",
"API 2.82 biolink: 64 hits\n",
"API 2.83 biolink: 31 hits\n",
"API 2.84 biolink: 6 hits\n",
"API 2.85 biolink: 98 hits\n",
"API 2.86 biolink: No hits\n",
"API 2.87 biolink: 24 hits\n",
"API 2.88 biolink: 5 hits\n",
"API 2.89 biolink: 104 hits\n",
"API 2.90 biolink: No hits\n",
"API 2.91 biolink: 58 hits\n",
"API 2.92 biolink: No hits\n",
"API 2.93 biolink: No hits\n",
"API 2.94 biolink: 21 hits\n",
"API 2.95 biolink: No hits\n",
"API 2.96 biolink: 80 hits\n",
"API 2.97 biolink: No hits\n",
"API 2.98 biolink: 26 hits\n",
"API 2.99 biolink: 57 hits\n",
"API 2.100 biolink: 198 hits\n",
"API 2.101 biolink: No hits\n",
"API 2.102 biolink: No hits\n",
"API 2.103 biolink: No hits\n",
"API 2.104 biolink: 14 hits\n",
"API 2.105 biolink: No hits\n",
"API 2.106 biolink: 40 hits\n",
"API 2.107 biolink: 148 hits\n",
"API 2.108 biolink: No hits\n",
"API 2.109 biolink: No hits\n",
"API 2.110 biolink: No hits\n",
"API 2.111 biolink: 1 hits\n",
"API 2.112 biolink: No hits\n",
"API 2.113 biolink: 2 hits\n",
"API 2.114 biolink: No hits\n",
"API 2.115 biolink: No hits\n",
"API 2.116 biolink: 76 hits\n",
"API 2.117 biolink: 61 hits\n",
"API 2.118 biolink: 49 hits\n",
"API 2.119 biolink: No hits\n",
"API 2.120 biolink: No hits\n",
"API 2.121 biolink: 32 hits\n",
"API 2.122 biolink: No hits\n",
"API 2.123 biolink: 200 hits\n",
"API 2.124 biolink: No hits\n",
"API 2.125 biolink: 14 hits\n",
"API 2.126 biolink: No hits\n",
"API 2.127 biolink: 6 hits\n",
"API 2.128 biolink: 7 hits\n",
"API 2.129 biolink: 5 hits\n",
"API 2.130 biolink: No hits\n",
"API 2.131 biolink: No hits\n",
"API 2.132 biolink: 200 hits\n",
"API 2.133 biolink: No hits\n",
"API 2.134 biolink: 200 hits\n",
"API 2.135 biolink: No hits\n",
"API 2.136 biolink: 66 hits\n",
"API 2.137 biolink: 73 hits\n",
"API 2.138 biolink: No hits\n",
"API 2.139 biolink: No hits\n",
"API 2.140 biolink: 1 hits\n",
"API 2.141 biolink: 200 hits\n",
"API 2.142 biolink: 200 hits\n",
"API 2.143 biolink: No hits\n",
"API 2.144 biolink: No hits\n",
"API 2.145 biolink: 16 hits\n",
"API 2.146 biolink: No hits\n",
"API 2.147 biolink: 1 hits\n",
"API 2.148 biolink: 169 hits\n",
"API 2.149 biolink: No hits\n",
"API 2.150 biolink: No hits\n",
"API 2.151 biolink: No hits\n",
"API 2.152 biolink: 92 hits\n",
"API 2.153 biolink: 200 hits\n",
"API 2.154 biolink: 159 hits\n",
"API 2.155 biolink: 39 hits\n",
"API 2.156 biolink: No hits\n",
"API 2.157 biolink: 43 hits\n",
"API 2.158 biolink: 8 hits\n",
"API 2.159 biolink: 19 hits\n",
"API 2.160 biolink: No hits\n",
"API 2.161 biolink: 10 hits\n",
"API 2.162 biolink: No hits\n",
"API 2.163 biolink: 200 hits\n",
"API 2.164 biolink: 200 hits\n",
"API 2.165 biolink: 200 hits\n",
"API 2.166 biolink: No hits\n",
"API 2.167 biolink: No hits\n",
"API 2.168 biolink: 22 hits\n",
"API 2.169 biolink: 200 hits\n",
"API 2.170 biolink: 36 hits\n",
"API 2.171 biolink: 30 hits\n",
"API 2.172 biolink: No hits\n",
"API 2.173 biolink: 199 hits\n",
"API 2.174 biolink: 144 hits\n",
"API 2.175 biolink: No hits\n",
"API 2.176 biolink: 1 hits\n",
"API 2.177 biolink: No hits\n",
"API 2.178 biolink: No hits\n",
"API 2.179 biolink: 1 hits\n",
"API 2.180 biolink: 27 hits\n",
"API 2.181 biolink: 200 hits\n",
"API 2.182 biolink: 200 hits\n",
"API 2.183 biolink: 29 hits\n",
"API 2.184 biolink: 113 hits\n",
"API 2.185 biolink: 200 hits\n",
"API 2.186 biolink: 66 hits\n",
"API 2.187 biolink: 200 hits\n",
"API 2.188 biolink: No hits\n",
"API 2.189 biolink: 99 hits\n",
"API 2.190 biolink: 200 hits\n",
"API 2.191 biolink: No hits\n",
"API 2.192 biolink: 22 hits\n",
"API 2.193 biolink: 21 hits\n",
"API 2.194 biolink: 95 hits\n",
"API 2.195 biolink: 62 hits\n",
"API 2.196 biolink: 25 hits\n",
"API 2.197 biolink: No hits\n",
"API 2.198 biolink: 200 hits\n",
"API 2.199 biolink: No hits\n",
"API 2.200 biolink: 200 hits\n",
"API 2.201 biolink: No hits\n",
"API 2.202 biolink: No hits\n",
"API 2.203 biolink: No hits\n",
"API 2.204 biolink: No hits\n",
"API 2.205 biolink: 14 hits\n",
"API 2.206 biolink: No hits\n",
"API 2.207 biolink: 111 hits\n",
"API 2.208 biolink: 200 hits\n",
"API 2.209 biolink: 34 hits\n",
"API 2.210 biolink: 44 hits\n",
"API 2.211 biolink: 200 hits\n",
"API 2.212 biolink: 6 hits\n",
"API 2.213 biolink: 77 hits\n",
"API 2.214 biolink: 200 hits\n",
"API 2.215 biolink: 17 hits\n",
"API 2.216 biolink: No hits\n",
"API 2.217 biolink: No hits\n",
"API 2.218 biolink: 33 hits\n",
"API 2.219 biolink: 17 hits\n",
"API 2.220 biolink: No hits\n",
"API 2.221 biolink: No hits\n",
"API 2.222 biolink: 6 hits\n",
"API 2.223 biolink: 3 hits\n",
"API 2.224 biolink: 200 hits\n",
"API 2.225 biolink: No hits\n",
"API 2.226 biolink: No hits\n",
"API 2.227 biolink: No hits\n",
"API 2.228 biolink: 200 hits\n",
"API 2.229 biolink: No hits\n",
"API 2.230 biolink: 196 hits\n",
"API 2.231 biolink: 200 hits\n",
"API 2.232 biolink: No hits\n",
"API 2.233 biolink: No hits\n",
"API 2.234 biolink: 47 hits\n",
"API 2.235 biolink: No hits\n",
"API 2.236 biolink: 46 hits\n",
"API 2.237 biolink: 163 hits\n",
"API 2.238 biolink: 70 hits\n",
"API 2.239 biolink: No hits\n",
"API 2.240 biolink: 78 hits\n",
"API 2.241 biolink: No hits\n",
"API 2.242 biolink: 5 hits\n",
"API 2.243 biolink: No hits\n",
"API 2.244 biolink: No hits\n",
"API 2.245 biolink: 34 hits\n",
"API 2.246 biolink: 16 hits\n",
"API 2.247 biolink: No hits\n",
"API 2.248 biolink: No hits\n",
"API 2.249 biolink: No hits\n",
"API 2.250 biolink: 35 hits\n",
"API 2.251 biolink: 18 hits\n",
"API 2.252 biolink: 17 hits\n",
"API 2.253 biolink: No hits\n",
"API 2.254 biolink: No hits\n",
"API 2.255 biolink: 200 hits\n",
"API 2.256 biolink: No hits\n",
"API 2.257 biolink: 15 hits\n",
"API 2.258 biolink: No hits\n",
"API 2.259 biolink: No hits\n",
"API 2.260 biolink: 200 hits\n",
"API 2.261 biolink: 200 hits\n",
"API 2.262 biolink: No hits\n",
"API 2.263 biolink: No hits\n",
"API 2.264 biolink: No hits\n",
"API 2.265 biolink: No hits\n",
"API 2.266 biolink: 43 hits\n",
"API 2.267 biolink: No hits\n",
"API 2.268 biolink: 36 hits\n",
"API 2.269 biolink: 200 hits\n",
"API 2.270 biolink: No hits\n",
"API 2.271 biolink: 7 hits\n",
"API 2.272 biolink: 40 hits\n",
"API 2.273 biolink: No hits\n",
"API 2.274 biolink: No hits\n",
"API 2.275 biolink: No hits\n",
"API 2.276 biolink: No hits\n",
"API 2.277 biolink: 200 hits\n",
"API 2.278 biolink: No hits\n",
"API 2.279 biolink: No hits\n",
"API 2.280 biolink: 67 hits\n",
"API 2.281 biolink: 63 hits\n",
"API 2.282 biolink: 200 hits\n",
"API 2.283 biolink: 200 hits\n",
"API 2.284 biolink: No hits\n",
"API 2.285 biolink: No hits\n",
"API 2.286 biolink: No hits\n",
"API 2.287 biolink: 200 hits\n",
"API 2.288 biolink: 19 hits\n",
"API 2.289 biolink: 200 hits\n",
"API 2.290 biolink: No hits\n",
"API 2.291 biolink: No hits\n",
"API 2.292 biolink: No hits\n",
"API 2.293 biolink: 200 hits\n",
"API 2.294 biolink: 36 hits\n",
"API 2.295 biolink: No hits\n",
"API 2.296 biolink: 1 hits\n",
"API 2.297 biolink: No hits\n",
"API 2.298 biolink: No hits\n",
"API 2.299 biolink: 2 hits\n",
"API 2.300 biolink: 56 hits\n",
"API 2.301 biolink: No hits\n",
"API 2.302 biolink: No hits\n",
"API 2.303 biolink: No hits\n",
"API 2.304 biolink: 15 hits\n",
"API 2.305 biolink: 11 hits\n",
"API 2.306 biolink: 199 hits\n",
"API 2.307 biolink: 21 hits\n",
"API 2.308 biolink: 6 hits\n",
"API 2.309 biolink: 200 hits\n",
"API 2.310 biolink: No hits\n",
"API 2.311 biolink: 13 hits\n",
"API 2.312 biolink: No hits\n",
"API 2.313 biolink: No hits\n",
"API 2.314 biolink: 8 hits\n",
"API 2.315 biolink: 200 hits\n",
"API 2.316 biolink: 4 hits\n",
"API 2.317 biolink: No hits\n",
"API 2.318 biolink: No hits\n",
"API 2.319 biolink: 200 hits\n",
"API 2.320 biolink: No hits\n",
"API 2.321 biolink: No hits\n",
"API 2.322 biolink: No hits\n",
"API 2.323 biolink: 122 hits\n",
"API 2.324 biolink: 1 hits\n",
"API 2.325 biolink: No hits\n",
"API 2.326 biolink: No hits\n",
"API 2.327 biolink: 167 hits\n",
"API 2.328 biolink: No hits\n",
"API 2.329 biolink: 12 hits\n",
"API 2.330 biolink: No hits\n",
"API 2.331 biolink: 200 hits\n",
"API 2.332 biolink: No hits\n",
"API 2.333 biolink: 71 hits\n",
"API 2.334 biolink: 2 hits\n",
"API 2.335 biolink: No hits\n",
"API 2.336 biolink: No hits\n",
"API 2.337 biolink: 58 hits\n",
"API 2.338 biolink: No hits\n",
"API 2.339 biolink: No hits\n",
"API 2.340 biolink: 49 hits\n",
"API 2.341 biolink: 150 hits\n",
"API 2.342 biolink: No hits\n",
"API 2.343 biolink: 84 hits\n",
"API 2.344 biolink: 200 hits\n",
"API 2.345 biolink: 107 hits\n",
"API 2.346 biolink: 73 hits\n",
"API 2.347 biolink: No hits\n",
"API 2.348 biolink: 3 hits\n",
"API 2.349 biolink: No hits\n",
"API 2.350 biolink: 1 hits\n",
"API 2.351 biolink: 200 hits\n",
"API 2.352 biolink: 64 hits\n",
"API 2.353 biolink: 62 hits\n",
"API 2.354 biolink: No hits\n",
"API 2.355 biolink: 200 hits\n",
"API 2.356 biolink: 6 hits\n",
"API 2.357 biolink: No hits\n",
"API 2.358 biolink: 61 hits\n",
"API 2.359 biolink: 200 hits\n",
"API 2.360 biolink: No hits\n",
"API 2.361 biolink: No hits\n",
"API 2.362 biolink: 32 hits\n",
"API 2.363 biolink: No hits\n",
"API 2.364 biolink: No hits\n",
"API 2.365 biolink: No hits\n",
"API 2.366 biolink: No hits\n",
"API 2.367 biolink: 200 hits\n",
"API 2.368 biolink: 80 hits\n",
"API 2.369 biolink: 31 hits\n",
"API 2.370 biolink: No hits\n",
"API 2.371 biolink: No hits\n",
"API 2.372 biolink: 6 hits\n",
"API 2.373 biolink: No hits\n",
"API 2.374 biolink: No hits\n",
"API 2.375 biolink: 33 hits\n",
"API 2.376 biolink: No hits\n",
"API 2.377 biolink: 32 hits\n",
"API 2.378 biolink: No hits\n",
"API 2.379 biolink: 158 hits\n",
"API 2.380 biolink: 200 hits\n",
"API 2.381 biolink: No hits\n",
"API 2.382 biolink: 15 hits\n",
"API 2.383 biolink: No hits\n",
"API 2.384 biolink: No hits\n",
"API 2.385 biolink: 15 hits\n",
"API 2.386 biolink: No hits\n",
"API 2.387 biolink: 41 hits\n",
"API 2.388 biolink: 200 hits\n",
"API 2.389 biolink: 75 hits\n",
"API 2.390 biolink: 149 hits\n",
"API 2.391 biolink: 23 hits\n",
"API 2.392 biolink: No hits\n",
"API 2.393 biolink: 65 hits\n",
"API 2.394 biolink: 200 hits\n",
"API 2.395 biolink: No hits\n",
"API 2.396 biolink: No hits\n",
"API 2.397 biolink: 200 hits\n",
"API 2.398 biolink: 200 hits\n",
"API 2.399 biolink: 5 hits\n",
"API 2.400 biolink: No hits\n",
"API 2.401 biolink: 17 hits\n",
"API 2.402 biolink: No hits\n",
"API 2.403 biolink: 43 hits\n",
"API 2.404 biolink: 114 hits\n",
"API 2.405 biolink: 200 hits\n",
"API 2.406 biolink: No hits\n",
"API 2.407 biolink: No hits\n",
"API 2.408 biolink: 3 hits\n",
"API 2.409 biolink: 63 hits\n",
"API 2.410 biolink: No hits\n",
"API 2.411 biolink: 15 hits\n",
"API 2.412 biolink: No hits\n",
"API 2.413 biolink: 200 hits\n",
"API 3.6 semmed_disease: 15 hits\n",
"API 3.7 semmed_disease: 70 hits\n",
"API 3.8 semmed_disease: 702 hits\n",
"API 3.9 semmed_disease: 1 hits\n",
"API 3.10 semmed_disease: 1 hits\n",
"API 3.11 semmed_disease: 1 hits\n",
"\n",
"After id-to-object translation, BTE retrieved 3659 unique objects.\n",
"\n",
"==========\n",
"========== Final assembly of results ==========\n",
"==========\n",
"\n",
"\n",
"In the #1 query, BTE found 3 unique Gene nodes\n",
"In the #2 query, BTE found 473 unique Disease nodes\n",
"In the #3 query, BTE found 3659 unique PhenotypicFeature nodes\n"
]
}
],
"source": [
"fc.connect(verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"df = fc.display_table_view()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>input</th>\n",
" <th>input_type</th>\n",
" <th>pred1</th>\n",
" <th>pred1_source</th>\n",
" <th>pred1_api</th>\n",
" <th>pred1_pubmed</th>\n",
" <th>node1_type</th>\n",
" <th>node1_name</th>\n",
" <th>node1_id</th>\n",
" <th>pred2</th>\n",
" <th>...</th>\n",
" <th>node2_type</th>\n",
" <th>node2_name</th>\n",
" <th>node2_id</th>\n",
" <th>pred3</th>\n",
" <th>pred3_source</th>\n",
" <th>pred3_api</th>\n",
" <th>pred3_pubmed</th>\n",
" <th>output_type</th>\n",
" <th>output_name</th>\n",
" <th>output_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>ANISINDIONE</td>\n",
" <td>ChemicalSubstance</td>\n",
" <td>physically_interacts_with</td>\n",
" <td>None</td>\n",
" <td>DGIdb API</td>\n",
" <td>None</td>\n",
" <td>Gene</td>\n",
" <td>GC</td>\n",
" <td>NCBIGene:2638</td>\n",
" <td>disrupts</td>\n",
" <td>...</td>\n",
" <td>Disease</td>\n",
" <td>C0600688</td>\n",
" <td>UMLS:C0600688</td>\n",
" <td>affected_by</td>\n",
" <td>SEMMED</td>\n",
" <td>SEMMED Disease API</td>\n",
" <td>3995681,19668867</td>\n",
" <td>PhenotypicFeature</td>\n",
" <td>REDUCED GLUTATHIONE</td>\n",
" <td>name:REDUCED GLUTATHIONE</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>ANISINDIONE</td>\n",
" <td>ChemicalSubstance</td>\n",
" <td>physically_interacts_with</td>\n",
" <td>None</td>\n",
" <td>DGIdb API</td>\n",
" <td>None</td>\n",
" <td>Gene</td>\n",
" <td>GC</td>\n",
" <td>NCBIGene:2638</td>\n",
" <td>causes</td>\n",
" <td>...</td>\n",
" <td>Disease</td>\n",
" <td>C0600688</td>\n",
" <td>UMLS:C0600688</td>\n",
" <td>affected_by</td>\n",
" <td>SEMMED</td>\n",
" <td>SEMMED Disease API</td>\n",
" <td>3995681,19668867</td>\n",
" <td>PhenotypicFeature</td>\n",
" <td>REDUCED GLUTATHIONE</td>\n",
" <td>name:REDUCED GLUTATHIONE</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>ANISINDIONE</td>\n",
" <td>ChemicalSubstance</td>\n",
" <td>physically_interacts_with</td>\n",
" <td>None</td>\n",
" <td>DGIdb API</td>\n",
" <td>None</td>\n",
" <td>Gene</td>\n",
" <td>GC</td>\n",
" <td>NCBIGene:2638</td>\n",
" <td>prevents</td>\n",
" <td>...</td>\n",
" <td>Disease</td>\n",
" <td>C0600688</td>\n",
" <td>UMLS:C0600688</td>\n",
" <td>affected_by</td>\n",
" <td>SEMMED</td>\n",
" <td>SEMMED Disease API</td>\n",
" <td>3995681,19668867</td>\n",
" <td>PhenotypicFeature</td>\n",
" <td>REDUCED GLUTATHIONE</td>\n",
" <td>name:REDUCED GLUTATHIONE</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>ANISINDIONE</td>\n",
" <td>ChemicalSubstance</td>\n",
" <td>physically_interacts_with</td>\n",
" <td>None</td>\n",
" <td>DGIdb API</td>\n",
" <td>None</td>\n",
" <td>Gene</td>\n",
" <td>GC</td>\n",
" <td>NCBIGene:2638</td>\n",
" <td>treats</td>\n",
" <td>...</td>\n",
" <td>Disease</td>\n",
" <td>C0600688</td>\n",
" <td>UMLS:C0600688</td>\n",
" <td>affected_by</td>\n",
" <td>SEMMED</td>\n",
" <td>SEMMED Disease API</td>\n",
" <td>3995681,19668867</td>\n",
" <td>PhenotypicFeature</td>\n",
" <td>REDUCED GLUTATHIONE</td>\n",
" <td>name:REDUCED GLUTATHIONE</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>ANISINDIONE</td>\n",
" <td>ChemicalSubstance</td>\n",
" <td>physically_interacts_with</td>\n",
" <td>None</td>\n",
" <td>DGIdb API</td>\n",
" <td>None</td>\n",
" <td>Gene</td>\n",
" <td>GC</td>\n",
" <td>NCBIGene:2638</td>\n",
" <td>related_to</td>\n",
" <td>...</td>\n",
" <td>Disease</td>\n",
" <td>C0600688</td>\n",
" <td>UMLS:C0600688</td>\n",
" <td>affected_by</td>\n",
" <td>SEMMED</td>\n",
" <td>SEMMED Disease API</td>\n",
" <td>3995681,19668867</td>\n",
" <td>PhenotypicFeature</td>\n",
" <td>REDUCED GLUTATHIONE</td>\n",
" <td>name:REDUCED GLUTATHIONE</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 23 columns</p>\n",
"</div>"
],
"text/plain": [
" input input_type pred1 pred1_source \\\n",
"0 ANISINDIONE ChemicalSubstance physically_interacts_with None \n",
"1 ANISINDIONE ChemicalSubstance physically_interacts_with None \n",
"2 ANISINDIONE ChemicalSubstance physically_interacts_with None \n",
"3 ANISINDIONE ChemicalSubstance physically_interacts_with None \n",
"4 ANISINDIONE ChemicalSubstance physically_interacts_with None \n",
"\n",
" pred1_api pred1_pubmed node1_type node1_name node1_id pred2 \\\n",
"0 DGIdb API None Gene GC NCBIGene:2638 disrupts \n",
"1 DGIdb API None Gene GC NCBIGene:2638 causes \n",
"2 DGIdb API None Gene GC NCBIGene:2638 prevents \n",
"3 DGIdb API None Gene GC NCBIGene:2638 treats \n",
"4 DGIdb API None Gene GC NCBIGene:2638 related_to \n",
"\n",
" ... node2_type node2_name node2_id pred3 pred3_source \\\n",
"0 ... Disease C0600688 UMLS:C0600688 affected_by SEMMED \n",
"1 ... Disease C0600688 UMLS:C0600688 affected_by SEMMED \n",
"2 ... Disease C0600688 UMLS:C0600688 affected_by SEMMED \n",
"3 ... Disease C0600688 UMLS:C0600688 affected_by SEMMED \n",
"4 ... Disease C0600688 UMLS:C0600688 affected_by SEMMED \n",
"\n",
" pred3_api pred3_pubmed output_type \\\n",
"0 SEMMED Disease API 3995681,19668867 PhenotypicFeature \n",
"1 SEMMED Disease API 3995681,19668867 PhenotypicFeature \n",
"2 SEMMED Disease API 3995681,19668867 PhenotypicFeature \n",
"3 SEMMED Disease API 3995681,19668867 PhenotypicFeature \n",
"4 SEMMED Disease API 3995681,19668867 PhenotypicFeature \n",
"\n",
" output_name output_id \n",
"0 REDUCED GLUTATHIONE name:REDUCED GLUTATHIONE \n",
"1 REDUCED GLUTATHIONE name:REDUCED GLUTATHIONE \n",
"2 REDUCED GLUTATHIONE name:REDUCED GLUTATHIONE \n",
"3 REDUCED GLUTATHIONE name:REDUCED GLUTATHIONE \n",
"4 REDUCED GLUTATHIONE name:REDUCED GLUTATHIONE \n",
"\n",
"[5 rows x 23 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The df object contains the full output from BioThings Explorer. Each row shows one path that joins the input node (ANISINDIONE) to an intermediate node (a gene or protein) to another intermediate node (a DisseaseOrPhenotypicFeature) to an ending node (a Phenotypic Feature). The data frame includes a set of columns with additional details on each node and edge (including human-readable labels, identifiers, and sources). Let's remove all examples where the output_name (the phenotype label) is None, and specifically focus on paths with specific mechanistic predicates **target** and **causes**."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"dfFilt = df.loc[df['output_name'].notnull()].query('pred1 == \"physically_interacts_with\" and pred2 == \"prevents\"')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>input</th>\n",
" <th>input_type</th>\n",
" <th>pred1</th>\n",
" <th>pred1_source</th>\n",
" <th>pred1_api</th>\n",
" <th>pred1_pubmed</th>\n",
" <th>node1_type</th>\n",
" <th>node1_name</th>\n",
" <th>node1_id</th>\n",
" <th>pred2</th>\n",
" <th>...</th>\n",
" <th>node2_type</th>\n",
" <th>node2_name</th>\n",
" <th>node2_id</th>\n",
" <th>pred3</th>\n",
" <th>pred3_source</th>\n",
" <th>pred3_api</th>\n",
" <th>pred3_pubmed</th>\n",
" <th>output_type</th>\n",
" <th>output_name</th>\n",
" <th>output_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>ANISINDIONE</td>\n",
" <td>ChemicalSubstance</td>\n",
" <td>physically_interacts_with</td>\n",
" <td>None</td>\n",
" <td>DGIdb API</td>\n",
" <td>None</td>\n",
" <td>Gene</td>\n",
" <td>GC</td>\n",
" <td>NCBIGene:2638</td>\n",
" <td>prevents</td>\n",
" <td>...</td>\n",
" <td>Disease</td>\n",
" <td>C0600688</td>\n",
" <td>UMLS:C0600688</td>\n",
" <td>affected_by</td>\n",
" <td>SEMMED</td>\n",
" <td>SEMMED Disease API</td>\n",
" <td>3995681,19668867</td>\n",
" <td>PhenotypicFeature</td>\n",
" <td>REDUCED GLUTATHIONE</td>\n",
" <td>name:REDUCED GLUTATHIONE</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>ANISINDIONE</td>\n",
" <td>ChemicalSubstance</td>\n",
" <td>physically_interacts_with</td>\n",
" <td>None</td>\n",
" <td>DGIdb API</td>\n",
" <td>None</td>\n",
" <td>Gene</td>\n",
" <td>GC</td>\n",
" <td>NCBIGene:2638</td>\n",
" <td>prevents</td>\n",
" <td>...</td>\n",
" <td>Disease</td>\n",
" <td>C0600688</td>\n",
" <td>UMLS:C0600688</td>\n",
" <td>disrupted_by</td>\n",
" <td>SEMMED</td>\n",
" <td>SEMMED Disease API</td>\n",
" <td>17628682</td>\n",
" <td>PhenotypicFeature</td>\n",
" <td>REDUCED GLUTATHIONE</td>\n",
" <td>name:REDUCED GLUTATHIONE</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>ANISINDIONE</td>\n",
" <td>ChemicalSubstance</td>\n",
" <td>physically_interacts_with</td>\n",
" <td>None</td>\n",
" <td>DGIdb API</td>\n",
" <td>None</td>\n",
" <td>Gene</td>\n",
" <td>GC</td>\n",
" <td>NCBIGene:2638</td>\n",
" <td>prevents</td>\n",
" <td>...</td>\n",
" <td>Disease</td>\n",
" <td>C0600688</td>\n",
" <td>UMLS:C0600688</td>\n",
" <td>caused_by</td>\n",
" <td>SEMMED</td>\n",
" <td>SEMMED Disease API</td>\n",
" <td>1449514</td>\n",
" <td>PhenotypicFeature</td>\n",
" <td>REDUCED GLUTATHIONE</td>\n",
" <td>name:REDUCED GLUTATHIONE</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>ANISINDIONE</td>\n",
" <td>ChemicalSubstance</td>\n",
" <td>physically_interacts_with</td>\n",
" <td>None</td>\n",
" <td>DGIdb API</td>\n",
" <td>None</td>\n",
" <td>Gene</td>\n",
" <td>GC</td>\n",
" <td>NCBIGene:2638</td>\n",
" <td>prevents</td>\n",
" <td>...</td>\n",
" <td>Disease</td>\n",
" <td>C0600688</td>\n",
" <td>UMLS:C0600688</td>\n",
" <td>treated_by</td>\n",
" <td>SEMMED</td>\n",
" <td>SEMMED Disease API</td>\n",
" <td>10865999</td>\n",
" <td>PhenotypicFeature</td>\n",
" <td>REDUCED GLUTATHIONE</td>\n",
" <td>name:REDUCED GLUTATHIONE</td>\n",
" </tr>\n",
" <tr>\n",
" <td>22</td>\n",
" <td>ANISINDIONE</td>\n",
" <td>ChemicalSubstance</td>\n",
" <td>physically_interacts_with</td>\n",
" <td>None</td>\n",
" <td>DGIdb API</td>\n",
" <td>None</td>\n",
" <td>Gene</td>\n",
" <td>GC</td>\n",
" <td>NCBIGene:2638</td>\n",
" <td>prevents</td>\n",
" <td>...</td>\n",
" <td>Disease</td>\n",
" <td>C0600688</td>\n",
" <td>UMLS:C0600688</td>\n",
" <td>prevented_by</td>\n",
" <td>SEMMED</td>\n",
" <td>SEMMED Disease API</td>\n",
" <td>1610167,6975248,6381340,3697994,12495814,96435...</td>\n",
" <td>PhenotypicFeature</td>\n",
" <td>REDUCED GLUTATHIONE</td>\n",
" <td>name:REDUCED GLUTATHIONE</td>\n",
" </tr>\n",
" <tr>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>47532</td>\n",
" <td>ANISINDIONE</td>\n",
" <td>ChemicalSubstance</td>\n",
" <td>physically_interacts_with</td>\n",
" <td>None</td>\n",
" <td>DGIdb API</td>\n",
" <td>None</td>\n",
" <td>Gene</td>\n",
" <td>GC</td>\n",
" <td>NCBIGene:2638</td>\n",
" <td>prevents</td>\n",
" <td>...</td>\n",
" <td>Disease</td>\n",
" <td>BREAST CANCER</td>\n",
" <td>MONDO:MONDO:0007254</td>\n",
" <td>related_to</td>\n",
" <td>None</td>\n",
" <td>BioLink API</td>\n",
" <td>None</td>\n",
" <td>PhenotypicFeature</td>\n",
" <td>HP:0002861</td>\n",
" <td>HP:HP:0002861</td>\n",
" </tr>\n",
" <tr>\n",
" <td>47631</td>\n",
" <td>ANISINDIONE</td>\n",
" <td>ChemicalSubstance</td>\n",
" <td>physically_interacts_with</td>\n",
" <td>None</td>\n",
" <td>DGIdb API</td>\n",
" <td>None</td>\n",
" <td>Gene</td>\n",
" <td>GC</td>\n",
" <td>NCBIGene:2638</td>\n",
" <td>prevents</td>\n",
" <td>...</td>\n",
" <td>Disease</td>\n",
" <td>BREAST CANCER</td>\n",
" <td>MONDO:MONDO:0007254</td>\n",
" <td>related_to</td>\n",
" <td>None</td>\n",
" <td>BioLink API</td>\n",
" <td>22538716</td>\n",
" <td>PhenotypicFeature</td>\n",
" <td>HP:0025318</td>\n",
" <td>HP:HP:0025318</td>\n",
" </tr>\n",
" <tr>\n",
" <td>51259</td>\n",
" <td>ANISINDIONE</td>\n",
" <td>ChemicalSubstance</td>\n",
" <td>physically_interacts_with</td>\n",
" <td>None</td>\n",
" <td>DGIdb API</td>\n",
" <td>None</td>\n",
" <td>Gene</td>\n",
" <td>GC</td>\n",
" <td>NCBIGene:2638</td>\n",
" <td>prevents</td>\n",
" <td>...</td>\n",
" <td>Disease</td>\n",
" <td>BREAST CANCER</td>\n",
" <td>MONDO:MONDO:0007254</td>\n",
" <td>related_to</td>\n",
" <td>None</td>\n",
" <td>BioLink API</td>\n",
" <td>None</td>\n",
" <td>PhenotypicFeature</td>\n",
" <td>HP:0030406</td>\n",
" <td>HP:HP:0030406</td>\n",
" </tr>\n",
" <tr>\n",
" <td>54475</td>\n",
" <td>ANISINDIONE</td>\n",
" <td>ChemicalSubstance</td>\n",
" <td>physically_interacts_with</td>\n",
" <td>None</td>\n",
" <td>DGIdb API</td>\n",
" <td>None</td>\n",
" <td>Gene</td>\n",
" <td>GC</td>\n",
" <td>NCBIGene:2638</td>\n",
" <td>prevents</td>\n",
" <td>...</td>\n",
" <td>Disease</td>\n",
" <td>SHOCK</td>\n",
" <td>MONDO:C0036974</td>\n",
" <td>coexists_with</td>\n",
" <td>SEMMED</td>\n",
" <td>SEMMED Disease API</td>\n",
" <td>26825935</td>\n",
" <td>PhenotypicFeature</td>\n",
" <td>PREGNANCY TEST NEGATIVE</td>\n",
" <td>name:PREGNANCY TEST NEGATIVE</td>\n",
" </tr>\n",
" <tr>\n",
" <td>54477</td>\n",
" <td>ANISINDIONE</td>\n",
" <td>ChemicalSubstance</td>\n",
" <td>physically_interacts_with</td>\n",
" <td>None</td>\n",
" <td>DGIdb API</td>\n",
" <td>None</td>\n",
" <td>Gene</td>\n",
" <td>GC</td>\n",
" <td>NCBIGene:2638</td>\n",
" <td>prevents</td>\n",
" <td>...</td>\n",
" <td>Disease</td>\n",
" <td>SHOCK</td>\n",
" <td>MONDO:C0036974</td>\n",
" <td>coexists_with</td>\n",
" <td>SEMMED</td>\n",
" <td>SEMMED Disease API</td>\n",
" <td>2106223</td>\n",
" <td>PhenotypicFeature</td>\n",
" <td>HEMATURIA PRESENT</td>\n",
" <td>name:HEMATURIA PRESENT</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>73 rows × 23 columns</p>\n",
"</div>"
],
"text/plain": [
" input input_type pred1 pred1_source \\\n",
"2 ANISINDIONE ChemicalSubstance physically_interacts_with None \n",
"7 ANISINDIONE ChemicalSubstance physically_interacts_with None \n",
"12 ANISINDIONE ChemicalSubstance physically_interacts_with None \n",
"17 ANISINDIONE ChemicalSubstance physically_interacts_with None \n",
"22 ANISINDIONE ChemicalSubstance physically_interacts_with None \n",
"... ... ... ... ... \n",
"47532 ANISINDIONE ChemicalSubstance physically_interacts_with None \n",
"47631 ANISINDIONE ChemicalSubstance physically_interacts_with None \n",
"51259 ANISINDIONE ChemicalSubstance physically_interacts_with None \n",
"54475 ANISINDIONE ChemicalSubstance physically_interacts_with None \n",
"54477 ANISINDIONE ChemicalSubstance physically_interacts_with None \n",
"\n",
" pred1_api pred1_pubmed node1_type node1_name node1_id pred2 \\\n",
"2 DGIdb API None Gene GC NCBIGene:2638 prevents \n",
"7 DGIdb API None Gene GC NCBIGene:2638 prevents \n",
"12 DGIdb API None Gene GC NCBIGene:2638 prevents \n",
"17 DGIdb API None Gene GC NCBIGene:2638 prevents \n",
"22 DGIdb API None Gene GC NCBIGene:2638 prevents \n",
"... ... ... ... ... ... ... \n",
"47532 DGIdb API None Gene GC NCBIGene:2638 prevents \n",
"47631 DGIdb API None Gene GC NCBIGene:2638 prevents \n",
"51259 DGIdb API None Gene GC NCBIGene:2638 prevents \n",
"54475 DGIdb API None Gene GC NCBIGene:2638 prevents \n",
"54477 DGIdb API None Gene GC NCBIGene:2638 prevents \n",
"\n",
" ... node2_type node2_name node2_id pred3 \\\n",
"2 ... Disease C0600688 UMLS:C0600688 affected_by \n",
"7 ... Disease C0600688 UMLS:C0600688 disrupted_by \n",
"12 ... Disease C0600688 UMLS:C0600688 caused_by \n",
"17 ... Disease C0600688 UMLS:C0600688 treated_by \n",
"22 ... Disease C0600688 UMLS:C0600688 prevented_by \n",
"... ... ... ... ... ... \n",
"47532 ... Disease BREAST CANCER MONDO:MONDO:0007254 related_to \n",
"47631 ... Disease BREAST CANCER MONDO:MONDO:0007254 related_to \n",
"51259 ... Disease BREAST CANCER MONDO:MONDO:0007254 related_to \n",
"54475 ... Disease SHOCK MONDO:C0036974 coexists_with \n",
"54477 ... Disease SHOCK MONDO:C0036974 coexists_with \n",
"\n",
" pred3_source pred3_api \\\n",
"2 SEMMED SEMMED Disease API \n",
"7 SEMMED SEMMED Disease API \n",
"12 SEMMED SEMMED Disease API \n",
"17 SEMMED SEMMED Disease API \n",
"22 SEMMED SEMMED Disease API \n",
"... ... ... \n",
"47532 None BioLink API \n",
"47631 None BioLink API \n",
"51259 None BioLink API \n",
"54475 SEMMED SEMMED Disease API \n",
"54477 SEMMED SEMMED Disease API \n",
"\n",
" pred3_pubmed output_type \\\n",
"2 3995681,19668867 PhenotypicFeature \n",
"7 17628682 PhenotypicFeature \n",
"12 1449514 PhenotypicFeature \n",
"17 10865999 PhenotypicFeature \n",
"22 1610167,6975248,6381340,3697994,12495814,96435... PhenotypicFeature \n",
"... ... ... \n",
"47532 None PhenotypicFeature \n",
"47631 22538716 PhenotypicFeature \n",
"51259 None PhenotypicFeature \n",
"54475 26825935 PhenotypicFeature \n",
"54477 2106223 PhenotypicFeature \n",
"\n",
" output_name output_id \n",
"2 REDUCED GLUTATHIONE name:REDUCED GLUTATHIONE \n",
"7 REDUCED GLUTATHIONE name:REDUCED GLUTATHIONE \n",
"12 REDUCED GLUTATHIONE name:REDUCED GLUTATHIONE \n",
"17 REDUCED GLUTATHIONE name:REDUCED GLUTATHIONE \n",
"22 REDUCED GLUTATHIONE name:REDUCED GLUTATHIONE \n",
"... ... ... \n",
"47532 HP:0002861 HP:HP:0002861 \n",
"47631 HP:0025318 HP:HP:0025318 \n",
"51259 HP:0030406 HP:HP:0030406 \n",
"54475 PREGNANCY TEST NEGATIVE name:PREGNANCY TEST NEGATIVE \n",
"54477 HEMATURIA PRESENT name:HEMATURIA PRESENT \n",
"\n",
"[73 rows x 23 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfFilt"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
AlJohri/DAT-DC-12 | homework/homework1.ipynb | 1 | 19390 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Homework 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The goal of this homework is to ensure you have a __decent understanding of Python__ AND __know how to read and interpret documentation__. Please make heavy use of Google, [StackOverflow](http://stackoverflow.com/questions/tagged/python), the [Python 3 Documentation](https://docs.python.org/3.5/), and of course the `help` function.\n",
"\n",
"Each problem has an associated test via the `assert` statement that will tell you if you implemented it properly.\n",
"\n",
"Feel free to use any previously functions for future problems (i.e. feel free to use a `square` function when implementing `square_and_add_one`)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run this First"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This line import several useful modules into memory for later use."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os, sys, csv, json, math, random, collections, time, itertools, functools"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This line create a CSV (comma seperated file) called `hw1data.csv` in the current working directory."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting hw1data.csv\n"
]
}
],
"source": [
"%%file hw1data.csv\n",
"id,sex,weight\n",
"1,M,190\n",
"2,F,120\n",
"3,F,110\n",
"4,M,150\n",
"5,O,120\n",
"6,M,120\n",
"7,F,140"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basic"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def double(x):\n",
" \"\"\"\n",
" double the value x\n",
" \"\"\"\n",
"\n",
"assert double(10) == 20"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def apply_to_100(f):\n",
" \"\"\"\n",
" runs some abitrary function f on the value 100 and returns the output\n",
" \"\"\"\n",
"\n",
"assert(apply_to_100(double) == 200)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"create a an anonymous function using lambda that takes some value x and adds 1 to x\n",
"\"\"\"\n",
"add_one = lambda x: x\n",
"\n",
"assert apply_to_100(add_one) == 101"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def get_up_to_first_three_elements(l):\n",
" \"\"\"\n",
" get up to the first three elements in list l\n",
" \"\"\"\n",
" return \n",
"\n",
"assert get_up_to_first_three_elements([1,2,3,4]) == [1,2,3]\n",
"assert get_up_to_first_three_elements([1,2]) == [1,2]\n",
"assert get_up_to_first_three_elements([1]) == [1]\n",
"assert get_up_to_first_three_elements([]) == []"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def caesar_cipher(s, key):\n",
" \"\"\"\n",
" https://www.hackerrank.com/challenges/caesar-cipher-1\n",
" Given an unencrypted string s and an encryption key (an integer), compute the caesar cipher.\n",
" \n",
" Basically just shift each letter by the value of key. A becomes C if key = 2. This is case sensitive.\n",
" \n",
" What is a Caesar Cipher? https://en.wikipedia.org/wiki/Caesar_cipher\n",
" \n",
" Hint: ord function https://docs.python.org/2/library/functions.html#ord\n",
" Hint: chr function https://docs.python.org/2/library/functions.html#chr\n",
"\n",
" print(ord('A'), ord('Z'), ord('a'), ord('z'))\n",
" print(chr(65), chr(90), chr(97), chr(122))\n",
" \"\"\"\n",
"\n",
" new_s = []\n",
"\n",
" for c in s:\n",
" if ord('A') <= ord(c) <= ord('Z'):\n",
" new_c = chr(ord('A') + (ord(c) - ord('A') + 2) % 26)\n",
" new_s.append(new_c)\n",
" elif ord('a') <= ord(c) <= ord('z'):\n",
" new_c = chr(ord('a') + (ord(c) - ord('a') + 2) % 26)\n",
" new_s.append(new_c)\n",
" else:\n",
" new_s.append(c)\n",
" \n",
" return \"\".join(new_s)\n",
"\n",
"assert caesar_cipher(\"middle-Outz\", 2) == \"okffng-Qwvb\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Working with Files"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def create_list_of_lines_in_hw1data():\n",
" \"\"\"\n",
" Read each line of hw1data.csv into a list and return the list of lines.\n",
" Remove the newline character (\"\\n\") at the end of each line.\n",
" \n",
" What is a newline character? https://en.wikipedia.org/wiki/Newline\n",
" \n",
" Hint: Reading a File (https://docs.python.org/3/tutorial/inputoutput.html#methods-of-file-objects)\n",
" \"\"\"\n",
" with open(\"hw1data.csv\", \"r\") as f:\n",
" return [line.strip() for line in f]\n",
" # lines = f.read().splitlines() # alternative 1\n",
" # lines = [line.strip() for line in f.readlines()] # altenative 2\n",
"\n",
"assert create_list_of_lines_in_hw1data() == [\n",
" \"id,sex,weight\", \"1,M,190\", \"2,F,120\", \"3,F,110\",\n",
" \"4,M,150\", \"5,O,120\", \"6,M,120\", \"7,F,140\",\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def filter_to_lines_with_just_M():\n",
" \"\"\"\n",
" Read each line in like last time except filter down to only the rows with \"M\" in them.\n",
" \n",
" Hint: Filter using List Comprehensions (http://www.diveintopython.net/power_of_introspection/filtering_lists.html)\n",
" \"\"\"\n",
" lines = create_list_of_lines_in_hw1data()\n",
" return [line for line in lines ]\n",
"\n",
"assert filter_to_lines_with_just_M() == [\"1,M,190\", \"4,M,150\", \"6,M,120\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def filter_to_lines_with_just_F():\n",
" \"\"\"\n",
" Read each line in like last time except filter down to only the rows with \"F\" in them.\n",
" \"\"\"\n",
" lines = create_list_of_lines_in_hw1data()\n",
" return [line for line in lines ]\n",
"\n",
"assert filter_to_lines_with_just_F() == [\"2,F,120\", \"3,F,110\", \"7,F,140\"]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def filter_to_lines_with_any_sex(sex):\n",
" \"\"\"\n",
" Read each line in like last time except filter down to only the rows with \"M\" in them.\n",
" \"\"\"\n",
" lines = create_list_of_lines_in_hw1data()\n",
" return [line for line in lines ]\n",
"\n",
"assert filter_to_lines_with_any_sex(\"O\") == [\"5,O,120\"]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_average_weight():\n",
" \"\"\"\n",
" This time instead of just reading the file, parse the csv using csv.reader.\n",
" \n",
" get the average weight of all people rounded to the hundredth place\n",
" \n",
" Hint: https://docs.python.org/3/library/csv.html#csv.reader\n",
" \"\"\"\n",
" weights = []\n",
" with open(\"hw1data.csv\", \"r\") as f:\n",
" reader = csv.reader(f)\n",
" next(reader)\n",
" for row in reader:\n",
" print(int(row[2]))\n",
" return round(avg_weight, 2)\n",
"\n",
"assert get_average_weight() == 135.71"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def create_list_of_dicts_in_hw1data():\n",
" \"\"\"\n",
" create list of dicts for each line in the hw1data (except the header)\n",
" \"\"\"\n",
" with open(\"hw1data.csv\", \"r\") as f:\n",
" return []\n",
"\n",
"assert create_list_of_dicts_in_hw1data() == [\n",
" {\"id\": \"1\", \"sex\": \"M\", \"weight\": \"190\"},\n",
" {\"id\": \"2\", \"sex\": \"F\", \"weight\": \"120\"},\n",
" {\"id\": \"3\", \"sex\": \"F\", \"weight\": \"110\"},\n",
" {\"id\": \"4\", \"sex\": \"M\", \"weight\": \"150\"},\n",
" {\"id\": \"5\", \"sex\": \"O\", \"weight\": \"120\"},\n",
" {\"id\": \"6\", \"sex\": \"M\", \"weight\": \"120\"},\n",
" {\"id\": \"7\", \"sex\": \"F\", \"weight\": \"140\"}\n",
" ]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Project Euler"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def sum_of_multiples_of_three_and_five_below_1000():\n",
" \"\"\"\n",
" https://projecteuler.net/problem=1\n",
" If we list all the natural numbers below 10 that are multiples of 3 or 5, we get 3, 5, 6 and 9.\n",
" The sum of these multiples is 23.\n",
" Find the sum of all the multiples of 3 or 5 below 1000.\n",
"\n",
" Hint: Modulo Operator (https://docs.python.org/3/reference/expressions.html#binary-arithmetic-operations)\n",
" Hint: List Comprehension (https://docs.python.org/3/tutorial/datastructures.html#list-comprehensions)\n",
" Hint: Range Function (https://docs.python.org/3/library/functions.html#func-range)\n",
" \"\"\"\n",
" return \n",
"\n",
"\n",
"def sum_of_even_fibonacci_under_4million():\n",
" \"\"\"\n",
" https://projecteuler.net/problem=2\n",
" Each new term in the Fibonacci sequence is generated by adding the previous two terms.\n",
" By starting with 1 and 2, the first 10 terms will be:\n",
" 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, ...\n",
" By considering the terms in the Fibonacci sequence whose values do not exceed four million,\n",
" find the sum of the even-valued terms.\n",
" \n",
" Hint: While Loops (http://learnpythonthehardway.org/book/ex33.html)\n",
" \"\"\"\n",
" the_sum = 0\n",
" a, b = 1, 2\n",
" while b < 4000000:\n",
" \n",
" return the_sum\n",
"\n",
"def test_all():\n",
" assert sum_of_multiples_of_three_and_five_below_1000() == 233168\n",
" assert sum_of_even_fibonacci_under_4million() == 4613732\n",
" \n",
"test_all()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Strings"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from collections import Counter\n",
"\n",
"def remove_punctuation(s):\n",
" \"\"\"remove periods, commas, and semicolons\n",
" \"\"\"\n",
" return s.replace()\n",
"\n",
"def tokenize(s):\n",
" \"\"\"return a list of lowercased tokens (words) in a string without punctuation\n",
" \"\"\"\n",
" return remove_punctuation(s.lower())\n",
"\n",
"def word_count(s):\n",
" \"\"\"count the number of times each word (lowercased) appears and return a dictionary\n",
" \"\"\"\n",
" return Counter(words)\n",
"\n",
"def test_all():\n",
" test_string1 = \"A quick brown Al, jumps over the lazy dog; sometimes...\"\n",
" test_string2 = \"This this is a sentence sentence with words multiple multiple times.\"\n",
" \n",
" # ---------------------------------------------------------------------------------- #\n",
" \n",
" test_punctuation1 = \"A quick brown Al jumps over the lazy dog sometimes\"\n",
" test_punctuation2 = \"This this is a sentence sentence with words multiple multiple times\"\n",
" \n",
" assert remove_punctuation(test_string1) == test_punctuation1\n",
" assert remove_punctuation(test_string2) == test_punctuation2\n",
" \n",
" # ---------------------------------------------------------------------------------- #\n",
" \n",
" test_tokens1 = [\"a\", \"quick\", \"brown\", \"al\", \"jumps\", \"over\", \"the\", \"lazy\", \"dog\", \"sometimes\"]\n",
" test_tokens2 = [\n",
" \"this\", \"this\", \"is\", \"a\", \"sentence\", \"sentence\", \"with\", \"words\", \"multiple\", \"multiple\", \"times\"\n",
" ]\n",
"\n",
" assert tokenize(test_string1) == test_tokens1\n",
" assert tokenize(test_string2) == test_tokens2\n",
"\n",
" # ---------------------------------------------------------------------------------- #\n",
"\n",
" test_wordcount1 = {\n",
" \"a\": 1, \"quick\": 1, \"brown\": 1, \"al\": 1, \"jumps\": 1, \"over\": 1, \"the\": 1, \"lazy\": 1, \"dog\": 1, \"sometimes\": 1\n",
" }\n",
" test_wordcount2 = {\"this\": 2, \"is\": 1, \"a\": 1, \"sentence\": 2, \"with\": 1, \"words\": 1, \"multiple\": 2, \"times\": 1}\n",
" \n",
" assert word_count(test_string1) == test_wordcount1\n",
" assert word_count(test_string2) == test_wordcount2\n",
"\n",
"test_all()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Linear Algebra"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Please find the following empty functions and write the code to complete the logic.\n",
"These functions are focused around implementing vector algebra operations. The vectors can be of any length. If a function accepts two vectors, assume they are the same length.\n",
"Khan Academy has a decent introduction:\n",
"[https://www.khanacademy.org/math/linear-algebra/vectors_and_spaces/vectors/v/vector-introduction-linear-algebra]"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Vector Add [4, 5, 1] [9, 8, 1] [13, 13, 2]\n",
"Vector Subtract [4, 5, 1] [9, 8, 1] [-5, -3, 0]\n",
"Vector Sum [[1, 2], [4, 5], [8, 3]] [13, 10]\n",
"Scalar Multiply 3 [9, 8, 1] [27, 24, 3]\n",
"Dot [4, 5, 1] [9, 8, 1] 77\n",
"Sum of Squares [4, 5, 1] 42\n",
"Magnitude [4, 5, 1] 6.48074069840786\n",
"Distance [4, 5, 1] [9, 8, 1] 5.830951894845301\n",
"Cross Product [[36, 32, 4], [45, 40, 5], [9, 8, 1]]\n"
]
}
],
"source": [
"def vector_add(v, w):\n",
" \"\"\"adds two vectors componentwise and returns the result\n",
" hint: use zip()\n",
" v + w = [4, 5, 1] + [9, 8, 1] = [13, 13, 2]\n",
" \"\"\"\n",
" return []\n",
"\n",
"def vector_subtract(v, w):\n",
" \"\"\"subtracts two vectors componentwise and returns the result\n",
" hint use zip()\n",
" v + w = [4, 5, 1] - [9, 8, 1] = [-5, -3, 0]\n",
" \"\"\"\n",
" return []\n",
"\n",
"def vector_sum(vectors):\n",
" \"\"\"sums a list of vectors or arbitrary length and returns the resulting vector\n",
" [[1,2], [4,5], [8,3]] = [13,10]\n",
" \"\"\"\n",
" v_copy = list(vectors)\n",
" result = v_copy.pop()\n",
" for v in v_copy:\n",
" result = \n",
" return result\n",
"\n",
"def scalar_multiply(c, v):\n",
" \"\"\"returns a vector where components are multplied by c\"\"\"\n",
" return []\n",
"\n",
"def dot(v, w):\n",
" \"\"\"dot product v.w\n",
" v_1 * w_1 + ... + v_n * w_n\"\"\"\n",
" return sum()\n",
"\n",
"def sum_of_squares(v):\n",
" \"\"\" v.v square each component and sum them\n",
" v_1 * v_1 + ... + v_n * v_n\"\"\"\n",
" return \n",
"\n",
"def magnitude(v):\n",
" \"\"\"the Norm of a vector, the sqrt of the sum of the squares of the components\"\"\"\n",
" return math.sqrt()\n",
"\n",
"def distance(v, w):\n",
" \"\"\" the distance of v to w\"\"\"\n",
" return \n",
"\n",
"def cross_product(v, w): # or outer_product(v, w)\n",
" \"\"\"Bonus:\n",
" The outer/cross product of v and w\"\"\"\n",
" for i in v:\n",
" yield scalar_multiply(i, w)\n",
"\n",
"def test_all():\n",
" test_v = [4, 5, 1] \n",
" test_w = [9, 8, 1] \n",
" list_v = [[1,2], [4,5], [8,3]]\n",
" \n",
" print(\"Vector Add\", test_v, test_w, vector_add(test_v, test_w))\n",
" print(\"Vector Subtract\", test_v, test_w, vector_subtract(test_v, test_w))\n",
" print(\"Vector Sum\", list_v, vector_sum(list_v))\n",
" print(\"Scalar Multiply\", 3, test_w, scalar_multiply(3, test_w))\n",
" print(\"Dot\", test_v, test_w, dot(test_v, test_w))\n",
" print(\"Sum of Squares\", test_v, sum_of_squares(test_v))\n",
" print(\"Magnitude\", test_v, magnitude(test_v))\n",
" print(\"Distance\", test_v, test_w, distance(test_v, test_w))\n",
" print(\"Cross Product\", list(cross_product(test_v, test_w)))\n",
"\n",
" assert vector_add(test_v, test_w) == [13, 13, 2]\n",
" assert vector_subtract(test_v, test_w) == [-5, -3, 0]\n",
" assert vector_sum(list_v) == [13,10] \n",
" assert scalar_multiply(3, test_w) == [27, 24, 3]\n",
" assert dot(test_v, test_w) == 77\n",
" assert sum_of_squares(test_v) == 42\n",
" assert magnitude(test_v) == 6.48074069840786\n",
" assert distance(test_v, test_w) == 5.830951894845301\n",
" assert list(cross_product(test_v, test_w)) == [[36, 32, 4], [45, 40, 5], [9, 8, 1]]\n",
"\n",
"test_all()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
tleonhardt/LearningCython | Learning_Cython_video/Chapter04/timing/timing.ipynb | 1 | 3663 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Two functions each doing exactly the same thing, which is to add up a sequence of integers\n",
"\n",
"def f1(n):\n",
" \"\"\" Use an explicit loop \"\"\"\n",
" result = 0\n",
" for i in range(n + 1):\n",
" result = result + i\n",
" return result\n",
" \n",
"def f2(n):\n",
" \"\"\" Use the sum() function \"\"\"\n",
" return sum(range(n + 1))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"100 loops, best of 3: 6.2 ms per loop\n",
"100 loops, best of 3: 2.01 ms per loop\n"
]
}
],
"source": [
"# IPython line magic %timeit will perform timing of code by running your code many times \n",
"n = int(1e5)\n",
"%timeit f1(n)\n",
"%timeit f2(n)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"100 loops, best of 3: 5.88 ms per loop\n"
]
}
],
"source": [
"%%timeit\n",
"# %% Implies an IPython cell magic function - needs to be on very first line of cell\n",
"tot = 0\n",
"for i in range(n+1):\n",
" tot += i"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" "
]
}
],
"source": [
"# %prun is a magic command for the Python profiler - it can be used as either a line or cell magic function\n",
"%prun f1(n)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Arithmetic progression\n",
"\n",
"The _fastest_ way to calculate the sum of a sequence of integers is with the following formula:\n",
"\n",
"$$\n",
"\\sum_{i=1}^{N} = \\frac{n(n+1)}{2}\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def f3(n):\n",
" \"\"\" Algorithm implementing the above sum formulat \"\"\"\n",
" return n * (n + 1) / 2"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"100 loops, best of 3: 6.6 ms per loop\n",
"1000 loops, best of 3: 1.88 ms per loop\n",
"The slowest run took 6.79 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
"1000000 loops, best of 3: 190 ns per loop\n"
]
}
],
"source": [
"%timeit f1(n)\n",
"%timeit f2(n)\n",
"%timeit f3(n)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
molpopgen/fwdpy | docs/examples/trajectories.ipynb | 1 | 34875 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tracking mutation frequencies"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python2.7/dist-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
" warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%matplotlib inline\n",
"%pylab inline\n",
"import fwdpy as fp\n",
"import pandas as pd\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import copy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run a simulation"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nregions = [fp.Region(0,1,1),fp.Region(2,3,1)]\n",
"sregions = [fp.ExpS(1,2,1,-0.1),fp.ExpS(1,2,0.01,0.001)]\n",
"rregions = [fp.Region(0,3,1)]\n",
"rng = fp.GSLrng(101)\n",
"popsizes = np.array([1000],dtype=np.uint32)\n",
"popsizes=np.tile(popsizes,10000)\n",
"#Initialize a vector with 1 population of size N = 1,000\n",
"pops=fp.SpopVec(1,1000)\n",
"#This sampler object will record selected mutation\n",
"#frequencies over time. A sampler gets the length\n",
"#of pops as a constructor argument because you \n",
"#need a different sampler object in memory for\n",
"#each population.\n",
"sampler=fp.FreqSampler(len(pops))\n",
"#Record mutation frequencies every generation\n",
"#The function evolve_regions sampler takes any\n",
"#of fwdpy's temporal samplers and applies them.\n",
"#For users familiar with C++, custom samplers will be written,\n",
"#and we plan to allow for custom samplers to be written primarily \n",
"#using Cython, but we are still experimenting with how best to do so.\n",
"rawTraj=fp.evolve_regions_sampler(rng,pops,sampler,\n",
" popsizes[0:],0.001,0.001,0.001,\n",
" nregions,sregions,rregions,\n",
" #The one means we sample every generation.\n",
" 1)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" esize freq generation origin pos\n",
"0 -0.314966 0.0005 1 0 1.382760\n",
"1 -0.021193 0.0005 1 0 1.367676\n",
"2 -0.066601 0.0005 1 0 1.125086\n",
"3 -0.066601 0.0005 2 0 1.125086\n",
"4 -0.066601 0.0010 3 0 1.125086\n",
" esize freq generation origin pos\n",
"104420 -0.016016 0.0005 9999 9998 1.773315\n",
"104421 -0.155373 0.0005 9999 9998 1.912775\n",
"104422 -0.155373 0.0005 10000 9998 1.912775\n",
"104423 -0.042471 0.0005 10000 9999 1.738310\n",
"104424 -0.030944 0.0005 10000 9999 1.805271\n",
"1.0\n"
]
}
],
"source": [
"rawTraj = [i for i in sampler]\n",
"#This example has only 1 set of trajectories, so let's make a variable for thet\n",
"#single replicate\n",
"traj=rawTraj[0]\n",
"print traj.head()\n",
"print traj.tail()\n",
"print traj.freq.max()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Group mutation trajectories by position and effect size"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Max mutation frequencies\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>pos</th>\n",
" <th>esize</th>\n",
" <th>freq</th>\n",
" <th>generation</th>\n",
" <th>origin</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2701</th>\n",
" <td>1.134096</td>\n",
" <td>0.001812</td>\n",
" <td>1.0</td>\n",
" <td>2612</td>\n",
" <td>43</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" pos esize freq generation origin\n",
"2701 1.134096 0.001812 1.0 2612 43"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mfreq = traj.groupby(['pos','esize']).max().reset_index()\n",
"#Print out info for all mutations that hit a frequency of 1 (e.g., fixed)\n",
"mfreq[mfreq['freq']==1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The only fixation has an 'esize' $> 0$, which means that it was positively selected,"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Frequency trajectory of fixations "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Get positions of mutations that hit q = 1\n",
"mpos=mfreq[mfreq['freq']==1]['pos']"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAAEPCAYAAAC6Kkg/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmcXHWZ7/HPkx1ICEEMWwgBwuKNYASEyGbPsGYEokMY\niWFYxuugDMsw6gWXIYnjqOgoyqZXLyIjQgRGILJGgj3AIBAhAYRsLAKByBIJJIGklzz3j985qdPV\nVd2nuuv0qeX7fr3qdZY6dc5TlUo9/Tu/zdwdERGRSgzKOwAREak/Sh4iIlIxJQ8REamYkoeIiFRM\nyUNERCqm5CEiIhXLNHmY2dVm9pqZPdnDMZeZ2QozW2xmk7OMR0REqiPrksc1wLHlnjSzqcAe7r4n\ncBbw44zjERGRKsg0ebj7g8BbPRwyDfjP6NhHgNFmtn2WMYmISP/lXeexM/ByYvuVaJ+IiNSwvJOH\nldin8VJERGrckJyvvxLYJbE9Dni11IFmpqQiItIH7l7qD/V+GYiSh1G6hAEwDzgNwMymAGvc/bVy\nJ3J3PdyZNWtWv88RCnjhMXFi/u8pz8+iUR76LPRZlHpkJdOSh5ldD7QA7zOzl4BZwDDA3f0n7n6n\nmf2NmT0LrAfOzDIe6W7mTLjuuryjEJF6k2nycPdPpzjmnCxjkK6eew6mTSts/+IX+cUiIvUr7zoP\n6YOWlpY+v3bixMJ6ZydY1e+EDqz+fBaNRp9FgT6L7FmW98Sqycy8XmKtZclkoY9TpPGZGV6nFeYi\nItJglDyayJe+VFjfe+/84hCR+qfbVk0kvmX1/PMwbhwMHZpvPCKSPd22krLcS9dfmMGCBd33b7+9\nEoeI9I+SRwP46Edh+vTSz/361yGxvPBCYd+WWw5MXCLSuHTbqgHEt6OKP554//XXw5tvwnnnlT5O\nRBqXbltJRTZuLKy//jp85Sv5xSIijUcljwZQquTxxhswdmxYHzQINm0qPKePUaR5qOQhFdmwobCe\nTBwPPDDwsYhI41HyaFDPP19YT1am77bbwMciIo1HyaOBJJvsJof2GT++sD5ixICGJCINSsmjziVv\nST32WKjfuPbawr45c6C9vbA9fPjAxSYijUvJo861t4cOfyNHwuOPh31Llxae3267rslDfTxEpBqU\nPOpcRwcMGQLDhsFZZ4V9ixYVnh86NCSPj3wEHn44lExERPpLPyV17rnn4L33ug43cs89YfmXvxSS\nx8aNqu8QkepR8qhzDz0UlsOGdX9uzJhC8tiwQfUdIlI9Sh51rqMjLIeUmRNSJQ8RyYKmoa1zkyaF\nZXLgQyg0zx06FG6+Oayr5CEi1aKSR51ra4Ojj+6+/7TTwjJZF6KSh4hUi0oede6447rv23JLmDEj\nrCeTh0oeIlItKnk0mOHD4d13YdSosK3kISJZUPJoMPFQ7OPGhWWyFdbgwQMfj4g0JiWPBhUP067S\nhohkQcmjjiXHtQLYYYewnDixsK9cE14Rkf5Q8qhjP/tZWB5zTBhNd9WqsJ2s52hrG/i4RKTxKXnU\nsVdfDcsbb+y6P1naiMeyuuCCgYlJRJqDbmrUsXjuji226Lp/2bLC+gEHwJ13wtSpAxeXiDQ+lTzq\nWGdnWCZvU0HXW1WDBilxiEj1KXnUsbhZrlV9ansRkZ4pedSxOHmIiAw0JY86tmFD3hGISLNS8qhj\n22+fdwQi0qyUPOrY+98PZ5/dff8uuwx8LCLSXNRUt461t3dvaTVvHmy7bT7xiEjzyLzkYWbHmdlS\nM1tuZheWeH4XM7vPzB43s8VmpoalKbW1dZ9+9oQT4NBD84lHRJpHpsnDzAYBVwDHApOAGWa2T9Fh\nXwN+5e77AzOAq7KMqZFcdRWsXJl3FCLSjLIueRwErHD3F929HZgLTCs6ZhOwdbS+DfBKxjE1jJde\nghtuyDsKEWlGWdd57Ay8nNheSUgoSXOA+WZ2HrAlcFTGMTWE117LOwIRaWZZJ49SfZ+9aHsGcI27\nX2pmU4DrCLe4upk9e/bm9ZaWFlpaWqoTZR1qbw9LzUsuIkmtra20trZmfh1zL/4tr+LJQzKY7e7H\nRdsXAe7ulySO+SNwrLu/Em0/Bxzs7m8WncuzjLXerFoFO+0URtQ9+eS8oxGRWmVmuHvVBzHKus5j\nITDRzHY1s2HAKcC8omNeJLpVZWYfAIYXJw7prqMjLJVPRSQPmSYPd+8EzgHmA08Dc919iZnNMbPj\no8O+CHzWzBYDvwROzzKmRvHuu3lHICLNLPNOgu5+N7B30b5ZifUlwGFZx9FoLr88LIs7CYqIDAQN\nT1KnVqwIy912yzcOEWlOSh51avLkrksRkYGk5FGn9tkHTlftkIjkJNOmutWkprpdxbMH6iMRkZ7U\na1NdERFpQEoeIiJSMSUPERGpmJKHiIhUTMlDREQqpmlo69QnPgHTp+cdhYg0K5U86tiWW+YdgYg0\nKyWPOtXeDkNUbhSRnCh51KFNm+COO+C55/KORESalZJHHbrllrBcvDjfOESkeSl51KH168Ny3bp8\n4xCR5tVr8jCz481MSaaGxHUdU6bkG4eINK80SeEUYIWZfSeaJlZyFg+K+MlP5huHiDSvXpOHu58K\nfBh4DrjGzH5vZv9oZqMyj05KikfS3WOPfOMQkeaV6naUu78D/BcwF9gR+CTwuJmdm2FsIiJSo9LU\neZxgZrcA9wFDgYPcfSrwIeCLGccnJYwdC8OH5x2FiDSzNN3MTgYudff7kzvd/V0z+4dswpKedHbC\nEUfkHYWINLM0yWMWsCreMLMtgO3d/U/uviCzyKSszk4YPDjvKESkmaWp87gJ2JTY7oz2SU7WrYM1\na/KOQkSaWZqSxxB3b4s33L3NzIZlGJP04tRTw9hWIiJ5SVPyeMPMTow3zGwa8GZ2IUlvlDhEJG/m\ncaeBcgeY7QH8EtgJMOBl4DR3fzb78LrE4b3F2iziToL6OESkN2aGu1vVz5v2B9nMRkbHr612ECmv\nr+QRUfIQkbSySh691nmY2XDgJGACMMSiXy53/3q1g5F0pk6Fhx/OOwoRaWZpKsxvA94GHgM2ZhuO\npLHffnD44XlHISLNLE3yGOfux2UeiaS2caN6mItIvtK0tnrIzPbNPBJJTclDRPKWpuRxGHCGmb1A\nuG1lgLv7fplGJmUpeYhI3tIkj6mZRyEVUfIQkbylmc/jRWAX4K+j9XfTvE6yo+QhInlLMyT7LOBC\n4MvRrqHAdVkGJT1ra4NhGiBGRHKUpgTxSeBEYD2Au78KpJ5F0MyOM7OlZrbczC4sc8zfmdnTZvaU\nmSkx9aKjA4YOzTsKEWlmaeo82tzdzcwBzGyrtCc3s0HAFcCRwKvAQjO7zd2XJo6ZSCjZfNTd3zGz\n7Sp6B02oowOGpPmXExHJSJqSx41m9n+Bbczss8C9wE9Tnv8gYIW7v+ju7YRpbKcVHfNZ4Mpoqlvc\nXYMu9qK9XclDRPLV60+Qu/+HmR0NvAPsDVzs7r9Nef6dCQMpxlYSEkrSXgBm9iAhmc1x93tSnr8p\nqeQhInlL9RMUJYu0CSOp1GBcxcP5DQEmAkcA44EHzGxSXBKR7lTnISJ5SzMw4loKP/jDCK2t1rv7\n1inOv5KQEGLjCHUfxcf83t03AX8ys2XAnoSxtLqYPXv25vWWlhZaWlpShNB4Vq9WyUNESmttbaW1\ntTXz66Qekn3zC8w+AUxx94tSHDsYWEaoMF8FPArMcPcliWOOjfadEVWWPwZMdve3is6lIdkjZnDr\nrTCtuPZIRKRIVkOyV9zZz91vBY5NeWwncA4wH3gamOvuS8xsjpkdHx1zD7DazJ4GFgBfLE4c0t1G\njW8sIjlKc9vqbxObg4ADgQ1pL+DudxMq2pP7ZhVtfwH4QtpzNrPrrw9L9TAXkTyluXN+QmK9A/gT\n3ZvbygCZOTMsd9wx3zhEpLlVXOeRF9V5wLHHwvz5Yb3JPwoRSSm3OczN7LKennf386oaUfk4mj55\nWOKfv8k/ChFJKc8K8xHA/sCK6DGZ0GT3MUo0pxURkcaXpuTxMHCYu3dE20OBB9x9ygDEl4xDJQ+V\nPESkQnmWPMYAyQ6BI6N9IiLSpNK0tvo2sMjMfhdtfwyYnVlEIiJS81K1tjKzHYCDo81H3P3PmUZV\nOgbdttJtKxGpUG63rczMgKOAD7n7bcAwMyseGVdERJpImjqPq4CPAjOi7bXAlZlFJCIiNS9NncfB\n7r6/mS0CcPe3zEwzaIuINLE0JY/2aHTceBra9wObMo1KRERqWprkcRlwCzDWzP4deBD4ZqZRSY8e\neSTvCESk2aVtbbUPYU4OAxYk5+MYKGptVWht1dammQRFJJ2sWlv1WOdhZoOAZ9x9H2BptS8u6a1a\nVVjXLIIikrceb1tFU8MuM7PxPR0n2UvMwNulv4eISB7S/A07BnjazB4F1sc73f3EzKKSbjo68o5A\nRKSgbPIws+HuvhH41wGMR8oYPDjvCERECnoqefyeMBT7/3b3vx+geKSMtWvD8rvfzTcOERHoOXkM\nM7NPA4cUzWMOgLv/OruwpFhbW1h+8Yv5xiEiAj0nj88BM4Ft6DqPOYQOg0oeA+gTn4Attsg7ChGR\noGzycPcHgQfN7A/ufvUAxiQltLerb4eI1I5ee5grcdSGjg4lDxGpHWmGJ5Gc3X03rFmjzoEiUjv0\nc1QHpk6FcePgb7s1WxARyUeq5GFmOwO7Jo939/uzCkq6W7NGfT1EpHb0OjCimV0CfAp4BuiMdvtA\n9zBv5oERNf2siPRVVgMjpkkey4D9ot7muVHyCJr0IxCRPsptDnPgeUDtfEREZLM0dR7vAovNbAGw\nufTh7udlFpWUNGJE3hGIiARpkse86CE5m6d/BRGpEb0mD3e/1syGAXtFu5a5e3u2YUkpW22VdwQi\nIkGvycPMWoBrgT8RpqHdxcxOb9Smups2haFAhg/PO5Lu1ElQRGpFmgrz7wHHuPvH3P0I4Fjg0mzD\nys/3vle7dQvq5yEitSJN8hjq7sviDXdfTgO3vnrmmbwjKG/durwjEBEJ0twI+YOZXQ38ItqeCTyW\nXUj52rQp7wjKe/PNvCMQEQnSlDw+DzwNnAecT+hp/rm0FzCz48xsqZktN7MLezhuupltMrP90567\n2r70JbjxxryuXlpnZ2FddR4iUit67WHer5ObDQKWA0cCrwILgVPcfWnRcSOBOwi3w85x98dLnCvz\nHua12JP7nXdg9Oiwfvvt8PGP5xuPiNSXAe9hbmY3RsunzOzJ4kfK8x8ErHD3F6PmvXOBaSWO+zfg\nEhKdECVoTzSKrsUWYCLSnHq6EXJ+tDy+H+ffGXg5sb2SkFA2M7PJwDh3v9PMvtSPazWkZPL44Afz\ni0NEJKlsycPdV0WrZ0clh80P4OyU5y9VVNp8Q8jMjNDs9wu9vKZpJZPHDjvkF4eISFKaKtijgeKK\n7qkl9pWyEhif2B5HqPuIjQImAa1RItkBuM3MTixV7zF79uzN6y0tLbS0tKQIob61t8Ouu8Ktt+Yd\niYjUg9bWVlpbWzO/TtkKczP7PKGEsTvwXOKpUcD/uPupvZ7cbDCwjFBhvgp4FJjh7kvKHP874F/c\nfVGJ55qywnzpUpg2DZYt6/1YEZFiWVWY91TyuB64C/gWcFFi/1p3/0uak7t7p5mdA8wn3CK72t2X\nmNkcYKG73178EnTbqouODjXRFZHak7qprpmNBTYP3OHuL2UVVJnrN2XJY/FiOP10eOKJvCMRkXqU\n22RQZnaCma0AXgD+mzBA4l3VDkRK6+xUyUNEak+aHubfAKYAy919N0L9xcOZRiWbdXZqQEQRqT1p\nkke7u68GBpnZIHf/HXBgxnFJRMlDRGpRmhsia6LhQ+4HfmlmrwPrsw1LYkoeIlKL0pQ8phHmMb8A\nuJvQbLc/vc4lpTFj4K67lDxEpPb02trKzC5x9wt725e1rFtbrV4N221X2O7shEFpUmuGzMLUs+vX\n107rLxGpL7m1tiL0MC82tdqB5O3BB7tuJ4dCH0hnnAGvJvrgr9cNQhGpQT2Nqvt5M3sK2LtoRN0X\ngLSj6taN3/wmLJcvh2HD8kse114Ld96Zz7VFRNLKtId5Pdl++7AcMiTUMWSZPNxh5UrYZZfSz9fy\nbIYiItDzqLpvu/ufCAMgeuIx0szGl3tdvRo3LiwHD84+edx6K4xvuE9QRJpJmqa6d1AYc2oEsBth\nsMNJGcY14M6OBpl3zz55/KXhym0i0mx6TR7uvm9yO5pjPO18HjXnmWdgxAjYfffCvuRtIrPsk8dd\nvQzusm4d/Ou/Znd9EZH+qrgxajTPxsEZxDIgJk2CPfboui854dLo0dknj6NLtV9L2GYb+OlPC9s/\n+lF2sYiI9EWvJQ8z+5fE5iBgf7pO6FT3OjoK66NHh+0sk0e5/iNxX44HH4QNGwr7jzgiu1hERPoi\nTcljVOIxnFAHMi3LoAZasuQB8NZb8KtfZXe9X/86LB99tOv+xx4Ly2uugbffLuzPu7OiiEixNHUe\ncwYikDzFyeNznyvse/75nl9z5ZWwYEEhEVTi7rvD8uCDu/YcP+GE0sdreBIRqTVlk4eZzevphe5+\nYvXDydb995feH9+2GjGisK+3vhbXXQcPV3lg+uTtsyQlDxGpNT2VPD4KvAzcADxCA0wP+7Wvld4f\nlzySt4fK/ZDHqlEnss8+XbfLXXPjxv5fS0Skmnq6m74D8BXgg8APCWNcvenu/+3u/z0QwVVbucEF\n4+Rx8cWFfVtsUf48zzwDCxf2PY4zzwzL4lZXyeTxvvcV1lXyEJFa01MP8053v9vdTyfMJPgs0Gpm\n5w5YdFU2cmTp/e3tsNdeoaUVwJQpcOihpY91h5NO6l8c224L738/DB/ePY7kdUREalWPFeZmNhz4\nODADmABcBvShirg2xD/If/d3Xfd3dMDQoYXt3Xcvf6voD3+ApUv7F0dnZyjZtLV1jyM2bFhhfYcd\n+nc9EZFq66nC/FrCLau7gDnu/scBiypjyYpxCH/xDxnS9flkP4uk4h/8vnj99XCN997ruj9Zj/Ln\nP8PYseHYrbfu/zVFRKqppzqPvwf2As4HHjKzd6LHWjN7Z2DCq674tlVHB7zyCvz2t2G7vb1ryaOn\n5HHjjf2LwR2uvz4M/Z7sRV7KKaf071oiIlnpqc5jkLuPih5bJx6j3L0u/xb+yEfCsrMzVFofc0zY\nriR5vPFG/2KopC6jt+QiIpKXpuq7/MADYdnRUSh1xNvJ5LHFFuWTR3F9SaV66z8yYUJh/ctfVsW5\niNSmpkoed9wRlsV9NCqp87jiiv5df82ano+ZPr2wfsABfb+WiEiWGjp5vPBC6c58xZ3xKrlttWBB\nYT05rHsaxx8Pl1/edd9DD4VlPJbVFlvA178e1oub8oqI1IqGTh677w5XX919/+23F9affbay5JFU\nPKBiGvF54/qM118vxAGw5Zbwz/8c1pU8RKRWNXTyAFi/PvTZ+OpXYeZM+NSnuj4/fXooiaS9bZWU\nJnmYwapVhe14FsFPfzos4+vGdRsHHFDo3f5qQw18LyKNpOGSx+9/33X7jTfg2mvhm9+EtWu7D/Xx\nxBPdSx7DhvU+ntS994a+GGnstFNhffnysIyvFyeNOGEcfXQhoaxbl+78IiIDraGSx09+AoccUrgV\nBPCtb8FZZ4X1efNKz41xyilw002F7aFDw6i5995b/lof/nDfYoxH9o0TRFwnM3gw7L1312OLB04U\nEakVDZU84iTRkzQTK8U/7N/+dvnnxowJy7RNaYsnfrJojOK4x/q6dV2b8b71VkiEIiK1qNfJoOpR\nT3UR5eb0SIoTxLJlYdnWBqtXw447wjnnwPjx4cd/yJBwreQ4VEnJW18Hl5n1PT4m7sAY22ab3uMU\nEclLQ5U8Yj2NP7V2be+vj5PHypVhefHFhXqLZJ+QoUO7J6onnghNhCFU0vcmLsGIiNSThkweL75Y\n+WRNu+1WWB9SVB57883CerI3eqnkMXlyYTj33qayhfJTz4qI1LKGTB7f+175prblSh77719Yj5PH\nhz4Ulsn+Fr2VPKDQNHf9+vQxi4jUk8yTh5kdZ2ZLzWy5mV1Y4vkLzOxpM1tsZr81s136e80NG7oP\ndx6bObP0/mRl9XXXdT9fLNmst1zygFDyeffd0s/9+7933X7iidLHiYjUqkyTh5kNAq4AjgUmATPM\nrLgB6uPAAe4+Gfgv4Lv9ve5WW8Efy8w+8tOfFlo6HXoojBoV1pO3quKSRnxcXPcBpW9bvfgiXHBB\n1+tceimMG1c6huIpbidPhl13hcMP7/l9iYjUiqxLHgcBK9z9RXdvB+YC05IHRHOix3/bPwzs3JcL\nJUsat90Gf/VXpY9LdhI85JDCbazk/quuimMLy/nzw7Kjo+ttq3Xr4J134Oc/hx/8oOuYWbNmFZJP\nseLJqCD06fjKV0ofLyJSa7JOHjsDLye2V9JzcvgMYebCir38cu/HxOKkMGlSYV+yaWycHIp7mb/z\nDtx8c+H1b70F++0X9hef79134YYbSl8/LnkcdVRh34YNpZOKiEgtyrqfR6m/vUt2qzOzU4EDgI+V\nO9ns2bM3r7e0tNDS0rJ5u/hWUE++8IXQWTC+ZQVw9tndjyuu8F6yJCyLR+U9/HD4/vcLQ4/0Jr7t\n1dJS6MWu5CEi1dDa2kpra2vm18k6eawExie2xwHdhvszs6OALwNHRLe3Skomj2IdHeHW0/Dh5Suq\nY//xH2EZ346C0Ddj330L2zvv3LX5LsBhh4Vl2mbAW20VktJ3i2px4l7uySS0YYNG0RWR/iv+w3rO\nnDmZXCfr21YLgYlmtquZDQNOAeYlDzCzDwM/Bk5099V9vVBHR/ixP+mk9K+ZPLmwXjyO1LXXhlFt\n46HSk4pnAyw3r/lhh8FBB3XfH/dyP/XUwr62tvI91UVEak2mycPdO4FzgPnA08Bcd19iZnPM7Pjo\nsO8AWwE3mdkiM7u1L9eKh1UvHon2Bz8o/5qxYwvre+3V9blhw0Li2HPP7q+Lk8dnPhOW5eo27rmn\n662x2Mknh+UeexT29TTMiYhIrcl8bCt3vxvYu2jfrMT60dW4TtwK6pZbuu4/77wwudI//ANcdFH6\n8/X0Qx7ftto5Rbuwp57quv3DHxZufyW1tXUdFl5EpJY1zMCIyf4Xsb33LjSX/c534H3vS3++nuof\nDjwwLIuHMSllzRp45ZUwz8h994VkVopuW4lIPWmo5FH8Y/7444VlJYkDyv+Qn3pqYS6PeNTdnhx1\nVBhU8aSTeq6P0W0rEaknDTO2VankEZdE+jJxU7kf8uQ1yg18uN9+hfW01169WslDROpHwySPZM/v\nWJrbSu6lBzBM/pB/4AOF+o3krbHiKW9jc+cW1ivpf1LJsSIieWqY5FGqzqDc8CDFttyy+75kncfk\nybBwYVhPzkQ4YUJhPe6hftll8Pbbhf2VlCZUYS4i9aJhkscbb4TK6SefrM75kj/6I0aEWQSh67Sz\nI0cW1uNBEMeOLVSoi4g0qoapMI+HWo9/9OPZ/PoqecsrOWxIsoPg4YcXRu+Nj586NaynndtcRKQe\nNUzJIxaPjpu8pdQXyVJFMnkkh4xJDjsS3yLbeuvKrvPYYxWHJiKSu4ZJHnFv7bT1HL0xg5deCuuX\nXlrYnxyuJFlHMXFi366TnMFQRKReNMxtqxNPDPUOg6qYDnurwI6fX7YsVNjfdFP1ri0iUssaJnnE\nra0mTID/+Z/qnDO+dbXTTqWfj0s5o0aFFlujR/ftOnvu2X0EXxGRWtZwycMszBBYDVttFZaXXFLY\nlxwJNzZoUEgca9b07TqLFqmZrojUl4ZLHtUUlyziH/bjj4fp07se88ADsP32/btOnKREROqFkkcK\ncbPb3/ym+3OlRsgVEWl0DdPaav780sOMVEPx5E8iIs2uIZLHmWeGgQVX93kewvK++lU45pjqn1dE\npJ6Z10lXaDPzUrHefHNhZr5774UjjxzgwEREapiZ4e5V6gFXUPclj0WLCuvt7fnFISLSTOo+eWzY\nUFjXkOYiIgOj7pPHI48U1pPjUYmISHbqPnlMmVJY1zhRIiIDo+6TR1tbYb1agyKKiEjP6j55bLdd\n3hGIiDSfuk8eGzfmHYGISPOpy+TxzDOFXt9xa6ssOgiKiEhpdZk8Jk2CuXPD+saNcPnlsO22+cYk\nItJM6jJ5QEgaGzfClVfC8OF5RyMi0lzqNnlccAG8/HLeUYiINKe6TR5vvw0dHWG9szPfWEREmk3d\nJY84YQCsWxeWSh4iIgOr7pJHcs6OK68MSyUPEZGBVXfJIy5tAPz852FZJ6PKi4g0jLqbz2PZMthn\nn67PbdigFlciIqVoPo/I4sVdty++WIlDRGSg1V3yuP76sBw/PizPPDO/WEREmlXmycPMjjOzpWa2\n3MwuLPH8MDOba2YrzOz3Zja+p/PdfXdYfvazYTlhQtVDFhGRXmSaPMxsEHAFcCwwCZhhZkU1FnwG\n+Iu77wn8APhOufM98khhCPbBg7OIuD60trbmHULN0GdRoM+iQJ9F9rIueRwErHD3F929HZgLTCs6\nZhpwbbR+M3BkuZPFEz+dey780z/B7bdXPd66oP8YBfosCvRZFOizyF7WyWNnIDmIyMpoX8lj3L0T\nWGNmPQ5zeMIJsPXW8PGPVzNUERFJa0jG5y/VPKy4bXDxMVbiGCC0qjr3XDjkkGqEJiIifZVpPw8z\nmwLMdvfjou2LAHf3SxLH3BUd84iZDQZWufvYEueqjw4pIiI1Jot+HlmXPBYCE81sV2AVcAowo+iY\n3wCnA48AJwP3lTpRFm9eRET6JtPk4e6dZnYOMJ9Qv3K1uy8xsznAQne/Hbga+IWZrQBWExKMiIjU\nsLoZnkRERGpH3fUwb0RmNs7M7jOzZ8zsKTM7L9o/xszmm9kyM7vHzEYnXnNZ1LFysZlNTuw/PeqQ\nuczMTsvj/VSDmQ0ys8fNbF60PcHMHo7e1w1mNiTaX7aTqZl9Odq/xMyOyeu99IeZjTazm6L38LSZ\nHdys3wszu8DM/mhmT5rZL6N/+6b4XpjZ1Wb2mpk9mdhXte+Bme0ffa7LzewHqYJydz1yfgA7AJOj\n9ZHAMmCGJVyZAAAHd0lEQVQf4BLg/0T7LwS+Ha1PBe6I1g8GHo7WxwDPAaOBbeL1vN9fHz+TC4Dr\ngHnR9q+Ak6P1HwFnReufB66K1j8FzI3W/xewiHBrdgLwLFFJu54ewM+BM6P1IdG/bdN9L4CdgOeB\nYYnvw+nN8r0ADgMmA08m9lXte0Cocz4oWr8TOLbXmPL+UPQo+UW5FTgKWApsH+3bAVgSrf8Y+FTi\n+CXA9oT6oh8l9v8oeVy9PIBxwG+BlkTyeAMYFK1PAe6K1u8GDo7WBwOvR+sXARcmznlXfFy9PIBR\nwHMl9jfd9yJKHi9GP4BDgHnA0cDrzfK9AHYtSh5V+R5Er30msb/LceUeum1VY8xsAuEvjIcJX4zX\nANz9z0DchLlc58vi/a/QvVNmPbgU+BJRfx8zex/wlrtvip5PdjYt7mT6dtTJtBE+i92BN83smugW\n3k/MbEua8Hvh7q8C3wNeIsT/NvA4sKYJvxexsVX6HuwcHVN8fI+UPGqImY0kDNFyvruvo0xnScp3\nrEzTKbOmmdnHgdfcfTGF92N0f2+eeK5YQ3wWhL+w9weudPf9gfWEv5yb8XuxDWEoo10JpZCtCLdn\nijXD96I3lX4P+vSZKHnUiKii72bgF+5+W7T7NTPbPnp+B0IRHcJfBrskXj4OeDXaP77E/npyKHCi\nmT0P3AD8NWHAzNHRQJvQ9X1t/iyiTqaj3f0tyn9G9WQl8LK7/yHa/i9CMmnG78VRwPPu/peoJHEL\ncAiwTRN+L2LV+h706TNR8qgdPyPcd/xhYt884Ixo/QzgtsT+02BzL/41UfH1HuDoqIXOGMI94Xuy\nD7163P0r7j7e3Xcn3Hu9z91PBX5H6EQKoaI0+VmcHq0nO5nOA06JWt3sBkwEHh2I91At0b/py2a2\nV7TrSOBpmvB7QbhdNcXMRpiZUfgsmul7UVwCr8r3ILrl9Y6ZHRR9tqclzlVe3pVAejiEv7Y7gcWE\nliCPA8cB2wL3Elpf/RbYJvGaKwgtRZ4A9k/sPwNYASwHTsv7vfXzc/kYhQrz3QgtQpYTWtgMjfYP\nB26M3vPDwITE678cfUZLgGPyfj99/Aw+RBipYTHwa0JLmab8XgCzon/LJwkjcQ9tlu8FcD2hNLCR\nkEjPJDQeqMr3ADgAeCp67odpYlInQRERqZhuW4mISMWUPEREpGJKHiIiUjElDxERqZiSh4iIVEzJ\nQ0REKqbkITXBzLY1s0XRGE6rzGxltL7IzB7M6JqTzewnWZy7P8xsVzObkdg+IPUw2emvcYOZ7VHN\nc0pzUT8PqTlmdjGwzt2/n/F1bgT+zd2fyvI6Za492MMwG6WeawG+4O4nZHj9I4BT3f0fs7qGNDaV\nPKQWdRmozczWRsuPmVmrmd1qZs+a2bfM7NNm9oiZPRENN4GZbWdmN0f7HzGzQ7pdIAxCuW+cOKLX\nzLcwGddPzexP0SismNnM6DyPm9mPoiEcMLO1ZvaNaMKdh8zs/WWu/9Fo/ywz+8+oJPWfUQnjfjP7\nQ/SYEoX3LeCw6HrnR+/7N9E5xpjZLdH7fcjMPpg499Vm9rvoszk32r+lmd0eleCeNLN4KI8HgKMS\n40KJVERfHKkHyeLxfsA/Eib1+XtgT3c/GLgaODc65ofA96P904H/V+KcBwJ/TGzPAha4+76EASrj\nQfX2Icx5cIiHkW03ATOj12wFPOTukwk/xp8tc/2rE9f5APDX7j4TeA04yt0PJIzjdXl0zEXAA+6+\nvxfGOos/gznA4+7+IeCrwC8S596bMF7RwcCsaEDA44BX3P3D7r4fYZ4LPNxyWEEY/kSkYkPyDkCk\nQgvd/XUAM3sOmB/tf4oweRSEEVg/EJcQgJFmtpW7r0+cZ0fCBFOxw4BPALj7PWb2VrT/SMJItguj\n840A/hw91+bud0brj0XXLXv9aH2eu7dF68OAKyxME9oJ7Jni/R8G/G0U5++iuqJR0XN3uHsHsNrM\nXiNMAPQU8F0z+1b0fLL+6A3C8OaLUlxXpAslD6k3GxPrmxLbmyh8nw2YkviRLuU9QiIoJzmXyLXu\n/tUSxyTP39nb9aNckkxgFwB/dvf9olLCez3EUxxXUlwqKf5shrj7CjM7APgb4BtmtsDd/y06ZkTK\na4p0o9tWUg9K/WD2ZD5w3uYXm5W6NbOErn/pP0i4PYWZHUOY4xlgATA9UZ8xxsziuQ/KxZXm+hBG\nyF0VrZ9GmC4VYC1hCtpS7gdOjc7bArzpYeKwksxsR+A9d78e+C7w4cTTexGGNRepmJKH1INyTQLL\n7T8fODCqVP4jcFa3F7ovA7ZO3E76OmGugyeBkwi3pta6+xLga8B8M3uCkBh27O/1I1cBZ5jZIsIP\neVwqeRLojCq5zy96zez43MA3ieZtKCGObV/g0egaFwPfADCzscC7Hk1jKlIpNdWVphX9MK9195+Z\n2TCg0907o1ZPV0UV5A3JzP4ZeNvdr8k7FqlPqvOQZvZjQmsoCNNz3hg1Xd1IoeVUo3qLri21RCqi\nkoeIiFRMdR4iIlIxJQ8REamYkoeIiFRMyUNERCqm5CEiIhVT8hARkYr9f7396nBQKAUjAAAAAElF\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f396f0f7c10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Frequency trajectories of fixations\n",
"fig = plt.figure()\n",
"ax = plt.subplot(111)\n",
"plt.xlabel(\"Time (generations)\")\n",
"plt.ylabel(\"Mutation frequency\")\n",
"ax.set_xlim(traj['generation'].min(),traj['generation'].max())\n",
"for i in mpos:\n",
" plt.plot(traj[traj['pos']==i]['generation'],traj[traj['pos']==i]['freq'])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f396f0f7090>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAESCAYAAAAxG5hmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f396f0f7050>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Let's get histogram of effect sizes for all mutations that did not fix\n",
"fig = plt.figure()\n",
"ax = plt.subplot(111)\n",
"plt.xlabel(r'$s$ (selection coefficient)')\n",
"plt.ylabel(\"Number of mutations\")\n",
"mfreq[mfreq['freq']<1.0]['esize'].hist()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
florianwittkamp/FD_ACOUSTIC | JupyterNotebook/2D/FD_2D_DX4_DT2_fast.ipynb | 1 | 144086 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# FD_2D_DX4_DT2_fast 2-D acoustic Finite-Difference modelling\n",
"\n",
"GNU General Public License v3.0\n",
"\n",
"Author: Florian Wittkamp\n",
"\n",
"Finite-Difference acoustic seismic wave simulation\n",
"\n",
"Discretization of the first-order acoustic wave equation\n",
"\n",
"Temporal second-order accuracy $O(\\Delta T^2)$\n",
"\n",
"Spatial fourth-order accuracy $O(\\Delta X^4)$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialisation"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Input Parameter"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Discretization\n",
"c1=30 # Number of grid points per dominant wavelength\n",
"c2=0.2 # CFL-Number\n",
"nx=300 # Number of grid points in X\n",
"ny=300 # Number of grid points in Y\n",
"T=1 # Total propagation time\n",
"\n",
"# Source Signal\n",
"f0= 5 # Center frequency Ricker-wavelet\n",
"q0= 100 # Maximum amplitude Ricker-Wavelet\n",
"xscr = 150 # Source position (in grid points) in X\n",
"yscr = 150 # Source position (in grid points) in Y\n",
"\n",
"# Receiver\n",
"xrec1=150; yrec1=120; # Position Reciever 1 (in grid points)\n",
"xrec2=150; yrec2=150; # Position Reciever 2 (in grid points)\n",
"xrec3=150; yrec3=180;# Position Reciever 3 (in grid points)\n",
"\n",
"# Velocity and density\n",
"modell_v = 3000*np.ones((ny,nx))\n",
"rho=2.2*np.ones((ny,nx))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preparation"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model size: x: 3000.0 in m, y: 3000.0 in m\n",
"Temporal discretization: 0.0006666666666666668 s\n",
"Spatial discretization: 10.0 m\n",
"Number of gridpoints per minimum wavelength: 30.0\n"
]
}
],
"source": [
"# Init wavefields\n",
"vx=np.zeros(shape = (ny,nx))\n",
"vy=np.zeros(shape = (ny,nx))\n",
"p=np.zeros(shape = (ny,nx))\n",
"vx_x=np.zeros(shape = (ny,nx))\n",
"vy_y=np.zeros(shape = (ny,nx))\n",
"p_x=np.zeros(shape = (ny,nx))\n",
"p_y=np.zeros(shape = (ny,nx))\n",
"\n",
"# Calculate first Lame-Paramter\n",
"l=rho * modell_v * modell_v\n",
"\n",
"cmin=min(modell_v.flatten()) # Lowest P-wave velocity\n",
"cmax=max(modell_v.flatten()) # Highest P-wave velocity\n",
"fmax=2*f0 # Maximum frequency\n",
"dx=cmin/(fmax*c1) # Spatial discretization (in m)\n",
"dy=dx # Spatial discretization (in m)\n",
"dt=dx/(cmax)*c2 # Temporal discretization (in s)\n",
"lampda_min=cmin/fmax # Smallest wavelength\n",
"\n",
"# Output model parameter:\n",
"print(\"Model size: x:\",dx*nx,\"in m, y:\",dy*ny,\"in m\")\n",
"print(\"Temporal discretization: \",dt,\" s\")\n",
"print(\"Spatial discretization: \",dx,\" m\")\n",
"print(\"Number of gridpoints per minimum wavelength: \",lampda_min/dx)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create space and time vector"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"x=np.arange(0,dx*nx,dx) # Space vector in X\n",
"y=np.arange(0,dy*ny,dy) # Space vector in Y\n",
"t=np.arange(0,T,dt) # Time vector\n",
"nt=np.size(t) # Number of time steps\n",
"\n",
"# Plotting model\n",
"fig, (ax1, ax2) = plt.subplots(1, 2)\n",
"fig.subplots_adjust(wspace=0.4,right=1.6)\n",
"ax1.plot(x,modell_v)\n",
"ax1.set_ylabel('VP in m/s')\n",
"ax1.set_xlabel('Depth in m')\n",
"ax1.set_title('P-wave velocity')\n",
"\n",
"ax2.plot(x,rho)\n",
"ax2.set_ylabel('Density in g/cm^3')\n",
"ax2.set_xlabel('Depth in m')\n",
"ax2.set_title('Density');\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Source signal - Ricker-wavelet"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"tau=np.pi*f0*(t-1.5/f0)\n",
"q=q0*(1.0-2.0*tau**2.0)*np.exp(-tau**2)\n",
"\n",
"# Plotting source signal\n",
"plt.figure(3)\n",
"plt.plot(t,q)\n",
"plt.title('Source signal Ricker-Wavelet')\n",
"plt.ylabel('Amplitude')\n",
"plt.xlabel('Time in s')\n",
"plt.draw()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Time stepping"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Starting time stepping...\n",
"Finished time stepping!\n"
]
}
],
"source": [
"# Init Seismograms\n",
"Seismogramm=np.zeros((3,nt)); # Three seismograms\n",
"\n",
"# Calculation of some coefficients\n",
"i_dx=1.0/(dx)\n",
"i_dy=1.0/(dy)\n",
"c1=9.0/(8.0*dx)\n",
"c2=1.0/(24.0*dx)\n",
"c3=9.0/(8.0*dy)\n",
"c4=1.0/(24.0*dy)\n",
"c5=1.0/np.power(dx,3)\n",
"c6=1.0/np.power(dy,3)\n",
"c7=1.0/np.power(dx,2)\n",
"c8=1.0/np.power(dy,2)\n",
"c9=np.power(dt,3)/24.0\n",
"\n",
"# Prepare slicing parameter:\n",
"kxM2=slice(5-2,nx-4-2)\n",
"kxM1=slice(5-1,nx-4-1)\n",
"kx=slice(5,nx-4)\n",
"kxP1=slice(5+1,nx-4+1)\n",
"kxP2=slice(5+2,nx-4+2)\n",
"\n",
"kyM2=slice(5-2,ny-4-2)\n",
"kyM1=slice(5-1,ny-4-1)\n",
"ky=slice(5,ny-4)\n",
"kyP1=slice(5+1,ny-4+1)\n",
"kyP2=slice(5+2,ny-4+2)\n",
"\n",
"## Time stepping\n",
"print(\"Starting time stepping...\")\n",
"for n in range(2,nt):\n",
" \n",
" # Inject source wavelet\n",
" p[yscr,xscr]=p[yscr,xscr]+q[n]\n",
" \n",
" # Update velocity\n",
" p_x[ky,kx]=c1*(p[ky,kxP1]-p[ky,kx])-c2*(p[ky,kxP2]-p[ky,kxM1])\n",
" p_y[ky,kx]=c3*(p[kyP1,kx]-p[ky,kx])-c4*(p[kyP2,kx]-p[kyM1,kx])\n",
" \n",
" vx=vx-dt/rho*p_x\n",
" vy=vy-dt/rho*p_y\n",
" \n",
" # Update pressure\n",
" vx_x[ky,kx]=c1*(vx[ky,kx]-vx[ky,kxM1])-c2*(vx[ky,kxP1]-vx[ky,kxM2])\n",
" vy_y[ky,kx]=c3*(vy[ky,kx]-vy[kyM1,kx])-c4*(vy[kyP1,kx]-vy[kyM2,kx])\n",
" \n",
" p=p-l*dt*(vx_x+vy_y)\n",
" \n",
" # Save seismograms\n",
" Seismogramm[0,n]=p[yrec1,xrec1]\n",
" Seismogramm[1,n]=p[yrec2,xrec2]\n",
" Seismogramm[2,n]=p[yrec3,xrec3]\n",
" \n",
"print(\"Finished time stepping!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Save seismograms"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"## Save seismograms\n",
"np.save(\"Seismograms/FD_2D_DX4_DT2_fast\",Seismogramm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAr0AAAJZCAYAAACk8Y1MAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAACKSElEQVR4nOzdd3xc1Zn/8c+jblnNsuRuWXKvgG250G0ghE4gCaEmkEJg05cNu2m/ZFN3sylACgklQEKAEBJa6L3buOLee5Vsy7Zc1Gae3x8zsoUt2ZJmRjMafd+v1zBz7z33nEe6eObRmXPPMXdHRERERCSZpcQ7ABERERGRWFPSKyIiIiJJT0mviIiIiCQ9Jb0iIiIikvSU9IqIiIhI0lPSKyIiIiJJT0mviEiUmNk+Mxsc7zhERORoSnpFRJows9PM7F0z22Nmu8zsHTOb1Jpz3T3H3dfEOsZEZWZjzewFM9thZpoEXkQSipJeEZEwM8sD/gX8BigE+gP/DdTGM65oMbPUGDdRDzwKfC7G7YiItJmSXhGRw4YDuPvD7h5w94Pu/qK7L2gsYGafNbOlZlYV7tUc1OSYm9nQ8OsLzGyJmVWb2WYz+4/w/mlmtsnMbjWzCjPbamYfC5dfEe5d/naTOjPN7DYz2xJ+3GZmmU2O3xquY4uZff6IGO43szvN7Fkz2w9MN7MLzWyeme01s41m9oMmdZWGz78hfKzKzG4ys0lmtsDMdpvZb1v65bn7cne/F1gchWshIhJVSnpFRA5bAQTM7AEzO9/MejQ9aGaXAt8GLgeKgbeAh1uo617gi+6eC4wFXm1yrA+QRagn+f8BdwPXAhOB04HvmVlZuOx3gKnAScCJwGTgu+F4zgP+HTgHGApMayaOq4GfALnA28B+4NNAAXAhcLOZfeyIc6YAw4BPAbeFYzgHGANcYWZntvAzi4gkLCW9IiJh7r4XOA1wQolopZk9ZWa9w0VuAn7m7kvdvQH4KXBS097eJuqB0WaW5+5V7j73iGM/cfd64BGgCLjd3avdfTGwhFCCC3AN8EN3r3D3SkLDLa4LH7sCuM/dF7v7AeAHzcTxpLu/4+5Bd69x99fdfWF4ewGhpP3IJPZH4bIvEkqSHw63v5lQoj/+uL9MEZEEo6RXRKSJcEJ7vbsPINRD249QbyfAIOD28Nf8u4FdgBHqsT3Sx4ELgPVm9oaZndzk2E53D4RfHww/b29y/CCQE37dD1jf5Nj68L7GYxubHGv6utl9ZjbFzF4zs0oz20MokS864pwjY2kpNhGRTkNJr4hIC9x9GXA/oeQXQgnkF929oMmjm7u/28y5s9z9UqAX8AShG7zaYwuhZLtRSXgfwFZgQJNjA5v7MY7Yfgh4Chjo7vnAHwgl7iIiSU1Jr4hImJmNNLNbzGxAeHsgcBUwI1zkD8C3zGxM+Hi+mX2ymXoyzOwaM8sPD2HYCwTbGdbDwHfNrNjMigiNAX4wfOxR4AYzG2Vm2cD3WlFfLrDL3WvMbDKhMb9RYSFZQEZ4O6vpTXciIvGkpFdE5LBqQjdxzQzPdjADWATcAuDujwP/CzxiZnvDx85voa7rgHXhcjcRGpvbHj8GZgMLgIXA3PA+3P054A7gNWAVh5PzY02x9m/AD82smlAC3d4e6OYMIjT8oXH2hoPA8ijWLyLSbuau+cNFRJKBmY0ilIhnhm+0ExGRMPX0ioh0YmZ2WXgu3x6EeqGfVsIrInI0Jb0iIp3bF4EKYDUQAG6ObzgiIolJwxtEREREJOmpp1dEREREkp6SXhERERFJemnxDqAlZlYA3ENoUngHPuvu7zVXtqioyEtLSzsuOBERERGJizlz5uxw9+K2npewSS9wO/C8u3/CzDKA7JYKlpaWMnv27I6LTERERETiwszWH7/U0RIy6TWzfOAM4HoAd68D6uIZk4iIiIh0Xok6prcMqATuM7N5ZnaPmXWPd1AiIiIi0jklatKbBkwA7nT38cB+4L+aFjCzG81stpnNrqysjEeMIiIiItJJJGrSuwnY5O4zw9uPEUqCD3H3u9y93N3Li4vbPJZZRBKEuzNvQxX3vLWGP7+3jvU798c7JBERSUIJOabX3beZ2UYzG+Huy4GzgSXxjktEoqtibw23/P0D3lq540P7rz+llG9fMIqMtET9u1xERDqbhEx6w74C/DU8c8Ma4IY4xyMiUbRl90Gu+ON77NxXx3cvHMXlEwawv7aBe95aw/3vrmPDrgP88bqJpKcq8RURkcglbNLr7vOB8njHISLRd7AuwBf+PJs9B+r52xencsKAAgAKu2fw35eOZVjvXL77xCJ+8sxSfnDJmPgGKyIiSUFdKCLS4X74ryUs2bqXO64afyjhberaqYP47Kll3P/uOt5dtePoCkRERNpISa+IdKiZa3by8Psb+PxpZUwf2avFcreeN4LSntl86/GF1NQHOjBCERFJRkp6RaTDBILO955cxIAe3fjGR4Yfs2xWeio//tg41u88wF9nbuigCEVEJFkp6RWRDvP4vM2s2L6Pb50/iuyM499ScNqwIk4Z0pM7X1/FgbqGDohQRESSlZJeEekQdQ1Bbnt5BWP753H+2D6tPu+Wc4ezY18dD6m3V0REIqCkV0Q6xDMLt7Cp6iD//pHhpKRYq8+bOKiQSaU9+PN76wkEPYYRiohIMlPSKyIx5+7c+/ZahvbKYdrwlm9ea8lnTillw64DvLasIgbRiYhIV6CkV0RibubaXSzavJfPnVbWpl7eRh8d04c+eVn8Zcb6GEQnIiJdgZJeEYm5e99eS2H3DC4b379d56enpnD5hP68tbKSiuqaKEcnIiJdgZJeEYmpir01vLqsgk9NGkhWemq767l8Qn+CDk/N3xLF6EREpKtQ0isiMfWPuZsJBJ0rygdGVM/QXrmM65/PE/M3RykyERHpSpT0ikjMuDt/n72RyaWFlBV1j7i+y8b3Z9HmvazcXh2F6EREpCtR0isiMTN7fRVrduznikmR9fI2uvjEfqQYPL1ga1TqExGRrkNJr4jEzD/nbiI7I5ULxrV+MYpjKc7NpHxQIS8t2R6V+kREpOtQ0isiMVEfCPLcom18ZHTvVi053FrnjunN0q172bjrQNTqFBGR5KekV0Ri4u1VO9h9oJ6LT+gX1Xo/Mro3AC+qt1dERNpASa+IxMTTH2whLyuN04cXRbXeQT27M7JPLi8u3hbVekVEJLkldNJrZqlmNs/M/hXvWESk9WrqA7y0eDsfHdOHzLT2z83bknNH92bWul1U7a+Let0iIpKcEjrpBb4GLI13ECLSNm+sqKS6toGLT4zu0IZG00f2IujwzuodMalfRESST8ImvWY2ALgQuCfesYhI2zy7cCuF3TM4ZUjPmNR/woAC8rLSeHNFZUzqFxGR5JOwSS9wG3ArEGzuoJndaGazzWx2ZaU++EQSRUMgyOvLK5k+ohdpqbF5i0lNMU4bVsRbK3fg7jFpQ0REkktCJr1mdhFQ4e5zWirj7ne5e7m7lxcXF3dgdCJyLPM27mbPwXrOHtUrpu2cPqyYrXtqWF25L6btiIhIckjIpBc4FbjEzNYBjwBnmdmD8Q1JRFrjlaUVpIV7YmPp9HD9b6zQuF4RETm+hEx63f1b7j7A3UuBK4FX3f3aOIclIq3w6rLtTC4rJC8rPabtDOiRzeDi7ry1UsObRETk+BIy6RWRzmnjrgOs2L6Ps0bGdmhDozOGFTNjzU5qGwId0p6IiHReCZ/0uvvr7n5RvOMQkeN7bXkFQIclvacPK6KmPsicdVUd0p6IiHReCZ/0ikjn8crSCsqKujO4OKdD2ptUVkiKwYy1uzqkPRER6byU9IpIVByoa+C9NTs7rJcXIC8rnTH98pm5ZmeHtSkiIp2Tkl4RiYp3Vu2kriHYoUkvwJSyQuZt3E1Nvcb1iohIy5T0ikhUvLpsOzmZaUwqLezQdqcM7kldQ5APNu7u0HZFRKRzUdIrIhFzd15dVsEZw4vISOvYt5XJpYWYwUyN6xURkWNQ0isiEVu8ZS/b99YyfUTHDm0AyM9OZ1SfPGZoXK+IiByDkl4Ridiryyowg2lxSHoBpgwuZO6GKuoagnFpX0REEp+SXhGJ2KvLKjhxQAHFuZlxaX9KWU9q6oMs2LQ7Lu2LiEjiU9IrIhGprK7lg027O3zWhqYml4VunpulRSpERKQFSnpFJCKvL6/AveNWYWtOYfcMBhd3Z856Jb0iItI8Jb0iEpHXllfQOy+TMf3y4hrHxJIezN1QhbvHNQ4REUlMSnpFpN3qGoK8uWIHZ43shZnFNZaJg3qwa38da3fsj2scIiKSmJT0iki7zVq3i321DZw1sne8Q2HioB4AGuIgIiLNiijpNbNsM/uemd0d3h5mZhdFJzQRSXSvLqsgIy2FU4f2jHcoDCnOIb9bupJeERFpVqQ9vfcBtcDJ4e3NwI8jrFNEOolXl1Vw8uCeZGekxTsUUlKMCSUFSnpFRKRZkSa9Q9z950A9gLsfAOI7sE9EOsSayn2s3bGfs0fFb9aGI00c1IOVFfvYc6A+3qGIiEiCiTTprTOzboADmNkQQj2/ETGzgWb2mpktMbPFZva1SOsUkeh6dVkFQFyWHm7JhPC43rkb1NsrIiIfFmnS+33geWCgmf0VeAW4NeKooAG4xd1HA1OBL5nZ6CjUKyJR8uqyCob3zmFgYXa8QznkpIEFpKaYhjiIiMhRIhqI5+4vmdlcQompAV9z9x2RBuXuW4Gt4dfVZrYU6A8sibRuEYnc3pp63l+7i8+fPjjeoXxIdkYao/vmKekVEZGjtCvpNbMJR+zaGn4uMbMSd58bWVgfaqsUGA/MjFadIhKZt1fuoCHoCTWet9HEQT3426yN1AeCpKdqVkYREQlpb0/vL8PPWUA58AGhnt4TgNkcns0hImaWA/wD+Lq77z3i2I3AjQAlJSXRaE5EWumVpRXkd0tn/MCCeIdylImDenD/u+tYunUvJwwoiHc4IiKSINrVDeLu0919OqEe3gnuXu7uEwn1yG6ORmBmlk4o4f2ru/+zmRjuCrdbXlxcHI0mRaQVgkHn9eUVTBtRTFoC9qQeuplNQxxERKSJSD+xRrj7wsYNd18EjIqwTiy0num9wFJ3/1Wk9YlI9HywaTc799dx1sjEG9oA0C8/i955mczdsDveoYiISAKJdEb5BWZ2D/BgePsaYEGEdQKcClwHLDSz+eF933b3Z6NQt4hE4NVlFaQYnDk8Mb9hMTMmlPRg3kb19IqIyGGRJr03ADcDjfPovgncGWGduPvbaJELkYT0ytIKygcVUpCdEe9QWjShpAfPLdpGZXUtxbmZ8Q5HREQSQETDG9y9xt1/7e6XhR+/dveaaAUnIolly+6DLNm6l7MScNaGpiYMKgC0SIWIiBwWUU+vma0lvBpbU+6eWJN3ikhUvBJehe2cUb3jHMmxjemXT3qqMXdDFR8d0yfe4YiISAKIdHhDeZPXWcAngcII6xSRBPXyku2U9sxmSHH3eIdyTFnpqYzul8+89bvjHYqIiCSISIc37Gzy2OzutwEXRic0EUkk+2obeG/1Ts4Z1ZvQBCuJbUJJAQs276Y+EIx3KCIikgAiSnrNbEKTR7mZ3UTkvccikoDeXllJXSDIOaMTe2hDowklPaipD7J0697jFxYRkaQXaYL6yyavG4C1wBUR1ikiCeilJaFV2MrDiz8kusZFKuZt2K2V2UREJOKk93PuvqbpDjMri7BOEUkwgaDz2vIKpifoKmzNObxIRRWfOaU03uGIiEicRfrp9Vgr94lIJzZvQxW79td1mqENcHiRCk1bJiIi0M6eXjMbCYwB8s3s8iaH8gjN4iAiSeSlpdtJSzHOSNBV2FqiRSpERKRRe4c3jAAuAgqAi5vsrwa+EGFMIpJgXllawdTBPcnLSo93KG3SdJEKzdcrItK1tSvpdfcngSfN7GR3fy/KMYlIAlm5vZpVFfu4buqgeIfSZlqkQkREGrV3eMOt7v5z4Gozu+rI4+7+1YgjE5GE8OzCbZjB+WM7X9J4aJGKDbvjHYqIiMRZe4c3LA0/z45WICKSmJ5btJVJgwrpldc5h+tPKCng4fc3UB8Ikt5JZp6IB3entiFIbUOQhkCQtNQUcjLTSE1J/IVIRERao73DG54OPz8Q3XBEJJGsrtzHsm3VfP/i0fEOpd0mlPTgvnfWsWxrNeMG5Mc7nIRQ2xDg/bW7mLt+Nws27Wb9rgNs3X2Q/XWBo8pmpafQs3sm/Qqy6JvfjX4F3SgrymZorxyGFOdQkJ0Rh59ARKTt2ju84WnAWzru7pe0OyIRSRjPLdwKwHmdcGhDo8ZFKuZuqOrSSa+7M3dDFQ/O2MDLS7ZTXduAGQwtzmFocQ6nDS2iODeTzLQU0lNTqA8E2V8bYF9tPZXVtWzZU8P8jbt5ftE26pos7VyUk8GQ4hyG9go9hvXKZVjvHHrlZnaK5apFpOto7/CGX0Q1ChFJSM8u3MbEQT3om98t3qG0mxapgLdX7uAXLy5n/sbd5GWlcf64Ppw3tg+TSgvJbeOMHIGgs7nqIKsqQzc4rq7Yz6rKfTz9wRb21jQcKpebmcbQ3jkMa5IMD+2VQ/+CbqRoyISIxEF7hze80fjazDKAkYR6fpe7e12UYhOROFq3Yz9Ltu7luxeOincoEenKi1Rs2X2Q/356MS8s3s6AHt340aVj+PjEAWRntH8xztQUo6RnNiU9szlr5OHFStydyn21rKrYx6qKfazcHnp+dVklj87edKhct/RUhvTqztDiHEoKs+nfoxsDemQzoEc3+uZ3IyNN465FJDYiWobYzC4E/gCsBgwoM7MvuvtzkQZmZucBtwOpwD3u/j+R1ikirfdMeGjD+eP6xjmSyHXFRSpeWLyNWx9bQF1DkG9+dASfO62MrPTUmLVnZvTKzaJXbhanDCn60LHdB+pCiXBjMly5j1nrqnjqgy0EvWkd0Ds3i34FoXqKcjMozgk9F+VkUpybSXFOJnnd0snNTFOPsYi0SURJL/BLYLq7rwIwsyHAM0BESa+ZpQK/Az4CbAJmmdlT7r4kwnhFpBXcnX/O3cSk0h70L+i8QxsaNS5SMW9DFecm+Xy9waDzv88v449vrmFs/zx+e9UESou6xzWmguwMyksLKS8t/ND++kCQbXtq2FR1kE1VB8LPB9my+yCrK/cxc20tVQfqm63TLDSEIj87nbys0CO/Wzp53dLIzkijW0Yq3dJTyc5IJSv83C09tcn+NDLTQ+OX01ONjNTw67SU8GvTmGSRJBNp0lvdmPCGrSG0KlukJgOr3H0NgJk9AlwKNJv0HqwPsHjLng/tM45+szry/au597Mjz2u+zPHrObJU6+ppJuZWtNWamI86p7l6jtjZXDVH/Q5b83tuNoDI60lNMTLCN92kpegDKpoWbt7D6sr9fP70wfEOJSoOL1KxO6mT3rqGIN987AOenL+Fa6aU8P8uHk1mWux6dyOVnprCwMJsBhZmAz2bLVPXEGTX/joqq2vZsa+Wyn217D1YH3rUNLDn0Ot61u7Yz56D9Ryoa6CmPvihG+7aF5+Fk+LQIyPVSE9LITXFSEsxUsxITWnyMCMl/JyWevh4ioXKp6Y0HofUlBRSUzh0PDUl9C5oZphBioW2U8L7abovXMaO3A6Xp8m+FAu9v364fOh1SqjBQ2VC+8Pvx+G308Z31cb318PbH34OHbOj9h3z3Cbv6nZUe4drbf54k3NbiMmO+CFadW4L7XGc443X7Xg/w3F/n838DluMrcn+tvz+2tV2K85p0++qmXosBTJSU8hMS4nZ53mkSe9sM3sWeJTQmN5PEuqVvRzA3f/Zznr7AxubbG8CprRUeFXFPi684+12NiXJwCz0jyUj3EuTkRZ6ZKalkJuVTm5WWqg3qFsaueFeoaKcDPrkZ9EnL4ve+VnkZqYpcQ7759zNZKSlcEESDG2Aw4tUJPO43rqGIDf+ZTavL6/kmx8dwb9NG5IU/z9npKWE/p3mt32e6PpAkIP1AWrqAhyoC3CwPvRcE36uawhSHwglx/WB4KHt+oA3eR3arm04XCbgTjDoNARDzwF3AsHDj4ZgkNqG8LY7gSDh8kGCzofKNtYVcMc99C2Le+gDNRh+HXQPTZfU5HXjMZFk1Pg5npl25HMqg3pmt7veSJPeLGA7cGZ4uxLoBlxM6N9se5Pe4zKzG4EbAXoPKOWP1008dKz5N4IP72yuzJG7mi/TnnqO/87U7raO2NdcS0e234pfz1Ftt76t6NRzZKHmyjQE/NCHUF34g6uu4fAHV11DkJr6IPtqG9i1v451O/azt6aBvQfraQgeXWP3jFTKiruHpl8qzmFIrxzG9stnYGG3pEgeWqs+EOTpD7bwkVG9ye/Wtjv7E1kyL1IRCDrf+Nt8Xl9eyU8vG8fVU0riHVJCaOyhzWvjDBWdiR+RGDe+hqOTZg+G3pOb7guGM+ygh44F/fBnxlHv1eHtxvf1psf9UBk/YvvDJQ7Xcfx6j9Xecc89bjwtx3S4/ebPPfS51uSpNXEc8as4xs/btt9ja38Hzf0MzbXd6nib1NnSMZr5OVr62YLhBXLqwovkhJ4DR21Hcm9CREmvu98QyfnHsBkY2GR7QHhf07bvAu4CKC8v948m8VeWEl3uzsH6AJXVtWzfW8u2vTVs31PD5t0HWbNjP7PXVfHk/C2HyhflZHDSwB5MGFTAGcOKGd03L6lvoHlzRSU799dx2fj+8Q4lqpJ1kQp353tPLuKZhVv5zgWjlPB2MYeGQzQ/kEwkKf2ynedFOntDGfAVoLRpXVFYnGIWMCxc/2bgSuDqCOsUAUIfEtkZaQzqmcagns3f4HOgroHVFfv5YNNu5m6oYv6G3by8dDs/f345xbmZTBtezLlj+nDm8OKkm2Lp0dkbKeyewZkjiuMdSlQl6yIVf5mxnodmbuCmM4fwhTOSYwy2iEgsRDq84QngXuBpILI7Bppw9wYz+zLwAqEpy/7k7oujVb/I8WRnpDFuQD7jBuRz7dRBAFRW1/LmikpeW17Bi0u28/c5myjITufCcX25onwgJw4siG/QUbBtTw0vL63g86eXJd0QgGRcpGLmmp388OklnD2yF7d+dES8wxERSWiRJr017n5HVCI5grs/Czwbi7pF2qM4N5OPTxzAxycOoD4Q5O2VO3hi/mb+MXcTf525gQklBVx/ahnnj+3TaRPGR2ZtIOjONZMHxTuUqGtcpGLeht3xDiUqKqtr+dJDcynpmc2vrzwpqYfciIhEQ6RJ7+1m9n3gRaC2cae7z42wXpGElp6awvSRvZg+shfVNfU8NmcTD7y7jq8+PI+Bhd34yvRhXDahf6dKfhsCQR55fyNnDCumJIK7YxNZ4yIVO/bVUpTTeRepcHdufewDqmsaeOgLU5P6Ri0RkWiJ9BN5HPAF4H8IjSv+JfCLSIMS6Uxys9K54dQyXr1lGvd8upwe2Rnc+o8FnPOrN3jqgy2tmr0jEby8tIJte2sODedIRuNLCgCYu74qvoFE6MGZG3hteSXfOn8kw3vnxjscEZFOIdKk95PAYHc/092nhx9nRSMwkc4mJcU4Z3RvnvzSqdz96XKyM9L46sPz+NQfZxy1eEoi+vN76+iXn8VZI3vFO5SYGdv/8CIVndX6nfv5yTNLOGN4cdKMTRYR6QiRJr2LgIIoxCGSNMyMj4zuzb++cho/u3wcqyr3cfFv3uYHTy1mf21DvMNr1gcbd/Pu6p1cf2opqUk8NrSzL1Lh7nz3iUWkpaTw84+f0KXmjxYRiVSkSW8BsMzMXjCzp8KPJ6MQl0inl5piXDW5hNf+YxqfPrmUB95bx3m3v8m7q3bEO7Sj/OGN1eRlpXHV5OSf43VCSQELNu2mPsIlauPhqQ+28NbKHXzzoyPatUKZiEhXFmnS+33gMuCnwK8Iza87NNKgRJJJfrd0fnDJGB794smkpaRw9T0z+c7jCzlYF4h3aACsqdzH84u3cd3Jg8jtAjdETSjpQU19kGVbq+MdSpvsOVDPj/61lBObTKMnIiKtF1HS6+5vAHuBi4D7gbOAP0QelkjymVRayHNfO50bzxjMQ+9v4OLfvs3SrXvjHRa3v7KSzLQUrj+lLN6hdIjGRSrmbexcQxx+8eJyqg7U8dPLxyX1EBQRkVhpV9JrZsPN7Ptmtgz4DbABsPCNbL+JaoQiSSQrPZVvXzCKBz83hb0H67n0d+/w5/fWxW2Gh8Vb9vDk/C3ccGoZxbmddwqvtji0SEUnmsFh5fZq/jpzPddOKWFMv+RZTU5EpCO1t6d3GaFe3Yvc/bRwopsY39WKdAKnDi3iua+dzqlDevL/nlzMF/8yh90H6jo8jp8/v5z8buncdOaQDm87XhoXqehMMzj89NmldM9M42vnDI93KCIinVZ7k97Lga3Aa2Z2t5mdDej7NpE26JmTyZ+un8T3LhrNa8sruOD2t5izfleHtf/asgreWFHJv00bQn635B/L29SEkh5s2HWAHftqj184zt5aWclryyv5yllDKeyeEe9wREQ6rXYlve7+hLtfCYwEXgO+DvQyszvN7NwoxieS1MyMz51Wxj9uPoW01BSu+OMM7nx9NcFgbIc7HKhr4LtPLGJYrxxuOLVrjOVtasKgAgBmr+u4PzLaIxB0fvLMUgYWdtOcvCIiEYr0Rrb97v6Qu18MDADmAf8ZlchEupATBhTwr6+exnlj+/C/zy/j+vtnxbQX8lcvrmDz7oP87PJxZKR1nqWSo2Vc/wK6pacyY01iJ71Pf7CFZduq+c/zRpKZlhrvcEREOrWofdq5e5W73+XuZ0erTpGuJC8rnd9eNZ6fXDaWGWt2csHtb/He6p1Rb+e1ZRXc8/Zarps6iPLSwqjX3xlkpKVQXtojJr/faAkEnTteXcnIPrlcMLZvvMMREen0ul4Xj0gCMzOumTKIJ790KjlZaVxzzwxue3kFgSgNd9hUdYBvPDqf0X3z+M6Fo6JSZ2d18pCeLN9ezc4EHdf7zMKtrKncz1fOGkaKpigTEYmYkl6RBDSqbx5Pf/k0Pja+P7e9vJJr7pnBpqoDEdVZtb+OG+6bRSDg/P6aCWSld+2vy08e3BMgIYc4BIPOb15ZybBeOZw/tk+8wxERSQpKekUSVPfMNH51xUn84pMnsmDTHj7yqze5+801NLRj+dyK6hquvXcm63cd4O7PlFNa1D0GEXcuY/vn0z0jlffWJN6y0M8t2sbKin185Wz18oqIRIuSXpEE94mJA3jp38/k1KE9+cmzS7nwjrd5YfG2Vi9oMWd9FZf//l3WVO7nj9dNZGq4h7OrS09NYVJZYcL19AaDzh2vrGRIcXcuHKexvCIi0ZJwSa+Z/Z+ZLTOzBWb2uJkVxDsmkXjrX9CNuz9dzh+unUB9IMgX/zKHC+94m7/OXN/iohZrd+znv/6xgE/+4V0AHrlxKtNH9OrIsBPeyYN7sqpiHxXVNfEO5ZAXl2xj+fZqvnLWMC03LCISRWnxDqAZLwHfcvcGM/tf4FtoGjQRzIzzxvblnFG9+ee8zfzp7bV85/FFfPeJRYzpl0dZUQ4F3dKprqln6dZqlm+vJiM1hU+fXMot5w4nN6trLUDRGlObjOu95MR+cY4G3J3bX1lFWVF3LjpBvbwiItGUcEmvu7/YZHMG8Il4xSKSiNJSU7iifCCfnDiABZv28NryCmat28XCTbvZc7Ce7Iw0hvbK4eMT+3PpSf3pnZcV75AT1ph+eeRmpvHe6p0JkfS+tGQ7S7fu5ZefPJG01IT7Ik5EpFNLuKT3CJ8F/tbcATO7EbgRoKSkpCNjEkkIZsaJAws4cWBBvEPptNJSU5gyuJB3VsX/Zjb30Ly8g3pmc+lJ8U/ARUSSTVy6EszsZTNb1Mzj0iZlvgM0AH9tro7wQhjl7l5eXFzcUaGLSJI5Y3gxG3YdYO2O/XGN49VlFSzavJcvTR+qXl4RkRiIS0+vu59zrONmdj1wEXC2t/YWdRGRdpg2vBewmNeXV1BWVBaXGNxDMzYM6NGNy8b3j0sMIiLJLuG6E8zsPOBW4BJ3j2w2fhGR4yjpmc3gou68vrwybjG8saKSDzbt4UvTh5KuXl4RkZhIxHfX3wK5wEtmNt/M/hDvgEQkuZ05opgZa3ZSUx/o8LZDMzaspH9BNz4+YUCHty8i0lUkXNLr7kPdfaC7nxR+3BTvmEQkuU0b0YvahiDvrdnZ4W2/vWoH8zbs5uZpQ8hIS7i3ZBGRpKF3WBHp8qaUFZKVnsIbHTzEwd25/eWV9M3P4pPl6uUVEYklJb0i0uVlpady8uCevLa8otXLO0fDe6t3Mnt9FTdPG0JmWmqHtSsi0hUp6RURAc4e1Zv1Ow+wYvu+DmvztldW0jsvkyvKB3ZYmyIiXZWSXhER4NwxvTGD5xZt7ZD2ZqzZyftrd3HTmUPISlcvr4hIrCnpFREBeuVmMWlQIc8v2tYh7d328gqKczO5arJWlBQR6QhKekVEws4b24dl26pjvjrbu6t3MGPNLm5WL6+ISIdR0isiEvbRsX2A2A5xcHdueyk0lvfqKerlFRHpKEp6RUTC+hd0Y3xJAU/O2xKzWRzeWbWT99ft4kvTh6qXV0SkAynpFRFp4uMTBrB8ezWLt+yNet3uzq9fXkHf/Cw+NUkzNoiIdCQlvSIiTVx8Qj8y0lJ4bM6mqNf95sodzFlfxZemD9W8vCIiHUxJr4hIE/nZ6XxkVG+enL+ZuoZg1OoNBJ3/eW4ZA3p007y8IiJxoKRXROQIn5g4gKoD9by8dHvU6nx83maWbt3LreeNJCNNb70iIh1N77wiIkc4Y3gxAwu78ae310alvoN1AX7xwnJOHFjAxSf0jUqdIiLSNkp6RUSOkJpi3HBKGbPXVzF/4+6I67vnrTVs21vDdy4YhZlFHqCIiLSZkl4RkWZcMWkguZlp3Bthb++GnQf47WurOH9sHyaXFUYpOhERaSslvSIizcjJTOPKyQN5duHWdq/Q5u5898lFpKem8P2Lx0Q5QhERaYuETXrN7BYzczMrincsItI13XjGEDLTUvjFi8vbdf7j8zbz5opKbjl3OH3ys6IcnYiItEVCJr1mNhA4F9gQ71hEpOsqzs3k86eV8cyCrczdUNWmc9fv3M/3nljE5NJCPn1yaWwCFBGRVkvIpBf4NXArEJt1QEVEWunGM4fQNz+L//rHAmobAq06p7YhwFcfmU9qivHrK08iNUU3r4mIxFvCJb1mdimw2d0/iHcsIiI5mWn85LKxrNi+j1+9uOK45d2dWx9bwAcbd/PzT5xA/4JuHRCliIgcT1o8GjWzl4E+zRz6DvBtQkMbjlfHjcCNACUlJVGNT0SkqbNG9uaaKSX88c01DOudyycmDmi2XDDofO/JRTw5fwvf/OgIzhurOXlFRBJFXJJedz+nuf1mNg4oAz4Iz2U5AJhrZpPdfdsRddwF3AVQXl6uYRAiElM/uGQMa3fs55uPfcDeg/XccGrph+bc3bmvlv/8x0JeXrqdm84cwr9NGxLHaEVE5Ejmnrj5opmtA8rdfcexypWXl/vs2bM7JigR6bIO1gX46iPzeGnJdiYO6sHHxvenoFs68zbs5u+zN1LbEOTbF4zk+lPL4h2qiEjSMrM57l7e1vPi0tMrItIZdctI5Q/XTuSRWRv4wxur+d4TiwBISzHOG9uHr5w1jBF9cuMcpYiINCehk153L413DCIiTaWmGNdMGcTVk0vYvPsgB+oCDOjRjeyMhH47FRHp8vQuLSLSDmbGgB7Z8Q5DRERaKeGmLBMRERERiTYlvSIiIiKS9JT0ioiIiEjSU9IrIiIiIkkvoefpbS0zqwaWxzsOiZsi4JhzOUvS0rXv2nT9uy5d+65thLu3eX7IZJm9YXl7JimW5GBms3X9uyZd+65N17/r0rXv2sysXSuSaXiDiIiIiCQ9Jb0iIiIikvSSJem9K94BSFzp+ndduvZdm65/16Vr37W16/onxY1sIiIiIiLHkiw9vSIiIiIiLepUSa+ZnWdmy81slZn9VzPHM83sb+HjM82sNA5hSgy04tr/u5ktMbMFZvaKmQ2KR5wSG8e7/k3KfdzM3Mx0V3eSaM21N7Mrwv/+F5vZQx0do8ROK977S8zsNTObF37/vyAecUr0mdmfzKzCzBa1cNzM7I7w/xsLzGzC8ersNEmvmaUCvwPOB0YDV5nZ6COKfQ6ocvehwK+B/+3YKCUWWnnt5wHl7n4C8Bjw846NUmKlldcfM8sFvgbM7NgIJVZac+3NbBjwLeBUdx8DfL2j45TYaOW//e8Cj7r7eOBK4PcdG6XE0P3Aecc4fj4wLPy4EbjzeBV2mqQXmAyscvc17l4HPAJcekSZS4EHwq8fA842M+vAGCU2jnvt3f01dz8Q3pwBDOjgGCV2WvNvH+BHhP7QrenI4CSmWnPtvwD8zt2rANy9ooNjlNhpzfV3IC/8Oh/Y0oHxSQy5+5vArmMUuRT4s4fMAArMrO+x6uxMSW9/YGOT7U3hfc2WcfcGYA/Qs0Oik1hqzbVv6nPAczGNSDrSca9/+Gutge7+TEcGJjHXmn/7w4HhZvaOmc0ws2P1DEnn0prr/wPgWjPbBDwLfKVjQpME0NbcIGlWZBMBwMyuBcqBM+Mdi3QMM0sBfgVcH+dQJD7SCH29OY3QNzxvmtk4d98dz6Ckw1wF3O/uvzSzk4G/mNlYdw/GOzBJPJ2pp3czMLDJ9oDwvmbLmFkaoa86dnZIdBJLrbn2mNk5wHeAS9y9toNik9g73vXPBcYCr5vZOmAq8JRuZksKrfm3vwl4yt3r3X0tsIJQEiydX2uu/+eARwHc/T0gCyjqkOgk3lqVGzTVmZLeWcAwMyszswxCA9afOqLMU8Bnwq8/Abzqmog4GRz32pvZeOCPhBJejelLLse8/u6+x92L3L3U3UsJjem+xN3btTa7JJTWvO8/QaiXFzMrIjTcYU0Hxiix05rrvwE4G8DMRhFKeis7NEqJl6eAT4dncZgK7HH3rcc6odMMb3D3BjP7MvACkAr8yd0Xm9kPgdnu/hRwL6GvNlYRGvx8Zfwilmhp5bX/PyAH+Hv43sUN7n5J3IKWqGnl9Zck1Mpr/wJwrpktAQLAN91d3/AlgVZe/1uAu83sG4RuartenV3JwcweJvQHbVF4zPb3gXQAd/8DoTHcFwCrgAPADcetU/9viIiIiEiy60zDG0RERERE2kVJr4iIiIgkPSW9IiIiIpL0lPSKiIiISNJT0isiIiIiSU9Jr4hIDJlZTzObH35sM7PN4df7zOz3MWjvJjP7dLTrFRHp7DRlmYhIBzGzHwD73P0X8Y5FRKSrUU+viEgcmNk0M/tX+PUPzOwBM3vLzNab2eVm9nMzW2hmz5tZerjcRDN7w8zmmNkLZta3mXp/YGb/EX79upn9r5m9b2YrzOz0Zsr3NbM3w73Pi5orIyKSDJT0iogkhiHAWcAlwIPAa+4+DjgIXBhOfH8DfMLdJwJ/An7SinrT3H0y8HVCKxod6WrgBXc/CTgRmB/ZjyEikpg6zTLEIiJJ7jl3rzezhYSWXH0+vH8hUAqMAMYCL4WX2k4FjrnOfNg/w89zwvUcaRbwp3BS/YS7z29n/CIiCU09vSIiiaEWwN2DQL0fvuEiSKiDwoDF7n5S+DHO3c9tbb1AgGY6Otz9TeAMYDNwv26CE5FkpaRXRKRzWA4Um9nJAGaWbmZjIq3UzAYB2939buAeYEKkdYqIJCINbxAR6QTcvc7MPgHcYWb5hN6/bwMWR1j1NOCbZlYP7APU0ysiSUlTlomIiIhI0tPwBhERERFJekp6RURERCTpKekVERERkaSnpFdEREREkp6SXhERERFJekp6RURERCTpKekVERERkaSnpFdEREREkp6SXhERERFJekp6RURERCTpKekVERERkaSnpFdEJErMbJ+ZDY53HCIicjQlvSIiTZjZaWb2rpntMbNdZvaOmU1qzbnunuPua2IdY6Iys8+Y2Rwz22tmm8zs52aWFu+4RERASa+IyCFmlgf8C/gNUAj0B/4bqI1nXNFiZqkxbiIb+DpQBEwBzgb+I8Ztioi0ipJeEZHDhgO4+8PuHnD3g+7+orsvaCxgZp81s6VmVmVmL5jZoCbH3MyGhl9fYGZLzKzazDab2X+E908L94LeamYVZrbVzD4WLr8i3Lv87SZ1ZprZbWa2Jfy4zcwymxy/NVzHFjP7/BEx3G9md5rZs2a2H5huZhea2bxwb+xGM/tBk7pKw+ffED5WZWY3mdkkM1tgZrvN7Lct/fLc/U53f8vd69x9M/BX4NTIL4uISOSU9IqIHLYCCJjZA2Z2vpn1aHrQzC4Fvg1cDhQDbwEPt1DXvcAX3T0XGAu82uRYHyCLUE/y/wPuBq4FJgKnA98zs7Jw2e8AU4GTgBOBycB3w/GcB/w7cA4wFJjWTBxXAz8BcoG3gf3Ap4EC4ELgZjP72BHnTAGGAZ8CbgvHcA4wBrjCzM5s4Wc+0hnA4laWFRGJKSW9IiJh7r4XOA1wQolopZk9ZWa9w0VuAn7m7kvdvQH4KXBS097eJuqB0WaW5+5V7j73iGM/cfd64BFCwwFud/dqd18MLCGU4AJcA/zQ3SvcvZLQcIvrwseuAO5z98XufgD4QTNxPOnu77h70N1r3P11d18Y3l5AKGk/Mon9Ubjsi4SS5IfD7W8mlOiPP97v0sw+C5QDvzheWRGRjqCkV0SkiXBCe727DyDUQ9uPUG8nwCDg9vDX/LuBXYAR6rE90seBC4D1ZvaGmZ3c5NhOdw+EXx8MP29vcvwgkBN+3Q9Y3+TY+vC+xmMbmxxr+rrZfWY2xcxeM7NKM9tDKJEvOuKcI2NpKbZmhXuOfwac7+47jlVWRKSjKOkVEWmBuy8D7ieU/EIogfyiuxc0eXRz93ebOXeWu18K9AKeAB5tZxhbCCXbjUrC+wC2AgOaHBvY3I9xxPZDwFPAQHfPB/5AKHGPivCQi7uBi919YbTqFRGJlJJeEZEwMxtpZreY2YDw9kDgKmBGuMgfgG+Z2Zjw8Xwz+2Qz9WSY2TVmlh8ewrAXCLYzrIeB75pZsZkVERoD/GD42KPADWY2ysyyge+1or5cYJe715jZZEJjfqPCzM4idPPax939/WjVKyISDUp6RUQOqyZ0E9fM8GwHM4BFwC0A7v448L/AI2a2N3zs/Bbqug5YFy53E6Gxue3xY2A2sABYCMwN78PdnwPuAF4DVnE4OT/WFGv/BvzQzKoJJdDt7YFuzveAfODZ8EId+8zsuSjWLyLSbuZ+5DdfIiLSGZnZKEKJeGb4RjsREQlTT6+ISCdmZpeF5/LtQagX+mklvCIiR1PSKyLSuX0RqABWAwHg5viGIyKSmDS8QURERESSnnp6RURERCTpKekVERERkaSXFu8AoqGoqMhLS0vjHYaIiIiIxNicOXN2uHtxW89LiqS3tLSU2bNnxzsMEREREYkxM1t//FJH0/AGEREREUl6SnpFREREJOkp6RURERGRpJcUY3pFIhEMOou37GXuhioqqmvIzkhjaK8cThtaRPdM/RMRERFJBvpEly6rPhDk77M3cc/ba1hTuR+A1BQjEAwt2JKdkcr1p5TypelDlfyKiIh0cvokly5p3oYqvvXPhSzbVs24/vn83ydO4PRhxfTKzaS2Ici8jVU8NHMDd76xmucXb+POayYyok9uvMMWERGRdkqKZYjLy8tdU5ZJa7g797+7jh8/s5TinEz++9IxnDu6N2bWbPn3Vu/ka4/Mo7YhyF8+N5kTBhR0bMBdkHtouMnsdbuo3FdLemoKJYXZTB3ck34F3eIdnoiIxJmZzXH38jafp6RXuor6QJBv/XMhj83ZxDmjevOrT51IXlb6cc/buOsAV909g321DTz5pVMZ1LN7B0TbNT2/aBu3vbyCZduqgQ8PNwE4ZUhPPntqGWeP6tXiHyoiIpLclPQq6ZVjqG0I8KW/zuPlpdv56tnD+PrZw0hJaX3StH7nfi757Tv0ys3kyS+fSnaGRgZF04G6Bm59bAH/WrCVYb1y+NxpZZw5opg+eVnUB5zVlft4dVkFf52xni17apg6uJAfXjqW4b015EREpKtR0qukV1pQUx/gxr/M4c0Vlfzo0jFcd3Jpu+p5e+UOrr13Jp8+eRA/vHRsdIPswnYfqOPTf3qfRZv3cMu5I7jxjMGkpzY/m2JDIMjDszbyyxeXc7AuwHcvGs21U0rU6ysi0oW0N+nVPL2S1BoCQW56cA5vrazk5x8/od0JL8Bpw4r47Kll/Pm99by7akf0guzC9tc2cP19s1i2rZq7rivnS9OHtpjwAqSlpnDd1EG89I0zmTK4J997YhFffmgeB+sCHRi1iIh0Rkp6JWm5O997cjGvL6/kJx8bxxWTBkZc563njaCkMJvvP7WY+kAwClF2Xe7Of/5jAQs27eY3V43nnNG9W31ucW4m918/if88byTPLtrKVXfPYMe+2hhGKyIinZ2SXklaf3xzDQ+/v4F/mzaEq6eURKXOrPRUvnvhKFZW7OPBGeujUmdX9eDMDfxrwVb+46Mj+OiYPm0+PyXFuHnaEO68ZiLLtu3l8t+/y6aqAzGIVEREkoGSXklKzyzYyv88t4yLTujLf5w7Iqp1f2R0b04Z0pPfvbZKX6u306aqA/z0maWcObyYm84YElFd543tw8NfmMruA3V86o8zWL9zf5SiFBGRZKKkV5LOnPW7+Maj8ykf1INffPLENs3S0Bpmxjc+Mpwd++r460z19raVu/P/nlwMwE8uGxuV6zO+pAcPfWEqB+oa+NQfZ7C6cl/EdYqISHJR0itJZd2O/Xz+gdn0L+jGXZ8uJys9NSbtTCot5JQhPfnjm2uoqVdvb1u8sHg7ry6r4JZzhzOgR3bU6h3bP5+Hb5xKQzDIlXfNYN0O9fiKiMhhSnolaVTtr+OG+2cBcN/1kyjsnhHT9r569jAqq2v5++yNMW0nmQSCzi9eXM6Q4u5cf0pp1Osf2SePh78wlUDQueaemWzefTDqbYiISOcUt6TXzAaa2WtmtsTMFpvZ18L7f2Bmm81sfvhxQbxilM4jNBfvbDbvPsg9nymntCj2q6ZNKSvkhAH5PPDeepJhvuuO8MS8zayq2Mct544g7RhTk0ViWO9c/vzZyeytqeeau2dQsbcmJu2IiEjnEs+e3gbgFncfDUwFvmRmo8PHfu3uJ4Ufz8YvROkMgkHnm48tYNa6Kn51xYlMHFTYIe2aGZ8+uZRVFft4d/XODmmzM6sPBPn1yysY2z+P89oxW0NbjO2fz/03TKKiupZr751J1f66mLYnIiKJL25Jr7tvdfe54dfVwFKgf7zikc7rFy8u5+kPtvBf54/kohP6dWjbF53Ql8LuGTzw7roObbczembBVjZVHeQb5wyP+s2FzZk4qJB7Pl3Oup0H+PSf3mdvTX3M2xQRkcSVEGN6zawUGA/MDO/6spktMLM/mVmP+EUmie7h9zfw+9dXc9XkEr54xuAObz8rPZUrJw3k5aXb2bpH40db4u7c8/YahhR3Z/qIXh3W7ilDi/jDtRNYunUvn71vFgfqGjqsbRERSSxxT3rNLAf4B/B1d98L3AkMAU4CtgK/bOG8G81stpnNrqys7KhwJYG8saKS7z6xiDOHF/OjS8dgFvvew+Z8atJAgg6Pz9scl/Y7gxlrdrFo814+f/rgDunlbeqskb25/crxzN1QxecfmK3ZNkREuqi4Jr1mlk4o4f2ru/8TwN23u3vA3YPA3cDk5s5197vcvdzdy4uLizsuaEkIizbv4eYH5zC8dy6/vXp8zG6Kao1BPbszqbQH/5izSTe0teDet9fSs3sGl42PzwimC0/oyy8+eSLvrdnJF/6sxFdEpCuK5+wNBtwLLHX3XzXZ37dJscuARR0dmyS2jbsOcP19s+iRncH9N0wiNys93iHx8QkDWF25nw827Yl3KAln254aXl22nU9NGhizeZNb4/IJA/jfy0/grZU7+Le/zqWuIRi3WEREpOPFs6f3VOA64Kwjpif7uZktNLMFwHTgG3GMURLMrv11fOZP71MfCPLAZyfROy8r3iEBcMEJfclMS+EfczbFO5SE8/fZGwl6aBhIvF0xaSA/vWwcry6r4MsPzaU+oMRXRKSrSItXw+7+NtDc4D5NUSbNOlgX4PMPzGLz7oP89fNTGNorN94hHZKXlc5HRvfm2YVb+f7Fo+M63CKRBIPO32Zv5NShPRnUM/ZzJ7fG1VNKqA8E+f5Ti/n6I/O57cqTSNf1EhFJenqnl06hpj7AF/48m/kbd3P7lSdRXtoxc/G2xYXj+rJzfx3vr9sV71ASxrurd7Kp6iCfmlQS71A+5DOnlPLdC0fxzMKt3PzgHI3xFRHpApT0SsKrbQhw84NzeGf1Dv7vEydy3ti+xz8pDqaN6EW39FSeW7gt3qEkjH/O3UReVhrnju4d71CO8vnTB/Ojj43llWUVXH/f++yr1XRmIiLJTEmvJLS6hiBfeWgery2v5CcfG8fHJw6Id0gt6paRyvSRxTy/eBuBoGZxqKkP8OKS7Zw/tm9cb2A7luumDuLXV5zErHVVXHP3DHZp5TYRkaSlpFcS1sG6ADf+ZTYvLtnODy4ezdVTEusr8uacP7YvldW1zFlfFe9Q4u61ZRXsq23g4hM7dpW8tvrY+P788dqJLN1WzeW/f4fVlfviHZKIiMSAkl5JSHsO1nPdvTN5Y0UlP7t8HNefWhbvkFpl+sheZKSl8OzCrfEOJe6eXrCFopxMTh7SM96hHNc5o3vz0OenUF3TwGW/e4d3Vu2Id0giIhJlSnol4Wzdc5Ar75rBB5t289urJnDV5MTv4W2Uk5nGmcOLeWHxti69UMW+2gZeWVrBheP6kNrBK7C1V3lpIU986VT65GfxmT+9z33vrO3S11BEJNko6ZWEMndDFZf89h027NzPvZ+ZxIUnJOZNa8fykVG92bqnhqVbq+MdSty8tGQbtQ3BhB/acKSBhdn84+ZTmDaimP9+egk3PTiHPQfr4x2WiIhEgZJeSQjuzqOzNnLlH2fQLT2Vx790KmcM75zLS08bGYr7teUVcY4kfp5ZsI1++VlMKOkR71DaLDcrnbs/Xc53LhgV6q2+4y1maRo6EZFOT0mvxN3emnq+9sh8bv3HAiYO6sGTXzqV4b0TZ+GJtuqVm8W4/vm8uqxrJr0H6wK8vaqSj4zuTUonGdpwJDPjC2cM5tGbTgbgij++x/efXKRpzUREOjElvRJX767ewYV3vMUzC7dyy0eG8+Dnp9Cje0a8w4rYWSN7MW9DFVVdcAqst1ZWUlMf5COj+8Q7lIhNKOnBC18/g8+cXMqfZ6zno79+k+cXbdVYXxGRTkhJr8TFrv113PLoB1x990xSzHj0iyfzlbOHdZqbno7nrJG9CDq8saIy3qF0uJeXbic3K40pgxNv1bz26J6Zxg8uGcNjN51M98xUbnpwLp/8w3vM26Bp6UREOhMlvdKhDtYFuPP11Uz7v9d4cv5mvjR9CC98/QwmDup8Yz+PZVz/fIpyMrvcEIdA0HllaQXTRvQiPTW53l4mDirk2a+ezs8uH8e6nQe47PfvcsN97zNzzU71/IqIdAJp8Q5Auobqmnr+Nmsjd7+1hu17a5k+opj/On8UI/p03rG7x5KSYkwbUcxLS7bTEAiSlmQJYEvmbahi5/46PpKAyw5HQ1pqCldNLuGSE/tx3ztrue+ddXzqrhmMLyng0ycP4rwxfemWkZirz4mIdHVKeiWmVlXs42+zNvDI+xuprm1gcmkhd1w5nimDE3/BgkidObyYx+ZsYtGWvZw0sCDe4XSIl5ZsJz01lPAns+6ZaXz5rGF8/vTB/H3OJu59aw3f+NsH/L/MxVx0Yj8uOqEvk8sKk663W0SkM1PSK1G3cdcBXlqynSfmb2bBpj2kphgXjOvL508r48QukvwBnDq0CIB3Vu3oMknvq8sqmFLWk7ys9HiH0iGy0lO5buogrp1Swsy1u3h09kYen7eJh9/fQF5WGtNH9mL6iF5MLiukX0G3eIcrItKlJWzSa2bnAbcDqcA97v4/cQ5JWrBtTw3zNlQxa10Vb6yoYHXlfgDG9s/juxeO4pIT+9ErLyvOUXa8wu4ZjOmXx1srK/nS9KHxDifmtuw+yMqKfVxRPjDeoXQ4M2Pq4J5MHdyTH39sLG+t3MFLS7bzytLtPDl/CwD9C7oxqbQHY/rlM7JvLqP65lGUkxnnyEVEuo6ETHrNLBX4HfARYBMwy8yecvcl8Y2s6woEnZ37atlYdZDVlftCj4p9LN6yl617agDISEthSlkhV08ZxPQRxQwuzolz1PF32tAi7ntnHQfqGsjOSMh/blHzZnimis66qEi0ZGek8dExffjomD4Egs6ybXt5f+0u3l+7i/fW7OSJcBIMoT+MBhZmM7BHN0oKsxlYmE2fvCwKu2fQMyeDnt0zNUZYRCRKEvVTeDKwyt3XAJjZI8ClQEyT3qZ3YDe+9JaOH1EutO/o8z9c/9FlP7zv6LY+vI+jCrfU5uH4nIagU9cQpLYhSF1DkPpAkLpA6HXj/n21Dew9WE91TQN7a+qprqlnz8F6tu+tZfveGiqqawkEDzeQkZpCaVE25aWFjB9YwPiSAkb3yyMzTR/QTZ06tIg/vrmGWeuqODPJk8E3V1bSJy+L4b31x06j1BRjTL98xvTL54ZTywDYua+WZduqWbp1L6sq9rGx6gALNu3h+UXbaAge/caRnZFKj+wMcrPSyM5IpXtmGjmZaYeeu2Wkkp6aQmZaCumpRnpqChlpKU32hR5pqUaKGSkGqWaYGakpoe2UlNCx0H7C+43UlFAvdqqFtq3JjIJmoWMAFt4OvbYmrxv/c3i/HTrfmrwOHW88qen5LZU7KhbaNt2htXF2xLZOpmhtaKDtdccuFpFklqhJb39gY5PtTcCUlgov2ryHYd95tsWk78h9of3RCDM5dc9IJTcrnbxuafTKzWLIkCL65GfSJy+LfgXdGFKcw4Ae3brMjASRmFRaSEZqCu+s2pHUSW9DIMhbK3dw/tg++oA9jp45mZw6NPPQmO9GDYEg28J/YO7aV8fO/bXs2FfHrv2hx/7aBvbXNbC3poFte2rC2wEO1gWoCwTj9NOItC0Jj+UfD+2pPxl0tbfc0p7d231uoia9x2VmNwI3AvToX8YXTh8c3t+kTHM9Ah+u5Kh9bTn/UG9EC//HNe35aL7+lts8cv/hfU2OH6POpmVDPT1GRlqo5ycjLYWM1NRD+zLSUsjNDCW5OZlpSmajqFtGKhMH9eDtlTviHUpMfbBpN9U1DV1+aEMk0lJTGNAjmwE9stt8rrtTH/DQtzhHfJtTHwh909MQDBJ0J+ih4UpBd7zJ66A7wSAE3HF3AkEO7w9vf+gbqCbfNjX9Zsxb2I/7h74ha1rXh8//8LdXTb8V8xb2t+f31bbybaw/pnUnTuxtbaCtdcf6d5MMumIHXo/sDF5u57nHTXrNLBu4BShx9y+Y2TBghLv/q51ttsZmoOndMAPC+w5x97uAuwDKy8v91vNGxjAckfY7bVgR//fCcnbsq03aG5feWLGDFAuNYZaOZ2ZkpIX+iO2enP+LiYgccmM7z2tNl959QC1wcnh7M/DjdrbXWrOAYWZWZmYZwJXAUzFuUyQmGhPBd1fvjHMksfPGikpOHFhAQXZGvEMRERFpVmuS3iHu/nOgHsDdDxDjYTPu3gB8GXgBWAo86u6LY9mmSKyM7Z9PXlYa765KziEOVfvrWLBpN2cM09AGERFJXK0Z01tnZt0ID8cxsyGEen5jyt2fBZ6NdTsisZaaYkwuK2Tm2l3xDiUm3l29E3c4Y7iGNoiISOJqTU/v94HngYFm9lfgFeDWmEYlkmSmlPVk7Y79VOytiXcoUTdz7U6yM1I5YUBBvEMRERFp0XGTXnd/CbgcuB54GCh399djG5ZIcpkyuBCAGUnY2ztzzS4mDupBumb9EBGRBNbip5SZTWh8AIOArcAWoCS8T0RaaXTfPHIy05i5JrluZtu1v47l26uZOrhnvEMRERE5pmON6f1l+DkLKAc+IHQD2wnAbA7P5iAix5GWmkJ5aY+kG9f7/tpQEj+lrDDOkYiIiBxbiz297j7d3acT6uGd4O7l7j4RGM8Rc+aKyPFNKevJqop9VFbH/D7QDjNjzS6y0lM0nldERBJeawbhjXD3hY0b7r4IGBW7kESSU+O43veTqLd35tpdTCjpQUaaxvOKiEhia80n1QIzu8fMpoUfdwMLYh2YSLIZ1z+f7IxUZq5NjnG9ew7Us2zbXqaUaTyviIgkvtbM03sDcDPwtfD2m8CdMYtIJEmlp6YwcVAPZq5Jjp7e99ftwh2mDtZ4XhERSXzHTXrdvQb4dfghIhGYUlbIL15cwa79dRR279xL9s5Ys5OMtBROHFgQ71BERESO67hJr5mtJbwaW1PuPjgmEYkksSnhqb3eX7uL88b2iXM0kZm5difjBxaQlZ4a71BERESOqzXDG8qbvM4CPgno+0yRdjhhQD5Z6SnMXLuzUye9e2vqWbJlL18+a1i8QxEREWmV1qzItrPJY7O73wZcGPvQRJJPZloq4wf2YNa6zj2ud/a6XQQdpmp+XhER6SRaM7yh6eprKYR6flvTQywizZhUVshvX11JdU09uVnp8Q6nXWau2UV6qjG+pEe8QxEREWmV1iSvv2zyugFYC1wRm3BEkt+UskLucJizvoppI3rFO5x2mbF2FycNLKBbhsbziohI59CapPdz7r6m6Q4zK4tRPCJJb3xJAWkpxqx1uzpl0ruvtoFFm/dw85lD4h2KiIhIq7VmcYrHWrmv1czs/8xsmZktMLPHzawgvL/UzA6a2fzw4w+RtCOSiLIz0hjbP7/Trsw2e90uAkE/tMKciIhIZ9BiT6+ZjQTGAPlmdnmTQ3mEZnGIxEvAt9y9wcz+F/gW8J/hY6vd/aQI6xdJaJPLCrn/nXXU1Ac63ZRfM9fuIi3FmDhI43lFRKTzOFZP7wjgIqAAuLjJYwLwhUgadfcX3b0hvDkDGBBJfSKdzeTSQuoCQT7YuDveobTZzDU7GTcgn+wM3c8qIiKdR4ufWu7+JPCkmZ3s7u/FMIbPAn9rsl1mZvOAvcB33f2tGLYtEhflpaFe0vfX7jq0YEVncKCugQWb9vD507U2jYiIdC7HGt5wq7v/HLjazK468ri7f/VYFZvZy0Bzs+9/J5xQY2bfITQjxF/Dx7YCJe6+08wmAk+Y2Rh339tM/TcCNwKUlJQcKxSRhFOQncHIPrm838nm6527fjcNQWeqxvOKiEgnc6zvJ5eGn2e3p2J3P+dYx83sekLDJ852dw+fUwvUhl/PMbPVwPDmYnD3u4C7AMrLy49aJlkk0U0qLeSfczfREAiSltqae0rjb+banaSmGOWlSnpFRKRzOdbwhqfDzw9Eu1EzOw+4FTjT3Q802V8M7HL3gJkNBoYBa1qoRqRTm1xWyF9mrGfJ1r2cMKAg3uG0yow1OxnbL4+cTI3nFRGRzuVYwxueBlrsQXX3SyJo97dAJvCSmQHMcPebgDOAH5pZPRAEbnL3zvX9r0grTQ4v4fv+2l2dIumtqQ/wwcY9XH9qabxDERERabNjddf8IlaNuvvQFvb/A/hHrNoVSSS987IY1DOb99fu6hQ3hs3dUEVdIMiUMg1tEBGRzudYwxveaHxtZhnASEI9v8vdva4DYhNJepNKC3ll6XaCQSclxeIdzjHNXLMLMzSeV0REOqXj3j1jZhcCq4E7CA1LWGVm58c6MJGuYHJZIVUH6llduS/eoRzXzLU7Gd03j/xu6fEORUREpM1ac8v4L4Hp7j7N3c8EpgO/jm1YIl3D5HCv6cwEX5K4pj7AvA27mdqJ5hQWERFpqjVJb7W7r2qyvQaojlE8Il3KoJ7Z9MrNZFaCz9f7wcbd1DZoPK+IiHRerZl3aLaZPQs8SmhM7yeBWWZ2OYC7/zOG8YkkNTNjclkhM9fswt0Jz2aScGauDY3nnaykV0REOqnW9PRmAduBM4FpQCXQDbiY0OISIhKByWWFbNtbw6aqg/EOpUUz1+5kRO9cCrIz4h2KiIhIuxy3p9fdb+iIQES6qqbz9Q4szI5zNEerawgyZ30VV07Sct8iItJ5HTfpNbMy4CtAadPyES5OISJhw3vlkt8tnffX7uLjEwfEO5yjLNy8m5p6jecVEZHOrTVjep8A7gWeJrRKmohEUUqKMam0R8LezDZjTSgujecVEZHOrDVJb4273xHzSES6sEmlhby8tIKK6hp65WbFO5wPmbl2F8N759AzJzPeoYiIiLRba25ku93Mvm9mJ5vZhMZHzCMT6UIae1Fnr6uKcyQfVh8IMnvdLqaUaX5eERHp3FrT0zsOuA44i8PDGzy8LSJRMLZ/Pt3SU3l/7S4uGNc33uEcsmjzHg7UBZgyWEMbRESkc2tN0vtJYLC718U6GJGuKj01hQmDCng/wVZma1wpTuN5RUSks2vN8IZFQEGM4xDp8iaVFrJ02172HKyPdyiHzFyzk8HF3RNunLGIiEhbtaantwBYZmazgNrwPnf3S2MWlUgXNLmsEHeYu76K6SN7xTscGgJBZq2r4tKT+sU7FBERkYi1pqf3+8BlwE+BXwGzgKGRNGpmPzCzzWY2P/y4oMmxb5nZKjNbbmYfjaQdkc5k/MAepKfaoSEF8bZoy1721TYwdbBuYhMRkc6vNSuyvWFm44GrCY3vXQv8IQpt/9rdf9F0h5mNBq4ExgD9gJfNbLi7B6LQnkhC65aRyrj++QkzX++MNTsBlPSKiEhSaLGn18yGh6cqWwb8BtgAmLtPd/ffxCieS4FH3L3W3dcCq4DJMWpLJOFMKitkwabd1NTH/++8GWt2MrRXDsW5mp9XREQ6v2MNb1hGaFqyi9z9tHCiG81P4i+b2QIz+5OZ9Qjv6w9sbFJmU3ifSJcwpayQ+oAzb8PuuMZRHwgya+0upmqqMhERSRLHSnovB7YCr5nZ3WZ2NmCtrdjMXjazRc08LgXuBIYAJ4Xb+GVbAzezG81stpnNrqysbOvpIglp4qBCzIj71GWLNu9hf12AkwcXxTUOERGRaGlxTK+7PwE8YWbdCQ07+DrQy8zuBB539xePVbG7n9OaAMzsbuBf4c3NwMAmhweE9zVX/13AXQDl5eXemrZEEl1+t3RG9snj/XU7gWFxi2PGmlDSrUUpREQkWRx39gZ33+/uD7n7xYSS0HnAf0bSqJk1XXLqMkJzAQM8BVxpZplmVkboU//9SNoS6WymlBUyd/1u6gPB4xeOkffW7GRYrxyKcjSeV0REkkNrpiw7xN2r3P0udz87wnZ/bmYLzWwBMB34Rrj+xcCjwBLgeeBLmrlBuppJpYUcrA+waPOeuLRfHwgye90uTh6iWRtERCR5tGZxiqhz9+uOcewnwE86MByRhDKpLHRf56x1uxhf0uM4paNv4eY9HKgLaKoyERFJKm3q6RWR2OuVm0VZUfe43cz23urQ/LxTyjSeV0REkoeSXpEENLm0kFnrqggGO/4ezXdX72Bkn1x6ajyviIgkESW9IgloUlkhew7Ws6KiukPbPVgXYNbaKk4fpqnKREQkuSjpFUlAjUMLZnXwEIeZa3dSFwhy+rDiDm1XREQk1pT0iiSgAT260ScvixkdnPS+tXIHGWkpTNZ4XhERSTJKekUSkJlx8pCevLd6Z4eO631rZSVTygrJSk/tsDZFREQ6gpJekQR1xvAidu2vY9GWjpmvd9ueGlZs38dpQzWeV0REko+SXpEE1Tiu9s0VlR3S3lsrKz/UroiISDJR0iuSoIpyMhnbP483V+zokPbeXrWDopxMRvbJ7ZD2REREOpKSXpEEdsawYuZuqKK6pj6m7QSDztsrd3D6sCJSUiymbYmIiMSDkl6RBHbG8GIags674VXSYmXRlj3s3F+n+XlFRCRpKekVSWATSnrQPSOVN2I8rvflpRWkGEwb0Sum7YiIiMSLkl6RBJaRlsIpQ4t4Y3kl7rGbuuzlJdspH1RIYfeMmLUhIiIST0p6RRLcOaN6sXn3QZZs3RuT+hvrPme0enlFRCR5KekVSXDnjOpNisGLi7fHpP5Xlm4/1I6IiEiyikvSa2Z/M7P54cc6M5sf3l9qZgebHPtDPOITSSQ9czIpH1TIi0tik/S+tGQ7g4u7M7g4Jyb1i4iIJIK0eDTq7p9qfG1mvwSaLjm12t1P6vCgRBLYuWN68+NnlrJx1wEGFmZHrd7qmnpmrNnJZ08ti1qdIiIiiSiuwxvMzIArgIfjGYdIovvI6NDQg2j39r66rIL6gHPOaA1tEBGR5BbvMb2nA9vdfWWTfWVmNs/M3jCz0+MVmEgiGdSzOyP75PLi4m1RrffpD7bQJy+LiSU9olqviIhIoolZ0mtmL5vZomYelzYpdhUf7uXdCpS4+3jg34GHzCyvhfpvNLPZZja7sjK2c5iKJIJzx/Rh1rpdVOytiUp9uw/U8caKSi46oa9WYRMRkaQXs6TX3c9x97HNPJ4EMLM04HLgb03OqXX3neHXc4DVwPAW6r/L3cvdvby4uDhWP4ZIwrj0pH4EHZ76YEtU6nth8TbqA84lJ/WLSn0iIiKJLJ7DG84Blrn7psYdZlZsZqnh14OBYcCaOMUnklCGFOdw4oB8Hp+3OSr1PfXBFkp7ZjOuf35U6hMREUlk8Ux6r+ToG9jOABaEpzB7DLjJ3Xd1dGAiiepj4/uzeMteVmyvjqieTVUHeHf1Ti45sR+h+0lFRESSW9ySXne/3t3/cMS+f7j7GHc/yd0nuPvT8YpPJBFdfGI/UlOMf86NrLf3b7M2AnDFpIHRCEtERCThxXv2BhFpg6KcTKaP6MXfZ2+ktiHQrjrqA0H+Nmsj04YXM6BH9Ob8FRERSWRKekU6metPKWXn/jr+9cHWdp3/ytIKKqpruXrKoChHJiIikriU9Ip0MqcO7cnQXjnc/+463L3N5//5vXX0ycti+gjNeiIiIl2Hkl6RTsbMuP6UUhZu3sPs9VVtOnfehireXb2Tz51WRlqq/vmLiEjXoU89kU7o8gn96dk9g9tfXnn8wk387rXV5HdL5+opJTGKTEREJDEp6RXphLIz0rh52hDeXrWDd1fvaNU5s9bt4uWl2/ncaWV0z0yLcYQiIiKJRUmvSCd17dRB9M3P4sf/Wkp9IHjMssGg899PL6ZvfhafP72sgyIUERFJHEp6RTqprPRUvn/xGJZs3cvdbx174cK731rDos17+a/zR5KdoV5eERHpepT0inRi543twwXj+vDrl1bw3uqdzZaZu6GK/3thOeeP7cMlJ/br4AhFREQSg5JekU7uZ5efwKCe3fniX2Yz54jZHOZtqOKz98+iX0E3/ufyE7TksIiIdFn6nlOkk8vvls5910/i2ntn8qk/vscnJg7ghAEFLNy8m8fmbKJPfhYPfm4K+dnp8Q5VREQkbqw9k9snmvLycp89e3a8wxCJq90H6vi/F5bzz7mbOVgfIDMthcsn9Oc/zxtJQXZGvMMTERGJCjOb4+7lbT5PSa9IcqmpD7Bzfx1FORlkpqXGOxwREZGoam/Sq+ENIkkmKz2V/gXd4h2GiIhIQtGNbCIiIiKS9JT0ioiIiEjSU9IrIiIiIklPSa+IiIiIJL2kmL3BzKqB5fGOQ+KmCNgR7yAkLnTtuzZd/65L175rG+HuuW09KVlmb1jenqkrJDmY2Wxd/65J175r0/XvunTtuzYza9c8tRreICIiIiJJT0mviIiIiCS9ZEl674p3ABJXuv5dl65916br33Xp2ndt7br+SXEjm4iIiIjIsSRLT6+IiIiISIs6VdJrZueZ2XIzW2Vm/9XM8Uwz+1v4+EwzK41DmBIDrbj2/25mS8xsgZm9YmaD4hGnxMbxrn+Tch83Mzcz3dWdJFpz7c3sivC//8Vm9lBHxyix04r3/hIze83M5oXf/y+IR5wSfWb2JzOrMLNFLRw3M7sj/P/GAjObcLw6O03Sa2apwO+A84HRwFVmNvqIYp8Dqtx9KPBr4H87NkqJhVZe+3lAubufADwG/Lxjo5RYaeX1x8xyga8BMzs2QomV1lx7MxsGfAs41d3HAF/v6DglNlr5b/+7wKPuPh64Evh9x0YpMXQ/cN4xjp8PDAs/bgTuPF6FnSbpBSYDq9x9jbvXAY8Alx5R5lLggfDrx4Czzcw6MEaJjeNee3d/zd0PhDdnAAM6OEaJndb82wf4EaE/dGs6MjiJqdZc+y8Av3P3KgB3r+jgGCV2WnP9HcgLv84HtnRgfBJD7v4msOsYRS4F/uwhM4ACM+t7rDo7U9LbH9jYZHtTeF+zZdy9AdgD9OyQ6CSWWnPtm/oc8FxMI5KOdNzrH/5aa6C7P9ORgUnMtebf/nBguJm9Y2YzzOxYPUPSubTm+v8AuNbMNgHPAl/pmNAkAbQ1N0iaFdlEADCza4Fy4Mx4xyIdw8xSgF8B18c5FImPNEJfb04j9A3Pm2Y2zt13xzMo6TBXAfe7+y/N7GTgL2Y21t2D8Q5MEk9n6undDAxssj0gvK/ZMmaWRuirjp0dEp3EUmuuPWZ2DvAd4BJ3r+2g2CT2jnf9c4GxwOtmtg6YCjylm9mSQmv+7W8CnnL3endfC6wglARL59ea6/854FEAd38PyAKKOiQ6ibdW5QZNdaakdxYwzMzKzCyD0ID1p44o8xTwmfDrTwCvuiYiTgbHvfZmNh74I6GEV2P6kssxr7+773H3IncvdfdSQmO6L3H3dq3NLgmlNe/7TxDq5cXMiggNd1jTgTFK7LTm+m8AzgYws1GEkt7KDo1S4uUp4NPhWRymAnvcfeuxTug0wxvcvcHMvgy8AKQCf3L3xWb2Q2C2uz8F3Evoq41VhAY/Xxm/iCVaWnnt/w/IAf4evndxg7tfEregJWpaef0lCbXy2r8AnGtmS4AA8E131zd8SaCV1/8W4G4z+wahm9quV2dXcjCzhwn9QVsUHrP9fSAdwN3/QGgM9wXAKuAAcMNx69T/GyIiIiKS7DrT8AYRERERkXZR0isiIiIiSU9Jr4iIiIgkPSW9IiIiIpL0lPSKiIiISNJT0isiEkNm1tPM5ocf28xsc/j1PjP7fQzau8nMPh3tekVEOjtNWSYi0kHM7AfAPnf/RbxjERHpatTTKyISB2Y2zcz+FX79AzN7wMzeMrP1Zna5mf3czBaa2fNmlh4uN9HM3jCzOWb2gpn1babeH5jZf4Rfv25m/2tm75vZCjM7vZnyfc3szXDv86LmyoiIJAMlvSIiiWEIcBZwCfAg8Jq7jwMOAheGE9/fAJ9w94nAn4CftKLeNHefDHyd0IpGR7oaeMHdTwJOBOZH9mOIiCSmTrMMsYhIknvO3evNbCGhJVefD+9fCJQCI4CxwEvhpbZTgWOuMx/2z/DznHA9R5oF/CmcVD/h7vPbGb+ISEJTT6+ISGKoBXD3IFDvh2+4CBLqoDBgsbufFH6Mc/dzW1svEKCZjg53fxM4A9gM3K+b4EQkWSnpFRHpHJYDxWZ2MoCZpZvZmEgrNbNBwHZ3vxu4B5gQaZ0iIolIwxtERDoBd68zs08Ad5hZPqH379uAxRFWPQ34ppnVA/sA9fSKSFLSlGUiIiIikvQ0vEFEREREkp6SXhERERFJekp6RURERCTpKekVERERkaSnpFdEREREkp6SXhERERFJekp6RURERCTpKekVERERkaSnpFdEREREkp6SXhERERFJekp6RURERCTpKekVEYkSM9tnZoPjHYeIiBxNSa+ISBNmdpqZvWtme8xsl5m9Y2aTWnOuu+e4+5pYx5iozOxKM1se/t1VmNkDZpYX77hEREBJr4jIIeEE7V/Ab4BCoD/w30BtPOOKFjNLjXET7wCnuns+MBhIA34c4zZFRFpFSa+IyGHDAdz9YXcPuPtBd3/R3Rc0FjCzz5rZUjOrMrMXzGxQk2NuZkPDry8wsyVmVm1mm83sP8L7p5nZJjO7NdwbutXMPhYuvyLcu/ztJnVmmtltZrYl/LjNzDKbHL81XMcWM/v8ETHcb2Z3mtmzZrYfmG5mF5rZPDPba2YbzewHTeoqDZ9/Q/hYlZndZGaTzGyBme02s9+29Mtz943uvqPJrgAwtP2XQ0QkepT0iogctgIIhL+WP9/MejQ9aGaXAt8GLgeKgbeAh1uo617gi+6eC4wFXm1yrA+QRagn+f8BdwPXAhOB04HvmVlZuOx3gKnAScCJwGTgu+F4zgP+HTiHUHI5rZk4rgZ+AuQCbwP7gU8DBcCFwM1m9rEjzpkCDAM+BdwWjuEcYAxwhZmd2cLP3Dg8ZA9QDXw8fL6ISNwp6RURCXP3vcBpgBNKRCvN7Ckz6x0uchPwM3df6u4NwE+Bk5r29jZRD4w2szx3r3L3uUcc+4m71wOPAEXA7e5e7e6LgSWEElyAa4AfunuFu1cSGm5xXfjYFcB97r7Y3Q8AP2gmjifd/R13D7p7jbu/7u4Lw9sLCCXtRyaxPwqXfZFQkvxwuP3NhBL98cf4Hb4dHt4wAPg/YF1LZUVEOpKSXhGRJsIJ7fXuPoBQD20/DvdWDgJuD3/NvxvYBRihHtsjfRy4AFhvZm+Y2clNju1090D49cHw8/Ymxw8COeHX/YD1TY6tD+9rPLaxybGmr5vdZ2ZTzOw1M6sM98jeRCjpburIWFqKrUXhBPl5Qkm9iEjcKekVEWmBuy8D7ieU/EIogfyiuxc0eXRz93ebOXeWu18K9AKeAB5tZxhbCCXbjUrC+wC2EupRbTSwuR/jiO2HgKeAgeEe2T8QStxjIQ0YEqO6RUTaREmviEiYmY00s1vMbEB4eyBwFTAjXOQPwLfMbEz4eL6ZfbKZejLM7Bozyw8PYdgLBNsZ1sPAd82s2MyKCI0BfjB87FHgBjMbZWbZwPdaUV8usMvda8xsMqExv1ER/plLwq8HERpL/Eq06hcRiYSSXhGRw6oJ3cQ1MzzbwQxgEXALgLs/Dvwv8IiZ7Q0fO7+Fuq4D1oXL3URobG57/BiYDSwAFgJzw/tw9+eAO4DXgFUcTs6PNcXavwE/NLNqQgl0e3ugmzMaeDf8u3sHWA58IYr1i4i0m7kf+c2XiIh0RmY2ilAinhm+0U5ERMLU0ysi0omZ2WXhuXx7EOqFfloJr4jI0ZT0ioh0bl8EKoDVhBaDuDm+4YiIJCYNbxARERGRpKeeXhERERFJekp6RURERCTppcU7gJaYWQFwD6FJ4R34rLu/11zZoqIiLy0t7bjgRERERCQu5syZs8Pdi9t6XsImvcDtwPPu/gkzywCyWypYWlrK7NmzOy4yEREREYkLM1t//FJHS8ik18zygTOA6wHcvQ6oi2dMIiIiItJ5JeqY3jKgErjPzOaZ2T1m1r1pATO70cxmm9nsysrK+EQpIiIiIp1Coia9acAE4E53Hw/sB/6raQF3v8vdy929vLi4zcM6RERERKQLScjhDcAmYJO7zwxvP8YRSa+IJIf6QJBXlm5n9roqMtJSmD6yF+WDemBm8Q5NRESSSEImve6+zcw2mtkId18OnA0siXdcIhJdqyv38eWH5rF0614y01IIBJ3fv76a88b04RdXnEhOZkK+RYmISCeUyJ8oXwH+Gp65YQ1wQ5zjEZEoWrm9miv++B4pZvz26vGcP7YvtQ0B7n93Hb98cQXX3jOTh74wheyMRH6bEhGRziJhP03cfT5QHu84RCT6dh+o4/N/nk1qSgqP3XQypUWh+1SzM9L4t2lDGVKcw80PzuE//7GQ31w1Ps7RiohIMkjUG9lEJIl9+/GFbN1dwx+vm3go4W3qo2P68O8fGc7TH2zh+UVb4xChiIgkGyW9ItKhXlqynWcXbuNr5wxj4qAeLZa76cwhjOmXx/eeXEx1TX0HRigiIslISa+IdJjahgD//fRiRvTO5QunDz5m2bTUFH562Tgqq2v509vrOiZAERFJWkp6RaTDPDxzA5uqDvKdC0eRkXb8t58TBxZw7uje3PP2GvYcUG+viIi0n5JeEekQB+sC/Pa1VUwdXMjpw4pafd43PjKc6poGHnhvXeyCExGRpKekV0Q6xD/mbmLHvjq+cc7wNi08MapvHmcML+bBGeupawjGMEIREUlmSnpFJOaCQedP76zlhAH5TC4rbPP5N5xSSkV1Lc8v3haD6EREpCtQ0isiMff6igrWVO7nc6eVtWt54TOHFzOoZzYPzlgfg+hERKQrUNIrIjH3p7fX0Tc/iwvG9W3X+SkpxicmDOD9tbvYuOtAlKMTEZGuQEmviMTUxl0HeHvVDq6aXEJ6avvfcj42vj8AT32wJVqhiYhIF6KkV0Ri6u+zN2IGn5g4IKJ6BhZmM7m0kH/O3YS7Ryk6ERHpKpT0ikjMBILOY3M2ccawYvoVdIu4vo+N78/qyv0s3LwnCtGJiEhXoqRXRGLm7VU72LKnhivKB0alvgvH9SU91Xhm4dao1CciIl2Hkl4RiZkn5m0mv1s654zuFZX68rPTmTq4Jy8u3q4hDiIi0iZKekUkJmrqA7y4eBvnj+1DZlpq1Oo9d0wf1u7Yz+rKfVGrU0REkp+SXhGJideXV7C/LsBFJ/SLar0fGdUbgBeXbI9qvSIiktwSOuk1s1Qzm2dm/4p3LCLSNk9/sJWinAymDm77CmzH0ic/ixMH5PPiYiW9IiLSegmd9AJfA5bGOwgRaZv9tQ28smw7F4zrS1oEc/O25NwxfZi/cTcVe2uiXreIiCSnhE16zWwAcCFwT7xjEZG2eXnpdmrqg1Ef2tBo2ohiAN5auSMm9YuISPJJ2KQXuA24FQjGOQ4RaaPnFm6jd14m5YN6xKT+UX3yKMrJ4K2VlTGpX0REkk9CJr1mdhFQ4e5zjlHmRjObbWazKyv1wSeSKGobAry1spKzR/UmJcVi0kZKinHa0CLeWrmDYFBTl4mIyPElZNILnApcYmbrgEeAs8zswaYF3P0udy939/Li4uJ4xCgizZi1tor9dQHOHhmduXlbcsbwYnbur2PJ1r0xbUdERJJDQia97v4tdx/g7qXAlcCr7n5tnMMSkVZ4Zdl2MtNSOGVIUUzbOW1oqH6N6xURkdZIyKRXRDond+fVZRWcMqQn3TKityBFc3rlZTGyT67G9YqISKskfNLr7q+7+0XxjkNEjm/Njv2s33mAs8ILSMTaGcOLmb2uigN1DR3SnoiIdF4Jn/SKSOfx6tIKAM6K8XjeRqcPK6IuEOT9tbs6pD0REem8lPSKSNS8smw7I/vk0r+gW4e0N3FQD9JSjJlKekVE5DiU9IpIVOw5WM/sdVUd1ssLkJ2RxgkD8pmxZmeHtSkiIp2Tkl4RiYq3VlbSEPQOTXoBpg7uycJNezSuV0REjklJr4hExatLKyjITmd8SWxWYWvJlME9aQg6c9ZXdWi7IiLSuSjpFZGIBYLO6ysqmT6iF6kxWoWtJRMH9SA1xZi5RuN6RUSkZUp6RSRi8zfuZtf+OqZ38NAGgJzMNMb2z2fmWo3rFRGRlinpFZGIvbpsO6kpxpnD4rMk+NTBhXywcQ8H6wJxaV9ERBKfkl4RidiryyopH9SD/Oz0uLQ/tawndYEg8zZoXK+IiDRPSa+IRGTL7oMs3bq3w2dtaGpiaQ/MYLZuZhMRkRYo6RWRiLy6LLQK29mj4pf05mWlM6J3rpJeERFpkZJeEYnIa8sqKCnMZkhxTlzjmDCoB/PWVxEMelzjEBGRxKSkV0Ta7WBdgLdX7eCskb0w69ipyo40saQH1bUNrKzYF9c4REQkMSnpFZF2e2/NDmobgnEdz9uovDS0KIYWqRARkeZElPSaWbaZfc/M7g5vDzOzi6ITmogkuleXVZCdkcqUwYXxDoWSwmyKcjKYvV6LVIiIyNEi7em9D6gFTg5vbwZ+HGGdItIJuDuvLq3gtKFFZKalxjsczIwJJT2Yq55eERFpRqRJ7xB3/zlQD+DuB4D4DuwTkQ6xbFs1W/bUxHXWhiNNHNSDdTsPsGNfbbxDERGRBBNp0ltnZt0ABzCzIYR6fiNiZgPN7DUzW2Jmi83sa5HWKSLR1ThV2fQRiZP0alyviIi0JNKk9/vA88BAM/sr8Apwa8RRQQNwi7uPBqYCXzKz0VGoV0Si5NVlFYzrn0+vvKx4h3LImH75ZKSmaIiDiIgcJS2Sk939JTObSygxNeBr7r4j0qDcfSuwNfy62syWAv2BJZHWLSKR27W/jrkbqvjqWcPiHcqHZKWnMrZ/nnp6RUTkKO1Kes1swhG7toafS8ysxN3nRhbWh9oqBcYDM6NVp4hE5o0VFbjHdxW2lkwc1IMH3ltPbUMgIW6wExGRxNDent5fhp+zgHLgA0I9vScAszk8m0NEzCwH+AfwdXffe8SxG4EbAUpKSqLRnIi00itLKyjKyWRsv/x4h3KUiYMKufuttSzavIeJg+I/lZqIiCSGdo3pdffp7j6dUA/vBHcvd/eJhHpkN0cjMDNLJ5Tw/tXd/9lMDHeF2y0vLi6ORpMi0gr1gSBvrKjkrJHFpKQk3mQtEwYVADB3/e64xiEiIokl0hvZRrj7wsYNd18EjIqwTiy0num9wFJ3/1Wk9YlI9MxZX0V1TQNnjewd71Ca1Ss3iwE9ujF3g8b1iojIYZEmvQvM7B4zmxZ+3A0siEJcpwLXAWeZ2fzw44Io1CsiEXp1WQXpqcZpw4riHUqLJpT0YN6G3fEOQ0REEkhEszcANwA3A43z6L4J3Blhnbj722iRC5GE9MrS7Uwd3JOczEjfPmJnQkkBT32whS27D9KvoFu8wxERkQQQ6ZRlNcCvww8RSXLrduxndeV+rp06KN6hHNOEQaFFKuZuqFLSKyIiQITDG8xsrZmtOfIRreBEJLG8vHQ7AOeMSszxvI1G9c0jMy1FN7OJiMghkX4/Wd7kdRbwSUBzBIkkqZeXbmdE71wGFmbHO5RjSk9N4YQB+bqZTUREDomop9fddzZ5bHb324ALoxOaiCSSPQfqmbWuinNGJ96CFM2ZUNKDxVv2UFMfiHcoIiKSACLq6T1iZbYUQj2/iXt3i4i02+srKggEPeGHNjQaX9KD+jfXsHiLFqkQEZHIE9RfNnndAKwFroiwThFJQC8t2U5RTiYnDiiIdyit0rhIxbwNu5X0iohIxEnv59z9QzeumVlZhHWKSIKpawjyxvJKLhjXNyFXYWuOFqkQEZGmIl2c4rFW7hORTmzWul1U1zZwzujOMbSh0YSSHprBQUREgHb29JrZSGAMkG9mlzc5lEdoFgcRSSIvLdlOZloKpw1N3FXYmqNFKkREpFF7hzeMAC4CCoCLm+yvBr4QYUwikkDcnZeXbue0oUV0y0iNdzhtokUqRESkUbuSXnd/EnjSzE529/eiHJOIJJDFW/ayqeogXzlraLxDabORfQ4vUnHRCf3iHY6IiMRRe4c33OruPweuNrOrjjzu7l+NODIRSQjPLNxKaopx7ug+8Q6lzTLSQotUzNuom9lERLq69g5vWBp+nh2tQEQk8bg7zy3cyilDetKje0a8w2mXCSU9uO+dddQ2BMhM61zDMzpSdU09O/fVURcIUh8Ikp6aQvfMNHIy0sjNSus0s3aIiLSkvcMbng4/PxDdcEQkkSzdWs26nQf44plD4h1Ku40v6cEf31zDos17mRge49vV7dxXy6vLKpi7YTcLNu1mw84DVNc2tFg+IzWF3vmZ9M3vRv+CbpT27M7QXjkM6dWdsqLu+mNCRDqF9g5veBrwlo67+yXtjkhEEsazC7eSYnBuJ5uqrKnDi1RUdemktyEQ5IXF23lwxnpmrt1J0CEvK40TBxYwqbSQPvlZFOdkkpmeQlpKCg3BIPtrG6iuaaByXy1bd9ewdc9BZq7ZyePzNh+qN8WgpDA7nATnMKxXLsPCr3MytUCniCSO9r4j/SKqUYhIwnF3nl24lamDe9IzJzPe4bRbV1+kIhh0/jlvM79+aQWbdx9kUM9svjx9KB8d24fRffMwa/uwhYN1Adbs2Meqin2srtzP6op9rKyo5o0VldQHDveH9MvPYmjvUBI8tFfOoeeC7M45VEZEOrf2Dm94o/G1mWUAIwn1/C5397ooxSYicbR8ezVrduzns6d1/kUWJ5T04P21u+IdRodbunUv3/rnQuZv3M2JA/L5/sWjOXtUb1IjHJ/bLSOVMf3yGdMv/0P7GwJBNuw6wMqKUEK8KpwM/3XmTmrqg4fKFeVkMrRXd0oKs+lfkM2AHt1Cj8JseudmkpYa6bpJIiJHi+i7JzO7EPgDsBowoMzMvujuz0UamJmdB9wOpAL3uPv/RFqniLTevz4IDW346JjON2vDkbraIhXuzoMz1vOjZ5aSl5XOLz95IpeN7x/zm9HSUlMYXJzD4OIcPjrm8P5g0Nm8++ChJLgxIX59eSUV1bUfqiM1xeiTl0VxbiZFOZkU52ZSnJNBUW4mxTmZFOVmkt8tnfxu6eRlpZOVntKu3moR6XoiHXD1S2C6u68CMLMhwDNAREmvmaUCvwM+AmwCZpnZU+6+JMJ4RaQVgkHn8XmbOXVoEcW5nXdoQ6PGRSrmbdid9ElvXUOQWx/7gCfmb2HaiGJ+8ckTKYrz8JSUFGNgYTYDC7OZPrLXh47V1AfYuqeGTVUH2FR1kE1VB9iyu4Yd+2rZVHWA+Rur2Lm/Dm/hLpKM1BTyuqWRl5VOXrfQIzs9leyMVLIyUslOT6VbRvjRuD89leyMNDLTUkhPTSEjzcLP4e3U0HN6qn1on2awEOncIk16qxsT3rA1hFZli9RkYJW7rwEws0eAS4Fmk97qmnpeXbb9Q/uMZt6c7JibhNtqRZkj6zm61NFlmqsoOvUcFfPxf/RmyxxZqnX1NBNzK9o68mdtTUfNkWVSU+zQh1NmWugDq/EDKi3F1PsTgVnrdrF590G++dER8Q4lKg4tUrGhigtP6BvvcGKmuqaemx6cwzurdnLLR4bzpelDEz5Ry0pPpawoNAtESxoCQXYdqGNHdR079tWy52A9e2vq2XuwocnrevbWhLa37TnIwfoAB+sCHKgLcLA+0GLS3BapKUZ6qpGWkkKKhXq2U8xITYFUM1JSjLSU0HOqGakphx8pjdtN9ofKcei4GYeezULvkoe2w69pLMMRZZuUaayHD20frtOa7sNICe08tN1YFxx+323pc6bpe/nhfc2XOerzrMmOo8/5cJkj93PMc5s/p7mYaam9o5tplbb8f9aW/yW9lRW36X/zNsXa+sKt/R04EAg6gaBTHwjS0PR1wKkPBgkEnIYm+2oaAhRGMH1mpEnvbDN7Fng0HP8nCfXKXg7g7v9sZ739gY1NtjcBU5oWMLMbgRsBMvoM5bP3a8pgOcws1AOUmZZCblY6uVlpoV6grHTywq+LcjLonZdFn/ws+oSfc7PS4x16Qnh83mayM1I5d0znnbWhqcZFKpL5ZrZ9tQ1ce+/7LNq8h1988kQ+MXFAvEOKmrTUFHrlZtErN6td57s7tQ3BUBIcToYP1gWoCwSoawh9oNYHgtQ1BMPzFH94X33Aw8+h440f1IGgE3AneMR2IOgE3WkIhJ5D+yEQPHxuXfhDvvHcoDvuoeQi6KGYQ9t8+Fh4aHTjvqCH0pHmygfDOxrLBJuU8aPOj8qlEomKxj8q08J/SKanppCWaof+SG6vSJPeLGA7cGZ4uxLoBlxM6N9Ve5Pe43L3u4C7AMacMN4f+tKph481X/7D282WOWrPccu0pp7m/kI7ck9zbzhH/WXVbJm219N8mSPrOX7MzcfTirZa9Ts8/vVq/IuwtiF4+AOpyevaQJDa+iDVNQ2HeoI27z7Ispp69hysp7rm6HlJi3MzGVLcnSHFOQwpzmFs/3xOGJBPVnrXmYe0pj7AMwu2cv7YvmRnJM+UU8m8SEVNfYAvPDCbRZv3cOc1Ezg3CcZhR5NZ6MMyKz2Vrjtp3fG5H0644fD7buPbceP7+7He148sc7iO5utsurM95zofLny8mD/UrDf/8zR3blt6fJv9prmlsjH4IqZNsbahcFtCbW21jd98pIWHE6WlpBz6tuRYHvhsG4JpIqJPNHe/IZLzj2EzMLDJ9oDwvmZ1y0jlxIEFMQpFklVNfYDte2vYtqeGbXtr2LK7hrXhaZie/mALe8NJcVqKMapvHhNKCjh9WDGnDO2ZVMngkV5eup3q2gYun9A/3qFEVbIuUhEMOl97ZB7vrdnJrz91ohJeaTez0HCLtqU3Ip1HpLM3lAFfAUqb1hWFxSlmAcPC9W8GrgSujrBOkQ/JSk9lUM/uDOp59Fcl7k7lvloWbNzD3A1VzNuwm0dnb+KB99aTkZrClMGFnDumDxeN69tpl+dtyd9mbaRvfhZTB/eMdyhRlayLVNz2ykpeWLyd7100msvGJ8+QBhGRaIu0u+oJ4F7gaSB47KKt5+4NZvZl4AVCU5b9yd0XR6t+keMxM3rlZnHO6CzOCa9GVtsQYPa6Kl5bVsGryyv43hOL+O+nFjNtRDFXlA+Myvyn8bZ+537eWrmDb5wzvNP/LEdKxkUqXli8jTteWcknJg7gs6eWxjscEZGEFmnSW+Pud0QlkiO4+7PAs7GoW6Q9MtNSOXVoEacOLeI7F45i6dZqnpy/mSfmb+blpRUM6NGNz5xcyqcmDySvk94Q99DMDaSmGFdOHnj8wp3QhJIezFqXHItUbNx1gFse/YATB+Tz44+N1WwlIiLHEemyN7eb2ffN7GQzm9D4iEpkIgnMzBjdL49vXTCKd/7zLO68ZgL9Crrxk2eXctr/vModr6ykuqY+3mG2SU19gEdnb+Tc0b3pnde+u+QT3YSSArbuqWHrnoPxDiUiDYEgX//bfMzgd9dM6FI3WoqItFekPb3jgOuAszg8vMHD2yJdQlpqCueP68v54/qyaPMebn9lJb96aQV/emct/zZtCNefUkZGWuIvq/rcoq1UHajn2qmD4h1KzIwvCY3lnbt+Nxee0HkXqfj966uZs76K2688iQE9suMdjohIpxDpJ/EngcHufqa7Tw8/lPBKlzW2fz53f7qcp758KicOKOCnzy7jvNve5LXlFfEO7ZjcnfvfXc/gou6cMiS5bmBralTfw4tUdFZLtuzl9ldWcsmJ/bj0pOSaYUNEJJYiTXoXAQVRiEMkqZwwoIAHPjuZ+66fhAM33DeLm/4yh4rqmniH1qwZa3bxwcbdfO70sqQeG9rZF6kIBJ1vP76Qgm7p/PDSMfEOR0SkU4k06S0AlpnZC2b2VPjxZBTiEkkK00f24oWvn8F/njeSV5dXcO6v3+TxeZtavaRkR7nzjdUU5WTy8QnJP+XVhJIeLN68l9qGQLxDabOHZq5n/sbdfO+i0RRkJ9dUeSIisRZp0vt94DLgp8CvCM2vOzTSoESSSUZaCjdPG8JzXzudIcU5fONvH/CFP89m577aeIcGwKLNe3hzRSWfPa20S9wQNb6kB3WBIIs27413KG1SsbeGnz+/nNOGFnHpSf3iHY6ISKcTUdLr7m8Ae4GLgPsJ3cD2h8jDEkk+Q4pzePSLJ/O9i0bz5sodnH/7W7y7ake8w+LXL60gNyuNa6Yk7w1sTTVdpKIz+cmzS6kNBDU9mYhIO7Ur6TWz4eGpypYBvwE2ABa+ke03UY1QJImkphifO62MJ790Knnd0rnm3pn8/Pll1AeitrZLm8xat4tXllVw87Qh5HfrnHMLt1XjIhXzNuyOdyitNm9DFU/O38IXzxhMadHRKwiKiMjxtbendxmhXt2L3P20cKLb+QbIicTJqL55PPXlU7ly0kB+//pqrvjje2zcdaBDY3B3/ue5ZfTOy+SGU8o6tO14m1DSo9PczObu/PiZpRTlZPLFM4fEOxwRkU6rvUnv5cBW4DUzu9vMzgb0fZtIG2RnpPGzy0/gt1ePZ9X2fVx4x1s8v2hbh7X/5PwtzFlfxdfPGU63jOQfy9tUZ1qk4rlF25izvor/OHc4OZmRTq0uItJ1tSvpdfcn3P1KYCTwGvB1oJeZ3Wlm50YxPpGkd9EJ/Xjmq6dTVtSdmx6cww+eWhzzmQV2H6jjR/9awkkDC7iiPDmXHD6WiYMKAXh/bWIvSVzbEOBnzy1lZJ9cPtkFr5OISDRFeiPbfnd/yN0vBgYA84D/jEpkIl1ISc9s/n7TKXz+tDLuf3cdH7/zXdbu2B+z9n70r6XsOVjPzy4fR2pK1/uSZnS/PHKz0pixJrGT3kdnb2LjroP81/kju+R1EhGJpqitjeruVe5+l7ufHa06RbqSjLQUvnvRaO75dDmbqg5y0R1v8eT8zVFv559zN/GPuZu4edoQRvXNi3r9nUFqijGlrJAZa3bGO5QW1TUEufO1VUwoKeDM4cXxDkdEpNOLWtIrItFxzujePPvV0xnVN4+vPTKf//rHAg7WRWe4w/Jt1Xzn8UVMKSvka2cPi0qdndXUwT1Zu2M/2/Yk5ip5j83ZxJY9NXztnOGaokxEJAqU9IokoH4F3Xjkxql8afoQ/jZ7I5f+7m0Wbd4TUZ2bdx/k+vveJycrjd9cNZ601K79z3/q4J4AvLcm/nMlH6k+EOR3r63ixIEFnDGsKN7hiIgkha79qSeSwNJSU/jmR0fywA2TqTpQzyW/fZsf/WsJ+2sb2lzX2h37uequGeyrbeDPn51Mr7ysGETcuYzum0d+t3TeW514Qxwen7uZzbsP8vWzh6mXV0QkSpT0iiS4M4YX8/K/n8lVk0u49+21TP/F6/z5vXWtnuHhhcXbuOz377CvtoG/fG5Klx3He6SUQ+N6E+tmtvpAkN++topx/fOZNkJjeUVEoiXhkl4z+z8zW2ZmC8zscTMriHdMIvGW3y2dn1w2jn/cfAqlPbvz/55czBk/f41fvbicldurcfcPlQ8GnfdW7+SG+97ni3+Z8//bu/8gq8r7juPvD7vLrwVBd9ddlF/yGwSDsnVEE0UgFImRMTKJaW1qtLVJE41pfkzQzsRpJ39g0voz1phKTVtrTR2bUBOlAiLWRhMSCLDKwqqIAiuIsIALuMt++8e9bXa2K3uBvffcPft5zdzh3r3nPue7891z+N7nPOd5GH76AJ784sVMHzE0mV+gSM0cW8H295rZsb945uv96fqdbH+vmVvcy2tm1q2KcabzZ4HFEdEqaQmwGE+DZgbAjFGn8/ifXcQLW9/l4f96g/uea+DeVQ1UDurHmMpyhgws4+CRFuobD7KvuYUhA8r41hWTuOGSc+hbWnTfcRP3f+N6X9vLohnDE44GWo+1cf+qrUwZdhpzJ5+ZdDhmZqlSdEVvRPxnu5cvAYuSisWsGEni0glVXDqhip37D7Nmyx5+ue09duw7zFvvNVPer5R5U2q4eFwFH59SzcC+RXeYF42J1YM5fWBZ0RS9/7FhJ9v2NvPgdTPcy2tm1s2K/X/DG4DHO3tD0k3ATQAjR44sZExmReOsoQO49sKRXHuhj4GT0aePmDm2ghcb3iUiEi00j7UF961qYFLNYOZNqU4sDjOztErkeqekFZI2dfJY2G6b24FW4NHO2sguhFEbEbVVVb7Zw8xOzmUTqmg8cIT6dw4mGsdTG3by+p73uWXOePp49TUzs26XSE9vRMw93vuSrgeuBOZExzt0zMy60WUTMmNnV9fvYVJNMjNbtGV7eSdUD2L+uTWJxGBmlnZFd2eLpPnAN4GrIqI56XjMLN1qhvRnUs1gVtfvTiyGn2/aRcPuQ9w82728Zmb5UnRFL3A/MBh4VtJ6SQ8mHZCZpdusiWeydts+Dh5pKfi+29qC+1Y2MLaqnAXThhV8/2ZmvUXRFb0RMS4iRkTE9OzjC0nHZGbpNmtiFa1twYsNhV+dbXldI/XvHOTm2eMpcS+vmVneFF3Ra2ZWaDNGnc7gfqU8v2VPQffb1hbcs3IrYyrL+eRHzirovs3MehsXvWbW65WV9OGScZWsrt/9/1a3y6cVr77D5saDfOnyce7lNTPLMxe9ZmbA3CnV7Go6woa3mwqyv4hML++oioEsnO5eXjOzfHPRa2YGfHxyNaV9xNObGguyv1Wbd1O38wBfunwcpSU+FZuZ5ZvPtGZmwJCBZcwcW8Ezm3blfYhDRHD3iq2MOGMAV59/dl73ZWZmGS56zcyy5k+tYdveZjY35nd1thWv7mbjjiZunj2eMvfympkVhM+2ZmZZ86bUIJHXIQ4RwV3PbmFUxUA+5V5eM7OCcdFrZpZVNbgfM8dU8NP1O/I2xGF53Tu8susAt8we77G8ZmYF5DOumVk711wwnDf3NrP2zX3d3nZbW3D3ii2MqSz3jA1mZgXmotfMrJ35U2sY2LeEJ9a+3e1tP72pkc2NB/nKXPfympkVms+6ZmbtlPcr5Yqpw/jZxl0c/uBYt7X7QWsb312+mQnVg7jyPPfympkVmoteM7MOFs0YzqGjrTy1YWe3tfnoy2+ybW8zixdM9uprZmYJcNFrZtbBRWPOYEL1IJa+uK1bbmhrOtzCPSu3csm4CmZNqOqGCM3M7ES56DUz60ASN370HF7ddYBfvL73lNu7f9VWmg63cNuCyUju5TUzS4KLXjOzTiycfjYV5X15+IU3TqmdV3cdYOmL2/j0jBGce9aQborOzMxOlIteM7NO9C8r4bqLRrFy82427Wg6qTba2oLb/n0jQweUsXjBpG6O0MzMTkTRFr2SviYpJFUmHYuZ9U43fPQchgwo487l9Sf1+X/4722s276f2z8xmaED+3ZzdGZmdiKKsuiVNAKYB2xPOhYz672GDCjjy5ePY82WPTy/Zc8JfbZuZxNLnt7M3MnVXO3lhs3MEleURS9wF/BNID/rgJqZ5eiPZo5iTFU5tz25kfePtub0mYNHWrjlsXUMHVjGnYvO881rZmZFoOiKXkkLgR0R8dukYzEz619Wwp3XnMfOpsN8e1ldl1OYtRxr488f/Q1v7m3m7munc0a5hzWYmRWD0iR2KmkFUNPJW7cDt5EZ2tBVGzcBNwGMHDmyW+MzM2uvdvQZ3Dx7PPeu3MrYqkF8cdbYTrc72nqMWx5bxwtb32XJNdO4eKxvSTAzKxaJFL0RMbezn0uaBpwD/DZ7OXA48BtJF0ZEY4c2HgIeAqitrfUwCDPLq1vnjOe1PYdY8sxm9jd/wNd/fyJlJb+7WPbGu+/z1cfXs/6t/dzxySl85vf8ZdzMrJgkUvR+mIjYCJz5v68lbQNqI+LdxIIyMwP69BH3fGY6QweU8YM1r7O8rpErzzuLoQPLWLd9P8/UNVLet4QH/vACFkwblnS4ZmbWQVEVvWZmxay0pA/fuXoasyedyYPPv8YDqxtoC6go78vnLx7Nn3xsDDVD+icdppmZdaKoi96IGJ10DGZmHc2ZXM2cydUcaTnG0dY2Tutf6hkazMyKXFEXvWZmxax/WQn9y0qSDsPMzHJQdFOWmZmZmZl1Nxe9ZmZmZpZ6LnrNzMzMLPVc9JqZmZlZ6qmrJTV7AkkHgfqk47DEVAKey7l3cu57N+e/93Lue7eJETH4RD+Ultkb6iOiNukgLBmS1jr/vZNz37s5/72Xc9+7SVp7Mp/z8AYzMzMzSz0XvWZmZmaWemkpeh9KOgBLlPPfezn3vZvz33s5973bSeU/FTeymZmZmZkdT1p6es3MzMzMPlSPKnolzZdUL6lB0rc6eb+fpMez778saXQCYVoe5JD7v5D0iqQNklZKGpVEnJYfXeW/3XbXSApJvqs7JXLJvaRPZ4//Okn/UugYLX9yOPePlPScpHXZ8/+CJOK07idpqaTdkjZ9yPuSdG/2b2ODpAu6arPHFL2SSoDvA1cAU4DPSprSYbMbgX0RMQ64C1hS2CgtH3LM/TqgNiLOA54A7ixslJYvOeYfSYOBrwAvFzZCy5dcci9pPLAYuCQizgVuLXSclh85Hvt/Cfw4Is4HrgUeKGyUlkePAPOP8/4VwPjs4ybg77pqsMcUvcCFQENEvB4RHwD/CizssM1C4EfZ508AcySpgDFafnSZ+4h4LiKasy9fAoYXOEbLn1yOfYC/JvNF90ghg7O8yiX3fwp8PyL2AUTE7gLHaPmTS/4DOC37fAiws4DxWR5FxBrgveNsshD4x8h4CRgqadjx2uxJRe/ZwFvtXr+d/Vmn20REK9AEVBQkOsunXHLf3o3A03mNyAqpy/xnL2uNiIifFTIwy7tcjv0JwARJL0p6SdLxeoasZ8kl/3cA10l6G/g5cHNhQrMicKK1QWpWZDMDQNJ1QC1wWdKxWGFI6gP8LXB9wqFYMkrJXN6cReYKzxpJ0yJif5JBWcF8FngkIv5G0kzgnyRNjYi2pAOz4tOTenp3ACPavR6e/Vmn20gqJXOpY29BorN8yiX3SJoL3A5cFRFHCxSb5V9X+R8MTAVWS9oGXAQs881sqZDLsf82sCwiWiLiDWALmSLYer5c8n8j8GOAiPgF0B+oLEh0lrScaoP2elLR+ytgvKRzJPUlM2B9WYdtlgF/nH2+CFgVnog4DbrMvaTzgR+QKXg9pi9djpv/iGiKiMqIGB0Ro8mM6b4qIk5qbXYrKrmc939CppcXSZVkhju8XsAYLX9yyf92YA6ApMlkit49BY3SkrIM+Fx2FoeLgKaI2HW8D/SY4Q0R0Srpy8ByoARYGhF1kv4KWBsRy4CHyVzaaCAz+Pna5CK27pJj7r8LDAL+LXvv4vaIuCqxoK3b5Jh/S6Ecc78cmCfpFeAY8I2I8BW+FMgx/18Dfijpq2RuarvenV3pIOkxMl9oK7Njtr8NlAFExINkxnAvABqAZuDzXbbpvw0zMzMzS7ueNLzBzMzMzOykuOg1MzMzs9Rz0WtmZmZmqeei18zMzMxSz0WvmZmZmaWei14zszySVCFpffbRKGlH9vkhSQ/kYX9fkPS57m7XzKyn85RlZmYFIukO4FBEfC/pWMzMehv39JqZJUDSLElPZZ/fIelHkl6Q9KakT0m6U9JGSc9IKstuN0PS85J+LWm5pGGdtHuHpK9nn6+WtETSLyVtkfSxTrYfJmlNtvd5U2fbmJmlgYteM7PiMBaYDVwF/DPwXERMAw4Dn8gWvvcBiyJiBrAU+E4O7ZZGxIXArWRWNOroD4DlETEd+Aiw/tR+DTOz4tRjliE2M0u5pyOiRdJGMkuuPpP9+UZgNDARmAo8m11quwQ47jrzWU9m//11tp2OfgUszRbVP4mI9ScZv5lZUXNPr5lZcTgKEBFtQEv87oaLNjIdFALqImJ69jEtIubl2i5wjE46OiJiDXApsAN4xDfBmVlaueg1M+sZ6oEqSTMBJJVJOvdUG5U0CngnIn4I/D1wwam2aWZWjDy8wcysB4iIDyQtAu6VNITM+ftuoO4Um54FfENSC3AIcE+vmaWSpywzMzMzs9Tz8AYzMzMzSz0XvWZmZmaWei56zczMzCz1XPSamZmZWeq56DUzMzOz1HPRa2ZmZmap56LXzMzMzFLPRa+ZmZmZpd7/AFA8cYnm3/L3AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"## Image plot\n",
"fig, ax = plt.subplots(1,1)\n",
"img = ax.imshow(p);\n",
"ax.set_title('P-Wavefield')\n",
"ax.set_xticks(range(0,nx+1,int(nx/5)))\n",
"ax.set_yticks(range(0,ny+1,int(ny/5)))\n",
"ax.set_xlabel('Grid-points in X')\n",
"ax.set_ylabel('Grid-points in Y')\n",
"fig.colorbar(img)\n",
"\n",
"## Plot seismograms\n",
"fig, (ax1, ax2, ax3) = plt.subplots(3, 1)\n",
"fig.subplots_adjust(hspace=0.4,right=1.6, top = 2 )\n",
"\n",
"ax1.plot(t,Seismogramm[0,:])\n",
"ax1.set_title('Seismogram 1')\n",
"ax1.set_ylabel('Amplitude')\n",
"ax1.set_xlabel('Time in s')\n",
"ax1.set_xlim(0, T)\n",
"\n",
"ax2.plot(t,Seismogramm[1,:])\n",
"ax2.set_title('Seismogram 2')\n",
"ax2.set_ylabel('Amplitude')\n",
"ax2.set_xlabel('Time in s')\n",
"ax2.set_xlim(0, T)\n",
"\n",
"ax3.plot(t,Seismogramm[2,:])\n",
"ax3.set_title('Seismogram 3')\n",
"ax3.set_ylabel('Amplitude')\n",
"ax3.set_xlabel('Time in s')\n",
"ax3.set_xlim(0, T);"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| gpl-3.0 |
marcelomiky/PythonCodes | scikit-learn/scikit-learn-book/Chapter 3 - Unsupervised Learning - Principal Component Analysis.ipynb | 1 | 130056 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Learning Scikit-learn: Machine Learning in Python"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## IPython Notebook for Chapter 3: Unsupervised Learning - Principal Component Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_Principal Component Analysis (PCA) is useful for exploratory data analysis before building predictive models.\n",
"For our learning methods, PCA will allow us to reduce a high-dimensional space into a low-dimensional one while preserving as much variance as possible. We will use the handwritten digits recognition problem to show how it can be used_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Start by importing numpy, scikit-learn, and pyplot, the Python libraries we will be using in this chapter. Show the versions we will be using (in case you have problems running the notebooks)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n",
"IPython version: 2.1.0\n",
"numpy version: 1.8.2\n",
"scikit-learn version: 0.15.1\n",
"matplotlib version: 1.3.1\n"
]
}
],
"source": [
"%pylab inline\n",
"import IPython\n",
"import sklearn as sk\n",
"import numpy as np\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"\n",
"print 'IPython version:', IPython.__version__\n",
"print 'numpy version:', np.__version__\n",
"print 'scikit-learn version:', sk.__version__\n",
"print 'matplotlib version:', matplotlib.__version__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import the digits dataset (http://scikit-learn.org/stable/auto_examples/datasets/plot_digits_last_image.html) and show its attributes"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['images', 'data', 'target_names', 'DESCR', 'target']\n"
]
}
],
"source": [
"from sklearn.datasets import load_digits\n",
"digits = load_digits()\n",
"X_digits, y_digits = digits.data, digits.target\n",
"print digits.keys()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's show how the digits look like..."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAAEGCAYAAABhBk/MAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x104332210>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"n_row, n_col = 2, 5\n",
"\n",
"def print_digits(images, y, max_n=10):\n",
" # set up the figure size in inches\n",
" fig = plt.figure(figsize=(2. * n_col, 2.26 * n_row))\n",
" i=0\n",
" while i < max_n and i < images.shape[0]:\n",
" p = fig.add_subplot(n_row, n_col, i + 1, xticks=[], yticks=[])\n",
" p.imshow(images[i], cmap=plt.cm.bone, interpolation='nearest')\n",
" # label the image with the target value\n",
" p.text(0, -1, str(y[i]))\n",
" i = i + 1\n",
" \n",
"print_digits(digits.images, digits.target, max_n=10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's define a function that will plot a scatter with the two-dimensional points that will be obtained by a PCA transformation. Our data points will also be colored according to their classes. Recall that the target class will not be used to perform the transformation; we want to investigate if the distribution after PCA reveals the distribution of the different classes, and if they are clearly separable. We will use ten different colors for each of the digits, from 0 to 9.\n",
"Find components and plot first and second components"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def plot_pca_scatter():\n",
" colors = ['black', 'blue', 'purple', 'yellow', 'white', 'red', 'lime', 'cyan', 'orange', 'gray']\n",
" for i in xrange(len(colors)):\n",
" px = X_pca[:, 0][y_digits == i]\n",
" py = X_pca[:, 1][y_digits == i]\n",
" plt.scatter(px, py, c=colors[i])\n",
" plt.legend(digits.target_names)\n",
" plt.xlabel('First Principal Component')\n",
" plt.ylabel('Second Principal Component')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At this point, we are ready to perform the PCA transformation. In scikit-learn, PCA is implemented as a transformer object that learns n number of components through the fit method, and can be used on new data to project it onto these components. In scikit-learn, we have various classes that implement different kinds of PCA decompositions. In our case, we will work with the PCA class from the sklearn.decomposition module. The most important parameter we can change is n_components, which allows us to specify the number of features that the obtained instances will have."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEPCAYAAAC3NDh4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4FNXXx7/bd2ZbNrvpPYHQCRCkSAtIr1KkSpEiSC/S\nREgQEARBRRRQUfkhIKBgCYqiNJEmiEgRgQgqHUJPIG2/7x932SRCJEFCgHc+zzNPMjN3btvdc+be\nc+65gIKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgkIB0QDYDeBL97k3\ngLUADgH4FoBXEdVLQUFB4f896qKuAIAhAA4AoPt8DISSiAbwvftcQUFBQeH/IcEAvgNQF9kjiYMA\n/Nz/+7vPFRQUFBT+H7ICQEUAdZCtJC7muK/6x7mCgoKCwn2kKKebmgM4C2GPUOWRhsiehlJQUFBQ\nuM9oi7DsxwG0BNAUgBGAFcAiAGcgpplOAwiAUCS5iIqKYlJS0v2rqYKCgsKjQRKAYgV5oChHEi8A\nCAEQAaAjgHUAugL4AkB3d5ruAD7754NJSUkg+cAf8fHxRV4HpZ5KPZV6KnW8eQCIKqigfhC8m25y\nc1ppGoAGEC6w9dznCgoKCgpFQFFON+Vko/sAgAsA6hdhXRQUFBQU3DxII4lHjri4uKKuQr5Q6nlv\nUep5b3kY6vkw1PFuycur6EGH7vk1BQUFBYV8olKpgALK/QdluklBQUHhgcDb2xsXLz7cy7Psdjsu\nXLhwT/JSRhIKCgoKOVCpVHjY5UtebbibkYRik1BQUFBQyBNFSSgoKCgo5ImiJBQUFBQU8kRREgoK\nCgoKeaIoCQUFBYWHhAsXLqB169Ywm80IDw/H0qVLC71MxQVWQUFB4R5AEp988gl+/fVXREdHo0uX\nLlCr7+17+IABA2A0GnH27Fns3r0bzZo1Q0xMDEqXLn1Py8mJ4gKroKCgkIO83EcvX76Mjz/+GKmp\nqWjatClKlCiR637fvn2xePFipKSkwGQyoVGjRvjkk09uup3+Z1JSUuDt7Y39+/ejWDERyLV79+4I\nDAzE1KlT89UGxQVWQUFBoRC4cOECypYti2HDhmHMmDGoVKkSNm/e7Ll/4sQJLFy4ECkpKQCEQF+z\nZg327dvnSZOVlYWhQ4fCarXC29sb06dPL1AdDh06BK1W61EQABATE4P9+/f/x9b9O4qSUFBQULgD\ns2fPxpkzZ3D9+nWkp6cjNTUVAwYM8Ny/cuUKdDpdrmd0Oh0uX77sOZ88eTLeffddXL16FRcvXsTE\niRPx0Ucf5bsO165dg9VqzXXNYrHg6tWrd9mq/KEoCQUFBYU7cPbsWWRkZOS6lpyc7Pk/KioKdrvd\nY4NQqVTQarWIiYnxpPn000+RmprqOU9NTcXKlSvzXQez2YwrV67kunb58mVYLJYCtaWgKEpCQUFB\n4Q40a9YMsix7ziVJQvPmzT3ner0emzZtQpUqVWC1WlG+fHn88MMPuQS4w+HIladGo4GPj0++6xAd\nHY3MzEwcOXLEc23Pnj0oW7bs3TQp3yiGawUFBYUc5GX0nTNnDsaPH4+0tDS0bt0aCxYsgNFozHe+\nu3btQp06dZCeng61Wg2TyYTdu3cjNDQ033l06tQJKpUK7733Hn7++Wc0b94cW7duRalSpfLVhrsx\nXCtKQkFBQSEHhRng78iRI1i1ahW0Wi06deoEf3//Aj1/8eJF9OzZE2vXroXT6cS0adPQsWPHW9Ip\nSkJREgoKCoWEEgU2N4pNQkFBQUEhT4pSSRgBbAfwC4ADAG6uBvEGsBbAIQDfAvAqktopKCgoKBT5\ndJMMIBUiPMhmAM8DaAngPIDpAEYDsAMY84/nlOkmBQWFQkGZbspNUU833XQa1gPQALgIoSQWuq8v\nBPBkEdRLQUFBQQFFryTUENNNZwCsB7AfgJ/7HO6/fkVTNQUFBQWFoo4C6wJQAYANwDcA6v7jPt3H\nLSQkJHj+j4uLQ1xcXKFUUEFBQeFhZcOGDdiwYcN/yqOobRI5GQ/gOoDeAOIAnAYQADHCKPmPtIpN\nQkFBoVBQbBK5KcrpJieyPZckAA0A7AbwBYDu7uvdAXx2/6umoKCgoAAU7XRTAIRhWu0+FgH4HkJR\nLAfQC8AxAO2LqH4KCgoK/+8pypHEXgCVIGwS5QHMcF+/AKA+gGgADQFcKpLaKSgo3MKJEyewbdu2\nXBFQFe4Pc+bMQeXKlWE0GvHMM8/ct3KL2rtJQUHhIeHtt2ejfPliGDSoMUqWDMPXX39d1FV6oCCJ\nFStWYPz4CVi0aBFcLtc9zT8oKAjjx49Hz54972m+d+JBMlwXBMVwrVBgTp48idmzZ+Pgwd9Rs2YN\nDBgwAJIk3fG5pKQkHD9+HKVLly5QaOdHicOHD6NGjRjs2HEd4eHAli1AixYmnDhxvkCRUB8G7n77\n0iFYvHgjUlJawWT6Fo0aFcMnn/zvnm1fepPx48fj+PHj+OCDDwrchrsxXD+sUEHh3zh79iy/+eYb\n/vzzz9yyZQvHjh1Lo9FCoCuBYQTMDAsrwatXr/5rPvHxUyhJPrTZHqfJ5OQ333xzn1rwYLF69Wo2\nbGgjCc8RHCzz6NGjRVuxQuB28iU5OZnBwdGUpLbU6/tTlp384YcfPPePHz9Og8FO4BIBEkilLIfy\n119/9aTJzMzkkCGjaLH40m4P4iuvzLyr+o0bN449evQocBtuXi9i2X3fuKvOVXj4OHv2LFevXs0t\nW7bQ5XL9a9r09HT27z+cZrMvVSoLjcaS1Okc1GgcVKkGEahFoA6BNAILqVKFc/r0GdyzZw8ff7wR\nIyJi2KvXQKakpJAkf/75Z8pyEIHT7h/+JlosTmZmZnrKnDfvHfr6RtLLK5ADB45gRkZGofZHYZKR\nkcG9e/fyt99+Y1ZWVq57hw8fpo+PxCNHhILYuBF0Os28ceNG0VS2ELmdfImPn0idrpf7e0ACy1i+\nfE3P/QMHDtBsjspxn7TZquZSJAkJUyjLNQgcJbCfslyCixYtLnD9XnzxRUVJ5IMCd6zCw8eOHTto\nsfjSam1Ak6k4W7To4BFe586d4+uvv86pU6dy//79JMnnnx9HWa5L4A8COwmEEfAisMf9w81yK4qP\nCWwiUJy9e/ej1epHYB6BnTQa27F58/YkyeXLl9NqbZ3rh28w2Hn27FmS5BdffEFZDneXlURZrsPR\noycUTWf9R5KTk1mlShkWK2ZmSIjMJk1q8/r167nSvPPO2/TyMrJ8eSudTvMjO6q6nXx57rkhBF7N\n8V3Yw6CgUp77aWlpDAkpSbV6KoHjVKnm0uEI4ZUrVzxpypWrSWBdjjzeZ+vWXQtcP2UkkT8K3LEK\nDx9RUTEElrjf5K/QZKrGJUuW8NSpU/T1DafB0JVa7RAajQ7OmzePEREVCGzP8SN8jYCGwI0c17oR\nGE+gGtVqJ4cOHUqTqUOO+6nUaPRMT0/nvn37aDT6EviGwM8EVtJuD/Qoqu7d+xGYnePZbYyKqlTE\nvXZ39OnThf376+lygenp4JNPSpw0Kf6WdKdPn+bOnTt58eLFfOftcrnuOAp8kLidfElMTHS/EOwl\ncJaS1IJ9+w7Jlebo0aOsVq0+rVY/xsTU5IEDB3Ldj4trQWC+5/ui0Yzls88OKnD9lJFE/ihwxyo8\nfOj1JvdowEHASpWqJadMmcLRo8dRqx2QQzgvpVrtR63WSWBpjutDCIQT6EPggvstzkxAIiCxXLmq\n/Pjjj2k2NyDgcj9zkoCWarWOlSrVZkREWQJOAiFUq61MTEz01G/48NHUaIbmKG8JY2PrFmGP3T01\napTlhg3Z9oZFi8COHZv9pzzF9F9PyrKeNpvEhIRxD4WyyEu+vPnm2/TyCqAk2di5c69bRlp3YufO\nnTSZnNTpBtBgeIbe3kH8888/8/18ZmYmr1+/zjFjxrBr1668ceNGrqnP/LQBipJQeFTIysqiVmsj\nsMgtgH8hYOb777/Pnj37E3g9h3DeSaA8gWFUqcxUqYYS6EzAREBLoLRbOUS6p5UCaDA8xYSESbx2\n7RojI8tSr+9BYA6BKPdoI4UqVQOqVE8QSCfgolY7nK1bP+2p4/Hjx+l0hlCv70mNZgRl2clNmzYV\nYa/dPb16deKgQWIkkZEBtmkjceLEF/9TnvHxY/nEEzLPnQP//BMsX17m+++/d49qXHgUpnw5fPgw\np0+fzlmzZvHUqVMFejY+Pp4qlSrXMXHixNumzasNKCQlcTv/tqL2eSvwh6PwcHH69GkaDN657AF6\nfWN+8skn/OKLL6hWB7ingP5y2xnKEjhFo9FKhyOIQH/31NN4Ar1z5PMjVSpvVqhQwzNffPbsWdas\n+QQtllCq1cHuPEmgB4F3c00nFSsWe0s9Z8yYwQkTJnDv3r1F0VX3hPPnzzM2tiRLlDAzPNzEhg1r\nFPhN+Z/UrFmO69Zlj04+/BDs0qXVPalvYfIoyJe82oC7UBL5CcuxBWJl9J2uKSjcM+x2O1SqTAB7\nAMQAuAyt9jeEhoZCkiRotelIT28NEROyLYBVAOagVKkYmExmbN4cA6AKgBAAZaBSmUCGw2CYgWee\naY8fftgNm80LdnsAypUrjV271Lh+/S0APwKoDREdxhfAYgDdAOig0y1H+fKlc9UzKSkJ06a9gUuX\nzmHu3A/xxRfLUK1atfvTSfcQh8OBrVt/xf79+6HValG6dGmo1f9tra2Pjz/27duHunWFXNq7Vwun\nM+BeVFfhASEAQCyAgxAKIdb9N859rSi5z3pZoShYunQZJclJWa5OjcaLISHlOGPGa9y2bRstlnI5\n3vBdBILocATz0KFD3L17N81mHxqNz1CW29DHJ5T9+w9ir14D+NVXXzEgoDiBye5ppE0EZAK/5siv\nDjWaxpSkQJYoUYmyHEyzOZrR0RV55swZkuS1a9f43nvv0Wi0Eljgfu4z2mz+uTxa7sTatWvZrnt3\ndu7dm7t27SqsriwS9u3bR19fC7t3l/jUUzLDwnx58uTJoq7WHXkU5EtebcBdjCT+beVddwA9AFQG\nsDPH9asAPgSwsqCF3UPc7VV4lPnzzz9RpUoczp69CBHnsS5keS7atSuFjRs348SJNsjMbAONZgls\nto/Qpk1rBAX5ok+fPsjMzMTq1auh1+vRtm1b2O12AEBiYiJatGgHMQK5+fVv7M5fhDswGuujffsg\n9O3bF9WrV8fvv/+O9PR0lCpVCjqdDpcvX0ZsrVo46euL61YvYM0m4PpXACrDao3BunXvIzY29o7t\nS0xMRPs+fXB9wgQgNRXytGn44ZtvUKnSozNIP378OBITE6HVatG6dWs4HI6irtIdUUKFF5x29zrD\ne8B91MkK95vZs9+i1epHlcpGoCOBejne8rcSsLBkyaqMjCzP4ODSDAiIpsFQzu0FFUa12sT4+Cm3\nzVu4rRoJ/O7O7waBcBoMMQQ+pVY7kgEBUbx06VKe9Xtp8mTqu3YlXC4x2/7hh4SpDqEfQBiNNPv4\nMH7y5Dt68jxWvz7xySfZa5inT+fTffoUqK+ysrKYnJx8372GMjIyOHr0UEZG+rJs2TAuW/bxfS2/\nMHkU5EtebcBdjCTyM+mYCKALgHEAJgCId/9VUPhXsrKycOzYMZw/fz7fzyQmJmLMmFdx5coHIHUQ\nNol9AIYBOASgFYBJOHhwCk6csKJq1Qq4ePES0tJuAHgTwDG4XEcwffo8bNmy5Zb8zWYJIshwHQB9\nABSHWn0NYWE6VKr0Orp0uYpdu36AzWbLs47Hz5xBesWKwM2YPJUqiXqW/hH4/Xdc27IFM5Ytw4J/\nia0DABkZGUDO2FGyjPTMzPx2FdasWQObry8CIiLgFx6OnTt33vmhe0R8/Bhs3/4OEhPP4s03/8Sw\nYT3/8w5oCg8v3wBYBmAUgBE5jqLkPutlhYJy8uRJRkdXpCwHUq+3sl+/obnedo8fP85nnx3Eli07\n87333vfc69dvCIG+BGxuW8FHBHYTaOx2c306x6jiFAEDZbkYxaI5l+ee0did8+fPv6VeixYt4s11\nEoCOgC/Fgr05lCQHd+/efce2LV++nLrISOLPP4nUVGpat6YhIIBYsyZ7VPDRR2zSvv2/5rPggw8o\nR0URiYnEsmWU/Py4bt2626a9du0au/TpQ5+ICJaoXJmjRo2izmYjNm+mO/wojd7e/Omnn+5Y/3tB\nmTIh3L0723PplVfAYcMG3JeyC5tHQb7k1QYUkgvsvsLI9D9yn7tcoaDUq9eSWu1Yt+C+SJOpIpcu\nXUpSuJw6nSHUaEYR+JCyXI4TJkwiSSYkvOSeNqrndkG9qRDOE9BRpcoZJuMPAhbq9d4EfAh87r6e\nTFmO4oYNG3LV6eLFizSbfQispVgpHeD+/2Z+k/K1ArZ7977USlGEzkBoNESJElQHBBCvv+5REurx\n49mjXz/PM9euXeNPP/3EpKSkXHm99sYbjK5ShRXr1OFXX32VZ5lPdu5MY/v2xKFDRJs2hJ8fUb16\njnB7JIKCaLDb+d5773kM7IVF1aql+OWX2aUPHKjl+PEvFGqZ94tHQb7k1QYUkpJ4B2JToAeJ+9zl\nCgXF4QglkJRDAE/hiBGjSJJz586lJHXKcS+Jer2ZgYHRtNkC3TYDfwLNc6T5jYCVarWFwPME/uce\nWQyij08YK1R4nCqViRpNWRqNPhw+fOwtddq2bRut1lh3fn0IRBP4LkcZL7NPn4H/2q6UlBRqtUah\ntDSSENok8fPPhMlEXa9eNHbrRntgII8dO0ZSePk4Q0JojYmh5OvLZwcPpsvl4spVqyg7HLQ9/jiN\nDgdfTEjgRx99xK+//pqLFi3i8IEDOXv2bKalpVEny8TFi8SZM4TNRmzdSvj7E+fPi/KPHCGsVmL+\nfEpWK806HRMm3F0cqaNHjzIhYQLHjRvDPXv23DbNV199RV9fmRMmgH37ahkS4uSJEyfuqrwHjUdB\nvuTVBhSSkvgNQAbEhPBe9/FrYRRUAO5zlysUlNjYOKpUb7mFbzpluZ5n+ufNN9+k0ZgzouYKAnYC\nP7pHB5J7iimaYiHcawQCCXRniRIx1GoDCTQk8Bw1mk5s0aIjSfLChQvcvHkzDx8+fNs6/f333zQa\nvQkcJzCdQAzFKuzlBOZTrbZw586d/9quK1euUKuVCJwgjNZs4zVJfd267N69O2fPns1Tp07R5XLx\n5RkzqPP1JUqVIr78krh8maayZbl06VJqzWZi507x/J9/EmYzmzQxMdChZSmNmq8AbCJJbFijBi2+\nvsS+fUIphYeLZ158kQgNJZo0Iex2omJFIiSEkGWaY2MJb28+3bt3gT63w4cP08/PyqFDNRw3TkWn\nU84VyTQn27dv5wsvjObkyZMKvHr4QeZRkC95tQGFpCTC8ziKkvvc5QoF5cCBA3Q4gmmz1abZHM36\n9Vt6wmj/+eeftFh8qVK9SeB7arXhBKbmUBomAmcJJBOIpwirUZNGY1nOnfsOa8U1ICwWIi6OKm9v\njhk/nt9++y1feuklNmjQgtWqNeKAAcN58eJFpqWl5arXyy+/SlkOpNncimq1FzUaJzWaIBoMfly1\nalW+2ta0aTvqdC0JUzQxaTJx/Tqxdi1hlNhv8GCWq1mTZWJjWalqVepDQ4lNm4gvvhBTRM2aEcHB\ntPj5Eb6+uaaLdNVj+cknoEkHXnB3RibAUiYTS8fGEmYzERgoFMLkycTp08QLLxB6PeHtTcyeTTid\nxIYNIs9Ll2iMiODGjRvz/bkNGNCL8fEqT60WLgSbNKmR7+cfBR5U+ZKWlsaePXsyLCyMFouFFSpU\n4Ndff33btHm1AYUYu6kWgJubqvoAiLgHeYYAWA9gP4TdY7D7ujeAtRAjl28BeN3m2cL8LBTuERcv\nXuR3333Hbdu23bI/wd69e1m//pMsX74Wa9WqR52uTw4l0ZxADQKfEEhwTz+ZqdV6c8KESTTYbERS\nkhBjp05R6+VFozGEIhhgNwJfUq2uwJuB+lq27MjU1FSS5MfLlrFR69Zs0qoVX375ZY4aNYpvvfWW\n535+SE1NZZMmT1KlCiRMwYRaS8jhhFGmvnZtoQhiY4mXXxZ2g86diY8/FkrCaCSaNiWKFRPCfv16\n0Y7ffiPMJm7bBjqNYCrAhQBnAXTIMvXNmhFHjxIbNlDr5cUSsbE0ORwsU7Uqm7doQTRqRFy7RhgM\nuUY36rZtOWbMmHy3rXv3dnznnWzV9f33YM2a5fL9/KPA3coXl8vF5cuX88UXX+T//ve/W77z/5WU\nlBQmJCR4ggImJibSYrF4pjVzklcbUEhKIgHAl26hDQBBELEL/iv+ACq4/zcD+B1AKQDTITypAGA0\ngGm3efaedr7C3fHqq6/Txyec3t4hHDNmQoF+FF9++SV9fMKo1Rr52GNxdDpDKdZEPE/AkuNwukcS\nZwn8TZ3OTHOJErkNtmXKUMT6L0FhKH+fQAUCZwik0mhszeeeG8ZXZs6kHB1NzJtH1aBBhNlMQ/fu\nlCMiOH7SpHzXPS0tjV9//TUlyU7hGfUXgaqEViemj3x9hcAmidRUIiCAcDiIH38kzp4lunQhoqKI\ngQPFm3/JkmKUIBmZmQkG+KmptlioqlaN6j59xKjprbdEftevE5068an27T3hustUrCiUkctFREeL\ndRsk8ccfhI8PjV5e3LJlS77atmrVSoaHy/zxR/CXX8DYWJkzZ76S7755FMhLvly6dInz5s3jrFmz\nePDgwVvu93+2P0NMIayDOowwRbBDmw6Fvn6lfPnyXLly5S3X82oDCklJ7IFYT7E7x7XCsEl8BuHA\nfhCAn/uaP24fAqRQO17hzixatJiyXIIiOutByvJj+d6O8bfffqMkOQlsJHCVWu3zDAyMoFZb3j1y\nGEURtC/VLfSH8Kbrq9EYRo3ZTKxeLQTh5s2EJLsN0NEUGws9Q+CdHCOTrSxevDJlh0O8sdOtXDp2\nJObMIc6cocFm4969e/n1119z165def64z58/zxIlKlGSIqjXB9BgcNBo9CKMXmLa59tvhdBnDiUW\nHS0Uw83zkyfFiEKSiJ49iVathDHaoKdalsX/1apljwhuGqkvXxYjlJgYGurUoTM0lH/88QfN/v7C\n5tGli5h+MpnEtJTFQrz9NjFrVoEW6S1Y8C5Llw5hdHQAJ00qmPJ/FLidfElOTmZEcATLS+VZTV+N\nNtl2y/alZoOZYzCGCUjgOIyjU3besn3p8CHD6W3xpq/dlzNemfGf6nn69GkajUb+/vvv+WrDzesF\nFcz5CfCXBsCV49xU0ELyQTiAigC2QyiIM+7rZ5CtMBQKAZfLhUuXLsHLy6tAAd2WLUtEauoLEMH3\ngNTUyVi2bBpGjRr+r8+lp6djxYoVAJ6AmEmcgcxMK06ePAbgRYi1ms8C6ATg5kKzbgAaAuiLGzeS\nAZQE2nYFtJlQuzLhJdlw4foeAA532jSIGJR9AAAq1U8wGlVITUkBci6Ss1qB9HTAywsqsxlV4+Kg\nq1gRN/buRXhAANq1aIGoiAgc+P13hIeEoHfv3hg8eAwO/U1QygCCooAjv6JsVAT2XboEPN0PeP55\n4No1YPJkoHNnqD/9FOozZ5B19qwIk6BSAYcOAX5+wIwZwDPPQJOWBqtGg8tedrgSEoAjR4AFC4BT\np4DAQKB0aSA5GWjWDIiOBhYvRppKhYxp0/DcyJHw9fPDtcGDgX79AIMBUKuBiROBDh0AiwWYOfNm\nOIZ80bNnb/Ts2Tvf6f8/8ObsN+E440DzjOYAgOD0YAwfMBw79uwAAFy5cgVmnRnGNBEgWwcdbDob\nLl++7Mnj5ckv4/N3P0fX1K7IQAZmTZyFgMAAdHm6S4Hrk5GRgS5duqBHjx6Ijo6+By38b4wEMB/A\nUYhf7zZk2w/uBWYAuwA86T6/+I/7F27zDOPj4z3H+vXr/5NG/v/Khg0baLP5Ua+30m4PyNOL5XZ0\n69aXanVCjrf1uXziiSdvm/bmW/mhQ4cYGFiMRmMoxUI2I8UiuWcISDQa/dxeTRMJNCCQ4c47nkB1\nij0hfCn2qM50Tz+F8PPPP2fZstWoVuspSd4MCYmmyeRDk6k+TaZ2tNn8Wb5WLWELeOIJMe2zYIF4\n427bltDpxHqH5s2JTz8Vb+1TplD13HMizahRNDZqxJIVKtDPL4wIL0VcvSre8pcvp8ZkEqMBl4t4\n912iZUvCaqVXUBCjK1Wi3m6nyt+fqF+fGDBAvOEHBxNeXkSnTuyu09FpMmV7Ou3aRZQrJ0YNO3eK\n0UadOkSFCqLeO3aIkUZgIE2BgRw0aBChVlOl04lDq6XOZCLeeYd4+23KTid37NhBUmwE9Mxzz9Fg\nNtPk7c2Xpk4t8JTIBx8sYFCQnVarkV27tvXsCf6ogNu8hQ98biAboiETkMAEJLAf+jEyKNJzPy0t\njVEhUayvrs/hGM7mqub0d+QO9li5XGV2R3dPHq3Qih1adyhw/bKystihQwc2a9bsjpsOrV+/Ppes\nRCEarhsCeNV9NLiH+eogVnQPzXHtIMQ0EyAi0SrTTYVA9sKyb92C+CtaLL68fPlyvp4/cuQIbTZ/\narX9qNEMpcnkvMV99MCBAyxevAJVKjUDAiIZEVGOKtUbBDYQspno3Y9o3IaQShAYyCeeaEovrwDq\ndDItlkACQQRiKTYC2kPhGit2iQMqE9hEjSaKiYmJdLlcbNeuG02mkrRY2lOSnBw+fAT/97//8dSp\nU4ytV49YtUq4jT72GBETQ7XVStSsKQT+X38RYWHCS6hGDbHugCT69ydGjCBKlybKlKGqWDEh4C9f\nzrY5aDTUeHlRVzySBllHo7eZkpcXneHhwtbQt69Y3zB1qlBG1asT9eqJfM1mdgaEMjp0iPjhB2Gn\nmDiRGDZMTEuFhgrFU706Ub484eNDLF4sjPfdu1MnSfR2OBgREUFZlvnMM8+wTIUK1EoSG7drl8se\n8fy4cZQbNBC2kaQkyqVLc9FHH+X7e7Nu3ToGBcnctQs8exZs187IPn265Pv5h4HbyZfExET6yD58\nDs9xJEayjFSG/fv2z5Xm6NGjrF2tNh1WB6vEVLll+9JGcY3YHM09SqK2pjafe/a5AtXN5XKxR48e\nrFevHm+jZetvAAAgAElEQVTcuFGgNty8fg/ld6GjAvA/AK/94/p0CIM1AIyBYrguFHIvLBOH1Vq+\nQOGq//rrL06dOpWTJk2+ZV40LS2Nfn4RBN4k8BQBPcUucZcJS9Xcge069SDQgk8/nT1vvn//fvdK\n6obuEYSNgNWdV5Lb88lEaE3UGc1MSEigyVSGwHV3e3bQZPL2vCV/sHAh5chIsVZh2TLK/v4sUaWK\ncE3NzCTq1iV69BBv7q+8IgTzxYvC1bR8eWLkSFFXl4vo0IEYPFicz5nDYhUq0G7RsnlFcFI7MMQB\nGnQQ9okDB4RS0euFwDebienTRQiP6tWJwEBqJEmMRGrUIGrVIt5/nxg7VigOu10skmvdmqhdW4x6\nGjYUZe/fT8TFMTgkhBMmTGBCQgLbtWvHgIAA9u/fn0ZJuuUzK1m1qlBEN/v+3Xf5VB77JbtcLr4x\nZw7Dy5dnREwM573zDseOHcWJE7MtLkeOgGFhjnx/Zx4G8pIvc96cQ6eXk2bJzG6du93V9qVeJi9W\n01VjZUNl+nn7FWj7UpLs27cvq1WrxmvXrv1rurzagEJSEm0BHAZwBSJM+FX3//+VmhC2jl8gjOK7\nIWI2ewP4DooLbKHy119/uReWnXQL1eM0Gu33LN7/77//TrM5gsBIAk+6hXdZAosJc3Eh4OgWNdOm\nUWu088svv+S1a9e4ePFivvvuu6xUqbp7mukaxd4PLQkMdtd3LWGyEZMmEUOHUSWbKElP5lB6LqrV\nulyurR8sXMjYevVYtWFDDh82jLJRQ7UaNBlVwoicmUn88APlru0pBfkQvXuLkUWZMsT332fXd/Fi\nwmajytubOqeTDRs3ZpVI0PURyMXgkVmgUQeR7q23iCpViEuXiLQ04skniaFDsxfQGY00WK3CmD1p\nkjA4v/CCKPPcOaGUhg8nIiKES63DIZTWZ5+JEYXTydp16jAhIYEJCQkcPnw4TSYTW7ZsSaMkcfxL\nL+WaTqrdrBkxb56nLdqhQzloxIjbfobvvf8+TSVLElu2iH6JimKnLl3YqZPhZk8wMRGsWLHYPfnO\nPCgUpnz5L9uXHjt2jCqVipIk0Ww2e44lS5bckjavNqCQlEQShGvqg8RdfUAKuZk06RXKciAtlqco\ny4GcNi1/3kl34vjx45w5c6Y7fEWsmF4CCewi4E0YA4Vf/9mzxJ49hK8vhw4dxkuXLjEqqhzN5kaU\n5a7UaBwUAf5uCv7vCVQV/1vqEsuXZwvuMWOokawE9hFwUaWaxeLFK9y2fjt27KC/Q+KvU8GM/4FD\nmmtpktTE6tU02SXO7ga+0RU0G1WMLluWWi8vol07IiNDuKDWbkToLUKxeHkRBgPbVREKgovB6x+A\nGjXE2oiuXcXI4GY9f/hB2BNIsQDPYGBgeDjVo0cLhTBqlBg9vPSSZ+SimvoyrTY9Te3cNhOzWXhA\nbd5M1K9Pi8PB4cOHc8KECaxatSqtNhu1skx89hnlChX41rx5JMVc9qZNm2hyOmns1Yty+/b0i4jI\nU1g93qSJmKJjtnKs26oVy5SJ4JNPyhw0SE+nU+a33357T743DwqPgnzJqw0oJCVxL9ZE3Gvuc5c/\nuuzatYtLlizJV/TT23HhwoVcdox9+/bRavWjJHWlTtdCTAlhbA5B/wyBttSYw6gzmegVEMC33eE6\nXnppMvX6nFFem1Ol6srs6K6jqVI5CLxE2AKJjRuzBdibb7JG/QY0Gi3U6UyMjCzHXbt2ce3atdyx\nY0eut+lZs2ZxYGO9R6hfWyCEutnHwgV9soX9O73Ax6uUpxQRQVSuLN7iTSZCKkPIMqU61Wg2aykb\nQEkPrhgMJr0GdnoctJg1QhlUrSpcU2+6s06ZIkYCPXsKA3atWkLoG43UeXkRsiwM1LGxxOnTVL86\nnWFBen46FHy9KyjZTcKuodUSKSnEzz9TbTZTrdVSo9dTZzSKaammTYmsLGLSJPpERLBugwa0WyWa\nZR1Dg5wcPXo058+fz+Tk5Dw/20Zt2+YadWDWLLbp2pVXrlzh/Pnz+eqrrz7U+3rnxaMgX/JqAwpJ\nSbwBESq8E8TUU1sAbQqjoAJwn7tc4Z+kpqayYcPW1OnM1Olkduz4DDMyMli//pNUqWZ7BL1GM4Qa\njZVAHIG6FAvjztFsrnvLIqBnnx1EYGYOJbGZarXVPU1Vi0A4gQBWqlSFZi+HWI/w00/Ed99RCgri\nmjVrmJmZyUuXLnHPnj202wNps9WhyRTFZs2e8niCLFmyhLXKmJi5SCiDTeNB2WGhtVgglwzIVhIf\n9QeDAu3CjuFyCeP2tGmENoCyr41daoCHZop0ei1oDvWhyctI6bEYMaWk0dDHx0eMOGJihJHc4RDn\nsiwWu9G96E2ShAJyOKirWpV6o5EarZZGSc8dL2XXaUQzUGU1U2WxiDJSU8VoxWoVxvWWLUXeTqdY\n1W21EmPHUjJpuSVB5LG4Pxga5MP09PR//Yy3b99O2ekkJkygatw4mpxO/vLLL4X1lXpgeBTkS15t\nQCEpiQ/dxwf/OIqS+9zlCv9k0KCRNBrbut1Rr1GW63Hq1BksW7YGgfU5BP0HbNq0PRs2bEq9vhiB\nt6nX92FkZNlbjG+ffvopZTmawDECKTQa29PHJ4rAiwS+InCFwEoCdmq1rQjtOMJSnCqbnS+OH58r\nrzJlqjF77+k0mkw1+OGHH5IUbqCVK5Ri2RCwfTVQNuuEIli1ig6HgZ8NA1cNA4N9JPqHhhLPPCM8\nj4YNE4LcaKRGDaYvzBbeTSuqxBv8jRsira+vGCk0bCgUQ69eIraSv78YCRQvnnvBXalSxGOPUWcw\nMCY2lhMmTODYsWPp7+/HMS2yyxnWBIQsUa3T0SlJwiBeqpR442/aVCiHCxeEUvP3J1asIFavZtVK\nNk8eXAwG+ci3DefwT/bs2cNhI0dy+KhRt3jrPKo8CvIlrzagkBbT9ShopgpFT1ZWFk6ePAm73Q6z\n2XzP8//hhx24cWM8AD0APVJTe2PjxlVo2rQekpKm4vr1GAApkOXX0br1YHTo8BRmzpyFXbs2oUSJ\ncIwbtwkmU+51mW3atMFvvx3BpEllkZmZjgYNWuPChQicO5cMoAQAC4AjALKQmTkTQBRwdTKIl5Ce\ndiNXXn/99QeEHwQA6JGSUg9HjiQBAI4ePYp9B8/ixo3nse/c28DggUBzsUgqeVN/dFs0H2oXkQ4J\nrnLlxIK7SpWA8HDg8GEgJQWqksVx5jIQ7BBq6MQ1HfDbDkCWAb0e0GhE2oAA4O+/gfLlxQK7I0eA\nhQuBc+eAzZuBGjWA118H/vgDGDcO2r17UTU2Fmq1GgaDAbGxlTFv/beoFJGBv84Dc380Art/gWvf\nPqR0aotGpYCzWX/j93lvIHXDVqBiRZFXbKxYuLdvH6xfLsPhw1cw7zugX33g8GngSmoWnE7nHT/n\nMmXKoFf37lCpVChRosQd0x87dgy//PILgoODUbly5TumV3g0CAGwCsA59/EpgOAirdEjoOkLk0OH\nDrFEiRL09/enyWTia6+9ds/LaNu2KzWa8R5PIr3+WQ4cOILp6ens2vVZ6nQS9XoT+/UbzFWrVtFk\nctBsjqIs2/npp7fGmsnJxo0bOeaFFzh06FBKkjeB4gS8KNZG+LpdYVd7Rit6fXu+8kru+EKPP96Q\nGk2C255xnipVJF9//XWeP3+eHTt2pF5fThjUNUbxxv/qq8TcucIQbTKJ6aGcoTQWLSIqVRL/f/st\nNaWiGewNvtwerFxMTZ1RKzyhbr7dR0fnHimUKyemxh57TAT669aN8Pam0UtmiAPsUENNu01Ls9HA\nevXrMyEhgRMmTGDx4sVpNJvp6zRRtuiFoZ+kudZjfKe3GBW4PgKfrGEgXp4iyt20ybMJkd2s4hcj\nwK9Ggv42sEIY6OctccG779zxM75y5Qpja9emKTycpvBwVqlbl1evXs0z/eeff0anU2bz5laGhckc\nMqTvHct4EHkU5EtebUAhTTd9BxEBVuc+ekBEaS1K7nOXP1xUrlyZb7zxBkkRljskJISbN2++p2X8\n/fffDAiIotVajxbL4yxWrDwvXLjgub969WqaTN6U5VCKldWvuoX6Tsqyg6dPn75tvosWL6YcGEhM\nmEA82ZqQwwhcJnCBIsprXQLe1Gis1Gr7UKUKJqClXm/im2++7cln//79bnuGH0WgwKa02wMZGebP\nzjV1HNkMNBtlAm0pG1VUq8XaBlXbNsTevUKYN2qULeQTE4Vwf/ttsWfD1KlE7drUGXSUZZlxcXGM\nLlGCOqdTKAq7XXgvkcRXXxEmEw3e3tRLElUjR4rpqBYt6PDS8OoCIeyPvQ7qNKDWaKR/SAi9QkKo\ni4khrFY6Q0OEMnOvyrZEBvDXqdnTR7OeBo2xMTQ4HDRGRVGuVo12m4EfPJudZuVQ0MtHZqOWLW/p\n9+TkZH777be5jPwDhg+noVs3YQDPzKSxc2cOGTXqtp9bVlYW7XaZ27eLHrt8GYyMNN3z79394FGQ\nL3m1AYUY4C8/1+4n97nLHx6ysrKo0Why7aPQr18/zp49u0D5XL16lVu2bOGBAwdyeQZdvnzZowwu\nX77ML7/8kl999VWu9QjJyck0mRwUmwgJA7TYkvQCAdJmq5ZnCBCf8HBi27Zs4dy4DYG33fl0p1ot\nsV27p7ljxw4WK1aBGs1AihAdR2g0BnvcMbds2UKLJZZiE6PzBEiDwYfd62g9QvOrkaBN0rBbLeG2\nuv8V0NtXFvsx7N4tBP3vv4tQGjYbERkpRhm7dmWvMzAY2L9/fyYkJDA+Pp6hUVFi85/ISKFUbDZC\nr6dZp+NjlSuzdevW9A0Lo6ZDB6JXLz5WXJXLVmA3QdgsVq4U3lsZGWLVt1qVbfCuUoWSScdOj2uY\nthA89RYY6afmYzVrskOHVgwKMrJ6dZleZjVnd8vO+8O+oKVhLWoMhlxB+3bv3s0AXy/WKW9lVJCJ\nHdu1ZFZWFqs3bixsNTc/i1WrWKt589t+bmIFv445xk586inLbX34H3QeBfmSVxtwF0oiPxHdkgF0\nBaCBsGE8DeB8QQtSuD+o1WqEhoZi3bp1AIDU1FRs2bIFERH53wLkt99+Q+nSpTFo0CDUr18fvXv3\nRmZmJjp37gWnMxD+/mFo0KAVtFotmjdvjiZNmkCSJM/zSUlJ0GhCATzuvlIDYobyCICjSEs7hLCw\nsNuWnXLlCpDzXrEQiPWbyTCZNmPt2kSsWLEIjz32GE6e/BNZWRMgvppRuHGjBZ58sh02b94Mp9OJ\nzMzjEEH/HAAugK4riPTJ9GQd4Qu4mIVpHQCjHigdDPSufgPYsAE4cQIWkwmG2FggMRH4808gKUnY\nGgIDRQanTiErIwM2d9BAlUoFL4sFxkvnoJH0QEICsHIldMyAIyAAzZo3R0xMDHp06ADXihXAmjXY\n9yexdi/gcgFzvxOqUNJmAVevArVri/IOHACsNlGPFSugOX8eHTp1R4pXHCy9NYgYrkW3vmMwefx4\n7N37PQ4duoEtW1IxfZYLY5YBMxKB178GBiyXcLXnAE9Qx3fmzUWT+o+jWYOa6FvrEjaMvoL9U1Lw\n5/7vsXTpUsSULAnDypWici4XDKtWoULJkrf93Gw2G/z9ffDhhze/Q8DGjZmIiYnJ79cuT2bNmg5f\nXyusViP69u2G9PT0/5ynwr0lHGI/iZs2ic8BhBZlhfAIaPrCZMOGDfTx8WGjRo0YGRnJnj17FiiI\n2+OPP865c+eSFBudVK5cmZ06PU1Zrkux+jmNRuNT7N9/+C3PulwuHjlyhEajndl7XCcRkGixVKck\nOfnmm3PzLLt9jx40tm0r3ELXrCHMZppMUTQavTly5Iu50oaGliaQ6C7jY0IyEaXLEiYTm7RqxV69\n+tNkKk29fjAlKZKy7KCXrOIPE8Cjr4ONKhroZdbw61HZc/t1y2vEpkEOBw2BgWzeogW1N0NwkCJ0\nR/36YsV406bUFS/OUhUrcvDgwezYsSNNko4L+oBlI7TUFROxm/y9wPKlIz2rol944QWqdDoxyrDb\nKRnVVKnAciHggeng7G6gWVYLu0WdOiKm1MyZuRbklahShSR5/fp1z45/7733Hrt3l2+mossFqtWg\nJGkpl4oknn+ecng4SxuNbP1kS5YNl/n5cPDtZ0CHWYykuBh8oZWKL774Io8ePcqYxx+nuXhxmqKi\nWLFmzVwB6/7J3r17GRHhRx8fIy0WAxcu/CDf37m8WLFiBaOjZf7+u4gV1bixxNGjh/znfP+NR0G+\n5NUGPGSxm/4L97nLHz5OnjzJxMREbt++vcBRPh2O3DaDcePGMSgogrlXP69juXI1cz23YcMG2u0B\n1GqNNJt9aDA4aLM1oCT5cNKkqVy7di3/+OOPfy07JSWFXfr0oT04mOaAQOp0Mk0mJ8eNm3BL2nXr\n1lGnsxFoRRhl4pdfhHhMSiIsFlavV49Tp05lp06dGBNTjVrtGAKv0CzpKRtAo6QlevakbDWwU00N\nqxRTUXaYReC9rVuJ996jUdJSssvE55+LvIcPF4vfNBoh5GfOpLZLF+psNvp46bl+nBC0h2aCNpOW\n0OvZr4GKvl5aNqhfj926dWN4RDi11atTMoDNY9X0dWg5tUP2tNBvM8BwI6jTaESojkGDRKgO0hN5\ntlJc3C39sWPHDlqteg4cCH79NTh7NliqVCidZjP7ajTsJElcBPB5tZpBvlbunJxd5qjm4LhW4Ll5\nYFSAjlqNlia9iRXKVODatWv5yy+/5BlxNCeZmZk8efJkgeMa5cWzz3blW29lT2Ft3w5WqhR1T/LO\niwdZvnTp0oX+/v60WCyMiIjg5MmTb5surzagkJREFMRI4jyyRxKRhVFQASjMz+H/PXXq1OGsWbNI\nit24YmJiqNFIBHoye/XzODZq1MbzTHJysjuq7Br3/U9ptfpy5cqVPHLkSIHrMGLEC5SkBgROUWxq\nFM1ly5bfku61116jVhtEBEbk9iaqWZNqq5UOo4NhhjDqoCHQmJCCibnzRUylxo1F2oMHxRoGjYYY\nPVpc272bkiyE95zuoFWCiJUkSSIo4LVrwnjt7S0CASYksFONbKH761TQYTMS3boxNFTmT5PAJ6vo\nGOFvoDHQl8YgJz8eKNL+rx9YMhC88A6YtQgc9ATYQQ9+BlAly2K1tyyLdRqTJlFvt3tsLz/++CNH\njx7NKVOmsG7duqxduzbHjh3LgAB/Wq1a2u1GNomLYyujkUcArgHoI0ksFubL7TkW6Q1vAlpNWpok\nLb2MMkdgBOMRz1raWmwY1zBXn7tcLi5atIgjho3g/PnzPSOZwmDs2Oc5YEC2reODD8AGDaoVWnnk\n3SuJm9uXjh9fONuXkiKiwU0FfPDgQfr5+d12n+u82oBCUhLbIWwSN72bnnZfK0rueec/LKSkpOQy\nSv+TLVu2cMSIERw/fjz/+uuvuyrj8OHDLFasGKOjo+nl5cWGDRtSkkIptgStTqAuVSovLl261PPM\n5s2babNVzTHSIC2WUrl25vonp06d4q+//nrb/aWLF69MYGuO/N5m5869PfczMjI4f/58DhkxgrGx\n1QmDUewTQQrvJIeDsNtplpwsDg0nAYwAiDIVRJpPPhHhuukWP8nJQknIMjFzJo2li+V6u/9kCGiz\n66kpXozWuCo0N60nYid5eVGt0VCtVlOj0TDEqeOb3cEwPwMjQnwolwyn1m6hZFTTzylCeOh1YJhT\nGKm/GCGmuaoUA/U6FU0GsJQBPAfwL4Baq5WIjxfeVePGES1asGq9eiTF4kMvLy/GxcWxUqVKlCSJ\nS5YsYefOndm5c2fq9Rpu2AD6+FjYt1s3hjocLB8RwcTERL41ZzaLB8lcMgCc3klFH28z161bx5Ej\nR7K2qrYnnPVwDKfdYs/12fTt2ZdhchifwBMsLhdn80bNCzxa3bt3L2tUrsFg32C2bt6a586du226\ns2fPMjLSn+3aSXz2WSOdThN/+umnApVVUPKSL3favnTw4GcZE2PihAlgtWomPv10m0LdvvTgwYMM\nCgq6beTmvNqAQlISt9uqVPFuus9cvXpVRPY0GmkwGDhmzJhbvoCrV6+mr68vJ0+ezCFDhjAgICBX\nKOIrV65w9erVnDlzJj/66KPbCuebrFixgl5eXmzVqhXLlStHs9lGYJt7pPA+jUZvJiUledInJSVR\nknwInOPNqLIGg41nzpy5bf7x8VNoMHjRYilFb++gXLGjrl27xrJlq1PsVS2UhFY7mMOHjyYp3tga\nt2lDOS6OmDqVpsqVWS0uTrx1R0SIUBS1ahElShBvv03JbOb3AL+HezRw44YYCZQqRXTvTixYQG1M\nDFXlywvPpW7daLFo+Wb3bCWxZrQQ6mE+Kn45AnyvDyh7SYRWS7PF4gmwV6VKFcqykSaDsC281QM0\nmzTEsmXE4sU06rPn/re/BHqbwd0vgw4LiGLFWL1uXdokiVaDinoNKBnU1DZr7In9pJ44kV16C2UZ\nHR3Nbt26eWwdYWFhtFqtbNasGevVq0edTsutW0GVRsWoSpUYVbEi58ydy59++omhAaFUq1R0WvVs\n3rgu9+3bR5JcsGABo+Vojsd4JiCBbdGW5aLLeT6bkydP5tqm80W8SD+TX4FCzJ87d44+Xj5soWrB\ngRjI6rrqrFKxSp4C9cKFC5w/fz5nz56d6zt3O9LS0vjBBx/wlVde4datW/Ndp5zcTr4kJyczOjqY\nbdtK7N9fBDb85/aldruBly6J147UVDA0VL5l+9JRo4bQ19fCoCD7Xe8d/txzz1GWZWo0Go/tMD9t\nuHm9MATyKwDGQhiwwyH2epgGEdLbuzAKzAd31bkPK6tWrWLZsmXZvn17pqen89y5c6xQoQIXLlyY\nK121atX4xRdfeM6HDx/OsWPHkiS///572mw2mkwmdujQwfP2efXqVd64cYOHDh3ixYsXPc/6+vp6\nfmRpaWksV64c9XoLbbbalCQnX331jVvq2a/fEGq1AdRoWlOv9+PUqa/etj2bN2+mLIe5p5JIYDGD\ng0uQFBsVOZ2hNJlKEpCpVvekJHWgn1+4x07y888/0xQZKUJvk8TlyzTY7dy2bRubtG5NU3CwMC5f\nuiTuL1jABmYzSxmNRECACJ43fjy1xYszplo1Nn3qKapNJqJTJ2EgPnmS2ser0GEWQfvWjAYjfUHZ\nAE/8Iy4G41uDKrWKtWvX9gjqYcOGUWcw8O1nstMt7AdamsQRnTuzdKQ+l8trkB3UGrRUT3uZiIqi\nj11idKCWNUsI19Y/XhNlq+PiqO7Rg5K3Nw8fPkySDAgI4KBBgzxl2+12Pv30057zWrVqsXRpNVUB\n/sT69cSmTTRERdGgl9gO7RiPeHZCJ3pbvD1uzenp6axfpz5DzCEsZy1Hb4s3t2/f7vnsDh8+TB+T\nD+MR7xltRFmjuHHjRpLCFTYpKckzBXXp0iVu374916j2iy++YBlrGc/zEzCBJoOJZ8+evcMv4d9J\nS0tjnTqV+cQTJg4bpmVAgHxXxvPbyZeJE+PZq1f2tNeyZWDNmuU99w8cOMCoKLPnPglWrZp7H+wp\nUxJYo4bMo0fB/fvBEiVkLl68qMD1I8WL0vr16+lwOHJ9Pv/WhpvXCyps8+MC2wFi29L17qOf+9ou\nADsLWqBCwViwYAGGDRuGlJQUjB49GjqdDk6nE7169cKWLVtypU1NTYW/v7/n3N/fHykpKbhx4wY6\nduyI4OBgLFy4EB9//DHWrVuHiIgIxMfHo3jx4mjUqBFCQ0MxZ84cZGZmIjk5GY899hgAQK/Xo0qV\nKpg48QV8+ukEbN36HUqVKoa1a9ciLS0NAHDy5El8/PFyZGU1QVaWL9RqGaQLt2Pfvn0gn0D2BoQd\nceLEYWRkZKBjxz5ITh6DlJTfAGyHVrsWnTqZsH//Tvj5ie3OFy1ajFSjJMJfAIDFgnStFpPHjcPC\n+fMR6nTAeuYozE+3A3bsAPR6HCXxm04nwlbcuAG89RZcR49i2/r10KtUMGRdx3PnP8bTq8dCKhuN\nzF8P4OoN4PklQL8PVDieogeCApGe7UGLtCwAajWOHjsGl0u09e+//4bGJEPSZaeT9IDq6hVAp8PR\nMy4cPi2u7z4GJF8DsjJcUI0bD93xY5A01+FrycRLbQF/L+GmO6E14LV9C4p9uAU+qWZMHD8RgAhj\nsnbtWpw/fx5JSUm4fv06dLrsgg0GA44cNYAzXgXi4oBatZA2YwYyjRLKoixUUKEESsBL5YUDBw4A\nADZu3Iizp88iQ5eB4KrB+Hnvz6hSpYonz/DwcDj9ndio2YgLuIDtqu1I1aeiYsWKmDplKoL8glC1\nfFVEhUZhyZIliAyJRIcGHVAmugymvDQFAGAymXDVdRUuiD67gRvIcmXlcqO+Gz777DO4XAfx7bcp\nmDUrE998k4rhwwf9pzxvcuHCWZQqleE5L1kSSE5O9pxHRUVBp7Nj2jQ1TpwA5s1T4eRJbS4X4K+/\n/hSTJqUiPFxsWz56dCrWrFl5V/VRqVSIi4vDU089haVLl951ux5l7kr7PiycPHmSb731FufMmcOo\nqChu3bqVjRs35pw5c0iKt4guXbrwpZdeyvXcxIkTWb16de7evZvffPMNAwICuH79ev7xxx8MCQlh\nWFhYLiPy5MmT6XA4+JF7+8pjx44xMDCQu3btYrVq1Th58mS6XC7u27ePfn5+/OWXX3j48GFGRESw\nbt26rFKlCitXrszLly9z1qxZNBh65bAhHKCXV+Bt27d8+XKqVIGexXXAZ/TziyBJms3OHCMMUqUa\nxwkT4j3PfvbZZ5SkMEIKIma+Rhw5QtXo0QyRZfZXq+lrNbNEkI5fjgDn9wIli4GwWKmSzWIVN93v\nee69pns++ywDA7z4rjvExYk5YOkg0GzVUy5bXHgwvfEGcewYVe+9R18vFRf2A1/pKMKDN23WjHpJ\norevLyMiIqjTamkIcNJm1XDFYBEo0M8MqnVasc2pUU/JrOP/sffd8VFU6/vPzE7Zna1JNptsOglJ\ngKWdU4AAACAASURBVEA6EJJAAoI06b2FJigJRZReQlBRBEGaCIrSBC5NQbEhCGLj2hBp0qQrINJb\nSLLP748JG6OgcK94vff3ffjsh5zZc87MmZ1533POW56YimaaVIEGg8BMG+hrBNu1A6tHgi1TdWP5\nzdXGI43AWlIyC1DAURhFm9HGw4cP89q1a6xfvz4dDgfDwsIoywYGBzjYrVs3tmnThppRpkE1ElOn\nlo177lwKFhsfw2MsQAGHYRjtRjsPHDjA7du3026ysyM6Mg95rGKswtSEVLZt3pbjx433GkxPnDjB\nRvUa0e3nZu0atbl3715u2bKF/po/H8WjLEABmwpNaRJN7IIuLEABH8Nj9NP8+MUXX7CoqIi102qz\niqkK66M+Q7QQDnlkiPfZHj16KO12E61WlYMGPXxHXlUk+cILL7B3b9PNkfL6dVCSxDtufxO3ki/r\n1q1jRITGHTt0V9xmzUy/STty6NAh1q+fxoAAGzMzE36TELFZs2zOnVu20hg50sABA/re1bX9Gr17\n9+bo0aPvaAw3j98LgSwBaAFgIIBHATxW+v9/Ev/Wjf074+DBgwwKCmJOTg67d+9OTdP41ltvcdeu\nXXS73WzUqBFr1KjB5OTk3/isFxcXs6CggJUrV2ZycjJXrVpFUk/r7ePjw2bNmrFr1668evUq9+3b\nVypY5HJ7wV27duX8+fN55MgRpqamUlVV2mw2Lly4kLm5g2mxWJmfr7ujejwedu/enaNHj+akSZMo\ny7m/UBLf02YL8Pb7008/8YcffqDH42GDBq0IZFHPw1STgJWPPDKYJJmSkkVRnFraxwWazQlcuXIl\nSd1QW7VKHIFWehS3pRJFs4V1zGaeAOgBaDehXGrtkc1BRVAIu1unE2XpK7psGZGdTVSpQrtF5KbR\nYPFiMCEMHNEM3D8FnNJNoMkk6gRAJ0/quZ00jYlGsJ4MKgYDFUWhoig0Gw2c3UOPv3ggCYwPBd0O\n0GEGNYPIpnXrsnPLloyTZQo2mx7NrWl0uF0McouUJdCs6p/c+qC/FeyWCbZIBs2K4BW+4zCOgZZA\nfvPNN4yvUpGhvmD1CiLNikCXr4W9swVWj1ZZu4rKmCCZsgTKNhMxdozOm61prIZqtMPOOMTRITs4\nYugIkuRTTz3FDCmjnNFahsyWaMmqpqqsV7vebT12ZsyYwVrGWt62YzCGAoRy21LJlmQuXqxvr1y/\nfp3Tp0/n4IGD+Y9//MP7DM6ePZMpKRqPHQNPngSTk8HMjLQ7EvT6VqWJH3wAnj0L9u8vs2HDzD9s\n92vcTr7Mnj2TbreDdruJvXt3/pfoS51OM/PyZPbsqTI42Peu6EtPnz7NZcuW8fLlyywuLua7775L\nm83Gzz///I7HgHukJN4B8BqA8QDG/eLzn8Rd/Tj/TejZsyefeOIJb3nChAkMDg7mpk2bOGvWLFqt\nVk6bNu2uH9C1a9fS19eXQUFBNBgMNJvNnDFjBgMDA7lx40aS+n5yZGQkt2zZQlLnwe7V62Hm5j7C\nUaPGUtNq0m6vXm6fdf78+ezatSsPHjxY6gI7i8B71LQ0PvbYSBYVFbFbt2602Wz08/NjgwYN6HC4\nCTgJiNSZ60axVy+dVP7AgQMMCqpIq7USjUY/9ukzgB6Ph08+Po6Vwsyc2BFsFG+gRZVYEyYmAnQB\nnAzwUKmS+GRcmZIY0gS0KT4UNLNODbp7N3Hhgm7YnjqV2L2bko+NCRECN44CQ3zLaEi5BKwSAgLQ\nmevcbkJV6QswFGCgy8URI0YwPz+fKckJ7FpbT/mxeQyYEaOn+gjyM7JJq1aMSEhgWoMGOj/DTX7v\nCxcoO52UJAMD/Cx02iQObAg6jAbaFYUWWaGi2mg0aqwv1Gd/9GdVsSpdisowf3+mVSxLV/5CT9Bm\nFGhWRWbEGhjlNrJCqJOPPw7u3AkOfExi3YYSzSawD/qwNmpTgUJN0bh161Z6PB727NmTgYZAxiGO\nFlioQqUAgTGI4RAMoV22s1P7Tgz0DWSIK4SzZszyPgfr1q1jiDmEozCKBShgJ3SiIijsjM4ciqHM\nQAZVg8qCgoLfCPxz586xfav2DHIGMdht5T/+UTbbfucd0OWj8qknn7qj5/ytt95iVFQgLRaVzZrV\nva3X1O/hXsqXf4e+9KeffmJWVhYdDgftdjurV6/OtWvX3rLu7caAv9C76T+Nu/5x/lvQvHlzrl69\n2lteu3Yt4+PjmZ6ezoYNG95RwrRFixYxOTmZ8fHxnDp1qneWdvLkSX744Yf8/vvvvcc2bNhAp9PJ\n7OxsBgUFcVhpAreNGzeWZmAdRSCfBoMvgYU0GgewVavOvHHjBi9dusQ6depw2rRpJHXugfvvb83k\n5HqcMGESS0pKOGXKFNarV49XrlxhUVERu3TpQqPRRuBL6lwUQyiKbs6a9bz3+q9fv85vv/3WO8sq\nKiqiUZX4w6yyyOiUUHAmQD9JYkJcHCvHxtIky6yRmMCoII2L+4FPdwBNikCMHK0TBk2YoOdeMpt1\njojiYj1HU0QElewMqjJoNYIWI5hSAfzqSTDMT/dCeqo9qClgX4CPGMAMSWKTJk28RuK+ffsyNlgl\nl4BPtAU7pOnXWtFtoCE7W0/MN3s2IQjEjRu6CNy5k6rJxEGDBrGgoIAdO3ZkgEOiAFCUZMrZtYmx\nY6nGxrJixVj6+/jTIYpcA7CzAD7ZrkyZHZoGWhSBUYiiCJEAGBrs4IIFZQL37bdBh12gEUYGI5gP\n42FmI5vDhw1n/ez69BV8WRmVqUFjOMLZB33oC19WQAVWQRVq0OiCi/3Rn33RlwFaAJcvX+79jSqG\nVqQGjUEIogyZwVIwDTBQhkx/+NMII/1kP7ZuVt419L469zFVSeUADGCMMYwjRpRd8zMTwRgtmHVq\n1in3jBcVFXHqs1PZtUNXTnhyQrlJ09WrV9m9eztaLCoDA+2cM+d53g3+F+TL7caAf0FJCHdQ51no\nWV/fu9vO7wCvAGgK4DSAaqXHfKEz4YUDOAygPYDzv2pXOt7/PcycOROLFy/G6tWrIQgC2rZtiw4d\nOmDw4MG3bXP9+nU8++yz2L17NwRBwEcffYSFCxfCZDKhT58+yM3NRb9+/bz1i4qK8Nxzz2Hbtm2I\njIxEr169cOjQIbjdbsTFxaGwsBCRkTG4cOEiBEGCx1MdV69egijGweN5FprWAeQWeDzF6Nq1C+bO\nnQuDwXDLa+vSpQsaNmyInJwcAMBHH32E5s1zcP78odIahQDMuHr1kvf64+PjIUllVCfXrl2Dj8OK\ny/NKIJWeptUzwPE9Mpz16iGtVi0AwPvvv4/o6GhcvnwJG95ZA7nIiPMBgSg6eqDsgmJjdavhd98B\no0frHA+9ekF56SVIRw/gmU5A1wxg9RfAo68CDhPwzdOAzQQ4+gDxYUCr6sCUtwRYXRXRqVMniKKI\njz/+GNs+34LYgBvYdxJYNRD49ADw5Brg6okzgJ+ffn5XAPDUBODBB4EXX0TkpEnI6dbNe3mTJj4J\nT0kx2qeLqBrkwaQPNPz84CMIWLkKrTIyED1/PgYDWAlgvAvY8gTgYwaGLRUwdyNgKLSjB3rADDMW\ni4tQ4v4BK1aU4OxZIC/PgKuXbGhyrhkiS+Nh35bfxrWYa9i9azeMMKIIRYhGNI7iKGIRi2AEYxd2\n4SiOQoGCNmiDCtDzgH2Nr6G11rBs9TK89NJLmDhoIupeq4sbuIEDOIAzOIPjOI4O6IAIROASLuFF\nvAjZKOO9j95Damoqrl+/DpvFhhElI2CAAedxHi+apqPh/QJUScS7bxtQ5XoKgpoHYeWalQAAkmjX\nsh12bNiBmKsxOGw6DGeSExu2bIDBYEBubk+cOvUPvPjidfz4I/DAAxrmzFmNRo0a4U4gCAL+2+XL\n7cYgCAJwZ3LfizshHfoUOp+ECOCmeZ8AbHdzottgPoCZABb94tgI6EppEnR32xGln/8vkJeXh5Mn\nTyI+Ph4A8NBDD2HQoEG3re/xeNC6dWsoioI2bdpg+fLl8PX1RVZWFkRRxOTJkzFp0qRySqJ79+44\nc+YMcnJysH79enTp0gVbtmyBUuotNGnSJFSpEot1696EKIpo27Yb3n33BgyG12AwHAJpgqJY8MEH\nbyMpKQmFhYUgWU6w30RkZCTef/99dOvWDYIg4L333kNJSTGAEuiJ+bbDZnOievVsHDlyEYAH0dEu\nfPjh27BarQAAk8mEunXS0Xf+VgxvUoRP9wMb9wAVRRGuUo8nAHC5XFi+dCnk4hJc9AyEKs5B0eVz\nwLVrgMmkJ807exZ49lldWeTlAcXFkAsKUKGoCFdsQP/79b56ZwOT3gSuFAKVhgK9swCLEdg8BpAl\nICeTCHvkMF6ZORMWoxGHfvoJJhbj7Dn9pWo+RWe9uFps0EmLAN3M4nZDHjgEJfkF8Jz9GScNIi5f\nvgyLxYIjR47gRhFRIwJY2Ff3/Klf9SpqTZ6Onz0G/HT2LPwEASDRFsA/zgJBeYAqAyUewqcwBJVQ\nCQ44AADNPS2w9PKr6NBWwPmT51BNlbHHcwWrpZUIKg7BJeESLhkuoWRXCfqjP8wwYyd24hN8gn7o\nh5mYCQMMOI7jkCDBBRfO4ZxXSVwQLyDYGQwA2PHNDkRdi0IoQgEAdtixGIsBABGIAABYYUUwgnFR\nvIgLFy4AAGRZhiiKuFxyGfbSf74IxnvvnoWfxw+hBgf22fZhweQF3t/56NGj2LB+A/pf7w8ZMpKu\nJWHet/PwzTffICUlBe+//w7eeOM6/Px03dyv31Vs2PDOHSuJ/0N53ImSmAogDcBOALf2afzX8RFQ\n+gSVoTmArNK/FwLYjP+PlIQoipgwYQImTJhwR/X37t2LXbt24eDBg5AkCZ06dUJ4eDj27NmDQ4cO\nYcqUKTh+/Dj27duHmJgYnDp1Cu+88w5++OEHmEwmdOnSBUlJSdi6dSvq1KkDAPjqq6/w0EN9oaoq\nACA3tyc++qgr5s6dDUBfiZw+nY2hQ4fi8OHDOHLkCAwGAwYNGoSJEyfenK0AAIYNG4b69esjJSUF\nmqbh1KlTqFQpCnv21EZJSVUIwlqkpibj449DcePGXADE7t09MGbME5g+fRIA4OLFizh1zYOPvxWx\ncitgFQTUKiI2i0U4vXEjXC4XiouL8emHH2LsjRvQIGAUnoPFE4gz1y/DUzMNaN1Kz6Lati3w44+A\nJMFdUoLiq1chA7gK4NwV/eNjBi5fB05dBDQFCPEFZq4HzJoIWdJfAX8bYFGKEH/uHNIBPA0gBUDu\nGX0GlQ/gvBHwUUtwLaEarucXQPn0U3gOHECHa61w5toZbBDXI8xfwktzZsDP14EfTp1FIEqQ+ouE\nvUs/ASzFV1CzIvD++2uxTtcRcAL4yKDilVdeQWJiIr74/Avk9snFieITIAgBAn7Ejwhxh+HY4X3Y\nRqDi9evYByARwA3hBpKYhN0lu3EBF7Aaq3ESJyFAQAlKIEGCBg3bsR0OOBCGMDjhxNt4G0dxFACw\nw7MDJ9afgMvmgtFkRIlaghqFNSBDxk7shD/88QN+wH7sRzSicQ7ncARHYJSMSE5OBqCvCh579DG8\nPONlVL1WFaeMpxAQHYBP132Kt99+GyTRokULuN1u7z0pLCyEbJAhlYovAwxQBMWbHdbPzw979pxC\n5cp6/d27FVSqVDaZ+D/8+dgCfcp3rxABYMcvyud+8bfwq/JN/EU7e38Nrl27xmXLlnHu3LneQKk7\nxfbt2xkdHe3d4/V4PAwJCWFWVhbdbjfnzJnDcePG0eVy8eDBgzx+/DidTme5fDtpaWncuHEjV6xY\nQZfLRVVVmZOTQ4/HQ4/Hw/79+7NBgwbe+s8//zwrVarEN954g6+88gqdTic3bNjAlJQUzps3z3sd\n586do8fjYWFhITdt2sT333+fly5dYlFREZcvX86GDZvQZgugweBPncP6pmfUaqalNeS6dev4xhtv\nsEJ4AM02heb6mTq/Q1YWk0WREZLEiNBQKopCyWDgWFGkp7STRMjsgR4cgiGUIROKUY/ADgnxehYJ\nFgutADMADoBuj6gSDD7WBKwaotsgQn11G0PHWrrn0cAG4OJ+ugeSyw4KAP1UMN4EVpJAA0ARoEkG\nH20CvtIXrOQP1lAMHCGK9IfEaIQyGhIrKLrBed8UcNNo8L3hoMME+il6GpA3H9MN8adf0O0OR6aD\nqgQGwJcVEEiLYimXGmXVqlX0s/oxQopgsimZPhYfzps3jyk2G39xc1kBYG/09rrUKlCYilTmI58D\nMZAaNGYikyaYmItcBslBtBgtVKBQgkQNGhUolCEzDGHsi75sh3ZURZV2o51B5iCaDCYKEOhr86XN\nZKOPwYcSJIYGhHL79u0kyXfffZdOp5W+vir9/Mysm12XvXv3/sN0MsXFxUyKS2ItpRYfxIPMMmTR\n5edk27YNOWBAX65evZpOp8bcXJUtW2qsXDm8XKDoH+F/Qb7cbgy4RzaJhQAqQPdyupnIndBXGH8G\nIqAnELxpkzgHwOcX35/FbyO7OW5cmYNVdnY2srOz/6TL+Wtx9epV1KtXD5qmISIiAm+++SZWrVqF\nrKysP24MoLi4GOnp6ahRowbat2+PVatWYfPmzTh//jwWLVrkvS/Dhg2Doih44okncP/998PtdqNX\nr15Yv349XnvtNSxYsAAtWrTAW2+9hbCwMKSmpkLTNNhsNly7dg0ffPAB/P39AQApKSmYNm0aateu\nDQCYMGECzpw5g7i4OHzyySfo0aMHOnbsiMuXL8Nms2HlypVIT08vd91TpkxHfv5iXL26FMBTAK4D\nWAKAEMU2MHjWQ4IIUbmGsa2JdjWBxZ8ZMOnrYFwdPBzakMEItBbDbDHDoAXi4J7vcaykBHboD2kU\nZDRBTwQhCEuNS1G5YWWs3bwZyM0FJkwAzp+HWLMmwo4cwYEbNyAAqKoAiSlAdCCw6wTw3nbg0wKg\nWmli/PpPAVu+A2LdwOEzQGY0sGUvMKYFUCsGeGoVsOcAcFoAzCqQWgHYdgR4uB6wZD0QfF3AbqgI\nYyE+BzEHwLIoYOMYwCgDefOBtR8Bp28AVpMATwkR5gK+fabsvoXmiWh+/mG44MJhHMZHIR/hwLED\nKCkpwf79++HxeLB9+3acPHkSTZo0gZ+fHyoGBWN90Q3UALAVQD0IGIhhMMEEgpiMyeiMzggpZSV+\nG29jG7YhDWm4pF6CGC2i50M9MWrAKFRRInHFeBY/Xj+Pyzdu4GE8DJ/S1/U9vIfqA6sjJycHcXFx\nEEURiqLg6NGjyMt7GFs2f4BLl28gukI0DMZr+O67YwgKApYuBa5cAdq0Bq5fF6ApGj746AOkpqZC\nEIRyK9Ob2LZtG3rm9MSPP/wIm0OF3ecMBg++hm3bJLz2mh+WLVuLzz77DGazGR06dIDNdue74/9L\nNonNmzdj8+bN3uPjx48H7tImcScoKP3cdH29+fefhQiUX0l8h7JQXHdp+df4S7XyvcSMGTPYokUL\n70pgzZo1TExMvKs+zpw5wwcffJDp6ens3bs3z5w5w7i4uHKJ0MaPH8+hQ4eS1HM4DRgwgBkZGczJ\nyfEG7/XtWxbYc/HiRYqiyI0bN/4mx1P16tW5YcMGbzk/P5+PPvoo+/Tpw8GDB9PlcvG9994jSb75\n5psMCAj4TUxH9er1WcZTfZFANYpiAFXVTRFmRqMae6EXK7vU8mkswqxUw8PYJUOg51Xd0+mh+gqT\n4qJZVZL4JMDqAMPh5DiMYy5yaVEs3L9/P01OJ3H4cFmsxOOPM0JRvDPsQ6WrCUUCLapOJXr2xbJz\n98rSVwdcAh6Yqs/qsyqD517UvZmsRrB2rL7iODJdr/fdZD2DrEMTOfHpp9mkSRMOEkUSYBHAYAU0\nl3pVmRWwlgxaRJGfffYZZ86cSU3VXXNv0o9aZYPXzXQABjDQL5Bnz55lUlwSbQYbZchUFZWqolKR\nFT7U9yHaFCstkBgMiUaAEiRmI5sP42FmypnUJI2d0IkFKOBYjGWQEMT4+Hg2adCE99W7jzNmzGBm\nRiadmok9uhq4di3YorlAqyZ6VyQFKGA1oRqnTJlS7ne+cOECo6Lc7NoVnDoVDAuUaTEaOGsWWFSk\ne1z5++sxEZHBCnuiJ/3hTx+7kYpioNmscPz4Md73o6SkhM2a3U+zGXzgAfChh0BZ1tvf9Ihq2dLM\nBQsW3NU79Ev8L8iX240B98gF9iaspZ8/GxEoryRuGqwB3RYx8RZt/uJbfu8wZswYjhs3zls+cuQI\n3W73v93vxIkTWa1aNS5YsICvvPIK/f39+eWXX962/sqVK1mrVi2vD/vnn39Ol8tFUt86ev311zll\nyhRu3LiRixYtYnh4OBcsWMBJkybRYrEwLS2NVapU4bvvvsvU1NRyfVerVo1ff/11uWNpaXUJPEDg\nJQLXKAjj2bRpGw4YMIAKKrEN2jAXuQwwy7w2XxeSl18GrSaBNrPK1weXCe83HwN9bBIFi4Wy2cx4\ngEaARmiURZkL5usCI7F2bQpz5uiipLCQSEsjZJlPAjwGcAAEBjscTAT4NcDqCtg8WQ+sWzNYF/67\nJ5Wdt0KAgTWiBCZHgE4ruHoQ+GA2GOgj8o3HyuqF+IKybOLrr7/OdevWMVzTeKQ0+C8JoEMDP84H\nj80AH4gHK8tgXmkSv3B3OK2yTFUCNRmUIbMzOjMXuYzVYtn/4f7s3rk7U8VUdkIn2kw29ujRg/36\n9aPb5aZiUJiFLNZHfVphZRayGIEIqlCpQt8eGj58ODVFYzSi6Q9/BiGIPvChKqjMlDIZKAcSAMPD\ndCIjUhfwPj6gGWbej/uZghQ6zI7f5F+aN28eH3hA5U0BvmcPaDKVCXQSrF8fXLAANBkFDsIgRqiB\nrF8fvHgRPHYMrFpV4+LFi0iSo0aNoKqC3bvrbUtKQEUBz50r669jR40vv/zy3b4yXvwvyJfbjQH3\nSElUA7ANwNHSz1cAqv5JfS8D8AP0HYJjAHpC31raAGAfgPVAqatGefzFt/ze4f3332d4eDj37t3L\na9eusVevXuzcufO/3e+LL75Is9lMt9tNq9XK2bNn/279oqIiNmrUiGlpaezTpw9dLhdXrlxJj8fD\nXr16MTExkQMHDmRkZCQnTJjA1157jR07dmSnTp04YcIErlixgpcvX+bhw4fp5+fnDRQ6fvw4HQ4H\nf/jhB++5FixaRGNAAPHII0RmXcIcRqvVxf3793PJkiVUpWDGIIHDMZzBqo1+FrByEFgpWGJYcCAr\nx1Zi65oKixaBRYvAFimgUjuN+O47iiNH0qBpTDQY2LdvX86ZO5dhcXEMiIpiz4ceosnPT+dniI4m\nWrUiXn2VgsWXJphpFZx0qCrTTOB9JrAzQFNoII3+dmqRwZSkskC9HRNBowz6OcyMD9VXH1GBMpMS\n4tigQQM6fSx8rpvAD0bp9ZIA1gDooygcNmQITbJMq6TP7Ee3LFMo3z+nryoCfDW2b9WYTRo2Yqwx\nlnnIYxu0oUWyMCwgjBHuCD7S/xEWFhYysXIie6InU6QUNm7c2Bu70atXLxpVIyMRSQUKB2CAN2o7\nAhFsjdZMQhIVKHQZXAxHOLugC/ORz/7oTzPMTEEKTTCxMiozLFQopyT8/ES6fF10aA7WyazD48eP\n84nxTzA+Np51atXht99+yxkzZrB3b4U3BfjZs/rM/+hRvXz5MhgQANo0AwNFfwZqVvr4gK+/Ds6d\nCy5cCE6bBvbu3YkkWaVKCOvWBZ95pkwptGsHZmSA778PPvusSLfbcdeBar/E/4J8ud0YcI+UxGcA\n6v6inA3dLfY/ib/4lt9bzJ49m3a7nbIss3nz5jx//vy/1d/u3bvpcrm8RvA1a9YwODj4D0lQioqK\nuHLlSs6aNYtvv/029+/fzy+//JLh4eG8cuUKSZ0Dwmq1erOG3godOnSgr68fGzduTh8fX1aqVK1c\nlK3N5dKD2EjC46FYqxYrVqzIuLg4Pvzww2zatB1V0UyrYmCLZPCbp8CX+oA+NiOPHDnCK1eusFH9\n2nRooFMDzTZVT/1d2p/i50e7EXRYFFrtRp2LYedOarVqsWadOkS3bsTWrURJib79pJkJJBMw0WkB\n1z4KvtoPtBtBi0lgbKiBYWEaNRVU7SYGh1momiQaXH5UJYmJ4WCsG4ypGOEV0P3796ckGajJoPMX\nRuPhAKMDAlhYWMgdO3ZQlVS2rS54lcT6EaBdA/Nb62REsaEaq1WpQqNspN1s55TJU35zvzu17cQ0\npDFNTGN6err3Gtq2batvO0GhAIGjMdq7NZSABDZDM/ZDPzrgYAM0YCpSvd8PwACaYaYBBg7EQI7F\nWEZo/uzWDVy7FmzZEqxXr2a5oLi+vfrSBRc7oiPvw31URZXvvPMOLRaRS5aA27frW0QWC+jnB3br\nBkaGi6xkDGc/9GOGmMZ6dcFatUCHQ18tPPCArkQGDx5AkkxOjuLQoWB4OPjNN/o20333gbVqJTIr\nK5Ht2zfh3r177/RVuSX+F+TL7caAe6QkbsUd8X98En8yPB7PXSciux1WrFjBVq1alTvm6+vLU6dO\n8dChQ5w7dy4XL17MS5cu/aZtYWEhW7VqxYCAAIaEhDAhIYE1atTgsGHD2KNHDy5cuJChoaG3zevv\n8XhoMtkJ4UHCkkGobalp1bwpzD0eDw2KQly54t1wEHNyaDeZ+OGHH7J9+/Zs164dP//8c0oGgVfn\nl82yO9U2c/78+ST1vW7VYOA7AEVfX50jgiR276ZJ0RPrHZ8JPnifgea66fp3n35KZ1QUERxMHD9O\nlJRQ7t+fotVO4AdaTbW4NE+3c2RX1lcvI5rr5y5ZDDZPAQ0P9qTq8mXXbAOfaq97I9kUMDMGjK9W\nzSugR4wYQUUS6VT1FB5FAEcDDAfoALxR9UuXLqVZFdgsGRzSVOesaJJQNuZvngKtRoEJtgTaTXYu\neXXJb+75yZMnGR4UTiOMlCWZNVJrsE7tOpRlmXVRl4MwiGaYWRVVOQAD2A7tqEHjQAxkYzRmnY/l\n6QAAIABJREFUGMIYhzjKkGmDjXVQh/7wpz/8KUL05l8ajuEMUO202cDKsZHeicNNqAaVj+ARr6KJ\nRzxTU1IpCGB6OlilCjhwINiuDVgLtRiOcIYj3Nt/JS2U8+bpdefNK1spdOsGDhmi5/ZasuRVBgYK\nHDxYVx5GI5ienvin0aWS/x1KYt++fVRVlV27dr3l97cbA+5RqvBDAMZCtx1UADAGwPd3e6L/w+9D\nEITbRi0DwNdff40VK1Zg165df9hXxYoVsXXrVmzatAlXr17FZ599BgA4ePAgatSogU8//RRLly5F\nWlqaN6jpJp599lncuHEDR48exZEjR5CSkoI9e/bg0qVLyMjIwMSJE1FYWIiwsLBbnvvGjRu4VnwN\niP4KmJsLDIrBVRzD999/7x1nVsOGEPLygDNngE2bYFy9GvWKirDlww+xYMECvP766xg/dhgkkaj4\nKNB2GvDWNuD0RcGbTtpqtcKqabAA8L92DahdWw+Sa9QIDaoCLVOBYF/ghe4luP7xP4HCQuDYMZw7\nexaBogipYkVIFgsSd+1CiE8ogBMgbbh6A/jhHLDzOGA1AQ8k6uMSRaBlCmBcsRTtKl/A4j4lGNkC\nWPsYIMmArwX4bu8e7NixA6dPn8bbb6xG40QDLhXrL9kY6L7kbwBYAaBvh45o0rAJVry6Ak9Pno4v\nT/hh2xEBrVKBimXZ3qHKgEIVrS62QudrndGndx+MGT0Cnds1w1NPPo4bN27AYrEgLS0NHnjQqbgT\ntC818GOiYlFFGGCAD3zQGZ2xH/vxsvQy3pHegQwZ67AOH+ADXMEVEEQe8tACLbAVW3HecB4VEitA\ngoQP8AFu4AZ+wA84W3gVVS+m48DeY/jmm2/K/fYejwcslUEEcUY9jp27voTVCpw6BaxeDUyfDpz7\nSUYgAtERHXEGZ7BKXoX3pfdxtOQ0Vq9WcPGintH9JqpXB777bgd+/vlndO7cBRMmvIT160Ph5xeM\np5+ego8//hpGo/EP34t7DZJYuXIl8vPzsXjxYm/6+HuBvLw81KhR45aeX/8J+EKPiv669DMd5V1U\n/xO4hzr674fHH3+cISEhbN26NQMCAm7LRnUTBQUFtFqtDAsLo81mo8Ph4FtvvcWsrCwuWrTIW69r\n164cM2YMSXLXrl0sKChgfHy8N1MnSW7atIkhISHe8rFjx2g0Gtm3b18uXLiQhYWF/Omnn+jxeHjk\nyBFmZaRQMoDmiEDio4/0uWDzFuWM82fPnqXm40OjqtJtNnMdwGcADs7L47Fjx2g1CXykicSTs/Wt\nH7sGuqxgqNvJy5cve/t56623aFUUVgI4HWCuLLMuwPgwgSWLy/b4JVkkRoykpBrpp0n0sYgcM2YM\nL168yE2bNpXmqDpHg9iMFiP4eFs9DXivLLB3tr6KuDofTI/WbQ8jm5fN9PdNAQPt+t+fjAN9rArN\nJoVJFSQG+YAOs8Zgu522UmP4zW2npwGGI4it0IpBWhBHjhjJhKoxVGXdQD67p052VDUEdEhGDsZg\n5iOfNqOBbWupXPQw+ECqiS2aNmCX9l0Yb4ynFVbmIc87k09BCuujPgtQwI7oSJNgYpQtiiEBIezQ\noQMtJgtlQaYEiUMwxNuuOqrT1+pLTdUoG2Rq0ChAoAqVmchkTWM8LZrAoCBbuTxjFSMq0gwz4xHP\nWMQyJgacM0c3cEdHg5oG1qkDujQLR2Ik85HPCloFdu/enRMnTuQXX3zB++6rRYcDbNRIN1wfOgSG\nhYHh4Qr9/DSuW7fud5/9L7/8km3aNGSjRrU4d+7sf4k+9Hby5Y/pSwcyISGB+fn5TEtLY9euXe8J\nfemyZcvYvn17FhQU/CUrid+DCYDrFsddpd/9J/Gn3/i/K/bv30+Xy+X1Gvn+++9pt9v5888/37L+\npk2bGBkZ6a2/cOFCxsbqrG+VK5dxTq9cuZIWi4WyLDMoKIg2m409e/ZkVFQUW7ZsyZKSEno8Hg4a\nNIiVK1f29n/hwgXKsswZM2YwIiKCJpOJDoeD0dHRrBwdxifaGXhxni7cTb4W4sQJKl278vnnyydZ\n69+7NxsZDDwJ8BuALlHkQw89xNjYWCqSwOLFZYK4Qxo4oR3oazPyzTffLNfP8OHD+VipSykBnoS+\nPdMoWePolgJdNjBaiGSMEM1IP4mbRoPvDNNpQyWLhYLZTMFmp8lUgaoEvjUUrFNJT/OdFqVvOfla\ndMHdKhV020GjpHtUffu0rjhSIsoyx+Y2ACVRNz772VWOGTWMV69epQ3gW79QEv0AZiGTBShgLnLp\nsDgY4Gti9UhwQnv9XPXiwIfrg75GPUleLGIZ7Ft2bwoXgoF+JjosDtZHfUYjmk442QEdWB/1KUNm\nFKJYAzWoQvVyOzQQGzA9NZ3hgfoWlQKFD+JBr5Lwgx9DEcohGMJ0pLMaqlGBwsfwGGsa49m0ocTD\nh8FNm8CAABO3bt3KWTNn0Wlysh7qMQIRlAzg8OG67WH3bn3b6LPP9O0hk2RiDa0GoyxRzE7P5o0b\nN7y/59mzZ6lICmNNYZQNAhUFHDZMb//pp6Cvr/m2tLu7du2i02nm7Nm63aRqVfO/RBF6K/mi05fG\nsE2bNszNzaXT6bwFfamP15549epVhoWF3YK+dBhdLheDg4N/4y58J7hw4QJjYmJ44sQJjhs37j+u\nJF4C0OYWx1sBeOHPPNG/gLu+uf+t2LRpEzMzy+fEj46O/g2hyU3MmjWLDz1URoZSVFREURRZUlLC\nxo0bs1mzZty2bRudTie//vprejweTp06lRUqVOCxY8d44cIFBgcHMyQkhPHx8axcuTL9/f35/PPP\n85NPPmH9+vXZo0cP7ty5k/7+/l5u5IkTJ9Kslk+znZmkET160BYQwOPHj5e7zrFjxzLM3592o5GB\nNhtdTicbNGjAZcuWUTMp/P45vY/ixToRz6KHQYsExmgax5VSspJ6iuoYTWNuaXxEEsCE6GjOmTOH\n+WPHsknDJozSohhusvPtoWXX9tKDoLVShG64XrGCqsNBt5+Rnlf1lUJBG52fulcW2Kcu6G/T23VO\nBwVk0mrUDdsJEugngaE++opCEiW6bAI/LdA9oBIiJD42eBBtNn9aoPBx6JlkLTB4OSIGYAB9LQau\nfgQc1Agc2LDsOse3Fhim+rEGalCCxOgg2XuPSxaDbl+FRtXImMgYpiSk0CgZaYWVVWOrsmWzlkyK\nT2J0VDTTke5VAv3QjypUNkIjtkM7mmGmBRbWRm1WQRUaYWRXdGWWXItx1lCaZANDEUo77LRqBh4+\nXGYvGDVK4Lhx+TTJenR2D/SgBt2dtmplgZmZ5d1do6JMXLt2LefOnctVq1aVi/y/iScKnqDL7GJl\nsTJTU8q3j4gw3zYrwZgxIzlihOCt++WXYMWKrrtOF34r+TJ+/Hj27t3bW16+fHm591KnL40q16Zm\nzZq/oi+dwIyMDB46dIi7du1ibGwslyz5rY3p9zBw4EBOmjSJJP8WK4mvf+e73X/mif4F3NWN/bvi\nwoUL7NmzJytWrMjMzMxbkoecOnWKTqfTy/GwZs0aut3u286mNmzYwOjoaG8agpUrVzImJoYk+fTT\nTzMuLo4Gg4GNGzf2tvF4PFQUhU6nkzVr1uSjjz7KoKAgTpkyhdeuXeOOHTv4wAMPsFKlSqxSpQqv\nX7/ORYsWsWPHjt4+rly5QtkAHp1RNssN8zcwLTv7lgotPT2dmzdv9pbnzZvHnJwckuSMaVMZ5jJy\nSFM9WC0pHPTRQFkE/WVQNRi8M7aSkhLGBAWxEcBPAU4BGGC1evmwS0pKOGrkKLpsMl/NLRO+T3cA\njempxMWLBElz+/YMD3byyQ4GzuoOJkfocRnFi8HWqWCkPzi1i85JrSnRlNCC/vClEQJ9ZZ2s6Kn2\n+gpkTq+y80zporvA+tsEqrK+CgHq0wALm6AJu6IrI7QIRoU6uXEUeGo2GOPWx12vskhfxUQnnGyH\ndozSomiU9NVN3Srg/dX07a+EhIRyHk1Wi5UXL17kwYMH+fjjj7Ndu3YMNYZyJEbq7q9CBEWIFCEy\nAhG0wkojjBQhUoJEBQqDNDvbtJC4ciXYsrlAiyayAirQxyJx06Yyod2pk5EtW7SkCJEjMIJRiGIr\ntOJYjGWMMZRmM7h/v173q69Ah8PICROe5IABfblgwYLbbsds3LiRI0aMoMMhccsWcNQosH170GYz\n3vbZHzt2NIcOFb3XtnUr6OMj0G5X+dxzk7zp7f8It5IvgwYN4rPPlnG2b9++vdwKu7CwkJUqVeLT\nTz/N48eP84UXXmBoaGi5INLMzEx+8MEH3vIrr7zCbt26/eH13MS2bdsYFxfnXXn9HVYSt4p0vpPv\n/grc8Y39O6NFixbMycnhnj17uHjxYvr7+9+Sqeq9996j0+mkw+FgcHAwFy5cyOnTp3PZsmXlluo3\nMXToULpcLlavXp2BgYFe5fPxxx8zNDSUOTk5rFChgvdl2759O202G4uLi/nII4/Q19eXNptOkflL\nnDx5koGBgXz++ec5Z84choWFeW0EX3zxBS1mI0P8VebdL7BGrJntWjW9rRBo2rQpX3zxRW952LBh\nHDRokLe8ceNG9u7dm8FuP2oKOP8hfZXy4Rg9p9IXX3xBj8fDNm260gDw6i+2choCXva8H3/8kW6H\ng32h51h6piM4rrUeBZ1eVaFWKZI4f55K9eqcO3cuK4S7qCgCLb4arRYDfS1gagW9XWI4GBMIWtQq\n1BQXgxwCTbIeDHfTC6tffZ3ZjkvAi/P07zaN1sv/fFw/r0kOJLCFCmrRbvTlpKcncdaM6YyL0PjB\nKHBZf32MMmSqUFkTNZmHPEqQ6GMGl+SCL/cB7SqoiSIbNGjgVRK5ubl0u9184flZNBoVBrkDWaVK\nFaqqSqNspEWx0AwzXXBRhEgBAiVIzEQmE5FIBxxUoTLABRYX68K2uBgM8Bf4EB5iFTWSVgsYEqK7\nqlotBjqtTgYjmHGIYzCCmYMcFqCA+chnjBhFzQRWq6bQ19fEpKQYPvCAkVOngqmpGgcOvD1954QJ\nBXQ6ZZpMYP/+4KRJoMulcsWK5besv2/fPvr7W/jMMwKXLgUjI8Hnn9djMlwumYpioMkk8b770m67\nXUveWsDq9KUR3LFjB0+fPs1mzZqVe17Jm/Sl9RkQEMDMzMxb0Jc249y5c73lkSNHcsCAAbe9jl9j\n2rRpNJvNDAwMZGBgIC0WC00mE1NSUu5oDDeP/5mCeAuAmrc4XqP0u/8k7vjG/l1RWFhIWZZ5/fp1\n77HOnTt7XTx/jeLiYv70009cunQpAwICmJuby8zMTDZo0KDckv3TTz9lfHw8bTYba9asyT179pTr\nZ+bMmVRVlWazmS6Xi+Ex4TRpJi5atIh79uzh6NGjaTQa+corr9zyOnbu3MnGjRszPj6eCQkJDA0N\nZePGjel0Ovn666+zX79+rFatGpcvX/67cRk6laOTubm5zMnJYUhICI8dO/abel999RV9LWUzcy4B\n06IFb/I/TYunAQaeLlUQ1wCGGwz0MRvYt3cOn3rqKSbKMttpGjsaDHSpYJvq4M5n9L6a1ZCIypUp\n+Phw+fLlVCwWnarU4yHeeYe+NtEb8X3lFV3Iq5LAiR30Yx+OASP8ywfDmVUwrx7Yq46eILDctVcE\nQ3wNBAwUIDHAL4TFxcX0eDycOWMa01Iqs06tBK5atYp2q52KJHpZ4hwmgQVtwAoBMk2KyJhgmb4G\n0Gw2Mzc3l8OHD2dMxRiaVBNNqsSYqFDm5+ezoKCAXbp0YWhoKE0GE0XofdZFXcYilkYY2RqtWYAC\nJiGJNtgY7BZZUlIW1ewOEJhkrMQG2RL37QPXrwetFrA5mrMxGtMII+MRTw0a/eDHPujDHuhBM8wM\ndAby3Xff5Zo1a1itmsWrfM6dAy0WuVzyvYsXL/LHH3/kuXPnaLEoHDIEzMsrW7ls2AAmJkbe9rna\nuXMnu3ZtTbsdfPXVsnYtW4LPPacrvH79ZHbo8MBt+7idfJk9ezbdbjftdjt79+79L9KXOpmXl8ee\nPXsyODj4ruhLr169ylOnTvHUqVM8efIkhwwZwrZt2/LMmTN3PAb8yUqiBnTSnwIAzaCn8B5feizt\nzzzRv4C7+nH+Uzhx4gS///77WwrLkpISmkwmb8ZLj8fD7OxsrlixghcvXuShQ4d+s0rweDx0uVze\n9BolJSXMyMjgihUrvOfz9/fnqlWrePr0aY4aNYo1atTgmTNn2KxZM6qqyqCgIC5ZsoRNWjehGC8S\n00DhfoGVkipRc2qUHpQo1hMZVS2KFy5c+N3xeTwerlmzhv7+/mzYsCG7dOlCp9PJzp07c9iwYb9Z\nifwaBw4c4OTJkzl9+nSeOnXqlnUuXrxIiybzYKmN4vxLYLC/id988w1nzJhBo/FhymjJWIh8EWBF\nReD91cA3HgNz7xepGUFEhBMvvkjpwQfL5VXiEnBkCxCNGxGTJ9MZEkGoRuL773XR8vnnrOCWvHU9\nr4JBPvosf2YOWClEpiyJVBSZnWqBB5/T2eJMMhgJ0AKRqiRw77PwZnH1s+ieWr3QiyMwghXUCnx2\n0rO/GfekSRMYFAQ+/TTYoqmBYZof/VUjLSaJnTt35vDhw1k7M51GRaYsyFQllbIoM0FKYBKSGOgw\nMDOjLLBuyJAhlGWZCUigDNkbzzAO4xiCELoEF82SmbJBpkky0ayB3bqC774Ldu0CWjSBZg08eLBM\n8I4cAWajDgtQwFCEMkqJYrKaTIuqR4WH+Iewf15/72rznXfeYd26Nm/7khLQ6TR6uc9HjnyUmibT\nz8/IpKRYBgWZOGwYOH582Tm3bwdjY4P+8Ll0u324fr3e5swZMDgY/OQTvXzwIBgW5nfb9vdSvvw7\n9KW/RkFBwW23q243BtyDYLoAAI8DWF36eRy39nj6q/Fv3dx7jeLiYubk5Hg5pdPT02+5vH3mmWcY\nExPDZ555hu3atWNqaionTJhATdO8wWw7duzg5cuX2blzZ6qqSlEUy60++vbt6/UcWrVqFZs3b+79\nzuPx0Gq1Mjg4mGazmVFRUczJyaHD4aBskYkLIAiiGIQviDWlZYJqR5WTJ0/29vN7s6aff/6ZL7/8\nMvv27cuAgABOnTqVQ4cOpdvt9irBU6dOcfny5Vy7du1dz8DmzJ5Ft1NjlywLo4LNfHSQzof9ySef\n0GQKJjCfgJlGNKBJMfD6gjKhXiUExJIlXtOnFhrA5in63v/W8aDdqREff0z06ElRrkpBUqhVqEDM\nn09DXh41Vfc22vssmN9K324a2wpUFZnJSUkcPnw4e/ToQUWRaZdkhqk+dMNNp5+LimKhqvjSrOor\nCB8zWNkN2hWRiUo0I01+jFFCmFkzk0ePHuXFixf5z3/+k3v37qWmKd7UFR4PmJYs0wADo6KivIJ/\n3LhxNIgGtkEbBiDAa5iuhVo0KQb62k3s378/x44dy+TkZCqywiEYQhEix2Kst34IQmg1W5mbm8uh\nQ4cyKjKKqqzSX7XSYRdYUQ1hDGJot4jcvPkX9oj2BjZAA+Yjn6HmUA4cOJCzZs3iiRMnbvk7njt3\njqGhfpw2TeDOneCAATIzMhLp8Xi4cuVKVq1q5pkz+ngHDzbQ6TRywACBLhf4xhu6ITo93cQxY4bd\n9lk5ffo0mzbNotEoUdMEVq+u0ddXZuXKBu/K6NVXwVq1qt62j7+7fLkT3G4MuMcJ/v5O+Itv+d1h\nxowZzM7O5pUrV1hSUsLc3Fz26NHjlnVff/11Pvroo5w0aRLffPNN+vj48NChQyR1Y25ERAT79u3L\nNm3a8Pz588zIyOCAAQN45coVfvbZZ/T39/e62W3cuJHVqlXzbj8dPnyYZrOZ48aNY6vOrahWUYme\noCHAQNEqEiVlSgF2EPt/UX4cHDpiKDdv3szg4GBKksTw8HBmZGQwMjKSLVu2LJePiSQTExPLZYcd\nNGgQ8/PzuWvXLrrdbrZo0YIZGRlMTU29IwPi2bNnuXz5cq5YsYIfffQRFyxY4DXgk/qWXULVGPqY\nwQA7qKkijTLKKYnKwSDWrPEqCaltW4YE6uk0zKpA1K5HNGpJwWwl8BJlGJklgjGKxLaSxBoAK6h6\nVtjoQJ3zOtIFiqLAUaNGeQV2YmICFYiMMwj0K/V8MhptfO6551hNUdgHYIYAdgSoSeCIB3QPqJ5Z\net8OgDZBZJA5iDajjZIksLAQ/Oc/weFDwEoxoAEi/Xx8OHbsWBYUFHDQoEEURZHRiKIKlc3RnA2E\nBnSYHZw65VlqJp1nQxAE2mQbTTCxF3oxGtFMQhIHYiDboi0lUSpn1+jXrx9tio1GQd9CCkAAJUiU\nYKDNBubng506gWaTwGxkM84Yx0CfQJqNZgb6BXoTKh47doxPPvkk88fme5/RvXv3slGjDMbGBrFz\n5xY8c+YM169fz7i4EMbHg6tWlc32g4IczMhIoKIY6O8vMyYmkOPGjfzdzAQNGqRz4ECZ587p+Z/s\ndoVr1qxh7drJrFHDwtatrXS5rOUyJP8af3f5cie43Rjwf0ri74HevXtzzpw53vLnn3/OpKSkP2zX\nqlUrtm/fniS9hD8Gg4EhISFe28KpU6cYFxdHSZIYHBzM1157zdteT6PcjHXq1OHw4cMZGhrKgIAA\nbt26leYKZpYG1hJHQSigoY+B+BbEJFCwCRRaCsR5EDtAY6iRK1eupL+/P999911euHCBAQEBnDx5\nMvfu3cuRI0cyMTGx3AtbqVIlbtu2zVseP348+/fvz4YNG3LWrFnecbVu3bqcm+6tcPToUYa7XGxq\ntbKR1cqKQUG/WZ5PfGoCm6aaWLhQVwj9GhhoNQmsX1XimsFg33qgZlWI++8nPvmEmDWLFpeLa9eu\npdHoS2AogW5UFF82btyMilKPJlnhyObg2JagVQErAjwPPe6hTXVdGV2cB/rZJPbt29c7ow8ODmKM\nANaO0OMn3h8J2jWBFSpUYCDAWAW8PxpsGqfbLA5MLXNj9TODiwDOAGiHwofxMH2sCrOyQH8jOK7U\nbdYogC5ZZnBgINPSatDp0DiqhUC/0jiOapUqMadzjpfU59y5c2zetDkzS+MxOqADTTAxHnrgnSSI\ntEkKIxDB+Grx5Tyk3IqbGjSGIYwKFCYjmU3QhDaDjXaLhQF+Lt5f/362bd2WwUEuBhh92R3dmYMc\n+ph8uGzZMlo1K30t8v9j773jo6qz//8zd+bOzJ3e03shoYUaIBBKaAGkdwHpBOkIiNREFBSl2EHA\nQlOaq4gVsaDgAiIWsGEDwVVBEBakBJLn748bJmSB3XVX3P1+fnsej3kkc+eWeb9n5pz7Pue8Xi/c\ndhNOu8qbb755xef85ptvEgpprFghrFsnxMXpf5ctE3Jzfxtl/sWLFzGZFMrUdAFh0CAbixcvpri4\nmBdeeIE1a9Zcc6Vzyf7b/cs/Y9cag/wvSPx32Jw5c+jcuXPYgd5+++1069btHx7Xs2dP4uPj6d27\nN1arFbfbjdVqJT4+nvXr14f36927N16vl3Xr1l1R77hw4QLLly/nzjvvZMOGDTidTp566ilcrVxc\ntk5Ai9TwxnjxJnhp3LYxH330EQmZCYgqGB1GHln8CA899BCxsbHk5eUxduxY6tWrF75OaWkp8fHx\nFXrWx48fT/Xq1dmxYwfPPvtsGGQXDAaZNGlSmOvnwQcfxOXyhtNOhw4dYvXq1WzatClchxnQowcz\njcZy8JnRSEpsAg0atOTuu+dx8eJF+vXuzOPDyusL2wsFlxaHarwNl1Ybi8OCHDqE3HorUrcuSplS\nX4cOHbDb7VitVoLBSFatWs2ZM2eIDvmYf2P5+RYNFDpZhA9EiPUIb00XGqTpr60aIXidFrKz65KY\nEIfVopJiFfbMLj/+7l6CXRPiQkJBXvn22T2E7vX0/888oXddHSob52Ax0kAakKlk4jMpbLysa6tA\nETQRjAYdFf7WdP0cY1rrQU0zC8uXL+epp54iJS6F6EA0mUmZdJSO4dRSW2mLUYzYzTpIcWFfIcYj\nmFWV1JRU6tSog81kI07iqCW1aC/tyZCMCsR/brsb0NN9Pp+VO+/UVxeapndk2cRGfGw8HpeBl14S\nvvpKyM1RSI4vryWUlJTw4IP3kZ4ewSOPlDv1P/1JiI83EhXlqQBE+2estLQUr9fGjh068O6DD4QG\nDWwVfjugK0F+9913V+0MhP8Fif8r9gdP+W+zs2fPkpeXR0ZGBtnZ2aSmpv5DSUbQ76rcbjf5+fmc\nOHGC/fv3ExMTQ0xMDE6nk2HDhtGuXTuSkpJITk6mdu3aYZnRa9mUKVNIS0tDdavIi4KcEwz3GYhN\nj6VatWoVNCYeeOABKlWqRHp6Ot27d8fv97Ns2TJefvllqlatSiAQ4LPPPmP//v2cOHECr9dbIeW0\nfPlyatSoQc2aNWnUqBFDhw4lMjKSWbNm0bZtW7Kzszl06BBpaTWwWAJ88cUX/PnPf8bhCOJwdMfh\nqEedOk04d+4crerVCyOUt4ngNJkIBkNYLE4slgT69RvG7Dtup2O2xoUV+kpidCtBM7co86k/IlYH\n8tNPyP79yLFj2FNTGTp0KFWqVGH69OnMmDGDrKwsxo8fz7PPPovfqbD6MizFs+OFupowUHSg3NHF\nQqRHWDtaOPuEMPkGXZhINQoOo5GgVXhhYvnxN7cQrBYj9WvrYMBL29+YqmtMLBuqI7YbmIVPRJik\nCJlGwShGFFFwifDeZUHiXhE0o+AwlwsRXVghNEwXnhopZPoFm8mE1+plgAxglIwiyhyFx+RhkAxi\nmAwjIAE8otEkvfz9HLhPR4knJiSiGHT8hKZoTJEpNJNmVJfq4SBxq9yK2WTm/oX34/daWLq03MEv\nWCBUs6WQL/nY7bqYkMMhTJ+uPzRNCa80J0wYRb16Npo1Ex58sPwc69YJ2dkZvxkAd8kmTZqAwyFU\nqaITAMbFuSvUwNavX4fbrREVZSM62suf//znK87x3+5f/hm71hjkdw4Sm/7O4/nf80JAuCIFAAAg\nAElEQVT/gv3BU/7b7eLFi+zYsYOtW7dW4Bu6mp09e5ahQ4cSDAbxeDx8+OGH4dcWLFhAbm4uTqeT\nzMxMOnbsiN/v57XXXuPMmTMEAgGaNWtG165d2bp16xXnLi0tZePGjQwYMAB3tBvFqJBaI5WpU6fS\ntGlTmjdvzs8//8wXX3xBSkoKzzzzDKdPn8br9TJ16tTwebZv347RaUTxK6gRKvaQnUGDBlW41saN\nG6lXrx4lJSWUlJRgt9vD9ZXS0lJq166NophQ1QLMZicnT56kUqU6iKwt84MlaFobHn74YYqmTqW1\npnFahCiT3tFTVFTE5MmTcThCKIreOtm6eSOSouxUTnQSF+VH06oishmRx1GsdjSrQjBgxmI24AkG\nyMvLo3v37uHUSp8+fWjYsCGVUmKY0UlIiRDeniG8WyTEBwwYTEbEYsFmcdG5jpm7e+oCQ4pBrzu0\nFuG8CJtFMIsBh1W4vaswLM+A2WhAUawoBp2D6dijehttuxp6h1RHizDEoK8OXKqO9L69q65Q1026\nUU9qUl2EZWWpKJ9B8Jnt9JE+uMwmOtbWsRtta+g0IRGqYBIhWrOTogVoa2jNMBlGyBsiOSYZq1hp\nIA2wiYGudcuDxOnHyqhERGjnchGpacQHo2ikNiJDMrCIhY7SkaEylBRJwWP3EG2NJuiy8Oyz5Q5+\n5Uoh4DST6ohkzhx92/79gtutU36PGiUEgzri2mIx8fPP+h1/ICA88ojw+OOC32+kbdsWvP/++7/p\n91ZaWsrw4f3xeg3cfbd+7XPnhCZNbGFswsGDB/H7Nfbs0V9/7jkhKsrD+fPnK5zr/wX/8o/sWmOQ\n3zlINP0Hj/+k/cFTfn1t1KhRtG/fnsOHD1OnTp0KBHuDBg3i9ttvZ/PmzXi9Xrxeb7jo9vTTTxMK\nhVizZg2PPfYYwWCQ7du3X/UaJ06cYEbRDDr26ojLpauX9evXL5x2sVqtzJ8/n9LSUkaNGoXVamXc\nuHF88sknDB01lMQqiUg9QS7o3VCGngb6DanYfldcXEzTpk3Jz89n6tSpGI3GCj/ATp06Y7VmYbOF\nWLpUx2F4PNGIHKT8hrmQKVOmcf78eQb07IlZUTAYDBQWFoYde5UqdTEYFBYsWMDbb7/Nxx9/zO7d\nuzl37hx33XUPvphEVF8Am1UJU3Hsm6uDz7xOJ9nZ2RQWFlJYWEjDhg0ZMGAAIb+Tww8KSwbr6Ok4\nn2CKikCOH0c++QQlFCLg85EcF8RrMfKMCG+KkCHCkrI330gsiMzBqExAJBOrVSMvL4/q1atjtZhQ\njTpCulu2YFN0UsKHRKhh1lNTl5z2Y0MFr2ZggAzAIQaqiOAXwasJCZYABVLAzXIzfvFjVASHUYhS\nhf4iuKy6zOkbU4VKQZU6hhrUyKzBuXPncGpOukgXLEa9FffJAp2K/IaagsMsfFU2jsMi2EWIDkTj\ntrjJlVySJZlIiaSqVMWpOhkkg3Aa7CTEGNm6VXj9dSEqZMQqVqyqwokTuiNeuFDHKFwKJK+9JiQl\nBTCbFU6e1Ldt2yakpip4PAYGDxbuvlsIBGzs3LkTgJ07d9K8eUOiotzUrJnK5s2br/h+/+lPfyIr\ny05CQjnCG/QW4sqVU5g9+07Wr19Py5blLbggxMbawjcyl+z/gn+51hjkf+mm/zctOTk5zCq5Y8cO\n3G43ffr0oXXr1mRkZHD8+HE+/PBDQqEQlSpV4t577+X48eNUr169AuHdwoULGTJkCFu2bKFZs2Zk\nZGQwfPhwfvjhB9Ky0jD1NSEPCWqmyq1lbYR33HEHHo+H5ORkFi9ezDPPPEPVqlX54IMP8Hg8GB1G\n5HZB7hckKMjzZVWNlwU1oF6RFjh37pzOmzRzJjk5OQwaNIivv/6adevW4ff7WbRoUQUGzTZtuqGq\nIxG5iMh32GwpvPTSSxXOl5CQQJcuXSgqKmLChAnYbHYUJQartQCLJYJu3XqyY8cOALr374+1Uyfk\n1Vfx+sxXANlMBiEyMpK4uDhCoRCqquKx2WjZLIceOVb+8pDwzkzBZTMga9eWu5PevUiI8RLtt9HR\nIBQZhDFGvaicV4b4jhTBoibjtkVgNpsZPHhwOLBVzsykfooerBpn6E55cI7QL1t3/k8WlL/PTRN0\nEaNIxcVMERYbhFSPsGaUXkNwqEYizU6qxOjSqTaz4BUjLpPCgr7l53lruuC1K7z88ssAPPPMMzis\nDlyasPtOndqjcoyQEBB8l6W1EKGGWIhT4nDb3XiMHgqkgDEyhhRbCjH+GPpKX2IllqqGyiS5fCS5\nfNSQLBziwOkwcOONwoQJQs+eOqXGJad88KDgcgmRkUaaNFHYvFmYPduAzSYkJupBpbRUT0H16dOJ\nmTMn4/Mp1K+vH6eq+qN169wKehZ33303Y8Yo5OTo+hO//iqcOiXUqqXXSlq1MpGWFkdkpMaRI/p7\n2bdPcLutV6z0/y/4l2uNQa5TkEgXkQ0i8pno2hLfyn9eT+IPnvLra7Vq1apAgdy+fXtq1qyJ0+lk\n7dq17N69m5ycHDweD7Nnz6ZOnTpYrVb8fn9YzAf01FTLli1xuVxER0dz55130qlTJwKBALbGNqS0\nzMH/JJgsJi5evMiGDRuoUqUKBQUFZGZmYjKZmDhxImfPnsUd40aKLit3PytIE0FKBWWoQkxqzDW5\nY0BfvVxC+tatW/eqq5xPP/2U6OhkRGwoisqcOfdesc8HH3xAMBjE6/WhqiomkxuRU6gyHb9odBIh\nTtO4c+ZMVJsNOXYMOXMGi0sLF5L/8pBeV/jTOMHvMhPp9TJahF9F2CWCX9Po2qkNAa+DlIRIYlJT\nkA0bdNf2yit43UZenSy8dpuOuh7QSJh3oxB0CnajkGy1Eul30KKawvoxgs2qMm7cuHCQaJjTAKMi\n2CK9ODWp4Mw71RZCLv3uf3uhzs3ULVsnNNwlQmVN18B+farQvrZKhEelRdVyMsVpHYUIiwOb0cSM\ny6RQN94i1KqmB91NmzZx8uRJvvnmG/weGy+WrbC+mKfrhtuMRjaXBYjtIrhFpY/0IShBUiUVVVS8\nTi8Tx03kqaeewmfzkSM5mMVMM2lGc2mO2WCmb9++uFwK3bvrd/Eej86ftGOH8MUXQny8it2ukplp\nJjraSFpaEL9f4ckn9dRT1arCAw8IK1YILVo0IDbWxtGjwpIlQu3awrFjwvnzQpcuagVKj7Vr1+L1\n6sdnZuoBxesVhgzRj+3UScjPt9O9e0eio220besiGLSxevXKK75v/xf8y7XGINcpSGwXkRYi8rGI\nJIiOwL7jelzoN9gfPOX/vh0+fJjZs2czc+ZM3nvvPZYsWcLs2bPZvn07GzduxOFwMHr0aDp06IDX\n6w2nkbKzs6latSqFhYXs2bOHvLw8oqKiiI2NJTMzk6ioKFatWsXixYtxOp14PB58Ph979+4NXzsz\nMxNTO1O5sz8nKGaFTz75hJo1a9KmTRumlDGrrlq1iqysLBYtWoQp2YTcd1mQeEMwBA04qjgwOAy0\nb98eq9VagcSspKSEF198kccff/yqnPuX29GjRwmFEjGZRiKyAJstgUcfXXrVfc+cOcO+fftYu3Yt\nbnceIgexi5WfyhzbfEN5EdmemYx89x2yfj2aplArUXfCl1I6NzXVsF7WOYUIXZxO1qxZE77e1q1b\nsQUCWAsKcCZF81B//dhHB+ua2qwWFg824LCpREToK5LLMRpD8kwkJSUycuRIevfujceh4nSYMMfG\n4nHoAeeSM181QvCZbAQcQkaU3gnlMAuqKKQb9fbWyjGCxWymXbt2tG3bFs2isrWsu+nNaUJAU4gW\nD25VKOws3N9PV80LeAOkOlPJcGYQFxnHd999x7Zt24gIuEmJcWDXTHi9Kn376kHJJYKjLEC0k3Yk\nSAJe1YvL6cJmszF+/HhKS0t5+eWX6dujLx3bd+SGNjcw8KaBvP7667hcGm3blq8c9u0TXC4LiYlB\nLBaVmjWzGDduHD179sRmM5GRkcLixfrq4c47dc0Jk0nw+RTGjRtHp05Ozp7VdawvL5K/+65Qt256\n+PO67bYJ9OxZrsE9YYJOCHipY6pNG6F/fxtLlizh448/ZuPGjddUV/x/0b/8rV1rDHKdgsQlNti9\nV9l2vSxfdBLBL0Vk8lVe/4On/N+zAwcOEAqFyM7Opm7dulgsFrKzs5k0aRIRERFERERgMplQVZXO\nnTtTv359kpOTyc/Px2w2Y7VaufXWW8P6Ds2bN2fs2LF4vV7MZjMRERH06NGDd955h5iYGDRNq6CT\n3aNHD0wuE/KwILsFpZOC4lDw+Xx06NABv98fbjcsLS1l5MiROBwOpKMgEYI8I8jrgiQKEyZN4PXX\nX8dkMhEdHU1UVBTffPMNoBfrO3XqRM2aNenbty+BQICNGzdec14WLlyIxdLvMl/9HqFQ0t+dy8OH\nD2OxOBFpQ7TYoGwlEGXT74pLV+mIaHtyNOYBAwglJqKahA1jJcxOWynWiqooPClCadlqIsVm45ln\nnqnAg/X555/zwAMP0Kp5E2Z20XWoF/bV8ReHHhCcNhNjxowJE+uZTCZqJQofzNa7nwIuI36Xmarx\nZka3EuxWA/fccw93zZ5FToaVQw/oeInUCANREotZUXBpdhyWWGxmNy7NgMWkp57qpJjp1KlTeGXS\nvn176qebKV4u9KotBE2CRQxsE2GYUehlErqJEK1Eh+VBG0tjGtdvDOhB9/PPPycy0sP77+vO9Px5\noX59IUqNIkMysIudKHMU+fn54aaBmJiYKzQ9Ltmrr75KWpqV4cOFQ4eEhg1VrFYFs9nM66+/jqLo\nYk/hFFzlTOLjY7jjDgNr1ujypocO6amitm2FNm2a4fGY8fn0VcGNN+rpqq5ddY3r5GS92+7EiRP0\n7Nm2AlfTG28INWrof5OThYICIRCwX1F/uJr9N/uXJk2aYLVacTgcOBwOMjIyrrrftcYg/0KQ+Gfk\nS8+JiFFEvhKRUSLSRUTsv/VCv8GMIvKQ6IGisoj0FpHM63i9626jR4+W06dPy88//yyff/65xMTE\nyJEjR2Tu3LmSmJgogwYNkuLiYnnjjTfkzTfflMjISPn5559FURQ5fvy4fPfdd/LGG2/I0qVLZc2a\nNdK5c2d55ZVXZMeOHbJ//35JTEyUpKQkadSokTRs2FDsdrsMGTJEvvzyS3nuuefkhRdekCcfeVI8\nd3vE0tYiGYcyxK26JTIyUj7++GMRERk4cKDs2rVLDAaDPPTQQ7Jq1SqxvGsRuVVE7hORQSKBkoC0\nbN5SCgsLJT09XcaNGycWi0ViY2NFRGTjxo3y/fffy86dO2XlypWyceNGGT58+DXn5cyZM1JSErxs\nS1DOnTt7zf0vXrwoffv2ldjYkOTknJQTNpFhBoPsEJEbaoukR4kYDCLTOoqcO/CDTE9Jkbb5+UJe\nS+m/wiadltglbrxRvj1SKpUqV5YxLpekWa2SYjXJ9xfOyuB+3cXjUMVrt8vK5culUqVKMnr0aHnw\nkSWyeKtDpqw1yKFjIqu2iTz2lkjA5xGfzyciIqFQSCJ8NulQW6TV3SKjnhQ5c84nv54plk8PFcuf\ntnqk+rn6Mv3WGXL2vCI5bYZK+kRFqk62yMGjDvlBjkppaZT89WyaiHhl0g1/lRNLEa9d5PkJIn5n\nRXlbo9EoHxwUCQwRObVXpPFFEYvZLOMUgzxpVOU5RZUXVEXcpW4xiC5xaRKT7P74bTGbjdKhQ564\n3W45deqMJCfr5zSbRWrXNkuz7s3EV98nQWtQfi75WbKyskRERNM0SUpKkr17L79fLDev1ytnzxpk\n/XqR3FxVzOYGMm7crdKtWzfp0qWLWCwW+etf/yoiIoCc/OWkHP/hpMyfb5b580WGDxeJjRX58EOR\n48dFdux4S0pKLsq6dSLffCPy3nsi1auLVK4ssmqViKL8LLVqZUpEhFe+/vqArFxplXPnRC5eFHn0\nUYP89JNNBg50yZkzLvn00xrywguvS2Ji4jW/X/+uQZl86YwZ102+1GAwyMMPPyynTp2SU6dOyWef\nffa7X+NfsWwRcYpInIg8KSJ/kutL8NdARF657PltZY/L7fqF6t/Zjh07hsPhCLf0ffTRR/h8Pjwe\nD59++ikWi4XTp0+zd+9eIiIi6NmzJ/n5+djtdp599lkAvv76azIzM7FYLLjdbpo2bcqiRYvYuHEj\nXq+XyMhI7HY7L730Eo0bN6ZOnTq43W6cTicul4s777wT0LuPVq5cydy5c9m2bRsNGjRg0qRJfP/9\n96xZs4ZQKBTGPZSWltK2XVvEJohJcEW56NChA3Xq1MHj8WAymcjIyGDv3r188MEHNGvWjFAoVIF+\n5Pz58xiNxmuywe7duxebLYDIOkTeR9NaMGzYmGvO5SW65kvMpmPHjsWoKBgNBirHCMXLy9MvcdE6\ngVuzjh312sJnnyGPP45R0xg6dChFRUVMmzaNQCBA0KPy/UP6XX2PbCHBqHcXJUT76NKhNb07daJG\nSgpZmekUDB3E7NmzCXgsqKqJgoICioqKGDx4MG67iRNLhXopgtXkQeRe4iWB6TI9jDNQxYmmxbB9\n+3aSkipjEpWO0pFsU3XcVoW0SMFiEn5erI8lyiN8OV/HbPjdGj169KB79+7YNCu5IrwhQpJZMCqC\nXTPj8bgZPny4Thke4cOqKkyRKQyX4TjtBt5+W79TnzzZRJMmtenVqz19+lg4dEh49VUhGLSxd+9e\nLl68yIL5C/D7/HTs2JGioiKmTp1KfHw8GzZsuOrnU1payo03diItTZ+byzvSqlevzqBBg3C73TRp\n3IT05HRi1VgGyABiI2Lx+1z0uVH47DO9JXbVKp2SJCdHrzEkJAjVq+syqCDcfLPQt69QXKzLnNaq\nJdSsmYnPZyEUstKiRYMKadDfYtfyL/9QvnTYMLLsdmaKUN9up2+XLr+7fGnTpk1ZtmzZP9zvWmOQ\n69zd5Cp7XG/rJroq3iXrK7rG9uX2u0789bT333//iiVhnTp1SEhI4KWXXiIQCLB582Y6dOjAwoUL\nWbNmDY888gi9evUiLy+PCxcukJGRwT333MPJkyd59NFHUVwKqkfF6DGyatUqQF/qa5pGv379mDNn\nDv3792fatGlXpREGPXg5nc4KX+IOHTqEHcDy5cupVasWx48f5+LFixQUFIRFgUpLS8NaFH/5y1+I\niIhg2bJlrFu3Dq/Xy759+ygpKaGwsJDc3Ny/Oz9vvfUWWVm5JCRUY9y4yddEwQKsXLmSWrVqhR3P\nzJkzMRqN7Nixgy4dWlMtyUGPRk4CXlu4o2fGrFlo7dohZ88iv/yCQVGucF6tqhvZfJuOf+heT0iN\n0IWO9s0VhjcXEq3C2yKMVVUSgkH8VivZLhdWk55KcToduGwmXpykB6pYny4sJOLDZXAxTaZRJEWM\nlbEooqFpN3HHHXegqg4iJZX6UpcYr/DLEh0cF+PVEdS/Pq4XpSvH6EXofo0Em9WC02bBaxIcBgM2\nsw6kK10l1E8307Vr1/DYevfujU2zoIgBgxjo3s3AyZPChQs6ZbbJpHDs2DH69etKZKSLKlUSwvMG\nOnfWokWL8Hq9JCUl4fP5GDBgwN91fCUlJaxcuRJVLS/cz5gxg+joaKZNm0blypXxiY820oZpMo3h\nMpyEyAQee+wxnHaFpASlAjX4wYM6zuKrr4Rp04TISOH774VQSK9duN06vmLpUiE3txYHDhwIo6mn\nT59ERkYMtWun8dxzz4Xf4/r164iN9WG1mrjhhmYVfiPHjx+/qoM9duwY6bGxdNU0RpjNBGy2K+VL\nLRZOlOVOz4gQb7NdKV86diwhp5MYr5f5c3+7tGrTpk0JBoMEAgEaNmxYQbjrcruWj5TrFCTqil6P\nOFj2+EhE6lyPC5VZV/kngsSlPvfCwsKrcsL8t9jPP/+M1+sNf1k+/fRTXC4Xdrsdr9dLo0aN8Pl8\n+P1+qlatSpMmTRg8eDAejwe73R7WagDdOVetVxUZL8gBQR4T3JHusJ51amoqdevWpVOnTjz00EM0\naNAgzJH0/PPPk5eXR9WqVenVqxd79uxB07Qwj82FCxfIysritddeA3TsxsKFC8Pj+Pjjj4mJiWH8\n+PG8+OKL4e2rV6+ma9eu4edPPPEEZrMZVVWJi4tj0KBBFRzPv2M7d+7EYrHQu3dvJk2aRHaDBqhx\ncbgiIvjyyy/ZvHkzq1evrlCQPH/+PO26d8fsdmPxeHB7POEc+4gRI3A4HET7TLhtwubbylHMOWm6\n+E/JSsFn0bWzPxO9sPtdmSP4swhem40hA/tTKdbGjM5CnSQVzVwbkSWIzMNt9+EVI0GxoIkVkTux\n23Uwo83WAqv4sItCQa4OaquXphIVchMIBHDazCRGWqmSkUxs0EGWRVgkwvMibBGhelISqTH2cAG8\nTyMTeXl54SCRn59PnRSVeL9eCPc5BLMq2DRhxnRdy2H48AHcd9/CK4Lzq6++itVqJSoqCo/HQ7Vq\n1SqAPK9m+/bt4+mnn2bnzp3Mnz8/7MiSk5NJSkoiISGBZs2a6Y0Xplj6Sl/itDjyW7ekR4825Ofn\n4XV7wwVnED78UOdzukQtHhWl036PGqUHu08+0XW0q1YVPB6FhIQA6elRxMW5SE9X+fBDne48IkLj\nnXfeYc+ePYRCGn/+s94iO2KEyg03NAN0Ua5QyHlVB3t7YSGDVTXc6LBWhEbVq4df//TTT0lxOCo0\nQ9RzuyvKlxYV0dBm41vREfaVbDZWr7yyu+of/QZOnz5NcXExy5cvx+l0XrUAf2kMb775ZgVfKdcp\nSOwVkdzLnjcSvdPpell9qZhumiJXFq9/08T+p23GjBlomkZGRgaaphEbG8v+/fsBXSQoGAwSCoXI\nzc0N36Vt3rwZp9PJokWLsFqt/Pjjj/zwww+oXrW8lRXB0cLB888/z+eff47T6aRy5crh9M6RI0fQ\nNJ0u2uv1EggEuP/++ykqKsLn8zFu3DhSU1OZPn06eXl55Ofnh/mm7rnnHjp16hQ+18KFC0lOTuae\ne+4hMTGRRx55BNBZbBs1ahR+3z/88AOqqpKUlERBQQHz5s0jISGBpUuv3rX0W6xW41oofRTMTgtG\nmw21QQPUypUx2WwkJSdfwUp7uR05coSffvqJmwcPxmY2YzYaMRmNJMREUT3BiEUVTj1W3nE0upUu\nPXp8iWAzCidFeE6EnDIHsEKEJBFsIvTq2JF169ZRUDAMs9mByB2IPILVGsBrsfBUWUCpKwZsRo0x\nY27ltddew26vgiI1yRUh2S3c0tZAjWoZ4XRabm4uNWvWJD4mRHKECacqWBS9C8luFKqnpWFV9SL6\nJeU71WSidu3a1KuXjddh4sM5+orI79ApQ2J9wqJBgs8uJCQYeeABoVUrjY4dW4Q/6wsXLuB0OGnX\ntl14JRAXFxemjr+aLV28mJCm0c3pJMFmY+qECbz11lvce++9PPzwwzidTqZMmRJO8zmdTqplVCMn\npyZNm9pYvVoYNsxM5cqJxMcHGTPGxIMP6iuHe+7Rg8TFi0J8vI6TuATWA2HMGL0rqmdPPVjs2SNs\n3aqr523apO8ze7YwYcIYFixYwMiRlvCxp04JFouRiRPHEAyaGTlSrhokxt58M/MuCwAfiZAZExN+\n/fz582TExXGXonBYhEUGA3F+f0X50mrVeOOyczwuQr/OnX/bj+BvLD8/nwcffPCK7dfykXKdgsQH\nV9l2PbubTCLytYgkiohZRD6UKwvX/9bE/pF25swZQqEQa9euZfv27WzZsgW3282BAwcoLS0lLi6O\n559/ntmzZzNp0qTwcUeOHMFqtQI6BXdCQgK9e/dGVEGOlIWIC3rHUXZ2dphEr3FjvXvlxIkTpNdM\nR6oKSgMFk8tUQTpx5syZjBkzhhdffJHCwkKWLl1a4W7yzJkzYSfVtGlT3G53mIl27969BINBCgsL\nGTRoEElJSXTr1o158+ZRpUoV8vPzufHGG9m/fz8bNmxg1apVxMfH/9152rJlC5MmTWLUqFG88sor\nV6Q0zp8/j2JSdO2LGi7kxRcx+f106dKFCRMm0KhRI2JjY8Myr6NGjarQqQR6y63TbOaICL+IcFoE\nt6aT8uVV0VM7pav0GkDAIYxooXcWBVVhrgiNjUYcIqwSIUaEnaKjk2+wWhlZRlHy8ccf079/AT16\nDKR///5MuKzV9isRolwu4BJjb0+s1hQixUCuIljNFTuYBg4ciN1mISkoBB06gd+Y1jonVMApaGad\nATbGq6eiEgK6nrZBDGSn6OO4pJbnL1P3Wz5cT6kNaVbufM+fFxIT7eHV7tdff43FbAl3bhUVFdG8\nefMwSeWGDetJTAzidmv07HkDhw8fxmk282XZOI+JEK1p4Tbszz77jIiIiAppvuTkZDZv3ozNpnLq\nlP4+SkuFhg2drFy5kttum8jw4f3p2LEtqakKc+YITZoIPp+JiAgd4X1pddG0qTB6tL7CuFx/e9Ei\nYdAg/f/Ro43MnDmdFStWkJtr48UXdfT3K68ITqfC8OEqTzwhZGdfPUi88MILJNps7BXhiAjtNY2x\nf8Nk/O2339Kifn0iXC4aZWVdKV/atCmPXhYkphiNjB52bfnWf8b+W4LEfSLyqJTTcSwSkYUiUqvs\ncT2sjYh8IXpH1ZSrvP5vTewfafv37yc+Pp6nnnqKtLQ0oqOjiY2N5aabbuLXX3/FbDZTUlLCnDlz\ncDgcjB07ll27djF06FBiyu5U7r//fqpUqUJWVhZSVZDKoqOgWwhiF9asWcNXX33FyZMnSUxMZO7c\nudw05CaUvkr5quN2oX7z+uH3tXDhwgp03YcPH2bXrl0VWmeLi4t57bXXGDx4MAMHDgxvP3ToEA6H\ng5tuuolHHnmEmjVrkpeXx5gxY1i7di133XUXrdu0xha04ergQovV0DzaNeeoaE4RlgQLMlIwZQhO\np5FeHTpUCBSlpaXYPDZkryB3m5CUGGIzMioI8JjNZgYMGMD48eNJS0tj+vTpFdT+JoUAACAASURB\nVK7z1VdfEW+3h3+kO0THErw+Vfj+IR2RbTYJFqPgMQjJJuE2EWaJYDcYUJVUjKJgFeGOy37sX4iQ\nHApVuNaxY8do0yaf6iaF18v2e08Ep0Fh7tz5gB4oFi9ejEMzMrKl0LSygbi4OKZNm8bMmTPJql6N\nQc1MlK7SxY4uYTVYrbPARnmE1tWEXbN0Ko83pwsGETRzEi5NeGSgsGWKzu80upV+3IP9hd4NhCqx\nwtq15Q41K8sVpsH4+eefsZgt5NTPobCwkMmTJ+P3+Zk9ezbvvfceoZDGtm3C0aPCTTdZaNmyIYHL\n5gMR6htNvPLKK+HvUVpaWphNuE2bNkRHR5d9j9QK1N7Nm7sqAERLS0tZu3Yt48eP4oYbbkBRdAEi\nl0tvia1fX2jRQi90+/3C6tXl55o6VWjUSA8QMTE+Dh8+zLvvvoumKSQn6wVxTROaNi1fWRw7dvUg\nAfDIgw8S5fHg1jQG33jjvyZfarczUlUZaLEQ4/P9JvnSEydO8Morr3D27FkuXLjAqlWrsNvtFZiY\nL9m1xiDXKUi8JSJvXvb42+f/CftNH85/0k6dOoXFYiEyMpJt27bx7bff0rRpUyIjI8N0261atSIl\nJQWXy0WdOnXw+/04nU6aNWtGs2bNiI6O1sFaViuSIch6QaaWBQpVOHr0KBs2bGDTpk18+umnulRp\n0II8eRkQ7h3BHNT71Z955hlcLhcpKSnExcVRP7c+Fq8FZ5YTs8uMO9qNP97PjFkzKC0t5fPPPycQ\nCLBu3Tr27dtHdnY29erVo7S0VAfkNa6JOIW23dty9OhR3n//fcQiyL6yax8TTCFTBa2JS3by5ElU\nu4r8pWzfs4I9QkjVtAr0HADLVy7HGrJiGmpCiTTj9njCAjwTJkzAZDKFhYAGDhxIVlZWheMvXLhA\nRnw8cxWFn0UXAGolQoxdB59NaidoJiHGYsFUhp9AhB9EqCSCSF+MotFIGtFHFHaI8IQI80SolZYW\nvs4vv/xCWnIM/ZuozO4hhOxCXxG8YkakNRaLi7S0NIYNG8bgAX0oLMNgFC8X6qaasFgs2Gw20qJV\nTizVnXudZOHlW/8GgGfXQYIrhuupsYltdX4qkZuxqnG4NSFoFxyqcG9v4e6eui5GfFBw24Tx4/Sc\nf1GRiYyMOM6ePcuXX35J0OXEqur04RaLBaPRSEZaBiUlJdx9993ccouJS0716FHBbjfhECNrpRyx\nbRPhq6++Cs/Jd999R/PmzQmFQjRs2JAvvvgCgM6dW9O1q5UtW4TCQiPJyZFXyOaWlpYyePCNpKUp\n+P26vvbq1TrDbEGBjqi+VKtwOAzMnCmMH68QCNjp378P/fr15emnn+bDDz/E7Vbp0EHHYaSlCXXr\nKrRpo4bHc+bMtYPE72H/jnzp0aNHqVu3bhg026BBgwoiX5fbtcYg17m76b/JfvOH83vbnj176NKl\nCy1atOC+++67ZpsnQHp6ergNFfQil9frBfQuokAgQFxcXPjO669//StJSUlYLBb8fj9Lly7l1KlT\nLFu2DKPLiNQUDAUGxCO0btOamJgYatSoQXJyMpmZmXz11VeY7CakoSB/FaRYkE6CK+QiNjaWjIwM\n/H4/W7Zs4cUXX8ToMyKHBHlCkEq66JB8KiiZClNm6Ejst99+m5ycHNLT02nVqhUdO3bk2LFjeKO9\nGB42IJ8L6miVGg1rcPDgQSwRlgr6Fa5811WBdd999x1alFZhX2ddIaQpRGdG03tw73D3yccff0zA\n4aCyxUKixULQ5yM1NZXGjRvj8/lISEgIryw6dOhAs2bNrrjeN998Q5PatXFrGgkRETTVNF4WoUAV\nehiFKI+HDz74ALPRyC9lq4SgyURmpUqkpFRGNWnESAIOxYJD06iWkYHNZgt3foFOud6rkTXs0HfO\nEhwWBb+Ycatm6tWrR//+/alduzaRkSGWDil3/lumCEkxPlq2aEbTTAkLKjWupHc5fXav8NFdemqp\ndTV9m0vT02Vum+DWzPTtO5Sbbx7L6BEj6Gm1sq1sfDVMgtMiOMwqtaQWXo+ZjIxounRpxaFDh/T0\nZyBAQBPeu0M4sUSnB3HZDeFU5KOPPkq7drYwsnnbNiE62k2cLY6A2LCLEYeomBTT3+1Uu2Rnzpxh\n4sRRNG6cRd++na9Kqb9r1y6Skuz8+quwZYu+YsjL0zuc0tJ0ZPUrrwjNm9u45557uO22icyYMY2P\nPvqIOnUyqV3bSU6Oi1DIyty55SuN0aP183g8Kvfeq7B1q9C+vfW/Gkz3z9q1xiC/c5DoV/Z3gojc\nctnj0vP/pP3BU17R9u/fTyAQ4OGHH+all16idu3a3H777dfcPy8vr4Jg+YsvvojP5wsrtmVnZ6Mo\nSoVAc+ONN5KSkkK1atUqnCslJYX8/HwGDBhAYWEhmqbh9/upXLkyQ4cOxe1207JlS5zVncgwQTRB\nHLpk6aUusKKiojANx9q1a3F0cujuuasgT1/mrjcJqtfAmIICfvnll/B7+Omnn4iKiqKgoAB7nr18\n/xLB4rNw+PBhAnEBZE3Z9vcFza9dFe168eJFXexojuia2xsEgyZIP0H+JFjidF3v6OhoqqWmhhlX\nS8pqAb169aKwsJDly5cTFRVFrVq1qF+/Ph6Ph127dv3dz/H8+fM0rVuXRg4HgzWNgKaFO7fGDBtG\njs1GtqrSskWLcPCpX78+HrsRVVWZOHEiRUVFjBo1Ck3TwtKqd911F7e0M4Ud/+EHBU3Vu5ISfb5w\nbn7mzJm4XC7iQlbeLdKdf+VoA5WUFHwWH06rXkuI9wuRLsFrEtyqTuhXOVMIefQ6xbf36df5/iHB\nYTWEUxinT5+mUc2aVHM4yHG5iPP76d61O43qNqJX115XdMUcO3YMs6JwS6vyoHXsUT0F9+OPPwLw\n66+/Urt2Bu3a2Rg/3kQopPHUU09RrVI1appr0kJaEGuLZfiw4ezcufOq2u6X7MKFC0yfPomqVeNp\n0KAKr776KqDX4z7++OMw8d6DDz5IWprKs8/qxesPPtAL1TabviLYvFlPLcXHB8Lp0pKSEgoKBtK9\nuxrWtk5NrVizWL5ciI9XmD59Kl27tiYnpwq33jrmf0HiN1hB2d8iESm8yuM/aX/wlFe0O++8k3Hj\nxoWff/rpp3+3MBsfHx9mdp00aRIOh4OioiIee+wx7HY7drudpKQklixZAuh3136/nz59+uD3+8MO\n+sSJE7jdbr766quwVGlcXByapmEymcjOzuaNN97A5XJh8ViQ7YKcEmSzYPPbwnfkCxYsoE+fPoAO\n7rNEWJDDggwUZPZlQeI+we0SHG7BHXJw4MCBCmOuX78+SmpZMbksraTaVE6ePMmsWbNQ7Aomtwmj\nzUgwFLymkMw333xD5XqVMah6QJOgTiKoNlBpkNuAqVOnMmDAAMxmMy9dlveeK8Ito0aFz3PkyBHu\nv/9+7r333nD32D+y4uJi1q5dy6JFiyoUGktKSrh/wQIig0H69etXgQ6jUrSCx6kxbtw4Bg8ejN1u\nJxQK4XK5mDhxIh999BFBr43nJ+h3/vk1VUKq3uEU63aHu5emT5+O0+kkq2oGPoeCSzPQRMlhpswk\nJCHyJI+axiq4zEK8RUj3CjmxQsgsxEXoBdvUiHKHzmohI1oqECkWFxfzzjvvsGLFCtrk5lItIYFB\nvXpVCPqX72s2GmmRXk4euHW6YLdKhSaAX3/9laVLlzJ37tywaNWWLVuIj/ficJhISQnhdluoVcuF\n32+r0DJ9uU2ePI4mTWzs3q3rUQeDNiZOHI/bbSEz00lkpJvbby/C7zdz441CzZp6jcHt1kWFfD6h\ncWOhXj3B7VbCKaxffvmFGjXS8PkMeL0671NxsdCnj9Csmd7RdPSoTgNSpUoq7ds3oXPnFuH27/+0\nf/k97FpjkP+lm/4Yu+uuuxgxYkT4+YcffkhiYuIV+/3000/MmjULVVVZv3498+fP5/bbb6d58+Y8\n8cQTAIRCIRITE7Hb7dhsNgKBQJmsZpBgMEjnzp1JT09n5MiRpKWl4XK5OHjwIFu3biU6OpqqVaty\n8OBBfvnlF5o2bcqoUaMwm81UqlQJxaZgT7Jj89nY9EI5387x48dJT0+nb9++zJgxA4fXgepW0dI1\nRBOU4QoyQr+jN/bR+Z5kohCVGsW5c+fC57lw4QL1m9dHa6ch9wj2GnZGTxwN6Cue7du38+OPP1Jc\nXMxNN93E/Pnz/+Hc7tq1C1uCDTkjGIyGcM2hqKiI6tWq0dRo5KIIR0WoZrdXIOW7HjZjxgwqVarE\n5MmTufnmm7FaLGSkJ1G1ShWsVisul4tevXqFuY0iIiJ488032bx5M3WyKhEb6SYqYMdpFboZhbqq\nSvXMTDp37kxGRgZul4MZXRS2zRR6NzCQbolhukzHIAZmykyKpIiA2GmdqeM3WC3M7Sn4rEYMBsFm\nEV4pIwt8a7pgtxiuAFD+8ssvxAUCzFMU9ogwxGwmr6ymdLm99tprVEmPx2XVi/iDmuj4CpvJdE3Q\nFuiAylDIybp1wo8/6uR6cXE6Wrp5c8Ht1irQel+ypKQgn31Wfmc/bZoBp9PEwYP68+eeE6xWvW4C\nOi6iUiVh1iz9+c8/6+pzNptCvXqZTJ48jtOnT1O3bib9++udT2fPCi1b6hoVPXoIWVk6CM9sVrjh\nhlZER2s89ZS+qgiFNN54443/BYl/wZaLiOey514Refx6XOg32B885RXt4MGDREREcMcdd7Bq1Soy\nMjJYsGBBhX1++uknEhISGDJkCEVFRQSDQZ566ilKS0upU6cO3bt3Z/ny5Wiaxrp16/j+++8ZO3Ys\n8fHxjBw5khkzZqCqKg6HA5vNRk5ODhs3buS+++4jMjKS+vXrEx8fX6Gtdfv27cTFxdGuXTtKS0vp\n1KkTEydOvKoy3vHjx5k3bx4zZszg3Xff5fDhw+zcuTNMLW42CLayO3pB/2urZGPPnj0VznPu3Dnu\nu+8+Ro4fyRNPPMHu3bvLiOMiw+mlL774giZNmtCmTZtw2uJaVlJSQm5+LtaOVoyakREjRoRTM4mJ\niVRJTcVpNmM1mZgyYcLvTnvwt1ZcXMyQIUNQVRVFUahdq2Y4aLVr1w4RCa8MioqKyM7OZvHixYAO\nZIoKaLw+Vccq1EkSzIpgVpxYLDFkZdWgfqYzvAq4uFJwWxUGySAsYmGADKBIiqhkSmDhZdTie+8W\nIq02Is2RVK+WiVUVPDa9NuHQTKxZs4a3336bnTt3cvHiRV544QWau1zhFdhFETwWSxiECTpIK+i1\nsX6M8NoUvXMqzaTLpTpVwWMzXjN1tGHDBtq3d3LJ2ZeWCna7sHGjMHeu/v+rr75K584tCQYdZGUl\n8/bbb1O5chxvv10eJIYONVKlit6KOneujnVQFD3FdGmfbt0qssF26GAgN9fIc88JPXtayc2thc9n\n4J13yvd54gmdIDA/Xzh9Whg+XGXevHm0bduwQofXo48KN97Y4X9B4l+wD//JbX+k/cFTfqXt37+f\nIUOG0L17d1asWHGFs5o9ezZDhw6ltLSUW265JYxCjoiIIDk5mVtuuYWkpCRSU1PDx5SUlGA2m8PK\nc7feeiuxsbF06NCBYDDI6tWrAb2Au3TpUlwuV4UVzcMPP0xMTEw4rbNgwQJGjx591fdfUlLCsmXL\nyM/Pp1+/fuH8dGlpKS+88AKV4uMxegU5VxYkzgumCL176FIr3quvvhqm5zhw4ACx6bE4qzrRojWS\nqyTTsmVLVq9ejcVrQW4W1P4q/lj/P9T7Pnv2LIV3FFKzfk0cTgcNGjQgJSWF5s2bU1xczLFjx656\nZ3o97dChQwT9Ptq2bRsOCEOGDMFiNocFkSZOnEgwGGTbtm0A3FwwhN4N9E6ko4t1PIbTGlvmq5fQ\npElrqic7KFmpO/9fHxfMRsGmWrCYApgMBjyaEavJQFa8gb8uK9PybmkgyRIiOhhNx7Z5LBks/PCw\nvtJ44Ca9g6lStBDjE/xOlca1a5NktVJSFiROiGBX1QpdRBNvGcesbnqhfMsUoX+ukBahc0iVrBSG\nNBV6dG0X3n/btm00bVqLrKwkevXqSvXqDi5c0J3t4cN6a+nZs/rdfFqagtvtJCFBlzt99lmdkfX+\n++8jJsbGvHnCmDFGIiPduFwG8vKE8eN1LQqXS5gyRU8X7dypdzQtWKBf57vv9JTT7t3lK42oKAuR\nkUZuu60cR9Ghg1CpksLu3cK8eYLXa+WTTz6hXbtGrFlTEVfRp0/H/wWJf8E+EhHfZc99UpE2/D9h\nf/CU/3a7hC5dtmwZderU4fjx4xQXF9OlSxeGDh0K6Hfzdrs9XGg8dOgQFouF4uJiPvroI2JiYsJ3\nb/v27cPlcoWdMujpAafTSYsWLejRowdOp5P27dtz4cIFjhw5QvXq1cOB5ZIVFxdTUFCAxWLBbDZj\nMpkwGAy43W4OHz4c3u/06dMkZSag5CrII4LSVCGjVgYNGjTAFrThbOjE2cBJUtUkjh49SuO2jTHO\nNobbWG25Nlq1aoUW0pDHyuscxluNjBg3gn/Wdu3axcKFC3n66aevAMf9XlZaWsquXbvYsmXLVXP1\nBw4cwKeqOEVwu92MHTuWyZMnk5yUhEdRwrTpDoeDWbNmAfDjjz8S8jvIqyx0rqMD3pYOFpxaBiK/\noFmisZqNmIxCpWiFRwcLGVEKTquR+/oJs7vrOhIbxgi779AL2JrZgMemF66DXhdBlwmvXZjTQw8y\nTxQIVrOCYjBQK1mlbyMjHqcFr9mMwyCkKAoPiVDPZmPUkCEVxjht6m2Mam2gXopQO0mI8wv33li+\nevn8XiElIQLQwXGBgI6Sfu89oUULK2lpUTRpYue22wxERgqFhbrjLSxUiIgI0r9/fzp37ozbrVNl\n9OrlYMWKFbz22muMGVPA1KmTWbx4MTVqmNi9W08p1aihI6azsvQVhcul4nZbcTj0dldNMxAVpYY7\nrS5cEGJiNFwuK1FROulfWprgdhtp0yYPm81QJpNqZvbsQjZt2kRUlMaTTwrz5wsul5E2bXL/FyT+\nBbtJdGDbHSJyZ9n/N12PC/0G+4On/Lfb9u3biYiIoF27duH0A+jL+lq1anHu3DlmzZpFMBgkPj6e\nCRMmEBUVFe462rRpE/n5+RXOGRUVdQX45t5776VSpUoUFRXRuXNnoqKisFgsqKpKampqOGD0Htyb\nrMZZZNXLIjEtkeSayVTNqUp0dDQGgwGz2cy8efMqnLu4uJjxE8bjCDq45957KC4upveg3sjo8hSU\nOkpl6OihhJJDyBeXFb3nCiPHj6Ryg8rI1su2LxW6D+h+nWb9t9vFixfp2rENqTF2GldzExPpY9++\nfRX2ycvOZkJZR9V0RcFkNGJUFBwWE5PbCXWTBJ/LRIM6VVhdRrg4ZmQBY9uUdzjN7qG3qBqNVoxG\nExkxJv7ykPDXZULrLBNV0uPx2AysGVXumO/uJQxtpv//1EjBZfEi8jUWUxY5acKfi3QEtc2sEwK6\n7BZGjBjBzJkzyc1thNvlZOjQobRt2waHZiLSLdSvVYOlS5Zc0bL9zDPPYFWFXvX11crCvkJ+loRX\nOYsHG2jWqA4A06ZNIy7OgtmsEBensmyZYLOZycnJ4YYbbqBjx7ZkZ2ssXy54PGaGDx8eXn3l5jYi\nJ0dwOoVQyM7cubPDq/CuXbtisZiIj3djsZhYtKj8Ln/BAqFy5XgmTVL59Vfho4+ENm2sJCQEGTLE\nwvPPC927W4iIsBETYyImRu9+iorysmPHDuLiAmzcqJ/rxx+FuDhdQ/ull16ifftm+P0WRo0y8vjj\n1xcn8UfZtcYg/0KQMP0T+6wQkfdFJK/sAp1F5NPfeqH/v1lOTo4sXrxYRowYIXa7XYYNGyYGg0He\neecdURRFevToIefPn5eHH35YNm3aJEuWLJFWrVrJnDlzRESkWrVqsnv3btmzZ4/UqlVL1q9fL0aj\nUaKjo8PXePPNN6VobpGciTkjsx+aLYZigxz/y3EpLS0Vo9EoderUkXfffVcGjR4k3+V9JxcKL4hM\nFDFoBuEhRL4XMReYxWKxSElJiZSUlFQYg6qq0r1rd3nrjbdk0sRJIiLy7fffiowu28EgcqHZBdm/\nfL9kZmbKsXXHpGR6ichZEdsmm9QcUFN8Pp8cmH5Azqw4I3JKxHaPTbre2fUP+Qz+GVu+fLkc+Wqr\nfDLnjJhNIkvfNMjwwX3knR3lGdX9+/fLo6KLr9xRWio2EZltFnn1tlL561mRFdtFlgy8KGbTJzJm\n4jAx/n/svXd8FOX+PX62ze7OluxueiMFUgglhRRKIBCKECAJNSAg0oRQQhXpRESKItJEURBUEAQL\ngnRQFBG4SFMJCiq9BQgJpJCye35/zDIkUtQrer+f+7uH177YmXnmeWZmJ095l3NUKly5dA5pQRVy\nHfHVgdCQEGz74gCGZfZFU9N6eFulY1PSK9Bx/hWYFYBZf+/aXPRAmbOKM7mAhrcAnINCkYO1WYCv\nDagfAuz7CZi1AahdJxweHh4AgKSkpti79xv4+PjA19cXl86dQp/on/Hc2h/Qp29fKJX3pGRKS0sx\nNLMfYoOBVnUlTY5BzSXdjJrPAl5m4LtLGuzZtxzL316OV155BbGxsejZswkuXryIwYNXIyAgAK6u\nrjh+/DhatGiBJ554Cdu27YBK9TXKysqqtHXunKQZUVpahNTUKQCUaNnyCezatQtDhmTBbDbjp59+\nwoQJ6zBwYAUUCuD6dSVu3bqF9PRyiKKkK9G58x3s3NkSBoMPXn/9e5w/n4v4+J/xySeAUgk88wzw\n6acl8PHxQW7uTaSmStfg6QkkJSmQk5ODXr164fLly1Aq/4WFC0sBAH37PqaX678Ef2SQACSVuHxn\neQKoBuDc33VR/y1IT09HcnIymjVrhsaNG8NiseDQoUMwmUw4ceIEbty4Aa1Wi86dOyMyMhJZWVny\nuQEBAViyZAmaN28OtVoNvV6P9evXQ62WfrKcnBy0SW2D0s6lwPNAuakcqAkcOnQITZo0AQBYLBZ8\n9913yFXkonxuOaAAUARwHYHaAOxA2Q9lUM9WQyto0alTJ7z19lt4/9P3YTVbMe25aYiKikJeXh5a\ntWqFhg0boqygDIqFCrAFAQL6t/VoHN8YA54egMQnEpG/Oh8VeRVo3aI1nn76aQBAfkE+3qn/DtSC\nGpPGTEJG14w//Sz37duHF8eNQ+GtW0jv0QPDR4+GQqH4qz8Rfv3lZzQPkwYIAGhdl5i64UyVMqEh\nIdhy8CCGASgHsAUAFQoEuhOjVwEvdAE6xUtly+3FGNC/J0STB86YdWgdeQeCGnhlmx5PpKTDYrHA\nxy8QR77XOGsDDp0BNGV2TCkmRiwF1AOB4lJg/AdAck1g+HJgzZdAcLkKN3AYSoUdN4ukQQIAbhUC\ndgeQf+kS7HY7VCoVLl68CIPBAIVCAZIoKi6BhwtAEmVlZdDr741GFy5cgKAsR+d44J09QJcEQKcB\nQtyAc0eAwCvAUZ0SFy9exLNDn0V5WTmaN28BhUKB4OBgBAYGoVatWoiMjEStWrXw6quv4pVXriMz\ncyiWLl2KCRMmICEhAbdv38bRo0fx1luQhY6ys+0YPnwStFo9goODYTZLagRhYWFYu5bIzgaKixV4\n+20tmjZtiCVLtuPixQrYbMC6dTokJzfEs89KDggPDwETJwJ3tZm6dQN27LiDVatWwWIxYfXqfHTv\nDly9Cuze7QCwGYMGDUBFhR3p6X/9XfpvxR8ZJIZByovIBVB5qlnnb7mi/zKYzWbs3bsXO3fuRGlp\nKVasWIHS0lJERUVVmc3pdLr7lKw6duyI9u3b48aNG3B3d5eVyQ4ePIgmrZugdEApUAZJFuprAA2A\nUaNG4a233sKWLVtw9epV1KlTByyVOnQoIFEmvgEpZq0cQBDgqHBg686t+HD9h5i2fBqKs4uhOKfA\njuQdmD5pOq6WXMW50HPY+flOmM+ZkRSVhG88vgEItEhpgTHDx+CF2S/AP8gfCbYEjB81HtHR0XIn\nvuDlBVjw8oJ/+xl+//33SG3RAi8VF8MfwHMnT6K4qAgTpv71dJ2o6BhMW21AVusiWERg2ZcqREXW\nrVLmtXffRctGjbCqqAhXKipgDghA5wYxyFr5GWi/g5J7E2WczgU0Dg2UBcXIyS+D91AlAAWezGgP\nRzlh1utRXlEBs0mLczcFmHTE1mMONHYAfWEHbwLPzwd+AOBwaHHzSBkiHMR0AKNQBh1GIywiGikv\nH8O49g6cuABsOAa4AgjJz8e7ixfD4u6OnF9+gU6nw8GDB3Hu3Dmcv5iLBdsVaNq4QZUB4vLly7h2\n7RpuFlageS3gu3OA92DAQcAdgGAHQgCo7tzBjOdfQExJDHYrdyMvLw+urq6w2+3Iy7sBURQBSKtP\npVKJ8nJpAOzfvz/c3NywfPlykERERCBu3Tolt3/qFODn58CpU6dw9uxZFBYWwmg04pdffoFGo8GS\nJURgoANWK1BWZsfatRXYtQu4cwdQqdT4+OPhAIDy8nIUFFTgvfeAzp2llcTatYBaDUyfPhHu7kTf\nvsCECVrcuqVAo0ZJOHlyEy5cqEBeHlCvHjF/PhDzd7HRPUasWbMGzz//PM6fPw8vLy+sWLECiYmJ\n/9Fr+gXSO/j/Ev5B696/j+PHj7NOnTq02WyMjY3l0aNH+cMPPzA/P5+RkZFMT0/n1q1bOWTIEPr5\n+VVxSj8KzVKbEW9WsvNnSxnKgodAX19f1qlTh+np6Tx9+jTLy8sZ0ziG2m5aYhGoCFAQPpD0KO6A\nyACVRiVPnz5Nm6+NGA9iq+RvUI1UUWfWEQec7VwCtbFa1q9fnx4eHjQajfT29mb1utWp66wj1oNC\nf4E1Y2uytLRUvt78/Hz27duXERERfOKJJ3j8+PE/9RwnjR/P8QqFHMJ5FGCIl9efquO3yMnJ4dq1\na3nw4EGOGTmUZoNAPw+RdSOq8/z58/eVv3nzJnfs2MF9+/bRbrezqKiIs6gkDgAAIABJREFUA/r0\noJvVSKNOwbk9wZldJf/AjK5SGGliqIJtWjVjWVkZl775JqNEkRcA5gFsptMxrW1bLlu2jHv37qWb\nKPITgGcg8TwFAjQC9LfZqAUYDkn46B2AiZGRXLduHZOTGrBRgwTqdEbq0YRu0LM69FSrVKxbty7T\n0tIYHR1NDzcrh7QCRa1S1iMnyecnTqRFq2WEyURXo4GuLlom1xZo1kuhr54Abzuf+WGAWqWSUcoo\n1lPUo8GgY2xsPfr4eFKnExgdHU1PT08aDAYGBARUIb/7+uuv6eZmYP/+eiYm6qnXS8py/fpJnEt9\n+yqZnZ1Nf38v6nQa+vq6UhA0NBikxDm9Hpw0CdRqJUqONWvA3bslgr7evZ+U24mMrE5vb4muIyhI\nyqnQ6cDvvpN8Efv3S9s7duxgt25t+d5793wey5ZJ12K1/vs+CYfDwbVr13LSZIlt9lFUPf8utm/f\nzoCAAJmM8dKlS7ImTGU87B7wNzmuvwCg+Tsq/gt47A//caOgoIAWi4UvvfQSz549yxkzZtBgMDAk\nJIQWi4VGo5EDBgxgcnIye/ToQavVWiWj+VGIbhpNbKs0SCwHlRYlBw0bxEaNGlUpe+nSJWZlZVFj\n0VDpoqRCqyC6VDr3JxBuoGARpEzn/iBqghgCKkcqqVArJLqMZSCskAgG9aDZ3czoptHs0LUDFRaF\nRFvudGabapu4b98++RpatWrFvn378tixY1y0aBF9fHwemn1NSkl6lQfM7ClTOKIS5fY3AGv6+f3J\nX+Qelr31Jj1senZoYKafh8ipk8bx2rVr/PXXX2U9jYchNzeXe/bsqRJAcODAAfZ6shN1agXbR6pk\nx3P+W6CgVrKiooJPpqZyRaVs8S8Ahnt7c9WqVVJOjacnzQBdAfZwhqlOABjg7s6Zlc77LePsxYsX\nqdd7UJr7f0tgG/V6G/v27Ss7i9PS0pjRUMMQP6OcVf7ll18ySBS5B2CGRsNIrZZmnRQ51KKFFAFk\nFATGAgwFqFOrabVaqVFr6Kfyo14nZTuvWQNu3gwKgoZdu3ZlZmYmIyIiqsjYNmhQu0o+Qtu2Srq4\nKDlgADhzphQOu2jRIiYmGnn6NPjNN1Jk0pgxUs7FpUsSpYZKBT7//L16vv0WDAiwyu2cPHmSoaH+\nNBgUNBpBf38bIyLulSelwaNFi2SOGJHJoUNV8v7Zs6XMbKXy4YPE78mXPpP1DA2RBmIKaKhvYMee\nj1++tEGDBnz77bd/t9zD7gF/0yDxNiRjxnhIvE3/v+du+iOYO3cug4ODq+zz9/fnt99+y5UrV9Lf\n37/KsYSEhCoqVo/CS3NforqumjguZUNrAjQcljWM9erV46xZs+RyZ8+epdXHSriBWOrsxHNAmEEc\ncW6/DyIehAESNQchkQJ6gdBJXEzo4hwgTjqPH4Ckfb0OFNoIVFh/M0jUMfGbb74hKc3ADQZDlfDV\ntm3b8qOPPrrvvhwOB0ePG02VVkWloGTiE4m8efMmf/31V3qYzcxWKrkMYJAocsqkSVyyZAk//fTT\nPzVjKygooNmo5U9zpI782hugl6v+Pu7/yrDb7fz88885duxYmT3XbDbfl0DZLqUdm9e6F52U+zqo\n06ppt9s5IjOTI9VqubOfC9AAHyoUsfRw9WYfQaA3JH6nu2U+Bli7WjWGGwy8BLDcmS3ds5JQTVlZ\nGY1GNwJfEsilKLSgXqthXFwcp06dysmTJ7NWWBB7Jiro7WGRB9/XXnuN3bVa2tRqNklMZHJyMl1c\nXFirlpLVq3uxQ4cOdHV1pVqlokZ9T8s7MzOTarWagqBhjx4KkuDLL4P169eTB6XRo0fT7NTNIMka\nNTyZk3Ovo549G2zWrBGTk+uxQ4eWPHToECdOnMjQUIHPPy/Jk9psUhTS3XPGjQMFARw16t6+XbvA\n8HDv+96h3NxclpaW8ty5cxRF8ORJqfyxY1JiX3x8JK9evcpq1dyYlAR27Aj6+EjCRGaz4oEd7I0b\nN+gX6kd9Jz2FwQJFt/vlS7VWLZHv/DsoBsVq98uXDh87nCYPE62+Vs5+5c/Jl1ZUVFAQBM6aNYs1\natSgn58fhw4d+kDK8of1kfibBolsVOVvuvv9P4k/9XD/KZSWljI7O5tPPPEEo6OjabPZ5KSvwsJC\nms1mfvfdd5w3bx6tVitjYmK4fv1653LcrUoG7KNgt9v53KTnJJU6IwhfEC5gfOP4Kh3mwKyBVI5U\nSgMAQVwGUV/q/KEBEeXkSVoHwq/S6oKgop6Cb7zxBq9fv06bj00qW/mft3N1USoNOqoOKmIDKAwU\nGF4vXDY3FRcXU6vVyisHh8PB+Pj4+yRN79y5w6hGUYQJhAeIFqDmaQ3Te6STlGaJmX36sGd6Okdk\nZdFDr2dfUWS00chObdr84YHi1KlTDPQ2VOE7ahbpwu3btz+wfEVFBdPS0ujr60uNRsN+/foxOzub\nI0eOpMVikWeUby15gyaDQFEAh7eWQlbjQ0WOGSUlM16+fJlBnp7sYDCwA0AdDASOE7BTCxvnAxwB\nsB7A65CEbWLVar4yezanT51KnVpNrUrFJxITOWroUPparQx0d+fCefO4bds2iqIrTXodh7QEv5oM\n+rhqaDIZqdfrqRU0dLcauH//fvm+du7cSVeNhtUDA6nT6ejj40OdTkeNRsVu3bpRFEVmZGSwZ8+e\ndHV1lQeA7Oxs+vj4sGPHjjSbTezTR+pca9YMlY8PHDiQnp6ecludO7dnQoKGu3aBP/4IBgWJTv6w\ndKalNafNZqUgCExKSmK9ejF0cVHTbAbfeONe/kNCAtirl2R6mjhRWmWYTKC/v9cjZ+vVqrnRYADr\n1ZMS80QRnDRJCjPPz8/nk09mUKdT0d9fZGCgBz/99NMHdrBTn59KTT/Nvff/A7BuYlX5UmN1Y5W/\nEZeEqvKl2S9mU2wkEqdBHJcYDN5b9cflSy9evEiFQsG4uDheuXKF169fZ6NGjThx4sT7yj6sj8T/\nuJv+s3jqqafYunVrjhgxgqIo0mq1snbt2pw2bRojIyPp7+/P119/nWFhYdy5cyfXr19Pi8VCnU73\np3WgHQ6H5EPY6nwl80BDsEFmIiXJDr06EG85VwEHQKSAGCvN9nHeuVro6Nwvgngd0opgI6iz6pje\nPZ3VY6qzVlwt6fjdPIj9kAanl5yDhAh2f7o7G7RuwAHDBjAvL4+kNJgNGTWESq2SCo2CjZIbsVOn\nTqxRowb7DOrDGbNmyIPo5GmTqX5CLdVXDqIHiL6gW6DbffdtEUUec862ywBGGY0PJZH7Le7cuUMf\nTys/GiENEPuyQTer+EC7LkmuXbuWQUFBHDp0KM1mc5XOMiIigp999hm/+uorertqmfMSeGY+GBkA\nWg3g6JEjaLfb+emnn7J541gmxtdm3z5PE1AQ+Jn3Fg1JDFSp+DPAfgDVADUARw4eTLvdzoKCAqam\nplIQBBoMBlYXBJ4EeASgv07HAf37c+HChfSyaWVivrJ3wLoekt7FCoAt4uLuu7f6sbEUBEGmPrm7\nSqhXrx5r1arF7Oxsjh07ljqdTs51GDx4MPV6PUePHs22bdvSW62mAaBWKzAysi5btmxJg8Egr2hf\nf/11WiwW1qwpUaobDFqmprajzabjwoVgQoKGZrOZ3bt3l59rXFwcq1dXUK8HExLUDAlR091dycRE\nLePiwCZNFDSZ9KxXrzZdXMxVBLF+i0uXLtHLy0KbTUmbTclmzRKq8I+RUlLrqVOnZGrzB/UvmcMz\niTmVhoBjoG/NqvKl/uH+VM5UEhdAxesKuvpXlS+tk1iH+LxSHW+DHXr9cfnSvLw8KhQKvvvuu/K+\njz76iNHR0feVfVgficecJzEfwHAAGx/USQNI/bON/TejpKQEH3zwAb799lskJyfj8OHDCAkJwbhx\n4/DCCy/Az88Pt2/fRnZ2Nt577z00b94cADB9+nQsWbIErVu3/lPt3blzB/m5+UAr5w4rgETg1KlT\naNxYkiTvktIF26ZuQ3F2MZACoATAckhRTn6QyODXACgG8AqkqKchAERAoVZgs8tmlL1eBnwEKdwm\nDkAwgPOQoqRyAbQHEhsnYtXbq+4LSZ0zbw6W718OxzkHoAAOtD0Anws+uG66juXhy6Hbo8PaVmtx\n4PMD2Hd0Hyr6Vkj1AkBvAKMAbx/vKnWWlpai6M4d1HZuawCEOxx4++23cfLkSfTu3RtWq/Whz02r\n1WL9xm3omNYG/d8uhEKpxrsrP6iSf1IZ58+fh7e3NywWCyoqKnD69GkEBQXhxo0buHjxIsLCwvDB\nBx+gR/1S1PSVztn+HBDyLHD56q/YtWsXBvXrjtd6FkMvAMNW/QqlUguH41UAL0GSiz+MsKQkRO/d\nizISCocDCqUSamcI64ABA3DhwgWMGjUK+fn5WL1iBc4COKcAShR3UHxiOV7brkVhcTmKSgGjTsp1\nKJWionEWgFpzv1tx7IQJ6N+/v5xb4enpCbPZhKNHj8Dd3QMOhwOiKCI5ORlLly6Fi4sLCgoK0KRJ\nE5hMJuReugQfEoIgAF5l6NDhO+TlKeHq6sCRI1/j1q1MjB49Gv3794fNZkNxcTEWLFiAY8e2YPx4\nO4YOBWbNkiKiTCaTfF0uLi6oX1+J2rXtmDTJjjZtuqFHj6exY8cObN/+Gn74oQwDBw6ExWJBaWkp\n3nzzTRw9ehRRUVEAAIfDgbNnz0Kn08Hb2xtnz17FDz/8AEEQEBERUSWqEACsVusj3xkAaNuyLd4Z\n+g6KnygGPAH9JD3atWonHxcEAV9t+QrdB3RHzrwcBIUEYfW21VXuy9XqCpwC0EzaVp1Swd3i/sh2\nf3udfn5+f7j8P4F6zv+TcE+69O4n6T9xQZXwh0fffwp3zSrr1q27L1Pax8eHZ86c4fnz5xkREcF1\n69bJx1544QWmpqZy48aN95Hn3cX169f5xRdf3BcV5FPD557+wwVQ9BPlqIe7GDRkkDTrdwPhCeJD\nZ/lypy9ioXMVcfefHRS7ilQYFYTdue8HpwnIC0SIc2XiB6IBqPHSyHTRv0Xjdo2JTyrV/QGoEBTE\njXttGWONXLx4MZu2akp1L7W0ynGAGCKtVtK7pd9nToivVYvTnGyw2wEaAMYCTARo02qrRPA8DHa7\nnVevXv1dqo89e/bQzc2Nw4cPZ69evajVamm1Wmk0GmVq97feeotJEUp5Fr/1ObCaD9ilS2v26t6R\nb/S9Z9r6bAxYO9SPSqWZgIqASE9PLxYWFrJ79+6Mi4vjlClT+Oyzz9LPz4+rVq2iq6srR44cKc+0\nExMTqVaAOhVk30rJctDDRcX4UB3n9QKbRYB1BPBNgN6iyI0bN953b7m5udRo1PIqQaJrUbNmzZrU\n6/U0GAz08vKi2Wxm06ZNabPZGBMTQ6vVyrDQUGo0GtaPjWX79u3p5mbh888rSUqaDX6+Jp46dYoe\nHh5VVl+hoV5MSbnHvxQXJzA8PIzVqlXj4MGD+dRTT9Fk0vLzz6WopOrVwdhYSfWvrKyM8fG1aTTq\nq9RZrdo9wa7r168zJiaGNpuNRqORPXv2fGgwwsNEkR7WvyxcvJAWbwv1Lno+2e/fky81uBmoGaKh\nto+WNt8/J19KStr0cXFxzM3NZV5eHhMTEzllypQ/fA/4G8xNagDvP+5KHwP+1IP9p9C3b1/Wr1+f\nbm5uMg/SgQMHaDabZY7/zZs3093dnfPmzeO0adPo4uJCi8XC1q1b09/fn6NHj2ZZWRlHjBhBX19f\n+vn50Wq1slGjRvT29ubIkSPlTvPw4cN09XOlKcRErVnLWa/Muu+aNmzYQNRxdsobQNhAJIOacA3h\nCkm1zhfEZ/f8FnpfPQUXgSh27rsKSbzI6SjHt05fxheg1kX7wEiloqIiNm/fnMqpSnmQUI5TUilW\n0p8gqIvSUXAVqO2rpcJNQQSDqAXpmn8GDbEGvvPOO1XqPnv2LBvUqUOVQkERYH+ADqftJhtgUmzs\nY/1d58+fT51OR1EUWbNmTW7durXKPZeUlLB2eBBjgsBeSaDVBLq6arlx40b2692dcypxIK0ZCvq4\n6WkyCAyu5sEJEybw9u3bJMnAwEDZ9JOdnc1WrVpx8ODBDA0NZY8ePWQt7zoRNSQaDr0kVHS37vT6\nRg4cOJBDBvVj1rBh7J6Wxu7t28sd6IPQs+eTVKvVtFgsVKvVVKlUdHFxYb169divXz82atSIFouF\nEydOZEhICJs0aUJBEGiz2Rgaes8PMXz4cBqNKpaWgp3SVNTrFCwqKqKnpyc7d+4sS8paLGp++ino\n7i6xs778MqhWq+jmZqNOp6NOp+XQoVLUU2KixN8UFRUqO9w3bNhArVbDdu3accqUKezZsyc1Gg2H\nDh3KhQsXMjk5mQ0aNODUqVM5YcIE1qhRg6+99lqVe87JyWGdOsFUKhX083Plrl27qhz/O/uXvyJf\nSkqRf4MHD6bFYqGXlxeHDx9eJdz8Lh52D/ibfBJfA9A+5jq7ADgOKTnvt+kr4yEtyn7EPWPKb/Gn\nH+4/gfLycs6cOZN16tShi4sLY2NjKYoiGzRoQD8/P06cOJFr165lamoqw8LCGBMTQ7PZLIeL5ufn\ns1q1ajQaJebTX375hf7+/rLsZ35+PsPCwmRxFFLqoI4fP/7QkNJr165RY9YQs5yriQYgTGDdenUJ\nCyRFuJWQvgeDWquW02ZNY8ceHSkmi1I+RltIoa8ukBzWFlDhpaDoJXLeonn3tXn+/Hn6+/vTaDQS\nIqhsr6SYIdLqY2VUYhSF/gJxDFQsVEiDz7eQI0JQHRI3VIlz38t4KCFgWVkZrUol36sUEfQ5wLBK\nTtPHhdLSUt64ceOhTtLS0lIOH57FsLBqbNCgthy9dejQIbpZRc7uJjG0mvWSY/v6G+CC3goGVfOU\nZ6SJiYls166dTI1ep04dzp49m9u2baNGo2FUVBQjQqqxboCGhcvA1Hpg94YSxfieKZJv5UHqf4/C\n0qVL6eXlxaeeeopjxoxhjRo1aDQaZfrzqVOn0tPTk02aNKFGo6FGo2GtWrVYrVo12W9x13ehVCpp\n0CkZKvpRq9HS4XDw22+/pY+PD/V6PTUatczO+sYboM2mZPPmsRw8OJMNG8ZRr1ewTh0FjUYjo6Ki\nqNVqZb2V0NBQ5ubmcsuWLfT01NBkMlGhUNBsNjM5OZlms46DBuloMOjkSKzs7GympKSwb9++8v2W\nl5ezenVvvvGGgna7pGrn5mbgpUuX5DL/r/YvfwYPuwf8TYPEewAOApiMxxcCGw4gFFIORuVBIgIS\nDbkGQCCAnyFR5vwW//Aj//M4dOgQDQaDbP45fvw4jUYjk5KS2Lx5c5rNZoaFhVGtVlc5r23btvT0\n9OSRI0dYUVFBpVJZZbk8YMAALl68+IFtFhcXc9myZZwzZw6PHDki79+9e7cU4bTD2fHeBnVBOgqe\nAtEGRCiIlqBKq5LJ7crLyzl33lymdkuVZv/ZTlPT1yD2gEKgwJmzZvLmzZty0t5dpKSkUKVS3X0h\nqdFomJ6ezqtXr/LmzZvs+nRX+kX4sUGrBlSoFJKz+m5UVS8F0fmeSUzfWs8FCxY89DnX8PVlAsAC\ngHcAtgHYqW3bh5b/J7F161aOHzuWY8aMYb+nn2Ra25YM9ROrRFbVDDDx2LFjJMnvv/+e7u7urFOn\nDoOCghgXFyc79sOrVaNeCU5KlyjFby8DgzwFhlb3p0qloJe7hZs2beI333zD1q1bMzExka+//vrv\nxun37t2bbdu2lTvVbt26UavVcuLEifJgZTKZ6OHhwdDQUMbHxzM7O5sjRoygTqdj27Zt2bdvXwYH\nB1On0zEKUfTSe3HGCzPkNhwOB/Py8vjVV1/Rx8dGi0WgxSJy/fpP5DK7d+9mZKSRarWKY8aMYVxc\nHOPj4zl16lROnTqVDRo0YKdOnTh8+HDq9VomJibKglRSoqaWJNiihYZNmzaVr7127dqcPfteqOmZ\nM2fo6yuycu5Ey5Yu3Lx5s1zm/0L/8nt42D3gHwqBvft5HPjtIDEewHOVtrcCqP+A8/7hR/7ncfz4\ncYaGhsrbI0eO5MCBA+Xt6dOny6apZs2akZTCPG02GyMjI/nxxx+TJGvVqsVly5aRvBup4cWoqChO\nnTq1ik21qKiIYTFh1LfSUxgmUO8hiRmR0oxboazkYyCo76NnQtMEGmsbqRukoxggcsbL9/6wSemP\nO7VLKpUeSskP8VEl/8JqMCg6iIJRoOgn0j/Mnz///DNJMjg4WB4g7n66d+/+wOcUnxxP9bNqKft7\nP6hz09HiaaG5iZnGcCMbt278wOX0Xfz444901+upcUYE1Q4I+MOZ638nFr76KgNFkdMAdtDpGBcR\nwWPHjtHbTc/by6QB4uaboKuLroq+Rm5uLj/88ENu3ryZpaWlvHbtGocPf5bNmrWlSRBo1IJJ4aC3\nVcXBA/vQ4XCwrKyMJ0+e5MyZM2kwGJiamsonn3ySPj4+VXI58vPzmZ2dzWeeeYbLli3jRx99xIyM\nDPl9umviuiunm5qayho1ajA4OJhTpkxhTExMlQGlQ4cONBqlMNvGjRszMjKSTZOayu9uZbz66qvU\n6/W0Wq0MCAi4Ly/lwoULFASBer3kbwgMDKS3tzf1ej29vb3ZqlUriqLIRo0aMTAwkCqVihEREXzq\nqafo4eHGWbOknI0zZ0CdTmBQUBB9fHyYlJRUJZrp1q1bNBoFnjkjDRC3b4PVqok8dOiQXOb/Qv/y\ne3jYPeBvGCSiIZmGaj7uip347SCxEECPSttLATyIMvQffuR/HsXFxfTy8uL69etJSlnH7713LyZ6\n165dbNKkCffs2UMXFxe6ubnRYDAwIiKC27Zto5ubG8eOHcuUlBQajUZ5ptauXTvu3LmTLVu2lHUp\nDh48yMAagUQz3FOS+1Jy/GY8ncGysjIG1wmmYolCOnYGFH0lJ/emTZu4cOFC7tmz575ZZ8/+PSVz\n0DUQaZBCZO/+exVUeaiIK05/wytK1m0oxY136dKFGo1GHiBEUeSrr75Ku91+Xz7DlStXWL9FfSrV\nSlq8Lfzwow+Zn5/P7du3c+/evfIqKj8/nykpKbI9/P3335frKC0t5eHDh3nq1KnH/0P+G3A4HDTp\ndDzlNIE5ADYzGrl69WoO7P8Uo6obOLa9irUDDRyZlfnQem7dukV//zBqNEMIvE29PpJt2rTn3Llz\nuX//fm7evJktW7ZhWFhdqtUWKhTVmJSUJHfi/fr1kycqRUVFrFmzJmNiYuQwVbPZTG9vb+p0Onp5\nebJ27QC6uqrZtKmaoih1tEqlUs4NSU1NpclkYmZmJkeOHMnAwEBGRUXRZrPx2WefpYeHxwNlTvft\n20dXV1eOGDGC2dnZbN26NV1dXXno0CF27tyZDRo0YEJCAv39/Wmz2diyZUuazWY2adKEY8aMYceO\nHSkIAn18fCgIgmxiCgoKosFgoE6n4bRp4JEjYFoa6OIicuXKldy3bx+vXbvGjIwMBgUFsVmzZjxx\n4gQXLJhLX1+RffroGRKiY5s2zeSwbfJ/g8Rv8agQ2CkAekKiCX8JwEwAb/6JuncA8HrA/gl4cFjt\nw/DAm8rOzpa/N23aFE2bNv0TVf790Ov1+OSTT9C5c2f07t0bDocDBQUFaNeuHbRaLV599VU0atQI\nLi4u0Ol0yMzMRFZWFtLS0jBx4kTUrl0bixYtQkZGBk6cOIElS5Zg3759WLduHXQ6HerVqwcvLy90\n6tQJnTt3RrFQDKRBCm8FJIOeBthwaQOmzZyGFye8iEGjB+HWuFvgbYJm4vLly2jYsCG+++479BjY\nAxdOXoDR1YhZU2chJSUFaz9cC/gAcAMwEVIY7UUABIRFAhxtHYCn1JxjgAM5E3IwYfIEWN2s8PLy\nwvXr10ESLVu2RM4vORBEASTRolULDOo7CMnJyfD09MS+HftAskoIbcuWLeXvP/30Ezp06IBTp06h\noqICeXl56N+/P4KDg5GQkABBEBAdHf1P/Kx/CA6HA3fKy3E3WFEBoJrDgcLCQrz+5gp8/HEqfvrp\nJ0zvVwupqfciyd9dsQIrFi6EWq1G1uTJKCwsxM2bQSgvXwTgAFQlOnyz5TMkJtTDhx9+iDfeeAN+\nfoE4d+4XOByZANSoqNgn13eXERYANm7cCJJo1aoV3nnnHbi6ukKlUuHChQvw8/PDxYsXUb16OcaM\nIV57TQOlUoX69eujrOwMVq58FyaTC+7cuQNvb2+sXLkSxcXFMJlMuHDhAtRqNebMmQO1Wo2ff/4Z\nSUlVgx8PHz6MGjVqwGKRVJDj4uKwdetW1K9fH66urigoKIDFYoHD4UCrVq3w8ccfQ6FQoFmzZlAo\nFKhbty4OHDiAK1euAJBIA81mM6xWKz777DP4+wfgpZeuYPr0MsTExCAszIGRI0di//79SEtLg91u\nR+vWrXHmzBnEx8ejY8eOGDJkEhYtmgubrQjkQURFhWL37n8hKCjo73ot/iPYvXs3du/e/bfVnwNA\ndH53BfDt39DGb1cS45yfu9gKIOEB5/2DY/Jfg91u57Vr11hWVsasrCwKgkCVSsUmTZpwx44djIuL\nY2BgID/44AOWlZUxNTWVvr6+DA8PZ/XqEtncihUraDQa6ePjQy8vL+7du5dXrlyhXq9naGgoMzIy\niKedIar7QeSB6A6iqxTR5F7dnYZggxQ9ZHQ6h11AmEG1TS2dN8HpLP4ShAHs/GRnGmsapWijhSCm\nQsrUNoIGVwOfe+456iP0UlKeA1L7ehDtpTIIBBWiguEx4UzvlC7t83SWqQ5qm2jpEejxuyGAe/fu\npcHNQDwDooPzfIAqlYozZsx45Ln/DhwOB99ftYp9MzI4duRIXr16tcrxO3fucPny5Xz55ZdlmdkH\nIa1FCz6l0fBXgB8CdDMYZInYB+HdFSsYLIr8zFneS6/n2LFjaTB0JpBDPQx8GxLnU5ROR71ez2ef\nfZbZ2dkcNWoU1WoNlWhNtdqFzZu3YHp6Oq1WK5cuXcpdu3bRzc0bCmCEAAAgAElEQVSNoaGhbNq0\nKevWrSubl9q0acMaNWpw1KhRVKlUFDQapqSkyKu2kJAafOKJJ2g0GuVwy4YNG1IQBPmTkJDASZMm\nccCAART1etkfVlFRwaNHj8rO75iYGPkctVrNp59+WqbxMJlMsunqbpTVmDFjmJ2dzUmTJtFqtVIU\nRarVak6ZMoWTJ0+mVquVw3fHjx9PFxcXmbMqKSmJvXv3psFgqKJBHhAQwOjoaJrNZiYlqWRVuxkz\nlOzaNYXk/1YSv8WjVhKlkNKsAOAGHuxAfhyonIG1AVLI7VwAvpBYiv/1N7X7j0CpVMLNzQ0AMH/+\nfLz88suYPXs25s2bh379+kGpVKJmzZro2LEjFi9ejJKSEvz6668QBAHZ2dno378/Dh8+jIMHDyI8\nPBybNm1CamoqAgICMGzYMLz55psIDw+HYbsBRS8VScbB65CS63IA5VAl8iryYN9nlxLhPgbwDCQv\nU0ugYmEFsArANAAqAE0ApAKfbv4UJoMJ6ANgAYBLAIYCGAAUzSzCSwtekqYQIYDSRQmH3SEN7/MB\nfAOgDsBDxI/JP+LHnB+lhLwUSCEQKUDpnlLceOsGssZnYf2q9Q99flkTs1A0r+ieEXIQgKVSUpyr\n6+MnJ37pxRfxzsyZGF5cjB80GjRcswYHjx+H1WpFaWkpmtevD/2pU6hVXo52KhUWLF+Orhn362N0\n6NkTwz7/HB8DUCoUqBcf/8hZ6oqFCzG/uBhtndvXSkrwxfHj0GgOAxiKJiiGGkAtAMPv3MFYNzcY\nDAYAEh29i2iG9dY5nKnwwZdf7oNGI2DYsAFITk5GdHQ0kpOTsW3bNpSVlSEiIkJetfn7++Pw4cMw\nm83QarVo3rw56tWTUqTUajVOnTqFBg0aIDg4GMuWLcOpU6dQVFSEGjVqoGPHjpgxYwZatGgBtVoN\nX19fhIaF4euvv4ZGo0Hz5s2Rn5+P8vJyqFQqCIKArKws5Ofn45133kFgYCAAwGQywdvbG2fOnIFW\nq8WQIUNw8OBBLFu2DBERETh79izKy8tRUlICg8GAjRs3Ii4uDiTh5SUZK7RaLby8vHDr1i35mRQW\nFsJut6O0tBR6vR4OhwOlpaWoU6cOoqOjsWnTSigUkvpBo0YOfPbZ2b/07vy34lEdfzAks9DdT+Xt\nDX+x3Q6Q8nbrA9gESccFkFYva53/bwEwGP9lXCO3b99GVFQU5syZgwkTJmDevHnYuHEj1Go1fvrp\nJ7Rr107KYIWkJ3Hy5EnExcUhPDwcANC2bVuQROfOnTF9+nSUlJRg+/btiLPGQTdZJ635FIAYIMLY\n3QjjViO0DbRAAQALpKcZCmAEpB5nsfPC9jr/rwBwCLALdowfMR6hn4UCVwB4QOr8owFsBJhMUEHg\nNcChcEhvhwJADdxTGqkHwB+AC6QBApCytmsByAHsiXacPn/6kc8r72YeEFZpRy1ApVMhICAAPXv2\nfOh5ZWVleG74cEQFByM5Nhb79+9/ZDt3MXvmTHxWXIyBABaWlyOyoAAfffQRAGDt2rXQnjqF7UVF\nmFdWhg0lJRiVmXlfHQ6HA4MHD8ZthwOFAG6R2L9/Pz7//POH32deHooqbRcBUCiV2LDhA5hUX8IO\nYj2kZffrAG7n5+PUqVMgiZycHJQWl6IL0gD8DJKIjY1CUlIS9uzZg+rVqyMyMhK9evVCYWEhDhw4\ngKKiIlRUVGDv3r3w9/dHTk4OHHa7LGoFSIOENPkERFF0akfkwWazoV69elCr1RBFEVevXpXv+8qV\nK3B3d0dqairi4uIQ7FQXstvtUCgUEAQBPj4+UKlU+Oijj7BkyRIsXboUp0+fRkVFBWrVqgWj0Yhm\nzZqhefPm+Oabb3Dp0iWEh4cjMTERZWVluH79OtasWQNAEtkCgCtXruD06dPQarW4ePEi/vWvfyEj\nIwMDBgzAmjVrsG/fPqxevRparRYBAQEwGAy4fRsoKABKS4EpUwQ4HDosWPDv6578t+JRK4m032y/\nUun7X+24P3F+HoQZzs9/HU6cOIEWLVqgdu3auHbtGsxmM7Zu3SrbjWvVqoV169ZhwIAB0Ol0WLVq\nFUJCQnDkyBFcuXIFXl5eOHjwIO7cuYMdO3bAZrOhbdu2+Pnnn3Hp0iVYy6woOFmAXbt24cKFCyAJ\nv8l+aJrSVPItFEAamq9DylBRAbgFwAGgM4B2AL6HJERkc2D8zPGouF0BNADwubP8UkjUHp8BOAKJ\nYqCrs553IK05f4TkE/kO0gqkBJIyehikAecEAC9AN0WHpPqPTt5Pa52GJROXoGR5CXAD0LyswcA+\nA/HSSy9VEc/5LYY/8wxOr12LpSUl+PH0abRv0QLfHDmCkJCQR7ZXWl4Ol0rbFodDlt/My8tDzYoK\neelbE8CN27fvq6O4uBh37typsq+kpAQHDx6U6Vh+i/O3bmEApJ+mGNJCL5lEt65dIajVCAEw127H\ny5AchBsqKpCxZg1uORzQqlRIqGgMgoDCAVeNAzUOHcC4jAy4162LvDxJ0tbb2xvdu3fH64sXY96c\nOaBCAa1ajZLycuTk5KBRaSm2btkCjZPCY8uWLYiKisKlS5ewfds2mBUKlKhUsNls+OWXX1C9enW0\nadMGK1euRHh4OHJzc3Hz5k34+vriwoUL0Ov1KCgowHPPPQelUonVq1dj0aJFGDBgAMxmM65fv46U\nlBQUFBTg008/hY+PD06ePAlPT0/cuXMHxcXFEAQBsbGxso/Kw8MDhw8fxqhRo5CTk4NPPvkEO3bs\nkAW7Nm/eDKPRiMmTJ6NLly7o3LkzYmNj8dlnn+H8+fNISUnBlStX8MUXXyAsLAyenjkgHTCZKpCZ\neRhffXX8ke/H//B/B/+Ybe9xomXLlly0aBFJyVeRlpbGOXPmyMcrKir45JNP0tXVlV5eXrRYLPTw\n8GBmZiY9PDyYmJhIURQ5bNgwbtq0ibVq1eLs2bM5aNAgRkREMDk5uUooH0nOmTeHgo9A9HPmOdyl\n6GgJYi4kynERhBJEPUiMsBXOxDY1CC2I6ZWimn4BUa3SthFEEIh0ENMgZWLrnbkXZhC9JR8HbCCS\nICXtaUGlTsk2ndr8brhqaWkp+w3pR4OrgVZfK+cvmv+HnrVFr+flSkl2mYJwH7X3gzCgZ0+21uv5\nDSRKC3ejUdb5OHr0KN31en4N8CbAgYLA1ObNH1iPVqu9Lww4LS3toe16eXlJkWAAdZWiwjp37szM\nzEzWCg5mL7WaWwG6AGyjVtPbZGJNX19q1WqaoKar0kCdUskTAOcArA5QD1Cr1dLHx4f169en1Wql\nTq3mCYBFkESFAkSR3Tp25IsAY1QqutlsDA4OZsOGDSmKIn18fKhVq5kF0KjRMMbJcOzl5UWbzUa1\nWk1RFKlUKtkAYJozD0ir1bJTp06yP6BXr1602Wy0Wi0UBIFDhw6t4ivQaDRUq9VUq9V0dXWVI7BS\nUlLkcn369KGvry+nTp1Kb29vhoaGsnv37oyPj6efnx9HjBjBhIQEdu3a9T5/165duxgXF8ewsDCO\nGzdO1i3x9rbw8GHJN+Fw4H8+id/g7/Iz/A8PwNmzZ9GsmcTupVQq0aRJE5w7d08qXKVSYcKECQCA\n5cuXIzc3F2+++SY2bNiAL7/8Et7e3sjMzMSCBQuQkpKCpUuXYtWqVXj99ddx/Phx7Nq1CzGV9BeL\ni4sxYeIElO0rk1YA3wEamwYp8SkYEjYE/U71g3hJlExAfpAilzwhrRh+BWCAtCJYCuAapBXHXNwL\niH4LkonpKoCdkExXwZCMkUZIpic7pOgoB4AoAO8C6AiYXc2IDIvEjh07AADvvfcegoKC4OPjg4kT\nJ8ozQ0EQsHTRUhReL0TehTxkDbmnA/4o6AQBeZW281Qq6HS63z1v0bJliBo0CMNDQ/FJYiK2ffUV\nAgICAACRkZF48/330cPdHf5aLa4mJWH52rXyuV9//TU6dOiA1NRUeHp63lf3b4nlKmPQoEEQRRHF\nAO5AIr2rXbs2ateuDU9PT6R06oSPSLyi18MrKAhHXV3Rb/hwZAwYgLapqXAXlIhTlkJJYg+AFZBc\nSWqNBr169UJ8fDyKiyUX4ytz5yJZFDFEFBFvMKB5ejpqRkZipkaDNnY7vG7fxpnTp3HgwAHUi4xE\n3ZAQhFRUwA2AubwcZ374AcLt28i7cQMFBQVo1aoVunbtimeeeQZH1WpcvnQJS5Ysgd1ux9mz9+z8\n586dg7u7O4qKiqHXS6srAPjxxx+Rm5uLwMBAjBs3DuPGjYObmxvq1q2L4uJifPHFFzh79iyuXr2K\nzZs34/bt21i8eDGuX7+Orl27IiwsDG3atEF+fj42b96MsLAwXL9+HQ0bNkR+fr7cvr+/PwICAuDp\n6QmLxQKFQgG9Xo/bt0vgdI/gMcim/20wGo0wmUzyR61WIyvrj/09/BX8EY3r/+ExIT4+HosWLcKi\nRYuQn5+PlStXYtSoqsnrx48fR1JSkswKm5aWhj59+mDLli0oLCyU/RWAxAR711T1IOTn50MpKoFq\nzh06QFFdAaVSiXVr12Hx4sV455N3ABOkjv1HSL6DLgDWQxoszkHqtQIhkbN4QxK0NUKakyicx1WQ\n6EZtuDd3fg5ABoDukMxR85zX0RrIF/MxSzELhrEGpH2chvXr1sud2Lx58yAIAqY+QMP6+PHj2L17\nN6xWKzp16gSt9sGMMROys5E6cSKGFxfjR40GB11csDgjA0eOHMGnGz6FQTTg6aefhrt7VRZOQRAw\nc+5cYO5cAFLHtm/fPoSFhcFmsyE9PR3p6en3tffVV1+hdevWKCkpgQqAQaGAVqFAGQklALVWi6ee\neuqhv9WUKVNgNBqxatUqmM1mJCQkYOvWrfLxoqIilCkUqNahAxqGhGDXrl2y/yA4OBg77XZ8b7fD\nA8BrkOIQHACqeXrCz88Pfn5+iIqKwiuvvILmrVohoWFDHDlyBJ28vDB16lTsOXgQVi8vzL58Ge5m\nM0y3buHZ8nLc3LcPb0GyGhogUS34lJejCEC5QgGtVosjR44AAEjC5OKC2nFxyMjIwMSJE3H8+HFc\nvnwZOp0ON27cQN26dXH16lVYrflYvXo1mjRpgmPHjsFsNst+DgCIjo7GoUOHoNPpUFhYiPfffx9K\npRJWqxWFhYUoKyurEjJdVlaG4uJidOnSBRqNBsHBwTh9+jTWrl2LLl26oH///tiyZYv8bN966y1c\nunQJ8+fPR7t2T2Do0G2YObMUJ0489Cf6XZDEhx9+iO++/x6hISHo0aPHIycGfxaFhYXy96KiInh5\neaFr166Prf7/NvyjS7fHhRs3bjApKYkWi4WiKHL06NH3JbAdOnSIfn5+sgDR119/TZ1Oxy5durBd\nu3YURZEzZ87k8uXLGRgYyBUrVjy0PbvdzoCIACpeVkjmo02g0d3Iixcvcvbs2ezQoYNkTrpayXzU\nz2liau40LX3uNBv1B3ERxBeQhIGskNhh3UC0ch73lkJfEQoaPAxU+aqIBGdobhTuJfpddIbTVoC4\n7GSG/Y1pJiREYv7cvXs3Z8yYwRUrVnD9+vUURZE6nY4Gg4HR0dH3aQNUxscff8xBvXtzwtixvHr1\nKnfs2EHRXaRynJJCH4EeAR68cuXKQ89/8cUXqdPpaDabaTAYuHPnzgc+46ysLCoU0j3oATYFuBig\nBeAAgNMAugqCnFj5KOzbt48rVqzgrl27GBwczNjYWLZq1Yru7u6cP18ytW3atIne3t4cM2YMp06d\nyuQmTdhEo+G3ALUA3QDOg6QFbhYEdu3alePGjWNmZiY1GhUjI2uwsLCQJLlkyRKGh4fTx8eH4eHh\nbNasGY1GI7s4NcWPAAyqZLY74TSJ9QHorVYzJiZGps6Ijo6mWq1m3bp12a1bN5lNVqVSycJGLi4u\ntFqt1Oul7GoXFxeq1WrabLYqNBwJCQkMDAykIAgcP348ExISqNPpaDQaKQgC+/Tpw5o1azIsLEzO\nGlcqlRw/frxsmvLz86Orqyvj4+MZGxvLZ555hi1atKDFYuHQoUMpiiJJ8vbt2+zduwu9vV1Yu3bA\nQ001vy9fmkVDZCQxZQoN9euzY8+ej12+9C5WrFjB6tWrP/T4w+4BjzkQqHJk04YHbP8n8bc8+H8C\nDoeD165dk5k/H4Rp06bRw8ODjRs3pslkqkJpkZWVxYiICD755JMPpD+ojNLSUiYkJFD0EAkVaPWz\n8ssvvyRJrlmzhunp6dS4aIjvKw0S7Z2DxC+V9k1wDhQKp38hQdrWBemoEBVEptMvMQOSLOoAUOmi\nJJ4DscTpjzBBEjeaBSLAWdZJF64UlXIne/cTExPDBQsWUK/XU6lUUhTFKlnccNrs/4je711E1I8g\n1t+7L3WmmpOnTn5g2WPHjlEUxSrtmUym+2inFyxYUKWcwckhlQ0ws1LnuhVgPefA9zBMmzSJ1USR\nPQwG+osiJz/3HLOzs5mQ0IAeHoGsWTNBJnucPHmykzVVR1dB4CyAnkol+zsHB1eAwRoNLSYTPT09\nqdVqaTKpuWoV2L69RH1x48YNTp8+nWFhYQwMDJRzJ7KysqhVKukAWALQCHAWwLMAEwAaBIFqlYoG\ng6GKWFBGRgb1ej3r1q1Lk8nEp556ir169ZKIAdVqVq9ene7u7tTrtPT19eW4cePkXA21Wk29Xk9P\nT096eHhQo9HQaDTSYrGwZcuWfPbZZykIgvweDB8+nJMmTWJSUhJtNhtFUWRkZCQDAgLYqVMnmdI8\nICCgCmHhXf9Hx44daTAYHvg7PKh/keRLQ6nv1InC4MEU3dweIF9qJfLzJUao4mKK1ao9QL50LE0e\nHrT6+nL2K6888n14FJo1a8bnn3/+occf1kfiMfskXnF+foUUn/ImJCt0oXPf//BvQKFQwM3NDUaj\n8aFlJk+ejD179uD5559Hw4YN0bFjR/lYy5YtUa1aNaxatQodOnR4ZFurV6+WQv0u38ai+Yvga/GF\nzWbDsWPHMG3aNKSmpmLOi3OgbKWUfA39IJmcvCCZju7ia0i2Cxsk6sV5gIIKOPIdEBoIUrSTKyTm\nrSgAbwAOlUMKYFZCimr6FVIk1HpAeV0pRVq1AlAL8Pb2htlslk1noihi5syZGDFmBEoqSuAQHChW\nFKO8vLzK/ZWVleHatWu/88Tv4datW5LZzImKoArcKLjxwLInT56sEg56t70bN6qW37Rpk2wmg/N2\nVZBCWH0rlfMBUFhUhIfh7NmzmD9nDg4WF2NlURH+VVyMRfPno6CgCN9/b0du7lqcODEZ3bo9gz17\n9mDatGm4evUqDh48iGeysrCzQQNchxoKAJGQUlrg5YVhI0YgMzMTycnNULOmEpGRwDc7S7Bo+nT4\neHjg8x07cP78eZhMJtl8YzabUUZiO4A9kGzS6wDEAziiVqNNaipGjR4Nd3d3fPvtt7Db7fj/2jvz\nuCir749/Zt8YBgTZEZRFVNw3NE0wt0hc0nLBFLV+qamZWmqL2Vcr/Wr6LZdyKdNS075piaS55JIo\n+rVccKvcSjBBxQABUZjz++M+M8wwMzCDLAPdt6/nNTP3uXOfc5/Be557z7nnFBUV4fTp06hfvz7S\n09PRp08fNGrUCCEhIYiLi0N7AE9dvgxxVhZatGwFf39/o42oZcuWRvuAp6cnfHx8IJVK8cILL2DQ\noEFITU1FWloadDodnnrqKahUKiQmJho9qfLy8iAWi+Hn54eQkBAcO3YM58+fx8iRI0FEKCoqMv7t\n6PV65OXlITk5GRMnTrT5e5Tmo2XLkNG1Kwr++188WL4c+cuX46VZs4znc3JyIKtXD9AJvnEqFWS+\nvsjOzjbWmbdgAVYnJyP32DHc3b0b76xahS83Op6J4Y8//sChQ4cwatQoh79bEcqySRwQXj9ASQIi\ngM0ifq4qgTiM8PBwhIeHIzU1FYsWLUKXLl0gkUiwYMECxMXF2dVGZmYmWrZsCbFYjAkTJiAjIwOd\nOnWCl5cXXnrpJSQkJEAkEiFpZxJ2b90N9ARwFMBCAAPARprTYHvtD4PtkdgOoD9AhYQH5x8wg/ca\nsEVwg1ttPthWTBWYS+1gMOP1dACxgLK7EvnL8lmAlgAg640sLHp/ETJuZuD+/ft49tlnmdumVs+u\nHQQWJOY/gKRAguJitgFKLpfj8ccft/ueDo4bjJXTVqJgZQGQAag+VGHg59YVbUREBIqKiszKlEql\nxQa+wMBASKVSY91iAEMBdAEwD8wnwA9M/w4oY/345s2bCJbL4SW4zvoACJTLsWnTNuTnbxRaAgoK\npmLTpm/QtWtXuLq6IjIyEu8tXIjXX38Tx87+jq3FtxFSAPwsEqNheDhSUlJw5MgRFBcXQyoF+vcA\nYu6LcEitQIeWLfHrpUtQqVS4cOECzp07B19fXxz88UfotFq87ecHkUiEcIUCt06dwkwAy7y9ERnJ\n8gIOHz4cixYtwsKFCyESidCgQQOoVCo8ePDAzAW4oKAAfiIRPgTws0SCk6mpkMvliImJgUqlwtmz\nZ+Hp6Yns7GxcvnwZWq0WCQkJ0Ol0yMjIQF5eHr777js8++yzyMnJgZeXFzw8PLB27VrUq1cPSqUS\nRUVFOHz4MAoKCiCXy9G5c2ccPnwY7u7uiI6OxubNmxEREYErV9jz7cyZM/Hyyy+X+zdjIDMrCw+b\nmISwi4gwe2AICQmBu0yG/PnzoX/uOYgSEyG9cQMtW7Y01vlm507kz50Lg5U8f8YMbN21CyOGD7db\nDoA5eXTt2tXoUOEMXAAQYvK5kVBWk1R4mlbbKC4uFsIzaEitVtOUKVNsZtoqzdGjR8nX15fOnDlD\nhYWFNGnSJOrbt69FvbNnz5LGU0OiuSLCRyCVt4rq+dQjcT0xiZQilsHO9F89kCxMxrLbGWwYMmHJ\nabGwHOUCwmdggQE7oyQc+L/Astq5CK/1QXgH1LFXRzOZFi1aRJhgcs0cEKSgqKgokkgk5OrqapGM\nqDwePHhA418ZT/UC65FfYz9a90XZ31+yZInRJqHVao1LdabcuHGDvL29SaPRkEajITc3N3riscfI\nXyymJwBqIbiiukgkNjOhEbH1bm9XV0oUAgJuA8hHp6Pw8HYEJBlWrUgsnkbTp8+0+P7atWvJ1dWd\npk8HDenL7AauWi15eHjQxIkTaerUqeTv708SiYQUYrEx091bb71F9T09qS9ALeRy8pXJ6CmZjBrW\nr29su7i4mAY89RS5SiSk1WrpzTffNIbTkEgkJJNKqV2rVtSwYUNydXWlDh06kEwmoyeeeIKio6NJ\nKZHQlwCdA0gll5NSqaRGjRqRQqEgtVpNcrmcunfvTvXr16ewsDBSKFgo8N69exttGjExMRQXF0cu\nLi7GBEyRkZG0adMm8vT0NPZn9uzZFBoaSs2aNaMZM2ZQdnY2FRcX08cff0wJCQn07rvvlut2bW18\n2bFjB6mDgwmpqYTMTFLFxdGLL79sVufq1asU1aMHuXp7U8suXSwi3UbHxRFWrjQGKZfMmkX/N2lS\nmbJYIywsjNauXetwHwzlVTEg9wHzcTkoHH8A6F0VF3IAh29sbUev11fICLZ+/XqjL3vv3r1tJic6\ne/YsjZkwhoY/P5yi+0STZJKEGbvjweI8GYzbZwWloBGM1TEg5ILFfYoFiwv1hLCOHwG2LyJQMGqH\nCa+TQXhLsFNsZ0bw0kpi8+bNpO6gZpnzCITvQR4NPIz3wlEyMjLo0qVL5aYrLc3Nmzfpl19+MUto\nX5qsrCxat24dffbZZ3Tz5k0qKCigjpGR1F+loncAClarael/LJMzlSY5OZkCPDxIIZFQg/r1KSUl\nhb799ltSqbwJWEBi8XTS6XyM+zZMKSoqoujoJ0mlElNcnIi0AHnK5RQXF2dci09ISCAXtZoUEonR\n/jBnzhyKiIigIIWC7gBUDNAkmYyGxsVZXEOv19PAgQMpODiYOnXqRFqtlurXr0+tZDJ6T7DDtBOL\nyU0mow8BGi0Wk4dMZozLpJDLycPDg3r06EEBAQHUunVrGjFiBLVo0YLUajWlpKTQnTt3aPDgweTr\n60uBgYHUpEkTat68OXXu3JlCQ0PJ39+fnn32WeratSsFBATQ3bt3qWvXrtSrVy9jDKegoCDasGGD\nQ7+zKbbGl6UrVpCbry+pdDoaPnZsBdOXepLspZdIMXo01fP3dzh9aXJyMmk0GqPjgS1s9QEVUBL2\negUrwTzmCWzVutDRC1UyQn859mIaEbQ8mj3WDOffPw98CeAvAA3B4js1A5tD/kd4XQ62RPU3mGts\nTwCTAbiC7Z3wAHAXwGtgPpTbwRY4E8CWoQ6ArcscBJa+v9RsjfjChQt4esTT+PXvX0GNCKKjInz4\n/oeYNGmSQ/0mIkyePBmrVq2CVCqFt7c3Dh48iMDAQIfacZT8/HysWbMGGX/9hW4xMejVy1aSRUt5\n8/PzoVarjTaCQ4cO4auvtkKjUWHixHE2lxmICHv27MGePXvwyX/+g6FEuNi+PXoI7tQnTpzAqZMn\ncevWLbRp2xaPPfYYrl+/jh9++AHPDhiAz7/4AjK5HGqVCp9/8QViY2MtrlFcXIwtW7bg2rVraNu2\nLWJiYjDoySdxLiUFHhIJ0kQitC0sROL9+xgvleLnZs0QO2AAiAiJiYnIzc2FVqvF1atXMXnyZIjF\nYuj1eqxcuRJ79uxB8+bNLa5peu1FixZh//798Pf3x9y5c+Hn54erV6+ie/fuePjwIe7du4enn34a\nn376qZmLrCOIRCJU1fhy6dIlbNu2DVKpFMOGDTPGnrKXcePGoaCgAOvWrSuznq0+CPfEoRtjb+XO\nYEOFFCWaaL0jF6pkuJKoQoaOGYrN4s0sGOB5AJ+C2SmeBFtoDwEL79EUbKn8VQDHwJRGGFiMp5tg\nFtQWYEHj/wJbqDwOFtuJwBREGIAiICQ1BKkpqVCpVNi1axeefvppFIgKWEiQAQAUgMsbLrjwywV4\nenqisLAQOp1pAA3rfP311xg9ejTyBKOxRCJBp06d8NNPP5IfnxMAACAASURBVFXGrQLA7AlnzpyB\nj48PWrRoUWntPgoRAQGYk56OqVIpfMPDAbkcqWfPYkxREUQiEdYrFBBJJAgMDMSaNWtw/PhxfPDB\nB+jWrRvy8vJw4MAB7N+/3yIEOxHhm2++walffkFIWBikUim+/PJLFBUVYdiwYejevTu6tmuHCdnZ\n+EomQ8vBgxEWFga9Xo8LFy7gwoULuHjxIpRKJaZOnWpUEosXL8aECRMwf/78Cg3uhYWFuHjxIlxd\nXR853HdVKonqojKVhD18CfbffgVYUiDDUZM4NEXj2Mft27epc8/OJJIKIcU1IJwS9j68CkJXE9vC\nUmYjwD3mxoocsLAbS03sCPEgtBfe6wW7Ra7J+edA8ADhDEjbRUuJiYmUk5NDbm5urG0V2P4KbxAO\ng7T9tdSvXz+SSqUkk8koKirKLFkMEQvlnZaWZlxWmjVrlsUeDFdX1wrdn6KiIvruu+9ozZo1Rj/5\nffv2kUajIZ1OR2q1ml588cUq8413hJ07d5KnWk0jlEoKlcvJRSajBSYuubNFIpooJK0iIgoPDzcm\nF5ozZw5FR0fT1KlTLdqdNnEitdBoaA5AoXI5uel09Mwzz1BsbCyp1Wp6+eWX6ciRIzRy8GAK9vOj\nRo0akaurqzEftU6no2HDhlFwcDBFRkbSM888Q02aNCE/Pz/y9fWlTz75pDpvk1Xqwvhiqw+owHKT\nPTuu24I9M9Zu1copl2HPD8P/Gv8P9D0Bv4P5PPYA81hqDbbTOgBs53UmmL/nNgCTwHZdq8FCcQDM\nxfUAWDiPBWB/QfUAPA82K0kFi/M7FcAggEIIN27cQGhoKAul4A7gLJhr0PcABgO5RbnY/nA7u+4D\nlsxm7Nix2Lp1KwDg22+/xXDBU0SpVCIpKQlhYWEs3IXgpioSieDj44OkpCRERkZaXbrJysrCzz//\nDDc3N7Rr1w4ikQhFRUXo2bMnTpw4ASKCXq/H5s2bMWrUKOMsBQC+/PJLDBkyxBh+xR4yMjIwb948\npKWl4amnnsLYsWMrvFRioE+fPjh04gQOHDiAWDc3fDR3LtqbbCf2JcJ1Iaw2wCK+GrzGAOvLk5mZ\nmVizejWuFhbCHcDXIhH6DxhgfHLPz8/Hj8uX479ffIGjp04hKysLHTt2xLBhwxAQEIDDhw/j4MGD\nCAkJQcOGDXHo0CHs378fOp0OCQkJuHr1KjZt2oQXX3zxkfrOqVzsURJnwVacb1SxLJwaJvlQMh5e\neMj+KpoA4pfEGPHXCCT/LxmXx11mA30QgBfAArz/CLYXwpA6ahnYXonjYHGgCsBCjJ8W6hcAuAS2\n3NQAwCYwJfQuIMuT4bO7nyEzO5OFNO8EpiAAFirkLgQ/UrCgRD8CD/If4MiRIwCA9PR0xMfHG+MB\nFRQUIDY2Funp6fjvf/+LgwcPQiKR4MGDB/jzzz8RHx+PBw8e4NNPP8WwYcOM9+DkyZPo3r270b++\nR48eWL9+PebOnYuUlBQz187Ro0eb+cEbuHLlit1K4u7du2jVqhVu376NoqIi7NmzB5cuXcL8+fPt\n+n5ZNGnSBE0Et82baWmYNmcOVuXnIxfAe2o1Pn7uOWPd6G7dsH79ejzRowfy7t1DSkoKXnvtNbP2\ncnNzoZNK4VbITJLWNll11+uhyM7GkgUL0K5zZzRt2tSYN6Jbt244evQoTpw4gaioKLRv3x6nTp1C\nXFwc5HI57t27Z8yRwaldHAAzTe4G33FdJ0hKSqIhQ4ZQfHw8JScnG8sbNG1ASCrZCa3uqabVq1dT\nQUEBtYlqw7yackrOi3xELCSH6T8FCFMFryYpCM1A2ADCJGF56SfBA8rQzv/Yd3bt2kVSdynzhBoH\ngh8Ifwl1dgpLX4awHoUwZqhr164dERH98MMPpNPpzJaVNBoNXbp0ifR6PR09epR69+5tsbNbqVRS\nXl4eZWdnU3Z2NkVERFjs6q5Xr57VqK5isZgCAwMt6pfOWFdUVESpqal0/vx5ixzfa9euJY1GY9aG\nXC6vlCUrvV5PaWlplJ6eTsXFxfT+v/5FTQMCqFWjRvRFKffhzs2a0esADZbLaZRUShMAmjJhglmd\nhw8fUmTDhvSORELXAXpOJCKtVkuDBw+m2NhY0kildAqgjwEaM2QI7dmzhwICAowusxMnTiSFQkGN\nGjUid3d3owts165dqVu3bqTT6ejo0aOP3O9HpS6ML7b6gCpaEYoWjm7CYXhfk1TzLa87fPvtt+Tn\n50dTpkyhRq0bkdRDSnPnziUitr6uqqciVV8VaTpqqF23dsbYSMuWLWMuq0UmCiEMBH8Q8oTPqSDI\nBRtGrGBv0IDQD4TGgm2hEIQXQPACoQ9Y7KcXQCIXEbNZNBfamg8WziMULM5TAxMlkc/KtFotnT59\nmoiILly4QCqVymywVSgURtfV8ePHWx3olUoldezYkSQSCYnFluFBICiD0mUQbBsffPCBWVmHDh3M\nBvj09HRyd3cnACQSiSgyMtLMT3/VqlUW4T+kUqmFMinNH3/8QcuWLaOVK1fS7du3Lc7n5+dTTEyM\nMXRHz549y3TZ7NC4MR0wsVn8GzCzWRj4888/qU+XLuSj01GHJk3IRaGgCIWCOslk1BigsQA1Uqtp\n27ZtbH/FgAEUFBRkDFO+evVqKioqouvXr1Nubi5dvHiRXnvtNZo6dSqdOnWqzD5XF3VhfLHVB1Sh\n2cAHQBxYWhqvqrqIA1TzLa879OzZk2bPnk0qHxXhKxC2giQ+Etq8ZTO9v+h9kuvkpAxTksZTYzbL\nWLhwIYkaidjmuWMgvMue5iM7RrIB/BlBKShBkIAZvtVgMZr0gnJoIgz2vcH2UGwF4QqMG/TgJ7we\nFsp+A0l0ElKr1UyJjAJhE0jRQ0Htu7W3CM43e/ZsUqvV5OrqSiqVymzDkVartTrQl3fYUhC2jtKB\nAIODgy3qPP/888bz6enpRsMuAFKpVDRixIgyf8MzZ86QVqslpZDr2svLi9LT083qvPLKK6RUKo3X\nVKlUNGPGDJttrli6lJqo1fQ9QBsAqq9SUXJyMm3atIlmTJ9OK1eutNhj8uGHH9JohcKoWK4CpBKJ\n6JPly411iouLadu2bbRs2TI6fvx4mf1yFurC+GKrD6giJfEs2Aa69cJxDSyYdE1Szbe87tCjRw9q\nH9OesNpkRvANqHmn5qT2VxPShLKtoPoN6hufio8ePUpKbyVTBq1BaAZq+3hbKi4uphfGvUB+IX7U\ntENT+uGHHyh2UCyJQkSEiSA0BWEaa1M5TklhEWFMiXiAeUYRCEuEWcZGEJYJCmYqCO1BjVo0ohs3\nbtCnn35KcYPiqMfAHvSv9/5ldffyn3/+SdOmTaOEhATav3+/sfz+/fsOD/ZyuZzkcjkFBQU5rCTW\nrFljvLa1mUl4eLjx/KVLl2jUqFHk7+9PDRs2pOnTp1NhYWGZv+ETTzxh1q5UKqVx48aZ1YmKirK4\nbteuXW22qdfraeWKFRTTujX16dyZ9u7dS1PGjaNWGg3NAyharaYBvXqZzXCsKQkPG0HzahN1YXyx\n1QdUkZI4A/PZQ32hrCap5lted/jqq69I5alig7Hh3wZQeMtwchnuYmZfkCqlZjuN13y2hpSuShJL\nxdQuup3VMNuXLl0ilZeKkC20cldQCPtBaj81HT9+nPLy8mjYmGGkaaYh6QtSNnvYa3LlfwkzjjGg\ngKYBdvXrt99+I51OR3K5nGTCLt9z585RcXExbd++naRSqUOzh9mzZ9OdO3fo6aefdkhJqFQqWrBg\nAX3yySd0/vx5i8i1AKh3795GmUvPcORyOcXHx5e5NBQZGWnRZumsdwkJCSSRSIznRSIRjRw50q57\nScR2qOsUCrorKIBCgEI1Gjp27JixTlpaGvnodPSeWExbAWqnVtObr71m9zWclbowvtjqA6pISaTC\nfPOFWCh7FBaC7dk9DbZly3RX1CwwB8yLYHFCrVHNt7xuMW/ePBJrxYSPQPiExWpasmQJqRuoCZnC\nQL0L5O7rbmFA1ev1ZT7pHj9+nFxbu5obs4OYwvlk9Sdm7SQlJdEHH3xAsvoywj6UhP3oBxYHyhPk\n4u1CX3/9NRUXF1NaWprVsCIPHz6kmJgYs6drkUhEffr0oVatWpktuwDCTMUVbGnMxkB/4sQJ2rlz\np+V3yzikUikFBweTi4sLqdVqUqlUNHr0aDO55HK5UbmOGzfOajsKhYLGjh1r8x7PmjXLzI6hVCrp\nrbfeMsutkZqaajGLMRj57eHy5csUoFaTHiV2ii6urrRv3z6zer/99huNHDyY+nbtSkuXLHGKPSKP\nSl0YX2z1AVWkJBaCeTYlABgNYBdYLvZHoSdKPOjmCwfAnCxPgQWkDgZzmLTmaVfNt7zuceTIERo0\nchANGDHA+B9/1pxZpPRUkq6DjrRe1gPalUdubi55BHqw4H7ZINEqEXkEepQZ+2j5x8tJHiQnvAaW\nzGg4WJDATiBI2aBZP7g+KT2VJHeVU/zz8VRcXEx6vZ7Wr19P9YPrs+Wt1jB6PQEgT09Pyyd5FZhy\nPAHC02B2ExPFolKpjIED4+Pjy1UMSqWS1Go1RUdH02OPPUZyudzsvJeXF+3YsYOGDh1KQ4cOpV27\ndhlnCSNGjLDZro+Pj8379eDBAxo7dizJ5XISi8UklUpJIpGQRCKh2NhYunPnDn311Vfk4uJiocTK\nymNiSlFREUUEBNAssZiuALRMJKJADw/6+++/HfhrqJ048/hy/fp16tu3rzHH+MSJE60G/LTVB1SR\nkgCAQWAZBxYDKDuJgeMMBNvVDbBZxAyTc7sARFn5TlX/Fv9Yrly5QsnJyRY7mR3hzJkzFN4mnGRq\nGUW0i6Bz586V+50vNnzBZjfJMC6BQSUoDRWYt1QRCLkgdRc1LVuxjGbNmkVSuZQZwouF7y0AQcNc\nUb29vS0H4O4mM5xCpoQgKJTCwkK6desWHT9+nDIyMmj8+PE2bRlisZjEYjH5+PhQp06dLDyUDIdc\nLqf79+/T448/ThqNhrRaLTVo0IDS0tJo3bp1NpVEcHBwufds6dKlFh5dIpGIWrduTYmJiVaVRFmR\naE1ZsGABKZVK0opEpAEoqF49u37HukBFxxe9Xk9btmyht958k9avX1+uh1pFGDhwICUkJFBhYSHd\nvHmTmjdvTh999JFFPVt9QBUpiYZgmQEMqGCWuuWRSQRgCKi+FEC8ybk1YAqqNJV+8zmVy7Vr12jg\niIHUJqYNTX9jeplpRonYfzCxTMzcW6+wpSacEQbz78AM24aIsB+DhowewuwMCmFmYPh3CiTSiWjm\nzJnUuXNnywHYNI3qLTAjurCmv2LFCiGDm5YUCgUNGjTIYvA3hOCw18ahUqkoKCjIbIYhkUjoqaee\nolOnTtlUQkqlkn777bcy79kLL7xg85pXrlyhNm3aGJWIRqOhOXPm2PXb5eTkWMyINBqNxf6Puoqt\n8aW89KWTX/o/atlIQ7MHgqIiNDRi6NOVvvwWHh5OO3fuNH5+9dVX6cUXX7SoZ6sPqCIl8TMAucln\nBVgqmPLYA2a7KH2YZsx5A8A3Jp+tKYmnYYkxF+7bb79t5snCqXmysrKoflB9krwjIewGqZ5S0dPx\nT5f5nYKCApLqpCyM+DYQesLcrlEfhD/YjEHSX0Iu9V2YXQEgRIIZyotBGMvya48ZM8aqZxHUIAwG\nM9yHg+3rAKhx48Z2DfqVdfj5+dHOnTvJx8fH5gxkyZIlRMQMxMeOHaO7d++a3bPly5db3fshl8vp\n1q1bVFBQQB999BFNnz7drvzaBq5du2ahHHU6HX3//feO/zHUQmBlgL1z5w6FhwTQoE4qmtBLTp7u\naov0pe6uCvp7NYg2gPLXghp4qy3Sl7427WXy8tCSv487fbBwgcOyTZo0iZ577jnKz8+ntLQ0ioyM\ntPrbGvqwf/9+s7ESVaQkTlkpO10J7SYASAYLQ25gpnAY2AWgo5XvOnxzOdXHli1bSBurLRng80ES\nhaRMj52TJ08ybxwNWC5t0xwWp8BmDM1BkjAJibxFbK/GLrANfgphsDfszShvkBbDptG6Og6RSEQS\niYR0Oh1ptVpq3ry51dnAihUrjMs+rq6u5OLiYvZAVFRURH379jVThiqVip577rlH+v2KiorI39/f\nrF0XFxf666+/Hqnd2oK18eWdOW/T2O4yog1MCWyeBOrSsYXx/Pnz5ynE38V4njaAOjbRmSmSd+fO\noceaqunqf0DnFoAaB6ppw5dfOCTbnTt3qHXr1iSVSkkkEtHo0aPt7oOh3O4RWqCsHNcGbgPob/K5\nv1D2KPQBCzDdHyw0nIHtYNkf5WDLXGFgkYA4tQiJRAI8MCl4CIBYcL2UlBS8PP1lzHpzFv74oySR\ntpubGwttnMfqIh9AOJhF6jEACkACCYpvFLMAhB3AUl+9BmiUGoiLxCz7ej7KRw/zvzoAYrE9/xXK\nRyQSWeTGLg0Robi4GNnZ2cjNzcW9e/ewaNEiqNVqACzYnk6nQ2RkJN555x3cv38fOTk5uHfvHvr3\n728MxCeRSLB9+3acOXMGkydPxogRI7BkyRJ8/vnnj9QHiUSCH3/8EWFhYRCJRPD29kZSUpLDuQ/q\nEll3MtHEpyTHeoQfLNKXylTumJ8oRnoW8Mk+EW5kS83Sl+7c8Q3mDshHcH2gaQAw48l87EraarcM\nRITevXvjmWeeQX5+Pm7fvo2srCzMmDGj/C9XMaFg2QKuC8dRlMT6rCi/g23QOykcK0zOvQ7m1XQR\ntjPgOaR9OdVLTk4OBTYOJNlkGWETSP24mkaPH027du0itZeaMA8kmSohnY+Orly5YvxeTEyM9adv\nf5RsvIsA4QfjHIWkE6Q08/WZFgZcRw6ZTGZ1P0N1HRKJhPr27Uu9evWiwYMH0/Tp0ykjI4O2bNlC\nrq6uZnUVCgVlZGRU229ZFcZXZ8fa+LJjxw4K9lFT6nxQ5seguPYqenmiuS3g6tWr1KNbFHl7ulKX\nqJYW6Uvj+kTTyrElM41Z/SU0acL/2S1XZmYmiUQiM0/Bbdu2UWRkpF19MJTbN0RXDBcA2qq8gAPY\nfWM5NUNmZiaNe3kc9Rrci/69+N9UVFRELbq0YKE4hH/iGWJ6eXpJnmC9Xk8JCQkWRlMMLfkOvgGz\nRcxjCsLd3538/f3LNSSr1WqrNgqRSESvv/46hYWFlbtEJBaLKTQ0lNq3b2/d3vEIy08A8z7y9vam\nO3fuUHFxMR0+fNhin4arq6vdOc45FcPW+LJi2VLy9XIjnVZFY0cNr1D6Uk93Db3UW0ajYxTk71PP\nofSler2e/Pz8aMGCBVRUVER3796lAQMGUHx8vN19QBUpCR+w3GS7hM9NwYI21yQO/Tgc5yCkTQjh\niMmAvwQ0ZsIYi3pXrlwxd990gzHGk2iZiPxD/emVV1+h2XNm08iRI23OAkwHcrVaTUePHiUfHx/j\nTmSFQkERERF2DfgSiYS6dOlCo0ePpmPHjlF4eLjDoT7sOWQyGQ0aNIiUSiWJxWLjDnJXV1fSarV0\n4MABtmN92DDSarXk4+NDmzdvroFfs+5SlePL77//Tv/+979p8eLFFbLxpKSkUJcuXcjNzY08PT1p\nyJAhlJmZaVHPVh9QRUpiF4AhKAnFIQPLMVGTOHxzOTXPO++9Q+oOaraRbTcL07Fnzx6rdU+ePEnd\nunWjpk2bUs8+PUmukZPKW0X+Yf5m7od9+vSxa/DVarW0ceNGunbtGsXGxlJYWBiNHDnSItS3PYdG\nCE8xZswYs9AXVXmsXbuW/v77b9Lr9dSqVSuLWdKRI0eq62es89SF8cVWH1BFSsLg7nrSpMyax1N1\nUs23nFMZFBcX0+y5symwWSCFtQ2jzVvsfwLOy8ujtLQ0s6WWjz76yCIXg61DIpFQUlKSRbseHh4V\nGrRHjRpFhw8fdigm1KMcbdq0ISLm9lr6nEgkotmzZz/6D8QhIq4kKsIBAB4oURJRAA5WxYUcoJpv\nOcfZ2LRpk81dzgCsDv7WQnA7GsDPcAwZMoSIiOLi4qpFSYSGhhIRUadOnawqiWbNmtGAAQPo8OHD\ndt2/7Oxs+u6772jHjh2Ul5dXeT9MHaAujC+2+oAqUhJtARwBy1p8BMwzqWWZ36h6qvmWc5yNfv36\n2RxQlUql1TwOSqWStmzZQt988w3l5OTQ4cOH7Z6JlD4WLVpERESFhYX0+OOP2/09W4mNyjpkMhlN\nmzaNvv/+e+uhRkwOtVpdrqK4fv06+fj4kFarJa1WS40aNaI7d+5Ux89WK6gL44utPqCKlATA7BCR\nwiGrqos4QDXfck5NcOnSJVq7di19++23FglvEhISrA62UqmUIiMjbdoaDANjYGAgNWvWrMJP9lqt\nljIzM6lly5YOKRo3NzdKTEy0W5nIZDJKSEigL774wurMydo9GDhwYJn3ddCgQWa2FLlcTi+99FJV\n/pS1irowvtjqAypZSXQA4GvyeRTYZrePANSrzAtVgGq+5ZzqZu/evaT2VJMmXkMuHVyoc8/OZsHp\nfv/9d6thvDUaDeXk5ND48ePLfTqv6CzCcJ2EhASrYTHKOlq3bl2uu61UKqXPPvvMGPdn06ZNVvva\noUMHat26tUV5v379yry31r7Tp0+fKv09axN1YXyx1QdUQEmUtc10JYBC4f3jYOG81wHIAbDK0Qtx\nOI4w6qVRyP8iH3lf5uHekXs4XXQaGzduNJ4PDQ3FxIkTIRKJLL6r1WrxwQcfoEOHDjbbf/jwIdzc\n3KBUKm3WKQsiwrVr11BYWFh+ZQE3NzeMHTsWN2/eLLNeUVERTp8+DZFIhE2bNmHs2LG4f/++Rb02\nbdpg1qxZxp3aAKBWqzFp0qQy2+/WrZtZv9VqNbp162Z3PzgcA6bxmZYDmGPjXE1QvWqZU+0oXZWE\nOyjZWT1dSu+//75ZnWPHjpnttFYoFDRw4EBasGABubm5lbn2r1ar6d1336UhQ4Y4ZCPQaDSkVqtp\n27ZtNHPmTLuTEikUCrp16xZt3brVrnzbYWFhRETUsWNHm/KnpKQQEdHXX39NnTt3pq5du9oVhC8/\nP5+efPJJkslkJJVKadiwYRbLef9k6sL4YqsPqOTlprMosT/8CsD0UeNcZV6oAlTzLedUN4/HPk7S\nV6Qsh8RvIHWAmg4dOmRRb/PmzeTl5UVqtZoGDBhAq1evtun1JJFIjMH1EhISjO60LVq0sDmwN27c\nmOLj4ykxMZGuXLlCBw4cMG6CKigooG7dutkV0iMggKVhzcrKIk9Pz3IVU/v27YmI6LHHHrM4V69e\nPYsMcRXh77//LjMZ1D+VujC+2OoDKllJvAHmzbQdzP3VsDQVBha9tSap5lvOqW4yMjKoXXQ7EsvE\npHBR0IqVK+z63sCBA8scfD/55BOL3BZvvPGG1bqLFi2iBQsW0Ntvv02nTp2yej29Xk979+61DCVi\ncshkMrNwzpcvX6bevXtTcHAwBQUFUbt27Uij0RgzzclkMnr++efpypUrlJSUZLGz25Ajm1M11IXx\nxVYfUMlKAgA6gWWO05iUhQNoU9kXcpBqvuWcmqKgoMChIHMvvvhimbugZ86cSdu3b6fIyEgKDQ2l\n999/n4qKiiy8oUQiEcnlcuPArVarbT69nz9/3iILnOEQi8V27Yb+66+/aMqUKSSTyYwpSbVaLSUm\nJloYx5VKJV29epVu3rxJycnJlJ6ebvf94ZSPM48v58+fp5iYGNLpdBQaGkrbtm2zWs9WH1AFSsJZ\nqcrfgVOL+fPPP8nDw8Om19GgQYPM7BhqtZree++9cr2hAFDz5s2tXtOW3QBgnkr2LunExsZaKKoe\nPXpY2DC0Wi0tWLCAVCoVubq6kkqlojVr1lTmbfxHU9HxxZC+9M0qSl/68OFDCgsLoyVLlpBer6cf\nf/yRNBqN1QyGtvoAriQ4HKKbN2/SkiVLqHv37hYKwdoO6ZCQEFq8eHG54caDgoKsXs/Ly8umgjCE\n07CHqKgoizb8/f0tZiYBAQEWsqpUKkpLS6ukO/jPxtb4Ul760gkTJlBgYCB169aNGjZsSEOGDKnU\n9KWpqank4uJiVtarVy966623LOra6gMqoCQqJ9MKh+NEeHt7Y8qUKdi9ezemTp2KoKAgNG3aFJs3\nb0bDhg0t3GZVKhXGjx+P5s2bw8XFBa6urnBxcYFCoTDWUavVGDx4MADg/v372LdvH/bu3Yv8/HxE\nRERYlaNNmzbYuXOn3XIPHz7czJ1VLpfj1q1bZnW0Wi1WrVoFmcx8T6tcLsfVq1ftvhbHMbKystC6\ndWusWLECW7ZsQceOHXH48GHj+fT0dKxfvx7x8fGIiYnB8OHDsW/fPpw9WxILtbi4GNOmTYOHhwe8\nvb2xaNGiR5ZLr9ebXaMqKDuFFodTi5FIJJg3bx7mzZtnLLt16xbLgGdCQkIClEolkpOTcejQIeTl\n5aFz585ITEzEG2+8gfv372P48OGYP38+srKyEBUVZdzrUK9ePaxatQo//fSTWbtKpRKbN2+Gl5eX\n3fJOnDgRf//9N5YuXQqRSISIiAgcOnTIok+dOnVCUVGRWfmDBw8QEhJi97U4jrF06VJ4eHigb9++\nAICAgABMnToVx4+zxJk5OTlwcXEx7j+RyWTQ6XTIzs42tvHee+/hu+++w3PPPYeHDx9i8eLF8PX1\nRXx8vF0yNG7cGF5eXli4cCGmTJmC/fv349ChQ+jevXsl99YcPpPg/KP49NNPLcoM/9GlUim6d++O\nuLg4eHh4ICEhAenp6cjMzMTkyZORnp6O119/HX/88Qdyc3ORm5uLGzdu4Msvv8T//d//Qa1WQyaT\nQaPRYOzYsQgODnZINpFIhLfeeguZmZnIyMiwmFkAgLu7O9zc3LBx40ao1Wq4urpCpVJhzZo18PX1\ntdEy51G5ffs26tUrCTTh6elpkb5Uo9Hg8OHDyMnJwYkTJ5CXl2eWvnT79u3o0qUL3N3d4eXlhQ4d\nOiAxMdFuGWQyGb799lskJSXB19cXS5YswbPPPouAvsugagAADeZJREFUgIDK6aQN+EyC84/iwYMH\ndpUZyMrKQkxMDC5dugS9Xg+NRmNW/+HDh/j111+RkpKCvn374sKFC2jatCliY2PLlCMvLw/vvfce\nzp07h6ioKEyfPt0iN/aYMWOwevVq/Pzzz8ay9PR0JCUloX///khPT8e1a9fQoEEDswGMU/n06dMH\nmzdvRkhICFxcXPDTTz+hT58+xvNyuRx79+7FqFGjsG7dOoSEhODHH3+EVluSzNPDwwN37txBw4YN\nAQB3795Fs2bNHJKjefPmOHDggPFz586dMXr06EfrnJMyF2zX9ikA+wAEmpybBRZp9iKAXja+X2nG\nIM4/i9KB8tRqNe3evdtm/aFDh5rtgZBKpWY5JJRKJb3yyisOyfDw4UNq3bq10QNLrVZT//79Ler9\n9ttvVjcGduzY0eF+c+zH1viybNky8vT0JBcXFxo5cmSF0pe6ublRVFQUtWvXjry9vR1KX0pEdObM\nGSooKKC8vDxauHAhNWrUyCymWXl9QC3ybjLNlT0JwBrhfVMwxSEDEAzgEqwviTl0YzkcU9atW0dt\n27aljh070vbt28usGx4ebjFI+/j4kEKhIIVCQU888QTl5+c7dP3k5GSLfRUKhYJu3LhhVi80NNSq\n11Tbtm0d7jPHfqpyfHnU9KWvvvoqubu7k4uLC8XGxtLly5et1rPVB1RASVhGR6t+ZgHQAZgpvNcD\nWCCc2wUWMyql1HeE/nI4VcuAAQOQlJRkNBSrVCrMnDkT48aNAxHBy8vLapDBnTt3YteuXfD19cWE\nCRPg6upqPHfw4EH069cPOTk5xjKVSoULFy4gKCgIAPOg0mg00Ov1Zu3K5XKsXr0aI0eOrIrucsBs\nQ7V9fLHVB+Fv1RnGfbt4F8CfYHGhdELZUgCmpv41AAZZ+a7DGpjDqQg3btygoKAg0mq15OLiQlFR\nUeUuM3z44YfGZSKFQkGNGjWi3Nxc4/m8vDxq0KCBcdlKoVBQu3btzHzq9Xq9xSY6sVhMr776apX1\nlcOoC+OLrT7AyWYSewD4WCl/HYCpSX8mgMYARoMpiRQAG4RzawB8D2BrqTaE/nI4VU9BQQFOnjwJ\nuVyO1q1bQyKRlFnfxcUFeXl5xs8ajQbLly/HqFGjjGV//fUXJkyYgF9//RUdOnTAhx9+CJ1OZ9bO\njh07MGTIEEilUhQXF6Nfv37YsGGD1ZkLp/LgMwlzqtK7qaed9TaCKQIASIe5ETtAKLNgzpw5xvfR\n0dGIjo52WEAOxx5UKhU6d+5sV10issgxUVxcjHv37pmV+fr6Ytu2bWW21bdvX5w9exYnTpyAj48P\nunTpwhUExyEOHDhg5g1VEWrqLy4MzIMJYIbrDgCeAzNcbxQ++wPYCyAUllMkPpPgOC39+/fH7t27\njYmCNBoNTp48ibCwsBqWjGMPfCZhTk1tpnsfQCqYJ1M0gGlC+XkAW4TXnQAmoBa5bHE4ALBx40YM\nHjwY3t7eaNasGXbt2sUVBKfWUlvnrnwmweFwqgQ+kzCH77jmcDgcE9zd3Wu97cfd3b3S2qqtd4LP\nJDgcDsdBapNNgsPhcDi1AK4kOBwOh2MTriQ4HA6HYxOuJDgcDodjE64kOBwOh2MTriQ4HA6HYxOu\nJDgcDodjE64kOBwOh2MTriQ4HA6HYxOuJDgcDodjE64kOBwOh2MTriQ4HA6HYxOuJDgcDodjE64k\nOBwOh2MTriQ4HA6HYxOuJDgcDodjE64kOBwOh2OTmlYS0wDoAdQzKZsF4HcAFwH0qgmhOBwOh8Oo\nSSURCKAngD9MypoCGCK89gGwAjWvyCrMgQMHaloEu+ByVi5czsqlNshZG2SsKDU5AC8G8Fqpsv4A\nNgF4COAagEsAOlSvWJVHbfnD4XJWLlzOyqU2yFkbZKwoNaUk+gNIA3CmVLmfUG4gDYB/dQnF4XA4\nHHOkVdj2HgA+VsrfALM7mNobRGW0Q5UpFIfD4XDsp6zBuaqIBLAPQL7wOQBAOoCOAEYLZfOF110A\n3gZwrFQblwCEVK2YHA6HU+e4DCC0poVwlKso8W5qCuAUADmAhmAdqglFxuFwOBxU7XKTvZguJ50H\nsEV4LQIwAXy5icPhcDgcDofD4VQWzr4Jby6A02BLZ/vA9oQYcCY5FwK4ACbrVgA6k3POIuczAM4B\nKAbQptQ5Z5HRQB8wWX4HMKOGZTHlMwAZAFJNyuqBOZf8BmA3ALcakKs0gQD2g/3eZwFMFsqdTVYl\nmJ30FNiqx/tCubPJCQASACcBJAqfnVHGSicQzKBtzZYhAxAMZtiuyT0gWpP3kwCsEd47m5w9Ta4/\nHyUOA84kZwSAcLDBw1RJOJOMAPvPeEmQRQYmW5MalMeUrgBaw1xJ/Bsl+5RmoOS3r0l8ALQS3rsA\n+BXsHjqjrGrhVQogBUAXOKecUwFsALBd+OyMMlY6XwNoAXMlMQvmT267AERVs1y2mIWSH8KZ5RwI\n4EvhvTPKWVpJOJuMnQQZDMwUDmchGOZK4iIAb+G9j/DZ2fgWQA84t6xqAP8D0AzOJ2cAgL0AYlAy\nk3BYxtoW8qI2bcJ7F8CfABJQMh11RjkNjAHwvfDemeU04Gwy+gO4bvK5puUpD2+wJSgIr95l1K0J\ngsFmP8fgnLKKwWaLGShZInM2OZcAeBVsad6AwzI6g3dTaWrLJjxbcr4OprXfEI6ZAP6Dkj0gpalp\nOQEm5wMAG8topyrltEdGe6hJT7ja7IVHcC75XQB8A+BlALmlzjmLrHqwpTEdgB/AntZNqWk5+wLI\nBLNHRNuoY5eMzqgketoojwTbO3Fa+BwA4GewTXjpMDcOGzboVSW25CzNRpQ8oTujnAkAYgE8YVJW\n3XLaey9NqYl7WRal5QmE+UzH2cgAU8w3AfiCDSjOgAxMQXwBttwEOK+sAJANIAlAWziXnJ0B9AP7\nv60E4Ap2T51JxirHmTfhhZm8nwT24wDOJ2cfsGmyZ6lyZ5MTYFP6tiafnU1GqSBDsCCTMxmuAUub\nxL9RYtOZCecwYIoArAdbJjHF2WT1RIlXkArAIbCHLGeT00A3lMzInVXGKuEKzF1gXwfzLrkIoHeN\nSFTCf8H+Q54CeyryMjnnTHL+Dhaq/aRwrDA55yxyDgRb6y8Ae/rZaXLOWWQ08CSYR84lsKVRZ2ET\ngBtgS4rXwZY+64EZNZ3JFbIL2DLOKZT8TfaB88naHMAvYHKeAVv3B5xPTgPdUOLd5KwycjgcDofD\n4XA4HA6Hw+FwOBwOh8PhcDgcDofD4XA4HA6Hw+FwOByOs1OMEl/3XwAEAUh2sI0pYJuVrHEAbH/E\nKQCHwSLFWmM1Kr6pzVF5TTkA881/BmRgG5h+A4sYcARsL0BtJgjAsJoWgsPh1C5Kx9yxRVmhYa4C\n8LBxzjQq7AsAvrNSpyYDWJaOWmtgPoC1YMoCYBstn6kuoaqIaDgWY4vD4XCsKol7wms0gJ/ABvaL\nYOGWk8BmBakAngULZ1IItqN1n5W2TAfhCLDwIoZrLBLaegzsib6Nybl5wrmjKNkJ7w1gm1B+CiUh\nx03lPQRghyDvxygJAbICLFT0WQBzbMhnQA3gNlgAO2sME/qbCvNwCffAwimcBQuGGAXgIFgYkDih\nTgLY/dwPNkuZbfL9qUKbqWBB8wAWpuMCgFVCuz+AxfgBgBCwne0nhH43Fso/B/Ah2AzrMoBBQnkK\ngL/BZo2G9jkcDqdMilCy3PSNUGZQHNFgA1+Q8HkQ2GBlwJC0yTQ+V2lMYzm9ChZ6AmBhHQaXqtfG\n5NxTwvsFYNFvAWAzSrKgicGCoZWWtwBsYBWDhTMwDJDuwqtEuFZzK9c10AJs6c0afmAhUjyEtvaB\nhcY3yG0IO7JVuL5EaO+kUJ4AFnrDHWywTwW7P23BFI8KgAZMIbQS+vJQaMNwD+KF9/sAhArvO6JE\nSX8u1APYEt7vwnvT2ECcOoYzRoHl1A0KwPIB2OI42KAIsEFsEdjT8w4wG0N5iMAybhWAKZNJQnkx\nSpRSaR6AzVgAZg8wRJ+NATBCeK8HkGND3mvC+01gcYa+ATAEbLlLChZVswnMA+nZS3swxXJH+LwB\nwONgs4MHYE/6ENq+D9bPs2CDvYHdAO4K77cKMpLwvsCkvCtYLJ+rKMnN8rPQlgYsgujXJu3KhVdC\nSWTWCyjJRVDTwR85VQhXEpyaIs/k/e9gCuUpsOWgfWB5wsuCAAyH5ZP5fdiOkf/Q5L0e5n//5Q10\npm2KhM/BYPnW24GFjF6LkiUba1wC0ABspmQtT4KpDIZrWJP7gY0+mGL6fVvtFpqUFwuyi8EUjS0F\n/8DkPVcO/wBqW2Y6Tt3EF2xw3wA2ozAMULkoWfqxRmUNUvsAjBfeS2xcswNKlpueBbOpuIIpuxyw\np+ony7lOPoBPwdb1DYbr+mDLY8fBlm0My01DwewOjtATbLlJBbZUdViQcwBKlpsGCGXW7p0I7J5f\nRcmSnQglS1K2yIV5XndOHYIrCU5VYe1pnmy8bw6WpvIkmMF1nlC+CixvtDXDtT3XKO/6hs8vgy05\nnQEz1jaxUv9/AJYBOA8Wpn6bUP8kmDF7A+xbJnsTwC2hnVSwtfxssDDoM8GWnE4JchjW+Uv3ydZ9\nPA62BHYaLFz9L4J8nwvnUsBcgk9b+a7p53gAYwU5zoIlrynr2qfBZiKnwA3XHA7nH0g0nN8wmwBg\naU0Lwal78JkEh1M+NZ2v2B5qg4wcDofD4XA4HA6Hw+FwOBwOh8PhcDgcDofD4XA4HA6Hw+FwOBwO\nh1PV/D/SOgmM65cYYgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1075c4dd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.decomposition import PCA\n",
"\n",
"n_components = n_row * n_col # 10\n",
"estimator = PCA(n_components=n_components)\n",
"X_pca = estimator.fit_transform(X_digits)\n",
"plot_pca_scatter() # Note that we only plot the first and second principal component"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To finish, let us look at principal component transformations. We will take the principal components from the estimator by accessing the components attribute. Each of its components is a matrix that is used to transform a vector from the original space to the transformed space. In the scatter we previously plotted, we only took into account the first two components."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def print_pca_components(images, n_col, n_row):\n",
" plt.figure(figsize=(2. * n_col, 2.26 * n_row))\n",
" for i, comp in enumerate(images):\n",
" plt.subplot(n_row, n_col, i + 1)\n",
" plt.imshow(comp.reshape((8, 8)), interpolation='nearest')\n",
" plt.text(0, -1, str(i + 1) + '-component')\n",
" plt.xticks(())\n",
" plt.yticks(())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAjwAAAEGCAYAAABhBk/MAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x107fb6a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print_pca_components(estimator.components_[:n_components], n_col, n_row)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
ES-DOC/esdoc-jupyterhub | notebooks/mpi-m/cmip6/models/mpi-esm-1-2-hr/aerosol.ipynb | 1 | 84302 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"# ES-DOC CMIP6 Model Properties - Aerosol \n",
"**MIP Era**: CMIP6 \n",
"**Institute**: MPI-M \n",
"**Source ID**: MPI-ESM-1-2-HR \n",
"**Topic**: Aerosol \n",
"**Sub-Topics**: Transport, Emissions, Concentrations, Optical Radiative Properties, Model. \n",
"**Properties**: 69 (37 required) \n",
"**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/aerosol?client=jupyter-notebook) \n",
"**Initialized From**: -- \n",
"\n",
"**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n",
"**Notebook Initialised**: 2018-02-15 16:54:17"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### Document Setup \n",
"**IMPORTANT: to be executed each time you run the notebook** "
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# DO NOT EDIT ! \n",
"from pyesdoc.ipython.model_topic import NotebookOutput \n",
"\n",
"# DO NOT EDIT ! \n",
"DOC = NotebookOutput('cmip6', 'mpi-m', 'mpi-esm-1-2-hr', 'aerosol')"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Authors \n",
"*Set document authors*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set as follows: DOC.set_author(\"name\", \"email\") \n",
"# TODO - please enter value(s)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Contributors \n",
"*Specify document contributors* "
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set as follows: DOC.set_contributor(\"name\", \"email\") \n",
"# TODO - please enter value(s)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Publication \n",
"*Specify document publication status* "
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set publication status: \n",
"# 0=do not publish, 1=publish. \n",
"DOC.set_publication_status(0)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Table of Contents \n",
"[1. Key Properties](#1.-Key-Properties) \n",
"[2. Key Properties --> Software Properties](#2.-Key-Properties--->-Software-Properties) \n",
"[3. Key Properties --> Timestep Framework](#3.-Key-Properties--->-Timestep-Framework) \n",
"[4. Key Properties --> Meteorological Forcings](#4.-Key-Properties--->-Meteorological-Forcings) \n",
"[5. Key Properties --> Resolution](#5.-Key-Properties--->-Resolution) \n",
"[6. Key Properties --> Tuning Applied](#6.-Key-Properties--->-Tuning-Applied) \n",
"[7. Transport](#7.-Transport) \n",
"[8. Emissions](#8.-Emissions) \n",
"[9. Concentrations](#9.-Concentrations) \n",
"[10. Optical Radiative Properties](#10.-Optical-Radiative-Properties) \n",
"[11. Optical Radiative Properties --> Absorption](#11.-Optical-Radiative-Properties--->-Absorption) \n",
"[12. Optical Radiative Properties --> Mixtures](#12.-Optical-Radiative-Properties--->-Mixtures) \n",
"[13. Optical Radiative Properties --> Impact Of H2o](#13.-Optical-Radiative-Properties--->-Impact-Of-H2o) \n",
"[14. Optical Radiative Properties --> Radiative Scheme](#14.-Optical-Radiative-Properties--->-Radiative-Scheme) \n",
"[15. Optical Radiative Properties --> Cloud Interactions](#15.-Optical-Radiative-Properties--->-Cloud-Interactions) \n",
"[16. Model](#16.-Model) \n",
"\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"# 1. Key Properties \n",
"*Key properties of the aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 1.1. Model Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of aerosol model.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.model_overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.2. Model Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Name of aerosol model code*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.model_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.3. Scheme Scope\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Atmospheric domains covered by the aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.scheme_scope') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"troposhere\" \n",
"# \"stratosphere\" \n",
"# \"mesosphere\" \n",
"# \"mesosphere\" \n",
"# \"whole atmosphere\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.4. Basic Approximations\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Basic approximations made in the aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.basic_approximations') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.5. Prognostic Variables Form\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Prognostic variables in the aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.prognostic_variables_form') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"3D mass/volume ratio for aerosols\" \n",
"# \"3D number concenttration for aerosols\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.6. Number Of Tracers\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Number of tracers in the aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.number_of_tracers') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.7. Family Approach\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Are aerosol calculations generalized into families of species?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.family_approach') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 2. Key Properties --> Software Properties \n",
"*Software properties of aerosol code*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 2.1. Repository\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Location of code for this component.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.software_properties.repository') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.2. Code Version\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Code version identifier.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.software_properties.code_version') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.3. Code Languages\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.N\n",
"### *Code language(s).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.software_properties.code_languages') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 3. Key Properties --> Timestep Framework \n",
"*Physical properties of seawater in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 3.1. Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Mathematical method deployed to solve the time evolution of the prognostic variables*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Uses atmospheric chemistry time stepping\" \n",
"# \"Specific timestepping (operator splitting)\" \n",
"# \"Specific timestepping (integrated)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.2. Split Operator Advection Timestep\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Timestep for aerosol advection (in seconds)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.split_operator_advection_timestep') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.3. Split Operator Physical Timestep\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Timestep for aerosol physics (in seconds).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.split_operator_physical_timestep') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.4. Integrated Timestep\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Timestep for the aerosol model (in seconds)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.integrated_timestep') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.5. Integrated Scheme Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Specify the type of timestep scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.integrated_scheme_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Explicit\" \n",
"# \"Implicit\" \n",
"# \"Semi-implicit\" \n",
"# \"Semi-analytic\" \n",
"# \"Impact solver\" \n",
"# \"Back Euler\" \n",
"# \"Newton Raphson\" \n",
"# \"Rosenbrock\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 4. Key Properties --> Meteorological Forcings \n",
"**"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 4.1. Variables 3D\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Three dimensionsal forcing variables, e.g. U, V, W, T, Q, P, conventive mass flux*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.variables_3D') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.2. Variables 2D\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Two dimensionsal forcing variables, e.g. land-sea mask definition*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.variables_2D') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.3. Frequency\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Frequency with which meteological forcings are applied (in seconds).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.frequency') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 5. Key Properties --> Resolution \n",
"*Resolution in the aersosol model grid*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 5.1. Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *This is a string usually used by the modelling group to describe the resolution of this grid, e.g. ORCA025, N512L180, T512L70 etc.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.resolution.name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 5.2. Canonical Horizontal Resolution\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Expression quoted for gross comparisons of resolution, eg. 50km or 0.1 degrees etc.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.resolution.canonical_horizontal_resolution') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 5.3. Number Of Horizontal Gridpoints\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Total number of horizontal (XY) points (or degrees of freedom) on computational grid.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.resolution.number_of_horizontal_gridpoints') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 5.4. Number Of Vertical Levels\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Number of vertical levels resolved on computational grid.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.resolution.number_of_vertical_levels') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 5.5. Is Adaptive Grid\n",
"**Is Required:** FALSE **Type:** BOOLEAN **Cardinality:** 0.1\n",
"### *Default is False. Set true if grid resolution changes during execution.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.resolution.is_adaptive_grid') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 6. Key Properties --> Tuning Applied \n",
"*Tuning methodology for aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 6.1. Description\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *General overview description of tuning: explain and motivate the main targets and metrics retained. &Document the relative weight given to climate performance metrics versus process oriented metrics, &and on the possible conflicts with parameterization level tuning. In particular describe any struggle &with a parameter value that required pushing it to its limits to solve a particular model deficiency.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.description') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.2. Global Mean Metrics Used\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.N\n",
"### *List set of metrics of the global mean state used in tuning model/component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.global_mean_metrics_used') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.3. Regional Metrics Used\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.N\n",
"### *List of regional metrics of mean state used in tuning model/component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.regional_metrics_used') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.4. Trend Metrics Used\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.N\n",
"### *List observed trend metrics used in tuning model/component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.trend_metrics_used') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 7. Transport \n",
"*Aerosol transport*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 7.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of transport in atmosperic aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.transport.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 7.2. Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Method for aerosol transport modeling*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.transport.scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Uses Atmospheric chemistry transport scheme\" \n",
"# \"Specific transport scheme (eulerian)\" \n",
"# \"Specific transport scheme (semi-lagrangian)\" \n",
"# \"Specific transport scheme (eulerian and semi-lagrangian)\" \n",
"# \"Specific transport scheme (lagrangian)\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 7.3. Mass Conservation Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Method used to ensure mass conservation.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.transport.mass_conservation_scheme') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Uses Atmospheric chemistry transport scheme\" \n",
"# \"Mass adjustment\" \n",
"# \"Concentrations positivity\" \n",
"# \"Gradients monotonicity\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 7.4. Convention\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Transport by convention*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.transport.convention') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Uses Atmospheric chemistry transport scheme\" \n",
"# \"Convective fluxes connected to tracers\" \n",
"# \"Vertical velocities connected to tracers\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 8. Emissions \n",
"*Atmospheric aerosol emissions*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 8.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of emissions in atmosperic aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.2. Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Method used to define aerosol species (several methods allowed because the different species may not use the same method).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.method') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"None\" \n",
"# \"Prescribed (climatology)\" \n",
"# \"Prescribed CMIP6\" \n",
"# \"Prescribed above surface\" \n",
"# \"Interactive\" \n",
"# \"Interactive above surface\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.3. Sources\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Sources of the aerosol species are taken into account in the emissions scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.sources') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Vegetation\" \n",
"# \"Volcanos\" \n",
"# \"Bare ground\" \n",
"# \"Sea surface\" \n",
"# \"Lightning\" \n",
"# \"Fires\" \n",
"# \"Aircraft\" \n",
"# \"Anthropogenic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.4. Prescribed Climatology\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Specify the climatology type for aerosol emissions*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.prescribed_climatology') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Constant\" \n",
"# \"Interannual\" \n",
"# \"Annual\" \n",
"# \"Monthly\" \n",
"# \"Daily\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.5. Prescribed Climatology Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of aerosol species emitted and prescribed via a climatology*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.prescribed_climatology_emitted_species') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.6. Prescribed Spatially Uniform Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of aerosol species emitted and prescribed as spatially uniform*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.prescribed_spatially_uniform_emitted_species') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.7. Interactive Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of aerosol species emitted and specified via an interactive method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.interactive_emitted_species') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.8. Other Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of aerosol species emitted and specified via an "other method"*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.other_emitted_species') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.9. Other Method Characteristics\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Characteristics of the "other method" used for aerosol emissions*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.other_method_characteristics') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 9. Concentrations \n",
"*Atmospheric aerosol concentrations*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 9.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of concentrations in atmosperic aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.concentrations.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 9.2. Prescribed Lower Boundary\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of species prescribed at the lower boundary.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.concentrations.prescribed_lower_boundary') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 9.3. Prescribed Upper Boundary\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of species prescribed at the upper boundary.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.concentrations.prescribed_upper_boundary') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 9.4. Prescribed Fields Mmr\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of species prescribed as mass mixing ratios.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.concentrations.prescribed_fields_mmr') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 9.5. Prescribed Fields Mmr\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of species prescribed as AOD plus CCNs.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.concentrations.prescribed_fields_mmr') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 10. Optical Radiative Properties \n",
"*Aerosol optical and radiative properties*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 10.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of optical and radiative properties*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 11. Optical Radiative Properties --> Absorption \n",
"*Absortion properties in aerosol scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 11.1. Black Carbon\n",
"**Is Required:** FALSE **Type:** FLOAT **Cardinality:** 0.1\n",
"### *Absorption mass coefficient of black carbon at 550nm (if non-absorbing enter 0)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.black_carbon') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.2. Dust\n",
"**Is Required:** FALSE **Type:** FLOAT **Cardinality:** 0.1\n",
"### *Absorption mass coefficient of dust at 550nm (if non-absorbing enter 0)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.dust') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.3. Organics\n",
"**Is Required:** FALSE **Type:** FLOAT **Cardinality:** 0.1\n",
"### *Absorption mass coefficient of organics at 550nm (if non-absorbing enter 0)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.organics') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 12. Optical Radiative Properties --> Mixtures \n",
"**"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 12.1. External\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there external mixing with respect to chemical composition?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.external') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 12.2. Internal\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there internal mixing with respect to chemical composition?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.internal') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 12.3. Mixing Rule\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *If there is internal mixing with respect to chemical composition then indicate the mixinrg rule*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.mixing_rule') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 13. Optical Radiative Properties --> Impact Of H2o \n",
"**"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 13.1. Size\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Does H2O impact size?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.impact_of_h2o.size') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.2. Internal Mixture\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Does H2O impact internal mixture?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.impact_of_h2o.internal_mixture') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 14. Optical Radiative Properties --> Radiative Scheme \n",
"*Radiative scheme for aerosol*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 14.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of radiative scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.2. Shortwave Bands\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Number of shortwave bands*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.shortwave_bands') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.3. Longwave Bands\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Number of longwave bands*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.longwave_bands') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 15. Optical Radiative Properties --> Cloud Interactions \n",
"*Aerosol-cloud interactions*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 15.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of aerosol-cloud interactions*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.2. Twomey\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is the Twomey effect included?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.twomey') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.3. Twomey Minimum Ccn\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *If the Twomey effect is included, then what is the minimum CCN number?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.twomey_minimum_ccn') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.4. Drizzle\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Does the scheme affect drizzle?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.drizzle') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.5. Cloud Lifetime\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Does the scheme affect cloud lifetime?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.cloud_lifetime') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.6. Longwave Bands\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Number of longwave bands*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.longwave_bands') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 16. Model \n",
"*Aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 16.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of atmosperic aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.model.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 16.2. Processes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Processes included in the Aerosol model.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.model.processes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Dry deposition\" \n",
"# \"Sedimentation\" \n",
"# \"Wet deposition (impaction scavenging)\" \n",
"# \"Wet deposition (nucleation scavenging)\" \n",
"# \"Coagulation\" \n",
"# \"Oxidation (gas phase)\" \n",
"# \"Oxidation (in cloud)\" \n",
"# \"Condensation\" \n",
"# \"Ageing\" \n",
"# \"Advection (horizontal)\" \n",
"# \"Advection (vertical)\" \n",
"# \"Heterogeneous chemistry\" \n",
"# \"Nucleation\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 16.3. Coupling\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Other model components coupled to the Aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.model.coupling') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Radiation\" \n",
"# \"Land surface\" \n",
"# \"Heterogeneous chemistry\" \n",
"# \"Clouds\" \n",
"# \"Ocean\" \n",
"# \"Cryosphere\" \n",
"# \"Gas phase chemistry\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 16.4. Gas Phase Precursors\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *List of gas phase aerosol precursors.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.model.gas_phase_precursors') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"DMS\" \n",
"# \"SO2\" \n",
"# \"Ammonia\" \n",
"# \"Iodine\" \n",
"# \"Terpene\" \n",
"# \"Isoprene\" \n",
"# \"VOC\" \n",
"# \"NOx\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 16.5. Scheme Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Type(s) of aerosol scheme used by the aerosols model (potentially multiple: some species may be covered by one type of aerosol scheme and other species covered by another type).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.model.scheme_type') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Bulk\" \n",
"# \"Modal\" \n",
"# \"Bin\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 16.6. Bulk Scheme Species\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *List of species covered by the bulk scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.model.bulk_scheme_species') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Sulphate\" \n",
"# \"Nitrate\" \n",
"# \"Sea salt\" \n",
"# \"Dust\" \n",
"# \"Ice\" \n",
"# \"Organic\" \n",
"# \"Black carbon / soot\" \n",
"# \"SOA (secondary organic aerosols)\" \n",
"# \"POM (particulate organic matter)\" \n",
"# \"Polar stratospheric ice\" \n",
"# \"NAT (Nitric acid trihydrate)\" \n",
"# \"NAD (Nitric acid dihydrate)\" \n",
"# \"STS (supercooled ternary solution aerosol particule)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### \u00a92017 [ES-DOC](https://es-doc.org) \n"
],
"cell_type": "markdown",
"metadata": {}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"name": "python2",
"language": "python"
},
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"name": "python",
"file_extension": ".py",
"version": "2.7.10",
"pygments_lexer": "ipython2",
"codemirror_mode": {
"version": 2,
"name": "ipython"
}
}
}
} | gpl-3.0 |
google/patents-public-data | examples/Document_representation_from_BERT.ipynb | 1 | 15265 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Document representation from BERT",
"provenance": [
{
"file_id": "1hccaqNncyxDG32f5U1Qncz6ipWiLV0TQ",
"timestamp": 1614125265907
},
{
"file_id": "1d9KurXhXvrV-jo-x2f7DkZ40qx75YAh_",
"timestamp": 1604694308174
}
],
"collapsed_sections": [],
"last_runtime": {
"build_target": "//corp/legal/patents/colab:dst_colab_notebook",
"kind": "shared"
},
"toc_visible": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "ED6tBdZtOjlU"
},
"source": [
"# Document representation from BERT"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CqNm7ioGOgSm"
},
"source": [
"Copyright 2021 Google Inc.\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
"\n",
"http://www.apache.org/licenses/LICENSE-2.0\n",
"\n",
"Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
]
},
{
"cell_type": "code",
"metadata": {
"id": "c1vLcDJINTGg"
},
"source": [
"import collections\n",
"import math\n",
"import random\n",
"import sys\n",
"import time\n",
"from typing import Dict, List, Tuple\n",
"from sklearn.metrics import pairwise\n",
"# Use Tensorflow 2.0\n",
"import tensorflow as tf\n",
"import numpy as np"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "vfSIZaeaPHpZ",
"colab": {
"height": 53
},
"executionInfo": {
"status": "ok",
"timestamp": 1614125346371,
"user_tz": 300,
"elapsed": 155,
"user": {
"displayName": "Rob Srebrovic",
"photoUrl": "",
"userId": "06004353344935214283"
}
},
"outputId": "c0bca557-2962-4f3b-a8f9-71be6d820897"
},
"source": [
"# Set BigQuery application credentials\n",
"from google.cloud import bigquery\n",
"import os\n",
"os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = \"path/to/file.json\"\n",
"\n",
"project_id = \"your_bq_project_id\"\n",
"bq_client = bigquery.Client(project=project_id)"
],
"execution_count": 2,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'# Set BigQuery application credentials\\nfrom google.cloud import bigquery\\nimport os\\nos.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = \"path/to/file.json\"\\n\\nproject_id = \"your_bq_project_id\"\\nbq_client = bigquery.Client(project=project_id)'"
]
},
"metadata": {
"tags": []
},
"execution_count": 2
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "7BojUHDYrESY"
},
"source": [
"# You will have to clone the BERT repo\n",
"!test -d bert_repo || git clone https://github.com/google-research/bert bert_repo\n",
"if not 'bert_repo' in sys.path:\n",
" sys.path += ['bert_repo']"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "QeoX7LfgPLGP"
},
"source": [
"The BERT repo uses Tensorflow 1 and thus a few of the functions have been moved/changed/renamed in Tensorflow 2. In order for the BERT tokenizer to be used, one of the lines in the repo that was just cloned needs to be modified to comply with Tensorflow 2. Line 125 in the BERT tokenization.py file must be changed as follows:\n",
"\n",
"From => `with tf.gfile.GFile(vocab_file, \"r\") as reader:`\n",
"\n",
"To => `with tf.io.gfile.GFile(vocab_file, \"r\") as reader:`\n",
"\n",
"Once that is complete and the file is saved, the tokenization library can be imported."
]
},
{
"cell_type": "code",
"metadata": {
"id": "HsSJXKPDPLXn"
},
"source": [
"import tokenization"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "JBqRRfigQxxK"
},
"source": [
"# Load BERT"
]
},
{
"cell_type": "code",
"metadata": {
"id": "kp2fx508lWBG"
},
"source": [
"MAX_SEQ_LENGTH = 512\n",
"MODEL_DIR = 'path/to/model'\n",
"VOCAB = 'path/to/vocab'\n",
"\n",
"tokenizer = tokenization.FullTokenizer(VOCAB, do_lower_case=True)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "sNf96pSxxXg2"
},
"source": [
"model = tf.compat.v2.saved_model.load(export_dir=MODEL_DIR, tags=['serve'])\n",
"model = model.signatures['serving_default']"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "-BWnaHqoT7db"
},
"source": [
"# Mean pooling layer for combining\n",
"pooling = tf.keras.layers.GlobalAveragePooling1D()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "rtzZg5LESCxF"
},
"source": [
"# Get a couple of Patents\n",
"\n",
"Here we do a simple query from the BigQuery patents data to collect the claims for a sample set of patents."
]
},
{
"cell_type": "code",
"metadata": {
"id": "u3iTTJQ5SFba"
},
"source": [
"# Put your publications here.\n",
"test_pubs = (\n",
" 'US-8000000-B2', 'US-2007186831-A1', 'US-2009030261-A1', 'US-10722718-B2'\n",
")\n",
"\n",
"js = r\"\"\"\n",
" // Regex to find the separations of the claims data\n",
" var pattern = new RegExp(/[.][\\\\s]+[0-9]+[\\\\s]*[.]/, 'g');\n",
" if (pattern.test(text)) {\n",
" return text.split(pattern);\n",
" }\n",
"\"\"\"\n",
"\n",
"query = r'''\n",
" #standardSQL\n",
" CREATE TEMPORARY FUNCTION breakout_claims(text STRING) RETURNS ARRAY<STRING> \n",
" LANGUAGE js AS \"\"\"\n",
" {}\n",
" \"\"\"; \n",
"\n",
" SELECT \n",
" pubs.publication_number, \n",
" title.text as title, \n",
" breakout_claims(claims.text) as claims\n",
" FROM `patents-public-data.patents.publications` as pubs,\n",
" UNNEST(claims_localized) as claims,\n",
" UNNEST(title_localized) as title\n",
" WHERE\n",
" publication_number in {}\n",
"'''.format(js, test_pubs)\n",
"\n",
"df = bq_client.query(query).to_dataframe()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"height": 241
},
"id": "ORcVOefPsT0U",
"executionInfo": {
"status": "ok",
"timestamp": 1614011849900,
"user_tz": 300,
"elapsed": 309,
"user": {
"displayName": "Jay Yonamine",
"photoUrl": "",
"userId": "01949405773282057831"
}
},
"outputId": "5299f3c1-b64e-4cbd-9206-273d1fb1d300"
},
"source": [
"df.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>publication_number</th>\n",
" <th>title</th>\n",
" <th>claims</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>US-2009030261-A1</td>\n",
" <td>Drug delivery system</td>\n",
" <td>[1 . A drug delivery system comprising:\\n a ca...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>US-2007186831-A1</td>\n",
" <td>Sewing machine</td>\n",
" <td>[1 . A sewing machine comprising:\\n a needle b...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>US-8000000-B2</td>\n",
" <td>Visual prosthesis</td>\n",
" <td>[1. A visual prosthesis apparatus comprising:\\...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>US-10722718-B2</td>\n",
" <td>Systems and methods for treatment of dry eye</td>\n",
" <td>[What is claimed is: \\n \\n 1. A meth...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" publication_number ... claims\n",
"0 US-2009030261-A1 ... [1 . A drug delivery system comprising:\\n a ca...\n",
"1 US-2007186831-A1 ... [1 . A sewing machine comprising:\\n a needle b...\n",
"2 US-8000000-B2 ... [1. A visual prosthesis apparatus comprising:\\...\n",
"3 US-10722718-B2 ... [What is claimed is: \\n \\n 1. A meth...\n",
"\n",
"[4 rows x 3 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "NeFzKlMw1DQd"
},
"source": [
"def get_bert_token_input(texts):\n",
" input_ids = []\n",
" input_mask = []\n",
" segment_ids = []\n",
"\n",
" for text in texts:\n",
" tokens = tokenizer.tokenize(text)\n",
" if len(tokens) > MAX_SEQ_LENGTH - 2:\n",
" tokens = tokens[0:(MAX_SEQ_LENGTH - 2)]\n",
" tokens = ['[CLS]'] + tokens + ['[SEP]']\n",
"\n",
"\n",
" ids = tokenizer.convert_tokens_to_ids(tokens)\n",
" token_pad = MAX_SEQ_LENGTH - len(ids)\n",
" input_mask.append([1] * len(ids) + [0] * token_pad)\n",
" input_ids.append(ids + [0] * token_pad)\n",
" segment_ids.append([0] * MAX_SEQ_LENGTH)\n",
" \n",
" return {\n",
" 'segment_ids': tf.convert_to_tensor(segment_ids, dtype=tf.int64),\n",
" 'input_mask': tf.convert_to_tensor(input_mask, dtype=tf.int64),\n",
" 'input_ids': tf.convert_to_tensor(input_ids, dtype=tf.int64),\n",
" 'mlm_positions': tf.convert_to_tensor([], dtype=tf.int64)\n",
" }"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "MlrVU10IOlSZ"
},
"source": [
"docs_embeddings = []\n",
"for _, row in df.iterrows():\n",
" inputs = get_bert_token_input(row['claims'])\n",
" response = model(**inputs)\n",
" avg_embeddings = pooling(\n",
" tf.reshape(response['encoder_layer'], shape=[1, -1, 1024]))\n",
" docs_embeddings.append(avg_embeddings.numpy()[0])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "DhF2-w2yU52U",
"executionInfo": {
"status": "ok",
"timestamp": 1614012215102,
"user_tz": 300,
"elapsed": 240,
"user": {
"displayName": "Jay Yonamine",
"photoUrl": "",
"userId": "01949405773282057831"
}
},
"outputId": "c6148de6-f1c2-40c3-d75d-90cc0f4e0469"
},
"source": [
"pairwise.cosine_similarity(docs_embeddings)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[0.9999988 , 0.68387157, 0.83200616, 0.86913264],\n",
" [0.68387157, 1.0000013 , 0.7299322 , 0.73105675],\n",
" [0.83200616, 0.7299322 , 0.99999964, 0.9027555 ],\n",
" [0.86913264, 0.73105675, 0.9027555 , 0.9999996 ]], dtype=float32)"
]
},
"metadata": {
"tags": []
},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "TFWxL-IGU9-6",
"executionInfo": {
"status": "ok",
"timestamp": 1614012321633,
"user_tz": 300,
"elapsed": 227,
"user": {
"displayName": "Jay Yonamine",
"photoUrl": "",
"userId": "01949405773282057831"
}
},
"outputId": "9fffcf1d-0c2c-4d84-eb8e-847d6054f125"
},
"source": [
"docs_embeddings[0].shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1024,)"
]
},
"metadata": {
"tags": []
},
"execution_count": 23
}
]
}
]
}
| apache-2.0 |
gfabieno/SeisCL | docs/notebooks/ForwardModeling/5_Attenuation.ipynb | 1 | 609 | {
"cells": [
{
"cell_type": "markdown",
"id": "laughing-tolerance",
"metadata": {},
"source": [
"# Attenuation\n",
"\n",
"Under Development ..."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| gpl-3.0 |
google/starthinker | colabs/dbm_to_sheets.ipynb | 1 | 7159 | {
"license": "Licensed under the Apache License, Version 2.0",
"copyright": "Copyright 2020 Google LLC",
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "DV360 Report To Sheets",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "28923e0d-001"
},
"source": [
"#DV360 Report To Sheets\n",
"Move existing DV360 report into a Sheets tab.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "28923e0d-002"
},
"source": [
"#License\n",
"\n",
"Copyright 2020 Google LLC,\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"you may not use this file except in compliance with the License.\n",
"You may obtain a copy of the License at\n",
"\n",
" https://www.apache.org/licenses/LICENSE-2.0\n",
"\n",
"Unless required by applicable law or agreed to in writing, software\n",
"distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"See the License for the specific language governing permissions and\n",
"limitations under the License.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "28923e0d-003"
},
"source": [
"#Disclaimer\n",
"This is not an officially supported Google product. It is a reference implementation. There is absolutely NO WARRANTY provided for using this code. The code is Apache Licensed and CAN BE fully modified, white labeled, and disassembled by your team.\n",
"\n",
"This code generated (see starthinker/scripts for possible source):\n",
" - **Command**: \"python starthinker_ui/manage.py colab\"\n",
" - **Command**: \"python starthinker/tools/colab.py [JSON RECIPE]\"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "28923e0d-004"
},
"source": [
"#1. Install Dependencies\n",
"First install the libraries needed to execute recipes, this only needs to be done once, then click play.\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "28923e0d-005"
},
"source": [
"!pip install git+https://github.com/google/starthinker\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "28923e0d-006"
},
"source": [
"#2. Set Configuration\n",
"\n",
"This code is required to initialize the project. Fill in required fields and press play.\n",
"\n",
"1. If the recipe uses a Google Cloud Project:\n",
" - Set the configuration **project** value to the project identifier from [these instructions](https://github.com/google/starthinker/blob/master/tutorials/cloud_project.md).\n",
"\n",
"1. If the recipe has **auth** set to **user**:\n",
" - If you have user credentials:\n",
" - Set the configuration **user** value to your user credentials JSON.\n",
" - If you DO NOT have user credentials:\n",
" - Set the configuration **client** value to [downloaded client credentials](https://github.com/google/starthinker/blob/master/tutorials/cloud_client_installed.md).\n",
"\n",
"1. If the recipe has **auth** set to **service**:\n",
" - Set the configuration **service** value to [downloaded service credentials](https://github.com/google/starthinker/blob/master/tutorials/cloud_service.md).\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "28923e0d-007"
},
"source": [
"from starthinker.util.configuration import Configuration\n",
"\n",
"\n",
"CONFIG = Configuration(\n",
" project=\"\",\n",
" client={},\n",
" service={},\n",
" user=\"/content/user.json\",\n",
" verbose=True\n",
")\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "28923e0d-008"
},
"source": [
"#3. Enter DV360 Report To Sheets Recipe Parameters\n",
" 1. Specify either report name or report id to move a report.\n",
" 1. The most recent valid file will be moved to the sheet.\n",
"Modify the values below for your use case, can be done multiple times, then click play.\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "28923e0d-009"
},
"source": [
"FIELDS = {\n",
" 'auth_read':'user', # Credentials used for reading data.\n",
" 'report_id':'', # DV360 report ID given in UI, not needed if name used.\n",
" 'report_name':'', # Name of report, not needed if ID used.\n",
" 'sheet':'', # Full URL to sheet being written to.\n",
" 'tab':'', # Existing tab in sheet to write to.\n",
"}\n",
"\n",
"print(\"Parameters Set To: %s\" % FIELDS)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "28923e0d-010"
},
"source": [
"#4. Execute DV360 Report To Sheets\n",
"This does NOT need to be modified unless you are changing the recipe, click play.\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "28923e0d-011"
},
"source": [
"from starthinker.util.configuration import execute\n",
"from starthinker.util.recipe import json_set_fields\n",
"\n",
"TASKS = [\n",
" {\n",
" 'dbm':{\n",
" 'auth':{'field':{'name':'auth_read','kind':'authentication','order':1,'default':'user','description':'Credentials used for reading data.'}},\n",
" 'report':{\n",
" 'report_id':{'field':{'name':'report_id','kind':'integer','order':1,'default':'','description':'DV360 report ID given in UI, not needed if name used.'}},\n",
" 'name':{'field':{'name':'report_name','kind':'string','order':2,'default':'','description':'Name of report, not needed if ID used.'}}\n",
" },\n",
" 'out':{\n",
" 'sheets':{\n",
" 'sheet':{'field':{'name':'sheet','kind':'string','order':3,'default':'','description':'Full URL to sheet being written to.'}},\n",
" 'tab':{'field':{'name':'tab','kind':'string','order':4,'default':'','description':'Existing tab in sheet to write to.'}},\n",
" 'range':'A1'\n",
" }\n",
" }\n",
" }\n",
" }\n",
"]\n",
"\n",
"json_set_fields(TASKS, FIELDS)\n",
"\n",
"execute(CONFIG, TASKS, force=True)\n"
]
}
]
}
| apache-2.0 |
vphill/eot-cdx-analysis | code/20160612-eot-cdx-analysis.ipynb | 1 | 14277 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## EOT CDX file analysis\n",
"\n",
"Working with the already-transformed parquet files."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from datetime import datetime\n",
"\n",
"from pyspark.sql import Row\n",
"from pyspark.sql.functions import lit, udf\n",
"from pyspark.sql.types import ArrayType, DateType, StringType"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df08 = sqlContext.read.parquet(\"eot2008.parquet\")"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df12 = sqlContext.read.parquet(\"eot2012.parquet\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 2.4 ms, sys: 196 µs, total: 2.6 ms\n",
"Wall time: 7.32 s\n"
]
},
{
"data": {
"text/plain": [
"160212140"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time df08.count()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1.83 ms, sys: 152 µs, total: 1.98 ms\n",
"Wall time: 5.96 s\n"
]
},
{
"data": {
"text/plain": [
"194066937"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time df12.count()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[('surt_uri', 'string'),\n",
" ('capture_time', 'string'),\n",
" ('original_uri', 'string'),\n",
" ('mime_type', 'string'),\n",
" ('response_code', 'string'),\n",
" ('hash_sha1', 'string'),\n",
" ('redirect_url', 'string'),\n",
" ('meta_tags', 'string'),\n",
" ('length_compressed', 'string'),\n",
" ('warc_offset', 'string'),\n",
" ('warc_name', 'string')]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df08.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Questions for reformatting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Extract date to new Date column"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def extract_date(s):\n",
" return '%s-%s-%s' % (s[0:4], s[4:6], s[6:8])\n",
"\n",
"date_extractor = udf(extract_date, StringType())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df08_date = df08.withColumn(\"capture_date\", date_extractor(df08[\"capture_time\"]).cast(\"date\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df08_date.take(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Extract domain components to columns"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def extract_domain(s, level):\n",
" domain = s.split(')')[0].split(',')\n",
" level = int(level)\n",
" if len(domain) > level:\n",
" return domain[level]\n",
" return ''\n",
"\n",
"domain_extractor = udf(extract_domain, StringType())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"...this is inelegant..."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df08_dom1 = df08.withColumn(\"dom1\", domain_extractor(df08[\"surt_uri\"], lit('0')))\n",
"df08_dom2 = df08_dom1.withColumn(\"dom2\", domain_extractor(df08_dom1[\"surt_uri\"], lit('1')))\n",
"df08_dom3 = df08_dom2.withColumn(\"dom3\", domain_extractor(df08_dom2[\"surt_uri\"], lit('2')))\n",
"df08_dom4 = df08_dom3.withColumn(\"dom4\", domain_extractor(df08_dom3[\"surt_uri\"], lit('3')))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"DataFrame[surt_uri: string, capture_time: string, original_uri: string, mime_type: string, response_code: string, hash_sha1: string, redirect_url: string, meta_tags: string, length_compressed: string, warc_offset: string, warc_name: string, dom1: string, dom2: string, dom3: string, dom4: string]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df08_dom4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hmm, that worked, but let's just go the sql route for now."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df08.registerTempTable(\"eot08\")"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sql = \"\"\"\n",
" SELECT SUBSTRING(surt_uri, 0, INSTR(surt_uri, \")\") - 1) AS surt, COUNT(*) AS count\n",
" FROM eot08\n",
" GROUP BY SUBSTRING(surt_uri, 0, INSTR(surt_uri, \")\") - 1)\n",
" ORDER BY count DESC\n",
" \"\"\"\n",
"domains08 = sqlContext.sql(sql)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"domains08.rdd.map(lambda x: \"\\t\".join(map(str, x))).coalesce(1).saveAsTextFile(\"eot08-domains.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df12.registerTempTable(\"eot12\")"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sql = \"\"\"\n",
" SELECT SUBSTRING(surt_uri, 0, INSTR(surt_uri, \")\") - 1) AS surt, COUNT(*) AS count\n",
" FROM eot12\n",
" GROUP BY SUBSTRING(surt_uri, 0, INSTR(surt_uri, \")\") - 1)\n",
" ORDER BY count DESC\n",
" \"\"\"\n",
"domains12 = sqlContext.sql(sql)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"domains12.rdd.map(lambda x: \"\\t\".join(map(str, x))).coalesce(1).saveAsTextFile(\"eot12-domains.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"141090"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"domains08.count()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"domains08 = domains08.withColumnRenamed(\"surt\", \"surt08\").withColumnRenamed(\"count\", \"count08\")"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"353280"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"domains12.count()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"domains12 = domains12.withColumnRenamed(\"surt\", \"surt12\").withColumnRenamed(\"count\", \"count12\")"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"domains_combined = domains08.join(domains12, domains08.surt08 == domains12.surt12, 'outer')"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[('surt08', 'string'),\n",
" ('count08', 'bigint'),\n",
" ('surt12', 'string'),\n",
" ('count12', 'bigint')]"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"domains_combined.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"domains_combined.rdd.map(lambda x: \"\\t\".join(map(str, x))).coalesce(1).saveAsTextFile(\"combined-domains.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"444186"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"domains_combined.count()"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def either(a, b):\n",
" if a:\n",
" return a\n",
" return b\n",
"\n",
"udf_either = udf(either, StringType())"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"domains_combined = domains_combined.withColumn(\"surt\", udf_either(domains_combined[\"surt08\"], domains_combined[\"surt12\"]))"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[Row(surt08=None, count08=None, surt12='100,99,67,134', count12=31, surt='100,99,67,134'),\n",
" Row(surt08=None, count08=None, surt12='118,72,133,174', count12=13, surt='118,72,133,174'),\n",
" Row(surt08='2,94,223,66', count08=31, surt12='2,94,223,66', count12=37, surt='2,94,223,66'),\n",
" Row(surt08=None, count08=None, surt12='218,120,100,94', count12=3, surt='218,120,100,94'),\n",
" Row(surt08=None, count08=None, surt12='249,58,254,173', count12=3, surt='249,58,254,173'),\n",
" Row(surt08='58,78,12,76', count08=2, surt12=None, count12=None, surt='58,78,12,76'),\n",
" Row(surt08=None, count08=None, surt12='67,254,207,130:7123', count12=2, surt='67,254,207,130:7123'),\n",
" Row(surt08=None, count08=None, surt12='af,afghanistan,cdn', count12=38, surt='af,afghanistan,cdn'),\n",
" Row(surt08='am,circle', count08=50, surt12='am,circle', count12=62, surt='am,circle'),\n",
" Row(surt08=None, count08=None, surt12='ar,com,latinvia', count12=9, surt='ar,com,latinvia'),\n",
" Row(surt08=None, count08=None, surt12='ar,com,tutoloquequieras,com,flickr', count12=2, surt='ar,com,tutoloquequieras,com,flickr'),\n",
" Row(surt08='at,ac,tuwien,atp,magnet', count08=15, surt12=None, count12=None, surt='at,ac,tuwien,atp,magnet'),\n",
" Row(surt08=None, count08=None, surt12='at,oewabox,ichkoche', count12=5, surt='at,oewabox,ichkoche'),\n",
" Row(surt08=None, count08=None, surt12='at,pressreleases', count12=32, surt='at,pressreleases'),\n",
" Row(surt08=None, count08=None, surt12='au,com,fairfax', count12=2, surt='au,com,fairfax'),\n",
" Row(surt08=None, count08=None, surt12='au,com,fishpond', count12=108, surt='au,com,fishpond'),\n",
" Row(surt08=None, count08=None, surt12='au,com,google,books,bks6', count12=55, surt='au,com,google,books,bks6'),\n",
" Row(surt08=None, count08=None, surt12='au,com,lgnews,staging', count12=7, surt='au,com,lgnews,staging'),\n",
" Row(surt08=None, count08=None, surt12='au,com,lwt', count12=5, surt='au,com,lwt'),\n",
" Row(surt08='au,com,milkwoodpermaculture', count08=3, surt12=None, count12=None, surt='au,com,milkwoodpermaculture')]"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"domains_combined.take(20)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"domains_combined = domains_combined.drop(\"surt08\").drop(\"surt12\")"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"domains_combined = domains_combined.na.fill(0)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"domains_combined.rdd.map(lambda x: \"\\t\".join(map(str, x))).coalesce(1).saveAsTextFile(\"domains-combined.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| cc0-1.0 |
kcarnold/clubdl | notebooks/NN-partes.ipynb | 1 | 141006 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Softmax"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def softmax(scores):\n",
" # ???"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEACAYAAABVtcpZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEJCkhhkCktQwQ0CSGmYISFLD/g9RM+rVd+ZYZgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115dcbb38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"a = np.array([2., -1, 1])\n",
"plt.stem(a)\n",
"plt.xlim([-1, len(a)]);"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Softmax probabilities sum to 1.0\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115e38358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"a2 = softmax(a)\n",
"plt.stem(a2)\n",
"plt.xlim([-1, len(a)])\n",
"print(\"Softmax probabilities sum to\", np.sum(a2))"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Softmax probabilities sum to 1.0\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAE2tJREFUeJzt3X+s3fd91/HnywkZq/tjq8oyckOM5awtmYq6ormZWqmn\nZFlvEcwRTODU3eYiWIRIKEJozsQsX8uTaKQiMRrGsDB4mwYZWiF1KaU2Wo9Q1aXx2vwqtWPXNSax\n044tdKhuAdd+88c5SQ439/oc3/u995z78fMhXfn743O+562Pv/d1vvfz/X7PN1WFJKlNm6ZdgCRp\n7RjyktQwQ16SGmbIS1LDDHlJapghL0kNmyjkk8wnOZnkVJI9S6x/fZIjSZ5M8kyS3Z1XKkm6Zhl3\nnXySTcAp4C7gAnAc2FlVJ0fa/ALw+qr6hSRvAp4Fbq6q765Z5ZKksSY5kt8OnK6qc1V1CXgE2LGo\nTQGvG06/DvhDA16Spm+SkJ8DnhuZf364bNTDwB1JLgBPAR/upjxJ0mp0deL1fcATVXUL8CPAP03y\n2o62LUlaoRsnaHMeuG1k/tbhslEfAv4hQFWdSXIWeCvwe6ONkvhFOZK0AlWVlbxukiP548DtSbYk\nuQnYCRxZ1OYc8OMASW4G3gx8bZlCZ/5n3759U6/BOq1zo9Zond3/rMbYI/mqupzkfuAogw+FQ1V1\nIsl9g9V1EPgl4HCSp4cv+/mqenFVlUmSVm2S4Rqq6j8Bb1m07J+PTL/AYFxekjRDvON1Cb1eb9ol\nTMQ6u7UR6twINYJ1zpKxN0N1+mZJref7SVILklBreOJVkrRBGfKS1DBDXpIaZshLUsMMeUlqmCEv\nSQ0z5CWpYYa8JDXMkJekhhnyktQwQ16SGmbIS1LDDHlJapghL0kNM+QlqWGGvCQ1zJCXpIZNFPJJ\n5pOcTHIqyZ4l1v/9JE8k+VKSZ5J8N8n3dV+uAM6ePccHP7if9753Hx/84H7Onj037ZIkzaixj/9L\nsgk4BdwFXACOAzur6uQy7f8i8Her6seXWOfj/1bp7Nlz3H33xzhzZj+wGbjItm37OHbsAbZu3TLt\n8iStgbV+/N924HRVnauqS8AjwI6rtL8X+DcrKUbj7d17eCTgATZz5sx+9u49PMWqJM2qSUJ+Dnhu\nZP754bJXSfK9wDzw8dWXpqWcP3+FVwL+JZu5cOHKNMqRNOO6PvH6l4DPVdU3O96uhubmNgEXFy29\nyC23eA5d0qvdOEGb88BtI/O3DpctZSdjhmoWFhZenu71evR6vQlK0EsOHNjNY4/te9WY/IEDD0y5\nMkld6ff79Pv9TrY1yYnXG4BnGZx4fQF4HLi3qk4savcG4GvArVX1nWW25YnXDpw9e469ew/zm7+5\nj1279nPgwG5PukoNW82J17EhP3yDeeCXGQzvHKqqjyS5D6iqOjhs87PA+6rqA1fZjiHfoQTsTql9\nax7yXTHku2XIS9eHtb6EUpK0QRnyktQwQ16SGmbIS1LDDHlJapghL0kNM+QlqWGGvCQ1zJCXpIYZ\n8pLUMENekhpmyEtSwwx5SWqYIS9JDTPkJalhhrwkNcyQl6SGGfKS1DBDXpIaNlHIJ5lPcjLJqSR7\nlmnTS/JEki8n+Wy3ZUqSVmLsg7yTbAJOAXcBF4DjwM6qOjnS5g3A54GfqKrzSd5UVX+wxLZ8kHeH\nfJC3dH1Y6wd5bwdOV9W5qroEPALsWNTmA8DHq+o8wFIBL0laf5OE/Bzw3Mj888Nlo94MvDHJZ5Mc\nT/LTXRUoSVq5GzvczjuAPw9sBn43ye9W1VcXN1xYWHh5utfr0ev1OipBktrQ7/fp9/udbGuSMfk7\ngYWqmh/OPwhUVT000mYP8Merav9w/l8An66qjy/almPyHXJMXro+rPWY/HHg9iRbktwE7ASOLGrz\nCeDdSW5I8hrgncCJlRQkSerO2OGaqrqc5H7gKIMPhUNVdSLJfYPVdbCqTib5DPA0cBk4WFVfWdPK\nJUljjR2u6fTNHK7plMM10vVhrYdrJEkblCEvSQ0z5CWpYYa8JDXMkJekhhnyktQwQ16SGmbIS1LD\nDHlJapghL0kNM+QlqWGGvCQ1zJCXpIYZ8pLUMENekhpmyEtSwwx5SWqYIS9JDTPkJalhE4V8kvkk\nJ5OcSrJnifXvSfLNJF8a/vxi96VKkq7VjeMaJNkEPAzcBVwAjif5RFWdXNT0v1TVT65BjZKkFZrk\nSH47cLqqzlXVJeARYMcS7Vb0JHFJ0tqZJOTngOdG5p8fLlvsx5I8meRTSe7opDpJ0qqMHa6Z0BeB\n26rq20neDzwKvHmphgsLCy9P93o9er1eRyVIUhv6/T79fr+TbaWqrt4guRNYqKr54fyDQFXVQ1d5\nzVngz1XVi4uW17j30+QSsDul9iWhqlY0JD7JcM1x4PYkW5LcBOwEjiwq4OaR6e0MPjxeRJI0VWOH\na6rqcpL7gaMMPhQOVdWJJPcNVtdB4KeS/C3gEvAd4K+tZdGSpMmMHa7p9M0crumUwzXS9WGth2sk\nSRuUIS9JDTPkJalhhrwkNcyQl6SGGfKS1DBDXpIaZshLUsMMeUlqmCEvSQ0z5CWpYYa8JDXMkJek\nhhnyktQwQ16SGmbIS1LDDHlJapghL0kNM+QlqWEThXyS+SQnk5xKsucq7X40yaUkf7m7EiVJKzU2\n5JNsAh4G3gf8MHBvkrcu0+4jwGe6LlKStDKTHMlvB05X1bmqugQ8AuxYot0DwG8Dv99hfZKkVZgk\n5OeA50bmnx8ue1mSW4B7quqfAemuPEnSatzY0Xb+MTA6Vr9s0C8sLLw83ev16PV6HZUgSW3o9/v0\n+/1OtpWqunqD5E5goarmh/MPAlVVD420+dpLk8CbgIvAz1XVkUXbqnHvp8klYHdK7UtCVa1olGSS\nkL8BeBa4C3gBeBy4t6pOLNP+XwGfrKp/t8Q6Q75Dhrx0fVhNyI8drqmqy0nuB44yGMM/VFUnktw3\nWF0HF79kJYVIkro39ki+0zfzSL5THslL14fVHMl7x6skNcyQl6SGGfKS1DBDXpIaZshLUsMMeUlq\nmCEvSQ0z5CWpYYa8JDXMkJekhhnyktQwQ16SGmbIS1LDDHlJapghL0kNM+QlqWGGvCQ1zJCXpIYZ\n8pLUsIlCPsl8kpNJTiXZs8T6n0zyVJInkjye5F3dlypJulZjH+SdZBNwCrgLuAAcB3ZW1cmRNq+p\nqm8Pp98G/Nuq+jNLbMsHeXfIB3lL14e1fpD3duB0VZ2rqkvAI8CO0QYvBfzQa4ErKylGktStSUJ+\nDnhuZP754bL/T5J7kpwAPgn89W7KkyStxo1dbaiqHgUeTfJu4JeAu5dqt7Cw8PJ0r9ej1+t1VYIk\nNaHf79Pv9zvZ1iRj8ncCC1U1P5x/EKiqeugqrzkD/GhVvbhouWPyHXJMXro+rPWY/HHg9iRbktwE\n7ASOLCpg28j0O4CbFge8JGn9jR2uqarLSe4HjjL4UDhUVSeS3DdYXQeBv5LkZ4D/C3wH+KtrWbQk\naTJjh2s6fTOHazrlcI10fVjr4RpJ0gZlyEtSwwx5SWpYZ9fJS9LZs+fYu/cw589fYW5uEwcO7Gbr\n1i3TLuu65onXDcwTr5olZ8+e4+67P8aZM/uBzcBFtm3bx7FjDxj0q+SJV0lTt3fv4ZGAB9jMmTP7\n2bv38BSrkiEvqRPnz1/hlYB/yWYuXPD7CqfJkJfUibm5TcDFRUsvcsstxsw02fuSOnHgwG62bdvH\nK0E/GJM/cGD31GqSIS+pI1u3buHYsQfYteujAOza9VFPus4Ar67ZwLy6RrPKfbNbXl0jSVqSIS9J\nDTPkJalhhrwkNcyQl6SGGfKS1DBDXpIaZshLUsMmCvkk80lOJjmVZM8S6z+Q5Knhz+eSvK37UiVJ\n12rsHa9JNgGngLuAC8BxYGdVnRxpcydwoqr+KMk8sFBVdy6xLe947ZB3FWpWuW92a63veN0OnK6q\nc1V1CXgE2DHaoKoeq6o/Gs4+BsytpBhJUrcmCfk54LmR+ee5eoj/DeDTqylKktSNTp/xmuS9wIeA\ndy/XZmFh4eXpXq9Hr9frsgRJ2vD6/T79fr+TbU0yJn8ngzH2+eH8g0BV1UOL2v1Z4OPAfFWdWWZb\njsl3yHFPzSr3zW6t9Zj8ceD2JFuS3ATsBI4sKuA2BgH/08sFvCRp/Y0drqmqy0nuB44y+FA4VFUn\nktw3WF0Hgb3AG4FfSRLgUlVtX8vCJUnj+dCQDcw/iTWr3De75UNDJElLMuQlqWGGvCQ1zJCXpIYZ\n8pLUMENekhpmyEtSwwx5SWqYIS9JDTPkJalhhrwkNcyQl6SGGfKS1DBDXpIaZshLUsMMeUlqmCEv\nSQ0z5CWpYROFfJL5JCeTnEqyZ4n1b0ny+ST/O8nf675MSdJKjH2Qd5JNwMPAXcAF4HiST1TVyZFm\nfwg8ANyzJlVKklZkkiP57cDpqjpXVZeAR4Adow2q6g+q6ovAd9egRknSCk0S8nPAcyPzzw+XSZJm\nnCdeJalhY8fkgfPAbSPztw6XrcjCwsLL071ej16vt9JNSVKT+v0+/X6/k22lqq7eILkBeJbBidcX\ngMeBe6vqxBJt9wHfqqp/tMy2atz7aXIJ2J2aRe6b3UpCVWUlrx17JF9Vl5PcDxxlMLxzqKpOJLlv\nsLoOJrkZ+D3gdcCVJB8G7qiqb62kKElSN8YeyXf6Zh7Jd8qjJc0q981ureZI3hOvktQwQ16SGmbI\nS1LDDHlJapghL0kNM+QlqWGGvCQ1zJCXpIYZ8pLUMENekhpmyEtSwyb5qmGpaWfPnmPv3sOcP3+F\nublNHDiwm61bt0y7LKkTfkHZBuaXQK3e2bPnuPvuj3HmzH5gM3CRbdv2cezYAwb9KrhvdssvKJNW\naO/ewyMBD7CZM2f2s3fv4SlWJXXHkNd17fz5K7wS8C/ZzIULV6ZRjtQ5Q17Xtbm5TcDFRUsvcsst\n/mqoDe7Juq4dOLCbbdv28UrQD8bkDxzYPbWapC4Z8rqubd26hWPHHmDXro8CsGvXRz3pqqZ4dc0G\n5hUM3bI/u2NfdmvNr65JMp/kZJJTSfYs0+afJDmd5Mkkb19JMZKkbo0N+SSbgIeB9wE/DNyb5K2L\n2rwf2FZVPwTcB/zqGtS6bvr9/rRLmFB/2gVMxP7sjn3ZrY3Tnys3yZH8duB0VZ2rqkvAI8CORW12\nAL8OUFVfAN6Q5OZOK11HG+c/vj/tAiZif3bHvuzWxunPlZsk5OeA50bmnx8uu1qb80u0kSStM6+u\nkaSGjb26JsmdwEJVzQ/nHwSqqh4aafOrwGer6reG8yeB91TVNxZty/PtkrQCK726ZpJvoTwO3J5k\nC/ACsBO4d1GbI8DfBn5r+KHwzcUBv5oiJUkrMzbkq+pykvuBowyGdw5V1Ykk9w1W18Gq+o9J/kKS\nrzK4dfBDa1u2JGkS63ozlCRpfa3pidckP5Xky0kuJ3nHVdqNvdlqLSX5/iRHkzyb5DNJ3rBMu/+W\n5KkkTyR5fB3rm/mb0cbVmOQ9Sb6Z5EvDn19c7xqHdRxK8o0kT1+lzdRv7BtX5yz0Z5Jbk/xOkv+a\n5Jkkf2eZdtPeN8fWOSP9+T1JvjDMl2eS7Fum3bX1Z1Wt2Q/wFuCHgN8B3rFMm03AV4EtwB8DngTe\nupZ1LVHDQ8DPD6f3AB9Zpt3XgO9f59rG9g/wfuBTw+l3Ao/NYI3vAY6sZ13L1Ppu4O3A08usn2pf\nXkOdU+9P4AeBtw+nXws8O2v75jXUOfX+HNbxmuG/NwCPAdtX259reiRfVc9W1WngaidcJ7nZaq3t\nAH5tOP1rwD3LtAvrf9npRrgZbdL/w6mfeK+qzwH/8ypNpt2XDN97XJ0w5f6sqq9X1ZPD6W8BJ3j1\n/TFT788J64TZ2D+/PZz8HgbnTBePp19zf87CdfKT3Gy11n6ghlcDVdXXgR9Ypl0Bx5IcT/I316m2\njXAz2qT/hz82/BPzU0nuWJ/Srtm0+/JazEx/JvnTDP7y+MKiVTPVn1epE2agP5NsSvIE8HXgWFUd\nX9Tkmvtz1Q/yTnIMGP0kCYMw/AdV9cnVbr8rV6lzqbG35c5Gv6uqXkjyJxiE/YnhEZfG+yJwW1V9\ne/hdR48Cb55yTRvZzPRnktcCvw18eHikPJPG1DkT/VlVV4AfSfJ64NEkd1TVV1azzVWHfFXdvcpN\nnAduG5m/dbisU1erc3iC6+aq+kaSHwR+f5ltvDD8938k+fcMhinWOuQn6Z/zwJ8a02Ytja1x9Jeq\nqj6d5FeSvLGqXlynGic17b6cyKz0Z5IbGQTnb1TVJ5ZoMhP9Oa7OWenPkRr+V5LPAvPAaMhfc3+u\n53DNcuNdL99sleQmBjdbHVm/smD4fruH0z8LvGonSPKa4ZEASTYDPwF8eR1qm6R/jgA/M6xt2ZvR\nplnj6Lhhku0MLt+dVsCH5ffHafflqGXrnKH+/JfAV6rql5dZPyv9edU6Z6E/k7zppSv7knwvcDdw\nclGza+/PNT5TfA+D8aPvMLhb9tPD5X8S+A8j7eYZnPE+DTw4hTPabwT+87CGo8D3La4T2MrgqpEn\ngGfWs86l+ofBVzr/3Eibhxlc4fIUy1zJNM0aGdwR/eVh/30eeOd61zis418DF4D/A/x3BjfuzVRf\nTlLnLPQn8C7g8sjvxZeG+8FM9eckdc5If75tWNuTwNMMhrxX/bvuzVCS1LBZuLpGkrRGDHlJapgh\nL0kNM+QlqWGGvCQ1zJCXpIYZ8pLUMENekhr2/wDB7YYhkHNgCQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115e38358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"a2 = softmax(a)\n",
"plt.stem(a2)\n",
"plt.xlim([-1, len(a)])\n",
"print(\"Softmax probabilities sum to\", np.sum(a2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Dense (similarities)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.90453403, 0.30151134, 0.30151134],\n",
" [ 0.30151134, 0.90453403, 0.30151134],\n",
" [ 0.30151134, 0.30151134, 0.90453403]])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prototypes_raw = np.array([\n",
" [3, 1, 1],\n",
" [1, 3, 1],\n",
" [1, 1, 3]], dtype=float)\n",
"prototype_magnitudes = np.linalg.norm(prototypes_raw, axis=1, keepdims=True)\n",
"prototypes = prototypes_raw / prototype_magnitudes\n",
"prototypes"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x1083806a0>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAPcAAAD7CAYAAAC2TgIoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10810dba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.matshow(prototypes)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 3.61813613, 3.01511345, 2.41209076])"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def similarities(vec, prototypes):\n",
" # ???\n",
"# similarities([1, 2, 3], prototypes)\n",
"similarities([3, 2, 1], prototypes)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.16213016, 0.29631473, 0.54155511])"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"softmax(similarities([1, 2, 3], prototypes))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Nonlinearity"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1083080b8>]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5AGgw/4PcThtjk9TpLIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1081add68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def relu(vector):\n",
" # ???\n",
"x = np.linspace(-2, 2, 100)\n",
"plt.plot(x, relu(x))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x10825def0>]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEACAYAAABVtcpZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nksP+HxbxqEw80EipAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10830f0f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def tanh(vector):\n",
" # ???\n",
"plt.plot(x, tanh(x));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Convolution"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from scipy.misc import imsave, fromimage, toimage\n",
"from PIL import Image, ImageOps"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"DATA = '../data/'\n",
"WIDTH = HEIGHT = 224\n",
"def load_and_crop_image(filename, target_size):\n",
" return ImageOps.fit(Image.open(filename), target_size)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"gatos = np.array([fromimage(load_and_crop_image(DATA+'/gatos/cat.{:04}.jpg'.format(i), (WIDTH, HEIGHT))) for i in range(1,100)])\n",
"gatos_nocolor = np.mean(gatos, axis=3)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAOAAAADgCAAAAAA/RjU9AACNvElEQVR4nGz9aZDkWXIfiLn7e/8z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\nJeSVQroSBMfKafw/x0vYifkyWYkAAAAASUVORK5CYII=\n",
"text/plain": [
"<PIL.Image.Image image mode=L size=224x224 at 0x10EEC5EF0>"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img = gatos_nocolor[0]\n",
"toimage(img)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import scipy.signal"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cambia `filt` hasta que la imagen parece como lo siguiente"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"filt = np.array([\n",
" [0, 0, 0],\n",
" [0, 1, 0],\n",
" [0, 0, 0]\n",
" ])\n",
"convolved = scipy.signal.convolve2d(img, filt)\n",
"toimage(convolved)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAOIAAADiCAAAAAB2e0QLAAB8BklEQVR4nGz9x5Jly5IliCkxs80O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\ngg==\n",
"text/plain": [
"<PIL.Image.Image image mode=L size=226x226 at 0x10EED3780>"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
RaspberryJamBe/ipython-notebooks | notebooks/en-gb/Communication - Cloud message 1 - Send message.ipynb | 2 | 1643 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"APPKEY is the Application Key for a (free) http://www.realtime.co/ \"Realtime Messaging Free\" subscription. \n",
"See \"[104 - Remote deurbel - Een cloud API gebruiken om berichten te sturen](104%20-%20Remote%20door%20bell%20-%20Using%20a%20cloud%20API%20to%20send%20messages.ipynb)\" voor meer gedetailleerde info."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"APPKEY = \"******\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import ortc\n",
"oc = ortc.OrtcClient()\n",
"oc.cluster_url = \"http://ortc-developers.realtime.co/server/2.1\"\n",
"oc.connect(APPKEY)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"oc.send(\"doorbell\", \"Here I am!\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.8"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| cc0-1.0 |
jdhp-docs/python_notebooks | nb_dev_python/python_scipy_integrate.ipynb | 2 | 3081 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import scipy.integrate"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- https://docs.scipy.org/doc/scipy-1.3.0/reference/tutorial/integrate.html\n",
"- https://docs.scipy.org/doc/scipy-1.3.0/reference/integrate.html"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Integrating functions, given callable object (scipy.integrate.quad)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"See:\n",
"- https://docs.scipy.org/doc/scipy-1.3.0/reference/tutorial/integrate.html#general-integration-quad\n",
"- https://docs.scipy.org/doc/scipy-1.3.0/reference/generated/scipy.integrate.quad.html#scipy.integrate.quad\n",
"\n",
"Example:\n",
"\n",
"$$I = \\int_{0}^{3} x^2 dx = \\frac{1}{3} 3^3 = 9$$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"f = lambda x: np.power(x, 2)\n",
"\n",
"result = scipy.integrate.quad(f, 0, 3)\n",
"result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The return value is a tuple, with the first element holding the estimated value of the integral and the second element holding an upper bound on the error."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Integrating functions, given fixed samples"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://docs.scipy.org/doc/scipy-1.3.0/reference/tutorial/integrate.html#integrating-using-samples"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x = np.linspace(0., 3., 100)\n",
"y = f(x)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.plot(x, y);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In case of arbitrary spaced samples, the two functions trapz and simps are available."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"result = scipy.integrate.simps(y, x)\n",
"result"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"result = scipy.integrate.trapz(y, x)\n",
"result"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
yingjun2/project-spring2017 | part1/bin/Q1+suppliment01.ipynb | 1 | 6932 | {
"cells": [
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import plotly\n",
"import plotly.plotly as py\n",
"from plotly.graph_objs import *\n",
"import pandas as pd\n",
"import math\n",
"from IPython.display import Image\n",
"import time\n",
"\n",
"plotly.tools.set_credentials_file(username='xjiang36', api_key='uZyWsdSH3xd9bxUefIFf')"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:9: SettingWithCopyWarning:\n",
"\n",
"\n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"\n",
"/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:18: SettingWithCopyWarning:\n",
"\n",
"\n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"\n"
]
},
{
"data": {
"text/html": [
"<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~xjiang36/8.embed\" height=\"525px\" width=\"100%\"></iframe>"
],
"text/plain": [
"<plotly.tools.PlotlyDisplay object>"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfroutes = pd.read_csv(\"routes.txt\",encoding='iso-8859-1')\n",
"dftrips = pd.read_csv(\"trips.txt\",encoding='iso-8859-1')\n",
"routeclean=dftrips[\"route_id\"].value_counts().reset_index().rename(columns={'index': 'x'})\n",
"def Nameclean(dataset,a):\n",
" wordlist=[\"SILVER\",\"ILLINI\",\"TEAL\",\"YELLOW\",\"GREEN\",\"BROWN\",\"GREY\",\"GOLD\",\"LIME\",\"BLUE\",\"RED\",\"BROWN\",\"BRONZE\",\"ORANGE\",\"LAVENDER\",\"RUBY\"]\n",
" for j in range(len(wordlist)):\n",
" for i in range(len(dataset)):\n",
" if dataset[a][i].find(wordlist[j])>=0:\n",
" dataset[a][i]=wordlist[j]\n",
"Nameclean(routeclean,\"x\")\n",
"sumroute=routeclean[:18]\n",
"cleanedroute=routeclean[\"x\"].value_counts().reset_index().rename(columns={'index': 'name'})\n",
"for j in range(len(cleanedroute[\"name\"])):\n",
" rsum=0\n",
" for i in range(len(routeclean)):\n",
" if routeclean[\"x\"][i]==cleanedroute[\"name\"][j]:\n",
" rsum+=routeclean[\"route_id\"][i]\n",
" cleanedroute[\"x\"][j]=rsum\n",
"colorbar0=[]\n",
"for i in range(len(cleanedroute['name'])):\n",
" for j in range(len(dfroutes['route_id'])):\n",
" if cleanedroute['name'][i]==dfroutes['route_id'][j]:\n",
" colorbar0.append(\"#%s\"%dfroutes['route_color'][j])\n",
" break\n",
"import plotly.plotly as py\n",
"import plotly.graph_objs as go\n",
"\n",
"trace0 = go.Bar(\n",
" x=cleanedroute[\"name\"],\n",
" y=cleanedroute[\"x\"],\n",
" marker=dict(\n",
" #color=['#66FF66','#FFFF66','#E0E0E0','','#666600','#A0A0A0','#FF6666','#B266FF','#CCCC00','#663300','#FFFF99','#FF9933','#FF0000','#66FFFF','#0000FF','#FF66B2','#000066','#330000']),\n",
" color=colorbar0),\n",
")\n",
"\n",
"data = [trace0]\n",
"layout = go.Layout(\n",
" title='Buses on each route',\n",
")\n",
"\n",
"fig = go.Figure(data=data, layout=layout)\n",
"py.iplot(fig, filename='color-bar')"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:9: SettingWithCopyWarning:\n",
"\n",
"\n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"\n"
]
},
{
"data": {
"text/html": [
"<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~xjiang36/8.embed\" height=\"525px\" width=\"100%\"></iframe>"
],
"text/plain": [
"<plotly.tools.PlotlyDisplay object>"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfroutes = pd.read_csv(\"routes.txt\",encoding='iso-8859-1')\n",
"dftrips = pd.read_csv(\"trips.txt\",encoding='iso-8859-1')\n",
"routeclean=dftrips[\"route_id\"].value_counts().reset_index().rename(columns={'index': 'x'})\n",
"def Nameclean(dataset,a):\n",
" wordlist=[\"SILVER\",\"ILLINI\",\"TEAL\",\"YELLOW\",\"GREEN\",\"BROWN\",\"GREY\",\"GOLD\",\"LIME\",\"BLUE\",\"RED\",\"BROWN\",\"BRONZE\",\"ORANGE\",\"LAVENDER\",\"RUBY\"]\n",
" for j in range(len(wordlist)):\n",
" for i in range(len(dataset)):\n",
" if dataset[a][i].find(wordlist[j])>=0:\n",
" dataset[a][i]=wordlist[j]\n",
"Nameclean(routeclean,\"x\")\n",
"sumroute=routeclean[:18]\n",
"cleanedroute=routeclean[\"x\"].value_counts().reset_index().rename(columns={'index': 'name'})\n",
"trace0 = go.Bar(\n",
" x=cleanedroute[\"name\"],\n",
" y=cleanedroute[\"x\"],\n",
" marker=dict(\n",
" #color=['#66FF66','#FFFF66','#E0E0E0','','#666600','#A0A0A0','#FF6666','#B266FF','#CCCC00','#663300','#FFFF99','#FF9933','#FF0000','#66FFFF','#0000FF','#FF66B2','#000066','#330000']),\n",
" color=colorbar0),\n",
")\n",
"\n",
"data = [trace0]\n",
"layout = go.Layout(\n",
" title='Distribution of each route by color',\n",
")\n",
"\n",
"fig = go.Figure(data=data, layout=layout)\n",
"py.iplot(fig, filename='color-bar')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| bsd-3-clause |
CNS-OIST/STEPS_Example | other_tutorials/OCNC2017/.ipynb_checkpoints/OCNC2017 STEPS tutorial execises-checkpoint.ipynb | 1 | 174959 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# OCNC2017 STEPS Tutorial Execise"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this notebook we will try to create a STEPS simulation script from scratch by modifying the examples given in the tutorial. Please follow the tutor's instruction step by step."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build a biochemical model\n",
"\n",
"Here is the example of a 2nd order reaction $A+B\\overset{k}{\\rightarrow}C$, where the reaction constant $k$ is set to 200 /uM.s:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Import biochemical model module\n",
"import steps.model as smod\n",
"\n",
"# Create model container\n",
"mdl = smod.Model()\n",
"\n",
"# Create chemical species\n",
"A = smod.Spec('A', mdl)\n",
"B = smod.Spec('B', mdl)\n",
"C = smod.Spec('C', mdl)\n",
"\n",
"# Create reaction set container\n",
"vsys = smod.Volsys('vsys', mdl)\n",
"\n",
"# Create reaction\n",
"# A + B - > C with rate 200 /uM.s\n",
"reac_f = smod.Reac('reac_f', vsys, lhs=[A,B], rhs = [C])\n",
"reac_f.setKcst(200e6)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For complex model, we can break it down into elementary reactions, for example, the following model\n",
"\n",
"$E+S\\underset{k_{-1}}{\\overset{k_{1}}{\\rightleftarrows}}ES\\overset{k_{2}}{\\rightarrow}E+P$\n",
"\n",
"is broken down into 3 reactions in STEPS\n",
"\n",
"1: $E+S\\overset{k_{1}}{\\rightarrow}ES$\n",
"\n",
"2: $ES\\overset{k_{-1}}{\\rightarrow}E+S$\n",
"\n",
"3: $ES\\overset{k_{2}}{\\rightarrow}E+P$\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Execise 1: Create a kinase reaction model in STEPS\n",
"\n",
"Modify the script below for this kinase reaction system:\n",
"\n",
"$MEKp+ERK\\underset{0.6}{\\overset{16.2*10^{6}}{\\rightleftarrows}}MEKpERK\\overset{0.15}{\\rightarrow}MEKp+ERKp$\n",
"\n",
"**Hint**: Break it down in to these elementary reactions\n",
"\n",
"1: $MEKp+ERK\\overset{16.2*10^{6}}{\\rightarrow}MEKpERK$\n",
"\n",
"2: $MEKpERK\\overset{0.6}{\\rightarrow}MEKp+ERK$\n",
"\n",
"3: $MEKpERK\\overset{0.15}{\\rightarrow}MEKp+ERKp$"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Import biochemical model module\n",
"import steps.model as smod\n",
"\n",
"# Create model container\n",
"execise_mdl = smod.Model()\n",
"\n",
"# Create chemical species\n",
"MEKp = smod.Spec('MEKp', execise_mdl)\n",
"ERK = smod.Spec('ERK', execise_mdl)\n",
"MEKpERK = smod.Spec('MEKpERK', execise_mdl)\n",
"ERKp = smod.Spec('ERKp', execise_mdl)\n",
"\n",
"# Create reaction set container (volume system)\n",
"execise_vsys = smod.Volsys('execise_vsys', execise_mdl)\n",
"\n",
"# Create reactions (Do it yourself)\n",
"# MEKp + ERK -> MEKpERK, rate constant 16.2*10e6\n",
"\n",
"# MEKpERK -> MEKp + ERK, rate constant 0.6\n",
"\n",
"# MEKpERK -> MEKp + ERKp, rate constant 0.15\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup geometry\n",
"\n",
"You can easily setup the geometry of a well-mixed model by providing the volume of the geometry as well as the voume system it associated with."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Import geometry module\n",
"import steps.geom as sgeom\n",
"\n",
"# Create well-mixed geometry container\n",
"wmgeom = sgeom.Geom()\n",
"\n",
"# Create cytosol compartment\n",
"cyt = sgeom.Comp('cyt', wmgeom)\n",
"\n",
"# Give volume to cyt (1um^3)\n",
"cyt.setVol(1.0e-18)\n",
"\n",
"# Assign reaction set to compartment\n",
"cyt.addVolsys('vsys')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create a random number generator\n",
"\n",
"You can use the follow code to create a random number generator for the simulation, currently available generators are \"mt19937\" and \"r123\"."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Import random number generator module\n",
"import steps.rng as srng\n",
"\n",
"# Create random number generator, with buffer size as 256\n",
"r = srng.create('mt19937', 256)\n",
"\n",
"# Initialise with some seed\n",
"r.initialize(899)\n",
"\n",
"# Could use time to get random seed\n",
"#import time\n",
"#r.initialize(int(time.time()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Execise 2: Create the geometry and random number generator for the kinase reaction model\n",
" \n",
"Let's continue our kinase reaction simulation script, here are the tasks:\n",
"1. Create a compartment of $0.1um^{3}$ (note that STEPS uses S.I units)\n",
"2. Associate the compartment with the volume system we've previously created\n",
"3. Create a \"r123\" random number generator and initialize it with with some seed"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Import biochemical model module\n",
"import steps.model as smod\n",
"\n",
"# Create model container\n",
"execise_mdl = smod.Model()\n",
"\n",
"# Create chemical species\n",
"MEKp = smod.Spec('MEKp', execise_mdl)\n",
"ERK = smod.Spec('ERK', execise_mdl)\n",
"MEKpERK = smod.Spec('MEKpERK', execise_mdl)\n",
"ERKp = smod.Spec('ERKp', execise_mdl)\n",
"\n",
"# Create reaction set container (volume system)\n",
"execise_vsys = smod.Volsys('execise_vsys', execise_mdl)\n",
"\n",
"# Create reactions (Do it yourself)\n",
"# MEKp + ERK -> MEKpERK, rate constant 16.2*10e6\n",
"MEKp_ERK_to_MEKpERK = smod.Reac('MEKp_ERK_to_MEKpERK', execise_vsys, lhs=[MEKp,ERK], rhs = [MEKpERK])\n",
"MEKp_ERK_to_MEKpERK.setKcst(16.2e6)\n",
"# MEKpERK -> MEKp + ERK, rate constant 0.6\n",
"MEKpERK_to_MEKp_ERK = smod.Reac('MEKpERK_to_MEKp_ERK', execise_vsys, lhs = [MEKpERK], rhs=[MEKp,ERK])\n",
"MEKpERK_to_MEKp_ERK.setKcst(0.6)\n",
"# MEKpERK -> MEKp + ERKp, rate constant 0.15\n",
"MEKpERK_to_MEKp_ERKp = smod.Reac('MEKpERK_to_MEKp_ERKp', execise_vsys, lhs = [MEKpERK], rhs=[MEKp,ERKp])\n",
"MEKpERK_to_MEKp_ERKp.setKcst(0.15)\n",
"\n",
"####### You script after execise 1 should look like above #######\n",
"\n",
"# Create a compartment of 0.1um^3\n",
"\n",
"# Associate the compartment with the volume system 'vsys'\n",
"\n",
"# Create and initialize a 'r123' random number generator\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create and initialize a solver\n",
"\n",
"For well-mixed simulation, we create a \"wmdirect\" solver and initialize it by adding molecules to the compartment. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Import solver module\n",
"import steps.solver as ssolv\n",
"\n",
"# Create Well-mixed Direct solver\n",
"sim_direct = ssolv.Wmdirect(mdl, wmgeom, r)\n",
"\n",
"# Inject 10 ‘A’ molecules\n",
"sim_direct.setCompCount('cyt','A', 10)\n",
"\n",
"# Set concentration of ‘B’ molecules\n",
"sim_direct.setCompConc('cyt', 'B', 0.0332e-6)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run the solver and gather simulation data\n",
"After that we can run the solver until it reaches a specific time point, say 0.1 second. You can gather simulation data such as molecule counts using many STEPS APIs, for example"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6.0"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Run simulation for 0.1s\n",
"sim_direct.run(0.1)\n",
"\n",
"# Return the number of A molecules\n",
"sim_direct.getCompCount('cyt', 'A')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In practice, it is often necessary to store simulation data in a numpy array or a file for plotting or further analysis. For example, here we record the number of molcules using numpy array."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Reset the solver and reinitizlize molecule counts\n",
"\n",
"sim_direct.reset()\n",
"\n",
"# Inject 10 ‘A’ molecules\n",
"sim_direct.setCompCount('cyt','A', 10)\n",
"\n",
"# Set concentration of ‘B’ molecules\n",
"sim_direct.setCompConc('cyt', 'B', 0.0332e-6)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Import numpy\n",
"import numpy as np\n",
"\n",
"# Create time-point numpy array, starting at time 0, end at 0.5 second and record data every 0.001 second\n",
"tpnt = np.arange(0.0, 0.501, 0.001)\n",
"\n",
"# Calculate number of time points\n",
"n_tpnts = len(tpnt)\n",
"\n",
"# Create data array, initialised with zeros\n",
"res_direct = np.zeros([n_tpnts, 3])\n",
"\n",
"# Run simulation and record data\n",
"for t in range(0, n_tpnts):\n",
" sim_direct.run(tpnt[t])\n",
" res_direct[t,0] = sim_direct.getCompCount('cyt','A')\n",
" res_direct[t,1] = sim_direct.getCompCount('cyt','B')\n",
" res_direct[t,2] = sim_direct.getCompCount('cyt','C')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's check what is inside the array now:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 10. 20. 0.]\n",
" [ 10. 20. 0.]\n",
" [ 10. 20. 0.]\n",
" ..., \n",
" [ 2. 12. 8.]\n",
" [ 2. 12. 8.]\n",
" [ 2. 12. 8.]]\n"
]
}
],
"source": [
"print(res_direct")
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Execise 3: Run your kinase model in STEPS\n",
"Here are the tasks:\n",
"1. Create a \"wmdirect\" solver and set the initial condition:\n",
" * MEKp = 1uM\n",
" * ERK = 1.5uM\n",
"2. Run the simulation for 30 seconds, record concerntrations of each molecule every 0.01 seconds."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Import biochemical model module\n",
"import steps.model as smod\n",
"\n",
"# Create model container\n",
"execise_mdl = smod.Model()\n",
"\n",
"# Create chemical species\n",
"MEKp = smod.Spec('MEKp', execise_mdl)\n",
"ERK = smod.Spec('ERK', execise_mdl)\n",
"MEKpERK = smod.Spec('MEKpERK', execise_mdl)\n",
"ERKp = smod.Spec('ERKp', execise_mdl)\n",
"\n",
"# Create reaction set container (volume system)\n",
"execise_vsys = smod.Volsys('execise_vsys', execise_mdl)\n",
"\n",
"# Create reactions (Do it yourself)\n",
"# MEKp + ERK -> MEKpERK, rate constant 16.2*10e6\n",
"MEKp_ERK_to_MEKpERK = smod.Reac('MEKp_ERK_to_MEKpERK', execise_vsys, lhs=[MEKp,ERK], rhs = [MEKpERK])\n",
"MEKp_ERK_to_MEKpERK.setKcst(16.2e6)\n",
"# MEKpERK -> MEKp + ERK, rate constant 0.6\n",
"MEKpERK_to_MEKp_ERK = smod.Reac('MEKpERK_to_MEKp_ERK', execise_vsys, lhs = [MEKpERK], rhs=[MEKp,ERK])\n",
"MEKpERK_to_MEKp_ERK.setKcst(0.6)\n",
"# MEKpERK -> MEKp + ERKp, rate constant 0.15\n",
"MEKpERK_to_MEKp_ERKp = smod.Reac('MEKpERK_to_MEKp_ERKp', execise_vsys, lhs = [MEKpERK], rhs=[MEKp,ERKp])\n",
"MEKpERK_to_MEKp_ERKp.setKcst(0.15)\n",
"\n",
"####### You script after execise 1 should look like above #######\n",
"\n",
"# Create a compartment of 0.1um^3\n",
"import steps.geom as sgeom\n",
"execise_wmgeom = sgeom.Geom()\n",
"execise_cyt = sgeom.Comp('execise_cyt', execise_wmgeom)\n",
"execise_cyt.setVol(0.1e-18)\n",
"\n",
"# Associate the compartment with the volume system 'vsys'\n",
"execise_cyt.addVolsys('execise_vsys')\n",
"\n",
"# Create and initialize a 'r123' random number generator\n",
"import steps.rng as srng\n",
"execise_r = srng.create('r123', 256)\n",
"execise_r.initialize(1)\n",
"\n",
"####### You script after execise 2 should look like above #######\n",
"\n",
"# Create a \"wmdirect\" solver and set the initial condition:\n",
"# MEKp = 1uM\n",
"# ERK = 1.5uM\n",
"\n",
"\n",
"# Run the simulation for 30 seconds, record concerntrations of each molecule every 0.01 seconds.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visuzalize simulation data\n",
"\n",
"Visuzliation is often needed to analyze the behavior of the simulation, here we use Matplotlib to plot the data."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEKCAYAAAAB0GKPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x116dfbd10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from pylab import *\n",
"%matplotlib inline\n",
"plot(tpnt, res_direct[:,0], label='A')\n",
"plot(tpnt, res_direct[:,1], label='B')\n",
"plot(tpnt, res_direct[:,2], label='C')\n",
"ylabel('Number of molecules')\n",
"xlabel('Time(sec)')\n",
"legend()\n",
"show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Execise 4: Plot the results of the kinase simulation\n",
"Let's now plot the result of our execise."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Import biochemical model module\n",
"import steps.model as smod\n",
"\n",
"# Create model container\n",
"execise_mdl = smod.Model()\n",
"\n",
"# Create chemical species\n",
"MEKp = smod.Spec('MEKp', execise_mdl)\n",
"ERK = smod.Spec('ERK', execise_mdl)\n",
"MEKpERK = smod.Spec('MEKpERK', execise_mdl)\n",
"ERKp = smod.Spec('ERKp', execise_mdl)\n",
"\n",
"# Create reaction set container (volume system)\n",
"execise_vsys = smod.Volsys('execise_vsys', execise_mdl)\n",
"\n",
"# Create reactions (Do it yourself)\n",
"# MEKp + ERK -> MEKpERK, rate constant 16.2*10e6\n",
"MEKp_ERK_to_MEKpERK = smod.Reac('MEKp_ERK_to_MEKpERK', execise_vsys, lhs=[MEKp,ERK], rhs = [MEKpERK])\n",
"MEKp_ERK_to_MEKpERK.setKcst(16.2e6)\n",
"# MEKpERK -> MEKp + ERK, rate constant 0.6\n",
"MEKpERK_to_MEKp_ERK = smod.Reac('MEKpERK_to_MEKp_ERK', execise_vsys, lhs = [MEKpERK], rhs=[MEKp,ERK])\n",
"MEKpERK_to_MEKp_ERK.setKcst(0.6)\n",
"# MEKpERK -> MEKp + ERKp, rate constant 0.15\n",
"MEKpERK_to_MEKp_ERKp = smod.Reac('MEKpERK_to_MEKp_ERKp', execise_vsys, lhs = [MEKpERK], rhs=[MEKp,ERKp])\n",
"MEKpERK_to_MEKp_ERKp.setKcst(0.15)\n",
"\n",
"####### You script after execise 1 should look like above #######\n",
"\n",
"# Create a compartment of 0.1um^3\n",
"import steps.geom as sgeom\n",
"execise_wmgeom = sgeom.Geom()\n",
"execise_cyt = sgeom.Comp('execise_cyt', execise_wmgeom)\n",
"execise_cyt.setVol(0.1e-18)\n",
"\n",
"# Associate the compartment with the volume system 'vsys'\n",
"execise_cyt.addVolsys('execise_vsys')\n",
"\n",
"# Create and initialize a 'r123' random number generator\n",
"import steps.rng as srng\n",
"execise_r = srng.create('r123', 256)\n",
"execise_r.initialize(143)\n",
"\n",
"####### You script after execise 2 should look like above #######\n",
"\n",
"# Create a \"wmdirect\" solver and set the initial condition:\n",
"# MEKp = 1uM\n",
"# ERK = 1.5uM\n",
"import steps.solver as ssolv\n",
"execise_sim = ssolv.Wmdirect(execise_mdl, execise_wmgeom, execise_r)\n",
"execise_sim.setCompConc('execise_cyt','MEKp', 1e-6)\n",
"execise_sim.setCompConc('execise_cyt','ERK', 1.5e-6)\n",
"\n",
"# Run the simulation for 30 seconds, record concerntrations of each molecule every 0.01 seconds.\n",
"import numpy as np\n",
"execise_tpnts = np.arange(0.0, 30.01, 0.01)\n",
"n_tpnts = len(execise_tpnts)\n",
"execise_res = np.zeros([n_tpnts, 4])\n",
"\n",
"# Run simulation and record data\n",
"for t in range(0, n_tpnts):\n",
" execise_sim.run(execise_tpnts[t])\n",
" execise_res[t,0] = execise_sim.getCompCount('execise_cyt','MEKp')\n",
" execise_res[t,1] = execise_sim.getCompCount('execise_cyt','ERK')\n",
" execise_res[t,2] = execise_sim.getCompCount('execise_cyt','MEKpERK')\n",
" execise_res[t,3] = execise_sim.getCompCount('execise_cyt','ERKp')\n",
"\n",
"####### You script after execise 3 should look like above #######\n",
"\n",
"# Plot execise_res"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here is the complete script for our well-mixed kinase simulation:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEKCAYAAAAfGVI8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWhBoNBpNA0cLAo1Go2ng/B9wAdoWV8dR2gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x116ff2710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Import biochemical model module\n",
"import steps.model as smod\n",
"\n",
"# Create model container\n",
"execise_mdl = smod.Model()\n",
"\n",
"# Create chemical species\n",
"MEKp = smod.Spec('MEKp', execise_mdl)\n",
"ERK = smod.Spec('ERK', execise_mdl)\n",
"MEKpERK = smod.Spec('MEKpERK', execise_mdl)\n",
"ERKp = smod.Spec('ERKp', execise_mdl)\n",
"\n",
"# Create reaction set container (volume system)\n",
"execise_vsys = smod.Volsys('execise_vsys', execise_mdl)\n",
"\n",
"# Create reactions (Do it yourself)\n",
"# MEKp + ERK -> MEKpERK, rate constant 16.2*10e6\n",
"MEKp_ERK_to_MEKpERK = smod.Reac('MEKp_ERK_to_MEKpERK', execise_vsys, lhs=[MEKp,ERK], rhs = [MEKpERK])\n",
"MEKp_ERK_to_MEKpERK.setKcst(16.2e6)\n",
"# MEKpERK -> MEKp + ERK, rate constant 0.6\n",
"MEKpERK_to_MEKp_ERK = smod.Reac('MEKpERK_to_MEKp_ERK', execise_vsys, lhs = [MEKpERK], rhs=[MEKp,ERK])\n",
"MEKpERK_to_MEKp_ERK.setKcst(0.6)\n",
"# MEKpERK -> MEKp + ERKp, rate constant 0.15\n",
"MEKpERK_to_MEKp_ERKp = smod.Reac('MEKpERK_to_MEKp_ERKp', execise_vsys, lhs = [MEKpERK], rhs=[MEKp,ERKp])\n",
"MEKpERK_to_MEKp_ERKp.setKcst(0.15)\n",
"\n",
"####### You script after execise 1 should look like above #######\n",
"\n",
"# Create a compartment of 0.1um^3\n",
"import steps.geom as sgeom\n",
"execise_wmgeom = sgeom.Geom()\n",
"execise_cyt = sgeom.Comp('execise_cyt', execise_wmgeom)\n",
"execise_cyt.setVol(0.1e-18)\n",
"\n",
"# Associate the compartment with the volume system 'vsys'\n",
"execise_cyt.addVolsys('execise_vsys')\n",
"\n",
"# Create and initialize a 'r123' random number generator\n",
"import steps.rng as srng\n",
"execise_r = srng.create('r123', 256)\n",
"execise_r.initialize(143)\n",
"\n",
"####### You script after execise 2 should look like above #######\n",
"\n",
"# Create a \"wmdirect\" solver and set the initial condition:\n",
"# MEKp = 1uM\n",
"# ERK = 1.5uM\n",
"import steps.solver as ssolv\n",
"execise_sim = ssolv.Wmdirect(execise_mdl, execise_wmgeom, execise_r)\n",
"execise_sim.setCompConc('execise_cyt','MEKp', 1e-6)\n",
"execise_sim.setCompConc('execise_cyt','ERK', 1.5e-6)\n",
"\n",
"# Run the simulation for 30 seconds, record concerntrations of each molecule every 0.01 seconds.\n",
"import numpy as np\n",
"execise_tpnts = np.arange(0.0, 30.01, 0.01)\n",
"n_tpnts = len(execise_tpnts)\n",
"execise_res = np.zeros([n_tpnts, 4])\n",
"\n",
"# Run simulation and record data\n",
"for t in range(0, n_tpnts):\n",
" execise_sim.run(execise_tpnts[t])\n",
" execise_res[t,0] = execise_sim.getCompCount('execise_cyt','MEKp')\n",
" execise_res[t,1] = execise_sim.getCompCount('execise_cyt','ERK')\n",
" execise_res[t,2] = execise_sim.getCompCount('execise_cyt','MEKpERK')\n",
" execise_res[t,3] = execise_sim.getCompCount('execise_cyt','ERKp')\n",
"\n",
"####### You script after execise 3 should look like above #######\n",
"\n",
"# Plot execise_res\n",
"from pylab import *\n",
"plot(execise_tpnts, execise_res[:,0], label='MEKp')\n",
"plot(execise_tpnts, execise_res[:,1], label='ERK')\n",
"plot(execise_tpnts, execise_res[:,2], label='MEKpERK')\n",
"plot(execise_tpnts, execise_res[:,3], label='ERKp')\n",
"ylabel('Number of molecules')\n",
"xlabel('Time(sec)')\n",
"legend()\n",
"show()\n",
"\n",
"####### You script after execise 4 should look like above #######"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## From well-mixed simulation to spatial simulation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To convert a well-mixed simulation to a spatial one, here are the basic steps:\n",
"1. Add diffusion rules for every diffusive species in the biochemical model.\n",
"2. Change the well-mixed geoemtry to a tetrahedral mesh.\n",
"3. Change the solver to \"Tetexact\".\n",
"\n",
"First, let's see how to add diffusion rules in our example well-mixed model:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Import biochemical model module\n",
"import steps.model as smod\n",
"\n",
"# Create model container\n",
"mdl = smod.Model()\n",
"\n",
"# Create chemical species\n",
"A = smod.Spec('A', mdl)\n",
"B = smod.Spec('B', mdl)\n",
"C = smod.Spec('C', mdl)\n",
"\n",
"# Create reaction set container\n",
"vsys = smod.Volsys('vsys', mdl)\n",
"\n",
"# Create reaction\n",
"# A + B - > C with rate 200 /uM.s\n",
"reac_f = smod.Reac('reac_f', vsys, lhs=[A,B], rhs = [C])\n",
"reac_f.setKcst(200e6)\n",
"\n",
"###### Above is the previous well-mixed biochemical model\n",
"\n",
"# We add diffusion rules for species A, B and C\n",
"diff_a = smod.Diff('diff_a', vsys, A)\n",
"diff_a.setDcst(0.02e-9)\n",
"diff_b = smod.Diff('diff_b', vsys, B)\n",
"diff_b.setDcst(0.02e-9)\n",
"diff_c = smod.Diff('diff_c', vsys, C)\n",
"diff_c.setDcst(0.02e-9)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"\n",
"We now import a tetrahedral mesh using the steps.utilities.meshio module to replace the well-mixed geometry:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reading Abaqus file...\n",
"Number of nodes imported: 1841\n",
"Number of tetrahedrons imported: 8828\n",
"Number of triangles imported: 0\n",
"creating Tetmesh object in STEPS...\n",
"Tetmesh object created.\n"
]
}
],
"source": [
"'''\n",
"# Import geometry module\n",
"import steps.geom as sgeom\n",
"\n",
"# Create well-mixed geometry container\n",
"wmgeom = sgeom.Geom()\n",
"\n",
"# Create cytosol compartment\n",
"cyt = sgeom.Comp('cyt', wmgeom)\n",
"\n",
"# Give volume to cyt (1um^3)\n",
"cyt.setVol(1.0e-18)\n",
"\n",
"# Assign reaction set to compartment\n",
"cyt.addVolsys('vsys')\n",
"\n",
"'''\n",
"\n",
"##### above is the old well-mixed geometry ##########\n",
"\n",
"import steps.geom as sgeom\n",
"import steps.utilities.meshio as meshio\n",
"\n",
"# Import the mesh\n",
"mesh = meshio.importAbaqus('meshes/1x1x1_cube.inp', 1.0e-6)[0]\n",
"\n",
"# Create mesh-based compartment\n",
"cyt = sgeom.TmComp('cyt', mesh, range(mesh.ntets))\n",
"\n",
"# Add volume system to the compartment\n",
"cyt.addVolsys('vsys')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we replace the \"Wmdirect\" solver with the spatial \"Tetexact\" solver:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Import solver module\n",
"import steps.solver as ssolv\n",
"\n",
"'''\n",
"# Create Well-mixed Direct solver\n",
"sim_direct = ssolv.Wmdirect(mdl, wmgeom, r)\n",
"'''\n",
"\n",
"##### above is the old well-mixed Wmdirect solver ##########\n",
"\n",
"# Create a spatial Tetexact solver\n",
"sim_tetexact = ssolv.Tetexact(mdl, mesh, r)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The \"Wmdirect\" solver and the \"Tetexact\" solver share most of the APIs, so we can reuse our old script for simulation control and plotting:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEKCAYAAAAB0GKPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl0XXW5//H3k+SkJ22TlJYWOlBSCgUp0AqRQRRFBQsi\nFUQtFwEVb8UJuPC7P1G8grCWAwo4wBWKIOjvWsAKWLQIlfGCCC21hQKFVsbQ0okhCW2a6fn9cXba\nQzhJdk7OPvtk5/NaKytnz88mNE++s7k7IiIifSmLOwARERkclDBERCQUJQwREQlFCUNEREJRwhAR\nkVCUMEREJBQlDBERCUUJQ0REQlHCEBGRUCriDqCQdt55Z6+rq4s7DBGRQePxxx/f5O5jw5ybqIRR\nV1fH0qVL4w5DRGTQMLOXwp6rKikREQlFCUNEREJRwhARkVAS1YYhIhKHtrY2GhoaaGlpiTuUHqXT\naSZNmkQqlcr7HkoYIiID1NDQQHV1NXV1dZhZ3OG8i7uzefNmGhoamDJlSt73iaxKysx2M7P7zOwZ\nM3vKzM4O9o82s8Vmtjr4vlMP158enLPazE6PKk4RkYFqaWlhzJgxJZksAMyMMWPGDLgEFGUbRjtw\nnru/BzgU+LqZ7QucD9zj7nsB9wTb72Bmo4ELgUOAg4ELe0osIiKloFSTRZdCxBdZlZS7rwPWBZ+b\nzOwZYCIwG/hwcNqNwP3At7pd/nFgsbu/DmBmi4FZwPxIgn3gUuho27GdroFDvwZl5ZE8TkRkMCpK\nG4aZ1QHvBR4FdgmSCe6+zszG5bhkIvBK1nZDsC/XvecCcwEmT56cX4AP/QzatgQbwRrnu78fJh6U\n3/1ERGJw2223ceKJJ/LMM8+wzz77FPz+kXerNbORwB+Bc9y9MexlOfZ5rhPdfZ6717t7/dixoUa3\nv9sFa+GiNzNfX7ors2/rm/ndS0QkJvPnz+cDH/gAN910UyT3jzRhmFmKTLL4H3e/Ndi93szGB8fH\nAxtyXNoA7Ja1PQlYG2Ws26VrM99b3irK40RECqG5uZmHH36Y6667LrKEEVmVlGVaWK4DnnH3y7MO\nLQROB34UfP9TjsvvAn6Q1dB9NPDtqGJ9h66EsS1sYUhEZIfv3/EUT68t7O+PfSfUcOEnp/d6zu23\n386sWbOYNm0ao0ePZtmyZRx44IEFjSPKEsbhwKnAR8xsefB1LJlEcZSZrQaOCrYxs3oz+zVA0Nh9\nCbAk+Lq4qwE8csNqMt9VwhCRQWT+/PnMmTMHgDlz5jB/fuH7CEXZS+ohcrdFAHw0x/lLgS9nbV8P\nXB9NdL2oHAFWroQhInnpqyQQhc2bN3PvvfeycuVKzIyOjg7MjEsvvbSg3X01l1R3ZplqqRZVSYnI\n4LBgwQJOO+00XnrpJV588UVeeeUVpkyZwkMPPVTQ5yhh5JKuUQlDRAaN+fPnc8IJJ7xj36c//Wl+\n//vfF/Q5mksql3StEoaIDBr333//u/adddZZBX+OEkYu6VpY+0+4/WuFud/+n4GpRxbmXiIiMVHC\nyGXPo+D1F+CFBwd+r6bXYMtmJQwRGfSUMHI5/KzMVyHccJwa0EUkEdToHTW1h4hIQihhRE0JQ0QS\nQgkjaulaTTMiIomghBG1YTWZhNHZEXckIpJQ5eXlzJw5kxkzZnDggQfy97//PZLnqNE7atmTGVZp\n0UARKbyqqiqWL18OwF133cW3v/1tHnjggYI/RyWMqGm6dBEposbGRnbaKZo/TlXCiJoShsjQcuf5\n8NqThb3nrvvDMT/q8fDWrVuZOXMmLS0trFu3jnvvvbewzw8oYUStK2Es/Gbm84yTYea/xRuTiCRK\ndpXUI488wmmnnbZ95tpCUsKI2q77w15Hw7YmWLsCOjuVMESSrJeSQDEcdthhbNq0iY0bNzJu3LiC\n3jvKFfeuB44DNrj7fsG+m4G9g1NGAW+6+8wc174INAEdQLu710cVZ+SqRsEpf8h8/v0ceKsh3nhE\nJNFWrVpFR0cHY8aMKfi9oyxh3ABcCfy2a4e7f67rs5ldBvRWsX+ku2+KLLo4pGthw1NxRyEiCdPV\nhgHg7tx4442Ul5cX/DlRrrj3oJnV5ToWrPf9WeAjUT2/JGnUt4hEoKOjOOO84upW+0Fgvbuv7uG4\nA3eb2eNmNreIcUUrXZOZiLCzM+5IRET6La5G75OB3lYoP9zd15rZOGCxma1y95xzjQcJZS7A5MmT\nCx9pIaVrAYfW5kzyEBEZRIpewjCzCuBE4OaeznH3tcH3DcBtwMG9nDvP3evdvX7s2LGFDrewNCZD\nRAaxOKqkPgascvec3YXMbISZVXd9Bo4GVhYxvugoYYjIIBZZwjCz+cAjwN5m1mBmZwSH5tCtOsrM\nJpjZomBzF+AhM1sBPAb8xd3/GlWcRaWEISKDWJS9pE7uYf8XcuxbCxwbfH4emBFVXLHqShgLvgSV\nw999fPQe8G9/gDJN8SUipUcjvYtp3HR4379Dy5vvPrZpNaz5WzCr7ajixyYig9prr73GOeecw5Il\nSxg2bBh1dXX87Gc/Y9q0aQV7hhJGMVVUwid+mvvYst/Bwm9kqquUMESkH9ydE044gdNPP52bbroJ\ngOXLl7N+/XoljETKXjdDRKQf7rvvPlKpFGeeeeb2fV0jvwtJCaNUqEFcJBF+/NiPWfX6qoLec5/R\n+/Ctg7/V4/GVK1dy0EEHFfSZuah1tVR0DeRTwhCREqUSRqnYXsJQlZTIYNZbSSAq06dPZ8GCBZE/\nRyWMUpEOGrpVwhCRfvrIRz7Ctm3buPbaa7fvW7JkScHX9VbCKBXDVCUlIvkxM2677TYWL17M1KlT\nmT59OhdddBETJkwo6HNUJVUqyisgNUK9pEQkLxMmTOCWW26J9BlKGKWkaid45Cp4bF5+15dVwKd/\nDft8orBxiYighFFajr0UGpbkf/1DP4N1K5QwRCQSShilZJ9PDOyX/ZLr1QYiEhN3J7OYaGly9wHf\nQ43eSZKuVbdckRik02k2b95ckF/KUXB3Nm/eTDqdHtB9VMJIknSNShgiMZg0aRINDQ1s3Lgx7lB6\nlE6nmTRp0oDuoYSRJOlaJQyRGKRSKaZMmRJ3GJFTlVSSKGGISISiXHHvejPbYGYrs/ZdZGavmtny\n4OvYHq6dZWbPmtkaMzs/qhgTRwlDRCLUr4RhZmVmVhPy9BuAWTn2X+HuM4OvRd0Pmlk5cBVwDLAv\ncLKZ7dufOIesdC1sU8IQkWj02YZhZr8HzgQ6gMeBWjO73N1/0tt17v6gmdXlEdPBwJpgqVbM7CZg\nNvB0HvcaWobVZHpJNb0GViK1jcNqIDWwnhkiUhrCNHrv6+6NZnYKsAj4FpnE0WvC6MU3zOw0YClw\nnru/0e34ROCVrO0G4JA8nzW0DB8DOFy2d9yR7FAzCf5jJZRw/3QRCSdMwkiZWQr4FHClu7eZWb6d\njX8FXAJ48P0y4Evdzsn1m6XH55nZXGAuwOTJk/MMKyFmngypKuhsizuSjH/dB6v+DO0tmbhEZFAL\nkzCuAV4EVgAPmtnuQF6jw9x9fddnM7sW+HOO0xqA3bK2JwFre7nnPGAeQH19fWmOmimWdC0cdHrc\nUWSxTMJoaVTCEEmAPiu63f0X7j7R3Y/1jJeAI/N5mJmNz9o8AViZ47QlwF5mNsXMKoE5wMJ8nicx\n07KzIonSZ8Iws13M7DozuzPY3hfo889YM5sPPALsbWYNZnYGcKmZPWlmT5BJOv8RnDvBzBYBuHs7\n8A3gLuAZ4BZ3fyq/15NYKWGIJEqYKqkbgN8AFwTbzwE3A9f1dpG7n5xjd85r3H0tcGzW9iIyDewy\nmClhiCRKmL6XO7v7LUAnbC8BdEQalSRDV8LQ2BCRRAiTMN42s6C/JpjZoYB+A0jfVMIQSZQwVVLn\nkml0nmpmDwNjgZMijUqSQeuUiyRKnwnD3ZeZ2YeAvcmMkXjW3Uuko7+UtFRVZsT5q8vijkRECqDH\nhGFmJ/ZwaJqZ4e63RhSTJIUZVKThmYWwaTXsvFfcEYnIAPRWwvhkL8ccUMKQvs36IdxxNjSuVcIQ\nGeR6TBju/sViBiIJNfGgzHe1Y4gMemFmq/1erv3ufnHhw5HEUU8pkcQI00vq7azPaeA4MiOwRfqm\nhCGSGGF6SV2WvW1mP0VzO0lYldWAKWGIJEA+q+wMB/YodCCSUGVlmfEY2/Ka4FhESkiYNown2bEe\nRTmZgXtqv5DwtNa4SCKEacM4LutzO7A+mE9KJJx0jRKGSAKESRjjgafcvQnAzEaa2XR3fzTa0CQx\n0rXw+gvwdMRNXxNmwqghvuqiSITCJIxfAQdmbW/JsU+kZ7WT4KWH4ZZTo31O3QfhC7kWcRSRQgiT\nMMzdty996u6dZhbmOpGMT/4C3n9WtM+4+7vQtC7aZ4gMcWF+8T9vZmeRKVUAfA14vq+LzOx6Mu0f\nG9x9v2DfT8hMOdIK/Av4oru/mePaF4EmMututLt7fYg4pVSl0rDrftE+o3YSbFwV7TNEhrgw3WrP\nBN4PvAo0AIcAc0NcdwMwq9u+xcB+7n4AmZX7vt3L9Ue6+0wlCwlFPbFEIhdm4N4GYE5/b+zuD5pZ\nXbd9d2dt/gOtqyGFkq6Fti3Q0QblqbijEUmkPksYZjbNzO4xs5XB9gFm9t0CPPtLwJ09HHPgbjN7\n3Mx6Lc2Y2VwzW2pmSzdu3FiAsGRQ2j4FiQYIikQlTJXUtWSqjtoA3P0J8ihxZDOzC8iM6fifHk45\n3N0PBI4Bvm5mR/R0L3ef5+717l4/duzYgYQlg9n2hPGuJjERKZAwCWO4uz/WbV/eA/fM7HQyjeGn\nZPe+yubua4PvG4DbgIPzfZ4MEVoOViRyYRLGJjObSjA9iJmdBOTVf9HMZgHfAo539y09nDPCzKq7\nPgNHAyvzeZ4MIZoVVyRyYRLG14FrgH3M7FXgHOCrfV1kZvOBR4C9zazBzM4ArgSqgcVmttzMrg7O\nnWBmi4JLdwEeMrMVwGPAX9z9r/19MRliuhLGygXwxkvxxiKSUGF6ST0PfCz4a7+sa4qQENednGP3\ndT2cuxY4Nut5M8I8Q2S72olQORL++f+gvBKOuyLuiEQSp8eEYWbn9rAfAHe/PKKYRPqvaif4z3/B\n1YfDltfjjkYkkXorYVQXLQqRQkilM4lD7RgikegxYbj794sZiEhBpGtVwhCJSJiBe5PM7DYz22Bm\n683sj2Y2qRjBifRbular+4lEJEwvqd+QWcN7AjARuCPYJ1J6hmmxJpGohEkYY939N+7eHnzdQGaZ\nVpHS0zUJYe4xoSIyAGEH7n3ezMqDr88Dm6MOTCQv6VroaIX2lrgjEUmcMAnjS8BngdfIjPA+Kdgn\nUno04lskMmEG7r0MHF+EWEQGrith3HMxpEfFG0spqxwBHzwXUlVxRyKDSJ8Jw8ymAN8E6rLPd3cl\nESk9u0yHEePg6YVxR1K6vCOzdsju74epR8YdjQwiYZZovZ3MlB53AJ3RhiMyQOPeA/+5Ou4oStv6\np+BX71e1nfRbmITR4u6/iDwSESkOtfNInsIkjJ+b2YXA3cC2rp3uviyyqEQkOkoYkqcwCWN/4FTg\nI+yokvJgW0QGm8qRYGUaES/9FiZhnADs4e6tUQcjIkVgphHxkpcw4zBWAHn1TzSz64M5qFZm7Rtt\nZovNbHXwfacerj09OGd1sKyriBRK14h4kX4IkzB2AVaZ2V1mtrDrK+T9bwBmddt3PnCPu+8F3BNs\nv4OZjQYuBA4hs573hT0lFhHJgxKG5CFMldSF+d7c3R80s7puu2cDHw4+3wjcT2ad72wfBxa7++sA\nZraYTOKZn28sIpJFCUPyEGak9wMFfuYu7r4uuPc6MxuX45yJwCtZ2w3BvkicfdM/aW3fMcSkJp3i\n+7Onk06VR/VIkVg9XFnGHxtfhN99IO5QpACqK6r4/smLI39OmBJGHCzHvpzTj5rZXGAuwOTJk/N6\n2Aub3qalrQOAt7d18OqbW/ns+yZx0O6j87qfSKn74/BKHmhNMblNPaWSoLajOJNtxpEw1pvZ+KB0\nMR7YkOOcBnZUWwFMIlN19S7uPg+YB1BfX5/XnNYLv7Hjr6xlL7/Bif/9dxpb2vO5lcig0Dh8FPtW\nzuR3x/4u7lBkEOmx0dvM7gm+/7jAz1wIdPV6Oh34U45z7gKONrOdgsbuo4N9katJpwBoUsKQBGtq\nbaK6sjruMGSQ6a2EMd7MPgQcb2Y30a2aKMxIbzObT6aksLOZNZBpQP8RcIuZnQG8DHwmOLceONPd\nv+zur5vZJcCS4FYXdzWAR60mnflP0ri1rRiPE4lFc2szdTV1cYchg0xvCeN7ZLq8TgIu73Ys1Ehv\ndz+5h0MfzXHuUuDLWdvXA9f39YxCq6nKlDAaW5QwJLlUwpB89Jgw3H0BsMDM/svdLyliTLEaVlFG\nZXmZqqQksdxdCUPyEqZb7SVmdjxwRLDrfnf/c7RhxcfMqE5XqEpKEmtr+1bavV0JQ/qtz5HeZvZD\n4Gzg6eDr7GBfYtVUpdRLShKrqbUJQAlD+i1Mt9pPADPdvRPAzG4E/gl8O8rA4lSTrqBJbRiSUM1t\nzYAShvRf2HEYo4CuXkq1EcVSMqrTKZa88Dqzr3q4IPc79dDdOemgSQW5l/RuXfM6Lnj4AlraizOQ\naTDa0rYFgOqUEob0T5iE8UPgn2Z2H5mutUeQ4NIFwOfetxvlZbkGm/ffspff4C9PrFXCKJIVm1aw\n5LUlHDjuQKpSVXGHU5JqhtWwx6g92G/n/eIORQaZMI3e883sfuB9ZBLGt9z9tagDi9MnZ0zgkzMm\nFORep/z6H2oPKaKu+vlLj7iUXUbsEnM0IskSqkoqmCww7JTmkqUmnWJjU3PcYQwZza2qnxeJSpj1\nMGQAMl10VcIolqbWJsqtnKoKVUeJFJoSRsRq0imNGi+ixtZGqiurMStMG5SI7NBrwjCzsuzlVaX/\naqpSbGntoK2js++TZcA0glkkOr0mjGDsxQozy2+hCdk+maGmGikOJQyR6IRp9B4PPGVmjwFvd+10\n9+MjiypBuiYzbGppY/SIypijSb7mtmYlDJGIhEkY3488igSrDtbXUMN3cTS1NmnabpGIhFrT28x2\nB/Zy97+Z2XBAi12H1FUlder1j5IqL+Pzh+zO2R/bK+aokmdL2xZOvfNUnn/refbfef+4wxFJpDCT\nD/47sAC4Jtg1Ebg9yqCSZMZuo5h7xB4cs994ys14aM3GuENKpFebX+W5N57j0PGH8rm9Pxd3OCKJ\nFKZb7deBw4FGAHdfDYzL94FmtreZLc/6ajSzc7qd82EzeyvrnO/l+7y4pVPlfOfY9/DDE/fngEm1\navyOSNeEeqfvezrTd54eczQiyRSmDWObu7d29Ws3swoyK+7lxd2fBWYG9yoHXgVuy3Hq/7r7cfk+\npxRVp1NaZyMiXVOCjKwcGXMkIskVpoTxgJl9B6gys6OAPwB3FOj5HwX+5e4vFeh+Ja2mqkLzSkWk\nsbUR0JQgIlEKkzDOBzYCTwJfARYB3y3Q8+cA83s4dpiZrTCzO80sEXUMNekUzdva6ejMu4AmPdAc\nUiLRC9NLqjNYNOlRMlVRz7r7gH/jmVklcDy5p0pfBuzu7s1mdiyZRvacXYvMbC4wF2Dy5NIeX9g1\nJqO5pZ3a4amYo0kWrSInEr0wvaQ+AfwL+AVwJbDGzI4pwLOPAZa5+/ruB9y90d2bg8+LgJSZ7Zzr\nJu4+z93r3b1+7NixBQgrOl1dbDW3VOE1tTYxrHwYw8qHxR2KSGKFafS+DDjS3dcAmNlU4C/AnQN8\n9sn0UB1lZrsC693dzexgMolt8wCfF7vtg/iUMAquqa2JkSk1eItEKUzC2NCVLALPAxsG8tBg8N9R\nZNpEuvadCeDuVwMnAV81s3ZgKzCnENVgcaupyvznfuPttpyTEZabUVaglf5KTVtntEnyrW1vqTpK\nJGI9JgwzOzH4+JSZLQJuIdOG8RlgyUAe6u5bgDHd9l2d9flKMtVfiTKqKjOX1OevezTn8Uk7VfHA\nfx5ZsOVhS8WlSy7ld0//LvLnzBg7I/JniAxlvZUwPpn1eT3woeDzRmCnyCJKsH12reaS2dN5K8dY\njH++/Cb3rNpAU0sbo4Yna5LCZ19/lokjJ3LiXif2ffIAHLzrwZHeX2So6zFhuPsXixnIUFBWZpx6\nWF3OYwseb+CeVRto3NqeuITR1NrEnqP2ZO4Bc+MORUQGoM82DDObAnwTqMs+X9ObF1Z1gntQNbY2\nsseoPeIOQ0QGKEyj9+3AdWRGd2vZuIjUbJ8GPXkJo7mtmeqUGqRFBrswCaPF3X8ReSRDXFcPqqRN\nHeLuWgVPJCHCJIyfm9mFwN3Atq6d7r4ssqiGoJqEjtHY0r6FTu+kprIm7lBEZIDCJIz9gVOBj7Cj\nSsqDbSmQpFZJaRZZkeQIkzBOAPZw99aogxnKRgaN3klbL0NzPIkkR5iEsQIYxQBHd0vvysuM6mEV\nvPrmVl7a/Hbe95g4qoqutUtKwfotmanClDBEBr8wCWMXYJWZLeGdbRjqVltgY0ZWsuDxBhY83pD3\nPS799AF89n27FTCqgTn3/nMBGJ0eHXMkIjJQYRLGhZFHIQBcdcqBPPtaU97X/58/rOCVN7YUMKKB\na+toY9pO09h7p73jDkVEBijMehgPFCMQgekTapk+oTbv679/x9Ml1Wi+rWMb7d7OrLpZJVVNJiL5\nCTPSu4kda3hXAingbXdXP8kSU2pLwKrBWyRZwpQw3vGv3cw+BWiWtxJUk06VVAlDCUMkWcKs6f0O\n7n47GoNRkmrSqZLqlqt1tkWSJUyVVPac1GVAPTuqqKSE1FRV8NLm0mn07iphaJS3SDKE6SWVvS5G\nO/AiMHugDzazF4EmoANod/f6bscN+DlwLLAF+IKmI+lddYlVSTW2NQJo6VSRhAjThhHluhhHuvum\nHo4dA+wVfB0C/Cr4Lj2oSafU6C0ikeltidbv9XKdu/slEcSTbTbw22At73+Y2SgzG+/u6yJ+7qA1\nMg1by9bwm8fLKYVOrEs3LQeUMESSorcSRq75KUYAZ5BZj3ugCcOBu83MgWvcfV634xOBV7K2G4J9\n70gYZjYXmAswefLkAYY0uL3W+RDD667m8pVxR5KlM026PB13FCJSAL0t0XpZ12czqwbOBr4I3ARc\n1tN1/XC4u681s3HAYjNb5e4PZh3P9Ufyuxrbg0QzD6C+vn5IN8ZPHtcOr8G5+12OlUAZ45HnN3P3\nE9toae9keGW/O+SJSInptQ3DzEYD5wKnADcCB7r7G4V4sLuvDb5vMLPbyIztyE4YDUD2pEiTgLWF\neHZSvd3WxIjUCL540FFxhwJAZfvL3PX4kzRubWd4ZZj+FSJSynr8s8/MfgIsIdOTaX93v6hQycLM\nRgSlFsxsBHA00L0iZSFwmmUcCryl9oveldrKdjtWESydnlsikr/e/uw7j8zstN8FLsiaC8jINHoP\npHP9LsBtwT0rgN+7+1/N7EwyN78aWESmS+0aMt1qo+ytlQgllzCCRaGalDBEEqG3NozIKp3d/Xlg\nRo79V2d9duDrUcWQRM1tzVSnSihhVHWtIlg6XX1FJH9qiUyQUithVKdVJSWSJEoYCdLY2lhSCSOp\n65SLDFVKGAlSuiUMVUmJJIH6OpaQVa+v4rk3nsv7+ua25pJKGOlUORVlxh0r1vK1D0/VIkoig5wS\nRgk57/7zeLnp5QHdY9LISQWKpjAqK8pY9VoTT69rHNBqgiISPyWMEvJ6y+vMnjqbr8z4Sl7XV1gF\nu47YtcBRDcx/n3IgX/jNEl5/uzXuUERkgJQwSkRHZwfNbc1MGDmB3ap36/uCQWJ8bRWgrrUiSaBG\n7xLR3JZZnS5pa0eoa61IcihhlIiuhFFKjdaF0DV4T6O9RQY/JYwSkdTlTEdUllNmqpISSQIljBKR\n1NXpzIyaqpSqpEQSQAmjRDS2ButfVyarDQMy7Rga7S0y+ClhlIikljCg9NYaF5H8qFttEXV6Jzc/\nezON2xrfdeyJTU8AyWvDgEzCeG59E7+8Z3Wkz3n/nmM4aPfRkT5DZChTwiii1W+s5geP/qDH4xNH\nTkxct1qA6RNqeOT5zVy2OP9pT8K479lR3Pq1wyN9hshQpoRRRG9tewuAa4++lvpd6t91vMzKKLPk\n1RJ+97h9Of+YfSJ9xtk3LeeZ195dchORwil6wjCz3YDfArsCncA8d/95t3M+DPwJeCHYdau7X1zM\nOKPQ1U4xatgoKsqGVq6uKI82EdZUpdR1VyRicfzWagfOc/dlwbrej5vZYnd/utt5/+vux8UQX2Sa\n2jIJI4nVTnGrSVdocKBIxIpe/+Hu69x9WfC5CXgGmFjsOOKQ5J5QcaupSrGtvZOWto64QxFJrFgr\nzM2sDngv8GiOw4eZ2Qozu9PMpvdyj7lmttTMlm7cuDGiSAujK2GohFF4NcGcVU3qvisSmdgShpmN\nBP4InOPu3VsrlwG7u/sM4JfA7T3dx93nuXu9u9ePHTs2uoALoKm1iRGpEZSXlccdSuJUdy0Hq2op\nkcjEkjDMLEUmWfyPu9/a/bi7N7p7c/B5EZAys52LHGbBldqa20lSUxXMiqsR5SKRKXrCsMw6ndcB\nz7j75T2cs2twHmZ2MJk4Nxcvymg0t5bWEqpJUpPumhVXVVIiUYmjl9ThwKnAk2a2PNj3HWAygLtf\nDZwEfNXM2oGtwBx39xhiHbBtHdv45bJf0tzWzMpNK5lUXVpLqCZFV5XUtf/7PLVVKWbsNirmiESS\np+gJw90fAqyPc64ErixORNF6cuOT3Pj0jdQOq6WyrJLDJhwWd0iJtNvoKt4zvoaH12xifG1aCUMk\nAkNr9FgMunpGXXPUNUwf02NnLxmg4ZUV3Hn2Bzn6igc0gE8kIsmbh6LEdK2kV5NK3qSCpagmnaJp\nmxq+RaKghBGxJK9zUYoya2+ohCESBSWMiG0frKeEURRa3U8kOkoYEWtqbaKqoopUWSruUIaEmnRK\nYzFEIqKEEbGm1iaNvSiimqoKGlvaGaS9sEVKmhJGxJrbmhO5il6pqkmn6Oh0tmoSQpGCU8KIWGNr\noyYbLKLQlSvvAAAIl0lEQVTtc0qp4Vuk4DQOI4c7X7iTP635U0Hu9fSmp5k5bmZB7iV965pT6pvz\nl1FVqf+9ezKispwfnrg/o4ZXxh2KDCL6F5XDratv5clNTzK1duqA7zWldgofr/t4AaKSMN47eScO\n22MMW9s6aFPjd05bWtt5bn0zcw6ezIemlfYMz1JalDByaG5t5r3j3suvPvaruEORfpo4qor5cw+N\nO4yStnp9E0dd8aBWKJR+UxtGDk1tTVSn1LNJkqmmSu08kh8ljBzUFVaSrDpYnVADHKW/lDC6cXcl\nDEm0qlQ5FWWmAY7Sb0oY3Wzr2EZbZ5um8pDEMjNNoSJ5iWuJ1llm9qyZrTGz83McH2ZmNwfHHzWz\numLF1jX3kwbbSZLVpCu0OqH0WxxLtJYDVwHHAPsCJ5vZvt1OOwN4w933BK4Aflys+JraMglDVVKS\nZDVVmnNL+i+OEsbBwBp3f97dW4GbgNndzpkN3Bh8XgB8tGuN76htn11Wo7MlwarTmTm3RPojjnEY\nE4FXsrYbgEN6Osfd283sLWAMsCmKgD7358+xrX0bAFvbtwIqYUiy1aRTLHlxA0dd/kDcoUgB7DS8\nklvOjH755zgSRq6SQvepRcOckznRbC4wF2Dy5Ml5BTSldgqtHa3btw+bcBj7jN4nr3uJDAYnHzyZ\n4pTZpRhq0sVZPiGOhNEA7Ja1PQlY28M5DWZWAdQCr+e6mbvPA+YB1NfX5zWn9Y8++KN8LhMZtI6Y\nNpYjNC2I9FMcbRhLgL3MbIqZVQJzgIXdzlkInB58Pgm417XAgYhIrIpewgjaJL4B3AWUA9e7+1Nm\ndjGw1N0XAtcBvzOzNWRKFnOKHaeIiLxTLJMPuvsiYFG3fd/L+twCfKbYcYmISM800ltEREJRwhAR\nkVCUMEREJBQlDBERCUUJQ0REQrEkDW8ws43AS3levjMRTT1SwvTOyTfU3hf0zv21u7uHGsWZqIQx\nEGa21N3r446jmPTOyTfU3hf0zlFSlZSIiISihCEiIqEoYewwL+4AYqB3Tr6h9r6gd46M2jBERCQU\nlTBERCSUIZcwzGyWmT1rZmvM7Pwcx4eZ2c3B8UfNrK74URZOiPc9wsyWmVm7mZ0UR4yFFuKdzzWz\np83sCTO7x8x2jyPOQgrxzmea2ZNmttzMHjKzfeOIs5D6eues804yMzezQd9zKsTP+QtmtjH4OS83\nsy8XNAB3HzJfZKZT/xewB1AJrAD27XbO14Crg89zgJvjjjvi960DDgB+C5wUd8xFeucjgeHB568O\n5p9xP965Juvz8cBf44476ncOzqsGHgT+AdTHHXcRfs5fAK6MKoahVsI4GFjj7s+7eytwEzC72zmz\ngRuDzwuAj5oN2sUs+3xfd3/R3Z8AOuMIMAJh3vk+d98SbP6DzKqPg1mYd27M2hxBD0seDyJh/i0D\nXAJcCrQUM7iIhH3nyAy1hDEReCVruyHYl/Mcd28H3gLGFCW6wgvzvknT33c+A7gz0oiiF+qdzezr\nZvYvMr9AzypSbFHp853N7L3Abu7+52IGFqGw/29/OqhuXWBmu+U4nrehljBylRS6/6UV5pzBIknv\nElbodzazzwP1wE8ijSh6od7Z3a9y96nAt4DvRh5VtHp9ZzMrA64AzitaRNEL83O+A6hz9wOAv7Gj\ntqQghlrCaACyM+4kYG1P55hZBVBLZpnYwSjM+yZNqHc2s48BFwDHu/u2IsUWlf7+nG8CPhVpRNHr\n652rgf2A+83sReBQYOEgb/ju8+fs7puz/n++FjiokAEMtYSxBNjLzKaYWSWZRu2F3c5ZCJwefD4J\nuNeD1qRBKMz7Jk2f7xxUVVxDJllsiCHGQgvzzntlbX4CWF3E+KLQ6zu7+1vuvrO717l7HZm2quPd\nfWk84RZEmJ/z+KzN44FnChpB3C3/MfQ0OBZ4jkxvgwuCfReT+Z8JIA38AVgDPAbsEXfMEb/v+8j8\n5fI2sBl4Ku6Yi/DOfwPWA8uDr4Vxx1yEd/458FTwvvcB0+OOOep37nbu/QzyXlIhf84/DH7OK4Kf\n8z6FfL5GeouISChDrUpKRETypIQhIiKhKGGIiEgoShgiIhKKEoaIiISihCEiIqFUxB2ASNzMbAxw\nT7C5K9ABbAy2t7j7+wv0nE8BB7j7xQW630+BRe5+byHuJ9IXjcMQyWJmFwHN7v7TCO79dzIDrDYV\n6H67A9e6+9GFuJ9IX1QlJdILM2sOvn/YzB4ws1vM7Dkz+5GZnWJmjwULE00NzhtrZn80syXB1+HB\n/mnAtq5kYWafMbOVZrbCzB4M9pWb2U+C654ws69kxfF/g+esMLMfAbj7S8AYM9u1uP9VZKhSlZRI\neDOA95CZjPJ54NfufrCZnQ18EziHzBQcV7j7Q2Y2GbgruOZwYFnWvb4HfNzdXzWzUcG+M4C33P19\nZjYMeNjM7gb2ITNZ4CHuvsXMRmfdZ1lw7z9G9M4i2ylhiIS3xN3XAQTrStwd7H+SzCp+AB8D9s1a\nc6vGzKqB8exoFwF4GLjBzG4Bbg32HQ0ckLVUbi2wV3DP33iw6JO7Z8+evAGYUJjXE+mdEoZIeNnT\noHdmbXey499SGXCYu2/NvtDMtpJJAAC4+5lmdgiZmWOXm9lMMusdfNPd7+p27Sx6XsckDWzt4ZhI\nQakNQ6Sw7ga+0bURJALITDO9Z9b+qe7+qLt/D9hEZp2Du4CvmlkqOGeamY0I7vklMxse7M+ukpoG\nrIzwfUS2UwlDpLDOAq4ysyfI/Pt6EDgz+H6ZmZlnuib+JFijwsh06V0BPAHUAcuCdeQ3Ap9y978G\niWepmbUCi4DvBIllT2Awr/Egg4i61YoUiZn9HLjD3f9WoPudABzo7v9ViPuJ9EVVUiLF8wNgeAHv\nVwFcVsD7ifRKJQwREQlFJQwREQlFCUNEREJRwhARkVCUMEREJBQlDBERCeX/A3Hktue+VnZqAAAA\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x116ed6c50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Inject 10 ‘A’ molecules\n",
"sim_tetexact.setCompCount('cyt','A', 10)\n",
"\n",
"# Set concentration of ‘B’ molecules\n",
"sim_tetexact.setCompConc('cyt', 'B', 0.0332e-6)\n",
"\n",
"# Import numpy\n",
"import numpy as np\n",
"\n",
"# Create time-point numpy array, starting at time 0, end at 0.5 second and record data every 0.001 second\n",
"tpnt = np.arange(0.0, 0.501, 0.001)\n",
"\n",
"# Calculate number of time points\n",
"n_tpnts = len(tpnt)\n",
"\n",
"# Create data array, initialised with zeros\n",
"res_tetexact = np.zeros([n_tpnts, 3])\n",
"\n",
"# Run simulation and record data\n",
"for t in range(0, n_tpnts):\n",
" sim_tetexact.run(tpnt[t])\n",
" res_tetexact[t,0] = sim_tetexact.getCompCount('cyt','A')\n",
" res_tetexact[t,1] = sim_tetexact.getCompCount('cyt','B')\n",
" res_tetexact[t,2] = sim_tetexact.getCompCount('cyt','C')\n",
" \n",
"from pylab import *\n",
"plot(tpnt, res_tetexact[:,0], label='A')\n",
"plot(tpnt, res_tetexact[:,1], label='B')\n",
"plot(tpnt, res_tetexact[:,2], label='C')\n",
"ylabel('Number of molecules')\n",
"xlabel('Time(sec)')\n",
"legend()\n",
"show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Execise 5: Modify your well-mixed kinase simulation to a spatial one"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's now convert the below well-mixed kinase model to a spatial one, here are the tasks:\n",
"\n",
"1. Add diffusion constants:\n",
" * MEKp = 30e-12 $m^2/s$\n",
" * ERK = 30e-12 $m^2/s$\n",
" * MEKpERK = 10e-12 $m^2/s$\n",
"2. Replace the geometry to use mesh 'meshes/sp_0.1v_1046.inp'\n",
"3. Change the solver to Tetexact\n",
"4. Run the simulation again"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEKCAYAAAAfGVI8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd8VFXagJ8zySST3huE3ntHwEJVEBBZRQHRRbFX1sUC\nurou6oprWV1Z/RQVEJGisoJSBEGkg/QqPZCEAOltMslk5nx/3MkkQ9okzKRxnt9vzL3nnHvuOzHc\n9573vEVIKVEoFArFtYuutgVQKBQKRe2iFIFCoVBc4yhFoFAoFNc4ShEoFArFNY5SBAqFQnGNoxSB\nQqFQXOMoRaBQKBTXOEoRKBQKxTWOUgQKhUJxjeNZ2wI4Q3h4uGzevHlti6FQKBT1ij179qRIKSMq\nG1cvFEHz5s3ZvXt3bYuhUCgU9QohxDlnxinTkEKhUFzjKEWgUCgU1zhKESgUCsU1Tr3YIygLs9lM\nQkICJpOptkWp1xgMBmJjY9Hr9bUtikKhqCXqrSJISEggICCA5s2bI4SobXHqJVJKUlNTSUhIoEWL\nFrUtjkKhqCXqrWnIZDIRFhamlMBVIIQgLCxMraoUimuceqsIAKUEXID6HSoUinqtCCrFmAa5KbUt\nhUKhUNRpGrYiyEsHY6rbphdCcN9999nPCwsLiYiIYPTo0QDMmzePiIgIunfvbv8cPXqUuLg4Onfu\nbL9uzpw59OzZk/T0dLfJqlAoFOVRbzeLnUe6bWY/Pz8OHz5MXl4ePj4+rFu3jsaNGzuMGT9+PLNn\nz3Zoi4uLsx8vWLCAjz76iA0bNhASEuI2WRWKa52C8+cxHTlS22JUGb8bb8TD39+t97gGFIF7ufXW\nW1m5ciXjxo1j0aJFTJw4kc2bNzt17dKlS5k1axbr168nPDwcgEGDBtG9e3d27dpFVlYWX375JX37\n9nXnV1AorgkuvDidvH37aluMKtNy1UqlCJzhHz8e4eiFrNIdhXkgJeirbnLp2CiQv9/WqdJxEyZM\nYObMmYwePZqDBw8yZcoUB0WwZMkStmzZYj/fvn07AOfOneOpp55i3759REdHO8yZm5vLtm3b2LRp\nE1OmTOHw4cNVll+hqM9YcnIovHTJpXOaExIIuPlmIqY+49J53Y0+Ntbt92gQiqA26dq1K3FxcSxa\ntIiRI0eW6i/LNAQQERFBaGgoS5cu5dlnn3XomzhxIgA33XQTWVlZZGRkEBwc7J4voFDUQc5NvIf8\nkyddPq93mzZ4t27t8nnrOw1CEZT75p56GixmiGzv1vuPGTOG5557jo0bN5Ka6tzmtK+vL6tXr+aG\nG24gMjKSSZMm2fuudOlULp6KawkpJQVxcfgPG0pQGS9X1Uanw+/66103XwOiQSiC8qmZB+iUKVMI\nCgqiS5cubNy40enrIiIiWLNmDYMGDSI8PJzhw4cDmjlp8ODBbNmyhaCgIIKCgtwkuULhPnJ37CD+\n0ceQZnPVLpQSpMSvTx8CXakIFOXSwBVBzRAbG8vUqVPL7Ltyj+Djjz+mUaNG9vMWLVqwYsUKRo4c\nybJlywAICQlhwIAB9s1ihaI+knfoEDI/n7BHHgGPqnmqC09PAm1u2Ar3I6R0n3ulq+jdu7e8sjDN\nsWPH6NChQ8UXpp4BS4HbTUOuZNCgQbz77rv07t27xu7p1O9SoXACq8lE/OOPY0lLpzA5GWky0W7v\nntoW65pFCLFHSlnpw6RhrwgEuDOOQKFQOFJw9izG7Tvw6dYNn5498OnWrbZFUjhBw1YE9ZCq7DEo\nFDVN3qFDpH21QLPjl0FhqpbSJfLFF/Ht2aMmRVNcBUoRKBQKp8n47nuyVq1CH9u43DGGrl3xbqNc\nNOsTShEoFAqnydu7F+9WrWi5Ynlti6JwIQ076ZxCoXAployMcs1CivpLA18RqEAshaIsCtPSyNt/\noOrXZWQQdPsYN0ikqE0auCIAd3oNeXh40KVLF/v5hAkTmD59OoMGDSIpKQmDwYCXlxdz5syhe/fu\nADRv3pzdu3cTHh7Onj17GDduHMuWLaNHD7Wxpqg5Lr/9NpnLV1TrWn3Tpi6WRlHbNHxF4MZVrI+P\nD/v37y+zb+HChfTu3Zu5c+fy/PPPs27dOof+gwcPMm7cOJYsWaKUgMIprLm5FLqoZkV+XByGTp2I\n/sc/qnSd8PTAu00bl8igqDs0fEVQy/Tv35933nnHoe3YsWNMnjyZBQsWqBTTCqc5c8cdmM+dd9l8\ngbfdhk/nyjPsKho+DUMRrJ4OFw+Vbi/MA2kFvV/V54zuArfOqnBIXl6e3eQDMGPGDMaPH+8wZs2a\nNYwdO9ah7fbbb+frr7/mhhtuqLpcimsSWVCA+dx5Am65Bf/Bg10yp1//fi6ZR1H/aRiKoFzcu1lc\nkWlo0qRJ5ObmYrFY2Lt3r0PfsGHD+Pzzzxk+fDgeHh5ulVFRf8k/c4Zzk+7FajLZPXX8brie4D+N\nreRKhaJquFURCCGeBR5Cs9QfAh4AYoDFQCiwF7hPSllwVTcq7809LQ7MRojqeFXTV4eFCxfSrVs3\npk+fzpNPPmlPKAcwe/ZsHnvsMZ544gk+/fTTGpdNUT8wHT2GJT2d4LvGoQsIRHjpCbj55toWS9EA\ncZsiEEI0Bp4BOkop84QQS4EJwEjg31LKxUKI/wMeBD5xjxBQm7mG9Ho9b7zxBq1atXJI7KbT6Vi0\naBHDhw/n1VdfZebMmbUmo+LqkAUFJPzlWQpTUlw+t8U2Z+Tzz+MRGOjy+RWKItxtGvIEfIQQZsAX\nSAKGAPfY+ucDr+EuReBmrtwjGDFiBLNmOa5OfHx8mDZtGu+++y5ffPGFvd3b25vly5czcOBAoqKi\nePLJJ2tMboXrKIiPJ2fDBrw7dsAzLNylc3sEBeE/ZAi6gACXzqtQXInbFIGUMlEI8S5wHsgD1gJ7\ngAwpZaFtWAJQftKSOo7FYimz/crEcdOmTbMfx8XF2Y+DgoLK3WNQ1D1Sv/iS/DOnHdosKVpFuqgX\nXsSv33W1IZZCcdW40zQUAtwOtAAygG+BW8sYWqbtRgjxCPAIQFMVwKKoZaz5+Vx+5x10AQHo/By9\n0LzbtMa7XdtakkyhuHrcaRoaBpyVUiYDCCGWAQOAYCGEp21VEAtcKOtiKeVnwGegFaapnggqxYTC\nNeQd0NIxRL34AsHjxtWyNAqFa3Fn0rnzQD8hhK/Qqq8PBY4CvwJF/5ImAyqNoaLOk/HddwAqqlbR\nIHHnHsFOIcR3aC6ihcA+tDf8lcBiIcQbtrYvyp9Foag98o4cwZqZCUDBuXN4t22rKm4pGiRu9RqS\nUv4d+PsVzWcAlVdBUacpSEgk7k5HE1DArSNqSRqFwr008MhiVO50RSmklFhSUyv82zAdOwpA1Msv\nY+ioxX94t1UbwoqGiSpMcxUIIbjvvvvs54WFhURERDB69GgA5s2bR0REBN27d7d/jh49SlxcHJ07\nd7ZfN2fOHHr27El6BZklK5rLx8eH7t2707FjR/785z9jNpsBzY21SBaAv/3tbwwfPpz8/HxX/yrq\nFSn//ZiTN9zIyRtvKveT+PQzAPhdfz2+vXrh26sXHsqfX9FAadgrAjc7Dfn5+XH48GHy8vLw8fFh\n3bp1NG7sGBYxfvx4Zs+e7dBWMpZgwYIFfPTRR2zYsIGQkJAK71feXK1atWL//v1YLBZuvvlmli5d\nyqRJkxzGvfnmm2zdupVVq1bh7e1djW/bcMg/eRLPyEjCn3i8wnEewSF4tWheIzIpFLVJw1YENeA+\neuutt7Jy5UrGjRvHokWLmDhxIps3b3bq2qVLlzJr1izWr19PeLgWlTpo0CC6d+/Orl27yMrK4ssv\nv3Q6VbWHhwd9+/YlMTHRof29995j1apV/Pzzz/j4+FTtC9ZDEp5+GuPvu8vtt+Tk4NuzJyETJtSg\nVApF3aVBKIK3d73NH2l/lO4ozAdrIXhVPQ11+9D2vNj3xUrHTZgwgZkzZzJ69GgOHjzIlClTHBTB\nkiVL2LJli/18+/btAJw7d46nnnqKffv2ER0d7TBnbm4u27ZtY9OmTUyZMoXDhw9XOFcRJpOJnTt3\n8uGHH9rbtm7dyvHjx9mzZw/+/v5V+A3UT6SU5Py2Ce82bSr08Am4eVgNSqVQ1G0ahCKoTbp27Upc\nXByLFi1i5MiRpfrLMucAREREEBoaytKlS3n22Wcd+iZOnAjATTfdRFZWFhkZGRXOdfr0abp3787J\nkycZN24cXbt2tfe1bt2a9PR01q5dy7gGHgh16V/vkHfwALKggMDbRhN2//21LZJCUS9oEIqg3Df3\nzEQwpkCMe32/x4wZw3PPPcfGjRtJTU116hpfX19Wr17NDTfcQGRkpINNX4u/o9zzKynaI0hKSmLQ\noEGsWLGCMWO0AuNRUVEsXLiQoUOHEhYWxmAXFTWpa0irlbSvvkIfGYnfgAH4q6I/CoXTNAhFUC5C\n1Ij76JQpUwgKCqJLly6lEs5VREREBGvWrGHQoEGEh4czfPhwQDMBDR48mC1bthAUFERQUJBT88XE\nxDBr1izeeustuyIAaNu2LcuWLWPs2LGsXLnSIWNqfabg3DnSvl4IFgvSbIbCQkLvv5/QP99X+cUK\nhcJOw1YECGqiHkFsbCxTp04ts+9Ku/7HH39Mo0aN7OctWrRgxYoVjBw50l68JiQkhAEDBtg3i52d\nC2Ds2LG89tprpTas+/Tpw9y5cxkzZgy//vorrVq1qv4XriNkLPsf6QsW4BEcDIBnZCQ+3VXkr0JR\nVYSsBwFXvXv3lrt3O3qBlCz0Ui5ZSZBzEWK6a6uDesCgQYN499136d27d43d06nfZR3DuHcvl997\nH3N8PG02/Vbb4igUdRIhxB4pZaUPk4a9Iqgfz35FNUh4ZiqWlBT8BgyobVEUinpPw1YE9VATVGWP\n4VqlID4eS0oKoQ9OIfIvf6ltcRSKes81kmKi7pu/FM4TN14LBPNu0wah19eyNApF/aeBrwgU9RFL\nTi5Yyy4DKgsLsaSl4TdgAEGjRtWwZApFw+TaUASS+mgluibJWr2axGf/Wum4gFtHqNWAQuEiGrgi\nKHr6K9NQfcF07A/w9CTq+efKH6TXE3hr6ShuhUJRPRq2InDzKsDDw4MuXbrYzydMmMD06dMZNGgQ\nSUlJGAwGvLy8mDNnjj2Iq3nz5uzevZvw8HD27NnDuHHjWLZsGT169HCvsHWItK8XkjpnTpl9lqws\nPENDCZ08uYalUiiuXRq2InAzPj4+7N+/v8y+hQsX0rt3b+bOncvzzz/PunXrHPoPHjzIuHHjWLJk\nyTWlBAByNv2GLCzEf/CgMvt9azCGQqFQVFERCCF0gL+UMstN8riY2jcN9e/fn3feeceh7dixY0ye\nPJkFCxbYU0y/9tprnD59msTEROLj43nhhRd4+OGHa0Nkt1KYnk7ups34DxxIozfeqG1xFAoFTigC\nIcQ3wGOABdgDBAkh3pdSvlPxlTXHxX/+k/xjZaShtpjBkm9LQ101O5F3h/ZEv/RShWPy8vIc8vbM\nmDGD8ePHO4xZs2YNY8eOdWi7/fbb+frrr7nhisRoBw8eZMeOHeTm5tKjRw9GjRpVKoVEfSdvn7aC\n8u5YvyKZFYqGjDMrgo5SyiwhxCRgFfAimkKoM4qgtqjINDRp0iRyc3OxWCzs3bvXoW/YsGF8/vnn\nDB8+HA8PD3v77bffjo+PDz4+PgwePJhdu3aVUiL1BUtmJqlz5yLzCxza80+dAiDk7rtrQyyFQlEG\nzigCvRBCD4wFZkspzUKIOuWGU+6be04yZCVAVBfwqNntkIULF9KtWzemT5/Ok08+aU8oBzB79mwe\ne+wxnnjiCT799FN7e1XTT9dlsn/9ldT/+xRhMIDOMW7Ru01rPG0V2RQKRe3jzNPxUyAOOABsEkI0\nA+rHHoH9OVo7ekuv1/PGG2/QqlUrh8RuOp2ORYsWMXz4cF599VVmzpwJwPLly5kxYwa5ubls3LiR\nWbNm1Yrc5VEQH49x9x6nxuZu3gRA221b0fn6ulMshUJxlVSqCKSU/wH+U6LpnBCiYVY3qSJX7hGM\nGDGi1MPbx8eHadOm8e677/LFF1/Y2729vVm+fDkDBw4kKioKgL59+zJq1CjOnz/PK6+8Uuf2By69\n+U9yqpALyTMmRikBhaIe4MxmcRTwT6CRlPJWIURHoD/wRcVXNnwslrLTIFyZOG7atGn247i4OPtx\nUFCQfY/htddeo23btnz22Wcul7M6SIuF/BMnkBarva3g3Dn8BvQn2raCqQyP4BB3iadQKFyIM6ah\necBc4GXb+QlgCfVCEdS++2h9Jf2bRVx6881S7X4DBuAVG1sLEikUCnfhjCIIl1IuFULMAJBSFgoh\nyn4VVlSb1157rbZFQJrNFBUqKjh7FuHrS+N33y0eIMC3Z89akk6hULgLZxRBrhAiDNtrtRCiH5Dp\nVqmcRErpnGeNWhCUS9GDP3v9ehKefgasxaYgrxYtCBiitoMUioaOM4rgr8AKoJUQYisQAYxzq1RO\nYDAYSE1NJSwsrAJlUH/dL2sCKSWpqakYDAZMR46AlET85S/2sp4+PRpGkXuFQlExzngN7RVCDATa\noT1Zj0spzW6XrBJiY2NJSEggOTm5/EEFuWBMhXRP0Km0SmVhMBiI9jYQ9/EneISEEP7Yo7UtkkKh\nqGHKfToKIe4op6utEAIp5bJy+msEvV5PixYtKh60/xv4+XF4Zj+EVjL2GiZ7w68ABAy/pZYlUSgU\ntUFFr8m3VdAngVpVBE4hiiJa1SZBeST/5yN7bED4o2o1oFBci5SrCKSUD9SkIO7BtkcglSIoC2k2\nk/Lxx3hEhOM/aBCekZG1LZJCoagFnAkoe7Wsdimlc1FFtUnRikApAjsFCYlkLluGlFakMQ+AiCef\nImTC+EquVCgUDRWn3EdLHBuA0cAx94jjYoq8iaS14nHXEBlLFpM653N7Ijjh44NBpYRWKK5pnPEa\neq/kuRDiXTR30koRQgQDnwOd0Qz1U4DjaJHJzdGS2d0tpUyvitBOI1Rk8ZVYMjLwjIykzabfalsU\nhUJRR9BVPqQUvkBLJ8d+CKyRUrYHuqGtJKYD66WUbYD1tnM3oVYEV2LJyMQjKLC2xVAoFHUIZ/YI\nDlH8Su2BFlBW6f6AECIQuAm4H0BKWQAUCCFuBwbZhs0HNqIVu3E5qcZCwuCaVATW/HzM58+Xajdf\nuoQuKKgWJFIoGg6XskxE+Huj0wmsVkmasYBwf2+HMRcy8sjJLyTUz6tU35WYzBayTGYiAwzuFLtc\nnNkjGF3iuBC4JKUsdOK6lkAyMFcI0Q2tqtlUIEpKmQQgpUwSQrjNVeWb3+N5WruRu25RZ0l6+W9k\n/fRTmX0BI0bUsDQKRcPh1OVshr2/ib/e3JZnhrbh001neHvNH+x8aShRgdqD/HyqkZve0eJzfL08\n2PvKzRj0HuXO+fBXu9l8MoWDr91CoEFfI9+jJM4oghjgiJQyG0AI4S+E6CSl3OnE3D2Bp6WUO4UQ\nH1IFM5AQ4hHgEYCmTZs6e5kD8ho2DRXExWHo2JGwRx4u1efTQyWOUyiqy9kUIwAbj1/mmaFt+N++\nBAAS0o12RRCXqvnY3NgmnM0nU0jOzqdJaPm1OTafTAEgKcNEYHTdVASfoD3QizCW0VYWCUBCCYXx\nHZoiuCSEiLGtBmKAy2VdLKX8DPgMoHfv3tV8pS8OKNuauJXHfnmMjXdvJMwnrHrT1QMyvv+epFde\nBauVoDvvIFC9/SsULNgexyvLj7h0zr3nM2g+faX9/M5Ptpcac0vHKDafTOHGf/3q1JzDP9hUqu2X\nvw6kdaR/9QV1AmcUgZCy2LYipbQKIZzxNroohIgXQrSTUh4HhgJHbZ/JwCzbz+XVE90JSriPfnX0\nKwCOph7lxtgb3XbL2ibv8GGEwUDYAw8QOGpkbYujUNQJ3lr9BwCRAd5M6Fs9C0NJziTn0DJCezhL\nKTlyIYvOjR333iL8vbirdxOMBRZyCyrO3G+1SlJzC4gIKL2XEOLr/hWCM4rgjBDiGbRVAMATwBkn\n538aWCiE8LJd8wDaa/pSIcSDwHngrqqJ7DyS4oCybRe22doa9n6BNTMTfWQkEU8/VduiKBSYLVYe\nmr+bdGMB79/djdaRAWWO23Mundd/Ospbd3ShQ0wgf19+mB1n0lwmh9H2IO7eJJi/3tzWZfM6w6MD\nW9Xo/aqDM4rgMbSaxX9D8x5aj812XxlSyv1A7zK6hjor4FVhDyMofvgbC401cuvaQnMPVV5BirpB\nUoaJ305oGYJ3nU0vVxH8eOAC++Mz2HoqhQ4xgSzbm0h4gDftosoeX1WCfPTsikvjqSGtXTJfQ8MZ\nE89lYEINyOJyJKWTzj3/2/N0DO1I00Btebj+3Hq2XtjKwNiBDGwysNI5zRYz/93/Xx7s8iABXq75\nI3Ulxj178O13XW2LoVCQkG5k5k/FdvkFO86x/UxqmWMPJ2q1rr7dncD++Ayy8wt5bFArnhysHtw1\ngTNxBG3RzEJRUsrOQoiuwBgp5Rtul+4qKVIE0upon3tmwzP8MPYHAP6y8S8AfHviWw5NPlTpnGvi\n1vDF4S/INefycr+XKx1fk0gpkQUFCFGdOEGFwrWsPXKJX45dpnPjQPy9Pbmclc+RxLKLGxYt3s0W\nK0cvZNE2yp/+rRquU0ddwxnT0BzgeeBTACnlQSHEN0CdVwRFm8VZ5hyH5tOZp/k57mdS8lIc2hNz\nErFarVzOu0yvqF4AFFgKWH56OSNbjMRP72ffY8g2Z9fAF3COnC1bMR0+jCwoACnx69+vtkVS1EOs\nVsnS3fHc0ikaDyHYeTaVWzpFV2kOKSWLf48nLbeALSdT0HsIVjx5AzqdqhZYl3FGEfhKKXddUQ7S\nmYCyOoAm84HM06V6nvvtuVJtXx/9mq+PfQ1gXx2sP7+emdtnkmHK4OGuD+Ol8wI0BVFXSJoxg8Ki\nSm2ennh3UEnkFFVnX3wG05cd4siFLM6lGdl0ItkhSMoZzqbkMmNZ8cq6V7MQpQTqAc4oghQhRCuK\ni9ePA5LcKpWrsCkvY2FepUM9dZ4cSytOqmoqNGHwNHAy/SQAi/5YxLBmw/g57megtCKIz44n2Du4\nxvcNrCYThcnJhD3yCBFPPQk6HcJTleW81jl1OZumoX54eTpvJtxiC2qKS83lfJrmVJGTX0hUiTFx\nKbmcvJxTxtUaJy9rK+X5U/rSv2UYnkoJ1AuceWI8iRbY1V4IkQicBe51q1QuQtps5TkWTRF0jejK\nweSDZY6N9Y/lXNY5+/mauDWMbT2WOYfmAJCcl8yYH8bY+/Mt+Q7Xj1w2kvah7fn2tm9d+h0qoyiN\nhD4mGuHlVaP3VtRNLmebGPb+Jib2bcpbd3Rx+rp//3ICAD8vTzxsD/DcfMfF/0Nf7eZUBYqgiJbh\nVVNCitrFGa+hM8AwIYQfoCtKNVE/sP0x21xGPx76MUazkVRTKhNXTrSPev3619l/eT8/nPoBX09f\njIVG8grzKLSWbwHLKsjCVGgi35LPZaMWHP1H2h9u/C5lk39SW7EE3VFeiWlFQ0FKSVKmiTB/L7w9\ny89bk55rBmBHOR46JTGZLRj0HlitxZ51hVYrSRkmAE4n59A1NphCi5ULGSYS0o3c0aMxU24ovwZ4\noEFfYToFRd2jouL1fy2nHQAp5ftuksllSJusOYUmBIIArwCCvIOI8Y9xGBfrH0tSbhIWabHHGRRY\nCpi+ufzUSEdTj9JnYR/3Ce8Elqws0uZ/hfDxQeddcXZDRf1nxYELTF28nxvbhLPgwfJdhN/5+ThA\npWaZFQcu8MyifayfNtDuvgnwy7HirC/PLjlA8zA/Fu06z9LdWk6dTo2DSkXRKuo3Fa0I6p6TfFWx\nK4I8/PR+6Mpxq9QJHW2DHaMN8y359v2Auor5wgUAQu+tF5Y6xVVy2maSuZBR8Z5XTr62IujTIrTC\ncSsPan8/x5KyiLftCbx/dzekhGnfHrCPO5Ocy+nkXNpFBfDE4FYM7RBV5nyK+ktFxev/UZOCuAft\nwZ9r0RRBSSJ8IkjO0zxtfDx9CAx0LNby0b6PakbEapK7bRsJTz0NgP/gQbUrjMIpFu86zz9+PMpH\nE3swrKPzD9PPN5/hjZUlHBnMVlJz8un1xi8AxM0axdTF+/j5yEUEgjyzFjfzzc7z9GgSzF29mzjM\n948fjzB3a5z9fOri/QAEGjy5o2cs4KgIXvz+IIVWye3dG3F798ZV+9KKeoEzAWWxwEfA9WieQ1uA\nqVLKBDfLdtXYN4sLTfjrHbP3fTPqG7YkbsFDeNAhrANWaWVqz6l8uPfDUvNcF3MdO5O0JKotg1oS\nGxDLpoTSWQJrkrwDB7AajURMfQafLs5vCCpqj73n08kzW9gXn14lRVBSCQAYCwpLee5sP51Ky3B/\nLmWZ7IoAYHdceilFUKQEogK9CfbxYlC7CAAHc8/3jw9g4Y5z6D10BNuSnikl0HBxxmtoLvANxcnh\n7rW13ewuoVxFUT2CXIsJPy/HFUG0XzTj2o6zn+uEjoe6PMRDXR6iy3zHB+uNjW8kw5TB8fTjjGs7\njmDv4FpTBJkrVpC24GvMSUl4BAUR/vjjtSKHoupk5WnOB4t3xbM7Lp0PJnQnJsin3PHHL2Yz7v+2\nObQFeHuSkWdm+vfF3m+3z95Cck4+4/s04fe4NFJLJGtbfTiJPy5mlTl/l8ZBfD657H2uXs1C6NUs\nxOnvpqjfOOPfFSGlnCulLLR95qGVq6wHFG8WX7kicJa7297NXW3vsruL+uv96d+oPzc3K9aDq/60\nCgAPUb4nh6vIWrnKVnSmA6FTprj9fgrXkZGnxZ60ivRn59k09p7LqHD8F1vOkG0q9lxrFubLG3/q\nzJB2kTQP115shIAQPy+GtItkeKdoJl3XjKHtI5ncvxkD20bQs1kIIX5eDp+iVMfj+1x9OmZFw8DZ\ngLJ7gUW284lA5X5pdQGhQwIHc87R3cf5t5uWQS05k6ll2n6l/ysAxGXFAeCn9yPcJ5z3B71vXzk0\nCWzCk92f5L/7/4vZaqbQWsi8w/N4qMtD6D1K5xLfnLAZD+HBgMYDqvyVcnftwrd3b5p+9lmVr1W4\nlo/WnyQ6CX+2AAAgAElEQVQzz0yvZiH8diKZiX2b0q1JMOdTjXy2+TRHLmQxqG0kU4e14VKWiR1n\n0ri5YxSz7uhCrzd+4eONpxjVNYaDCRmsOnSR61qEgoDB7bTqrYUlXDrjZo2yH1dkouncOIjbujVy\n35dWNEicUQRTgNnAv9H2CLbZ2uo8Jg9/4vTaV9yfvN/p617t/yr3r7m/zD4fz+Kl/KiWo2gf0h7A\nvuIwmo3MPTyXLw5/QZRfFHe0Ke3f/8T6JwCcSnJXEmm1IvPyEPqaL2WncCTDWMB767QArM+3nAVg\n8e/xxM0axYoDiXy94zwA+85nMHVYG9YdvQRo+fBDfLXAvyMXsjCZLcywpXX4v9+0VChFD31fL22F\neV+/ZjX3xRTXJM4ElJ0HxlQ2ri6S6xlEIVUPcS9KOFcWBdbi1BKzbpxlPy7ySnrg5we4lKv9oz+R\nfoK3d71N25C2+Hj6YLKYmL1vtv2ao6lH6RjW0Wm5TMe0TUO/AVVfSShcg5SS7/YkcCYlt8z+zzef\nsdefLdm25VQKQsAjN7VEpxO8fWcXXvz+EJ9s1FYOV44HOJSQSeNgH14f29k9X0ahsOGM11ALtEpj\nzUuOl1LWeeVgEZ6YbXqgTUibq5qrKOK4Q2jZCd08ddqvpig3EcDCYwsrnPOhnx9i2z3bKhxTkoyl\nWvoK77ZX910U1ef4pWye/67sNCVQ2sOnZFv76AD0Htq2XLvoQDx0gg/Xnyx3PMCQ9pFXK7JCUSnO\nmIZ+AL4AfgSs7hXHtViEHqNO+4f3Yp8Xr2qunZN2VthvspiqPGe2ORspJVdkdi0Ta34+ORs3om/a\nFL++fat8L8XVIaVk3dFLnErW3DYXPNiX5mF+9qLkn0zqyQ1twu3jDXoPCi0Sq9Q+AD76YmeC7k2C\nOfKP4Zgt2j8pvYfOvidQokQ4fl4qgaDC/TjzV2aSUv7H7ZK4ASk8Mdoesr6eVc990i6kndNjWwVV\nry7pgeQDdI/sXum49G8WUXjpEv4DK6+ipnA9i3bF89L/tD0dIaBNZAChfsVJ/tpGBxBgcNy70Vfi\nRGbQe2CobJBCUQM4owg+FEL8HVgL2FNuSin3uk0qFyGEINtmsvHVV00RbB6/GYOn83nYe0b1LLO9\nyKRURIxfDPNGzGP3pd28vOVlkvOS7Z5GJTeir6TgXBwAjd75l9MyKa6enPxC/Lw8WHEgEYBwfy8W\nP9Kf6CDtb2PzC4PJNhXSKqJ67skKRV3AGUXQBbgPGEKxaUjazus2AgqEtsyu6oog2BDsEhF6RvVk\nS+IW+3mIIYRG/o3ogxbIk12QzcNrH+Zo6lF23LOj3HxIudu2o4+NxeOKVBgK97F413mmLztE42Af\nEm35fe7p25TWkcUPfZVlU9EQcCag7E9ASynlQCnlYNun7isBG0ZbBsaK3rZdxeo7VpdqG958uMN5\nYo72ZllUwCa7IJs9l/aQV5hHRn4FAUZWK55RKtlXTbLhDy0LZ2KJJG9Th7Utb7hCUW9xRhEcAFzz\nelzDCARG2xu2j979iiA2ILZUW8ugloQZiotwB3ppb/S+el90Qse7u9+19w1cMpCcAm0zMu2rrzh+\nXT/7x5yQoHIKuZj98RkMfncj51ONzNt6lt5vrGPX2TR+j0ujwytrWGvz/S+id7MQe8EWhaIh4Yxp\nKAr4QwjxO457BHXefVQIyNMJdFLaaw27m9lDZnM8/bg9e2mQdxD/HfpfPj/0OWE+Ydzf6X5Ay23k\nr/cnq8DRh/x4+nF6RfUid9cuhE5H4ChbRKlOEHz3XShcx6Kd5zmbksvmU8n8cuwyKTkF7D2fzsbj\nlx0St93aOZoLmSaeG+6884BCUZ9wRhH83e1SuAkBnNQ1xpscp1w0XcHAJgMZ2GQgS48v5ZLxEn56\nP5oFNuPfg/9damyAV0ApRfDDqR/oFdULa0Ym3q1bE/23l2tE7obINzvP4+2po0/zUF7+4RAhvl5M\nuq4p4z/bQZCPnrwC7WH/2aYzpOVqgYLzt8WRlFnsCvzT0zeoIiyKBo8zkcW/1YQg7kAIMAkdhhJ+\n2TXFf4f+l+WnlzuYha5ElIh6DvQKJKsgix9O/cDr17+OJTMTr+YqtcDVUOTuOeuOLvZo3yyTVrQl\nM8/MdS1C2Xk2jUZBPjQK0jaEGwf7EBVoYH98BiO7RDtsDCsUDZUGHa0iEJiEwLsWFEG70Ha8EPpC\nhWP6NerHdye+Y0K7CUxsP5Hbl99Ou3jJxZkzMScmYujWtYakrX9kmczM3nCKgW0juL51OBuPX+aF\n7w7y4oj2WKXkYEJx6cWlu+PtxyXblzzav0ZlVijqKg1aEeh0kC/AYK15ReAMId5aRtSzWWftXkRj\ndlpJP7MUj6AgfHv3rk3x6jSbT6Tw2aYzrD1ykY3PD+axr/dgMluZ9u0BAgyeFBQWB8HHpRppG+WP\nyWwlJ19L6/zMUJWmQ6EooqLi9eullEOFEG9LKa8uP0MtoffQEe9txKuOOnoMbjKYOYfmkF2QbVcE\nfiaJb69eNPtqfi1LV3c5mJDB51u0xGxxqUasVonJXPzgzzYV8vzwdjw5uHVtiahQ1CsqWhHECCEG\nAmOEEIvBMY1nfYgs1nvoyPE0Ax5QWACeNeM55CxF7qZ3tL4Dbw+tWIh/HuT71S056xpjZm91OP/t\nZHKpMW2jAmpKHIWi3lORIngVmA7EAu9f0VcvIouLMj0CYM6tc4ogxBDC7nt325XAZzd/RuCsKeT5\nNuz8M8nZ+RxLyqJ9TACZRjMhfl6E+3tXeZ47e8by/d4E1h7R/P0/nNCdm9pEIAQE+9at/9cKRV2m\nXEUgpfwO+E4I8YqU8vUalMll6D1KLGIKcqEKVcpqiiIlABCRZsEsIaWw4hKG9Z0+b/5Sqq1kBa6K\nKJmZs3vTYH7Yn8iiXVoRmCahvoSo1ZRCUWWccR99XQgxBrjJ1rRRSvmTe8VyDTpb7MBdWdlQYKxk\ndO0TnmImCUjp2YKsgix7FLJCUwBZeYV464tXec1CfVn1zI2k5OTj6+VB9yb1MgBeoah1nClM8xbQ\nFyiqsjJVCHG9lHKGWyVzCdrbY6jFCrbUDXWZy9NfAmBO+o+8segnpvWaxv2d769doVzMoRLumyXZ\ncjLFIZ9/EbM3nOTdtSfs53f1Kk7jEernRbvoANqh9gMUiqvBmVxDo4CbpZRfSim/BEbY2pxCCOEh\nhNgnhPjJdt5CCLFTCHFSCLFECOG2tbwQtqIfSM00VIeRUmI1GkkK03EpRFvJxGXF1a5QbmDHmdQy\n25ftTSizvaQSANh7Ph2APs1D6NRIrZgUClfgjCIAx6RzVY23nwqUrN/3NvBvKWUbIB14sIrzOY2U\nWgoBvZSwZ567buMSCi9fRhYU8EuJGLLvT37PmzverD2h3MD6Py6V2W4tYfs3FhQy8sPNvFhGScjT\nyZpCn9CnaY2lDVEoGjrOKIK3gH1CiHlCiPnAHuCfzkwuhIhFWz18bjsXaN5G39mGzAfGVlVoZ5G6\nIkUAHP6u4sG1TEHcOQDSr8hosPj44lqQxn0UlV6c0KeJQ3vTEnn9z6bkcjQpiyUlIoK1a4u9qVRl\nL4XCdVSqCKSUi4B+wDLbp7+U0tmn0wfACxQXtAkDMqSUhbbzBKBxlSSuAgVSe3vU10KKiaqSMns2\nAGejS7/lxmXGAWC2mHljxxtcNl6uSdEqJa/Awoxlh0i3JW4ri/TcAmYsO8ilbBP9WoYyonM0AEPb\nRxLg7cnPRy7xxk9H+fHABV754bDDtb2aad5eg9pF0iRUSydu0Du7mFUoFJXhVIoJKWUSsKIqEwsh\nRgOXpZR7hBCDiprLmr6c6x8BHgFo2rRpVW5tJ9+SXXyDiA7VmqOmyDtyBIAHh7+Ezsub+Ox4vjj8\nBQA7knbQPKg5WxK3sOT4EtJMabw/6MrQjtrju70JLNp1HoNex99v61TmmI83nmLRLu0N/+aOUXRv\nEsygdhE8MbgVBxMzOX4pm+OXsh2uaR8dQKdGQQxoFYZVSkZ0jubWLtEs3hWvMoIqFC7EnbmGrkeL\nSh4JGIBAtBVCsBDC07YqiAUulHWxlPIz4DOA3r17V+uVvmizOCaoORiiqzNFjZA6bx7SaCTi2We5\nq/M9AGSYMuyKYOuFraw7t45BTQYBUGAp/83bHRxKyOT3uDSm3NCCTKOZnw5d4J6+mo1+z7k0dpzW\nNoDnbo3jxjbhDGnvWElNSsn87efs5wEGT4J9vZj3QF8AxnRrxBdbztr7g3317H/1Foc57izhLTS6\nayOXf0eF4lrGbetrKeUMKWWslLI5MAHYIKWcBPwKjLMNmwwsd5cM9sWGpw/kZ1c8tJawGo1cnvU2\nAD7dutnbA70DaROiJUbbGL+RXRd38a/ftcL1suxFlNt4fOEeZv50lJScfF764RAv/++wPYvnnZ9s\nZ+WhJPvYh+bvLnV9QnqeQxK4QIPeob9P8xACDMXvJNe3Lu1GqlAo3EeFKwIhhA44KKXs7MJ7vggs\nFkK8AewDvnDh3A4UOZUIT18wls5HUxfI+H4ZADFvvYVfv+vs7TqhY9mYZXSZX7o8pazhPY+EdK1m\n79mUXLad0vL6/29fosPDuwirhGNJWTQL82XRrnjC/LxKjbvyfETnGEZ0jnGT9AqFojIqVARSSqsQ\n4oAQoqmU8nx1byKl3AhstB2fQQtQczuyjq8IChISufSm5h7q3bKF09fV9IqgiLv+b7v9eN62OIeV\nQElun72V4Z2j+fGAZvVrH+0Y8JVhNLtPSIVCUWWcMQ3FAEeEEOuFECuKPu4WzBUU7UwLL1/Iz6pw\nbG1QcPoUAI3e+ZeDWagyEnMS2XtpL6l5ZQdnVZdMo5lTl3PYdz6dTNvD+tTl8iOyk7PzCfAufpd4\n8AZNmRVYrPxSovB70Ryvj9UWlrn5hSgUirqDM5vF/3C7FG6iaLNYFq0IpCy2F9UBkv/7MQCGzlWz\nvJ3NPMvkNZMBODT5kMvk6TZzrf1YJ2Djc4MZ9n7FlUqzSzzUOzcujvQtWfy90CppHOxDZ1skcCfl\n8aNQ1CmcqlkshGgGtJFS/iKE8AXqVTSP8PQFaQWzEbz8alscAKz5+Vhzc/Fq1gzvFuWbhVb+aSWJ\nOYk8su6RGpROs/XHpZZOy+Hn5UFugcWh7bGBrfhTj8a0CPfjhe8OYrYUm67WPnsTCelGmof50TLC\nn7XP3kTrCFUHWKGoS1RqGhJCPIwWCfyprakx8IM7hXIZwvZA0tuiVuvIPkHa/Pkc79adgtOn8bv+\n+grHNg1sSv9G5dfWfW3ba/T5us9Vy9T6pVWl2v785a5SbSM6xxAR4Fg7oE2kP+2iA/Dy1HFX7+KI\n4SahPrSNCmBI+yha2h7+baMC0OnqzqpMoVA4Zxp6Em1zdyeAlPKkECLSrVK5DE0RyJKKIKD24wny\nDh/BIziYsIceJGDErVW69oNBH5CUm8TKMys5nHqY709+f9XymC1WCm11ncP9vUjJKT9O4c5ejbm3\nX1P2nddqJkjglk7FcQOvjOqIAExmK3f2clvQuEKhcCHOKIJ8KWVBUYIvIYQn5UQD1zUc3EcB4ndC\nuK1o+bGfYM9cuPfqH6RVIemVV8letw5Du3aEPfRQla8f2mwoAGE+Ybyw6QV7u5Sy3CRshRYrrV9e\nDcDJN291rNwGnEkuNgFN7t+c99Y5ZvwsSc+mIRj0HvRoWnaRHx8vD978U2mXV4VCUXdxRhH8JoR4\nCfARQtwMPAH86F6xXEORm2VhSCutwVjCy2bJJO2n1QK6mtvyyFq9Gn1sY8IerpoSWDJ6CcdSi5O4\nXhdznUO/yWLCx9OnzGvTjMVv+Jez82kc7DjucrbJfjy0QxQD20Xwzs/Hub17Y7w8dUQHGjhyIRNj\ngUUle1MoGiDOKILpaKmiDwGPAquwZROt64gi05BPmNaw7lU4uQ7uL1FgrSAHDO73Ysla8zOZK1Zg\nzckh6LaHCRg2rErXdwzrSMewjvbzUEOoQ/+x1GP0jOpZ5rX55uKo3kKL1aFv7/l0PvzlZPF9bJ49\nCx50VDR9WzjeT6FQNBycyT5qRUsX/TqaK+l8WdOhrdXGpghKfs24zdoqoIj9i2pEkvTFi8ndsQND\n5874DRjgkjkf7/a4/fhgcunc/UXkFhS7eBqv8Pi54+Nt7D6nFXu5rZvK4aNQXIs44zU0CjgN/AeY\nDZwSQlRth7O2KDKZyyts558PLT6uoQRulsxM/K67jhbffYtPF9dk7Lit1W3246yCLFacXsGkVZPI\nzHcsB5mbX/zwP3k5hy+3nCWtjJTRr4yu2xlaFQqFe3Amsvg9YLCUcpCUciAwGPi3e8VyDUWmoVKb\nqBf2FR8HOxZIcReWzAw8glxrggr3CSfSV3PgysjP4OUtL3Mw+SDf/PGNwzhjiRXB19vPMfOno/xn\n/UmHnEWNg30I9nFb1VCFQlGHcUYRXJZSnipxfgaoW5VRysX2oJMCWg0pe8hRNyY/RaszkLlyJZa0\ndJcrAh9PH9bftZ7mgVqtgiLS8tIcxhW5egLsitP61h65yApbLqDnh7dj6/QheHmqYi8KxbVIuZvF\nQog7bIdHhBCrgKVoT9a7gN9rQLarpyigDAEdb4fTG0qPOfI/GPIKhLVyiwjnpzyINVMz1ehjYysZ\nXT3MVjNJucUJ4Py9HCN352+LK3XNhUwTUxfvB8DXS3kCKRTXMhV5Dd1W4vgSMNB2nAyU7URe55DF\n/+05GTrfqW0Ue3hB2mn4vxu0YaaMcmeo9p2lJP/ESayZmYROmULI3Xehb9bM5fcB6BTWicScRPv5\nhewUTqWfQ1gNSKsHqbkFjO4aw7qjl8i31QVY/Eg/Jny2AyiuI6xQKK5Nyn0CSCkfqElB3INtjwCd\nFl3mXSIdckAJDxnh+jfinF9/JeGJJwEwdOqIV/PmLr9HEccuO6aDXhX3A6vitCwgFlM08BdCfL2I\nCjRwPs0IQI+mwQQYPMk2FRLsq79ySoVCcQ1R6augEKIF8DTQvOR4KeUY94nlIipKaeMXBn0fhV2f\ngrRUMLB6FMRppRkbf/ABAUMGu3z+klzKvVTubo+H4SIAOfmFzHugD0Pe07KJent6sOzxAZxPM3JT\n2wi3yqdQKOo2ztgEfkCrIvYjYK1kbJ1ClNwsLov2IzVFUJjvsnsmPv8CWT9qgdfCYCBg+C3lpn5w\nFTp8i08sfuDhmDU0oMN0rN5/p2VEd3xjliP1F4FRtIkKoE2UY9EYhUJx7eGMIjBJKf/jdkncQtEe\nQTkPYk+D9rPQVHZ/Ncjbtw/vDh0IGDIE77Zt3a4EAKwWvX1FIKVHmd9WH7wTGIdH8PYyehUKxbWM\nM/6CHwoh/i6E6C+E6Fn0cbtkLqGE11BZeNrSKS/4ExTkwtxRsOndat+tID4ec0ICfv36EfH0UwQO\nv6Xac1UFq7XYxm81l+2i6u3pxb/3FId/WKWVf+78J+N/Gs/ZzLNul1GhUNRdnFkRdAHuA4ZQbBqS\ntvO6jT2yuJz+iBKRtCkn4NwW7XPTc9W6Xd4+LVCtKmUnXcH1QY/za84MTIkTENIbj6DdtA7ohtnz\nHJFBkgOZawk1hLLw2EL7NUazkUV/aOk1DqccpkWQ8zWTFQpFw8IZRfAnoKWUsmZyMbiUSkxDekPx\n8bkSJpP8HPCuehWtnI0bAfC7oeJiM67m5AUduZf+Zj9/+vqhTB3Wxn4+cMlA5h2Zh6co/t/96C+P\n2o/zCvNqRlCFQlEnccY0dAAIdrcg7kFTBDpnvmZKiRz8m96p1t0sGVrgmM6vZsthXlnxK93oqLOj\nfLXCMYWyONXEweSDNPLTXGiNZqObJVQoFHUZZxRBFPCHEOJnIcSKoo+7BXMFsmRAWWXsmVt8XDIX\nURWwGo34DRhQIxvERXy3J4HjF7MY3TXG3ma+ItX0Uz2esh93CNXMYTc2vpFVd2jlKd/b8x4/nq4X\nJSYUCoUbcMY09He3S+EmhKjEfRTAOxDysxzbqlng3mo04hkRXq1rq8PlLBPPfXsAgKjAYjPXqC4x\nDuNKFqzpHN6ZhOwEJrSfgEeJgjwvbXmJgU0GEugV6GapFQpFXaNSRSCl/K0mBHEHRdk1K1wRzIiH\n10p42gQ1BWMaJB2E4Kbg45xVzJKTS/6JExg6tK++wFUkPr3Yth/so+fsWyORsrSpyFdfHGdwZ5s7\neaXfK2WuWk6knaB3dG/3CaxQKOokztQjyBZCZNk+JiGERQiRVdl1dQEhKnEfLaIonqDdKC0NRfwO\n+PRG+MT5Td+0efO0qSIjqy5oNbnzk23242A/L4QQpZQAgK9nsSLw0fs4KIHBTYqjnh/4uQFkFVEo\nFFXGmRWBQ+ipEGIs0NdtErkU6fCjXF44A/nZ4BsGc0vU3MlKcPpOhZe1zNwRzzxTRRmdw2KVFFqt\neHuWnRfptq4xZbaDoyIweBgc+t4b9B5r49YyffN0QFtFWaUVY6ERT50nnsITIQT5lnys0kqAl4pE\nVigaGlVOOyml/EEIMd0dwrgLWdnCx8uveF8goXoZti1ZWXi1aoXQuyeB21Pf7GX14YvEzRoFwM4z\nqQ79ft7l/68saRq6ssC9Xqcnwqc419CiPxbx+8Xf+eX8L7QLacfx9ON0De9KUm4S6fnp7Ll3Dzqh\n6hYoFA0JZ5LO3VHiVAf0xklHnNqnKPuo+714LJkZeAS6b6N19WEteZzVKtHpBOfSHF0+9R7lP5wD\nvAKYO3wuQghCDKUziLcLbWc/zszP5JfzvwBwPP04AAdTiushZ+VnEWyop97ECoWiTJxZEZSsS1AI\nxAG3u0UaFyNtgdDSzWrLnJiIcfsO/Abe5N4bAS1fWoVBr8Nkrlr+v4o2gUt6Cn184OMK5/nq6Fek\nmdL4/uT3APjr/flk2Cd0j+wOwPYL23lk3SMADGs6jH8PrhdVTRWKa5pK1/hSygdKfB6WUr4ppawn\npSqLqIIp44mdVZ7ddFwLRvMfOLCSka6hpBJYP20gX95/dZ4+Qgj+M7h0XsGu4V1LtSXmJNqVAECO\nOYfjacft518e/tJ+fCD5wFXJpVAoaoaKSlW+WsF1Ukr5uhvkcSnS2c3ikkS2h4lLYNF47XzJfTDm\nP+BTdlG2tPnzSVvwNQABg11fd+D+ubvYeDy53P5WEf60iqh6OowrGdy0tOzP93me+1bf59C26uyq\nUuM+P/w5h1IO8edOf2ZH0g57e3JeMg+tfYicghx6RPbg6R5P88rWV5jWexqN/BuVmkehUNQOFb0q\n55bxAXgQeNHNcrkEp91Hr6TFjcXHx1bA7i/LHZqxfDlWk4ng8ePxjIqqupCVUJESePhG1yaK+2uv\nv9IssJn9uGNYR0INoRVeo9fpuZh7keWnl7MzqfRqamfSTo6kHmHZyWWsP7+etefW8sHeD1wqt0Kh\nuDoqKlX5XtGxECIAmAo8ACwG3ivvurpFFVJMlMTLD4a/BT/P0M5TTzt0Z61aRc7mLYBWiSxo1Ehi\n/vHa1YlaBQa2jWD+FNd78D7Q+QEe6OwYS/Db+N/oMr9LudeYrWb78b9+/5f9uLF/Y3sd5QifCJLz\nkll2chkA3h7erhRboVBcJRUaz4UQoUKIN4CDaEqjp5TyxfqzR2BTBBWlmCiPJtcVH+sc9WXyxx+T\ntWYNuTt34BkSgt+AAVcjZJWpwVRGDtzT/h6H88e7PV7uuH8M+AcAjfwa2YPWdl/aDUCYIcyNUioU\niqpS0R7BO8AdwGdAFyllTlUmFkI0Ab4CotHqGHwmpfxQCBEKLEGrgRwH3C2lTK+W9JVgTzpXHa+h\n2F7Fx0JoaSdOriU3pwkFp04Tcs9Eol+taBvl6vn5yMUy23W1pAkmd5rMH2l/sPfyXr4c/iV9ovvw\nyYFPSo2bcZ22kjo0+RAAJ9NPsvTEUnu/5Yoa0Sl5Kaw+u5rDKYdpG9KWbhHdVKoLhaIGqWhFMA1o\nBPwNuFAizUS2kykmCoFpUsoOQD/gSSFER2A6sF5K2QZYbzt3E0VxBNUMgOo9RftZYIQNr8P/HiXh\nL1MB8O7QoYILr568AguPLtjj0NY+WovqrWk1cH+n+/H19CXUEMqE9hMAaBnUEoBeUb0cxl7fqHRa\njis3hrMLsh3Ovz3+Lf/6/V+sOruKD/Z+wN+2/g2FQlFzlPuElFLqpJQ+UsoAKWVgiU+AlLLSyCkp\nZZKUcq/tOBs4BjRGi0GYbxs2Hxh79V+jXCkAMFuqGUgw6n2I7AgpJ7DEHSTngjfW7FzCHn6YkLvu\ncqGcxaTm5JOWW8CZlOIF2N9GdeDkm7fy7M1tgZo3DU3rPY0tE7dg8DRwa4tbOTT5EGE+mnln3oh5\nzOirrQDubHMnHw8rHYfgp/fjrrbFv68ccw5SSg4lHyLfks+hlEMO4y8bL7Ptwja2Jm7ldMZpMkwZ\nbE3cSmZ+JlkFWey7vA+rrFochUKhKJ8qp5ioDkKI5kAPYCcQJaVMAk1ZCCHKzNImhHgEeASgadOm\n1byvpgDOp1az8IoQml0paT+XjrQm85D28PNu27Z68zlBrzd+KdUWGWhA76GjRbiWBuPGNhGlxrgb\nva781BntQ7WMq9fFXFdu+ol+Mf349sS3AOQU5HA09Sj3rLqHBzs/yL7L+/D19MVYaMRP70euOZdH\n12kV1AweBgY2GcjPcT9zZ5s7STOl8Wv8r8wbMa/UakShUFQPtyeNEUL4A98Df5FSOp21VEr5mZSy\nt5Syd0RE9R58AQZNz3nrr0LftRqM1QIFaWa8g800n/UYgSNvrfw6FzLaVl+gbVQAO2YM5c/9m9Xo\n/SujZ1RPfhn3C7e2KP/3ckvzW1g3bh39YvqRnJds9yg6nHIYY6GR4c2Hs+GuDWwav4mZA2barzNZ\nTPbYhNS8VH6N/xWAjPwMN34jheLawq2KQAihR1MCC6WUy2zNl4QQMbb+GMBtHkhFJpTC6pqGAEJa\ncP7XMPISzXgFFOLT2B/hUXYGUHdRMrV0dJChRiugOUuUX+UxFNF+0QgEJ9JPMO23aQDsvLgTq7Ti\np3e8LGYAACAASURBVPcjwjcCLw8v+sY4usZm5mslQDcmbLS35Rfmu054heIax22KQGhPqy+AY1LK\n90t0rQAm244nA8vdJ4P202ypeFyFGIIoyPJEp7cS2S1LS1ddg2ydPqRG7+duYgNiy2xPM6XZjxv7\nN+bDwR+yfGz5fxr5FqUIFApX4c4VwfXAfcAQIcR+22ckMAu4WQhxErjZdu4WivYIrqzhWxWklz8W\ns46QNrl4+VscFcH8MbB+ZvkXVwNfr+LVxuT+zWgc7FPB6PpHl/Cyg9MifR23ioY0HULLoJY0DXDc\nH+oeoSW3e3Xbq6TmOabiVigU1cNtikBKuUVKKaSUXaWU3W2fVVLKVCnlUCllG9vPtMpnqx5FBpTC\nq1gRWKP6gBR4eNmUScn6xmd/g82uC7K2WiXGAgs9mgbzwPXNuee6urUX4ApGtBhRZvsT3Z8os33O\nLXMY0GgAb934FpM6TOLx7sVBbHFZce4QUaG45qgRr6HawmpLQ/3dnkReGFE9v//sXzcDFCuCXZ9B\ni4Hw+5ziQXkZTtc2roh0YwEAIzpF8+jAVlc9X13kysI4lbU38m/Epzd/CsDolqMptBba+9JMabzw\n2wtcMl7CR+/DP2/4Z6W5kRQKRWkadKkpi9WClDouZ1ffnmzcuxcA38iC4sYlk+DMxuLzvV9Ve/6S\nFBWbCTC4p8pZXeHeDvcCMLb1WFoEteDZXs86fa1niXQfx9OOszpuNRdzL7I1cSuHkg9VcKVCoSiP\nBr0iMFvNIKvn4ZOx7H/kHTyAccdODB074vXuMXinNeSWkQ1U76OloNj+X7j+GTAEOXUPq1Uy+9dT\njOnWiObhfry24ggAXWOdu76+8mLfF3mx79UnsP30oLZSeKjrQ8zcPpOnNjzFqJajCPYOZkrnKaX2\nHRQKRdk06BXB1SiCS2+/TeYPWoppv5tsaal7P1j2YL0v7P8GNr8L+xY6fY9TyTm8v+4Eb60+hsUq\nOZiguUk2C/Ot5Mprm95RjnmIbmh0g/145ZmVLDy2kHXn1tW0WApFvaVhrwgsZrw89LSJrFrhlsL0\ndKyZmUT8ZSrhjz1W3DF4BvxWhpPTb7Owb02f3gD9y974vJJjSdrGc2pOATmmYtt3QzcNXS1zR8zl\nfyf/x6vbXiXYO5gY/xiHfk/hyZbELaTkpWCxWojxj6FZQDNSTCnkmnOxSiv9Y/rTMrhlLX0DhaJu\n0aAVQaEsROCBqYpuQ1mrtCpc+tgmpTuFB1yRPZOM88XHFw/iLHvPaUlXY4J9yDKZKxmtKEnr4NYA\n9G/UH4AhTYawIX4DYYYwInwj2JK4hS2JW8q9PsIngg13b6gRWRWKuk6DVgRSSnRCVLnQuzVLe1MP\nHH5L6c7xC2DxPRDaCp7ZCx90hYxzxf15GXD5D8z+0aRfTiSyeady71O0ORyfZuRQYmaVZLzW6RLR\nhe0Tt+Or18xo7w96nxxzjnYuIduczcAlWg3pp3s8zUf7PgKgU1gnjqQeITkvmd8v/o7ZYkbvoa3A\ngryD7FHMueZcGvk3om2I+/JKKRR1hYatCJAIBHlVDC22ZGSi8/VF6Msw0RRV1yo0aT8bdXdUBJZ8\n+Pg6Lhva0th0gtznEvDzDyjzPn8kacFp++MzeGLh3irJqAB/r2KTn4fOgyDv4k32UI9iN9Ki1QNo\nabOPpGqb8lN+nlLpPdbeubaU6UmhaGg0aEUAIIQgv4orgsLkZHRB5XjuFOXSj9AybjLmIwhvB5v+\n5TCssekEANnpl/H188dskXh5Ou7NW8qomPPpfSqjpqvYPH4zQgj89f4suHUBAV4BxAbEcmPsjUgp\n+eLQF+y8uJN7O9zLnkt7OJZ2jDGtxrDn0h57Urz/b++846Oq0sb/PUkmvUBISIAkhEBIQpcSmgZW\nBGlSrCAgZS1gQeDn/kRXfVnftbyurLL7iqKrWFZFXSyoKM1CUTokICC9QwIJhED6zHn/OHcyM8kk\nGSAhmcn5fj7zuWXuOfece2fuc8/zPOd59p3fR4BPAF5eXoSYQuplnCeN5mrxaEEgpUQIQbHZgtki\n8faq/k8szWYuLF2Kb5tKJnRZ30Ljeqmlfxh0GVtBEFjJzz3LX9OLeHvtIQCm39iGWYOSAMhzYheI\nbxJUbRs1rtHI3zbJr0vTLmXrvZqpe7crexcbTm+gW1Q3svKz2J2zm86RnQkyBfHxno8BeGjVQ2Xl\nRrcZzbN9azakiEZTH/BsQYDEy/DmKSgxE+xXfXcteeqNP6BzZ+cHtLkJxi6CRDv7QWBEpfUV5J7l\n7bVny7bfWnOIWYOSKCo1U1hiIa1tJKv3qrkJTwxJJinauRpJU/OMTR5Ls6Bm9I/tT0qTFHo178WQ\n+CGYvE1c3+J6Zvw4Q7kgG/x+7vc6bK1GU3t49DwCoGwon19UWs2RCnOuMhYGpaY6P0AISBoCXnbz\nE/wqf3jnpH/DeO8VvG96AbDlSMgz3EVvSrFNeprYJ96lNmpqhkBTIEMThuLj5UOL4Bbc0fYOgn2D\n8fP2Iy0mjRGtRzgcvyt7F3vP7a2j1mo0tYdHCwKr1xBAfrFrBmOrIKjURuAMIeDWtxhW9Dxvlzom\nZ8nPO89fTQtJ896BLyWktlJGzAsF6k0z1N/E/HFdeWpYCv6ma5vnQFM193a8l8kdJvPOze+U7fv7\nlr9XUUKjcU88WxBgEwSXil0bEWS/rf703x66xNp9Z6s52kZ+8q38JuN5sXSsw35xMbNsPYR8vsk4\nxYS3N3Dj3J8BCA3wYWjHZtx7g57cVN+ICYlhVrdZ9Ijuwc3xNwOw7sQ6Hl/9OMXmYqSUjP5qNMO/\nGE5+yRWmQ9Vo6gEeLQiAshy6ro4ICnaoCWF/2VnI+Lc3uHyeL7edBKCknNmls9eBsvVQoR4Wa+wE\nTMcWVx+1VFP7zE6dDUBUYBRLDy3lUO4hLpZcZP/5/Ry5cITdObvruIUazZXj8cZiq7ffntN59Iiv\nOkRx0cGDlJ48RfiUKRTk+Dt8t+VIDv9YtZ+Fk3o4pI604uNt2/dh6QDG+awCIErYcuv+zbSAGSUP\ncVzacjBHhvhddr80156IgAh2TNzB1sytTPx+IgsyFuDnbbt3k76fxJD4IUQYjgPnC8/z07GfiAyM\npG+LvgAkhyeX2R3yivNYuHMhheZCLNLC/nP78fPxwyIttAprVVZvanQq/WP7X7uOahokni0I7GwE\nT3+5kwm9qk70cu7jRQAEdr0OVjqGrr7t9V8BWL3vDP2TKka1NNkJgvfMg7jdezWLzP2Z6GMLftbd\nay/vm17gxmKlZ56kjcNuR0JYAs2DmrP2xFoKSgscvvvu8HcEmYIoKi2iVCpVZF5uHpn5mRSbi/EW\n3tyScAtCCNadWMdbO94iwCegQj3bsrYBKi/z2hNrtSDQ1DoerRpSIwLXJgAV7tpFQUY6vvHxmPr9\nodLjLHaTwPZm5pF1Qc0wvlSkVE8bnxxA1+59SCp6D9MtFQ2LCYGFZetzBkRB+icqLIXGLWjk34hl\nty/jq5HO8ymvv3s9kzpMqrBv+nXTKTQXsv/8fgCWHV4GqJnLwabgCsevv3s9oxJHcSj3EHty9tR8\nRzQaOzxaECDBx8u1Lp6YOYvC9Ax8W7fmqS93VnqcxW6S8qBXVpP6vFIBXTTcU4P9fRjWSYUk6Ngi\nDLre41hBoziahvgR0zgA1s2DL+6Hre9dRqc09YGIgAh8hOOA2vrmnhBW0fBvjXS6IGMBhaWFrDy6\nEoBQv1DuSLoDgCBTEM2CbOEspPHScfe3d9d4+zUaezxaNQRKECRFhagHrxOkxULR3r2UnD5No7vu\nIvqpP7PyhR/Lvs+6UEjTUH/iwgM5mpPPxaJSpJQcOnvJVoeUHDxzES8BASZvbkiMZN9zQzB5e0Hz\neTDoOZAW+J+WYArk1ycGqIIf/q9aFuXVWv81tYPJ28SaMWswSzPBpmAs0oK3Mbfklta30Kd5H0L9\nQhHGhMa0mDRahrbk6IWj/HpSqRlnp87GS3gxs+tM7ut4H4E+jnkoCs1q9FhiKWH9qfU08mtE67DW\n7D23F4u0YJZmzNKMr5cvXl5eJDVOoshcxOHcw8SExFBYWkhWflaFtpulmRJLCR0iOlSaIlTTsPBo\nQSBRb1SNAk1lb+zlyVu+nBMzVKpEv8REhMnE+XzbbNLU51fx0b098TepkUVeUSm/HMhm3L9sHkWP\nfLyNbzJOAbYJbCZvYyTi5QX+obYTHluPd+5RaNwSDqjRRFkAO41b4RD0Dsc5IE0CmlQ4PjYklrUn\n1jL9x+mALRieEIIQ34qTEts3ac+3B78F4L7l9wHQKbITGWechzqfnTqb7Vnb+f7w9y61/7bE25jT\nZ45Lx2o8mwYhCEL8fTiV6/xhW3z4MACxC97A1CMVi6ViILitR8+VBYzLKyzhWI6jz7hVCLjM2X0Q\n2sK2XVpNTmWL2XEms8YtsUZHvTH2Rsa3G0+3qKoDDI5JHkNKeAqTl00u25dxJoMAnwCmdJjCa9tf\nA+C1Aa/x2M+PcTj3MEcuHHGoo3tUdyZ3mOywzxo/6WjeUYrNxZiN/Bq+Xr5loxqAUot6ebKG2dCj\nB8/FswWBEXQu2M+nwojg1ZV7eXXFXr77ah6YTJR0702HZ52nN3x5uS2swEvf10C8mU8nwAw7O8TG\nN2HIS+DMsD3HmOF83XiVBnOONiy7K2G+6l52iuxEj+ge1R5v8jLRPbo70UHRnL50umx/sCmYfjH9\nygRBWkwaUYFRLPp9UYU6UpqkkBaT5rT+Tac30e3fNmHUKaITHw5TqVbv/vZudpzd4XD8g10eZFrn\nadW2W+N+eLYgMPIRBPv7OKSCBHh15T4CDZVMaddUDmVfclZFzTLmY1g0FkryIb/crOWiC1Unvd/2\nb7WU0rnA0NR7JrafSExIDMMShl1WuX8P+Tef7/ucNzPepFSWEmgKJDk8mXEp47ihhcqnPafPHNLP\npCMQLNy5kHNFKvvdhJQJFer7etTXZJzNYO7mueQU5jAkfgg5RTlsydyCRVrwEl4OQmBgy4Gkn0ln\nV/auq+i9pj7j2V5DoASBn4k8JzaCVrlKpVOSNqAsbaSVxwcn13xjkofCsLlq/bVyQe12LVHLr2fA\nz85DWgPODcsnt6mRw9pXnJcxl6rv54SBWafErCuaBzdnQrsJhPtXPbGxPFFBUUzrMo1Huj4CgLfw\nRgjB7NTZZZPVukV1Y0qHKUzuMJl2Ee0ANVJwllQnPiyeEa1HlI0UBrcaTP+Y/pRaSun8fmcGLx7s\ncPzQVkNp06gNv5z4heFfDGfT6U2X3XdN/cbjBQEoG0FxqYWicrmLIwpVgDlzVLOyIHBW/Hy8eH9K\nJRFIgfAgX2YNbEtqNbOVK5A01HG7/xNqee6wWm5ZCD8+V3n5gnMV9y17Si1XznFeJj/btp532vkx\nmnrPwJYDGd1mdLXqmeubXw9QwV5Qnse6P8a0ztNIi0krEygAFmnzkY4KjKJHdA/uaXcPN8ffzNEL\nR7Ug8EA8WzVkZyMA+GV/NpPf3VTmAdQh+yAAk344S0qiLxHBvpy9WAyAn8mLtLaRzisGWkcGMX1A\nItMHJGKxSBKeXOpao0KbO273nw0/vQBrXlYfK0d+hT3fVCy/9hW45dXyPa36nPYzVy16ROCuxIbE\nupQYp0+LPuDCszrML4wHuzwI4BDWoltUN3Zl7+Jg7kFeSnuJML8w+rboS98WfVl3ch1LDixhd3bl\nsZVC/UJ5pvczDiE4NPUbjx4RWL2GrIJg8rvq32FNZh9Qorx1Lpn8ycwrdMhg1r650te3jrRlDOvT\n2uYSGGSX5MbLS3DfDa14Z1J31xo2QiVSZ9BfKz9m07/gV2OeQVicbb/V5dSe4otVn6/Yzv5RrKNk\nejpxIXEMiR/CS2lVqBir4FzRuTJPInsXWYBRbUYR6htKZn6m08/B3IMsObCEvTk6b4M70TBGBP7O\nu5mauZvdjVuCEBzLKeCObjF8tuU4AF1iVVTQiX3ieear3xjXM47nRndk6Y5TPPjhVny9HWXon4e1\nc71hXe+pOOPYnqbtHEcDfR6Gng/At/8Pdi627T+zF1Y8A6fSqz6fvSDI3gfRHVxvK8DpHXBsI/T4\no/PvLRY1O7rLOPDxrbquk9vh03vg0hm4ZR6c+R3iekPiTZfXJk2l+Hj58FK/yxcCb9z0BlNXTsVs\nMZe5jgZ4O7qMzuw2k5ndZlZax29nf2PMt2N4b9d7xIbE0q5JOwa2HHjZbdFcWzxaEIAyFrdoVNH/\n2WQuIaSkgFI7v+nu8Y1pEuzHj3tsszH7tG5C0xA/Rl+n/P5LzGo0UT4R/VXR8U7Y8altu2VfyLLz\n0GhqCBm/UCi8YPMceq16F0TAccRwdt/lt++TCXDuELQbCUFO0nLu/A98MwMuZkH/x6uu6/sn4Lyh\nu/78Ptv+ObmX3y5NjdK2cVuaBjRlfMp48kvzeWLNE0QGVq4edUZcaBxNA5qy6ugqzBYzIb4hWhC4\nAR4tCKyqoWS7PMBxF07TreQMT/eLJfNrWBlr86O+q4dSwcweYvMYatM0hI1/tr2tFpXWgiC47S31\nsc4ZGPYy/L4ULpxQ262UiyD+oSDNSmXUvKvzuja8CeGtVG5lq5vpMTuF8fFNUHQR/ByH/GTtgcJc\nyDsJF05Cm4EQ2VZ9d+6QWhacV8Lo9A6IMa5b7nE4bcx0XfMytB9tK2ePlHBsg6Ph2h6LRc3CdoUz\nv0NAuKqvWSdoFFd9mfpAfg4cWg1tBlSZ3rRGOLkdIpPB5F/9sQaRARGs6vYURHUH4cWQm94FH9fL\nA4T4hrDqTqW+fDPjTf657Z/8cPQHIgMiOZp3tMqypZZSfLzUI8nHy4e0mDQ9ie0a4fGCQCDwsVPj\nPLHpA+LzMslco7bPhEUBEBHsmmGrfXMVLmJQu6iabSyAKcg2l6DTncowbLKLP+NnhKpY/lTldXz3\nJ7V8eDNEJKr1jW/avt+3HD6bCOMXO5ab39Nxe9mTFd/Si3Jh1V+UIHpoI0QmwSvtbd+bi+HdYfAn\nJ6OOLQvhm8pVCmx4A3o/WPn39ti73sakwr3OJwLWO9bMVddu0HNK3Vdb5J2GN/tB57Ew+g3Xy51K\nhw9GwfWz1EtIxifw2H4IvrxRgZX40HgAHv3x0Ssq/3Svp7kz6c4rKqu5PDxaECBxCENtMpcSn5dJ\n6KhRRDxwP17+/nwSGUWpxeJylNL2zcPY9ezNBPrWwqV7/LBt/cZnIPV+CLL7E1Y14azNQNhv90A8\nla6OLy1Uk9c63K7sDqWFsH+lTb1kLlFvqs6wmG2jElDHHTYk6KkMCIupWOZSllJf2cdXAthvZ+Ru\nO1jZLax1gZoLUVkb8k5BQGPIOWQTblZyj6ll8SXVvkaxzuupDfIywVzk+ojkkjGJ8PQOyD4ATVq7\nVs5cotRu/qFqtBbaouKIzp6Lhmrz2EbX6reSZXgCndwK5wz1XWHuFQuCgS0HsmDgAh5Y8QCgckCP\nbD3S6bELMhbwzcFvmNhuIre1vY3RX41mV/Yufs+pgZn8BqWylOTGyQ5hNDQKjxYE1hGBlak7vgTA\nPykJv1Y2dznfy3SeqhUhAI6GVi+viq6mfuUervZEtXcUBIvLGXYDGkFIM5ua55d/QN9H4bNJzt1U\nAZY8Ats/tG1/eLtt/fN7Ia6P83JvD4SH7NJ8FpxzPEdIM/A2OQoCX5t3lgPfPQ6b3rJtdxnn+L2h\nSuDDO+DIOhW641oIg9IimGuowB7NUEEEq8M6ByRjkfpMWQ5xPasuA+pe7rLLfxB/A0yq5J6BbR6K\nz2W6b345VS1NQbbrWp1HWhUIIWjfxDZiTAlPIT4s3umx1gB8iY0TaRXWiuigaBbvW8zifYudHn+l\n3N/pfh657pEardMTqBNBIIQYDMwDvIF/SSlfrI3zSDv/+lYRQTTNV3/Exne56XDTXq889GWI6gAL\njVmgvR+GDrfB+vmQ/nHFskV5Sh30T8O2kPEp9H6kohCI6w1HVZhkh7d4Zxz9BYS3slsAtOgGJ7bA\nGSORipTqk3vcsZx/KDQ2BHFAOBTkKEFlj3XEYi8EoKKHVNEFtTyyTi3PHXIuCCwWVZ8QjmE67Ned\n2SmchfSQUo1SrOQerygInJXLz4awWLh+Jnw7C3IO2ARBZaFDpHQUAmCbFFi+H9akSUXGwzvWBSFj\nfx4rpgAwcn1TaBfbypqMw3odXSDML4wPhnzAhaJcereo5MUBGJcyjpTwZHpEK7Xf/AHzOZh70PX2\nV8PMn5RaMj0r3WHC3JUiJEj7S+Dk/gkJArDgWliYCnVa9yNcTrB1pVxzQSCE8AZeAwYCx4FNQogl\nUspaCWRivYC9EpoQUpxPafdeeAUGVlOqnmJ9a+4yDlINjxtvX6WbD45Un14POhcEwU0dVRGZO+G/\nnXgAJQ5UD9uSfLjowixkaTdb+7rxShCAzfANyoDsUEbaVDzJQ2H3N7B5obKJjHpD7Xu1IzTrXPF8\nmXbB+lrfCAd+gLP7bfu+mAqzjJ/Su8PVQy3tT/D2IOXx9Icnla1i3GL1xvzJeJiyDPZ8DT/8FSZ8\noVQxH90Jw/4Oy5+G2FQ4aOSo+OMKNTIqtLOfvGvMFu85DYa8CHu+hUV2yWTm5MLhtXBis/K86nSX\nEgRfTlN6/PXz4ef/UcZd3yDVBoD0RfDFAxWvQWmhUktZRyRzclU/ygv1LQuVMb37FMf9X0yDU9th\n8Ivw/giYuctm8AflBWbl/ZEw7Vc1glk3T+3rNkm5/rpCSQFd3rtdqcX+uFxdSyf471tBn/9MgXH/\ngYR+JDRKKEvmUxOk+EWyu+gMG05voPP7Tn5XtUC42cJrp7OY0jyagqt4jn816iunyY5qkroYEaQC\n+6WUBwGEEIuAkUCNCwJp95bz9PAU9s0zEx5VMU682xDdEYa/Csl2Qcse2mgLTwHqj3/LPOUFtOF1\ntW/0AvUAsid5eLm5CtOVTaHz3dD+VmUoRMBPzzuWi0iCs+X0tu1HQ8oISOjv3CB88GfjuFvht8/V\nm3FsLxVxNWWEEjynjSBnPz4PTVPUg/bQasd6wmJtNoHYntB1ohIE1oc0OD6graqnxEGAVHMXdn6u\n9p3apvTghefVCOjwWmN/um0ktOIZKLnkWP+GN9Q5uoxXD+2NC+y+e10JghNbK14D60im51Sl34/r\no0ZUhblKCBTmKi8oezYvtK1HtIVW/ZT6rzAXzpabsHV8k/Ikyz2u7DRWTmypKAjSP1JLqxPBsQ02\nl97U+yEwwvG+Z/6mPM8axysvouObK/avMnJPqOtuvQaVCAIOrVEvNKfSIaGf6/W7yPxDv/N+oA8B\nfaaD79W9CB44to5l2ekESsGk66Yxf/t8AEY2u54WUZ0AOHxiI0vPbOaHqHgKRCF3J4ygUagTm5q1\nfUYdd8QOIrJJG4fvGvs1vqr2uoKwf1heC4QQtwODpZT3GtsTgJ5SykrdKLp37y43b76MH5/BJ/cO\nIHpfNjEh6gYUHzpM47vuJPqZZ66s8e6G9a3c3vtnbrJSa8zYod66AUbOh+vGVSxvX4eVP2fCi3HK\nSGrllnnqLRFg0TjnNge/UBjxD2WT6HqPbXY1qBhJ9gHzwuIg187VsOX1cGQttBulXEfP7Iaxi5Rd\n5NWONvWSVa0VkaSG4lYVVWCELdqrdQQV0Nimsw+OUqqzknxbXZVhVYVNXafUQS+U+3NHJivVjb1K\nJTJZPQyL8uCpLNW2jM+UnSU8AXIOVqwDbO0H2z1c8V/qzTy0uc2QH5msjk37ExxdX872EgJhdrkv\nytcLEBytrom5GJ406rS/78FRynU4eSj4N4JtH0C5h1WllBTYhExghPN5KGC7ZoFNHB0kagprn8MT\n1G/gKthgzuPeMG/65Rfwv5YmTPXKZl1gAJ/lSpK91ah9uzmPCWHehHj5kmcp5udcQbh35a6wHYPV\njP/vciHGu5ygGrtIuYRfAUKILVLKakMe1MWIwNkgqYI0EkLcD9wPEBd3ZX7ioXEJWIr98WusfrR+\niYmEjXTuteCR3Px8RX/10Qtg7/cQGqNUAye3KRVLZYx6Qz1ITm5Tnjkmf/UQ3/O10iN7+6k5C1aG\nzXUUBKn3w8VM5eaZPFwZqPuUcyfscLt6O//tc2g9QL0xe3mrkUHTduotMqgJdJsICNjxmRoR+DdS\nqrALJ8A3WM18/nW+LZ5SwXn1cIvvq2wSl85CcZ7NY0dK2L0E4nqp4/NzIDAcSoth73dqtHLgB/Un\nzD6oRgcpw9UDLTIZvH1s6ilQI7bwBOVWm33ApsaKTFKfZl1suuKEftBpjIoDFZEEx9arh7a0qGNB\nqc8O/ASD/tvuWt0K548qYbTrhNGWJHWd2t+qzhEUqdp87jBYnGTmkxYlfNoOVvfKaqeIsZugOOYj\nNSLy8bepFLtNVu7MBeeoNr6VPfE3KAFgdVRwRmQSHN9im59S0wRFKgEZ3fGqq+oozdyWvYmxjTuD\ndxCPleQRl72N1lE3lN3fFGnhNnmWC5FtiMk6QOOoiCrtBHOLMtlWcJrmUZ0qHne5Rv8roC5GBL2B\nOVLKm43tJwCklC9UVuZKRwQajUbTkHF1RFAXQec2AYlCiFZCCF9gDLCkDtqh0Wg0GupANSSlLBVC\nPAwsQ7mPviOl/O1at0Oj0Wg0ijqZRyClXAq4GMBfo9FoNLWJR+cj0Gg0Gk31aEGg0Wg0DRwtCDQa\njaaBowWBRqPRNHC0INBoNJoGzjWfUHYlCCHOAEeusHgEcLYGm1OXeEpfPKUfoPtSX/GUvlxtP1pK\nKauN2eEWguBqEEJsdmVmnTvgKX3xlH6A7kt9xVP6cq36oVVDGo1G08DRgkCj0WgaOA1BELxZ/SFu\ng6f0xVP6Abov9RVP6cs16YfH2wg0Go1GUzUNYUSg0Wg0mirwaEEghBgshPhdCLFfCDG7rttzpQgh\nDgshdgghtgsh3CoxgxDiHSFElhBip92+cCHECiHEPmNZ+7n4aoBK+jJHCHHCuDfbhRBD67KNRA4j\n3wAABZ1JREFUriCEiBVC/CiE2C2E+E0I8aix3+3uSxV9ccf74i+E2CiESDf68hdjfyshxAbjvnxi\nhO+v2XN7qmpICOEN7AUGAsdReRDGSilrPDdybSOEOAx0l1K6nV+0ECINuAi8L6XsYOx7CciRUr5o\nCOjGUsrH67KdrlBJX+YAF6WUL9dl2y4HIUQzoJmUcqsQIgTYAowCJuFm96WKvtyJ+90XAQRJKS8K\nIUzAWuBRYBbwuZRykRDiDSBdSvl6TZ7bk0cEqcB+KeVBKWUxsAhoQHkq6wdSytVA+STAI4H3jPX3\nUH/cek8lfXE7pJSnpJRbjfU8YDfQAje8L1X0xe2QiovGpsn4SOBG4D/G/lq5L54sCFoAx+y2j+Om\nPxDUj2G5EGKLkcvZ3YmSUp4C9UcGmtZxe66Wh4UQGYbqqN6rU+wRQsQD1wEbcPP7Uq4v4Ib3RQjh\nLYTYDmQBK4ADwHkppTX5dK08xzxZEDjLFO2uerC+UsquwBDgIUNFoakfvA60BroAp4C5ddsc1xFC\nBAOLgRlSygt13Z6rwUlf3PK+SCnNUsouQAxKq5Hi7LCaPq8nC4LjQKzddgxwso7aclVIKU8ayyzg\nC9QPxJ3JNHS7Vh1vVh2354qRUmYaf14L8BZucm8MHfRi4EMp5efGbre8L8764q73xYqU8jzwE9AL\naCSEsGaTrJXnmCcLgk1AomFx9wXGAEvquE2XjRAiyDCCIYQIAgYBO6suVe9ZAkw01icCX9VhW64K\n64PTYDRucG8Mo+TbwG4p5d/tvnK7+1JZX9z0vkQKIRoZ6wHATSibx4/A7cZhtXJfPNZrCMBwGXsV\n8AbekVI+V8dNumyEEAmoUQCoHNMfuVM/hBAfA/1RURQzgf8CvgQ+BeKAo8AdUsp6b4StpC/9UeoH\nCRwGHrDq2esrQojrgTXADsBi7H4SpVt3q/tSRV/G4n73pRPKGOyNekn/VEr5rPEMWASEA9uA8VLK\noho9tycLAo1Go9FUjyerhjQajUbjAloQaDQaTQNHCwKNRqNp4GhBoNFoNA0cLQg0Go2mgaMFgUaj\n0TRwfKo/RKNxX4QQTYBVxmY0YAbOGNv5Uso+NXSeUUAnKeWzNVTfy8BSKeUPNVGfRlMVeh6BpsFQ\nmyGjhRC/ACNqKlS4EKIl8JaUclBN1KfRVIVWDWkaLEKIi8ayvxDiZyHEp0KIvUKIF4UQ44wkITuE\nEK2N4yKFEIuFEJuMT19jf1ugyCoEhBB3CCF2GglGVhv7vIUQfzPKZQghHrBrx/83zpMuhHgRQEp5\nBGgihIi+tldF0xDRqiGNRtEZFekxBzgI/EtKmWpkvHoEmAHMA16RUq4VQsQBy4wyfYGtdnU9A9ws\npTxhjR0D/BHIlVL2EEL4AeuEEMuBZFR8+Z5SynwhRLhdPVuNuhfXUp81GkALAo3GyiZrLBohxAFg\nubF/B/AHY/0moJ2KcwZAqBEQsBk2uwPAOuBdIcSngDWy5yCgkxDCGjwsDEg06lwopcwHKBfbJwto\nXjPd02gqRwsCjUZhH8TLYrdtwfY/8QJ6SykL7AsKIQpQD3YApJRThRA9gWHAdiFEF1R+jEeklMvK\nlR1M5fHl/YGCSr7TaGoMbSPQaFxnOfCwdcN4wIMKFdzGbn9rKeUGKeUzwFlUXoxlwDQjdj5CiLZG\nWPHlwBQhRKCx31411BY3CJ+scX/0iECjcZ3pwGtCiAzUf2c1MNVYzhVCCKnc8P4mhEhEjQJWAelA\nBhAPbDVi6J8BRkkpvzcEymYhRDGwFHjSEBhtgM3XtIeaBol2H9VoagAhxDzgaynlyhqqbzTQVUr5\ndE3Up9FUhVYNaTQ1w/NAYA3W54Ob5NnVuD96RKDRaDQNHD0i0Gg0mgaOFgQajUbTwNGCQKPRaBo4\nWhBoNBpNA0cLAo1Go2ng/B9wAdoWV8dR2gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f041cd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Import biochemical model module\n",
"import steps.model as smod\n",
"\n",
"# Create model container\n",
"execise_mdl = smod.Model()\n",
"\n",
"# Create chemical species\n",
"MEKp = smod.Spec('MEKp', execise_mdl)\n",
"ERK = smod.Spec('ERK', execise_mdl)\n",
"MEKpERK = smod.Spec('MEKpERK', execise_mdl)\n",
"ERKp = smod.Spec('ERKp', execise_mdl)\n",
"\n",
"# Create reaction set container (volume system)\n",
"execise_vsys = smod.Volsys('execise_vsys', execise_mdl)\n",
"\n",
"# Create reactions (Do it yourself)\n",
"# MEKp + ERK -> MEKpERK, rate constant 16.2*10e6\n",
"MEKp_ERK_to_MEKpERK = smod.Reac('MEKp_ERK_to_MEKpERK', execise_vsys, lhs=[MEKp,ERK], rhs = [MEKpERK])\n",
"MEKp_ERK_to_MEKpERK.setKcst(16.2e6)\n",
"# MEKpERK -> MEKp + ERK, rate constant 0.6\n",
"MEKpERK_to_MEKp_ERK = smod.Reac('MEKpERK_to_MEKp_ERK', execise_vsys, lhs = [MEKpERK], rhs=[MEKp,ERK])\n",
"MEKpERK_to_MEKp_ERK.setKcst(0.6)\n",
"# MEKpERK -> MEKp + ERKp, rate constant 0.15\n",
"MEKpERK_to_MEKp_ERKp = smod.Reac('MEKpERK_to_MEKp_ERKp', execise_vsys, lhs = [MEKpERK], rhs=[MEKp,ERKp])\n",
"MEKpERK_to_MEKp_ERKp.setKcst(0.15)\n",
"\n",
"########### execise 5.1: Add diffusion constants\n",
"\n",
"# * MEKp = 30e-12 m^2/s\n",
"# * ERK = 30e-12 m^2/s\n",
"# * MEKpERK = 10e-12 m^2/s\n",
"\n",
"\n",
"####### You script after execise 1 should look like above #######\n",
"\n",
"########### execise 5.2: Replace the geometry to use mesh 'meshes/sp_0.1v_1046.inp'\n",
"\n",
"# Create a compartment of 0.1um^3\n",
"import steps.geom as sgeom\n",
"execise_wmgeom = sgeom.Geom()\n",
"execise_cyt = sgeom.Comp('execise_cyt', execise_wmgeom)\n",
"execise_cyt.setVol(0.1e-18)\n",
"\n",
"# Associate the compartment with the volume system 'vsys'\n",
"execise_cyt.addVolsys('execise_vsys')\n",
"\n",
"# Create and initialize a 'r123' random number generator\n",
"import steps.rng as srng\n",
"execise_r = srng.create('r123', 256)\n",
"execise_r.initialize(143)\n",
"\n",
"####### You script after execise 2 should look like above #######\n",
"\n",
"# Create a \"wmdirect\" solver and set the initial condition:\n",
"# MEKp = 1uM\n",
"# ERK = 1.5uM\n",
"import steps.solver as ssolv\n",
"\n",
"########### execise 5.3: Change the solver to Tetexact\n",
"execise_sim = ssolv.Wmdirect(execise_mdl, execise_wmgeom, execise_r)\n",
"\n",
"execise_sim.setCompConc('execise_cyt','MEKp', 1e-6)\n",
"execise_sim.setCompConc('execise_cyt','ERK', 1.5e-6)\n",
"\n",
"# Run the simulation for 30 seconds, record concerntrations of each molecule every 0.01 seconds.\n",
"import numpy as np\n",
"execise_tpnts = np.arange(0.0, 30.01, 0.01)\n",
"n_tpnts = len(execise_tpnts)\n",
"execise_res = np.zeros([n_tpnts, 4])\n",
"\n",
"# Run simulation and record data\n",
"for t in range(0, n_tpnts):\n",
" execise_sim.run(execise_tpnts[t])\n",
" execise_res[t,0] = execise_sim.getCompCount('execise_cyt','MEKp')\n",
" execise_res[t,1] = execise_sim.getCompCount('execise_cyt','ERK')\n",
" execise_res[t,2] = execise_sim.getCompCount('execise_cyt','MEKpERK')\n",
" execise_res[t,3] = execise_sim.getCompCount('execise_cyt','ERKp')\n",
"\n",
"####### You script after execise 3 should look like above #######\n",
"\n",
"# Plot execise_res\n",
"from pylab import *\n",
"plot(execise_tpnts, execise_res[:,0], label='MEKp')\n",
"plot(execise_tpnts, execise_res[:,1], label='ERK')\n",
"plot(execise_tpnts, execise_res[:,2], label='MEKpERK')\n",
"plot(execise_tpnts, execise_res[:,3], label='ERKp')\n",
"ylabel('Number of molecules')\n",
"xlabel('Time(sec)')\n",
"legend()\n",
"show()\n",
"\n",
"####### You script after execise 4 should look like above #######"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here is the modified script"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reading Abaqus file...\n",
"Number of nodes imported: 247\n",
"Number of tetrahedrons imported: 1046\n",
"Number of triangles imported: 0\n",
"creating Tetmesh object in STEPS...\n",
"Tetmesh object created.\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEKCAYAAAAfGVI8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngUaj0bRztCDQaDSads7/Ax6ez/FGJC4RAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1161d3390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Import biochemical model module\n",
"import steps.model as smod\n",
"\n",
"# Create model container\n",
"execise_mdl = smod.Model()\n",
"\n",
"# Create chemical species\n",
"MEKp = smod.Spec('MEKp', execise_mdl)\n",
"ERK = smod.Spec('ERK', execise_mdl)\n",
"MEKpERK = smod.Spec('MEKpERK', execise_mdl)\n",
"ERKp = smod.Spec('ERKp', execise_mdl)\n",
"\n",
"# Create reaction set container (volume system)\n",
"execise_vsys = smod.Volsys('execise_vsys', execise_mdl)\n",
"\n",
"# Create reactions (Do it yourself)\n",
"# MEKp + ERK -> MEKpERK, rate constant 16.2*10e6\n",
"MEKp_ERK_to_MEKpERK = smod.Reac('MEKp_ERK_to_MEKpERK', execise_vsys, lhs=[MEKp,ERK], rhs = [MEKpERK])\n",
"MEKp_ERK_to_MEKpERK.setKcst(16.2e6)\n",
"# MEKpERK -> MEKp + ERK, rate constant 0.6\n",
"MEKpERK_to_MEKp_ERK = smod.Reac('MEKpERK_to_MEKp_ERK', execise_vsys, lhs = [MEKpERK], rhs=[MEKp,ERK])\n",
"MEKpERK_to_MEKp_ERK.setKcst(0.6)\n",
"# MEKpERK -> MEKp + ERKp, rate constant 0.15\n",
"MEKpERK_to_MEKp_ERKp = smod.Reac('MEKpERK_to_MEKp_ERKp', execise_vsys, lhs = [MEKpERK], rhs=[MEKp,ERKp])\n",
"MEKpERK_to_MEKp_ERKp.setKcst(0.15)\n",
"\n",
"########### execise 5.1: Add diffusion constants\n",
"\n",
"# * MEKp = 30e-12 m^2/s\n",
"# * ERK = 30e-12 m^2/s\n",
"# * MEKpERK = 10e-12 m^2/s\n",
"\n",
"diff_MEKp = smod.Diff('diff_MEKp', execise_vsys, MEKp)\n",
"diff_MEKp.setDcst(30e-12)\n",
"diff_ERK = smod.Diff('diff_ERK', execise_vsys, ERK)\n",
"diff_ERK.setDcst(30e-12)\n",
"diff_MEKpERK = smod.Diff('diff_MEKpERK', execise_vsys, MEKpERK)\n",
"diff_MEKpERK.setDcst(10e-12)\n",
"\n",
"####### You script after execise 1 should look like above #######\n",
"\n",
"########### execise 5.2: Replace the geometry to use mesh 'meshes/sp_0.1v_1046.inp'\n",
"import steps.geom as sgeom\n",
"import steps.utilities.meshio as meshio\n",
"\n",
"mesh = meshio.importAbaqus('meshes/sp_0.1v_1046.inp', 1.0e-6)[0]\n",
"execise_cyt = sgeom.TmComp('execise_cyt', mesh, range(mesh.ntets))\n",
"execise_cyt.addVolsys('execise_vsys')\n",
"\n",
"# Create and initialize a 'r123' random number generator\n",
"import steps.rng as srng\n",
"execise_r = srng.create('r123', 256)\n",
"execise_r.initialize(143)\n",
"\n",
"####### You script after execise 2 should look like above #######\n",
"\n",
"# Create a \"wmdirect\" solver and set the initial condition:\n",
"# MEKp = 1uM\n",
"# ERK = 1.5uM\n",
"import steps.solver as ssolv\n",
"\n",
"########### execise 5.3: Change the solver to Tetexact\n",
"execise_sim = ssolv.Tetexact(execise_mdl, mesh, execise_r)\n",
"\n",
"execise_sim.setCompConc('execise_cyt','MEKp', 1e-6)\n",
"execise_sim.setCompConc('execise_cyt','ERK', 1.5e-6)\n",
"\n",
"# Run the simulation for 30 seconds, record concerntrations of each molecule every 0.01 seconds.\n",
"import numpy as np\n",
"execise_tpnts = np.arange(0.0, 30.01, 0.01)\n",
"n_tpnts = len(execise_tpnts)\n",
"execise_res = np.zeros([n_tpnts, 4])\n",
"\n",
"# Run simulation and record data\n",
"for t in range(0, n_tpnts):\n",
" execise_sim.run(execise_tpnts[t])\n",
" execise_res[t,0] = execise_sim.getCompCount('execise_cyt','MEKp')\n",
" execise_res[t,1] = execise_sim.getCompCount('execise_cyt','ERK')\n",
" execise_res[t,2] = execise_sim.getCompCount('execise_cyt','MEKpERK')\n",
" execise_res[t,3] = execise_sim.getCompCount('execise_cyt','ERKp')\n",
"\n",
"####### You script after execise 3 should look like above #######\n",
"\n",
"# Plot execise_res\n",
"from pylab import *\n",
"plot(execise_tpnts, execise_res[:,0], label='MEKp')\n",
"plot(execise_tpnts, execise_res[:,1], label='ERK')\n",
"plot(execise_tpnts, execise_res[:,2], label='MEKpERK')\n",
"plot(execise_tpnts, execise_res[:,3], label='ERKp')\n",
"ylabel('Number of molecules')\n",
"xlabel('Time(sec)')\n",
"legend()\n",
"show()\n",
"\n",
"####### You script after execise 4 should look like above #######"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-2.0 |
chongxi/spiketag | demo/GT/kernel.ipynb | 1 | 570630 | {"cells":[{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"Populating the interactive namespace from numpy and matplotlib\n"}],"source":"%pylab inline"},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[],"source":"%gui qt"},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":"/disk0/anaconda3/lib/python3.7/site-packages/sklearn/externals/six.py:31: DeprecationWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n \"(https://pypi.org/project/six/).\", DeprecationWarning)\n/disk0/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n warnings.warn(msg, category=DeprecationWarning)\n"}],"source":"from spiketag.base import mua_kernel as kernel"},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[{"data":{"text/plain":"[<matplotlib.lines.Line2D at 0x7fbbfc26ef98>]"},"execution_count":3,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","image/svg+xml":"<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"248.518125pt\" version=\"1.1\" viewBox=\"0 0 386.845313 248.518125\" width=\"386.845313pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;white-space:pre;}\n </style>\n </defs>\n <g id=\"figure_1\">\n <g id=\"patch_1\">\n <path d=\"M 0 248.518125 \nL 386.845313 248.518125 \nL 386.845313 0 \nL 0 0 \nz\n\" style=\"fill:#ffffff;\"/>\n </g>\n <g id=\"axes_1\">\n <g id=\"patch_2\">\n <path d=\"M 44.845313 224.64 \nL 379.645313 224.64 \nL 379.645313 7.2 \nL 44.845313 7.2 \nz\n\" style=\"fill:#d9d9d9;\"/>\n </g>\n <g id=\"matplotlib.axis_1\">\n <g id=\"xtick_1\">\n <g id=\"line2d_1\">\n <path clip-path=\"url(#pa39a367b73)\" d=\"M 60.063494 224.64 \nL 60.063494 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_1\">\n <!-- 0 -->\n <defs>\n <path d=\"M 31.78125 66.40625 \nQ 24.171875 66.40625 20.328125 58.90625 \nQ 16.5 51.421875 16.5 36.375 \nQ 16.5 21.390625 20.328125 13.890625 \nQ 24.171875 6.390625 31.78125 6.390625 \nQ 39.453125 6.390625 43.28125 13.890625 \nQ 47.125 21.390625 47.125 36.375 \nQ 47.125 51.421875 43.28125 58.90625 \nQ 39.453125 66.40625 31.78125 66.40625 \nz\nM 31.78125 74.21875 \nQ 44.046875 74.21875 50.515625 64.515625 \nQ 56.984375 54.828125 56.984375 36.375 \nQ 56.984375 17.96875 50.515625 8.265625 \nQ 44.046875 -1.421875 31.78125 -1.421875 \nQ 19.53125 -1.421875 13.0625 8.265625 \nQ 6.59375 17.96875 6.59375 36.375 \nQ 6.59375 54.828125 13.0625 64.515625 \nQ 19.53125 74.21875 31.78125 74.21875 \nz\n\" id=\"DejaVuSans-48\"/>\n </defs>\n <g style=\"fill:#262626;\" transform=\"translate(56.882244 239.238437)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"xtick_2\">\n <g id=\"line2d_2\">\n <path clip-path=\"url(#pa39a367b73)\" d=\"M 103.544014 224.64 \nL 103.544014 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_2\">\n <!-- 10 -->\n <defs>\n <path d=\"M 12.40625 8.296875 \nL 28.515625 8.296875 \nL 28.515625 63.921875 \nL 10.984375 60.40625 \nL 10.984375 69.390625 \nL 28.421875 72.90625 \nL 38.28125 72.90625 \nL 38.28125 8.296875 \nL 54.390625 8.296875 \nL 54.390625 0 \nL 12.40625 0 \nz\n\" id=\"DejaVuSans-49\"/>\n </defs>\n <g style=\"fill:#262626;\" transform=\"translate(97.181514 239.238437)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-49\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"xtick_3\">\n <g id=\"line2d_3\">\n <path clip-path=\"url(#pa39a367b73)\" d=\"M 147.024533 224.64 \nL 147.024533 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_3\">\n <!-- 20 -->\n <defs>\n <path d=\"M 19.1875 8.296875 \nL 53.609375 8.296875 \nL 53.609375 0 \nL 7.328125 0 \nL 7.328125 8.296875 \nQ 12.9375 14.109375 22.625 23.890625 \nQ 32.328125 33.6875 34.8125 36.53125 \nQ 39.546875 41.84375 41.421875 45.53125 \nQ 43.3125 49.21875 43.3125 52.78125 \nQ 43.3125 58.59375 39.234375 62.25 \nQ 35.15625 65.921875 28.609375 65.921875 \nQ 23.96875 65.921875 18.8125 64.3125 \nQ 13.671875 62.703125 7.8125 59.421875 \nL 7.8125 69.390625 \nQ 13.765625 71.78125 18.9375 73 \nQ 24.125 74.21875 28.421875 74.21875 \nQ 39.75 74.21875 46.484375 68.546875 \nQ 53.21875 62.890625 53.21875 53.421875 \nQ 53.21875 48.921875 51.53125 44.890625 \nQ 49.859375 40.875 45.40625 35.40625 \nQ 44.1875 33.984375 37.640625 27.21875 \nQ 31.109375 20.453125 19.1875 8.296875 \nz\n\" id=\"DejaVuSans-50\"/>\n </defs>\n <g style=\"fill:#262626;\" transform=\"translate(140.662033 239.238437)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-50\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"xtick_4\">\n <g id=\"line2d_4\">\n <path clip-path=\"url(#pa39a367b73)\" d=\"M 190.505053 224.64 \nL 190.505053 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_4\">\n <!-- 30 -->\n <defs>\n <path d=\"M 40.578125 39.3125 \nQ 47.65625 37.796875 51.625 33 \nQ 55.609375 28.21875 55.609375 21.1875 \nQ 55.609375 10.40625 48.1875 4.484375 \nQ 40.765625 -1.421875 27.09375 -1.421875 \nQ 22.515625 -1.421875 17.65625 -0.515625 \nQ 12.796875 0.390625 7.625 2.203125 \nL 7.625 11.71875 \nQ 11.71875 9.328125 16.59375 8.109375 \nQ 21.484375 6.890625 26.8125 6.890625 \nQ 36.078125 6.890625 40.9375 10.546875 \nQ 45.796875 14.203125 45.796875 21.1875 \nQ 45.796875 27.640625 41.28125 31.265625 \nQ 36.765625 34.90625 28.71875 34.90625 \nL 20.21875 34.90625 \nL 20.21875 43.015625 \nL 29.109375 43.015625 \nQ 36.375 43.015625 40.234375 45.921875 \nQ 44.09375 48.828125 44.09375 54.296875 \nQ 44.09375 59.90625 40.109375 62.90625 \nQ 36.140625 65.921875 28.71875 65.921875 \nQ 24.65625 65.921875 20.015625 65.03125 \nQ 15.375 64.15625 9.8125 62.3125 \nL 9.8125 71.09375 \nQ 15.4375 72.65625 20.34375 73.4375 \nQ 25.25 74.21875 29.59375 74.21875 \nQ 40.828125 74.21875 47.359375 69.109375 \nQ 53.90625 64.015625 53.90625 55.328125 \nQ 53.90625 49.265625 50.4375 45.09375 \nQ 46.96875 40.921875 40.578125 39.3125 \nz\n\" id=\"DejaVuSans-51\"/>\n </defs>\n <g style=\"fill:#262626;\" transform=\"translate(184.142553 239.238437)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-51\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"xtick_5\">\n <g id=\"line2d_5\">\n <path clip-path=\"url(#pa39a367b73)\" d=\"M 233.985572 224.64 \nL 233.985572 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_5\">\n <!-- 40 -->\n <defs>\n <path d=\"M 37.796875 64.3125 \nL 12.890625 25.390625 \nL 37.796875 25.390625 \nz\nM 35.203125 72.90625 \nL 47.609375 72.90625 \nL 47.609375 25.390625 \nL 58.015625 25.390625 \nL 58.015625 17.1875 \nL 47.609375 17.1875 \nL 47.609375 0 \nL 37.796875 0 \nL 37.796875 17.1875 \nL 4.890625 17.1875 \nL 4.890625 26.703125 \nz\n\" id=\"DejaVuSans-52\"/>\n </defs>\n <g style=\"fill:#262626;\" transform=\"translate(227.623072 239.238437)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-52\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"xtick_6\">\n <g id=\"line2d_6\">\n <path clip-path=\"url(#pa39a367b73)\" d=\"M 277.466092 224.64 \nL 277.466092 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_6\">\n <!-- 50 -->\n <defs>\n <path d=\"M 10.796875 72.90625 \nL 49.515625 72.90625 \nL 49.515625 64.59375 \nL 19.828125 64.59375 \nL 19.828125 46.734375 \nQ 21.96875 47.46875 24.109375 47.828125 \nQ 26.265625 48.1875 28.421875 48.1875 \nQ 40.625 48.1875 47.75 41.5 \nQ 54.890625 34.8125 54.890625 23.390625 \nQ 54.890625 11.625 47.5625 5.09375 \nQ 40.234375 -1.421875 26.90625 -1.421875 \nQ 22.3125 -1.421875 17.546875 -0.640625 \nQ 12.796875 0.140625 7.71875 1.703125 \nL 7.71875 11.625 \nQ 12.109375 9.234375 16.796875 8.0625 \nQ 21.484375 6.890625 26.703125 6.890625 \nQ 35.15625 6.890625 40.078125 11.328125 \nQ 45.015625 15.765625 45.015625 23.390625 \nQ 45.015625 31 40.078125 35.4375 \nQ 35.15625 39.890625 26.703125 39.890625 \nQ 22.75 39.890625 18.8125 39.015625 \nQ 14.890625 38.140625 10.796875 36.28125 \nz\n\" id=\"DejaVuSans-53\"/>\n </defs>\n <g style=\"fill:#262626;\" transform=\"translate(271.103592 239.238437)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-53\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"xtick_7\">\n <g id=\"line2d_7\">\n <path clip-path=\"url(#pa39a367b73)\" d=\"M 320.946611 224.64 \nL 320.946611 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_7\">\n <!-- 60 -->\n <defs>\n <path d=\"M 33.015625 40.375 \nQ 26.375 40.375 22.484375 35.828125 \nQ 18.609375 31.296875 18.609375 23.390625 \nQ 18.609375 15.53125 22.484375 10.953125 \nQ 26.375 6.390625 33.015625 6.390625 \nQ 39.65625 6.390625 43.53125 10.953125 \nQ 47.40625 15.53125 47.40625 23.390625 \nQ 47.40625 31.296875 43.53125 35.828125 \nQ 39.65625 40.375 33.015625 40.375 \nz\nM 52.59375 71.296875 \nL 52.59375 62.3125 \nQ 48.875 64.0625 45.09375 64.984375 \nQ 41.3125 65.921875 37.59375 65.921875 \nQ 27.828125 65.921875 22.671875 59.328125 \nQ 17.53125 52.734375 16.796875 39.40625 \nQ 19.671875 43.65625 24.015625 45.921875 \nQ 28.375 48.1875 33.59375 48.1875 \nQ 44.578125 48.1875 50.953125 41.515625 \nQ 57.328125 34.859375 57.328125 23.390625 \nQ 57.328125 12.15625 50.6875 5.359375 \nQ 44.046875 -1.421875 33.015625 -1.421875 \nQ 20.359375 -1.421875 13.671875 8.265625 \nQ 6.984375 17.96875 6.984375 36.375 \nQ 6.984375 53.65625 15.1875 63.9375 \nQ 23.390625 74.21875 37.203125 74.21875 \nQ 40.921875 74.21875 44.703125 73.484375 \nQ 48.484375 72.75 52.59375 71.296875 \nz\n\" id=\"DejaVuSans-54\"/>\n </defs>\n <g style=\"fill:#262626;\" transform=\"translate(314.584111 239.238437)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-54\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"xtick_8\">\n <g id=\"line2d_8\">\n <path clip-path=\"url(#pa39a367b73)\" d=\"M 364.427131 224.64 \nL 364.427131 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_8\">\n <!-- 70 -->\n <defs>\n <path d=\"M 8.203125 72.90625 \nL 55.078125 72.90625 \nL 55.078125 68.703125 \nL 28.609375 0 \nL 18.3125 0 \nL 43.21875 64.59375 \nL 8.203125 64.59375 \nz\n\" id=\"DejaVuSans-55\"/>\n </defs>\n <g style=\"fill:#262626;\" transform=\"translate(358.064631 239.238437)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-55\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n </g>\n <g id=\"matplotlib.axis_2\">\n <g id=\"ytick_1\">\n <g id=\"line2d_9\">\n <path clip-path=\"url(#pa39a367b73)\" d=\"M 44.845313 204.02767 \nL 379.645313 204.02767 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_9\">\n <!-- −0.05 -->\n <defs>\n <path d=\"M 10.59375 35.5 \nL 73.1875 35.5 \nL 73.1875 27.203125 \nL 10.59375 27.203125 \nz\n\" id=\"DejaVuSans-8722\"/>\n <path d=\"M 10.6875 12.40625 \nL 21 12.40625 \nL 21 0 \nL 10.6875 0 \nz\n\" id=\"DejaVuSans-46\"/>\n </defs>\n <g style=\"fill:#262626;\" transform=\"translate(7.2 207.826889)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-8722\"/>\n <use x=\"83.789062\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"147.412109\" xlink:href=\"#DejaVuSans-46\"/>\n <use x=\"179.199219\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"242.822266\" xlink:href=\"#DejaVuSans-53\"/>\n </g>\n </g>\n </g>\n <g id=\"ytick_2\">\n <g id=\"line2d_10\">\n <path clip-path=\"url(#pa39a367b73)\" d=\"M 44.845313 169.194243 \nL 379.645313 169.194243 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_10\">\n <!-- 0.00 -->\n <g style=\"fill:#262626;\" transform=\"translate(15.579688 172.993462)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-46\"/>\n <use x=\"95.410156\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"159.033203\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"ytick_3\">\n <g id=\"line2d_11\">\n <path clip-path=\"url(#pa39a367b73)\" d=\"M 44.845313 134.360816 \nL 379.645313 134.360816 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_11\">\n <!-- 0.05 -->\n <g style=\"fill:#262626;\" transform=\"translate(15.579688 138.160034)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-46\"/>\n <use x=\"95.410156\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"159.033203\" xlink:href=\"#DejaVuSans-53\"/>\n </g>\n </g>\n </g>\n <g id=\"ytick_4\">\n <g id=\"line2d_12\">\n <path clip-path=\"url(#pa39a367b73)\" d=\"M 44.845313 99.527388 \nL 379.645313 99.527388 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_12\">\n <!-- 0.10 -->\n <g style=\"fill:#262626;\" transform=\"translate(15.579688 103.326607)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-46\"/>\n <use x=\"95.410156\" xlink:href=\"#DejaVuSans-49\"/>\n <use x=\"159.033203\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"ytick_5\">\n <g id=\"line2d_13\">\n <path clip-path=\"url(#pa39a367b73)\" d=\"M 44.845313 64.693961 \nL 379.645313 64.693961 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_13\">\n <!-- 0.15 -->\n <g style=\"fill:#262626;\" transform=\"translate(15.579688 68.49318)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-46\"/>\n <use x=\"95.410156\" xlink:href=\"#DejaVuSans-49\"/>\n <use x=\"159.033203\" xlink:href=\"#DejaVuSans-53\"/>\n </g>\n </g>\n </g>\n <g id=\"ytick_6\">\n <g id=\"line2d_14\">\n <path clip-path=\"url(#pa39a367b73)\" d=\"M 44.845313 29.860534 \nL 379.645313 29.860534 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_14\">\n <!-- 0.20 -->\n <g style=\"fill:#262626;\" transform=\"translate(15.579688 33.659752)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-46\"/>\n <use x=\"95.410156\" xlink:href=\"#DejaVuSans-50\"/>\n <use x=\"159.033203\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n </g>\n <g id=\"line2d_15\">\n <path clip-path=\"url(#pa39a367b73)\" d=\"M 60.063494 178.08861 \nL 64.411546 176.206211 \nL 68.759598 176.58102 \nL 73.10765 179.775245 \nL 77.455702 185.060869 \nL 81.803754 190.546438 \nL 86.151806 194.020025 \nL 90.499858 194.157968 \nL 94.84791 191.464646 \nL 99.195962 188.297591 \nL 103.544014 187.755583 \nL 107.892066 191.796259 \nL 112.240118 199.484694 \nL 116.58817 206.410276 \nL 120.936222 205.879415 \nL 125.284274 191.663894 \nL 129.632325 161.250828 \nL 133.980377 118.065739 \nL 138.328429 71.43771 \nL 142.676481 33.963907 \nL 147.024533 17.083636 \nL 151.372585 26.697659 \nL 155.720637 60.632386 \nL 160.068689 109.1651 \nL 164.416741 158.581891 \nL 168.764793 196.179002 \nL 173.112845 214.756364 \nL 177.460897 214.677643 \nL 181.808949 202.757643 \nL 186.157001 188.638959 \nL 190.505053 180.383437 \nL 194.853105 181.273082 \nL 199.201157 189.128019 \nL 203.549209 198.20352 \nL 207.897261 202.550734 \nL 212.245313 199.105011 \nL 216.593364 189.051387 \nL 220.941416 176.956524 \nL 225.289468 168.275476 \nL 229.63752 166.59971 \nL 233.985572 172.015751 \nL 238.333624 181.307219 \nL 242.681676 189.749448 \nL 247.029728 193.49056 \nL 251.37778 191.284208 \nL 255.725832 184.787079 \nL 260.073884 177.400302 \nL 264.421936 172.383731 \nL 268.769988 171.25053 \nL 273.11804 173.242096 \nL 277.466092 176.029676 \nL 281.814144 177.164131 \nL 286.162196 175.438692 \nL 290.510248 171.460993 \nL 294.8583 167.191251 \nL 299.206351 164.782589 \nL 303.554403 165.388064 \nL 307.902455 168.569721 \nL 312.250507 172.577753 \nL 316.598559 175.293785 \nL 320.946611 175.301797 \nL 325.294663 172.529404 \nL 329.642715 168.170697 \nL 333.990767 163.996677 \nL 338.338819 161.461222 \nL 342.686871 161.071784 \nL 347.034923 162.307744 \nL 351.382975 164.053177 \nL 355.731027 165.258065 \nL 360.079079 165.451948 \nL 364.427131 169.194243 \n\" style=\"fill:none;stroke:#8dd3c7;stroke-linecap:round;stroke-width:1.5;\"/>\n </g>\n <g id=\"patch_3\">\n <path d=\"M 44.845313 224.64 \nL 44.845313 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n </g>\n <g id=\"patch_4\">\n <path d=\"M 379.645313 224.64 \nL 379.645313 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n </g>\n <g id=\"patch_5\">\n <path d=\"M 44.845313 224.64 \nL 379.645313 224.64 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n </g>\n <g id=\"patch_6\">\n <path d=\"M 44.845313 7.2 \nL 379.645313 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n </g>\n </g>\n </g>\n <defs>\n <clipPath id=\"pa39a367b73\">\n <rect height=\"217.44\" width=\"334.8\" x=\"44.845313\" y=\"7.2\"/>\n </clipPath>\n </defs>\n</svg>\n","text/plain":"<Figure size 432x288 with 1 Axes>"},"metadata":{},"output_type":"display_data"}],"source":"plot(kernel)"},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[],"source":"from numpy.fft import fft, ifft"},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[],"source":"from spiketag.base import bload"},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[],"source":"bf = bload(nCh=3, fs=25000.)"},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":"2019-10-17 16:53:04,331 - spiketag - INFO - ############# load data ###################\n2019-10-17 16:53:04,333 - spiketag - INFO - /disk0/Work/pydev/spiketag/demo/GT/cell_0109_mua_25000Hz.bin loaded, it contains: \n2019-10-17 16:53:04,333 - spiketag - INFO - 11600119.0 * 3 points (139201428 bytes) \n2019-10-17 16:53:04,334 - spiketag - INFO - 3 channels with sampling rate of 25000.0000 \n2019-10-17 16:53:04,335 - spiketag - INFO - 464.005 secs (7.733 mins) of data\n2019-10-17 16:53:04,336 - spiketag - INFO - #############################################\n"}],"source":"folder = '/disk0/Work/pydev/spiketag/demo/GT/'\nbf.load(folder+'cell_0109_mua_25000Hz.bin', dtype='float32')"},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[],"source":"bf.show()"},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[],"source":"data = bf.data.numpy().reshape(-1,3)"},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[],"source":"x = data[:,2]"},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"data":{"text/plain":"(11600119,)"},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":"x.shape"},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[{"data":{"text/plain":"[<matplotlib.lines.Line2D at 0x7fbbd4837278>]"},"execution_count":14,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","image/svg+xml":"<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"248.518125pt\" version=\"1.1\" viewBox=\"0 0 406.627049 248.518125\" width=\"406.627049pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;white-space:pre;}\n </style>\n </defs>\n <g id=\"figure_1\">\n <g id=\"patch_1\">\n <path d=\"M 0 248.518125 \nL 406.627049 248.518125 \nL 406.627049 0 \nL 0 0 \nz\n\" style=\"fill:#ffffff;\"/>\n </g>\n <g id=\"axes_1\">\n <g id=\"patch_2\">\n <path d=\"M 60.754688 224.64 \nL 395.554688 224.64 \nL 395.554688 7.2 \nL 60.754688 7.2 \nz\n\" style=\"fill:#d9d9d9;\"/>\n </g>\n <g id=\"matplotlib.axis_1\">\n <g id=\"xtick_1\">\n <g id=\"line2d_1\">\n <path clip-path=\"url(#pd56fccd151)\" d=\"M 75.972869 224.64 \nL 75.972869 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_1\">\n <!-- 0 -->\n <defs>\n <path d=\"M 31.78125 66.40625 \nQ 24.171875 66.40625 20.328125 58.90625 \nQ 16.5 51.421875 16.5 36.375 \nQ 16.5 21.390625 20.328125 13.890625 \nQ 24.171875 6.390625 31.78125 6.390625 \nQ 39.453125 6.390625 43.28125 13.890625 \nQ 47.125 21.390625 47.125 36.375 \nQ 47.125 51.421875 43.28125 58.90625 \nQ 39.453125 66.40625 31.78125 66.40625 \nz\nM 31.78125 74.21875 \nQ 44.046875 74.21875 50.515625 64.515625 \nQ 56.984375 54.828125 56.984375 36.375 \nQ 56.984375 17.96875 50.515625 8.265625 \nQ 44.046875 -1.421875 31.78125 -1.421875 \nQ 19.53125 -1.421875 13.0625 8.265625 \nQ 6.59375 17.96875 6.59375 36.375 \nQ 6.59375 54.828125 13.0625 64.515625 \nQ 19.53125 74.21875 31.78125 74.21875 \nz\n\" id=\"DejaVuSans-48\"/>\n </defs>\n <g style=\"fill:#262626;\" transform=\"translate(72.791619 239.238437)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"xtick_2\">\n <g id=\"line2d_2\">\n <path clip-path=\"url(#pd56fccd151)\" d=\"M 136.846205 224.64 \nL 136.846205 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_2\">\n <!-- 20000 -->\n <defs>\n <path d=\"M 19.1875 8.296875 \nL 53.609375 8.296875 \nL 53.609375 0 \nL 7.328125 0 \nL 7.328125 8.296875 \nQ 12.9375 14.109375 22.625 23.890625 \nQ 32.328125 33.6875 34.8125 36.53125 \nQ 39.546875 41.84375 41.421875 45.53125 \nQ 43.3125 49.21875 43.3125 52.78125 \nQ 43.3125 58.59375 39.234375 62.25 \nQ 35.15625 65.921875 28.609375 65.921875 \nQ 23.96875 65.921875 18.8125 64.3125 \nQ 13.671875 62.703125 7.8125 59.421875 \nL 7.8125 69.390625 \nQ 13.765625 71.78125 18.9375 73 \nQ 24.125 74.21875 28.421875 74.21875 \nQ 39.75 74.21875 46.484375 68.546875 \nQ 53.21875 62.890625 53.21875 53.421875 \nQ 53.21875 48.921875 51.53125 44.890625 \nQ 49.859375 40.875 45.40625 35.40625 \nQ 44.1875 33.984375 37.640625 27.21875 \nQ 31.109375 20.453125 19.1875 8.296875 \nz\n\" id=\"DejaVuSans-50\"/>\n </defs>\n <g style=\"fill:#262626;\" transform=\"translate(120.939955 239.238437)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-50\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"254.492188\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"xtick_3\">\n <g id=\"line2d_3\">\n <path clip-path=\"url(#pd56fccd151)\" d=\"M 197.719541 224.64 \nL 197.719541 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_3\">\n <!-- 40000 -->\n <defs>\n <path d=\"M 37.796875 64.3125 \nL 12.890625 25.390625 \nL 37.796875 25.390625 \nz\nM 35.203125 72.90625 \nL 47.609375 72.90625 \nL 47.609375 25.390625 \nL 58.015625 25.390625 \nL 58.015625 17.1875 \nL 47.609375 17.1875 \nL 47.609375 0 \nL 37.796875 0 \nL 37.796875 17.1875 \nL 4.890625 17.1875 \nL 4.890625 26.703125 \nz\n\" id=\"DejaVuSans-52\"/>\n </defs>\n <g style=\"fill:#262626;\" transform=\"translate(181.813291 239.238437)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-52\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"254.492188\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"xtick_4\">\n <g id=\"line2d_4\">\n <path clip-path=\"url(#pd56fccd151)\" d=\"M 258.592877 224.64 \nL 258.592877 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_4\">\n <!-- 60000 -->\n <defs>\n <path d=\"M 33.015625 40.375 \nQ 26.375 40.375 22.484375 35.828125 \nQ 18.609375 31.296875 18.609375 23.390625 \nQ 18.609375 15.53125 22.484375 10.953125 \nQ 26.375 6.390625 33.015625 6.390625 \nQ 39.65625 6.390625 43.53125 10.953125 \nQ 47.40625 15.53125 47.40625 23.390625 \nQ 47.40625 31.296875 43.53125 35.828125 \nQ 39.65625 40.375 33.015625 40.375 \nz\nM 52.59375 71.296875 \nL 52.59375 62.3125 \nQ 48.875 64.0625 45.09375 64.984375 \nQ 41.3125 65.921875 37.59375 65.921875 \nQ 27.828125 65.921875 22.671875 59.328125 \nQ 17.53125 52.734375 16.796875 39.40625 \nQ 19.671875 43.65625 24.015625 45.921875 \nQ 28.375 48.1875 33.59375 48.1875 \nQ 44.578125 48.1875 50.953125 41.515625 \nQ 57.328125 34.859375 57.328125 23.390625 \nQ 57.328125 12.15625 50.6875 5.359375 \nQ 44.046875 -1.421875 33.015625 -1.421875 \nQ 20.359375 -1.421875 13.671875 8.265625 \nQ 6.984375 17.96875 6.984375 36.375 \nQ 6.984375 53.65625 15.1875 63.9375 \nQ 23.390625 74.21875 37.203125 74.21875 \nQ 40.921875 74.21875 44.703125 73.484375 \nQ 48.484375 72.75 52.59375 71.296875 \nz\n\" id=\"DejaVuSans-54\"/>\n </defs>\n <g style=\"fill:#262626;\" transform=\"translate(242.686627 239.238437)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-54\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"254.492188\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"xtick_5\">\n <g id=\"line2d_5\">\n <path clip-path=\"url(#pd56fccd151)\" d=\"M 319.466213 224.64 \nL 319.466213 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_5\">\n <!-- 80000 -->\n <defs>\n <path d=\"M 31.78125 34.625 \nQ 24.75 34.625 20.71875 30.859375 \nQ 16.703125 27.09375 16.703125 20.515625 \nQ 16.703125 13.921875 20.71875 10.15625 \nQ 24.75 6.390625 31.78125 6.390625 \nQ 38.8125 6.390625 42.859375 10.171875 \nQ 46.921875 13.96875 46.921875 20.515625 \nQ 46.921875 27.09375 42.890625 30.859375 \nQ 38.875 34.625 31.78125 34.625 \nz\nM 21.921875 38.8125 \nQ 15.578125 40.375 12.03125 44.71875 \nQ 8.5 49.078125 8.5 55.328125 \nQ 8.5 64.0625 14.71875 69.140625 \nQ 20.953125 74.21875 31.78125 74.21875 \nQ 42.671875 74.21875 48.875 69.140625 \nQ 55.078125 64.0625 55.078125 55.328125 \nQ 55.078125 49.078125 51.53125 44.71875 \nQ 48 40.375 41.703125 38.8125 \nQ 48.828125 37.15625 52.796875 32.3125 \nQ 56.78125 27.484375 56.78125 20.515625 \nQ 56.78125 9.90625 50.3125 4.234375 \nQ 43.84375 -1.421875 31.78125 -1.421875 \nQ 19.734375 -1.421875 13.25 4.234375 \nQ 6.78125 9.90625 6.78125 20.515625 \nQ 6.78125 27.484375 10.78125 32.3125 \nQ 14.796875 37.15625 21.921875 38.8125 \nz\nM 18.3125 54.390625 \nQ 18.3125 48.734375 21.84375 45.5625 \nQ 25.390625 42.390625 31.78125 42.390625 \nQ 38.140625 42.390625 41.71875 45.5625 \nQ 45.3125 48.734375 45.3125 54.390625 \nQ 45.3125 60.0625 41.71875 63.234375 \nQ 38.140625 66.40625 31.78125 66.40625 \nQ 25.390625 66.40625 21.84375 63.234375 \nQ 18.3125 60.0625 18.3125 54.390625 \nz\n\" id=\"DejaVuSans-56\"/>\n </defs>\n <g style=\"fill:#262626;\" transform=\"translate(303.559963 239.238437)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-56\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"254.492188\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"xtick_6\">\n <g id=\"line2d_6\">\n <path clip-path=\"url(#pd56fccd151)\" d=\"M 380.339549 224.64 \nL 380.339549 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_6\">\n <!-- 100000 -->\n <defs>\n <path d=\"M 12.40625 8.296875 \nL 28.515625 8.296875 \nL 28.515625 63.921875 \nL 10.984375 60.40625 \nL 10.984375 69.390625 \nL 28.421875 72.90625 \nL 38.28125 72.90625 \nL 38.28125 8.296875 \nL 54.390625 8.296875 \nL 54.390625 0 \nL 12.40625 0 \nz\n\" id=\"DejaVuSans-49\"/>\n </defs>\n <g style=\"fill:#262626;\" transform=\"translate(361.252049 239.238437)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-49\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"254.492188\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"318.115234\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n </g>\n <g id=\"matplotlib.axis_2\">\n <g id=\"ytick_1\">\n <g id=\"line2d_7\">\n <path clip-path=\"url(#pd56fccd151)\" d=\"M 60.754688 212.36592 \nL 395.554688 212.36592 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_7\">\n <!-- −200000 -->\n <defs>\n <path d=\"M 10.59375 35.5 \nL 73.1875 35.5 \nL 73.1875 27.203125 \nL 10.59375 27.203125 \nz\n\" id=\"DejaVuSans-8722\"/>\n </defs>\n <g style=\"fill:#262626;\" transform=\"translate(7.2 216.165139)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-8722\"/>\n <use x=\"83.789062\" xlink:href=\"#DejaVuSans-50\"/>\n <use x=\"147.412109\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"211.035156\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"274.658203\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"338.28125\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"401.904297\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"ytick_2\">\n <g id=\"line2d_8\">\n <path clip-path=\"url(#pd56fccd151)\" d=\"M 60.754688 176.876363 \nL 395.554688 176.876363 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_8\">\n <!-- −150000 -->\n <defs>\n <path d=\"M 10.796875 72.90625 \nL 49.515625 72.90625 \nL 49.515625 64.59375 \nL 19.828125 64.59375 \nL 19.828125 46.734375 \nQ 21.96875 47.46875 24.109375 47.828125 \nQ 26.265625 48.1875 28.421875 48.1875 \nQ 40.625 48.1875 47.75 41.5 \nQ 54.890625 34.8125 54.890625 23.390625 \nQ 54.890625 11.625 47.5625 5.09375 \nQ 40.234375 -1.421875 26.90625 -1.421875 \nQ 22.3125 -1.421875 17.546875 -0.640625 \nQ 12.796875 0.140625 7.71875 1.703125 \nL 7.71875 11.625 \nQ 12.109375 9.234375 16.796875 8.0625 \nQ 21.484375 6.890625 26.703125 6.890625 \nQ 35.15625 6.890625 40.078125 11.328125 \nQ 45.015625 15.765625 45.015625 23.390625 \nQ 45.015625 31 40.078125 35.4375 \nQ 35.15625 39.890625 26.703125 39.890625 \nQ 22.75 39.890625 18.8125 39.015625 \nQ 14.890625 38.140625 10.796875 36.28125 \nz\n\" id=\"DejaVuSans-53\"/>\n </defs>\n <g style=\"fill:#262626;\" transform=\"translate(7.2 180.675582)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-8722\"/>\n <use x=\"83.789062\" xlink:href=\"#DejaVuSans-49\"/>\n <use x=\"147.412109\" xlink:href=\"#DejaVuSans-53\"/>\n <use x=\"211.035156\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"274.658203\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"338.28125\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"401.904297\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"ytick_3\">\n <g id=\"line2d_9\">\n <path clip-path=\"url(#pd56fccd151)\" d=\"M 60.754688 141.386807 \nL 395.554688 141.386807 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_9\">\n <!-- −100000 -->\n <g style=\"fill:#262626;\" transform=\"translate(7.2 145.186026)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-8722\"/>\n <use x=\"83.789062\" xlink:href=\"#DejaVuSans-49\"/>\n <use x=\"147.412109\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"211.035156\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"274.658203\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"338.28125\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"401.904297\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"ytick_4\">\n <g id=\"line2d_10\">\n <path clip-path=\"url(#pd56fccd151)\" d=\"M 60.754688 105.89725 \nL 395.554688 105.89725 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_10\">\n <!-- −50000 -->\n <g style=\"fill:#262626;\" transform=\"translate(13.5625 109.696469)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-8722\"/>\n <use x=\"83.789062\" xlink:href=\"#DejaVuSans-53\"/>\n <use x=\"147.412109\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"211.035156\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"274.658203\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"338.28125\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"ytick_5\">\n <g id=\"line2d_11\">\n <path clip-path=\"url(#pd56fccd151)\" d=\"M 60.754688 70.407693 \nL 395.554688 70.407693 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_11\">\n <!-- 0 -->\n <g style=\"fill:#262626;\" transform=\"translate(47.392188 74.206912)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"ytick_6\">\n <g id=\"line2d_12\">\n <path clip-path=\"url(#pd56fccd151)\" d=\"M 60.754688 34.918137 \nL 395.554688 34.918137 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_12\">\n <!-- 50000 -->\n <g style=\"fill:#262626;\" transform=\"translate(21.942188 38.717355)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-53\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"254.492188\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n </g>\n <g id=\"line2d_13\">\n <path clip-path=\"url(#pd56fccd151)\" d=\"M 75.972869 70.407693 \nL 75.991131 70.313558 \nL 76.033743 66.081451 \nL 76.076354 64.474775 \nL 76.061136 78.739139 \nL 76.085485 75.684766 \nL 76.091572 81.444625 \nL 76.106791 63.667994 \nL 76.195057 75.275205 \nL 76.234625 84.101357 \nL 76.25593 55.322575 \nL 76.304629 74.838344 \nL 76.31376 77.682172 \nL 76.362459 55.068261 \nL 76.368546 51.33055 \nL 76.417245 80.136571 \nL 76.450725 74.489416 \nL 76.487249 66.262228 \nL 76.563341 78.395412 \nL 76.581603 63.320913 \nL 76.615083 81.946538 \nL 76.679 75.454169 \nL 76.709437 76.184746 \nL 76.718568 67.551246 \nL 76.782485 60.279204 \nL 76.742917 79.831867 \nL 76.825096 71.526351 \nL 76.907275 74.241913 \nL 76.849445 61.426947 \nL 76.925537 65.772991 \nL 77.04424 79.910431 \nL 77.068589 69.245807 \nL 77.089895 65.365256 \nL 77.123375 74.570022 \nL 77.205554 78.184031 \nL 77.150768 60.708454 \nL 77.229904 74.190409 \nL 77.315126 62.661183 \nL 77.342519 72.686627 \nL 77.354694 78.230751 \nL 77.369912 63.897522 \nL 77.44296 71.258807 \nL 77.455135 63.659595 \nL 77.503834 83.184111 \nL 77.555576 68.071125 \nL 77.646886 74.52738 \nL 77.5921 62.184277 \nL 77.656017 68.38164 \nL 77.665148 60.215149 \nL 77.71689 73.62336 \nL 77.765589 69.607334 \nL 77.838637 66.149541 \nL 77.796026 77.699036 \nL 77.884292 66.791674 \nL 77.942122 80.106029 \nL 77.963427 60.280317 \nL 78.021257 73.646082 \nL 78.118654 60.33209 \nL 78.094305 76.723425 \nL 78.149091 69.509654 \nL 78.246488 81.036319 \nL 78.258663 70.82279 \nL 78.362148 63.06051 \nL 78.313449 76.637347 \nL 78.371279 68.235357 \nL 78.47172 79.752406 \nL 78.426065 67.788563 \nL 78.480851 70.362008 \nL 78.56303 63.416593 \nL 78.502156 76.342491 \nL 78.590423 72.046701 \nL 78.651296 78.606512 \nL 78.623903 64.120399 \nL 78.690864 69.680884 \nL 78.693907 69.676663 \nL 78.760868 81.700688 \nL 78.773043 65.578546 \nL 78.879571 54.188653 \nL 78.843047 81.825861 \nL 78.888702 59.551496 \nL 78.916095 84.608941 \nL 79.016536 72.455114 \nL 79.080453 59.305108 \nL 79.062191 80.073067 \nL 79.113933 73.156674 \nL 79.141326 80.445339 \nL 79.162632 60.866997 \nL 79.220462 71.183912 \nL 79.281335 79.772147 \nL 79.342208 60.283737 \nL 79.348296 62.611093 \nL 79.36047 73.406845 \nL 79.463955 68.7776 \nL 79.512654 79.439797 \nL 79.552221 65.764895 \nL 79.588745 72.864868 \nL 79.673968 58.509153 \nL 79.655706 77.545939 \nL 79.710492 67.66491 \nL 79.722667 77.703846 \nL 79.81702 63.023218 \nL 79.887025 61.606444 \nL 79.868763 77.386128 \nL 79.896156 69.428791 \nL 80.005728 77.423665 \nL 79.96616 64.683093 \nL 80.011815 75.822725 \nL 80.118344 60.314043 \nL 80.084863 80.943852 \nL 80.139649 68.327126 \nL 80.142693 68.384581 \nL 80.157911 66.910889 \nL 80.197479 62.053263 \nL 80.179217 77.209651 \nL 80.221828 71.704936 \nL 80.29792 80.00963 \nL 80.270527 56.910082 \nL 80.337488 74.285434 \nL 80.407492 64.93666 \nL 80.453147 70.689042 \nL 80.517064 84.660732 \nL 80.498802 62.323681 \nL 80.556632 71.981965 \nL 80.638811 57.334087 \nL 80.580981 72.138361 \nL 80.669247 66.508191 \nL 80.678378 65.727748 \nL 80.693597 76.728927 \nL 80.775776 81.503645 \nL 80.714902 60.198165 \nL 80.806212 79.481954 \nL 80.943177 62.217782 \nL 80.964483 65.777756 \nL 80.988832 77.631841 \nL 81.074055 66.564417 \nL 81.083186 60.684602 \nL 81.168409 76.475622 \nL 81.183627 64.126394 \nL 81.287112 81.368884 \nL 81.299286 72.56104 \nL 81.311461 61.259713 \nL 81.363203 75.436606 \nL 81.414946 65.396209 \nL 81.436251 76.899867 \nL 81.454513 62.250483 \nL 81.527561 67.646512 \nL 81.637133 65.670058 \nL 81.594522 74.470322 \nL 81.640177 65.955285 \nL 81.740618 77.262288 \nL 81.688876 64.625358 \nL 81.75888 68.04562 \nL 81.828884 62.707704 \nL 81.786273 76.776527 \nL 81.844103 75.823238 \nL 81.904976 62.389151 \nL 81.953675 74.415171 \nL 82.03281 77.459141 \nL 81.978024 66.645141 \nL 82.048028 67.955649 \nL 82.051072 66.69823 \nL 82.139338 75.508265 \nL 82.145426 76.532476 \nL 82.206299 62.297157 \nL 82.212386 63.161642 \nL 82.254998 78.367182 \nL 82.334133 68.69325 \nL 82.422399 60.541645 \nL 82.404137 77.148016 \nL 82.437618 73.168109 \nL 82.501535 63.040107 \nL 82.52284 79.245273 \nL 82.538059 86.367935 \nL 82.574583 66.857359 \nL 82.617194 70.475184 \nL 82.717635 58.886989 \nL 82.699373 73.288406 \nL 82.72981 67.488498 \nL 82.778508 78.711164 \nL 82.818076 64.817535 \nL 82.8546 74.959829 \nL 82.94591 62.656019 \nL 82.967216 73.094091 \nL 82.973303 72.564625 \nL 83.088962 65.011527 \nL 83.055482 79.460994 \nL 83.09505 67.192099 \nL 83.110268 80.390032 \nL 83.16201 59.279268 \nL 83.204622 67.692205 \nL 83.213753 64.535225 \nL 83.259408 75.001177 \nL 83.298975 74.1281 \nL 83.372023 78.698179 \nL 83.347674 64.570899 \nL 83.399416 70.601948 \nL 83.466377 63.446568 \nL 83.484639 76.457718 \nL 83.508988 69.271688 \nL 83.612473 77.335501 \nL 83.539425 63.735328 \nL 83.627691 73.946515 \nL 83.658128 58.002932 \nL 83.67639 76.480698 \nL 83.749438 66.726892 \nL 83.764656 77.863247 \nL 83.862054 70.383552 \nL 83.922927 61.131059 \nL 83.941189 73.183609 \nL 83.971626 71.472405 \nL 83.992931 73.947768 \nL 84.026412 67.751303 \nL 84.035543 64.738037 \nL 84.053805 76.614416 \nL 84.126853 72.978191 \nL 84.129896 73.141238 \nL 84.142071 68.286779 \nL 84.151202 63.009865 \nL 84.193813 77.521529 \nL 84.248599 70.880773 \nL 84.260774 68.544243 \nL 84.266861 70.662099 \nL 84.376433 82.418376 \nL 84.294254 68.799863 \nL 84.382521 76.342854 \nL 84.422088 60.272352 \nL 84.495136 70.505306 \nL 84.543835 69.096206 \nL 84.516442 74.601729 \nL 84.55601 73.572871 \nL 84.619927 80.083474 \nL 84.583403 63.224953 \nL 84.656451 67.991179 \nL 84.726455 80.68615 \nL 84.686887 63.198744 \nL 84.753848 66.579677 \nL 84.769066 63.882477 \nL 84.808634 72.716231 \nL 84.836027 78.790139 \nL 84.884726 61.576715 \nL 84.909075 66.416414 \nL 84.95473 63.238524 \nL 84.930381 81.455603 \nL 84.997341 72.536112 \nL 85.076477 80.242982 \nL 85.015603 66.99053 \nL 85.106913 72.597226 \nL 85.152568 73.663112 \nL 85.243878 64.445335 \nL 85.304752 75.777541 \nL 85.383887 69.962294 \nL 85.46911 59.330505 \nL 85.408236 79.739524 \nL 85.490415 73.912987 \nL 85.5939 80.695462 \nL 85.539114 69.854187 \nL 85.599987 77.198378 \nL 85.694341 55.512947 \nL 85.673035 80.723243 \nL 85.71869 63.354679 \nL 85.825219 76.487389 \nL 85.858699 68.508053 \nL 85.864786 66.650953 \nL 85.883048 78.930411 \nL 85.968271 68.718178 \nL 86.05045 77.858353 \nL 85.998708 66.525796 \nL 86.059581 67.666854 \nL 86.14176 60.607541 \nL 86.114367 83.050117 \nL 86.166109 72.146377 \nL 86.226983 65.633864 \nL 86.263507 74.847192 \nL 86.32438 79.317942 \nL 86.287856 61.426652 \nL 86.373079 74.818295 \nL 86.376122 75.670633 \nL 86.430908 60.391219 \nL 86.433952 59.018521 \nL 86.470476 71.832283 \nL 86.519175 68.09157 \nL 86.56483 74.823499 \nL 86.583092 59.864059 \nL 86.643965 74.662769 \nL 86.692664 66.747902 \nL 86.674402 76.903337 \nL 86.774843 68.440085 \nL 86.860065 86.232316 \nL 86.875284 64.299155 \nL 86.957463 53.816285 \nL 86.920939 77.249346 \nL 86.981812 67.503018 \nL 87.018336 64.136232 \nL 87.000074 71.533326 \nL 87.05486 69.480775 \nL 87.070078 74.780902 \nL 87.106602 64.840543 \nL 87.173563 73.884834 \nL 87.313572 63.775517 \nL 87.228349 77.620928 \nL 87.325746 65.858755 \nL 87.383576 75.993994 \nL 87.401838 62.581294 \nL 87.444449 70.06827 \nL 87.462711 61.849494 \nL 87.514454 77.016363 \nL 87.526628 76.040617 \nL 87.532716 76.76675 \nL 87.54489 62.632603 \nL 87.550978 55.561477 \nL 87.587502 78.065039 \nL 87.648375 73.017612 \nL 87.681855 78.69442 \nL 87.767078 64.455078 \nL 87.770122 64.201938 \nL 87.78534 71.970183 \nL 87.827952 76.520117 \nL 87.80969 63.661027 \nL 87.891869 70.920722 \nL 87.925349 64.902791 \nL 87.910131 77.842777 \nL 87.964917 74.446295 \nL 87.971004 76.721161 \nL 87.995353 58.917787 \nL 88.065358 69.164414 \nL 88.144493 58.94959 \nL 88.101882 77.415569 \nL 88.17493 70.668838 \nL 88.202323 75.058175 \nL 88.226672 56.674693 \nL 88.238847 59.112268 \nL 88.247978 58.061912 \nL 88.260152 66.416262 \nL 88.275371 82.8124 \nL 88.366681 64.159282 \nL 88.369724 65.122302 \nL 88.384943 76.723627 \nL 88.488427 71.028327 \nL 88.500602 75.12414 \nL 88.518864 64.29773 \nL 88.582781 69.764887 \nL 88.594956 63.124468 \nL 88.613218 74.055709 \nL 88.692353 69.987659 \nL 88.762357 52.301024 \nL 88.774532 77.131971 \nL 88.780619 85.716137 \nL 88.84758 66.937786 \nL 88.88106 68.497961 \nL 88.920628 79.382148 \nL 88.963239 65.98362 \nL 88.981501 68.456686 \nL 89.051506 62.120831 \nL 89.011938 73.756482 \nL 89.08803 70.407133 \nL 89.12151 73.171834 \nL 89.185427 67.959931 \nL 89.188471 67.60848 \nL 89.2463 73.328347 \nL 89.267606 70.798139 \nL 89.389353 76.93653 \nL 89.331523 67.237387 \nL 89.392396 76.188555 \nL 89.407615 59.252389 \nL 89.425877 77.893665 \nL 89.517187 62.914317 \nL 89.663283 79.798899 \nL 89.559798 61.771676 \nL 89.690676 70.728406 \nL 89.778942 62.349113 \nL 89.76068 75.864761 \nL 89.809379 65.15268 \nL 89.812422 64.346285 \nL 89.864165 75.858014 \nL 89.888514 72.792748 \nL 89.918951 77.562369 \nL 89.931125 67.24813 \nL 90.00113 63.488927 \nL 89.982868 72.882763 \nL 90.010261 70.2887 \nL 90.12592 80.041742 \nL 90.077221 62.642476 \nL 90.128964 78.478404 \nL 90.247667 62.072714 \nL 90.253754 64.086378 \nL 90.311584 82.713305 \nL 90.34202 60.663577 \nL 90.36637 67.731467 \nL 90.387675 60.555908 \nL 90.412025 77.037595 \nL 90.463767 71.712723 \nL 90.576383 81.456464 \nL 90.533771 61.564788 \nL 90.58247 78.537198 \nL 90.676824 59.42106 \nL 90.695086 76.48221 \nL 90.82292 59.086406 \nL 90.935535 80.143687 \nL 90.959885 68.63571 \nL 91.048151 62.251217 \nL 90.987278 80.665504 \nL 91.063369 73.984281 \nL 91.121199 63.008597 \nL 91.099893 75.116136 \nL 91.157723 74.772314 \nL 91.166854 76.931216 \nL 91.227727 60.801963 \nL 91.258164 73.727666 \nL 91.303819 62.459525 \nL 91.285557 80.804312 \nL 91.37078 71.996096 \nL 91.401216 85.300096 \nL 91.416435 68.671592 \nL 91.480352 73.13064 \nL 91.565574 57.06652 \nL 91.532094 77.110966 \nL 91.589924 69.91394 \nL 91.626448 82.254073 \nL 91.684277 58.875203 \nL 91.696452 57.396931 \nL 91.708627 49.659428 \nL 91.742107 96.448441 \nL 91.745151 98.237633 \nL 91.806024 71.3236 \nL 91.854723 58.679924 \nL 91.897334 73.320684 \nL 91.915596 71.983771 \nL 91.91864 71.88152 \nL 91.930814 74.016143 \nL 91.991688 80.266309 \nL 92.019081 65.540052 \nL 92.028212 58.426048 \nL 92.046474 72.110674 \nL 92.122565 69.716671 \nL 92.219963 82.873341 \nL 92.159089 66.220751 \nL 92.235181 70.722059 \nL 92.244312 71.942198 \nL 92.256487 66.560448 \nL 92.262574 64.364729 \nL 92.286923 76.085036 \nL 92.353884 72.300168 \nL 92.399539 79.175339 \nL 92.381277 62.966662 \nL 92.451281 66.485213 \nL 92.542591 71.764617 \nL 92.566941 62.188995 \nL 92.661294 83.103321 \nL 92.627814 58.833717 \nL 92.700862 72.630892 \nL 92.761735 64.321376 \nL 92.74043 79.653516 \nL 92.801303 73.621232 \nL 92.80739 77.227571 \nL 92.825652 61.39079 \nL 92.901744 65.508435 \nL 92.904788 65.32368 \nL 92.935224 69.660472 \nL 93.01436 98.450194 \nL 92.974792 64.447338 \nL 93.053927 73.38523 \nL 93.078277 60.063085 \nL 93.193936 68.493596 \nL 93.257853 81.519102 \nL 93.279159 61.047147 \nL 93.312639 75.131612 \nL 93.343076 64.455431 \nL 93.352207 54.609205 \nL 93.376556 76.219577 \nL 93.44656 72.693658 \nL 93.547001 82.2676 \nL 93.498303 63.570372 \nL 93.559176 75.676767 \nL 93.574394 63.979588 \nL 93.674835 67.819408 \nL 93.677879 67.565309 \nL 93.693097 74.058342 \nL 93.72049 71.979991 \nL 93.729621 74.802525 \nL 93.790495 63.283039 \nL 93.827019 70.258895 \nL 93.839193 62.497346 \nL 93.854412 79.4178 \nL 93.942678 66.370174 \nL 93.99442 82.266013 \nL 94.015726 63.27404 \nL 94.088774 57.749553 \nL 94.11008 74.242586 \nL 94.125298 64.210865 \nL 94.134429 69.168415 \nL 94.216608 78.563045 \nL 94.173997 60.69178 \nL 94.244001 68.988847 \nL 94.286612 75.12369 \nL 94.347486 65.833388 \nL 94.353573 64.571771 \nL 94.374879 76.848132 \nL 94.41749 74.98104 \nL 94.438796 81.613881 \nL 94.460101 59.662798 \nL 94.524018 74.079625 \nL 94.56663 56.756328 \nL 94.651852 70.60784 \nL 94.730988 78.443258 \nL 94.685333 65.954921 \nL 94.752293 70.658992 \nL 94.764468 63.84471 \nL 94.813167 81.46288 \nL 94.861865 68.313281 \nL 94.96535 80.317698 \nL 94.880127 64.859826 \nL 94.974481 72.691013 \nL 95.053616 61.443592 \nL 95.004918 74.269552 \nL 95.087097 67.843864 \nL 95.178407 74.867481 \nL 95.132752 63.586393 \nL 95.196669 69.53027 \nL 95.202756 68.454111 \nL 95.227105 88.524025 \nL 95.230149 88.591403 \nL 95.233193 87.031605 \nL 95.275804 61.163853 \nL 95.357983 73.907306 \nL 95.367114 71.324518 \nL 95.44625 63.595797 \nL 95.412769 73.490688 \nL 95.473643 73.406428 \nL 95.555822 80.163049 \nL 95.504079 60.399458 \nL 95.564953 72.049824 \nL 95.577127 61.37378 \nL 95.668437 76.919626 \nL 95.671481 76.097029 \nL 95.726267 56.068698 \nL 95.704961 84.68079 \nL 95.790184 71.679412 \nL 95.872363 54.476309 \nL 95.823664 73.279535 \nL 95.884538 72.896193 \nL 96.000197 79.479975 \nL 95.9028 63.133634 \nL 96.003241 78.495589 \nL 96.021503 58.348668 \nL 96.134118 61.663524 \nL 96.15238 84.889182 \nL 96.185861 59.762153 \nL 96.255865 75.617931 \nL 96.258909 76.161678 \nL 96.310651 65.737837 \nL 96.319782 62.226614 \nL 96.365437 72.928522 \nL 96.414136 68.886101 \nL 96.447616 80.946565 \nL 96.481096 66.052018 \nL 96.535882 73.697663 \nL 96.639367 64.598871 \nL 96.554144 75.83457 \nL 96.651542 71.170814 \nL 96.709371 73.088226 \nL 96.691109 61.230195 \nL 96.751983 68.558976 \nL 96.803725 64.505377 \nL 96.776332 73.782253 \nL 96.864598 66.344273 \nL 96.97417 75.06604 \nL 96.934603 65.355327 \nL 96.983301 71.207562 \nL 97.059393 62.710743 \nL 97.038087 81.946962 \nL 97.08983 72.458394 \nL 97.178096 73.118278 \nL 97.138528 62.068075 \nL 97.190271 71.629653 \nL 97.211576 60.284014 \nL 97.263319 80.465076 \nL 97.269406 84.346136 \nL 97.315061 64.310647 \nL 97.366803 75.783519 \nL 97.369847 75.89853 \nL 97.375934 72.248984 \nL 97.45507 62.4455 \nL 97.476375 80.525524 \nL 97.57986 62.757517 \nL 97.622471 72.347634 \nL 97.634646 78.599795 \nL 97.674214 61.365089 \nL 97.725956 68.480641 \nL 97.808135 67.159116 \nL 97.768567 78.539917 \nL 97.832484 69.071681 \nL 97.847703 78.145191 \nL 97.878139 56.623909 \nL 97.9451 71.564791 \nL 98.03641 64.178711 \nL 98.009017 77.67263 \nL 98.066847 64.902343 \nL 98.158157 78.303206 \nL 98.176419 64.795991 \nL 98.200768 63.773072 \nL 98.188593 65.864939 \nL 98.206855 65.62417 \nL 98.31034 79.836319 \nL 98.258598 61.172045 \nL 98.322515 70.925833 \nL 98.40165 57.199057 \nL 98.422956 72.325745 \nL 98.425999 72.411386 \nL 98.43513 70.530124 \nL 98.517309 59.257142 \nL 98.45948 73.967681 \nL 98.532528 72.513754 \nL 98.566008 80.948477 \nL 98.61775 62.279482 \nL 98.6421 71.679723 \nL 98.727322 58.941671 \nL 98.681667 77.139682 \nL 98.748628 73.199751 \nL 98.751672 73.328827 \nL 98.769934 69.952796 \nL 98.861244 66.217873 \nL 98.791239 74.328701 \nL 98.885593 68.012242 \nL 98.897768 77.536826 \nL 98.946466 59.903519 \nL 98.995165 67.286348 \nL 99.001252 69.115277 \nL 99.031689 77.895939 \nL 99.077344 66.003858 \nL 99.110824 68.784475 \nL 99.156479 76.556783 \nL 99.177785 58.701193 \nL 99.214309 67.3808 \nL 99.31475 73.120073 \nL 99.296488 65.561039 \nL 99.354318 71.321834 \nL 99.436497 62.208003 \nL 99.418235 74.739728 \nL 99.466933 70.225277 \nL 99.573462 78.789526 \nL 99.512588 64.19713 \nL 99.579549 75.178427 \nL 99.591724 60.105994 \nL 99.692165 69.206148 \nL 99.73782 58.707129 \nL 99.753038 78.09343 \nL 99.801737 67.099049 \nL 99.86261 87.798653 \nL 99.883916 61.815801 \nL 99.914352 69.655849 \nL 99.941745 78.776394 \nL 99.9874 60.665667 \nL 100.030012 72.176968 \nL 100.118278 61.834616 \nL 100.139584 72.171513 \nL 100.166977 80.430976 \nL 100.182195 62.803834 \nL 100.209588 64.599881 \nL 100.258287 63.12008 \nL 100.243068 74.268722 \nL 100.267418 70.275624 \nL 100.310029 85.157831 \nL 100.337422 59.12997 \nL 100.380033 76.171787 \nL 100.483518 63.439974 \nL 100.504824 71.94549 \nL 100.635701 95.656043 \nL 100.587003 54.952901 \nL 100.638745 94.297192 \nL 100.705706 56.509028 \nL 100.778754 67.970096 \nL 100.815278 78.563713 \nL 100.854845 64.104838 \nL 100.9005 76.551459 \nL 100.930937 57.776788 \nL 100.982679 78.179192 \nL 101.034422 63.115253 \nL 101.092251 82.606961 \nL 101.046596 62.704778 \nL 101.137906 62.789663 \nL 101.226173 57.943214 \nL 101.17443 89.25639 \nL 101.244435 64.964424 \nL 101.262697 59.846286 \nL 101.302264 72.670066 \nL 101.311395 80.011035 \nL 101.396618 61.574061 \nL 101.405749 64.199066 \nL 101.533583 79.587921 \nL 101.451404 58.725313 \nL 101.542714 75.749792 \nL 101.685766 63.923313 \nL 101.576194 79.089347 \nL 101.716203 67.352635 \nL 101.795338 74.829942 \nL 101.810557 63.902291 \nL 101.816644 60.880618 \nL 101.90491 74.487029 \nL 101.926216 83.203552 \nL 101.968827 63.009403 \nL 101.99622 63.441274 \nL 102.008395 60.716989 \nL 102.066225 75.57807 \nL 102.081443 73.91278 \nL 102.093618 72.470505 \nL 102.206233 56.867923 \nL 102.169709 77.496554 \nL 102.224495 66.057147 \nL 102.23667 69.474405 \nL 102.303631 79.929008 \nL 102.267107 61.396016 \nL 102.349286 73.719949 \nL 102.358417 70.989749 \nL 102.413203 66.303093 \nL 102.382766 75.872674 \nL 102.467989 71.537098 \nL 102.5106 73.522185 \nL 102.528862 60.123891 \nL 102.556255 67.393625 \nL 102.662783 55.595941 \nL 102.607997 81.181609 \nL 102.674958 63.212213 \nL 102.75105 117.261738 \nL 102.717569 46.693328 \nL 102.778443 64.370425 \nL 102.796705 36.265484 \nL 102.830185 128.33026 \nL 102.891059 57.909483 \nL 102.90019 48.349316 \nL 102.988456 79.288175 \nL 102.9915 80.607402 \nL 103.046286 63.634317 \nL 103.079766 70.810721 \nL 103.11629 62.16363 \nL 103.128465 80.412906 \nL 103.140639 113.591453 \nL 103.225862 47.999964 \nL 103.231949 52.125689 \nL 103.33239 129.457647 \nL 103.301954 49.519333 \nL 103.359783 77.363929 \nL 103.451093 49.865659 \nL 103.475443 65.682202 \nL 103.478486 65.367094 \nL 103.493705 75.098288 \nL 103.508923 77.882767 \nL 103.545447 64.819211 \nL 103.548491 64.234738 \nL 103.581971 77.96374 \nL 103.661106 113.516619 \nL 103.636757 52.502777 \nL 103.694587 80.228138 \nL 103.75546 53.76993 \nL 103.810246 64.065349 \nL 103.84677 75.660106 \nL 103.928949 67.873418 \nL 103.941124 62.387404 \nL 104.026346 74.702403 \nL 104.105482 83.365265 \nL 104.056783 62.35676 \nL 104.1207 67.669534 \nL 104.15418 60.08922 \nL 104.184617 80.314956 \nL 104.196792 100.488071 \nL 104.278971 47.342261 \nL 104.282014 47.497657 \nL 104.306364 47.249979 \nL 104.315495 64.315085 \nL 104.330713 120.440599 \nL 104.391586 53.114965 \nL 104.422023 54.113246 \nL 104.516377 75.803316 \nL 104.555944 68.193778 \nL 104.641167 61.184078 \nL 104.580294 79.789918 \nL 104.662473 71.385448 \nL 104.790307 58.020884 \nL 104.729433 77.96463 \nL 104.796394 60.510271 \nL 104.902922 83.215673 \nL 104.921184 70.029054 \nL 104.939446 61.092434 \nL 104.991189 86.157106 \nL 105.027713 70.171924 \nL 105.079455 63.441379 \nL 105.146416 75.757719 \nL 105.259031 60.753101 \nL 105.268162 65.803574 \nL 105.36556 80.788959 \nL 105.283381 64.821487 \nL 105.386865 71.551193 \nL 105.420346 75.525094 \nL 105.441651 65.178965 \nL 105.481219 67.45413 \nL 105.536005 62.821512 \nL 105.560354 75.99816 \nL 105.584704 70.318488 \nL 105.663839 79.417399 \nL 105.682101 63.933409 \nL 105.691232 67.404445 \nL 105.739931 78.411752 \nL 105.776455 63.065709 \nL 105.806891 69.652583 \nL 105.809935 69.620599 \nL 105.812979 70.572445 \nL 105.85559 81.833382 \nL 105.901245 62.952018 \nL 105.904289 62.172996 \nL 105.928638 77.286941 \nL 105.983424 68.816181 \nL 106.102127 74.219223 \nL 106.022992 58.28125 \nL 106.105171 73.988274 \nL 106.202568 63.319111 \nL 106.184306 75.733719 \nL 106.229961 70.517673 \nL 106.25431 79.455921 \nL 106.27866 62.474465 \nL 106.339533 70.555762 \nL 106.348664 62.280927 \nL 106.366926 76.721661 \nL 106.446061 72.708106 \nL 106.449105 72.788677 \nL 106.455192 70.72351 \nL 106.49476 60.900405 \nL 106.513022 76.32865 \nL 106.558677 73.493304 \nL 106.625638 80.506521 \nL 106.579983 64.19349 \nL 106.662162 71.516098 \nL 106.723035 64.139054 \nL 106.738253 74.889068 \nL 106.765646 73.24926 \nL 106.869131 81.830859 \nL 106.796083 60.818549 \nL 106.878262 75.8965 \nL 106.945223 57.404696 \nL 106.926961 77.408073 \nL 106.987834 75.23577 \nL 107.000009 76.815393 \nL 107.015227 66.057543 \nL 107.045664 60.545479 \nL 107.066969 77.641367 \nL 107.115668 73.160755 \nL 107.188716 77.668579 \nL 107.173498 59.640217 \nL 107.22524 74.562763 \nL 107.292201 57.51594 \nL 107.331768 77.262664 \nL 107.337856 78.779385 \nL 107.389598 64.505664 \nL 107.423078 69.384995 \nL 107.535694 62.726244 \nL 107.450471 80.565614 \nL 107.541781 66.625696 \nL 107.581349 84.660596 \nL 107.611786 56.528271 \nL 107.660484 74.472435 \nL 107.68179 70.704295 \nL 107.760925 61.473197 \nL 107.776144 76.220145 \nL 107.815711 80.537838 \nL 107.846148 60.981796 \nL 107.870497 70.127379 \nL 107.89789 85.395578 \nL 107.986157 63.883351 \nL 108.037899 82.182879 \nL 108.071379 60.011525 \nL 108.126165 70.799559 \nL 108.159646 64.863935 \nL 108.180951 78.43655 \nL 108.190082 81.408484 \nL 108.211388 64.028724 \nL 108.275305 68.47077 \nL 108.366615 64.995561 \nL 108.345309 78.849855 \nL 108.384877 68.515859 \nL 108.421401 79.217147 \nL 108.442707 61.163967 \nL 108.506624 74.994387 \nL 108.546191 59.13118 \nL 108.567497 75.605271 \nL 108.62837 69.738062 \nL 108.713593 83.459502 \nL 108.734899 64.801153 \nL 108.77751 76.573262 \nL 108.807947 63.722276 \nL 108.865776 72.413984 \nL 108.954043 60.677992 \nL 108.923606 78.595776 \nL 108.981436 68.488597 \nL 109.063615 60.221088 \nL 109.045353 77.448347 \nL 109.075789 72.52486 \nL 109.112313 77.343094 \nL 109.094051 61.437046 \nL 109.191449 75.511103 \nL 109.197536 75.608664 \nL 109.206667 73.820205 \nL 109.273628 61.516474 \nL 109.227973 79.242543 \nL 109.301021 74.768373 \nL 109.307108 78.436531 \nL 109.328414 60.964882 \nL 109.407549 71.008438 \nL 109.419724 62.380523 \nL 109.486684 78.089676 \nL 109.526252 63.456594 \nL 109.602344 81.105159 \nL 109.644955 66.53943 \nL 109.651042 65.356454 \nL 109.669304 76.340066 \nL 109.742352 69.883409 \nL 109.751483 76.711773 \nL 109.842793 59.584055 \nL 109.845837 59.47505 \nL 109.851924 61.808738 \nL 109.961496 77.997801 \nL 109.982802 74.283544 \nL 110.083243 66.854451 \nL 110.092374 72.46481 \nL 110.098461 75.694047 \nL 110.144116 56.331304 \nL 110.20499 74.484499 \nL 110.220208 76.312743 \nL 110.235426 67.150354 \nL 110.299343 59.683769 \nL 110.274994 75.052985 \nL 110.338911 70.963938 \nL 110.348042 79.557214 \nL 110.390653 64.432435 \nL 110.445439 66.422595 \nL 110.497182 77.046599 \nL 110.5124 64.014206 \nL 110.564143 70.423581 \nL 110.66154 62.394043 \nL 110.600667 73.652228 \nL 110.676758 66.91448 \nL 110.71937 77.100091 \nL 110.765025 61.357043 \nL 110.795461 74.738794 \nL 110.883728 59.861739 \nL 110.850247 81.267856 \nL 110.914164 69.454742 \nL 110.938514 80.509195 \nL 110.959819 60.449623 \nL 111.023736 69.52358 \nL 111.102872 59.613742 \nL 111.072435 73.161626 \nL 111.130265 71.199176 \nL 111.215487 78.423794 \nL 111.182007 67.029587 \nL 111.245924 73.467516 \nL 111.258099 62.83958 \nL 111.303754 74.259211 \nL 111.361583 66.098027 \nL 111.437675 80.431457 \nL 111.410282 60.638932 \nL 111.474199 68.064869 \nL 111.486374 62.107891 \nL 111.532029 75.460938 \nL 111.580727 70.524807 \nL 111.675081 80.475498 \nL 111.63247 60.363392 \nL 111.702474 75.724171 \nL 111.802915 61.436254 \nL 111.754216 79.197302 \nL 111.818133 68.946848 \nL 111.88205 73.579689 \nL 111.915531 64.74817 \nL 111.921618 61.3162 \nL 111.982491 75.018336 \nL 112.012928 74.579124 \nL 112.092063 81.063637 \nL 112.070758 65.330789 \nL 112.110325 72.757389 \nL 112.216854 61.581725 \nL 112.198592 75.945007 \nL 112.229028 69.666422 \nL 112.265552 75.714595 \nL 112.24729 64.245783 \nL 112.341644 71.628001 \nL 112.457303 74.325067 \nL 112.369037 57.969691 \nL 112.460347 74.103287 \nL 112.52122 67.878786 \nL 112.499915 80.711421 \nL 112.572963 73.498743 \nL 112.633836 76.899575 \nL 112.664273 64.864444 \nL 112.67036 63.852419 \nL 112.70384 75.182552 \nL 112.749495 69.240284 \nL 112.764714 74.567782 \nL 112.816456 64.742839 \nL 112.856024 68.921022 \nL 112.919941 61.444619 \nL 112.94429 74.381776 \nL 112.956465 71.415205 \nL 112.996032 80.598142 \nL 113.029513 71.039073 \nL 113.038644 62.785954 \nL 113.090386 72.269228 \nL 113.142128 67.07274 \nL 113.245613 76.49679 \nL 113.18474 65.996676 \nL 113.254744 68.767396 \nL 113.263875 61.310165 \nL 113.30953 75.206664 \nL 113.364316 70.550802 \nL 113.422146 62.823673 \nL 113.443451 77.938816 \nL 113.45867 77.201484 \nL 113.531718 84.174748 \nL 113.513456 59.408708 \nL 113.54998 64.03255 \nL 113.671726 76.737946 \nL 113.626071 59.455223 \nL 113.696076 75.840566 \nL 113.738687 67.921678 \nL 113.76608 78.085202 \nL 113.814779 70.473877 \nL 113.860434 63.068425 \nL 113.839128 83.963458 \nL 113.903045 75.821407 \nL 113.909132 77.725268 \nL 113.930438 64.697204 \nL 113.997399 68.595372 \nL 114.079578 63.797314 \nL 114.058272 80.105511 \nL 114.09784 76.340169 \nL 114.134364 59.281286 \nL 114.231761 66.452588 \nL 114.283503 80.162317 \nL 114.335246 62.135949 \nL 114.338289 60.999314 \nL 114.390032 72.907991 \nL 114.429599 65.478046 \nL 114.502647 77.283805 \nL 114.523953 62.89624 \nL 114.539171 66.328003 \nL 114.575695 77.861352 \nL 114.584826 82.782799 \nL 114.627438 59.691732 \nL 114.673093 67.286318 \nL 114.676136 66.979532 \nL 114.715704 73.930667 \nL 114.724835 73.144739 \nL 114.727879 73.114915 \nL 114.73701 73.777805 \nL 114.740053 73.130098 \nL 114.849625 63.273361 \nL 114.800927 79.689985 \nL 114.867887 63.735837 \nL 114.947023 77.452637 \nL 114.898324 57.508586 \nL 115.001809 70.614821 \nL 115.01094 64.868935 \nL 115.029202 80.657137 \nL 115.111381 70.3595 \nL 115.19356 62.891608 \nL 115.129643 77.947511 \nL 115.202691 69.736419 \nL 115.31835 78.817459 \nL 115.22704 64.979418 \nL 115.321394 78.739645 \nL 115.342699 58.348149 \nL 115.449228 70.095087 \nL 115.549669 82.344713 \nL 115.516188 67.856131 \nL 115.5588 69.662906 \nL 115.567931 59.888861 \nL 115.656197 75.650298 \nL 115.668372 68.091002 \nL 115.714027 58.330887 \nL 115.692721 71.814981 \nL 115.765769 70.959671 \nL 115.844904 80.57141 \nL 115.793162 65.516329 \nL 115.863166 66.048331 \nL 115.951433 62.514824 \nL 115.890559 77.396917 \nL 115.960564 66.665994 \nL 116.073179 80.248348 \nL 116.012306 58.343046 \nL 116.076223 78.778354 \nL 116.158402 57.20623 \nL 116.194926 69.627942 \nL 116.28928 77.40174 \nL 116.237537 67.493664 \nL 116.304498 69.695705 \nL 116.310585 66.747559 \nL 116.353197 78.193893 \nL 116.411026 71.644864 \nL 116.435376 75.338661 \nL 116.44755 67.951527 \nL 116.468856 46.685573 \nL 116.508424 105.020955 \nL 116.554079 71.015739 \nL 116.56321 70.515314 \nL 116.575384 74.488289 \nL 116.584515 79.373912 \nL 116.633214 56.998706 \nL 116.669738 63.951262 \nL 116.675825 60.415916 \nL 116.739742 79.505205 \nL 116.767135 73.86202 \nL 116.809747 79.300841 \nL 116.83714 64.785058 \nL 116.879751 77.344285 \nL 116.925406 60.878487 \nL 116.998454 70.863864 \nL 117.016716 76.497145 \nL 117.050196 64.654499 \nL 117.120201 76.143264 \nL 117.229773 64.562788 \nL 117.241947 72.557934 \nL 117.251078 78.976316 \nL 117.296733 65.745881 \nL 117.345432 67.852087 \nL 117.421524 59.125995 \nL 117.439786 77.840823 \nL 117.455004 67.859181 \nL 117.546314 83.440063 \nL 117.485441 64.194608 \nL 117.558489 67.750818 \nL 117.564576 65.070161 \nL 117.628493 75.697957 \nL 117.668061 67.075357 \nL 117.686323 80.280526 \nL 117.753283 62.015474 \nL 117.786764 70.871407 \nL 117.87503 62.014302 \nL 117.811113 77.314597 \nL 117.899379 68.788656 \nL 117.911554 64.051458 \nL 117.990689 77.063132 \nL 117.996777 81.154476 \nL 118.015039 68.024788 \nL 118.088087 68.521743 \nL 118.197659 62.464153 \nL 118.145917 75.592392 \nL 118.203746 64.199154 \nL 118.218965 78.130909 \nL 118.261576 62.128138 \nL 118.319406 68.672044 \nL 118.365061 77.848708 \nL 118.340711 64.910484 \nL 118.441152 72.361372 \nL 118.459414 62.921894 \nL 118.523331 78.612217 \nL 118.556812 70.343244 \nL 118.648122 63.833589 \nL 118.581161 76.853454 \nL 118.660296 71.67603 \nL 118.745519 75.737929 \nL 118.724213 62.095209 \nL 118.757694 64.067381 \nL 118.763781 60.71901 \nL 118.818567 78.227373 \nL 118.864222 67.028636 \nL 118.912921 65.298663 \nL 118.894659 78.42301 \nL 118.958576 72.26574 \nL 119.007274 75.613698 \nL 118.985969 64.469283 \nL 119.046842 68.086916 \nL 119.068148 63.78116 \nL 119.110759 83.165025 \nL 119.156414 68.849821 \nL 119.171632 65.988926 \nL 119.186851 78.536567 \nL 119.256855 80.774212 \nL 119.2112 60.339132 \nL 119.287292 75.433583 \nL 119.384689 65.615576 \nL 119.339034 84.067883 \nL 119.399907 73.024533 \nL 119.409038 74.600946 \nL 119.469912 63.601131 \nL 119.475999 63.125982 \nL 119.506436 70.536412 \nL 119.561222 75.659261 \nL 119.606877 63.876566 \nL 119.60992 63.875073 \nL 119.698187 75.565366 \nL 119.719492 64.64643 \nL 119.728623 60.167123 \nL 119.774278 78.268766 \nL 119.822977 69.082211 \nL 119.829064 68.775613 \nL 119.835152 69.808603 \nL 119.85037 80.556847 \nL 119.886894 66.13524 \nL 119.94168 67.318975 \nL 119.969073 60.047828 \nL 120.008641 75.562895 \nL 120.051252 67.870276 \nL 120.115169 77.529693 \nL 120.096907 63.13475 \nL 120.166911 72.791951 \nL 120.288658 64.78266 \nL 120.224741 73.978132 \nL 120.291702 64.826274 \nL 120.337357 80.68101 \nL 120.322138 61.193588 \nL 120.404317 65.923996 \nL 120.410405 64.86457 \nL 120.465191 71.733894 \nL 120.48954 69.667948 \nL 120.52302 80.814235 \nL 120.544326 59.689976 \nL 120.605199 76.053773 \nL 120.696509 59.51813 \nL 120.669116 78.78674 \nL 120.720859 72.081635 \nL 120.745208 74.752881 \nL 120.757383 67.704477 \nL 120.79695 59.747996 \nL 120.85478 78.308483 \nL 120.857824 78.603853 \nL 120.873042 69.300203 \nL 120.900435 77.179837 \nL 120.991745 63.232199 \nL 121.040444 77.803526 \nL 121.153059 72.831331 \nL 121.238282 59.707157 \nL 121.198714 74.668036 \nL 121.265675 71.146462 \nL 121.347854 77.259121 \nL 121.31133 67.246809 \nL 121.384378 76.215949 \nL 121.430033 63.127221 \nL 121.484819 74.894235 \nL 121.49395 82.661955 \nL 121.536561 62.774893 \nL 121.591347 70.654031 \nL 121.655264 63.905899 \nL 121.637002 76.253886 \nL 121.71005 66.578475 \nL 121.795273 81.818843 \nL 121.749618 64.249594 \nL 121.82571 73.192275 \nL 121.883539 66.862202 \nL 121.904845 78.378867 \nL 121.935282 71.950796 \nL 122.011373 73.607285 \nL 121.956587 64.447744 \nL 122.023548 68.250615 \nL 122.102683 60.534122 \nL 122.081378 77.043851 \nL 122.13312 67.21097 \nL 122.193993 75.34729 \nL 122.212255 67.026846 \nL 122.242692 67.644306 \nL 122.251823 64.56047 \nL 122.306609 75.35523 \nL 122.34922 69.859952 \nL 122.385744 79.228106 \nL 122.400963 64.565521 \nL 122.455749 68.834733 \nL 122.474011 73.153676 \nL 122.492273 62.93595 \nL 122.550102 60.780023 \nL 122.53184 70.153548 \nL 122.583583 67.499865 \nL 122.604888 76.715803 \nL 122.696198 68.907991 \nL 122.699242 68.746104 \nL 122.720548 72.703241 \nL 122.729679 76.060755 \nL 122.77229 61.220747 \nL 122.820989 68.907501 \nL 122.824032 68.777764 \nL 122.833163 71.907006 \nL 122.903168 77.38745 \nL 122.8636 63.795044 \nL 122.936648 70.421422 \nL 122.948823 62.270619 \nL 123.037089 77.405559 \nL 123.040133 77.623483 \nL 123.058395 71.995765 \nL 123.073613 62.401328 \nL 123.125355 80.167281 \nL 123.174054 68.92047 \nL 123.19536 74.698905 \nL 123.237971 60.921467 \nL 123.244058 59.068774 \nL 123.271451 75.955507 \nL 123.332325 67.165919 \nL 123.335368 66.964382 \nL 123.344499 71.508322 \nL 123.350587 75.231881 \nL 123.441897 62.789504 \nL 123.44494 62.372498 \nL 123.454071 72.594129 \nL 123.463202 78.665428 \nL 123.527119 61.489916 \nL 123.566687 75.585677 \nL 123.624517 79.538171 \nL 123.609298 71.793526 \nL 123.657997 73.277578 \nL 123.670172 59.557548 \nL 123.770613 68.348314 \nL 123.81018 60.79375 \nL 123.828442 73.631662 \nL 123.864966 73.220139 \nL 123.941058 76.851055 \nL 123.892359 65.999377 \nL 123.956276 67.980627 \nL 123.962364 69.215878 \nL 124.020193 76.75879 \nL 123.989757 64.832702 \nL 124.078023 72.297049 \nL 124.169333 77.614077 \nL 124.105416 60.349023 \nL 124.181508 69.65701 \nL 124.260643 59.307987 \nL 124.239337 76.032341 \nL 124.288036 69.814089 \nL 124.300211 76.114508 \nL 124.339778 61.945491 \nL 124.403695 72.944867 \nL 124.421957 80.947987 \nL 124.488918 66.897641 \nL 124.513267 63.131742 \nL 124.537617 79.863028 \nL 124.577184 76.078409 \nL 124.589359 78.204218 \nL 124.622839 65.167985 \nL 124.708062 56.569036 \nL 124.680669 71.840596 \nL 124.726324 65.11383 \nL 124.759804 98.371565 \nL 124.83894 72.142513 \nL 124.866333 52.970995 \nL 124.915031 74.238005 \nL 124.948512 73.246711 \nL 125.039822 58.469432 \nL 124.978948 80.010445 \nL 125.05504 73.174055 \nL 125.131132 82.511434 \nL 125.149394 61.913209 \nL 125.152437 60.573847 \nL 125.188961 78.081685 \nL 125.243747 68.481918 \nL 125.29549 61.56975 \nL 125.262009 72.774468 \nL 125.304621 68.526712 \nL 125.42028 80.279069 \nL 125.371581 60.115328 \nL 125.423324 79.117785 \nL 125.475066 62.593554 \nL 125.548114 69.301801 \nL 125.599856 66.066605 \nL 125.621162 74.98392 \nL 125.654642 82.07823 \nL 125.639424 68.417136 \nL 125.666817 72.035493 \nL 125.745953 62.935686 \nL 125.685079 75.691969 \nL 125.779433 69.73531 \nL 125.797695 79.212099 \nL 125.819001 58.414432 \nL 125.882918 69.220036 \nL 125.892049 62.767492 \nL 125.93466 79.015742 \nL 125.986402 74.816364 \nL 126.050319 62.913874 \nL 126.086843 76.438491 \nL 126.092931 77.860947 \nL 126.114236 69.466038 \nL 126.181197 71.731117 \nL 126.269463 63.941141 \nL 126.248158 77.18645 \nL 126.309031 68.978707 \nL 126.372948 74.712694 \nL 126.336424 57.881577 \nL 126.42469 72.219981 \nL 126.491651 60.442064 \nL 126.455127 77.627174 \nL 126.528175 73.414777 \nL 126.543393 79.187415 \nL 126.564699 61.94111 \nL 126.619485 69.547585 \nL 126.622529 69.636984 \nL 126.628616 68.146438 \nL 126.637747 62.257117 \nL 126.689489 81.896751 \nL 126.735144 69.950469 \nL 126.838629 79.527071 \nL 126.783843 64.007286 \nL 126.84776 71.424228 \nL 126.856891 60.830079 \nL 126.945157 81.383051 \nL 126.954288 77.653948 \nL 127.006031 62.445431 \nL 126.981681 78.382434 \nL 127.076035 68.409087 \nL 127.139952 74.634031 \nL 127.170389 66.622957 \nL 127.182563 60.197735 \nL 127.209956 79.89402 \nL 127.264742 74.874178 \nL 127.27083 78.678309 \nL 127.316485 62.180504 \nL 127.365183 68.466811 \nL 127.377358 64.7766 \nL 127.419969 80.485624 \nL 127.477799 67.405693 \nL 127.514323 75.022494 \nL 127.553891 63.515383 \nL 127.587371 68.653152 \nL 127.599546 61.429864 \nL 127.657375 74.688339 \nL 127.693899 70.250605 \nL 127.699987 69.609176 \nL 127.715205 76.877106 \nL 127.76086 75.357084 \nL 127.766947 77.235249 \nL 127.843039 67.781284 \nL 127.861301 72.166377 \nL 127.903912 64.565992 \nL 127.882607 75.530905 \nL 127.949567 72.793428 \nL 127.958698 77.985489 \nL 128.046965 60.89897 \nL 128.053052 58.247911 \nL 128.107838 79.116625 \nL 128.153493 62.542374 \nL 128.18393 79.832042 \nL 128.263065 64.493804 \nL 128.357419 62.13722 \nL 128.290458 78.89423 \nL 128.369593 66.898589 \nL 128.506558 76.349234 \nL 128.418292 64.972622 \nL 128.512646 74.806627 \nL 128.573519 57.37623 \nL 128.533951 77.312677 \nL 128.619174 72.481442 \nL 128.695266 81.270646 \nL 128.658742 67.777066 \nL 128.728746 73.010655 \nL 128.740921 59.409819 \nL 128.759183 76.719822 \nL 128.841362 68.948812 \nL 128.926584 74.499169 \nL 128.862667 63.050414 \nL 128.950934 70.020735 \nL 129.0392 66.636265 \nL 128.999632 81.974019 \nL 129.063549 68.010547 \nL 129.078768 75.611674 \nL 129.167034 65.841194 \nL 129.170078 65.584083 \nL 129.179209 72.468701 \nL 129.191383 81.282488 \nL 129.227907 67.742997 \nL 129.294868 79.470317 \nL 129.316174 59.140947 \nL 129.419658 73.820794 \nL 129.428789 78.924613 \nL 129.474444 68.241108 \nL 129.526187 73.9074 \nL 129.623584 60.11976 \nL 129.590104 80.318506 \nL 129.641846 69.288258 \nL 129.7362 80.331247 \nL 129.705763 65.165925 \nL 129.751418 68.957285 \nL 129.763593 63.150896 \nL 129.815335 75.04129 \nL 129.86099 67.637537 \nL 129.897514 65.374292 \nL 129.949256 74.028707 \nL 129.961431 80.016392 \nL 129.979693 60.120937 \nL 130.055785 72.962255 \nL 130.153182 57.777109 \nL 130.110571 77.122491 \nL 130.1684 70.213659 \nL 130.262754 82.475417 \nL 130.201881 66.738169 \nL 130.277972 70.885935 \nL 130.302322 66.323756 \nL 130.31754 79.707171 \nL 130.320584 79.9261 \nL 130.332758 73.318356 \nL 130.354064 63.698429 \nL 130.405806 77.669416 \nL 130.454505 67.839391 \nL 130.491029 75.392576 \nL 130.542771 65.103955 \nL 130.567121 69.842077 \nL 130.603645 61.840659 \nL 130.646256 73.499738 \nL 130.685824 65.783395 \nL 130.749741 79.174856 \nL 130.801483 69.668984 \nL 130.810614 70.320292 \nL 130.819745 68.997423 \nL 130.941492 61.734419 \nL 130.880618 78.460295 \nL 130.944535 62.72255 \nL 131.008452 81.608601 \nL 131.029758 62.714915 \nL 131.075413 81.520282 \nL 131.124112 62.914471 \nL 131.215422 72.305867 \nL 131.227596 83.519618 \nL 131.315863 59.793748 \nL 131.328037 66.1952 \nL 131.443697 77.421558 \nL 131.388911 59.49037 \nL 131.449784 75.098349 \nL 131.50457 61.710481 \nL 131.525876 79.324741 \nL 131.565443 69.303443 \nL 131.665884 76.524879 \nL 131.614142 65.647046 \nL 131.678059 72.201176 \nL 131.690234 65.091 \nL 131.781544 76.371997 \nL 131.86981 62.85993 \nL 131.885028 75.710538 \nL 131.891116 79.334162 \nL 131.921552 66.526091 \nL 131.985469 68.72686 \nL 132.028081 82.371876 \nL 132.061561 68.700414 \nL 132.091998 71.648274 \nL 132.101129 62.805979 \nL 132.192439 81.191594 \nL 132.195482 81.343826 \nL 132.20157 76.294559 \nL 132.23505 62.259864 \nL 132.311142 72.388863 \nL 132.347666 80.620315 \nL 132.393321 65.697334 \nL 132.420714 74.584498 \nL 132.432888 65.404433 \nL 132.493762 78.157845 \nL 132.530286 73.579997 \nL 132.533329 73.808092 \nL 132.54246 66.633867 \nL 132.563766 87.243758 \nL 132.670294 55.200691 \nL 132.676382 53.824327 \nL 132.731168 69.900659 \nL 132.819434 214.214882 \nL 132.776823 46.033674 \nL 132.834652 68.874025 \nL 132.846827 23.044795 \nL 132.925962 79.984714 \nL 132.944224 70.915309 \nL 133.011185 64.488837 \nL 132.96553 75.74384 \nL 133.050753 72.261468 \nL 133.154237 76.938361 \nL 133.132932 61.628592 \nL 133.160325 74.956884 \nL 133.248591 65.133625 \nL 133.266853 78.557049 \nL 133.282072 84.995967 \nL 133.336858 65.867846 \nL 133.349032 58.428916 \nL 133.412949 75.377308 \nL 133.44643 63.632043 \nL 133.53774 77.327813 \nL 133.565133 72.13911 \nL 133.632093 64.916701 \nL 133.650355 76.050344 \nL 133.653399 76.144524 \nL 133.656443 74.728207 \nL 133.699054 63.389917 \nL 133.75384 77.246673 \nL 133.769058 71.352196 \nL 133.84515 62.226219 \nL 133.799495 74.637318 \nL 133.875587 68.076818 \nL 133.887761 81.046875 \nL 133.927329 62.358442 \nL 133.982115 69.793021 \nL 133.99429 55.816848 \nL 134.052119 79.519653 \nL 134.088643 72.128719 \nL 134.158648 76.272997 \nL 134.17691 66.021563 \nL 134.201259 73.941055 \nL 134.219521 60.821791 \nL 134.271263 78.41447 \nL 134.307787 74.417816 \nL 134.319962 78.045989 \nL 134.365617 58.654309 \nL 134.417359 74.170274 \nL 134.420403 74.495994 \nL 134.438665 67.4046 \nL 134.463014 68.1574 \nL 134.5178 64.995822 \nL 134.557368 77.68899 \nL 134.572586 82.127831 \nL 134.624329 65.048815 \nL 134.651722 72.045333 \nL 134.697377 60.938707 \nL 134.718682 76.927783 \nL 134.767381 67.585207 \nL 134.84956 79.890723 \nL 134.806949 64.49688 \nL 134.889128 72.008 \nL 134.916521 62.687374 \nL 134.937826 77.252227 \nL 135.001743 67.308514 \nL 135.071748 80.637623 \nL 135.056529 62.568527 \nL 135.108272 63.541175 \nL 135.187407 61.189985 \nL 135.153927 76.077714 \nL 135.211756 64.988981 \nL 135.342634 78.40031 \nL 135.418726 59.304147 \nL 135.482643 66.965022 \nL 135.595258 89.803788 \nL 135.552647 57.741683 \nL 135.61352 82.176212 \nL 135.70483 48.482819 \nL 135.738311 65.350236 \nL 135.817446 79.38487 \nL 135.850926 70.221358 \nL 135.869188 72.355266 \nL 135.896581 75.661657 \nL 135.9118 66.678865 \nL 135.920931 61.758062 \nL 136.012241 75.919718 \nL 136.030503 62.144168 \nL 136.121813 73.878214 \nL 136.200948 77.463457 \nL 136.149206 62.06556 \nL 136.225297 70.74709 \nL 136.280083 76.531396 \nL 136.295302 66.545859 \nL 136.374437 63.293185 \nL 136.344 83.629008 \nL 136.392699 72.599397 \nL 136.395743 72.91506 \nL 136.410961 66.784573 \nL 136.441398 66.845867 \nL 136.456616 61.86998 \nL 136.529664 76.370321 \nL 136.627061 78.56111 \nL 136.566188 67.314579 \nL 136.636192 75.053336 \nL 136.663585 61.38749 \nL 136.727502 75.083642 \nL 136.748808 71.191092 \nL 136.837074 75.119823 \nL 136.773157 65.334596 \nL 136.852293 68.499436 \nL 136.894904 66.773261 \nL 136.876642 78.569346 \nL 136.946646 72.625211 \nL 137.022738 80.22477 \nL 136.974039 56.053506 \nL 137.041 66.957076 \nL 137.089699 81.988331 \nL 137.123179 64.244281 \nL 137.177965 70.949418 \nL 137.187096 65.287715 \nL 137.251013 74.425455 \nL 137.287537 68.210444 \nL 137.351454 74.610354 \nL 137.336236 64.353633 \nL 137.397109 68.73046 \nL 137.491463 64.288539 \nL 137.427546 76.817825 \nL 137.506681 68.967581 \nL 137.613209 84.816783 \nL 137.591904 61.941224 \nL 137.625384 73.167499 \nL 137.710607 60.249424 \nL 137.692345 76.166458 \nL 137.753218 67.360433 \nL 137.878008 83.749486 \nL 137.817135 63.808003 \nL 137.884096 81.098108 \nL 138.002799 61.82867 \nL 138.008886 64.965735 \nL 138.130633 78.257569 \nL 138.133676 79.221183 \nL 138.158026 63.126873 \nL 138.221943 74.104419 \nL 138.301078 63.642933 \nL 138.316296 76.697636 \nL 138.337602 68.914649 \nL 138.358908 80.80723 \nL 138.44413 64.725328 \nL 138.529353 60.616069 \nL 138.471523 78.327119 \nL 138.550659 67.099866 \nL 138.568921 61.913086 \nL 138.587183 75.961869 \nL 138.611532 73.292585 \nL 138.660231 79.76404 \nL 138.632838 66.335194 \nL 138.669362 71.994479 \nL 138.751541 62.014449 \nL 138.690667 77.512367 \nL 138.788065 64.206752 \nL 138.806327 79.677227 \nL 138.858069 63.352372 \nL 138.912855 75.012981 \nL 138.973728 57.92117 \nL 139.025471 70.870609 \nL 139.104606 80.482156 \nL 139.061995 58.134832 \nL 139.144174 74.215254 \nL 139.202003 66.071525 \nL 139.244615 78.663958 \nL 139.247658 79.282301 \nL 139.268964 68.279464 \nL 139.302444 68.632792 \nL 139.335925 57.821602 \nL 139.35723 82.55286 \nL 139.369405 86.312448 \nL 139.433322 62.560694 \nL 139.445497 56.968849 \nL 139.512457 72.599337 \nL 139.625073 82.095303 \nL 139.588549 66.643632 \nL 139.634204 77.95369 \nL 139.743776 64.853058 \nL 139.698121 78.388573 \nL 139.752907 69.569425 \nL 139.847261 86.744189 \nL 139.822911 57.596792 \nL 139.877697 79.500545 \nL 139.941614 62.412126 \nL 139.990313 76.074761 \nL 140.069448 78.130631 \nL 140.017706 65.233287 \nL 140.078579 72.659227 \nL 140.157715 63.853402 \nL 140.17902 79.900292 \nL 140.185108 76.573833 \nL 140.306854 58.209388 \nL 140.312942 64.617972 \nL 140.36164 80.655548 \nL 140.337291 62.685229 \nL 140.428601 76.52538 \nL 140.556435 66.074076 \nL 140.56861 70.505647 \nL 140.656876 81.170796 \nL 140.629483 64.281586 \nL 140.675138 70.317791 \nL 140.775579 64.129928 \nL 140.736011 76.042877 \nL 140.78471 67.78176 \nL 140.87602 86.435319 \nL 140.830365 62.323193 \nL 140.90037 72.266113 \nL 140.918632 78.257441 \nL 140.946025 69.424138 \nL 140.997767 69.535862 \nL 141.055597 62.594514 \nL 141.095164 70.789124 \nL 141.104295 70.32177 \nL 141.125601 66.577304 \nL 141.14995 74.095656 \nL 141.162125 75.449043 \nL 141.198649 65.865978 \nL 141.232129 69.122732 \nL 141.29909 60.124438 \nL 141.256479 80.25329 \nL 141.33257 75.123868 \nL 141.408662 79.330469 \nL 141.350832 64.538133 \nL 141.417793 71.154049 \nL 141.426924 59.973925 \nL 141.445186 78.227062 \nL 141.533452 60.986684 \nL 141.53954 58.092938 \nL 141.612588 78.814245 \nL 141.627806 81.52574 \nL 141.673461 63.746045 \nL 141.682592 62.527502 \nL 141.700854 78.378477 \nL 141.706941 81.614936 \nL 141.725203 62.804966 \nL 141.807382 74.815815 \nL 141.810426 75.292678 \nL 141.853037 66.548869 \nL 141.865212 61.254423 \nL 141.919998 75.065937 \nL 141.956522 70.237577 \nL 141.99609 77.266599 \nL 142.017395 58.584844 \nL 142.038701 64.88092 \nL 142.044788 62.650246 \nL 142.069138 78.820228 \nL 142.136098 70.859865 \nL 142.224365 83.515871 \nL 142.242627 70.362224 \nL 142.24567 70.620124 \nL 142.291325 73.275158 \nL 142.27002 62.150803 \nL 142.306544 62.606837 \nL 142.312631 59.711799 \nL 142.373504 78.297566 \nL 142.406985 67.872528 \nL 142.449596 79.578927 \nL 142.492207 65.59379 \nL 142.528731 70.53225 \nL 142.616998 61.306203 \nL 142.595692 82.383765 \nL 142.632216 71.411368 \nL 142.720482 76.713105 \nL 142.70222 70.489488 \nL 142.744832 73.982224 \nL 142.781356 56.483441 \nL 142.820923 74.307295 \nL 142.857447 71.67581 \nL 142.933539 64.436334 \nL 142.893971 73.954978 \nL 142.939626 69.686239 \nL 142.951801 86.859896 \nL 142.991369 64.161938 \nL 143.049198 66.913801 \nL 143.134421 61.126083 \nL 143.113115 76.971515 \nL 143.155727 68.810274 \nL 143.225731 77.692639 \nL 143.207469 67.485333 \nL 143.268342 70.120115 \nL 143.350521 59.697363 \nL 143.332259 79.937729 \nL 143.380958 66.456662 \nL 143.444875 80.819446 \nL 143.502705 71.485611 \nL 143.578796 62.973888 \nL 143.514879 71.885322 \nL 143.606189 71.813155 \nL 143.612277 72.493904 \nL 143.648801 63.334745 \nL 143.651844 61.891627 \nL 143.70663 77.00658 \nL 143.746198 69.654006 \nL 143.85577 76.915017 \nL 143.804028 58.997863 \nL 143.861857 75.298075 \nL 143.877076 63.159073 \nL 143.922731 80.44088 \nL 143.974473 71.980464 \nL 144.007953 77.180865 \nL 144.056652 62.321175 \nL 144.077958 68.616309 \nL 144.093176 64.926861 \nL 144.151006 73.169067 \nL 144.160137 79.379257 \nL 144.190573 65.086155 \nL 144.257534 69.141851 \nL 144.303189 78.159937 \nL 144.321451 63.947911 \nL 144.37015 70.128941 \nL 144.397543 58.014151 \nL 144.455372 81.009254 \nL 144.473634 72.281705 \nL 144.55277 58.522355 \nL 144.589294 67.260784 \nL 144.601468 78.902318 \nL 144.647123 65.292351 \nL 144.71104 78.527147 \nL 144.729302 82.396049 \nL 144.753652 74.656006 \nL 144.771914 60.48754 \nL 144.8267 75.727145 \nL 144.875398 67.016749 \nL 144.936272 79.982024 \nL 144.914966 58.290512 \nL 144.98497 66.845191 \nL 144.991058 65.769637 \nL 145.064106 76.121136 \nL 145.067149 76.494839 \nL 145.094542 67.447853 \nL 145.137154 59.848543 \nL 145.179765 75.238824 \nL 145.19194 80.496401 \nL 145.210202 61.959312 \nL 145.280206 70.68282 \nL 145.362385 61.966623 \nL 145.301512 71.420726 \nL 145.389778 68.994346 \nL 145.490219 79.121351 \nL 145.44152 67.541525 \nL 145.502394 72.26472 \nL 145.514568 63.293569 \nL 145.566311 78.632279 \nL 145.615009 67.718827 \nL 145.71545 80.617036 \nL 145.666752 59.487492 \nL 145.727625 71.052157 \nL 145.761105 64.59331 \nL 145.834153 74.271062 \nL 145.873721 79.334495 \nL 145.913289 66.497917 \nL 145.931551 67.72954 \nL 145.937638 66.852576 \nL 145.9559 79.287379 \nL 146.022861 71.778799 \nL 146.101996 73.558489 \nL 146.086778 67.365956 \nL 146.111127 71.061742 \nL 146.126345 64.749226 \nL 146.156782 74.355813 \nL 146.22983 65.992308 \nL 146.235917 65.343098 \nL 146.257223 74.992483 \nL 146.305922 82.724652 \nL 146.281572 61.781022 \nL 146.333315 68.15907 \nL 146.345489 62.348078 \nL 146.403319 78.834687 \nL 146.433756 72.866954 \nL 146.439843 73.180044 \nL 146.461149 67.686038 \nL 146.531153 57.594494 \nL 146.491585 81.300762 \nL 146.558546 71.011996 \nL 146.573764 78.33637 \nL 146.613332 58.997991 \nL 146.671162 72.198253 \nL 146.683336 65.815545 \nL 146.698555 79.599465 \nL 146.77769 74.921907 \nL 146.783777 77.548131 \nL 146.814214 63.267599 \nL 146.887262 74.485681 \nL 146.966397 61.027352 \nL 147.033358 71.390795 \nL 147.100319 74.35983 \nL 147.112493 66.996635 \nL 147.121624 59.441553 \nL 147.194672 75.036404 \nL 147.215978 74.850372 \nL 147.246415 80.173342 \nL 147.282939 64.775347 \nL 147.328594 76.140371 \nL 147.386423 58.792875 \nL 147.429035 79.41143 \nL 147.456428 62.329662 \nL 147.535563 80.515963 \nL 147.514257 60.364093 \nL 147.578174 72.115402 \nL 147.675572 62.127034 \nL 147.648179 79.759298 \nL 147.69079 68.560428 \nL 147.757751 79.341877 \nL 147.776013 64.555104 \nL 147.806449 75.457515 \nL 147.885585 59.297123 \nL 147.93124 69.743665 \nL 148.025593 80.06625 \nL 147.989069 61.903289 \nL 148.052986 74.34985 \nL 148.110816 55.079339 \nL 148.135165 89.463758 \nL 148.141253 93.15811 \nL 148.211257 61.777665 \nL 148.311698 81.93197 \nL 148.259956 53.673077 \nL 148.32996 64.798541 \nL 148.339091 61.217624 \nL 148.403008 78.906762 \nL 148.430401 69.82367 \nL 148.539973 82.849064 \nL 148.448663 65.441116 \nL 148.546061 79.181112 \nL 148.561279 62.409531 \nL 148.66172 69.588286 \nL 148.673895 68.226377 \nL 148.686069 72.860094 \nL 148.762161 77.124121 \nL 148.707375 63.519796 \nL 148.774336 68.818374 \nL 148.78651 59.976913 \nL 148.838253 85.107283 \nL 148.883908 70.282507 \nL 148.932606 65.082022 \nL 148.914344 79.728746 \nL 148.975218 71.484579 \nL 148.987392 84.199655 \nL 149.008698 63.386022 \nL 149.078702 66.859283 \nL 149.093921 62.746561 \nL 149.139576 70.644267 \nL 149.166969 68.228412 \nL 149.224798 60.797291 \nL 149.288715 78.259492 \nL 149.407418 67.382402 \nL 149.319152 79.163697 \nL 149.413506 69.294668 \nL 149.428724 80.150293 \nL 149.477423 60.807401 \nL 149.523078 70.607022 \nL 149.599169 60.985637 \nL 149.54134 76.485378 \nL 149.638737 69.313281 \nL 149.678305 79.737973 \nL 149.720916 64.455664 \nL 149.75744 74.807832 \nL 149.854837 66.576606 \nL 149.806139 79.439396 \nL 149.891361 67.463607 \nL 150.025283 82.333798 \nL 149.924842 58.38281 \nL 150.03137 79.221202 \nL 150.055719 61.729465 \nL 150.101374 81.012312 \nL 150.153117 65.351176 \nL 150.174422 79.816305 \nL 150.192684 64.443724 \nL 150.287038 74.352654 \nL 150.293125 74.953025 \nL 150.335737 62.826595 \nL 150.341824 59.153651 \nL 150.381392 81.155403 \nL 150.448352 60.022921 \nL 150.551837 75.697195 \nL 150.494007 55.954167 \nL 150.570099 65.825294 \nL 150.67054 76.753769 \nL 150.682715 68.767055 \nL 150.713151 59.520557 \nL 150.72837 74.738851 \nL 150.774025 72.608706 \nL 150.810549 75.343214 \nL 150.859247 61.047169 \nL 150.880553 71.300866 \nL 150.895771 69.872834 \nL 150.904902 72.606081 \nL 150.999256 78.917711 \nL 150.932295 59.581056 \nL 151.014474 71.993127 \nL 151.06926 79.022592 \nL 151.038824 65.11183 \nL 151.124046 71.927783 \nL 151.136221 69.434023 \nL 151.163614 66.906088 \nL 151.206225 72.386588 \nL 151.297535 74.900053 \nL 151.230575 63.043372 \nL 151.30971 71.222202 \nL 151.324928 64.7913 \nL 151.36754 77.228934 \nL 151.422326 68.951509 \nL 151.513636 75.048761 \nL 151.449719 65.5559 \nL 151.52581 66.501327 \nL 151.571465 66.020584 \nL 151.580596 72.658718 \nL 151.589727 84.772019 \nL 151.677994 61.786146 \nL 151.687125 66.284091 \nL 151.738867 75.545957 \nL 151.70843 58.58227 \nL 151.805828 71.600993 \nL 151.821046 64.348906 \nL 151.881919 78.901063 \nL 151.921487 67.321677 \nL 151.961055 76.49684 \nL 152.009753 61.479571 \nL 152.012797 60.742057 \nL 152.034103 79.512308 \nL 152.058452 74.661515 \nL 152.064539 76.72823 \nL 152.125413 55.838686 \nL 152.158893 72.371368 \nL 152.195417 62.330954 \nL 152.213679 79.881351 \nL 152.274552 64.558287 \nL 152.326295 81.909795 \nL 152.399343 74.659653 \nL 152.49674 61.819899 \nL 152.511958 72.012483 \nL 152.548482 77.306747 \nL 152.566744 67.713898 \nL 152.585006 69.756363 \nL 152.600225 62.642854 \nL 152.64588 77.136943 \nL 152.691535 68.346751 \nL 152.77067 75.850726 \nL 152.746321 63.310705 \nL 152.807194 73.792546 \nL 152.877198 61.704108 \nL 152.858936 76.517033 \nL 152.910679 72.653931 \nL 152.91981 77.383377 \nL 152.938072 61.778924 \nL 153.01112 67.835592 \nL 153.017207 64.263374 \nL 153.10243 81.125598 \nL 153.120692 67.714293 \nL 153.132866 72.874595 \nL 153.22722 77.776478 \nL 153.160259 62.084029 \nL 153.239395 72.175784 \nL 153.251569 75.857844 \nL 153.2607 70.231788 \nL 153.275919 57.186375 \nL 153.327661 76.818854 \nL 153.367229 74.04894 \nL 153.37636 79.635639 \nL 153.461582 61.265904 \nL 153.464626 60.8662 \nL 153.479844 71.76828 \nL 153.55898 79.428154 \nL 153.537674 63.730903 \nL 153.598547 75.291142 \nL 153.680726 62.421802 \nL 153.619853 76.804884 \nL 153.708119 75.550409 \nL 153.83291 61.438032 \nL 153.796386 76.632256 \nL 153.842041 66.895686 \nL 153.918132 77.370413 \nL 153.936394 62.670203 \nL 153.9577 74.160824 \nL 154.006399 63.238809 \nL 153.979006 74.982761 \nL 154.061185 72.911318 \nL 154.070316 82.036303 \nL 154.137276 63.545262 \nL 154.164669 66.998806 \nL 154.234674 57.652637 \nL 154.182931 76.240159 \nL 154.249892 68.550579 \nL 154.32294 78.379525 \nL 154.344246 66.684595 \nL 154.365551 73.623127 \nL 154.426425 57.997173 \nL 154.402075 78.024217 \nL 154.475123 72.600038 \nL 154.532953 77.414618 \nL 154.548171 66.464815 \nL 154.566433 70.315536 \nL 154.578608 60.062985 \nL 154.618176 78.212266 \nL 154.676005 72.170618 \nL 154.791665 63.216847 \nL 154.776446 75.730724 \nL 154.797752 66.26645 \nL 154.855582 81.775394 \nL 154.901237 65.784887 \nL 154.916455 77.45042 \nL 154.922542 80.286226 \nL 154.946892 57.738078 \nL 155.010809 71.333935 \nL 155.022983 57.162575 \nL 155.068638 75.284153 \nL 155.117337 74.52521 \nL 155.217778 78.468634 \nL 155.169079 62.51957 \nL 155.226909 75.242034 \nL 155.248215 57.078681 \nL 155.303001 78.928719 \nL 155.345612 67.821181 \nL 155.354743 67.335203 \nL 155.363874 70.575561 \nL 155.376049 77.643346 \nL 155.415616 66.681891 \nL 155.461271 68.31811 \nL 155.470402 54.405524 \nL 155.519101 73.793343 \nL 155.5678 71.349876 \nL 155.598236 79.308733 \nL 155.616498 64.721536 \nL 155.677372 73.092058 \nL 155.69259 64.81494 \nL 155.75042 75.347989 \nL 155.789987 71.820133 \nL 155.893472 83.156429 \nL 155.844773 61.756298 \nL 155.902603 74.171901 \nL 156.003044 56.843949 \nL 155.969564 74.338111 \nL 156.015219 69.334301 \nL 156.045656 77.786062 \nL 156.063918 62.5116 \nL 156.127835 76.887271 \nL 156.234363 60.183858 \nL 156.158271 81.345791 \nL 156.252625 71.071031 \nL 156.267843 82.745959 \nL 156.353066 64.008802 \nL 156.359153 61.117549 \nL 156.420027 75.512281 \nL 156.465682 62.538385 \nL 156.563079 78.817302 \nL 156.602647 75.020243 \nL 156.657433 62.39417 \nL 156.639171 80.523304 \nL 156.693957 77.226944 \nL 156.697 79.274066 \nL 156.736568 59.542011 \nL 156.797441 72.953436 \nL 156.87962 61.410612 \nL 156.852227 75.825858 \nL 156.910057 71.523323 \nL 156.952668 64.922231 \nL 156.964843 81.316173 \nL 156.97093 84.474426 \nL 157.022673 57.736494 \nL 157.065284 74.073572 \nL 157.12007 65.801077 \nL 157.147463 76.007478 \nL 157.159638 80.035576 \nL 157.199205 63.684573 \nL 157.205293 59.058162 \nL 157.226598 74.985627 \nL 157.30269 70.483028 \nL 157.330083 61.00845 \nL 157.363563 81.589119 \nL 157.366607 82.186327 \nL 157.412262 69.637433 \nL 157.476179 58.100881 \nL 157.454873 82.23813 \nL 157.521834 70.000011 \nL 157.582707 73.165696 \nL 157.546183 65.994977 \nL 157.613144 69.513105 \nL 157.622275 61.207755 \nL 157.640537 74.13609 \nL 157.722716 70.411793 \nL 157.750109 78.375172 \nL 157.768371 66.533394 \nL 157.795764 53.493247 \nL 157.826201 87.109545 \nL 157.838375 114.879829 \nL 157.920554 49.202115 \nL 157.929685 58.042024 \nL 158.042301 76.678619 \nL 157.947947 52.403651 \nL 158.054476 70.134684 \nL 158.06665 63.270125 \nL 158.12448 83.598846 \nL 158.164048 70.224489 \nL 158.231008 79.614382 \nL 158.24927 65.937409 \nL 158.27362 70.968198 \nL 158.288838 65.316678 \nL 158.34058 80.685567 \nL 158.392323 67.031029 \nL 158.395366 67.030338 \nL 158.410585 62.055897 \nL 158.422759 70.784338 \nL 158.425803 70.975603 \nL 158.428847 67.908183 \nL 158.437978 51.392196 \nL 158.459283 113.884058 \nL 158.544506 58.14226 \nL 158.654078 78.441569 \nL 158.67234 73.982347 \nL 158.775825 63.285083 \nL 158.717995 77.084243 \nL 158.784956 72.146512 \nL 158.867135 78.611387 \nL 158.809305 62.480874 \nL 158.88844 66.00662 \nL 158.906702 60.098816 \nL 158.940183 79.616004 \nL 158.97975 71.646882 \nL 159.083235 77.877427 \nL 159.03758 61.008898 \nL 159.092366 73.952623 \nL 159.18672 61.684644 \nL 159.168458 80.58822 \nL 159.204982 72.719801 \nL 159.271942 50.322264 \nL 159.238462 73.334748 \nL 159.284117 70.793397 \nL 159.296292 101.288598 \nL 159.363252 59.535787 \nL 159.390645 65.532573 \nL 159.408907 57.543374 \nL 159.466737 73.922208 \nL 159.488043 72.27304 \nL 159.512392 69.541466 \nL 159.533698 84.421538 \nL 159.536741 85.471955 \nL 159.561091 61.10896 \nL 159.612833 74.24928 \nL 159.628051 59.924338 \nL 159.646313 76.623821 \nL 159.722405 72.963515 \nL 159.725449 73.122488 \nL 159.73458 68.019929 \nL 159.813715 59.221981 \nL 159.752842 75.551865 \nL 159.82589 70.079265 \nL 159.908069 83.113939 \nL 159.850239 65.341603 \nL 159.941549 76.195155 \nL 159.990248 69.474772 \nL 160.072427 61.30989 \nL 160.048077 74.661835 \nL 160.09982 67.82798 \nL 160.200261 77.660729 \nL 160.151562 66.251945 \nL 160.209392 70.613648 \nL 160.218523 60.733909 \nL 160.28244 78.496908 \nL 160.325051 63.629555 \nL 160.395055 82.611977 \nL 160.452885 68.28227 \nL 160.525933 55.84739 \nL 160.501584 82.946778 \nL 160.565501 67.083772 \nL 160.650723 83.456103 \nL 160.598981 63.244714 \nL 160.684204 75.528921 \nL 160.745077 61.838754 \nL 160.711597 80.254328 \nL 160.793776 76.381071 \nL 160.802907 76.823325 \nL 160.808994 74.452859 \nL 160.89726 62.233705 \nL 160.924653 70.225993 \nL 160.945959 78.076472 \nL 160.961177 67.593592 \nL 161.0464 61.891102 \nL 161.022051 78.016927 \nL 161.061618 70.920665 \nL 161.098142 73.472449 \nL 161.152928 61.357731 \nL 161.162059 56.787105 \nL 161.192496 100.512253 \nL 161.201627 126.478803 \nL 161.289893 49.736563 \nL 161.302068 54.375069 \nL 161.429902 77.408699 \nL 161.472513 72.556087 \nL 161.484688 57.394322 \nL 161.575998 79.243143 \nL 161.734269 63.963399 \nL 161.7434 69.412347 \nL 161.755574 75.934548 \nL 161.816448 61.43519 \nL 161.846884 62.713582 \nL 161.856015 58.567339 \nL 161.913845 76.967047 \nL 161.941238 71.761022 \nL 161.977762 76.716495 \nL 161.962544 68.658023 \nL 162.047766 68.658568 \nL 162.117771 80.072957 \nL 162.08429 64.801602 \nL 162.151251 69.155185 \nL 162.160382 64.099391 \nL 162.215168 81.463466 \nL 162.26691 65.198365 \nL 162.309522 63.088706 \nL 162.321696 72.954301 \nL 162.361264 79.060703 \nL 162.376482 57.131318 \nL 162.434312 74.183693 \nL 162.501273 80.632897 \nL 162.525622 64.486968 \nL 162.534753 68.068497 \nL 162.604757 63.796982 \nL 162.6565 75.43276 \nL 162.677805 64.929002 \nL 162.784334 70.795002 \nL 162.884775 81.710349 \nL 162.845207 58.646673 \nL 162.893906 73.539886 \nL 163.000434 62.90811 \nL 162.957823 75.016884 \nL 163.009565 67.689899 \nL 163.033914 80.444311 \nL 163.05522 57.708248 \nL 163.116093 66.915552 \nL 163.134355 62.35208 \nL 163.18001 76.01182 \nL 163.210447 72.259531 \nL 163.259146 77.547051 \nL 163.240884 67.167435 \nL 163.313932 69.602114 \nL 163.390023 62.137816 \nL 163.411329 77.198258 \nL 163.441766 85.505213 \nL 163.490464 63.279371 \nL 163.542207 55.137991 \nL 163.505683 68.313321 \nL 163.587862 68.051374 \nL 163.60308 70.703385 \nL 163.636561 59.683852 \nL 163.642648 53.813097 \nL 163.666997 85.974762 \nL 163.737002 73.522146 \nL 163.788744 59.548423 \nL 163.840486 77.833758 \nL 163.84353 78.404494 \nL 163.864836 62.853955 \nL 163.943971 57.993061 \nL 163.916578 78.606345 \nL 163.956146 68.030504 \nL 163.99267 81.880721 \nL 164.010932 62.664036 \nL 164.071805 76.470261 \nL 164.169202 62.73705 \nL 164.190508 69.23742 \nL 164.20877 86.556691 \nL 164.269643 58.468237 \nL 164.297036 63.655792 \nL 164.354866 58.799657 \nL 164.336604 73.522107 \nL 164.400521 69.929104 \nL 164.443132 77.184054 \nL 164.461394 61.026261 \nL 164.491831 67.171511 \nL 164.57401 63.450337 \nL 164.51618 83.463865 \nL 164.583141 71.126756 \nL 164.592272 77.361586 \nL 164.686626 65.56871 \nL 164.729237 64.302725 \nL 164.741412 75.320878 \nL 164.774892 80.642558 \nL 164.820547 60.520598 \nL 164.829678 53.678013 \nL 164.920988 72.227451 \nL 165.033604 78.080023 \nL 164.975774 62.948652 \nL 165.036647 77.982056 \nL 165.152307 64.177182 \nL 165.167525 70.229137 \nL 165.261879 82.322746 \nL 165.201005 61.038788 \nL 165.274053 72.480933 \nL 165.377538 57.617293 \nL 165.331883 79.736555 \nL 165.389713 60.630712 \nL 165.435368 83.633585 \nL 165.502328 62.806943 \nL 165.508416 60.4423 \nL 165.590595 75.588715 \nL 165.605813 78.186001 \nL 165.642337 61.804485 \nL 165.651468 56.094472 \nL 165.70321 81.234726 \nL 165.745822 65.178712 \nL 165.782346 81.015882 \nL 165.870612 71.006646 \nL 165.968009 64.180836 \nL 165.922354 81.946925 \nL 165.998446 66.619427 \nL 166.010621 75.403705 \nL 166.095843 63.838273 \nL 166.111062 68.755513 \nL 166.144542 75.679201 \nL 166.187153 63.668074 \nL 166.229765 71.984302 \nL 166.348468 65.832341 \nL 166.302813 78.791699 \nL 166.351511 66.265466 \nL 166.488476 80.575373 \nL 166.394123 60.655659 \nL 166.497607 75.07257 \nL 166.558481 58.518474 \nL 166.601092 81.999245 \nL 166.625441 60.916396 \nL 166.725882 72.897023 \nL 166.744144 80.150723 \nL 166.771537 59.386041 \nL 166.829367 74.373682 \nL 166.935895 61.193157 \nL 166.94807 65.944538 \nL 167.042424 80.783659 \nL 166.984594 64.390547 \nL 167.057642 68.247028 \nL 167.066773 64.51515 \nL 167.148952 77.202014 \nL 167.170258 67.139779 \nL 167.261568 77.479601 \nL 167.209825 64.163468 \nL 167.27983 66.167666 \nL 167.285917 62.941495 \nL 167.307223 78.145822 \nL 167.389402 67.030158 \nL 167.407664 76.372743 \nL 167.428969 63.087431 \nL 167.49593 68.800523 \nL 167.505061 62.457151 \nL 167.565934 83.944453 \nL 167.605502 68.478173 \nL 167.699856 60.88168 \nL 167.675506 73.219402 \nL 167.715074 68.257065 \nL 167.742467 77.018665 \nL 167.806384 62.591158 \nL 167.836821 73.339523 \nL 167.86117 78.326689 \nL 167.906825 62.757043 \nL 167.909869 62.178112 \nL 167.934218 76.086774 \nL 167.976829 70.483307 \nL 167.992048 74.911694 \nL 168.083358 83.299292 \nL 168.025528 66.185939 \nL 168.092489 70.158421 \nL 168.10162 58.036551 \nL 168.116838 73.746482 \nL 168.205104 65.10565 \nL 168.311633 78.891444 \nL 168.250759 59.246695 \nL 168.323807 72.223438 \nL 168.360331 63.115265 \nL 168.421205 80.215562 \nL 168.46686 66.71693 \nL 168.542951 62.817055 \nL 168.524689 76.671968 \nL 168.564257 75.850116 \nL 168.567301 76.25706 \nL 168.585563 65.561429 \nL 168.588606 64.211155 \nL 168.606868 80.052231 \nL 168.673829 74.86045 \nL 168.679916 78.037105 \nL 168.719484 61.59861 \nL 168.777314 70.744777 \nL 168.847318 64.707322 \nL 168.816881 81.765862 \nL 168.883842 71.508837 \nL 168.902104 74.421843 \nL 168.920366 66.782115 \nL 168.978196 68.663061 \nL 168.993414 63.393202 \nL 169.042113 75.900843 \nL 169.045156 76.216916 \nL 169.057331 68.796162 \nL 169.066462 62.160437 \nL 169.124292 77.636118 \nL 169.169947 62.752184 \nL 169.270388 82.023381 \nL 169.285606 65.815021 \nL 169.398222 61.673028 \nL 169.328217 77.016532 \nL 169.401265 61.892797 \nL 169.526056 76.212531 \nL 169.532143 75.039598 \nL 169.608235 64.670408 \nL 169.568667 77.097807 \nL 169.635628 76.052639 \nL 169.641715 78.869066 \nL 169.659977 66.617786 \nL 169.733025 67.933044 \nL 169.830422 64.761661 \nL 169.787811 77.805161 \nL 169.842597 68.399387 \nL 169.943038 79.864634 \nL 169.921732 59.696251 \nL 169.955213 69.157893 \nL 169.982606 63.502571 \nL 170.05261 74.792637 \nL 170.055654 74.586387 \nL 170.064785 76.969664 \nL 170.080003 82.516348 \nL 170.134789 63.701849 \nL 170.165226 75.118672 \nL 170.180444 63.0184 \nL 170.280885 67.305484 \nL 170.329584 63.096725 \nL 170.308278 72.468525 \nL 170.344802 69.708779 \nL 170.433068 76.283311 \nL 170.399588 67.390099 \nL 170.448287 68.127644 \nL 170.47568 54.996531 \nL 170.521335 79.197402 \nL 170.536553 101.227989 \nL 170.612645 64.190156 \nL 170.621776 64.270803 \nL 170.636994 53.772104 \nL 170.679605 75.62664 \nL 170.731348 64.428182 \nL 170.807439 78.481008 \nL 170.770915 62.885929 \nL 170.843963 68.032688 \nL 170.850051 65.03449 \nL 170.910924 75.940119 \nL 170.947448 70.903589 \nL 171.063107 82.529229 \nL 171.005278 63.921212 \nL 171.072238 75.877784 \nL 171.117893 60.388242 \nL 171.093544 76.139494 \nL 171.190941 68.598681 \nL 171.270077 77.222381 \nL 171.230509 62.879844 \nL 171.285295 65.279868 \nL 171.291383 63.785167 \nL 171.352256 80.097215 \nL 171.379649 71.911119 \nL 171.425304 83.114801 \nL 171.437479 69.395729 \nL 171.44661 61.832074 \nL 171.510527 70.947606 \nL 171.550094 63.568689 \nL 171.607924 75.828962 \nL 171.684016 69.420559 \nL 171.69619 63.01224 \nL 171.7875 73.749932 \nL 171.796631 77.732674 \nL 171.884898 67.257799 \nL 171.933596 62.489109 \nL 171.964033 77.929001 \nL 171.976208 83.916972 \nL 171.997513 65.189068 \nL 172.055343 68.173033 \nL 172.113173 57.977229 \nL 172.149697 75.125584 \nL 172.207526 76.605447 \nL 172.189264 63.637088 \nL 172.241007 71.378003 \nL 172.338404 63.430803 \nL 172.323186 74.270673 \nL 172.350579 69.999895 \nL 172.384059 78.848166 \nL 172.408408 63.804971 \nL 172.45102 66.613218 \nL 172.54233 82.14199 \nL 172.499718 63.727649 \nL 172.554504 65.527984 \nL 172.557548 63.670244 \nL 172.621465 80.115222 \nL 172.645814 74.917123 \nL 172.651902 78.356784 \nL 172.706688 61.632126 \nL 172.746255 67.14685 \nL 172.782779 63.897676 \nL 172.831478 77.726134 \nL 172.834522 77.89239 \nL 172.843653 72.793839 \nL 172.962356 63.188931 \nL 172.913657 78.641433 \nL 172.965399 63.563426 \nL 172.989749 78.831395 \nL 173.099321 72.41935 \nL 173.114539 62.703693 \nL 173.187587 81.647501 \nL 173.190631 81.621291 \nL 173.236286 57.422824 \nL 173.327596 62.645872 \nL 173.36412 77.534151 \nL 173.446299 70.472767 \nL 173.549783 64.886068 \nL 173.507172 78.170461 \nL 173.555871 67.238569 \nL 173.647181 78.755536 \nL 173.583264 63.102481 \nL 173.665443 66.247554 \nL 173.714141 63.28334 \nL 173.735447 74.802449 \nL 173.775015 66.17175 \nL 173.808495 82.264239 \nL 173.826757 63.500701 \nL 173.88763 72.168092 \nL 173.975897 65.111939 \nL 173.908936 79.133681 \nL 174.000246 69.632053 \nL 174.021552 78.225248 \nL 174.042857 61.906759 \nL 174.058076 62.18479 \nL 174.064163 60.021503 \nL 174.103731 76.983092 \nL 174.149386 69.472034 \nL 174.179822 78.520663 \nL 174.191997 63.991534 \nL 174.198084 59.899447 \nL 174.213303 78.115741 \nL 174.292438 72.334863 \nL 174.322875 81.896081 \nL 174.34418 61.394113 \nL 174.408097 75.046162 \nL 174.496364 52.58572 \nL 174.432447 79.284177 \nL 174.5268 64.774113 \nL 174.529844 64.492404 \nL 174.542019 72.809836 \nL 174.615067 80.445068 \nL 174.593761 63.706401 \nL 174.657678 76.995495 \nL 174.669853 72.583062 \nL 174.682027 65.943758 \nL 174.76725 83.409646 \nL 174.788556 62.902168 \nL 174.907259 76.146572 \nL 174.91639 79.297626 \nL 174.955957 59.647016 \nL 174.998569 71.634993 \nL 175.089879 65.039445 \nL 175.025962 76.180789 \nL 175.117272 66.460129 \nL 175.135534 86.192928 \nL 175.235975 71.187048 \nL 175.269455 60.726373 \nL 175.336416 73.080712 \nL 175.442944 90.740642 \nL 175.385114 60.818875 \nL 175.455119 76.343778 \nL 175.53121 59.529604 \nL 175.567734 73.556862 \nL 175.665132 79.919835 \nL 175.607302 60.631555 \nL 175.674263 72.140654 \nL 175.683394 60.169774 \nL 175.698612 78.16126 \nL 175.789922 60.732485 \nL 175.796009 58.96977 \nL 175.814271 75.657981 \nL 175.872101 72.748177 \nL 175.887319 83.333417 \nL 175.972542 63.272214 \nL 175.975586 62.953581 \nL 175.98776 72.938724 \nL 176.039503 76.018289 \nL 176.088201 67.087066 \nL 176.106463 77.712417 \nL 176.127769 60.633407 \nL 176.197773 67.461938 \nL 176.307345 62.8381 \nL 176.289083 74.766613 \nL 176.310389 64.558822 \nL 176.404743 88.696614 \nL 176.435179 76.970487 \nL 176.438223 77.533598 \nL 176.493009 68.978127 \nL 176.575188 61.796476 \nL 176.529533 72.440563 \nL 176.605625 66.676333 \nL 176.703022 76.866987 \nL 176.675629 64.227037 \nL 176.733459 69.194443 \nL 176.797376 65.110837 \nL 176.779114 80.649042 \nL 176.839987 69.816986 \nL 176.934341 79.534148 \nL 176.903904 65.061574 \nL 176.946515 68.878517 \nL 177.043913 60.310565 \nL 176.998258 77.634897 \nL 177.059131 64.356384 \nL 177.144354 79.77165 \nL 177.162616 64.935978 \nL 177.168703 62.135821 \nL 177.223489 77.357772 \nL 177.2661 70.02976 \nL 177.296537 78.833121 \nL 177.311755 65.37168 \nL 177.387847 59.371509 \nL 177.369585 82.275581 \nL 177.418284 66.052728 \nL 177.442633 80.623773 \nL 177.463939 58.611178 \nL 177.524812 67.487362 \nL 177.536987 58.530921 \nL 177.558292 81.498365 \nL 177.63134 69.002813 \nL 177.698301 86.056495 \nL 177.737869 68.576667 \nL 177.835266 64.967612 \nL 177.817004 72.687848 \nL 177.847441 68.237309 \nL 177.9631 76.305107 \nL 177.92962 66.999179 \nL 177.975275 71.019008 \nL 177.993537 59.221839 \nL 178.036148 79.943698 \nL 178.093978 62.705735 \nL 178.163982 79.197019 \nL 178.218768 68.92716 \nL 178.221812 68.879435 \nL 178.224855 69.521539 \nL 178.264423 77.782741 \nL 178.282685 58.249243 \nL 178.337471 72.507622 \nL 178.364864 58.422944 \nL 178.410519 82.931697 \nL 178.447043 69.637032 \nL 178.486611 81.007844 \nL 178.507916 60.932193 \nL 178.562702 74.03235 \nL 178.577921 58.185484 \nL 178.641838 81.980409 \nL 178.684449 65.600864 \nL 178.699667 76.701692 \nL 178.748366 65.034992 \nL 178.806196 69.841189 \nL 178.839676 60.601144 \nL 178.857938 75.500919 \nL 178.903593 73.775964 \nL 178.997947 82.845101 \nL 178.949248 63.282004 \nL 179.013165 74.970294 \nL 179.101432 59.457317 \nL 179.080126 75.74362 \nL 179.131868 67.951721 \nL 179.15013 74.29659 \nL 179.198829 67.099207 \nL 179.238397 69.09669 \nL 179.244484 65.971027 \nL 179.26579 75.724172 \nL 179.351012 67.99617 \nL 179.442322 78.694382 \nL 179.411886 67.066829 \nL 179.457541 67.713898 \nL 179.545807 60.406341 \nL 179.527545 80.961075 \nL 179.557982 74.5997 \nL 179.561025 75.113767 \nL 179.576244 66.051309 \nL 179.618855 68.174822 \nL 179.652335 60.410507 \nL 179.716252 75.204478 \nL 179.752776 84.567435 \nL 179.780169 69.937865 \nL 179.795388 62.918978 \nL 179.819737 72.644874 \nL 179.883654 69.958132 \nL 179.974964 80.048888 \nL 179.923222 63.649075 \nL 179.990182 67.011163 \nL 180.060187 65.691733 \nL 180.005401 71.644713 \nL 180.069318 71.139584 \nL 180.124104 76.238462 \nL 180.163671 63.264279 \nL 180.169759 61.280983 \nL 180.230632 82.327881 \nL 180.251938 73.17818 \nL 180.261069 78.479308 \nL 180.285418 65.594694 \nL 180.352379 67.833118 \nL 180.398034 61.058795 \nL 180.422383 84.539683 \nL 180.464994 63.680925 \nL 180.586741 79.747105 \nL 180.510649 61.374193 \nL 180.601959 73.600402 \nL 180.605003 73.613969 \nL 180.617178 77.274712 \nL 180.63544 55.835037 \nL 180.641527 52.215367 \nL 180.708488 81.463454 \nL 180.738924 62.794191 \nL 180.775448 78.749061 \nL 180.857627 72.9658 \nL 180.881977 58.647608 \nL 180.921544 75.69477 \nL 180.964156 71.904508 \nL 180.973287 78.505147 \nL 181.043291 64.948222 \nL 181.06764 66.208217 \nL 181.070684 65.887559 \nL 181.085902 75.228007 \nL 181.143732 76.834212 \nL 181.107208 64.718254 \nL 181.171125 66.213949 \nL 181.177212 64.999621 \nL 181.241129 77.063215 \nL 181.259391 72.450352 \nL 181.268522 79.156096 \nL 181.289828 64.723626 \nL 181.368963 72.650006 \nL 181.429837 61.043531 \nL 181.390269 79.456538 \nL 181.463317 78.143067 \nL 181.469404 80.162141 \nL 181.518103 66.728824 \nL 181.545496 69.962548 \nL 181.618544 61.081337 \nL 181.600282 79.327239 \nL 181.652024 72.837887 \nL 181.682461 77.977637 \nL 181.700723 65.091114 \nL 181.758553 72.429658 \nL 181.776815 62.088257 \nL 181.828557 82.194295 \nL 181.868125 71.324642 \nL 181.907692 75.275574 \nL 181.922911 60.829578 \nL 181.925954 60.503607 \nL 181.935085 69.116862 \nL 181.977697 79.591328 \nL 182.032483 64.605661 \nL 182.050745 73.103077 \nL 182.053788 73.155285 \nL 182.059876 72.009254 \nL 182.142055 57.138994 \nL 182.172491 70.683663 \nL 182.269889 80.595263 \nL 182.22119 66.597531 \nL 182.282063 72.867115 \nL 182.391635 63.133003 \nL 182.330762 75.458462 \nL 182.397723 64.975609 \nL 182.498164 77.715742 \nL 182.510338 68.378048 \nL 182.589474 57.975552 \nL 182.568168 75.796287 \nL 182.616867 70.98154 \nL 182.665565 76.298267 \nL 182.64426 66.290589 \nL 182.73557 73.672458 \nL 182.817749 63.805698 \nL 182.796443 81.903523 \nL 182.851229 70.396836 \nL 182.866447 77.467546 \nL 182.887753 62.227561 \nL 182.954714 67.744722 \nL 183.021674 79.09744 \nL 183.073417 55.993494 \nL 183.07646 56.013061 \nL 183.122115 86.124453 \nL 183.201251 65.37546 \nL 183.216469 55.488598 \nL 183.301692 74.981462 \nL 183.310823 78.840553 \nL 183.396045 65.456285 \nL 183.405176 56.897637 \nL 183.456919 73.066905 \nL 183.496486 72.982443 \nL 183.505617 76.981606 \nL 183.596927 68.423131 \nL 183.603015 70.049582 \nL 183.691281 78.188664 \nL 183.630408 58.941652 \nL 183.709543 68.562131 \nL 183.730849 64.09882 \nL 183.755198 76.747134 \nL 183.800853 73.033785 \nL 183.803897 73.060903 \nL 183.80694 72.414878 \nL 183.855639 59.347025 \nL 183.904338 75.969062 \nL 183.925643 67.474264 \nL 183.986517 79.396265 \nL 183.946949 64.705848 \nL 184.04739 75.786665 \nL 184.050434 75.555967 \nL 184.059565 81.741405 \nL 184.062608 83.628789 \nL 184.111307 63.949236 \nL 184.144787 68.17285 \nL 184.211748 57.484474 \nL 184.23001 88.380155 \nL 184.236097 94.318225 \nL 184.315233 59.119724 \nL 184.324364 53.863873 \nL 184.394368 76.534637 \nL 184.409586 71.616662 \nL 184.424805 77.822075 \nL 184.443067 64.272863 \nL 184.516115 72.606572 \nL 184.59525 60.659594 \nL 184.576988 83.259394 \nL 184.62873 70.839931 \nL 184.726128 80.451436 \nL 184.671342 61.003312 \nL 184.735259 72.938408 \nL 184.768739 74.956141 \nL 184.853962 63.444788 \nL 184.875267 81.971903 \nL 184.96049 63.328596 \nL 184.966577 59.093418 \nL 185.012232 76.616714 \nL 185.057887 75.006882 \nL 185.067018 77.543296 \nL 185.14311 63.481205 \nL 185.146154 64.112165 \nL 185.207027 81.04491 \nL 185.188765 58.66905 \nL 185.261813 67.216538 \nL 185.343992 64.915627 \nL 185.316599 82.837267 \nL 185.371385 67.159864 \nL 185.429215 84.992618 \nL 185.496175 70.406565 \nL 185.560092 56.772932 \nL 185.581398 71.731206 \nL 185.605747 70.395055 \nL 185.69097 74.04159 \nL 185.669664 67.714002 \nL 185.697057 70.239174 \nL 185.706188 61.974361 \nL 185.7488 78.339571 \nL 185.806629 71.234888 \nL 185.876634 78.594777 \nL 185.861415 63.26384 \nL 185.904027 68.658296 \nL 185.943594 57.468603 \nL 185.992293 73.56025 \nL 186.016642 67.947 \nL 186.098821 78.948404 \nL 186.083603 66.761281 \nL 186.129258 71.733763 \nL 186.168826 65.556372 \nL 186.211437 73.042551 \nL 186.217524 73.750432 \nL 186.275354 66.337135 \nL 186.29666 69.618166 \nL 186.384926 57.377999 \nL 186.321009 76.275266 \nL 186.406232 70.727445 \nL 186.427538 77.99369 \nL 186.436669 82.540889 \nL 186.454931 55.034459 \nL 186.527979 71.612244 \nL 186.534066 67.062426 \nL 186.573634 86.978404 \nL 186.643638 67.09885 \nL 186.747123 62.332999 \nL 186.728861 73.397941 \nL 186.75321 64.885467 \nL 186.765385 75.362572 \nL 186.871913 73.997154 \nL 186.899306 62.770024 \nL 186.951048 75.878885 \nL 186.999747 65.807109 \nL 187.063664 78.977287 \nL 187.103232 65.783889 \nL 187.121494 67.065256 \nL 187.197585 57.171278 \nL 187.173236 74.821088 \nL 187.224978 72.29402 \nL 187.255415 78.577633 \nL 187.237153 71.143664 \nL 187.261502 72.803593 \nL 187.273677 57.845709 \nL 187.328463 78.549425 \nL 187.377162 65.016744 \nL 187.401511 80.341279 \nL 187.489777 66.12357 \nL 187.498908 60.393207 \nL 187.550651 76.075422 \nL 187.599349 67.286877 \nL 187.614568 77.45436 \nL 187.648048 63.041416 \nL 187.708921 69.990378 \nL 187.721096 62.243254 \nL 187.739358 81.908887 \nL 187.81545 71.975685 \nL 187.830668 68.857368 \nL 187.873279 76.030076 \nL 187.879367 75.971776 \nL 187.894585 78.267331 \nL 187.921978 73.505857 \nL 187.94024 57.638364 \nL 187.970677 76.058613 \nL 188.03155 74.585905 \nL 188.086336 59.251937 \nL 188.107642 75.2355 \nL 188.138078 74.924507 \nL 188.144166 79.311595 \nL 188.189821 59.958165 \nL 188.24765 73.774089 \nL 188.256781 71.165081 \nL 188.308524 66.559322 \nL 188.284174 78.66462 \nL 188.357222 75.323563 \nL 188.36331 74.14599 \nL 188.460707 61.576808 \nL 188.439401 80.490286 \nL 188.491144 65.746544 \nL 188.625065 82.402192 \nL 188.54593 63.796586 \nL 188.628109 81.251001 \nL 188.710288 65.549606 \nL 188.740724 79.73035 \nL 188.743768 79.91096 \nL 188.749855 76.24032 \nL 188.835078 61.820764 \nL 188.862471 71.924086 \nL 188.874646 77.119644 \nL 188.932475 63.822358 \nL 188.968999 70.326731 \nL 188.990305 60.955344 \nL 189.032916 76.205032 \nL 189.115095 79.417159 \nL 189.054222 66.263921 \nL 189.142488 75.376535 \nL 189.145532 75.630272 \nL 189.157707 67.812945 \nL 189.242929 61.753459 \nL 189.221624 79.67061 \nL 189.261191 69.148349 \nL 189.331196 74.206069 \nL 189.282497 61.378754 \nL 189.349458 65.214306 \nL 189.352501 65.205625 \nL 189.389025 78.434384 \nL 189.407287 61.288324 \nL 189.483379 74.641717 \nL 189.504685 63.708099 \nL 189.556427 80.473266 \nL 189.599038 70.126161 \nL 189.644693 64.061529 \nL 189.626431 78.526093 \nL 189.696436 71.046459 \nL 189.705567 75.162867 \nL 189.723829 59.421204 \nL 189.787746 65.195034 \nL 189.793833 63.47627 \nL 189.842532 77.375905 \nL 189.879056 70.119365 \nL 189.927754 78.152894 \nL 189.94906 66.260707 \nL 189.988628 70.108871 \nL 190.052545 64.855043 \nL 190.082981 77.009963 \nL 190.089069 79.842486 \nL 190.162117 62.410463 \nL 190.174291 64.186808 \nL 190.186466 59.98638 \nL 190.22299 71.818539 \nL 190.265601 69.190657 \nL 190.378217 79.197741 \nL 190.317344 63.643367 \nL 190.381261 78.668295 \nL 190.469527 60.367201 \nL 190.512138 68.476247 \nL 190.548662 64.347172 \nL 190.533444 76.233107 \nL 190.62171 67.733082 \nL 190.703889 81.137278 \nL 190.685627 65.473243 \nL 190.731282 67.211557 \nL 190.816505 80.432938 \nL 190.767806 60.192839 \nL 190.834767 66.545598 \nL 190.913902 60.446198 \nL 190.89564 76.545153 \nL 190.938252 67.20429 \nL 191.038693 73.183034 \nL 190.993038 59.933186 \nL 191.050867 70.327383 \nL 191.087391 62.991277 \nL 191.111741 75.283227 \nL 191.151308 72.778395 \nL 191.263924 81.340546 \nL 191.218269 64.467646 \nL 191.273055 76.455119 \nL 191.309579 59.431604 \nL 191.394802 64.930149 \nL 191.416107 79.541241 \nL 191.458719 58.037014 \nL 191.513505 71.137703 \nL 191.583509 62.839588 \nL 191.598727 74.223076 \nL 191.616989 69.543694 \nL 191.635251 82.933373 \nL 191.693081 62.534886 \nL 191.726561 70.097412 \nL 191.80874 58.855225 \nL 191.787435 73.189349 \nL 191.839177 69.282612 \nL 191.878745 57.727154 \nL 191.860483 78.708988 \nL 191.9244 74.07647 \nL 191.933531 74.995275 \nL 191.954836 68.477035 \nL 192.009622 73.328838 \nL 192.027884 58.445174 \nL 192.088758 77.188752 \nL 192.122238 71.238479 \nL 192.189199 82.345726 \nL 192.207461 64.234146 \nL 192.237897 75.30206 \nL 192.32312 59.747028 \nL 192.359644 65.874087 \nL 192.38095 83.122461 \nL 192.402255 61.926142 \nL 192.47226 68.253956 \nL 192.481391 69.153547 \nL 192.493565 63.702238 \nL 192.496609 63.529595 \nL 192.502696 67.542613 \nL 192.530089 76.957914 \nL 192.578788 61.232951 \nL 192.609225 64.102617 \nL 192.651836 75.500932 \nL 192.712709 62.778949 \nL 192.715753 62.849914 \nL 192.816194 81.796818 \nL 192.794888 59.235728 \nL 192.867936 71.642416 \nL 192.877067 68.598821 \nL 192.898373 83.803279 \nL 192.968377 73.985126 \nL 192.974465 75.303603 \nL 192.99577 64.641253 \nL 193.056644 70.742059 \nL 193.166216 62.913912 \nL 193.108386 73.138725 \nL 193.172303 66.026632 \nL 193.184478 73.576122 \nL 193.263613 65.969219 \nL 193.287962 70.323595 \nL 193.318399 64.762211 \nL 193.342748 79.101703 \nL 193.397534 70.725097 \nL 193.458408 79.86911 \nL 193.47667 64.182351 \nL 193.504063 68.522528 \nL 193.54363 59.72707 \nL 193.598416 72.75222 \nL 193.625809 63.331433 \nL 193.714076 80.952583 \nL 193.741469 68.991087 \nL 193.799298 79.148284 \nL 193.814517 62.219333 \nL 193.866259 74.597107 \nL 193.981918 61.837103 \nL 193.945394 75.223265 \nL 193.991049 65.345033 \nL 194.085403 77.877852 \nL 194.033661 61.308554 \nL 194.100622 65.248214 \nL 194.112796 58.928512 \nL 194.164539 78.645771 \nL 194.201063 74.970933 \nL 194.261936 61.82026 \nL 194.24063 77.826293 \nL 194.310635 75.417043 \nL 194.313678 75.633118 \nL 194.319766 71.318583 \nL 194.33194 58.972486 \nL 194.386726 76.022152 \nL 194.432381 64.352925 \nL 194.535866 77.174206 \nL 194.551084 67.439189 \nL 194.584565 65.191733 \nL 194.639351 77.086021 \nL 194.648482 81.857476 \nL 194.685006 68.610483 \nL 194.745879 72.905301 \nL 194.751966 74.009637 \nL 194.803709 60.181672 \nL 194.809796 58.52441 \nL 194.867626 73.354568 \nL 194.895019 69.243991 \nL 194.93763 77.21925 \nL 194.919368 65.050074 \nL 195.013722 73.742214 \nL 195.059377 77.673571 \nL 195.077639 63.729676 \nL 195.101988 69.357109 \nL 195.147643 60.268654 \nL 195.132425 77.904896 \nL 195.205473 72.3764 \nL 195.241997 74.162708 \nL 195.226778 61.870658 \nL 195.305914 68.405027 \nL 195.351569 81.85922 \nL 195.330263 61.562084 \nL 195.409398 67.358262 \nL 195.418529 63.693763 \nL 195.461141 78.278629 \nL 195.503752 78.126454 \nL 195.595062 62.068307 \nL 195.63463 72.798346 \nL 195.652892 77.745644 \nL 195.674197 60.84322 \nL 195.732027 69.24452 \nL 195.844643 59.428228 \nL 195.802031 77.772206 \nL 195.85073 61.839606 \nL 195.938996 80.635962 \nL 195.97552 75.397168 \nL 196.094223 63.31139 \nL 196.106398 63.9827 \nL 196.121616 56.519744 \nL 196.142922 80.585628 \nL 196.197708 70.131766 \nL 196.243363 79.862493 \nL 196.264669 67.133962 \nL 196.310324 72.337229 \nL 196.322498 73.215449 \nL 196.334673 75.77696 \nL 196.377284 57.066533 \nL 196.410765 67.380289 \nL 196.529468 61.423648 \nL 196.43207 75.688832 \nL 196.532511 61.855633 \nL 196.620778 82.716956 \nL 196.602516 57.238563 \nL 196.678607 77.110311 \nL 196.815572 56.391783 \nL 196.693826 79.147061 \nL 196.827747 65.234413 \nL 196.842965 75.513602 \nL 196.864271 62.483322 \nL 196.943406 71.154544 \nL 196.967756 74.849561 \nL 196.989061 67.161554 \nL 197.089502 63.572975 \nL 197.068197 77.17245 \nL 197.098633 66.110095 \nL 197.208205 98.339009 \nL 197.156463 51.538067 \nL 197.241686 79.013539 \nL 197.305603 48.633546 \nL 197.363432 64.469837 \nL 197.375607 60.607381 \nL 197.439524 74.395617 \nL 197.46083 69.833232 \nL 197.506485 79.040222 \nL 197.530834 68.98009 \nL 197.579533 78.114194 \nL 197.582576 78.56999 \nL 197.594751 68.138898 \nL 197.606926 58.582648 \nL 197.652581 75.407429 \nL 197.701279 73.315501 \nL 197.728672 75.445602 \nL 197.76824 67.67527 \nL 197.789546 70.456902 \nL 197.871725 61.089445 \nL 197.886943 79.323381 \nL 197.889987 80.047473 \nL 197.929554 66.792912 \nL 197.981297 75.675653 \nL 197.98434 75.702409 \nL 197.990428 75.163211 \nL 198.048257 66.210681 \nL 198.026952 81.322285 \nL 198.096956 75.26715 \nL 198.103043 76.760366 \nL 198.124349 61.312638 \nL 198.182179 68.860739 \nL 198.227834 64.549025 \nL 198.255227 77.349083 \nL 198.288707 71.074852 \nL 198.346537 74.17406 \nL 198.355668 70.069027 \nL 198.367842 59.294975 \nL 198.401323 78.831679 \nL 198.462196 74.32567 \nL 198.523069 59.094642 \nL 198.541331 84.14909 \nL 198.586986 68.272923 \nL 198.62351 73.958706 \nL 198.660034 59.728342 \nL 198.663078 57.703084 \nL 198.68134 77.70929 \nL 198.757432 67.899333 \nL 198.769606 77.100774 \nL 198.86396 64.774305 \nL 198.867004 64.710449 \nL 198.870047 65.916367 \nL 198.92179 73.988574 \nL 198.946139 64.931122 \nL 198.979619 66.044793 \nL 198.982663 65.752488 \nL 198.997881 72.659207 \nL 199.022231 72.228184 \nL 199.070929 75.401929 \nL 199.04658 64.775793 \nL 199.08006 67.829353 \nL 199.089191 61.281466 \nL 199.128759 78.348464 \nL 199.189632 68.947924 \nL 199.19572 68.121693 \nL 199.259637 79.286015 \nL 199.265724 77.386301 \nL 199.311379 63.058947 \nL 199.293117 82.879206 \nL 199.387471 64.852233 \nL 199.390514 64.553588 \nL 199.433126 71.164677 \nL 199.542698 76.114238 \nL 199.460519 62.211305 \nL 199.548785 73.853015 \nL 199.612702 60.879093 \nL 199.591396 77.065564 \nL 199.65227 76.011275 \nL 199.734449 81.128121 \nL 199.716187 64.839876 \nL 199.749667 70.283854 \nL 199.840977 51.299595 \nL 199.770973 82.733408 \nL 199.853152 67.57992 \nL 199.86837 93.234195 \nL 199.953593 57.116136 \nL 199.962724 64.177824 \nL 200.035772 79.269156 \nL 200.078383 73.868822 \nL 200.127082 65.240078 \nL 200.105776 78.430425 \nL 200.194042 68.784826 \nL 200.224479 64.219659 \nL 200.254916 76.539647 \nL 200.261003 79.31263 \nL 200.346226 65.49232 \nL 200.419274 75.483974 \nL 200.483191 67.973332 \nL 200.492322 64.130782 \nL 200.537977 77.82652 \nL 200.589719 70.498345 \nL 200.607981 74.156422 \nL 200.641461 66.183952 \nL 200.720597 63.360236 \nL 200.702335 75.306854 \nL 200.732771 72.667399 \nL 200.784514 75.982614 \nL 200.757121 62.167397 \nL 200.836256 68.628588 \nL 200.8393 68.383635 \nL 200.851474 75.482482 \nL 200.857562 77.816885 \nL 200.900173 59.242312 \nL 200.948872 68.455555 \nL 200.982352 66.494411 \nL 201.000614 77.781594 \nL 201.058444 68.202769 \nL 201.073662 84.671018 \nL 201.101055 61.41913 \nL 201.158885 62.119642 \nL 201.168016 55.878585 \nL 201.253238 79.708285 \nL 201.350636 64.07793 \nL 201.371941 75.348207 \nL 201.444989 81.342043 \nL 201.463251 61.713136 \nL 201.493688 60.443821 \nL 201.508906 76.750217 \nL 201.518037 80.092672 \nL 201.542387 61.228633 \nL 201.60326 67.163322 \nL 201.615435 61.411592 \nL 201.664134 79.166334 \nL 201.712832 66.276356 \nL 201.819361 81.609561 \nL 201.849797 71.245954 \nL 201.93502 63.722621 \nL 201.886321 74.508058 \nL 201.950238 72.55949 \nL 201.962413 76.58115 \nL 201.995893 64.421083 \nL 202.053723 68.107129 \nL 202.166339 61.968186 \nL 202.120684 77.284112 \nL 202.172426 64.258766 \nL 202.190688 82.423501 \nL 202.218081 63.463825 \nL 202.294173 73.952323 \nL 202.355046 60.273729 \nL 202.39157 77.547934 \nL 202.412876 67.145983 \nL 202.522448 77.674152 \nL 202.434181 54.34391 \nL 202.528535 72.701207 \nL 202.580277 63.357282 \nL 202.555928 79.557811 \nL 202.644194 66.15759 \nL 202.653325 64.743866 \nL 202.705068 74.546555 \nL 202.781159 80.248873 \nL 202.75681 63.175785 \nL 202.799421 63.832771 \nL 202.805509 59.190441 \nL 202.854207 81.280574 \nL 202.908993 64.621293 \nL 202.933343 76.366771 \nL 202.975954 63.60001 \nL 203.021609 67.532746 \nL 203.027696 65.536884 \nL 203.070308 82.577143 \nL 203.128137 69.683052 \nL 203.146399 75.731527 \nL 203.182923 66.159591 \nL 203.234666 70.387138 \nL 203.292495 63.241 \nL 203.307714 75.192901 \nL 203.344238 68.730609 \nL 203.389893 77.059077 \nL 203.435548 64.818793 \nL 203.441635 61.391608 \nL 203.529901 78.929801 \nL 203.532945 79.281721 \nL 203.563382 71.333756 \nL 203.621211 61.368975 \nL 203.605993 76.61095 \nL 203.666866 73.713376 \nL 203.749045 78.390612 \nL 203.70339 60.771922 \nL 203.76122 66.855918 \nL 203.767307 63.052016 \nL 203.828181 75.857426 \nL 203.867748 69.992591 \nL 203.901229 74.375945 \nL 203.91036 68.270365 \nL 203.919491 61.10971 \nL 203.962102 76.751932 \nL 204.026019 62.477953 \nL 204.029063 62.424648 \nL 204.032106 64.232589 \nL 204.047325 81.0753 \nL 204.138635 62.018146 \nL 204.141678 60.411385 \nL 204.15994 77.785485 \nL 204.226901 72.389792 \nL 204.248207 66.630029 \nL 204.345604 81.102374 \nL 204.439958 58.688776 \nL 204.379084 84.241258 \nL 204.485613 72.943441 \nL 204.4917 78.482104 \nL 204.509962 62.198797 \nL 204.595185 72.467648 \nL 204.637796 81.862999 \nL 204.680407 61.853605 \nL 204.713888 77.716386 \nL 204.716931 77.918745 \nL 204.723019 72.931341 \nL 204.735193 61.494977 \nL 204.750412 76.246114 \nL 204.838678 65.827348 \nL 204.850853 78.946252 \nL 204.923901 62.175651 \nL 204.94825 68.971424 \nL 205.018254 72.081921 \nL 205.066953 62.234789 \nL 205.16435 81.201506 \nL 205.182612 64.968482 \nL 205.185656 64.516429 \nL 205.231311 73.485841 \nL 205.240442 75.804149 \nL 205.258704 64.452976 \nL 205.322621 68.745398 \nL 205.350014 78.549757 \nL 205.43828 64.098011 \nL 205.444368 63.420408 \nL 205.493066 72.575668 \nL 205.526547 78.591399 \nL 205.544809 66.882484 \nL 205.55394 64.536463 \nL 205.605682 80.097819 \nL 205.648293 68.713585 \nL 205.715254 73.925855 \nL 205.73656 67.101859 \nL 205.800477 62.279403 \nL 205.760909 74.838678 \nL 205.815695 70.182697 \nL 205.837001 77.573816 \nL 205.91918 68.498102 \nL 205.928311 71.743064 \nL 205.989184 83.024893 \nL 205.967878 59.582118 \nL 206.047014 74.296224 \nL 206.056145 71.636997 \nL 206.098756 59.437069 \nL 206.129193 79.347513 \nL 206.165717 69.954434 \nL 206.174848 77.012628 \nL 206.244852 58.794915 \nL 206.281376 76.314049 \nL 206.290507 71.628032 \nL 206.369642 65.710621 \nL 206.387904 77.581308 \nL 206.400079 72.588614 \nL 206.439647 58.434964 \nL 206.457909 79.667447 \nL 206.463996 82.914289 \nL 206.549219 63.741735 \nL 206.619223 61.010358 \nL 206.579655 81.356985 \nL 206.643572 67.666982 \nL 206.680096 84.510461 \nL 206.747057 66.728071 \nL 206.810974 58.103913 \nL 206.841411 73.001898 \nL 206.877935 79.148831 \nL 206.896197 69.805339 \nL 206.947939 73.787509 \nL 207.045336 59.474724 \nL 207.024031 74.784384 \nL 207.060555 69.75349 \nL 207.072729 75.515695 \nL 207.112297 62.212201 \nL 207.170127 68.739771 \nL 207.267524 55.959939 \nL 207.200563 78.46064 \nL 207.279699 67.989048 \nL 207.313179 88.918709 \nL 207.383183 60.750617 \nL 207.41362 74.771264 \nL 207.434926 59.454647 \nL 207.50493 68.338063 \nL 207.517105 60.040789 \nL 207.59624 76.282949 \nL 207.608415 73.685506 \nL 207.620589 78.338912 \nL 207.678419 66.609401 \nL 207.699725 67.497348 \nL 207.784947 62.804639 \nL 207.724074 79.320905 \nL 207.797122 69.809755 \nL 207.894519 76.41108 \nL 207.842777 69.312495 \nL 207.912781 73.397947 \nL 207.931043 63.648515 \nL 208.034528 66.609727 \nL 208.049746 64.921171 \nL 208.055834 66.821317 \nL 208.131925 83.73839 \nL 208.156275 65.109521 \nL 208.168449 69.629725 \nL 208.195842 75.078634 \nL 208.211061 67.522293 \nL 208.226279 60.723979 \nL 208.290196 73.811554 \nL 208.311502 71.48716 \nL 208.329764 73.955847 \nL 208.348026 70.159514 \nL 208.363244 71.255585 \nL 208.375419 63.327912 \nL 208.433248 78.604215 \nL 208.47586 69.188775 \nL 208.515427 63.061507 \nL 208.558039 75.41225 \nL 208.561082 75.505898 \nL 208.570213 73.218281 \nL 208.588475 73.437876 \nL 208.670654 61.603478 \nL 208.655436 81.904253 \nL 208.704135 69.812596 \nL 208.716309 80.436556 \nL 208.74979 54.186729 \nL 208.810663 66.698139 \nL 208.822838 64.630326 \nL 208.835012 68.229987 \nL 208.865449 87.762702 \nL 208.883711 64.831476 \nL 208.956759 71.36691 \nL 209.048069 65.656279 \nL 209.017632 75.578165 \nL 209.075462 68.910722 \nL 209.09068 75.158584 \nL 209.139379 64.932746 \nL 209.185034 70.156875 \nL 209.194165 63.922728 \nL 209.212427 72.535872 \nL 209.291563 69.706968 \nL 209.303737 81.452104 \nL 209.346349 60.277134 \nL 209.404178 75.150124 \nL 209.510707 61.637741 \nL 209.462008 81.396876 \nL 209.544187 67.912472 \nL 209.574624 71.701437 \nL 209.586798 66.353302 \nL 209.644628 64.347904 \nL 209.623322 77.293731 \nL 209.672021 72.859705 \nL 209.702458 76.359906 \nL 209.72072 66.783317 \nL 209.778549 71.746367 \nL 209.790724 66.039474 \nL 209.842466 76.748652 \nL 209.894209 68.51045 \nL 209.897252 68.438666 \nL 209.906383 70.037294 \nL 209.976388 84.449615 \nL 209.93682 59.221247 \nL 210.006824 66.215568 \nL 210.018999 59.717489 \nL 210.067698 78.192817 \nL 210.107265 72.152785 \nL 210.125527 67.622856 \nL 210.174226 79.449993 \nL 210.213794 71.801678 \nL 210.289885 78.355537 \nL 210.305104 66.433914 \nL 210.375108 64.683402 \nL 210.399457 73.509342 \nL 210.420763 79.605274 \nL 210.460331 63.435352 \nL 210.481636 67.766349 \nL 210.557728 63.978102 \nL 210.54251 73.373298 \nL 210.566859 71.071202 \nL 210.655125 79.642057 \nL 210.60947 68.410917 \nL 210.682518 74.686592 \nL 210.731217 79.248097 \nL 210.773828 63.557963 \nL 210.776872 63.232346 \nL 210.801221 70.857025 \nL 210.810352 75.529718 \nL 210.852964 62.369137 \nL 210.916881 74.854377 \nL 211.020365 60.417281 \nL 210.944274 77.394372 \nL 211.059933 70.61752 \nL 211.166461 76.768987 \nL 211.093413 65.622315 \nL 211.175592 72.934353 \nL 211.288208 59.371813 \nL 211.24864 81.863266 \nL 211.297339 63.414236 \nL 211.403867 74.364289 \nL 211.315601 61.873454 \nL 211.416042 69.047277 \nL 211.419086 68.540542 \nL 211.476915 76.422493 \nL 211.507352 79.444221 \nL 211.562138 65.575485 \nL 211.577356 61.364301 \nL 211.629099 77.194636 \nL 211.656492 72.799824 \nL 211.70519 79.295171 \nL 211.72954 60.659397 \nL 211.759976 67.961786 \nL 211.851286 83.424669 \nL 211.823893 61.940583 \nL 211.866505 68.900604 \nL 211.872592 65.683408 \nL 211.954771 81.915837 \nL 211.966946 76.430899 \nL 212.03695 63.071067 \nL 212.09478 65.394812 \nL 212.149566 76.748426 \nL 212.176959 61.784081 \nL 212.213483 72.660947 \nL 212.274356 78.621887 \nL 212.240876 63.794757 \nL 212.31088 69.445296 \nL 212.390015 58.350017 \nL 212.365666 78.483888 \nL 212.411321 75.253316 \nL 212.520893 78.124823 \nL 212.499587 68.339703 \nL 212.52698 77.410864 \nL 212.58481 60.736402 \nL 212.657858 74.045785 \nL 212.770474 81.254347 \nL 212.688295 62.139094 \nL 212.773517 79.440116 \nL 212.837434 58.222979 \nL 212.889177 74.490484 \nL 212.959181 80.366233 \nL 212.913526 63.451454 \nL 212.989618 67.497622 \nL 213.056578 61.722762 \nL 213.068753 79.033951 \nL 213.07484 85.396192 \nL 213.123539 59.839289 \nL 213.175281 74.232199 \nL 213.25746 80.995601 \nL 213.230067 66.59892 \nL 213.272679 66.907308 \nL 213.303115 63.991164 \nL 213.339639 79.276342 \nL 213.382251 66.75957 \nL 213.397469 63.016935 \nL 213.418775 76.895683 \nL 213.455299 71.149647 \nL 213.549652 82.135919 \nL 213.503997 61.056482 \nL 213.564871 72.983113 \nL 213.586176 60.553579 \nL 213.634875 74.960868 \nL 213.674443 72.97623 \nL 213.723141 67.824491 \nL 213.704879 79.240268 \nL 213.790102 70.300994 \nL 213.80532 76.871807 \nL 213.82967 55.048827 \nL 213.905761 73.637378 \nL 213.933154 75.320762 \nL 213.942285 69.665981 \nL 214.027508 62.168429 \nL 214.006202 79.755193 \nL 214.04577 74.635313 \nL 214.158386 79.696881 \nL 214.100556 62.540315 \nL 214.161429 78.018882 \nL 214.176648 62.078135 \nL 214.222303 81.052678 \nL 214.277089 72.570128 \nL 214.307525 60.443704 \nL 214.3197 49.504888 \nL 214.374486 92.198248 \nL 214.401879 78.550285 \nL 214.404923 81.197572 \nL 214.423185 53.597961 \nL 214.505364 73.176433 \nL 214.514495 73.676993 \nL 214.529713 70.24254 \nL 214.621023 59.343118 \nL 214.584499 78.920558 \nL 214.639285 70.687004 \nL 214.648416 65.952387 \nL 214.669722 78.925902 \nL 214.74277 74.312381 \nL 214.80973 77.565546 \nL 214.770163 64.196737 \nL 214.827992 68.039913 \nL 214.83408 66.113798 \nL 214.858429 74.11666 \nL 214.922346 72.664591 \nL 214.998438 78.824078 \nL 215.013656 67.479987 \nL 215.034962 75.002591 \nL 215.098879 59.457366 \nL 215.165839 64.431353 \nL 215.254106 77.03776 \nL 215.287586 71.252793 \nL 215.345416 78.523453 \nL 215.369765 66.741125 \nL 215.397158 71.026126 \nL 215.436726 61.030072 \nL 215.482381 79.177715 \nL 215.488468 81.214739 \nL 215.509774 65.665781 \nL 215.567603 68.893869 \nL 215.601084 61.082609 \nL 215.643695 76.242251 \nL 215.680219 67.582792 \nL 215.686306 65.995123 \nL 215.716743 76.537536 \nL 215.765442 74.022606 \nL 215.783704 79.907573 \nL 215.841533 64.873149 \nL 215.868926 71.363142 \nL 215.975455 63.161864 \nL 215.941974 78.552662 \nL 215.990673 66.274914 \nL 216.081983 81.831891 \nL 216.097201 65.561568 \nL 216.103289 63.160532 \nL 216.1459 71.019954 \nL 216.206773 65.368546 \nL 216.212861 66.569181 \nL 216.231123 83.736674 \nL 216.252428 61.370046 \nL 216.322433 67.804095 \nL 216.355913 60.34681 \nL 216.337651 71.180581 \nL 216.422874 68.401992 \nL 216.532446 78.175657 \nL 216.498965 65.129554 \nL 216.541577 73.368515 \nL 216.623756 66.240778 \nL 216.584188 74.600332 \nL 216.654192 69.413009 \nL 216.757677 78.629732 \nL 216.678542 58.761952 \nL 216.766808 71.568536 \nL 216.873337 62.192385 \nL 216.827682 75.021866 \nL 216.879424 64.63349 \nL 216.903773 64.2496 \nL 217.001171 77.415434 \nL 217.144223 65.931122 \nL 217.147267 66.175627 \nL 217.202053 74.402435 \nL 217.223358 65.496222 \nL 217.256839 66.834834 \nL 217.296406 64.942687 \nL 217.278144 80.018005 \nL 217.335974 72.194315 \nL 217.351192 79.841235 \nL 217.39076 60.525735 \nL 217.439459 70.284325 \nL 217.475983 64.353825 \nL 217.457721 77.577586 \nL 217.527725 72.463017 \nL 217.567293 81.623598 \nL 217.588598 64.242308 \nL 217.63121 68.662004 \nL 217.646428 74.880373 \nL 217.695127 57.900967 \nL 217.743825 69.721454 \nL 217.856441 76.842357 \nL 217.759044 68.289001 \nL 217.871659 71.954939 \nL 217.886878 63.632802 \nL 217.941664 77.497462 \nL 217.987319 68.985489 \nL 217.999493 73.534177 \nL 218.005581 74.605218 \nL 218.069498 62.11864 \nL 218.072541 61.758856 \nL 218.084716 70.076884 \nL 218.093847 76.638714 \nL 218.112109 64.304079 \nL 218.191244 67.847443 \nL 218.221681 66.421402 \nL 218.230812 72.598029 \nL 218.239943 80.535139 \nL 218.331253 67.567034 \nL 218.33734 69.22476 \nL 218.389083 74.691344 \nL 218.431694 65.002914 \nL 218.446912 68.569145 \nL 218.556484 56.784158 \nL 218.495611 84.785078 \nL 218.565615 65.315993 \nL 218.61127 84.194934 \nL 218.659969 63.304814 \nL 218.684318 71.834995 \nL 218.720842 77.731123 \nL 218.781716 67.351782 \nL 218.848676 61.610291 \nL 218.806065 78.2373 \nL 218.888244 67.59314 \nL 218.915637 78.402555 \nL 218.939986 57.073839 \nL 218.994772 66.399492 \nL 218.997816 65.042738 \nL 219.049558 78.772095 \nL 219.083039 76.509975 \nL 219.137825 78.663935 \nL 219.104344 65.011975 \nL 219.174349 69.737909 \nL 219.177392 69.564769 \nL 219.18348 73.191287 \nL 219.192611 79.728263 \nL 219.226091 60.994599 \nL 219.293052 74.382985 \nL 219.356969 76.845862 \nL 219.396536 63.616794 \nL 219.405667 58.373404 \nL 219.454366 75.065445 \nL 219.496977 71.448537 \nL 219.545676 83.820942 \nL 219.521327 64.700735 \nL 219.600462 67.056478 \nL 219.606549 66.804535 \nL 219.627855 71.531511 \nL 219.643073 70.919401 \nL 219.728296 75.230234 \nL 219.67351 60.171178 \nL 219.740471 66.682748 \nL 219.746558 64.262519 \nL 219.76482 75.699001 \nL 219.843955 71.027577 \nL 219.880479 79.023912 \nL 219.859174 67.63541 \nL 219.923091 69.669409 \nL 219.965702 60.388688 \nL 220.017444 79.101793 \nL 220.020488 79.852493 \nL 220.03875 65.111894 \nL 220.102667 76.110789 \nL 220.215283 57.683333 \nL 220.13006 76.609305 \nL 220.224414 61.115615 \nL 220.282243 81.895183 \nL 220.34616 71.505945 \nL 220.364422 62.307255 \nL 220.440514 71.880139 \nL 220.458776 70.614985 \nL 220.550086 80.037244 \nL 220.519649 65.472464 \nL 220.559217 68.980145 \nL 220.571392 56.263687 \nL 220.617047 94.531287 \nL 220.665745 71.966639 \nL 220.674876 75.769872 \nL 220.7114 54.389665 \nL 220.775317 72.276642 \nL 220.784448 69.113512 \nL 220.830103 85.617532 \nL 220.872715 76.297898 \nL 220.875758 76.61252 \nL 220.921413 70.076766 \nL 220.936632 64.087735 \nL 220.991418 70.836719 \nL 221.034029 68.099537 \nL 221.131426 77.409661 \nL 221.113164 63.926767 \nL 221.149688 70.723223 \nL 221.170994 66.610295 \nL 221.195343 73.303527 \nL 221.210562 77.514928 \nL 221.228824 64.582926 \nL 221.265348 67.593678 \nL 221.277522 64.048516 \nL 221.307959 77.365093 \nL 221.335352 75.86471 \nL 221.368832 81.007991 \nL 221.384051 62.532473 \nL 221.438837 74.121699 \nL 221.530147 60.252813 \nL 221.511885 75.318154 \nL 221.551452 71.375776 \nL 221.621457 77.945976 \nL 221.639719 69.713631 \nL 221.676243 66.842005 \nL 221.737116 74.780243 \nL 221.804077 78.466305 \nL 221.755378 66.333505 \nL 221.822339 69.613677 \nL 221.883212 79.398033 \nL 221.846688 63.466439 \nL 221.895387 67.080531 \nL 221.977566 60.222715 \nL 221.95626 76.526066 \nL 222.004959 67.395606 \nL 222.071919 80.678394 \nL 222.044526 61.810956 \nL 222.135836 70.315873 \nL 222.199753 61.13242 \nL 222.175404 75.435074 \nL 222.251496 67.396434 \nL 222.284976 78.154832 \nL 222.345849 59.359331 \nL 222.364111 70.528028 \nL 222.434116 58.720496 \nL 222.391504 75.195102 \nL 222.449334 68.799362 \nL 222.467596 88.901164 \nL 222.531513 56.251827 \nL 222.558906 69.284521 \nL 222.571081 62.213496 \nL 222.613692 79.108048 \nL 222.662391 72.698757 \nL 222.665434 73.052749 \nL 222.701958 65.353309 \nL 222.714133 57.254634 \nL 222.771963 74.149494 \nL 222.808487 68.732161 \nL 222.845011 78.483376 \nL 222.899797 65.291105 \nL 222.933277 72.652453 \nL 222.972845 53.99009 \nL 222.99415 89.058544 \nL 223.003281 112.496816 \nL 223.08546 50.734522 \nL 223.097635 63.520824 \nL 223.219382 83.122121 \nL 223.14329 58.727204 \nL 223.228513 80.387416 \nL 223.246775 64.837176 \nL 223.344172 74.97654 \nL 223.398958 82.830269 \nL 223.411133 70.411485 \nL 223.423307 61.732087 \nL 223.514617 76.459162 \nL 223.517661 76.951205 \nL 223.535923 65.139452 \nL 223.575491 69.055132 \nL 223.615058 48.32711 \nL 223.630277 80.693186 \nL 223.645495 120.080818 \nL 223.727674 50.82587 \nL 223.733761 54.515008 \nL 223.81594 78.710608 \nL 223.852464 70.9128 \nL 223.861595 62.103106 \nL 223.879857 81.320402 \nL 223.968124 64.290449 \nL 223.974211 61.572563 \nL 224.038128 77.341467 \nL 224.071608 67.868138 \nL 224.086827 72.99828 \nL 224.126394 52.090724 \nL 224.226835 40.787605 \nL 224.159875 128.232486 \nL 224.245097 40.906816 \nL 224.251185 44.404348 \nL 224.27249 101.3357 \nL 224.369888 62.999388 \nL 224.442936 75.469265 \nL 224.461198 60.881741 \nL 224.503809 74.511808 \nL 224.543377 64.119925 \nL 224.601207 75.034859 \nL 224.637731 83.172609 \nL 224.683386 68.235524 \nL 224.710779 76.888226 \nL 224.750346 60.321393 \nL 224.829482 69.375337 \nL 224.929923 78.02384 \nL 224.875137 62.649454 \nL 224.942097 70.712698 \nL 224.978621 62.203542 \nL 224.996883 75.292129 \nL 225.045582 74.438262 \nL 225.079062 78.675166 \nL 225.063844 69.336394 \nL 225.149067 74.436455 \nL 225.24342 56.918432 \nL 225.206896 75.928619 \nL 225.279944 63.555004 \nL 225.331687 76.564493 \nL 225.413866 71.865167 \nL 225.453433 76.49098 \nL 225.538656 60.678702 \nL 225.60866 77.516688 \nL 225.675621 72.325072 \nL 225.779106 62.015879 \nL 225.745625 75.12438 \nL 225.788237 69.714936 \nL 225.800411 85.205395 \nL 225.839979 53.681417 \nL 225.900852 73.542074 \nL 225.934333 65.811895 \nL 225.9739 74.526373 \nL 226.019555 68.096704 \nL 226.107822 77.083926 \nL 226.059123 59.524467 \nL 226.126084 68.028727 \nL 226.135215 62.206085 \nL 226.226525 75.94392 \nL 226.229568 75.362478 \nL 226.284354 60.781896 \nL 226.326966 79.15381 \nL 226.330009 79.204196 \nL 226.333053 78.287993 \nL 226.400014 62.442184 \nL 226.418276 79.312612 \nL 226.445669 76.848884 \nL 226.463931 60.946808 \nL 226.485236 78.131909 \nL 226.603939 65.575373 \nL 226.61307 68.68909 \nL 226.710468 83.831035 \nL 226.655682 58.434295 \nL 226.722642 71.389073 \nL 226.731773 62.15898 \nL 226.789603 73.613123 \nL 226.832214 70.824355 \nL 226.844389 76.485659 \nL 226.905262 66.00582 \nL 226.941786 72.416337 \nL 226.975267 57.53814 \nL 227.039184 79.057724 \nL 227.133537 63.840422 \nL 227.145712 77.131772 \nL 227.151799 80.684338 \nL 227.215716 69.12726 \nL 227.243109 69.510922 \nL 227.285721 64.978233 \nL 227.303983 70.999933 \nL 227.352681 68.305788 \nL 227.383118 81.102202 \nL 227.40138 59.832583 \nL 227.462253 68.997492 \nL 227.55052 61.678179 \nL 227.486603 73.62925 \nL 227.580956 66.539568 \nL 227.681397 79.469443 \nL 227.690528 68.370776 \nL 227.699659 58.50976 \nL 227.714878 77.601154 \nL 227.803144 63.420037 \nL 227.900541 80.949145 \nL 227.839668 62.064259 \nL 227.912716 65.585818 \nL 227.918803 61.564628 \nL 227.98272 76.770696 \nL 228.019244 66.289708 \nL 228.083161 76.667075 \nL 228.125773 67.761872 \nL 228.137947 60.133858 \nL 228.183602 74.054483 \nL 228.235345 67.799216 \nL 228.271869 75.030771 \nL 228.284043 65.797982 \nL 228.354048 59.838142 \nL 228.308393 84.0931 \nL 228.387528 66.748959 \nL 228.421008 82.305078 \nL 228.503187 72.164521 \nL 228.515362 60.450068 \nL 228.573192 79.064468 \nL 228.618847 63.611217 \nL 228.7132 77.787994 \nL 228.661458 57.511622 \nL 228.73755 67.801591 \nL 228.740593 67.807369 \nL 228.856253 76.610507 \nL 228.758855 67.029779 \nL 228.871471 71.56702 \nL 228.889733 58.921992 \nL 228.917126 79.077585 \nL 228.977999 72.929598 \nL 229.01148 76.243753 \nL 229.029742 60.777509 \nL 229.072353 65.657075 \nL 229.075397 65.446927 \nL 229.090615 72.054387 \nL 229.096702 70.511695 \nL 229.13627 64.122 \nL 229.166707 77.92915 \nL 229.206274 67.913698 \nL 229.221493 79.620936 \nL 229.28541 63.790865 \nL 229.31889 74.813517 \nL 229.334108 58.395425 \nL 229.419331 77.024007 \nL 229.434549 68.437542 \nL 229.516728 64.588327 \nL 229.471073 73.348103 \nL 229.528903 69.756375 \nL 229.604995 80.425705 \nL 229.586733 61.008673 \nL 229.623257 62.588648 \nL 229.629344 60.064857 \nL 229.68413 83.844636 \nL 229.720654 70.006542 \nL 229.77544 67.458778 \nL 229.74196 74.027038 \nL 229.821095 70.4165 \nL 229.83327 77.900075 \nL 229.851532 58.915882 \nL 229.939798 76.704495 \nL 229.955016 64.206691 \nL 230.037195 81.531746 \nL 230.043283 84.268868 \nL 230.073719 57.72583 \nL 230.137636 77.321184 \nL 230.238077 67.549876 \nL 230.192422 78.2056 \nL 230.250252 74.982501 \nL 230.256339 77.341878 \nL 230.292863 63.298041 \nL 230.344606 66.620654 \nL 230.35678 68.641016 \nL 230.368955 61.155228 \nL 230.375042 58.293192 \nL 230.408523 78.715607 \nL 230.475483 63.23109 \nL 230.499833 78.000688 \nL 230.585055 66.916918 \nL 230.621579 50.798032 \nL 230.642885 116.679418 \nL 230.645929 122.028684 \nL 230.712889 65.29121 \nL 230.725064 66.490901 \nL 230.758544 52.533405 \nL 230.798112 75.029416 \nL 230.828549 67.569252 \nL 230.932033 80.938495 \nL 230.889422 65.097413 \nL 230.944208 75.57409 \nL 230.96247 65.043792 \nL 231.023343 80.45613 \nL 231.065955 67.622671 \nL 231.163352 75.656543 \nL 231.120741 59.120102 \nL 231.175527 68.171163 \nL 231.187701 60.665802 \nL 231.242487 78.128241 \nL 231.279011 73.077336 \nL 231.391627 80.941867 \nL 231.318579 65.562641 \nL 231.394671 79.09031 \nL 231.409889 61.56852 \nL 231.51033 67.840969 \nL 231.519461 79.25899 \nL 231.562072 57.335589 \nL 231.619902 66.890168 \nL 231.63512 56.92766 \nL 231.717299 72.159623 \nL 231.72643 74.699944 \nL 231.808609 63.785797 \nL 231.814697 60.33741 \nL 231.839046 76.660105 \nL 231.90905 70.524451 \nL 231.9334 62.271322 \nL 231.972968 73.172816 \nL 231.985142 87.655292 \nL 232.006448 62.638515 \nL 232.076452 65.478079 \nL 232.091671 60.031569 \nL 232.143413 80.529404 \nL 232.186024 66.616296 \nL 232.292553 90.864072 \nL 232.26516 57.786395 \nL 232.316902 82.702926 \nL 232.405168 50.735954 \nL 232.44778 60.352755 \nL 232.466042 73.8078 \nL 232.526915 53.290355 \nL 232.533002 49.81734 \nL 232.560395 110.905492 \nL 232.615181 71.121515 \nL 232.642574 52.78184 \nL 232.712579 74.378852 \nL 232.718666 73.915694 \nL 232.733884 80.528492 \nL 232.773452 67.857002 \nL 232.776496 67.584279 \nL 232.78867 75.853552 \nL 232.791714 76.410897 \nL 232.825194 63.135132 \nL 232.8465 67.422396 \nL 232.855631 60.652098 \nL 232.940854 83.124579 \nL 232.943897 84.466463 \nL 232.989552 68.119993 \nL 233.032164 74.948634 \nL 233.050426 64.513084 \nL 233.156954 65.951066 \nL 233.159998 65.694222 \nL 233.17826 73.078023 \nL 233.202609 71.188439 \nL 233.257395 78.752458 \nL 233.230002 68.808398 \nL 233.30305 69.540521 \nL 233.348705 61.274806 \nL 233.376098 71.500873 \nL 233.385229 75.729775 \nL 233.436971 68.952513 \nL 233.48567 71.012451 \nL 233.503932 77.117397 \nL 233.564805 65.215474 \nL 233.589155 69.698199 \nL 233.640897 63.051376 \nL 233.622635 72.168146 \nL 233.686552 70.050671 \nL 233.698727 85.044105 \nL 233.790037 61.963625 \nL 233.802211 68.06841 \nL 233.917871 77.758911 \nL 233.896565 66.355527 \nL 233.920914 77.577952 \nL 234.045705 46.262698 \nL 234.051792 59.648226 \nL 234.070054 115.714496 \nL 234.15832 43.995686 \nL 234.362246 79.205909 \nL 234.380508 67.546695 \nL 234.480949 65.457722 \nL 234.423119 70.953661 \nL 234.487036 66.898308 \nL 234.544866 82.012676 \nL 234.520517 65.13611 \nL 234.596608 67.618309 \nL 234.690962 64.91125 \nL 234.651394 75.92942 \nL 234.703137 68.667723 \nL 234.70618 68.648817 \nL 234.751835 64.494281 \nL 234.733573 78.219014 \nL 234.800534 73.354546 \nL 234.815752 82.077784 \nL 234.897931 66.624317 \nL 234.983154 57.946481 \nL 234.961848 76.880403 \nL 235.007503 67.512033 \nL 235.110988 81.833036 \nL 235.053158 66.442547 \nL 235.123163 70.4915 \nL 235.150556 60.829906 \nL 235.174905 75.525195 \nL 235.232735 71.878689 \nL 235.324045 85.725164 \nL 235.281433 56.507025 \nL 235.339263 69.986013 \nL 235.427529 60.540983 \nL 235.378831 70.848599 \nL 235.457966 61.68631 \nL 235.476228 80.584208 \nL 235.582756 74.275151 \nL 235.5858 74.94122 \nL 235.64363 63.433093 \nL 235.652761 67.218422 \nL 235.750158 57.579282 \nL 235.777551 78.944663 \nL 235.865817 61.972218 \nL 235.914516 72.355553 \nL 235.95104 74.263317 \nL 235.966258 65.698401 \nL 236.011913 69.183987 \nL 236.094092 62.104921 \nL 236.069743 83.715136 \nL 236.118442 72.221129 \nL 236.185402 60.572518 \nL 236.136704 75.344088 \nL 236.22497 73.429375 \nL 236.231057 74.660069 \nL 236.246276 65.04589 \nL 236.31628 66.74626 \nL 236.389328 60.553593 \nL 236.368022 76.013184 \nL 236.416721 68.34113 \nL 236.514118 75.903685 \nL 236.46542 64.828861 \nL 236.529337 71.252604 \nL 236.608472 62.950805 \nL 236.565861 77.731086 \nL 236.64804 68.905701 \nL 236.669345 72.533858 \nL 236.687607 61.243582 \nL 236.721088 66.421502 \nL 236.760655 58.543579 \nL 236.800223 81.585817 \nL 236.803267 83.009077 \nL 236.873271 64.399665 \nL 236.876315 64.000638 \nL 236.918926 71.759483 \nL 236.946319 68.478885 \nL 236.970668 81.754916 \nL 237.01328 63.287725 \nL 237.058935 68.922656 \nL 237.10459 57.289235 \nL 237.135026 72.016085 \nL 237.147201 89.325497 \nL 237.232424 62.714735 \nL 237.241555 64.533066 \nL 237.250686 61.802926 \nL 237.281122 59.109449 \nL 237.317646 81.658984 \nL 237.466786 65.129277 \nL 237.53679 66.886096 \nL 237.564183 79.192949 \nL 237.606795 64.858832 \nL 237.649406 68.581485 \nL 237.725498 64.249435 \nL 237.670712 74.512453 \nL 237.752891 71.189272 \nL 237.795502 69.311527 \nL 237.81072 77.794662 \nL 237.813764 77.848618 \nL 237.819851 76.284049 \nL 237.832026 77.496793 \nL 237.953773 54.179765 \nL 237.914205 84.439552 \nL 237.965947 60.969154 \nL 238.042039 75.447903 \nL 237.978122 60.145442 \nL 238.166829 71.017471 \nL 238.249008 59.822069 \nL 238.203353 77.732821 \nL 238.279445 68.921016 \nL 238.319013 67.676516 \nL 238.337275 73.245798 \nL 238.355537 71.3948 \nL 238.452934 81.400545 \nL 238.395104 63.208999 \nL 238.465109 70.956089 \nL 238.547288 62.029161 \nL 238.571637 74.772661 \nL 238.574681 74.769995 \nL 238.577724 74.854841 \nL 238.595986 77.55499 \nL 238.626423 70.254089 \nL 238.672078 73.507772 \nL 238.696427 57.683551 \nL 238.74817 81.274578 \nL 238.790781 67.533197 \nL 238.845567 58.264853 \nL 238.827305 75.967554 \nL 238.87296 70.955402 \nL 238.967314 78.693573 \nL 238.939921 65.91649 \nL 238.976445 71.650335 \nL 238.988619 56.089458 \nL 239.052536 78.611198 \nL 239.08906 66.86625 \nL 239.104279 77.056342 \nL 239.177327 61.267073 \nL 239.195589 66.046803 \nL 239.289942 53.942352 \nL 239.268637 79.81071 \nL 239.299073 66.830811 \nL 239.350816 75.433004 \nL 239.408645 68.969806 \nL 239.436038 60.065377 \nL 239.460388 79.353183 \nL 239.502999 69.489935 \nL 239.51213 61.869979 \nL 239.609528 72.361479 \nL 239.624746 73.528534 \nL 239.639964 75.413274 \nL 239.655183 66.233261 \nL 239.667357 60.543706 \nL 239.725187 76.857131 \nL 239.767798 63.02782 \nL 239.78606 83.471408 \nL 239.895632 73.758899 \nL 239.956506 60.75808 \nL 239.983899 76.148282 \nL 240.008248 71.687869 \nL 240.038685 76.213184 \nL 240.056947 57.805685 \nL 240.102602 66.780857 \nL 240.108689 64.863741 \nL 240.187824 75.414628 \nL 240.206086 69.7477 \nL 240.315658 75.40744 \nL 240.254785 61.838948 \nL 240.340008 73.708387 \nL 240.443492 62.419836 \nL 240.42523 74.377967 \nL 240.480016 67.791655 \nL 240.604807 79.497514 \nL 240.498278 62.381985 \nL 240.616981 72.310921 \nL 240.656549 67.614678 \nL 240.638287 75.223933 \nL 240.726553 72.368669 \nL 240.744815 69.788189 \nL 240.753946 74.083303 \nL 240.75699 75.205837 \nL 240.775252 63.907787 \nL 240.857431 71.923712 \nL 240.921348 59.443317 \nL 240.88178 74.872062 \nL 240.951785 72.776368 \nL 241.046138 75.620509 \nL 241.003527 63.780091 \nL 241.055269 71.627686 \nL 241.067444 62.363594 \nL 241.128317 78.202697 \nL 241.167885 68.72229 \nL 241.18006 74.518214 \nL 241.192234 81.500582 \nL 241.27137 61.291906 \nL 241.286588 71.163854 \nL 241.289632 71.217787 \nL 241.292675 70.029122 \nL 241.365723 58.574032 \nL 241.344418 77.885491 \nL 241.408335 67.054347 \nL 241.417466 70.324207 \nL 241.42964 78.293472 \nL 241.514863 60.832879 \nL 241.517907 60.422654 \nL 241.533125 71.562842 \nL 241.554431 68.920683 \nL 241.569649 77.52855 \nL 241.627479 67.771131 \nL 241.664003 69.493395 \nL 241.721832 73.055151 \nL 241.737051 63.436186 \nL 241.767487 59.792641 \nL 241.79488 75.952407 \nL 241.822273 69.709131 \nL 241.94402 80.213006 \nL 241.889234 61.798449 \nL 241.947064 79.883535 \nL 241.962282 65.067382 \nL 242.06881 70.945733 \nL 242.074898 70.021355 \nL 242.114465 62.062424 \nL 242.157077 81.479336 \nL 242.175339 76.025594 \nL 242.205775 60.363899 \nL 242.306216 69.881936 \nL 242.418832 86.599768 \nL 242.336653 61.080488 \nL 242.427963 82.743571 \nL 242.449269 60.0618 \nL 242.552753 63.416786 \nL 242.555797 62.904857 \nL 242.592321 73.409329 \nL 242.607539 71.646661 \nL 242.689718 80.065012 \nL 242.631889 62.103737 \nL 242.704937 68.506438 \nL 242.738417 61.933363 \nL 242.759723 78.758377 \nL 242.808421 70.712862 \nL 242.908862 85.062558 \nL 242.887557 58.650433 \nL 242.917993 75.979677 \nL 243.003216 59.553523 \nL 243.033653 62.261632 \nL 243.128006 74.456848 \nL 243.146268 64.732094 \nL 243.149312 64.226058 \nL 243.167574 77.613211 \nL 243.194967 73.543893 \nL 243.210185 81.341206 \nL 243.252797 63.662531 \nL 243.292364 66.736558 \nL 243.298452 64.796182 \nL 243.31367 72.651488 \nL 243.395849 70.071682 \nL 243.423242 77.144211 \nL 243.468897 65.943646 \nL 243.508465 71.8301 \nL 243.520639 56.784227 \nL 243.557163 80.619019 \nL 243.614993 76.066734 \nL 243.630211 67.593574 \nL 243.657604 77.718981 \nL 243.724565 74.280959 \nL 243.733696 77.972632 \nL 243.755002 64.904668 \nL 243.834137 74.23369 \nL 243.946753 57.519752 \nL 243.928491 80.37105 \nL 243.955884 66.28257 \nL 243.992408 80.613363 \nL 244.041106 63.01662 \nL 244.065456 66.97678 \nL 244.068499 66.528322 \nL 244.105023 73.809863 \nL 244.147635 70.519268 \nL 244.175028 89.186486 \nL 244.19329 56.769894 \nL 244.26025 71.372295 \nL 244.369822 59.237015 \nL 244.35156 75.096275 \nL 244.378953 64.229626 \nL 244.439827 82.821371 \nL 244.491569 68.710069 \nL 244.509831 64.458302 \nL 244.540268 76.865539 \nL 244.576792 73.087994 \nL 244.637665 62.445231 \nL 244.692451 76.191079 \nL 244.701582 72.302333 \nL 244.783761 54.973619 \nL 244.732019 72.744172 \nL 244.817241 65.472638 \nL 244.914639 82.896546 \nL 244.862896 59.798865 \nL 244.929857 68.592295 \nL 245.039429 64.918478 \nL 244.978556 74.525884 \nL 245.042473 65.880366 \nL 245.133783 75.72323 \nL 245.112477 62.346939 \nL 245.161176 70.122685 \nL 245.249442 63.721762 \nL 245.215962 78.537403 \nL 245.267704 72.208614 \nL 245.292053 77.432821 \nL 245.310315 60.773595 \nL 245.313359 60.159206 \nL 245.343796 74.87725 \nL 245.35597 73.934021 \nL 245.362058 74.506255 \nL 245.377276 64.97528 \nL 245.386407 60.939739 \nL 245.4138 82.384132 \nL 245.486848 66.04714 \nL 245.50511 79.654361 \nL 245.532503 62.055465 \nL 245.590333 65.322344 \nL 245.599464 59.515998 \nL 245.660337 82.470369 \nL 245.68773 74.922548 \nL 245.693817 77.196156 \nL 245.751647 63.363603 \nL 245.794258 72.59188 \nL 245.861219 64.571077 \nL 245.809477 76.391502 \nL 245.888612 73.963182 \nL 245.958616 79.009148 \nL 245.937311 57.187189 \nL 245.970791 64.52642 \nL 245.976878 57.527929 \nL 245.99514 73.040494 \nL 246.080363 65.971016 \nL 246.208197 80.15054 \nL 246.214284 75.812367 \nL 246.317769 64.581435 \nL 246.3269 69.874921 \nL 246.439516 77.861272 \nL 246.41821 63.854 \nL 246.445603 75.857414 \nL 246.570393 63.953991 \nL 246.530826 76.660813 \nL 246.576481 67.780617 \nL 246.673878 78.657381 \nL 246.649529 63.397915 \nL 246.69214 72.796693 \nL 246.728664 65.489035 \nL 246.707358 75.379952 \nL 246.768232 73.210552 \nL 246.774319 76.739011 \nL 246.792581 60.158621 \nL 246.871716 67.082638 \nL 247.002594 79.195184 \nL 246.941721 64.388833 \nL 247.008681 75.792213 \nL 247.093904 64.054854 \nL 247.036074 77.651131 \nL 247.127384 65.612648 \nL 247.14869 79.838043 \nL 247.197389 65.210583 \nL 247.236957 66.240875 \nL 247.273481 60.357259 \nL 247.322179 76.000256 \nL 247.33131 82.96048 \nL 247.35566 62.549235 \nL 247.431751 75.983129 \nL 247.443926 82.657631 \nL 247.462188 62.178046 \nL 247.529149 70.739488 \nL 247.611328 62.926834 \nL 247.590022 72.579097 \nL 247.647852 65.358135 \nL 247.742205 80.074737 \nL 247.75438 65.511415 \nL 247.760467 59.610422 \nL 247.824384 81.539559 \nL 247.866996 63.125419 \nL 247.988742 103.823138 \nL 247.933956 62.324346 \nL 248.007004 86.217675 \nL 248.083096 47.903011 \nL 248.143969 73.603842 \nL 248.207886 64.086292 \nL 248.186581 80.773648 \nL 248.256585 72.539775 \nL 248.271803 79.23333 \nL 248.30224 66.370922 \nL 248.350939 67.800261 \nL 248.460511 60.728541 \nL 248.408768 78.288971 \nL 248.463554 61.26421 \nL 248.551821 96.898291 \nL 248.524428 58.276318 \nL 248.585301 67.459772 \nL 248.612694 58.862161 \nL 248.637043 81.607604 \nL 248.682698 67.551035 \nL 248.777052 81.630043 \nL 248.79227 69.202795 \nL 248.871406 58.952067 \nL 248.889668 75.58359 \nL 248.904886 65.750891 \nL 248.920104 74.667254 \nL 248.94141 61.363163 \nL 249.014458 67.432739 \nL 249.102724 56.101057 \nL 249.069244 82.295467 \nL 249.12403 68.236495 \nL 249.148379 96.936686 \nL 249.197078 64.034247 \nL 249.230558 67.10354 \nL 249.270126 60.881753 \nL 249.303606 76.353273 \nL 249.349261 62.54195 \nL 249.452746 79.252741 \nL 249.39796 60.694838 \nL 249.464921 67.989785 \nL 249.474052 61.533445 \nL 249.528838 76.705084 \nL 249.574493 69.491473 \nL 249.656672 76.522985 \nL 249.620148 64.384438 \nL 249.677977 67.154924 \nL 249.699283 54.997751 \nL 249.738851 87.96294 \nL 249.747982 97.188989 \nL 249.811899 60.302735 \nL 249.833204 67.703906 \nL 249.915383 59.735859 \nL 249.897121 75.535255 \nL 249.930602 73.495234 \nL 250.027999 78.795286 \nL 249.994519 68.076463 \nL 250.040174 74.367898 \nL 250.140615 61.153387 \nL 250.155833 69.197541 \nL 250.225837 76.558314 \nL 250.247143 65.516469 \nL 250.274536 74.743676 \nL 250.31106 67.633097 \nL 250.356715 60.190189 \nL 250.335409 77.798391 \nL 250.408457 74.934091 \nL 250.414545 80.597117 \nL 250.432807 62.417622 \nL 250.518029 74.067306 \nL 250.581946 58.223036 \nL 250.597165 78.974388 \nL 250.603252 84.143662 \nL 250.658038 57.3546 \nL 250.697606 66.570966 \nL 250.785872 75.1367 \nL 250.80109 65.272482 \nL 250.804134 64.167055 \nL 250.85892 77.980467 \nL 250.8924 71.71492 \nL 250.938055 79.119781 \nL 250.962405 59.906426 \nL 250.971536 60.194035 \nL 250.974579 60.068199 \nL 250.98371 63.037917 \nL 251.04154 82.913284 \nL 251.096326 65.040206 \nL 251.09937 64.960297 \nL 251.105457 67.500719 \nL 251.16633 76.527482 \nL 251.129806 60.001837 \nL 251.208942 64.237804 \nL 251.211985 64.095966 \nL 251.218073 67.709538 \nL 251.233291 81.953929 \nL 251.324601 66.428248 \nL 251.418955 62.264868 \nL 251.385474 73.793784 \nL 251.431129 65.761866 \nL 251.452435 79.822529 \nL 251.473741 58.316411 \nL 251.540701 67.657164 \nL 251.62288 59.374945 \nL 251.601575 81.311114 \nL 251.653317 62.920033 \nL 251.726365 83.3426 \nL 251.702016 60.956094 \nL 251.765933 68.457095 \nL 251.77202 62.077426 \nL 251.817675 80.27341 \nL 251.875505 68.807119 \nL 251.918116 58.731111 \nL 251.969858 79.089323 \nL 251.972902 79.53813 \nL 251.994208 67.163303 \nL 252.01247 68.992352 \nL 252.048994 66.873888 \nL 252.030732 73.358627 \nL 252.091605 72.406139 \nL 252.158566 77.115964 \nL 252.179871 65.889006 \nL 252.192046 57.122328 \nL 252.231614 90.125037 \nL 252.277269 76.240062 \nL 252.283356 79.565098 \nL 252.365535 60.650527 \nL 252.371622 62.11728 \nL 252.465976 76.074056 \nL 252.5025 72.239815 \nL 252.563373 68.36872 \nL 252.578592 79.10263 \nL 252.618159 68.810613 \nL 252.675989 73.956903 \nL 252.694251 65.10146 \nL 252.706426 60.309482 \nL 252.755124 75.695505 \nL 252.782517 70.863299 \nL 252.870784 81.334591 \nL 252.815998 64.67284 \nL 252.882958 68.204093 \nL 252.962094 60.812777 \nL 252.980356 76.506984 \nL 252.983399 77.040042 \nL 253.032098 66.331831 \nL 253.041229 62.38383 \nL 253.089928 81.172512 \nL 253.144714 63.194352 \nL 253.156888 70.245638 \nL 253.166019 78.646738 \nL 253.190369 57.140563 \nL 253.269504 74.514689 \nL 253.336465 65.929752 \nL 253.321246 80.73808 \nL 253.379076 74.660768 \nL 253.391251 73.295676 \nL 253.503866 62.811754 \nL 253.430818 75.785066 \nL 253.512997 65.93937 \nL 253.543434 79.989575 \nL 253.592133 64.428056 \nL 253.622569 65.798693 \nL 253.6317 58.864171 \nL 253.710836 74.592191 \nL 253.729098 70.082769 \nL 253.765622 79.800361 \nL 253.74736 66.232395 \nL 253.847801 71.873511 \nL 253.951285 63.447967 \nL 253.933023 76.711736 \nL 253.960416 68.324412 \nL 254.060857 79.238278 \nL 254.003028 66.250491 \nL 254.073032 69.61704 \nL 254.155211 59.744288 \nL 254.136949 83.376625 \nL 254.191735 64.91036 \nL 254.280001 81.253557 \nL 254.255652 61.545435 \nL 254.313482 69.876389 \nL 254.356093 82.752262 \nL 254.371311 61.47463 \nL 254.416966 67.629185 \nL 254.42001 67.679236 \nL 254.432185 66.709727 \nL 254.447403 62.51936 \nL 254.502189 73.166596 \nL 254.529582 70.881366 \nL 254.584368 79.349101 \nL 254.556975 64.038611 \nL 254.639154 69.941168 \nL 254.654372 77.240956 \nL 254.712202 63.004708 \nL 254.757857 74.5232 \nL 254.773076 61.045664 \nL 254.797425 79.817567 \nL 254.870473 69.550831 \nL 254.943521 75.456311 \nL 254.894822 59.823841 \nL 254.980045 71.107539 \nL 254.99222 62.620187 \nL 255.028744 81.018961 \nL 255.086573 73.876069 \nL 255.226582 64.755003 \nL 255.177883 74.720356 \nL 255.238757 66.164335 \nL 255.269193 80.079287 \nL 255.320936 62.791162 \nL 255.345285 66.078186 \nL 255.351372 65.293235 \nL 255.387896 76.945462 \nL 255.397027 82.349602 \nL 255.485294 64.993466 \nL 255.530949 73.222943 \nL 255.543123 80.026062 \nL 255.579647 66.552629 \nL 255.63139 66.99667 \nL 255.716612 60.006413 \nL 255.652695 80.361357 \nL 255.740962 67.618165 \nL 255.871839 77.07211 \nL 255.780529 61.026812 \nL 255.884014 71.09547 \nL 255.9388 65.184361 \nL 255.914451 77.110796 \nL 255.978368 72.595747 \nL 255.993586 82.730404 \nL 256.014892 60.804302 \nL 256.094027 76.846492 \nL 256.17925 66.579831 \nL 256.209686 73.325023 \nL 256.230992 60.718533 \nL 256.288822 75.407628 \nL 256.316215 70.882217 \nL 256.358826 83.052202 \nL 256.380132 64.854483 \nL 256.42883 73.712474 \nL 256.456223 62.380137 \nL 256.504922 76.969661 \nL 256.538402 72.164992 \nL 256.647974 77.78945 \nL 256.574926 65.575366 \nL 256.654062 75.841018 \nL 256.714935 64.447425 \nL 256.763634 75.061249 \nL 256.775808 77.310729 \nL 256.81842 64.464784 \nL 256.824507 61.479762 \nL 256.867118 84.107239 \nL 256.906686 78.559561 \nL 256.918861 81.691654 \nL 256.964516 60.366327 \nL 256.973647 55.954068 \nL 257.028433 73.811566 \nL 257.064957 66.987181 \nL 257.080175 75.819459 \nL 257.09235 83.155732 \nL 257.162354 66.12271 \nL 257.186703 72.978486 \nL 257.235402 63.07126 \nL 257.256708 77.500914 \nL 257.302363 66.84228 \nL 257.326712 80.029149 \nL 257.360192 65.362281 \nL 257.411935 68.401831 \nL 257.500201 58.07477 \nL 257.45759 74.051921 \nL 257.515419 73.584356 \nL 257.530638 66.267112 \nL 257.591511 75.379345 \nL 257.621948 80.069646 \nL 257.667603 68.738696 \nL 257.694996 74.963438 \nL 257.792393 59.110118 \nL 257.828917 69.379739 \nL 257.920227 82.653341 \nL 257.865441 61.456601 \nL 257.950664 78.493098 \nL 257.968926 63.211188 \nL 258.069367 74.080291 \nL 258.078498 79.966328 \nL 258.118065 61.844199 \nL 258.178939 72.531503 \nL 258.291554 77.596023 \nL 258.233725 61.444267 \nL 258.297642 76.502294 \nL 258.382864 60.279092 \nL 258.410257 75.147167 \nL 258.413301 74.98325 \nL 258.428519 79.306602 \nL 258.43765 81.410845 \nL 258.462 62.636867 \nL 258.522873 74.232943 \nL 258.611139 62.801655 \nL 258.638532 69.953109 \nL 258.653751 80.352306 \nL 258.684187 59.524603 \nL 258.748104 68.713389 \nL 258.757235 66.666975 \nL 258.76941 61.114334 \nL 258.812021 75.400023 \nL 258.857676 72.134148 \nL 258.958117 80.360537 \nL 258.930724 69.792644 \nL 258.967248 73.783199 \nL 258.979423 61.146247 \nL 259.067689 75.447277 \nL 259.082908 64.291522 \nL 259.152912 76.109855 \nL 259.265528 71.717787 \nL 259.292921 60.643369 \nL 259.332488 81.324032 \nL 259.369012 79.074084 \nL 259.448148 57.899389 \nL 259.475541 79.331494 \nL 259.481628 81.546206 \nL 259.536414 58.734362 \nL 259.566851 69.707651 \nL 259.618593 65.515867 \nL 259.627724 73.275747 \nL 259.706859 80.117896 \nL 259.725121 64.072028 \nL 259.731209 65.737977 \nL 259.782951 80.999981 \nL 259.843824 69.824128 \nL 259.962527 60.068407 \nL 259.926003 81.297534 \nL 259.965571 60.643382 \nL 260.075143 83.851017 \nL 260.087318 65.909051 \nL 260.093405 58.694504 \nL 260.154278 78.963358 \nL 260.193846 69.856571 \nL 260.245588 61.490664 \nL 260.227326 75.580122 \nL 260.327767 64.062107 \nL 260.452558 80.479029 \nL 260.458645 75.423961 \nL 260.47082 64.569329 \nL 260.571261 72.49651 \nL 260.656483 66.690434 \nL 260.598654 73.366164 \nL 260.662571 70.587781 \nL 260.671702 78.220865 \nL 260.763012 63.48734 \nL 260.769099 65.930999 \nL 260.881715 75.687943 \nL 260.793448 63.310162 \nL 260.887802 75.159424 \nL 260.90302 69.270922 \nL 260.915195 59.351 \nL 260.963894 76.710235 \nL 261.009549 72.66544 \nL 261.01868 77.029274 \nL 261.088684 64.117598 \nL 261.113033 68.281385 \nL 261.116077 68.163927 \nL 261.125208 71.779985 \nL 261.192169 77.038501 \nL 261.167819 66.246291 \nL 261.225649 68.773161 \nL 261.286522 64.528923 \nL 261.320003 72.587617 \nL 261.411313 81.223108 \nL 261.362614 66.506715 \nL 261.423487 73.249165 \nL 261.502623 66.40709 \nL 261.523928 81.716964 \nL 261.551321 65.39043 \nL 261.572627 62.903497 \nL 261.6335 73.166741 \nL 261.709592 76.154476 \nL 261.663937 63.451201 \nL 261.733941 66.831714 \nL 261.761334 80.06984 \nL 261.803946 65.469838 \nL 261.867863 72.610974 \nL 261.880037 62.555851 \nL 261.90743 77.493599 \nL 261.980478 68.369267 \nL 261.992653 76.067158 \nL 262.080919 63.288472 \nL 262.093094 59.473342 \nL 262.153967 77.957132 \nL 262.157011 77.987312 \nL 262.160055 77.11608 \nL 262.248321 66.026087 \nL 262.227015 77.874646 \nL 262.278758 71.555949 \nL 262.290932 76.721825 \nL 262.376155 66.027894 \nL 262.385286 61.937459 \nL 262.409636 77.150469 \nL 262.485727 66.314805 \nL 262.491815 66.251208 \nL 262.500946 68.995444 \nL 262.592256 77.298555 \nL 262.573994 61.63612 \nL 262.613561 71.136228 \nL 262.634867 65.968535 \nL 262.668347 75.898318 \nL 262.677478 79.728413 \nL 262.723133 68.063351 \nL 262.771832 71.398228 \nL 262.84488 59.849157 \nL 262.820531 73.718401 \nL 262.884448 69.410731 \nL 262.896622 79.637716 \nL 262.990976 65.365224 \nL 263.094461 62.800349 \nL 263.015325 74.729641 \nL 263.100548 64.139556 \nL 263.121854 77.650307 \nL 263.143159 63.25214 \nL 263.216207 68.49918 \nL 263.292299 61.917365 \nL 263.26795 80.592886 \nL 263.328823 66.327747 \nL 263.347085 78.683401 \nL 263.365347 59.665801 \nL 263.45057 73.043912 \nL 263.468832 71.850589 \nL 263.481006 79.082448 \nL 263.490137 84.981111 \nL 263.514487 63.898436 \nL 263.578404 69.384665 \nL 263.590578 55.522539 \nL 263.642321 75.093192 \nL 263.687976 69.959436 \nL 263.736674 64.069375 \nL 263.715369 76.143154 \nL 263.812766 65.561182 \nL 263.937556 77.111149 \nL 263.843203 62.899224 \nL 263.943644 75.957437 \nL 264.03191 62.710034 \nL 263.971037 77.184426 \nL 264.053216 74.034728 \nL 264.062347 77.730098 \nL 264.108002 58.464827 \nL 264.132351 59.181008 \nL 264.135395 57.239141 \nL 264.1567 78.521694 \nL 264.217574 74.447441 \nL 264.290622 79.749842 \nL 264.251054 64.479965 \nL 264.324102 75.457645 \nL 264.36367 58.78663 \nL 264.394106 82.219592 \nL 264.439761 68.018674 \nL 264.461067 62.527469 \nL 264.494547 75.331067 \nL 264.503678 83.003063 \nL 264.570639 61.710185 \nL 264.594988 65.183552 \nL 264.625425 62.359553 \nL 264.6376 69.622209 \nL 264.750215 82.707391 \nL 264.698473 66.638695 \nL 264.756303 79.851216 \nL 264.868918 65.244976 \nL 264.875006 67.621578 \nL 264.923704 60.589438 \nL 264.999796 80.532708 \nL 265.024145 60.440631 \nL 265.097193 80.773467 \nL 265.142848 69.408541 \nL 265.155023 80.947505 \nL 265.194591 64.070964 \nL 265.258508 76.375756 \nL 265.301119 64.761604 \nL 265.279813 78.042327 \nL 265.377211 71.712317 \nL 265.395473 64.740781 \nL 265.444171 73.446703 \nL 265.498957 67.066156 \nL 265.523307 75.166187 \nL 265.602442 65.062047 \nL 265.608529 63.869189 \nL 265.648097 74.621249 \nL 265.693752 70.980258 \nL 265.705927 78.593874 \nL 265.788106 64.375628 \nL 265.806368 73.346325 \nL 265.809411 73.645306 \nL 265.839848 66.540194 \nL 265.861154 68.510276 \nL 265.89159 61.537818 \nL 265.91594 76.215072 \nL 265.952464 70.418959 \nL 266.004206 76.785165 \nL 266.04073 61.048651 \nL 266.046817 57.832073 \nL 266.092472 81.036213 \nL 266.135084 71.992389 \nL 266.186826 62.888721 \nL 266.162477 78.076392 \nL 266.238568 74.261843 \nL 266.247699 78.027881 \nL 266.335966 63.768158 \nL 266.469887 83.725927 \nL 266.412057 59.529267 \nL 266.482062 72.231392 \nL 266.561197 58.623019 \nL 266.527717 81.808342 \nL 266.594677 67.587362 \nL 266.685987 80.33395 \nL 266.658594 61.548107 \nL 266.701206 67.849546 \nL 266.810778 61.460256 \nL 266.762079 76.570473 \nL 266.816865 63.12831 \nL 266.911219 77.240487 \nL 266.929481 64.737292 \nL 266.932524 64.504195 \nL 266.962961 69.862967 \nL 266.990354 82.117195 \nL 267.008616 65.426206 \nL 267.069489 70.577126 \nL 267.148625 59.517175 \nL 267.099926 73.445445 \nL 267.176018 71.079632 \nL 267.267328 75.042117 \nL 267.22776 63.140889 \nL 267.288633 72.761528 \nL 267.328201 63.309693 \nL 267.309939 76.55889 \nL 267.389074 71.351492 \nL 267.398205 76.06162 \nL 267.459079 58.825364 \nL 267.486472 60.22466 \nL 267.522996 91.40901 \nL 267.593 61.784643 \nL 267.599087 55.717921 \nL 267.663004 75.834993 \nL 267.699528 67.42178 \nL 267.702572 65.644758 \nL 267.787795 79.441819 \nL 267.921716 60.016771 \nL 267.882148 81.064194 \nL 267.946065 72.822374 \nL 268.028244 79.01217 \nL 268.043463 63.974029 \nL 268.046506 62.499648 \nL 268.101292 80.34656 \nL 268.128685 73.133703 \nL 268.180428 75.883276 \nL 268.156078 65.10857 \nL 268.23217 69.550558 \nL 268.26565 66.053329 \nL 268.277825 80.818504 \nL 268.280869 83.735376 \nL 268.350873 61.115794 \nL 268.372179 64.993838 \nL 268.41479 61.069308 \nL 268.430008 76.210759 \nL 268.484794 64.386093 \nL 268.493925 66.69292 \nL 268.548711 79.27334 \nL 268.606541 69.781469 \nL 268.624803 76.134957 \nL 268.658283 66.641312 \nL 268.706982 69.887659 \nL 268.792205 62.855791 \nL 268.755681 81.120728 \nL 268.816554 69.872257 \nL 268.846991 72.877787 \nL 268.856122 69.011992 \nL 268.944388 62.368548 \nL 268.926126 75.832414 \nL 268.96265 71.269506 \nL 269.023523 68.149451 \nL 268.983956 77.842328 \nL 269.05396 74.638218 \nL 269.066135 81.037369 \nL 269.090484 65.000052 \nL 269.133095 65.078045 \nL 269.291366 74.573874 \nL 269.160488 64.257634 \nL 269.297453 73.302271 \nL 269.315715 62.236084 \nL 269.364414 80.202677 \nL 269.4192 64.956044 \nL 269.507466 84.210112 \nL 269.461811 56.012329 \nL 269.531816 67.299033 \nL 269.638344 63.094293 \nL 269.580514 78.59519 \nL 269.644431 66.29762 \nL 269.72661 79.402576 \nL 269.674868 55.965074 \nL 269.766178 74.260069 \nL 269.781396 75.335907 \nL 269.787484 72.914543 \nL 269.814877 77.494219 \nL 269.906187 67.233519 \nL 269.954886 75.689911 \nL 269.99141 62.711971 \nL 270.052283 61.233635 \nL 270.034021 76.640195 \nL 270.061414 71.172097 \nL 270.070545 78.142987 \nL 270.131418 61.428641 \nL 270.17403 73.758762 \nL 270.180117 74.457701 \nL 270.237947 64.436533 \nL 270.262296 69.809097 \nL 270.271427 66.416321 \nL 270.335344 78.342555 \nL 270.365781 73.48639 \nL 270.405348 75.013165 \nL 270.42361 62.530531 \nL 270.460134 68.870757 \nL 270.502746 52.907699 \nL 270.530139 75.156405 \nL 270.545357 91.857313 \nL 270.578837 63.907498 \nL 270.633623 67.07743 \nL 270.752326 57.199831 \nL 270.700584 80.941112 \nL 270.75537 58.414178 \nL 270.782763 94.929106 \nL 270.871029 64.391929 \nL 270.99582 82.025271 \nL 270.944077 55.383342 \nL 271.001907 79.715466 \nL 271.093217 60.659419 \nL 271.111479 79.328335 \nL 271.138872 80.967817 \nL 271.160178 67.115908 \nL 271.184527 70.227941 \nL 271.224095 61.040693 \nL 271.205833 80.512299 \nL 271.284968 79.066248 \nL 271.291055 83.419349 \nL 271.379322 67.425061 \nL 271.388453 61.154119 \nL 271.440195 77.848543 \nL 271.488894 67.26147 \nL 271.525418 82.134707 \nL 271.561942 66.48584 \nL 271.604553 71.036695 \nL 271.656295 66.205442 \nL 271.631946 79.17347 \nL 271.720212 66.798273 \nL 271.741518 69.654407 \nL 271.750649 66.475977 \nL 271.75978 62.796178 \nL 271.778042 73.844244 \nL 271.860221 66.168609 \nL 271.863265 65.831173 \nL 271.88457 73.305402 \nL 271.893701 78.477633 \nL 271.911963 64.637498 \nL 271.985011 66.266221 \nL 272.06719 56.20912 \nL 272.015448 75.92337 \nL 272.082409 73.306408 \nL 272.115889 92.697816 \nL 272.179806 57.524635 \nL 272.331989 80.492677 \nL 272.350251 70.926086 \nL 272.435474 61.941431 \nL 272.405037 81.093674 \nL 272.459823 71.03784 \nL 272.468954 70.579009 \nL 272.478085 73.468693 \nL 272.557221 79.715602 \nL 272.529828 64.612662 \nL 272.575483 67.101223 \nL 272.630269 75.8769 \nL 272.651574 63.748451 \nL 272.669836 66.232771 \nL 272.727666 59.992007 \nL 272.70636 80.966078 \nL 272.76419 75.451192 \nL 272.8555 79.589503 \nL 272.79767 67.439788 \nL 272.867675 72.725792 \nL 272.949854 59.021559 \nL 272.892024 77.728009 \nL 272.983334 66.622156 \nL 273.068557 74.733064 \nL 273.050295 63.440187 \nL 273.111168 70.966042 \nL 273.126386 66.058093 \nL 273.147692 73.712506 \nL 273.217696 71.641263 \nL 273.299875 79.707594 \nL 273.245089 64.630511 \nL 273.318137 66.711345 \nL 273.342487 61.27184 \nL 273.382054 82.14764 \nL 273.427709 67.232603 \nL 273.442928 77.994046 \nL 273.479452 58.332488 \nL 273.540325 68.705472 \nL 273.628591 66.941068 \nL 273.601198 78.734761 \nL 273.64381 71.544858 \nL 273.753382 77.708 \nL 273.725989 64.348793 \nL 273.759469 75.755801 \nL 273.805124 64.825728 \nL 273.823386 76.954921 \nL 273.869041 75.10072 \nL 273.875128 77.47358 \nL 273.945133 61.318381 \nL 273.972526 71.900619 \nL 273.996875 41.295793 \nL 274.021224 113.270203 \nL 274.027312 118.888624 \nL 274.097316 55.081997 \nL 274.103403 53.902007 \nL 274.161233 72.002445 \nL 274.194713 78.960355 \nL 274.212975 64.582501 \nL 274.246456 68.573689 \nL 274.289067 60.756953 \nL 274.310373 77.974626 \nL 274.359071 66.833299 \nL 274.362115 66.727322 \nL 274.368202 70.023423 \nL 274.380377 77.207684 \nL 274.404726 62.176449 \nL 274.474731 66.391925 \nL 274.480818 67.276227 \nL 274.496036 81.320821 \nL 274.572128 59.298452 \nL 274.581259 49.026505 \nL 274.605608 107.970639 \nL 274.675613 64.094764 \nL 274.794316 81.17022 \nL 274.727355 52.892426 \nL 274.797359 80.0731 \nL 274.86432 84.920729 \nL 274.92215 66.81013 \nL 274.958674 61.655468 \nL 274.986067 71.815351 \nL 275.001285 73.169161 \nL 275.019547 68.222899 \nL 275.031722 63.446115 \nL 275.092595 83.928954 \nL 275.126075 70.368687 \nL 275.208254 64.004741 \nL 275.156512 72.631936 \nL 275.232604 70.143168 \nL 275.305652 78.416745 \nL 275.256953 62.213316 \nL 275.35435 74.242422 \nL 275.360438 74.221519 \nL 275.369569 75.772002 \nL 275.3787 79.410671 \nL 275.396962 66.407865 \nL 275.463922 69.924811 \nL 275.515665 61.696178 \nL 275.533927 83.064347 \nL 275.570451 72.776855 \nL 275.646542 48.333049 \nL 275.658717 78.91592 \nL 275.670892 114.381209 \nL 275.765245 61.528121 \nL 275.774376 53.984401 \nL 275.862643 73.173194 \nL 275.896123 82.945007 \nL 275.917429 62.075118 \nL 275.96004 69.781998 \nL 276.014826 62.231835 \nL 276.036132 75.395604 \nL 276.093961 65.325922 \nL 276.163966 78.90031 \nL 276.227883 77.076826 \nL 276.297887 63.915846 \nL 276.343542 73.468842 \nL 276.377022 63.671328 \nL 276.413546 76.190892 \nL 276.419634 79.462378 \nL 276.492682 62.607062 \nL 276.504856 63.574037 \nL 276.5079 63.386826 \nL 276.523118 68.019536 \nL 276.535293 67.592782 \nL 276.650952 83.977945 \nL 276.596166 62.623347 \nL 276.65704 79.477722 \nL 276.742262 61.657941 \nL 276.720957 80.243126 \nL 276.772699 69.185835 \nL 276.870096 73.761493 \nL 276.81531 62.941343 \nL 276.876184 68.523425 \nL 276.885315 59.991427 \nL 276.946188 76.607257 \nL 276.979668 75.870649 \nL 276.982712 76.279479 \nL 277.025323 67.679698 \nL 277.037498 63.729247 \nL 277.08924 80.526881 \nL 277.119677 72.953068 \nL 277.128808 75.633394 \nL 277.150114 67.005003 \nL 277.226205 70.985024 \nL 277.287079 57.205566 \nL 277.244467 78.031176 \nL 277.341865 68.464918 \nL 277.360127 67.65239 \nL 277.369258 70.242917 \nL 277.460568 84.261508 \nL 277.408825 67.209403 \nL 277.472742 71.029914 \nL 277.561009 61.886514 \nL 277.521441 76.116584 \nL 277.582315 72.281311 \nL 277.624926 73.254744 \nL 277.634057 70.526185 \nL 277.731454 64.376426 \nL 277.685799 80.270152 \nL 277.755804 67.758346 \nL 277.774066 65.560806 \nL 277.807546 73.29406 \nL 277.819721 82.197938 \nL 277.853201 58.37451 \nL 277.917118 74.942825 \nL 278.008428 58.905749 \nL 277.984079 80.439158 \nL 278.044952 65.574417 \nL 278.093651 76.842448 \nL 278.145393 63.136027 \nL 278.154524 68.207303 \nL 278.20931 79.133051 \nL 278.227572 63.382735 \nL 278.270183 76.099814 \nL 278.279314 73.261625 \nL 278.306707 63.013025 \nL 278.352362 79.497917 \nL 278.39193 71.216353 \nL 278.446716 55.84369 \nL 278.428454 82.046643 \nL 278.486284 72.952814 \nL 278.495415 78.098491 \nL 278.522808 58.731722 \nL 278.589768 67.250138 \nL 278.626292 62.088168 \nL 278.653685 79.637946 \nL 278.662816 83.526139 \nL 278.735864 64.188824 \nL 278.769345 61.716546 \nL 278.802825 77.057754 \nL 278.824131 73.599093 \nL 278.909353 75.552654 \nL 278.84848 65.39463 \nL 278.924572 70.84275 \nL 279.009794 65.254695 \nL 278.991532 73.433504 \nL 279.018925 70.427106 \nL 279.085886 76.047342 \nL 279.040231 60.180685 \nL 279.128497 70.241796 \nL 279.143716 69.356761 \nL 279.152847 71.241719 \nL 279.174152 88.865292 \nL 279.207633 69.295815 \nL 279.262419 70.643023 \nL 279.341554 60.801834 \nL 279.323292 71.611523 \nL 279.399384 67.798662 \nL 279.426777 89.591285 \nL 279.511999 69.011319 \nL 279.551567 57.174677 \nL 279.606353 74.024977 \nL 279.618528 82.233621 \nL 279.639833 64.238086 \nL 279.709838 68.718749 \nL 279.798104 62.686177 \nL 279.76158 77.681733 \nL 279.813322 73.014909 \nL 279.898545 76.641984 \nL 279.858977 55.325515 \nL 279.922894 72.755429 \nL 279.935069 60.184423 \nL 279.992899 81.58854 \nL 280.03551 71.131791 \nL 280.078121 58.752263 \nL 280.099427 76.347942 \nL 280.163344 66.863855 \nL 280.215086 75.446053 \nL 280.266829 65.294905 \nL 280.285091 73.807119 \nL 280.288134 74.815569 \nL 280.306396 61.269309 \nL 280.379444 68.444267 \nL 280.428143 77.919252 \nL 280.519453 74.468741 \nL 280.595545 63.576626 \nL 280.629025 73.162216 \nL 280.738597 78.818622 \nL 280.67468 59.595833 \nL 280.744684 76.505976 \nL 280.826863 61.208535 \nL 280.860344 70.104846 \nL 280.887737 75.58633 \nL 280.927304 70.061062 \nL 280.972959 72.51794 \nL 281.012527 59.453312 \nL 280.994265 75.98387 \nL 281.094706 63.248163 \nL 281.179929 82.683914 \nL 281.125143 58.942152 \nL 281.219496 73.135302 \nL 281.274282 67.362979 \nL 281.322981 76.199689 \nL 281.326025 76.43466 \nL 281.332112 71.968011 \nL 281.420378 61.921255 \nL 281.43864 75.448814 \nL 281.523863 67.166686 \nL 281.545169 76.55359 \nL 281.551256 79.414947 \nL 281.602998 60.326891 \nL 281.633435 67.608082 \nL 281.642566 60.796173 \nL 281.700396 83.821457 \nL 281.73692 77.216747 \nL 281.788662 64.679971 \nL 281.846492 76.031337 \nL 281.916496 77.169079 \nL 281.867797 57.412485 \nL 281.931714 70.109323 \nL 281.943889 61.953926 \nL 282.001719 78.543148 \nL 282.041286 70.338461 \nL 282.059548 77.429724 \nL 282.089985 59.405422 \nL 282.156946 72.200773 \nL 282.248256 63.077382 \nL 282.202601 81.181452 \nL 282.278692 65.238384 \nL 282.293911 81.282927 \nL 282.333478 60.011767 \nL 282.385221 60.546065 \nL 282.388264 60.545684 \nL 282.479574 74.402956 \nL 282.516098 72.703097 \nL 282.549579 93.597196 \nL 282.583059 68.515961 \nL 282.601321 70.008877 \nL 282.686544 52.15039 \nL 282.707849 71.264068 \nL 282.710893 70.343237 \nL 282.829596 59.620661 \nL 282.74133 80.677241 \nL 282.835683 62.460475 \nL 282.887426 84.15528 \nL 282.948299 65.968754 \nL 282.951343 65.336761 \nL 282.972648 81.203267 \nL 282.987867 79.421631 \nL 282.993954 81.547539 \nL 283.01526 66.81989 \nL 283.073089 69.239039 \nL 283.130919 58.87745 \nL 283.112657 75.075824 \nL 283.176574 70.069677 \nL 283.261797 77.650372 \nL 283.243535 64.657778 \nL 283.292233 73.556299 \nL 283.362238 62.311682 \nL 283.325714 75.244256 \nL 283.401805 73.199688 \nL 283.459635 78.595443 \nL 283.426155 64.337124 \nL 283.487028 69.828389 \nL 283.602687 59.347627 \nL 283.557032 81.852654 \nL 283.611818 64.985436 \nL 283.706172 78.112736 \nL 283.651386 61.634922 \nL 283.72139 63.875508 \nL 283.727478 60.994757 \nL 283.770089 77.974924 \nL 283.824875 69.94702 \nL 283.876617 64.591134 \nL 283.855312 75.815393 \nL 283.919229 74.521253 \nL 283.931403 78.368444 \nL 283.952709 58.725703 \nL 284.013582 71.215721 \nL 284.047063 62.625916 \nL 284.071412 77.029033 \nL 284.129242 70.083599 \nL 284.135329 69.861056 \nL 284.141416 70.315426 \nL 284.257076 78.468201 \nL 284.168809 58.189399 \nL 284.263163 76.599543 \nL 284.278381 65.104572 \nL 284.296643 78.2363 \nL 284.378822 72.578626 \nL 284.381866 72.919485 \nL 284.412303 65.499221 \nL 284.467089 60.317186 \nL 284.445783 81.157717 \nL 284.5097 70.989582 \nL 284.524918 78.685176 \nL 284.54318 64.741554 \nL 284.607097 68.647663 \nL 284.616228 64.815486 \nL 284.674058 74.554224 \nL 284.713626 70.771181 \nL 284.7258 69.308266 \nL 284.734931 72.327358 \nL 284.744062 79.455088 \nL 284.762324 64.911629 \nL 284.835372 66.966891 \nL 284.868853 62.204776 \nL 284.887115 71.42939 \nL 284.947988 65.198194 \nL 284.951032 65.024731 \nL 284.963206 70.294173 \nL 284.972337 73.311526 \nL 285.054516 61.243793 \nL 285.078866 46.451702 \nL 285.09104 89.122225 \nL 285.103215 143.862059 \nL 285.176263 39.603643 \nL 285.194525 49.477558 \nL 285.243224 81.775914 \nL 285.343665 76.78291 \nL 285.465412 49.393103 \nL 285.4228 80.806668 \nL 285.477586 57.557382 \nL 285.489761 53.352322 \nL 285.498892 75.793128 \nL 285.51411 138.402052 \nL 285.584115 51.341741 \nL 285.60542 57.278781 \nL 285.623682 50.870322 \nL 285.644988 70.143173 \nL 285.684556 68.042466 \nL 285.78804 78.833359 \nL 285.730211 67.238746 \nL 285.797171 73.724618 \nL 285.906743 55.790797 \nL 285.858045 77.181592 \nL 285.918918 59.3306 \nL 285.934136 50.287248 \nL 285.946311 80.930189 \nL 285.961529 123.720388 \nL 286.02849 52.522446 \nL 286.049796 60.492457 \nL 286.055883 57.808845 \nL 286.071101 69.726926 \nL 286.156324 61.84278 \nL 286.226328 77.931793 \nL 286.314595 73.698694 \nL 286.39373 51.416436 \nL 286.405905 80.74574 \nL 286.418079 110.20116 \nL 286.48504 58.193687 \nL 286.512433 70.283343 \nL 286.561132 71.071196 \nL 286.570263 65.857444 \nL 286.622005 55.525058 \nL 286.600699 71.381817 \nL 286.673747 70.12789 \nL 286.786363 95.361166 \nL 286.737664 64.480619 \nL 286.801581 76.289259 \nL 286.877673 58.918282 \nL 286.8168 77.269462 \nL 286.929415 67.83791 \nL 287.023769 63.600559 \nL 286.953765 75.521802 \nL 287.0329 68.030256 \nL 287.12421 82.853511 \nL 287.072468 60.459276 \nL 287.136385 68.572431 \nL 287.172909 59.916071 \nL 287.203345 77.377472 \nL 287.249 64.088774 \nL 287.279437 83.255009 \nL 287.370747 69.544943 \nL 287.385965 59.791361 \nL 287.434664 79.881143 \nL 287.483363 67.861117 \nL 287.574673 84.559078 \nL 287.532061 62.194957 \nL 287.602066 74.022507 \nL 287.611197 70.629674 \nL 287.662939 66.94387 \nL 287.644677 81.086837 \nL 287.711638 76.721414 \nL 287.714681 76.819185 \nL 287.720769 74.229906 \nL 287.739031 63.28766 \nL 287.79686 75.059653 \nL 287.833384 72.437254 \nL 287.918607 61.856393 \nL 287.933825 73.00407 \nL 288.031223 67.068399 \nL 288.049485 77.455881 \nL 288.183406 63.617685 \nL 288.232105 72.306443 \nL 288.296022 75.845064 \nL 288.265585 62.125181 \nL 288.326458 69.517965 \nL 288.411681 63.128369 \nL 288.393419 74.263983 \nL 288.432987 72.165396 \nL 288.484729 64.054014 \nL 288.463423 74.491219 \nL 288.524297 71.567702 \nL 288.579083 76.644401 \nL 288.597345 66.805535 \nL 288.630825 69.264531 \nL 288.639956 68.275355 \nL 288.703873 56.021798 \nL 288.722135 70.615793 \nL 288.743441 70.557443 \nL 288.831707 81.799029 \nL 288.804314 63.952671 \nL 288.856056 72.214745 \nL 288.865187 72.66833 \nL 288.877362 69.79626 \nL 288.929104 61.612131 \nL 288.95041 77.602867 \nL 288.971716 75.93091 \nL 288.980847 77.858218 \nL 289.023458 61.964118 \nL 289.029545 59.67766 \nL 289.056938 79.359854 \nL 289.117812 70.309524 \nL 289.233471 78.241402 \nL 289.187816 63.954807 \nL 289.236515 76.423364 \nL 289.297388 63.270792 \nL 289.336956 83.079227 \nL 289.339999 84.028179 \nL 289.388698 66.337925 \nL 289.400873 61.773451 \nL 289.425222 75.546677 \nL 289.495226 66.06731 \nL 289.559143 77.576267 \nL 289.522619 59.109174 \nL 289.613929 71.510972 \nL 289.674803 65.429281 \nL 289.656541 75.059145 \nL 289.717414 73.540186 \nL 289.80568 76.895356 \nL 289.741763 62.94931 \nL 289.814811 70.850772 \nL 289.890903 63.906265 \nL 289.875685 77.81899 \nL 289.924383 71.163604 \nL 290.021781 78.972568 \nL 290.000475 61.622108 \nL 290.027868 73.232734 \nL 290.043086 56.62916 \nL 290.067436 76.892196 \nL 290.13744 73.55216 \nL 290.173964 76.960846 \nL 290.192226 69.171902 \nL 290.225706 69.624778 \nL 290.22875 69.494633 \nL 290.240925 73.291755 \nL 290.317016 80.370937 \nL 290.268318 65.123556 \nL 290.335278 68.349339 \nL 290.420501 61.331011 \nL 290.396152 74.835552 \nL 290.435719 70.418101 \nL 290.499636 77.446285 \nL 290.484418 60.970792 \nL 290.545291 71.293951 \nL 290.563553 64.757254 \nL 290.581815 75.453279 \nL 290.609208 79.905048 \nL 290.633558 64.62089 \nL 290.663994 68.655378 \nL 290.749217 65.323392 \nL 290.682256 75.334215 \nL 290.758348 69.898575 \nL 290.767479 77.498391 \nL 290.785741 63.521395 \nL 290.874007 76.017939 \nL 290.944012 64.137044 \nL 290.986623 74.297525 \nL 290.99271 76.914108 \nL 291.044453 66.071901 \nL 291.090108 71.5618 \nL 291.096195 70.846278 \nL 291.230116 61.324986 \nL 291.172287 79.60512 \nL 291.23316 61.688135 \nL 291.263597 74.540268 \nL 291.300121 55.744044 \nL 291.306208 53.674079 \nL 291.330557 95.059258 \nL 291.336645 99.361821 \nL 291.379256 70.108575 \nL 291.41578 70.146038 \nL 291.455348 54.895729 \nL 291.497959 76.786106 \nL 291.504046 78.338725 \nL 291.571007 68.393659 \nL 291.589269 68.510527 \nL 291.665361 73.45628 \nL 291.704928 65.164474 \nL 291.720147 67.123902 \nL 291.735365 80.757565 \nL 291.753627 60.681933 \nL 291.835806 71.827913 \nL 291.881461 82.176467 \nL 291.902767 67.195448 \nL 291.921029 66.028114 \nL 291.933203 70.78921 \nL 291.945378 76.261444 \nL 292.000164 63.86746 \nL 292.039732 69.153297 \nL 292.121911 64.325477 \nL 292.103649 79.381178 \nL 292.149304 67.127425 \nL 292.167566 76.53209 \nL 292.225395 64.32284 \nL 292.261919 69.414528 \nL 292.310618 61.973394 \nL 292.325836 72.042817 \nL 292.341055 85.662854 \nL 292.401928 59.165364 \nL 292.435408 70.452301 \nL 292.502369 83.396098 \nL 292.520631 57.69802 \nL 292.551068 73.555352 \nL 292.614985 61.948878 \nL 292.645421 75.76789 \nL 292.651509 77.805005 \nL 292.666727 66.112952 \nL 292.75195 73.518394 \nL 292.837173 60.464921 \nL 292.852391 75.649461 \nL 292.864566 69.749384 \nL 292.879784 81.284392 \nL 292.93457 65.00192 \nL 292.940657 63.186798 \nL 293.004574 75.934042 \nL 293.02588 68.911729 \nL 293.050229 62.883118 \nL 293.144583 78.324456 \nL 293.147627 78.617551 \nL 293.184151 72.452659 \nL 293.235893 62.566025 \nL 293.217631 74.736216 \nL 293.293723 71.735576 \nL 293.327203 73.957014 \nL 293.381989 62.04178 \nL 293.400251 72.328687 \nL 293.461124 66.238023 \nL 293.439819 83.356104 \nL 293.506779 74.141348 \nL 293.51591 78.364302 \nL 293.537216 57.101824 \nL 293.598089 67.647642 \nL 293.677225 64.232458 \nL 293.622439 77.064231 \nL 293.69853 73.261066 \nL 293.741142 75.472783 \nL 293.750273 66.015604 \nL 293.835495 60.285878 \nL 293.771578 74.412752 \nL 293.856801 66.952079 \nL 293.88115 82.025535 \nL 293.929849 61.394402 \nL 293.975504 73.721821 \nL 293.987679 63.084802 \nL 294.036377 79.658986 \nL 294.091163 66.813666 \nL 294.100294 64.840212 \nL 294.115513 77.882448 \nL 294.170299 76.05975 \nL 294.173342 76.492794 \nL 294.200735 66.391784 \nL 294.225085 68.559079 \nL 294.289002 62.830899 \nL 294.307264 76.420941 \nL 294.325526 68.51477 \nL 294.334657 79.932516 \nL 294.349875 66.461099 \nL 294.435098 67.970736 \nL 294.450316 61.072735 \nL 294.480753 79.875291 \nL 294.526408 74.592129 \nL 294.562932 76.480947 \nL 294.581194 61.397342 \nL 294.584237 60.846347 \nL 294.620761 72.781813 \nL 294.648154 57.659882 \nL 294.736421 77.036009 \nL 294.842949 63.789553 \nL 294.855124 72.427538 \nL 294.891648 81.913793 \nL 294.958608 66.463055 \nL 295.019482 59.501957 \nL 295.00122 73.512931 \nL 295.059049 69.067893 \nL 295.15949 72.809482 \nL 295.101661 64.959339 \nL 295.180796 72.401154 \nL 295.199058 77.343463 \nL 295.241669 62.151605 \nL 295.269062 66.877386 \nL 295.345154 66.125122 \nL 295.296455 74.638319 \nL 295.363416 70.364485 \nL 295.439508 79.481308 \nL 295.39994 62.974702 \nL 295.454726 66.188928 \nL 295.466901 60.001025 \nL 295.49125 74.948527 \nL 295.567342 63.60886 \nL 295.58256 80.998494 \nL 295.61604 63.095451 \nL 295.683001 74.684402 \nL 295.76518 63.482665 \nL 295.743874 79.303743 \nL 295.795617 69.641899 \nL 295.859534 72.528704 \nL 295.816922 65.369305 \nL 295.902145 67.671581 \nL 295.917363 65.701161 \nL 295.935625 75.133079 \nL 295.959975 74.951284 \nL 295.969106 82.83473 \nL 296.023892 62.528116 \nL 296.069547 72.999957 \nL 296.191293 86.086206 \nL 296.142595 59.582085 \nL 296.194337 83.855073 \nL 296.209555 56.325039 \nL 296.316084 62.199144 \nL 296.319127 61.580301 \nL 296.337389 73.094055 \nL 296.392175 68.353845 \nL 296.410437 81.529969 \nL 296.431743 65.31684 \nL 296.498704 68.785097 \nL 296.504791 64.945036 \nL 296.523053 75.369492 \nL 296.611319 67.373538 \nL 296.650887 64.832347 \nL 296.693498 75.763814 \nL 296.787852 77.078942 \nL 296.726979 64.800747 \nL 296.793939 74.260468 \nL 296.830463 64.95455 \nL 296.909599 68.227354 \nL 296.930904 79.445094 \nL 296.976559 67.164855 \nL 297.022214 69.250506 \nL 297.10135 63.870019 \nL 297.083088 72.374053 \nL 297.131786 67.125633 \nL 297.195703 76.893162 \nL 297.213965 62.858677 \nL 297.238315 65.915422 \nL 297.320494 61.339754 \nL 297.274839 81.370193 \nL 297.335712 70.569311 \nL 297.378323 80.613943 \nL 297.399629 65.039544 \nL 297.439197 71.659609 \nL 297.448328 65.535391 \nL 297.518332 77.835362 \nL 297.554856 65.686545 \nL 297.567031 63.953714 \nL 297.603555 70.969495 \nL 297.652253 67.826056 \nL 297.725301 81.848964 \nL 297.764869 68.373497 \nL 297.770956 64.579522 \nL 297.828786 77.342596 \nL 297.874441 69.078303 \nL 297.877485 68.856894 \nL 297.901834 74.204074 \nL 297.962707 76.917415 \nL 297.9901 65.623051 \nL 297.993144 65.368937 \nL 298.002275 72.366113 \nL 298.114891 77.221932 \nL 298.093585 63.547838 \nL 298.117934 76.901837 \nL 298.151415 65.957494 \nL 298.194026 83.073979 \nL 298.260987 69.573964 \nL 298.294467 77.744484 \nL 298.35534 63.108174 \nL 298.364471 59.005071 \nL 298.422301 78.73002 \nL 298.458825 66.851724 \nL 298.477087 81.633131 \nL 298.525786 61.37472 \nL 298.580572 75.351129 \nL 298.662751 60.876628 \nL 298.644489 81.442463 \nL 298.693187 71.577915 \nL 298.757104 78.138041 \nL 298.741886 62.642675 \nL 298.799716 69.79411 \nL 298.854502 62.351967 \nL 298.900157 71.136769 \nL 298.912331 67.75585 \nL 298.915375 67.690721 \nL 298.924506 69.370545 \nL 299.01886 83.137785 \nL 298.957986 64.856872 \nL 299.031034 70.698784 \nL 299.043209 60.047596 \nL 299.08582 76.70602 \nL 299.14365 65.305584 \nL 299.222785 81.210739 \nL 299.244091 63.908715 \nL 299.262353 67.865538 \nL 299.271484 66.844022 \nL 299.338445 71.567254 \nL 299.460191 80.048917 \nL 299.420624 64.028613 \nL 299.463235 79.892932 \nL 299.551501 61.44702 \nL 299.588025 72.680601 \nL 299.591069 73.641367 \nL 299.630637 65.633043 \nL 299.679335 66.84965 \nL 299.682379 66.669936 \nL 299.703685 71.892807 \nL 299.752383 77.061727 \nL 299.776733 65.382762 \nL 299.822388 74.175475 \nL 299.828475 74.287553 \nL 299.837606 71.857045 \nL 299.849781 62.481616 \nL 299.90761 79.027866 \nL 299.944134 73.822256 \nL 300.017182 66.125238 \nL 299.974571 78.090321 \nL 300.047619 74.582128 \nL 300.05675 80.796609 \nL 300.078056 64.328619 \nL 300.154147 72.005782 \nL 300.230239 76.114315 \nL 300.175453 66.62589 \nL 300.245457 68.474559 \nL 300.248501 68.299945 \nL 300.254588 71.988058 \nL 300.35503 86.98109 \nL 300.309375 65.673443 \nL 300.367204 75.698175 \nL 300.449383 53.823758 \nL 300.431121 80.531284 \nL 300.485907 67.659077 \nL 300.498082 76.833821 \nL 300.592436 64.57337 \nL 300.60461 75.026076 \nL 300.650265 79.562766 \nL 300.632003 61.351029 \nL 300.708095 73.087661 \nL 300.817667 67.150106 \nL 300.759837 82.676992 \nL 300.823754 68.920417 \nL 300.908977 76.145568 \nL 300.890715 60.58865 \nL 300.945501 75.626155 \nL 300.9942 63.742172 \nL 301.070291 68.318321 \nL 301.08551 76.415205 \nL 301.115946 65.114375 \nL 301.179863 68.030068 \nL 301.188994 64.948486 \nL 301.225518 77.794334 \nL 301.292479 66.067061 \nL 301.319872 77.849221 \nL 301.338134 63.770407 \nL 301.399007 67.085119 \nL 301.411182 55.317064 \nL 301.465968 74.050888 \nL 301.508579 66.563104 \nL 301.539016 81.193477 \nL 301.590758 62.524038 \nL 301.624239 70.45405 \nL 301.715549 60.774415 \nL 301.688156 76.843463 \nL 301.730767 72.523171 \nL 301.739898 77.603977 \nL 301.819033 65.081423 \nL 301.837295 68.628891 \nL 301.879907 42.934557 \nL 301.898169 92.957844 \nL 301.910343 123.748314 \nL 301.983391 46.694123 \nL 301.99861 55.47671 \nL 302.138618 79.473672 \nL 302.141662 78.982464 \nL 302.166011 61.795897 \nL 302.202535 79.726339 \nL 302.266452 67.651152 \nL 302.284714 83.118256 \nL 302.30602 67.612666 \nL 302.376024 69.199403 \nL 302.39733 53.760753 \nL 302.421679 79.665203 \nL 302.458203 72.820419 \nL 302.476465 124.631743 \nL 302.519077 55.926926 \nL 302.564732 66.275113 \nL 302.668216 49.275678 \nL 302.616474 71.228536 \nL 302.677347 62.589906 \nL 302.695609 106.903088 \nL 302.744308 60.50953 \nL 302.79605 86.098195 \nL 302.847793 57.895494 \nL 302.914753 77.122733 \nL 302.923884 86.560412 \nL 302.97867 57.730438 \nL 303.030413 84.798381 \nL 303.082155 55.841691 \nL 303.155203 73.897729 \nL 303.16129 72.666532 \nL 303.225207 65.681831 \nL 303.243469 78.474408 \nL 303.267819 74.576854 \nL 303.283037 84.86173 \nL 303.365216 64.116268 \nL 303.371303 62.907019 \nL 303.395653 85.533596 \nL 303.398696 87.616549 \nL 303.423046 61.368268 \nL 303.480875 68.132587 \nL 303.49305 61.096515 \nL 303.55088 77.976733 \nL 303.58436 70.195302 \nL 303.599578 99.628536 \nL 303.636102 37.959301 \nL 303.696976 77.177118 \nL 303.739587 57.09794 \nL 303.797417 77.839654 \nL 303.803504 81.879836 \nL 303.89177 61.198458 \nL 303.894814 60.726884 \nL 303.910032 67.991771 \nL 303.955687 66.887675 \nL 303.980037 84.904432 \nL 304.053085 65.68276 \nL 304.07439 75.163276 \nL 304.089609 60.143985 \nL 304.19005 68.455197 \nL 304.287447 78.192441 \nL 304.296578 70.536371 \nL 304.305709 62.908169 \nL 304.400063 77.648703 \nL 304.415281 69.361446 \nL 304.436587 61.716288 \nL 304.512678 75.791994 \nL 304.579639 80.952748 \nL 304.591814 74.790014 \nL 304.616163 58.690278 \nL 304.689211 78.3237 \nL 304.71356 65.162525 \nL 304.807914 77.284126 \nL 304.823132 66.927815 \nL 304.82922 64.63144 \nL 304.887049 77.777081 \nL 304.932704 65.674494 \nL 304.950966 79.033655 \nL 304.975316 62.219187 \nL 305.042276 70.852895 \nL 305.057495 53.962904 \nL 305.100106 89.643736 \nL 305.160979 60.570318 \nL 305.200547 49.621974 \nL 305.182285 70.697934 \nL 305.243158 70.102527 \nL 305.331425 78.010332 \nL 305.276639 66.409295 \nL 305.340556 71.06578 \nL 305.425778 62.493501 \nL 305.401429 78.631761 \nL 305.450128 72.186113 \nL 305.495783 64.096968 \nL 305.538394 75.834087 \nL 305.541438 76.665057 \nL 305.605355 66.709972 \nL 305.623617 69.771633 \nL 305.672315 62.198509 \nL 305.687534 73.18953 \nL 305.784931 77.876373 \nL 305.739276 61.192596 \nL 305.800149 74.798565 \nL 305.86711 60.473089 \nL 305.836673 82.489979 \nL 305.918852 72.499607 \nL 306.001031 77.11774 \nL 305.970595 58.441014 \nL 306.013206 66.053749 \nL 306.022337 58.5355 \nL 306.074079 82.261664 \nL 306.119734 69.143974 \nL 306.150171 79.040232 \nL 306.189739 62.845838 \nL 306.192782 62.067157 \nL 306.253656 74.482773 \nL 306.262787 80.050419 \nL 306.284092 65.952566 \nL 306.369315 78.643556 \nL 306.463669 58.93519 \nL 306.509324 67.956783 \nL 306.573241 74.808957 \nL 306.536717 65.458665 \nL 306.643245 71.055925 \nL 306.685856 59.937083 \nL 306.72238 80.317494 \nL 306.728468 81.447413 \nL 306.761948 67.069218 \nL 306.795428 69.006231 \nL 306.834996 64.486655 \nL 306.880651 69.82312 \nL 306.96283 80.523983 \nL 306.938481 61.650588 \nL 306.975005 64.840614 \nL 306.981092 61.830895 \nL 306.99631 75.27812 \nL 307.075446 74.146875 \nL 307.090664 83.233213 \nL 307.133275 63.52805 \nL 307.181974 72.973996 \nL 307.23676 58.305649 \nL 307.29459 72.350752 \nL 307.41938 84.369436 \nL 307.352419 60.729231 \nL 307.422424 82.920457 \nL 307.535039 63.354791 \nL 307.547214 65.990862 \nL 307.611131 79.010559 \nL 307.672004 70.526188 \nL 307.684179 71.927541 \nL 307.717659 67.48623 \nL 307.729834 60.669162 \nL 307.815057 75.741962 \nL 307.8181 75.557548 \nL 307.878974 62.400906 \nL 307.857668 77.760181 \nL 307.924629 74.43873 \nL 307.930716 77.778131 \nL 307.982459 65.042172 \nL 308.02507 68.083574 \nL 308.055507 63.02086 \nL 308.0829 74.051566 \nL 308.11638 70.401211 \nL 308.216821 82.076581 \nL 308.177253 60.481167 \nL 308.225952 72.96791 \nL 308.24117 56.236304 \nL 308.302044 82.417601 \nL 308.338568 70.047449 \nL 308.365961 80.392976 \nL 308.42379 66.511205 \nL 308.502926 61.698763 \nL 308.48162 74.654868 \nL 308.5151 70.237001 \nL 308.594236 78.00261 \nL 308.566843 64.296017 \nL 308.627716 73.058373 \nL 308.761637 65.703722 \nL 308.667284 76.847251 \nL 308.764681 66.859875 \nL 308.825554 79.375971 \nL 308.886428 76.689999 \nL 308.916864 59.931451 \nL 308.901646 77.26998 \nL 309.008174 71.086765 \nL 309.041655 73.287436 \nL 309.053829 64.203239 \nL 309.06296 54.966228 \nL 309.12079 88.23511 \nL 309.160358 69.01023 \nL 309.24558 67.168542 \nL 309.184707 74.820364 \nL 309.257755 72.066575 \nL 309.342978 79.289754 \nL 309.321672 63.237327 \nL 309.367327 71.634083 \nL 309.397764 75.19917 \nL 309.431244 64.260832 \nL 309.437331 60.353242 \nL 309.455593 73.197866 \nL 309.534729 70.003467 \nL 309.598646 65.352612 \nL 309.616908 75.283276 \nL 309.641257 84.170298 \nL 309.671694 67.030384 \nL 309.680825 60.817262 \nL 309.72648 82.954465 \nL 309.781266 65.663188 \nL 309.826921 77.864526 \nL 309.805615 61.497486 \nL 309.903012 74.708206 \nL 309.918231 60.53581 \nL 309.997366 77.163093 \nL 310.012584 73.404158 \nL 310.046065 65.625098 \nL 310.073458 44.888063 \nL 310.097807 79.860467 \nL 310.113025 214.756364 \nL 310.140418 18.079346 \nL 310.204335 62.896834 \nL 310.319995 95.137942 \nL 310.286514 52.515015 \nL 310.332169 78.687878 \nL 310.344344 55.916322 \nL 310.444785 65.748886 \nL 310.533051 78.70709 \nL 310.551313 62.839338 \nL 310.554357 61.613179 \nL 310.612187 77.507571 \nL 310.636536 71.789515 \nL 310.660885 75.540971 \nL 310.70654 60.439981 \nL 310.709584 60.211817 \nL 310.727846 65.266755 \nL 310.828287 83.663585 \nL 310.806981 62.70255 \nL 310.843505 70.704393 \nL 310.943946 65.831245 \nL 310.901335 77.237783 \nL 310.953077 69.738367 \nL 310.977427 81.476307 \nL 310.998732 61.698768 \nL 311.059606 69.635747 \nL 311.074824 59.072399 \nL 311.099173 76.815239 \nL 311.166134 70.984983 \nL 311.169178 70.985451 \nL 311.184396 78.882681 \nL 311.248313 68.659801 \nL 311.272662 70.647773 \nL 311.293968 57.468122 \nL 311.354841 77.486519 \nL 311.376147 71.890242 \nL 311.415715 80.79274 \nL 311.458326 63.488907 \nL 311.485719 70.455804 \nL 311.497894 80.253743 \nL 311.540505 60.723909 \nL 311.58616 63.298253 \nL 311.589204 61.854704 \nL 311.634859 76.751592 \nL 311.671383 75.312828 \nL 311.67747 76.497507 \nL 311.738343 65.191652 \nL 311.756605 70.243235 \nL 311.76878 60.274609 \nL 311.799217 78.867337 \nL 311.857046 77.925597 \nL 311.863134 83.74424 \nL 311.884439 61.627022 \nL 311.954444 63.052435 \nL 312.024448 60.349616 \nL 312.003142 77.432086 \nL 312.054885 67.105325 \nL 312.091409 79.36054 \nL 312.106627 64.413651 \nL 312.1675 71.987832 \nL 312.264898 79.546514 \nL 312.185762 67.032037 \nL 312.270985 75.847237 \nL 312.28316 57.63479 \nL 312.325771 76.201173 \nL 312.383601 68.144639 \nL 312.471867 77.536926 \nL 312.410994 60.211288 \nL 312.49926 70.853596 \nL 312.505347 70.128456 \nL 312.560133 64.009705 \nL 312.538828 74.640337 \nL 312.608832 72.530597 \nL 312.614919 71.74013 \nL 312.703186 63.09586 \nL 312.675793 83.860648 \nL 312.730579 68.418988 \nL 312.745797 77.645501 \nL 312.800583 63.210761 \nL 312.837107 67.749947 \nL 312.843194 67.101753 \nL 312.885806 75.868466 \nL 312.891893 79.312212 \nL 312.937548 61.776456 \nL 312.986247 69.662641 \nL 312.998421 62.206918 \nL 313.056251 75.957077 \nL 313.098862 67.122721 \nL 313.101906 67.022257 \nL 313.107993 70.161928 \nL 313.208434 82.774845 \nL 313.144517 63.715986 \nL 313.217565 74.681681 \nL 313.248002 60.308897 \nL 313.281482 77.552524 \nL 313.330181 67.66455 \nL 313.427578 80.012061 \nL 313.439753 71.518687 \nL 313.451928 62.41823 \nL 313.476277 74.979186 \nL 313.552369 67.083973 \nL 313.555412 67.02369 \nL 313.573674 68.780125 \nL 313.649766 77.379034 \nL 313.664984 64.398612 \nL 313.674115 53.659157 \nL 313.692377 77.60301 \nL 313.768469 76.889747 \nL 313.801949 78.302892 \nL 313.817168 68.220252 \nL 313.844561 60.178516 \nL 313.878041 81.685087 \nL 313.890216 98.378344 \nL 313.969351 47.421848 \nL 313.975438 53.829537 \nL 314.011962 76.792683 \nL 314.072836 50.693203 \nL 314.091098 65.567715 \nL 314.170233 74.66512 \nL 314.218932 67.399538 \nL 314.261543 40.179275 \nL 314.270674 79.016909 \nL 314.285892 211.378035 \nL 314.313285 17.083636 \nL 314.380246 71.518411 \nL 314.431988 78.438169 \nL 314.480687 66.593456 \nL 314.483731 66.654721 \nL 314.571997 98.609761 \nL 314.523298 56.598291 \nL 314.611565 73.862649 \nL 314.672438 57.175391 \nL 314.72418 70.540823 \nL 314.727224 70.292982 \nL 314.757661 76.277202 \nL 314.766792 83.739721 \nL 314.81549 64.666355 \nL 314.861145 69.044372 \nL 314.955499 63.456608 \nL 314.912888 79.385789 \nL 314.970717 69.316428 \nL 314.992023 77.586233 \nL 315.010285 66.641249 \nL 315.077246 70.085276 \nL 315.159425 66.256601 \nL 315.144206 75.514997 \nL 315.174643 75.302473 \nL 315.183774 77.657264 \nL 315.262909 66.226866 \nL 315.311608 57.461172 \nL 315.287259 80.88658 \nL 315.360307 70.460416 \nL 315.378569 61.133166 \nL 315.439442 75.656657 \nL 315.47901 67.795784 \nL 315.51249 81.070954 \nL 315.555102 64.717201 \nL 315.591626 71.724692 \nL 315.606844 60.442819 \nL 315.640324 77.036506 \nL 315.704241 69.07293 \nL 315.740765 81.798064 \nL 315.762071 63.666219 \nL 315.774246 64.73391 \nL 315.780333 64.555459 \nL 315.792508 68.84076 \nL 315.87773 78.371933 \nL 315.832075 60.114199 \nL 315.892949 66.522743 \nL 315.899036 65.274378 \nL 315.953822 82.991779 \nL 315.978171 71.482831 \nL 316.093831 78.788567 \nL 316.048176 64.797877 \nL 316.096874 78.459213 \nL 316.15166 57.849349 \nL 316.227752 68.448349 \nL 316.246014 80.024833 \nL 316.288625 65.240924 \nL 316.337324 69.897316 \nL 316.422547 64.890775 \nL 316.386023 74.115399 \nL 316.446896 68.896978 \nL 316.465158 80.551637 \nL 316.535162 66.840304 \nL 316.556468 71.468382 \nL 316.568643 62.805552 \nL 316.599079 71.555188 \nL 316.672127 66.686979 \nL 316.681258 64.872808 \nL 316.705608 78.904509 \nL 316.75735 72.833857 \nL 316.839529 78.947116 \nL 316.799961 65.43058 \nL 316.857791 72.337027 \nL 316.903446 62.984762 \nL 316.924752 80.20317 \nL 316.970407 70.392521 \nL 317.08911 65.392444 \nL 316.994756 75.664224 \nL 317.095197 66.450734 \nL 317.195638 97.561505 \nL 317.119546 63.044713 \nL 317.207813 71.293455 \nL 317.238249 63.026871 \nL 317.256511 81.898976 \nL 317.320428 69.051358 \nL 317.375214 74.262055 \nL 317.411738 63.435755 \nL 317.417826 61.276681 \nL 317.503048 74.855865 \nL 317.515223 79.939121 \nL 317.548703 61.600207 \nL 317.600446 69.396625 \nL 317.61262 62.187827 \nL 317.664363 78.511679 \nL 317.706974 70.245889 \nL 317.789153 79.037817 \nL 317.755673 67.584038 \nL 317.816546 70.195347 \nL 317.9109 60.464077 \nL 317.892638 74.837445 \nL 317.926118 69.126195 \nL 318.032646 75.817847 \nL 317.965686 67.309229 \nL 318.038734 71.493016 \nL 318.050908 55.762265 \nL 318.075258 87.439932 \nL 318.154393 62.260647 \nL 318.254834 81.824781 \nL 318.206135 59.757291 \nL 318.270052 67.932386 \nL 318.282227 59.996365 \nL 318.361362 77.634364 \nL 318.385712 61.197035 \nL 318.391799 58.279764 \nL 318.410061 80.795817 \nL 318.473978 73.395902 \nL 318.556157 86.42336 \nL 318.528764 65.005835 \nL 318.586594 75.558362 \nL 318.589637 75.518623 \nL 318.656598 61.339245 \nL 318.711384 68.692704 \nL 318.726602 71.753207 \nL 318.775301 84.197948 \nL 318.796607 63.697765 \nL 318.817912 65.259328 \nL 318.820956 65.031525 \nL 318.851393 70.986405 \nL 318.854436 71.007391 \nL 318.85748 70.490455 \nL 318.869655 67.669524 \nL 318.927484 73.650749 \nL 318.954877 72.597472 \nL 318.994445 80.105726 \nL 319.018794 64.435079 \nL 319.067493 73.622201 \nL 319.128366 61.111536 \nL 319.082711 73.850054 \nL 319.198371 63.607196 \nL 319.286637 84.866089 \nL 319.34751 69.426441 \nL 319.414471 57.317858 \nL 319.380991 74.129116 \nL 319.450995 72.547734 \nL 319.475344 88.331271 \nL 319.539261 66.236271 \nL 319.557523 68.537798 \nL 319.609266 56.037234 \nL 319.648833 75.666049 \nL 319.664052 68.552665 \nL 319.740143 78.529732 \nL 319.721881 60.285784 \nL 319.779711 72.906045 \nL 319.846672 62.265945 \nL 319.810148 83.29863 \nL 319.886239 73.32358 \nL 319.889283 73.715719 \nL 319.913632 64.097701 \nL 319.922763 60.853883 \nL 319.971462 74.273165 \nL 320.007986 70.879759 \nL 320.020161 70.488252 \nL 320.032335 73.823779 \nL 320.117558 80.75012 \nL 320.065816 66.327677 \nL 320.129733 67.390458 \nL 320.211912 63.921952 \nL 320.172344 75.471837 \nL 320.233217 72.656032 \nL 320.309309 63.629483 \nL 320.297134 76.3501 \nL 320.321484 76.282819 \nL 320.324527 78.8941 \nL 320.364095 68.080826 \nL 320.418881 68.530113 \nL 320.437143 56.555159 \nL 320.464536 74.946957 \nL 320.519322 73.741167 \nL 320.561933 79.755562 \nL 320.586283 68.25921 \nL 320.622807 68.907057 \nL 320.62585 68.873782 \nL 320.631938 69.990056 \nL 320.68368 76.70931 \nL 320.662374 65.13477 \nL 320.735422 67.601064 \nL 320.747597 61.678403 \nL 320.790208 74.521351 \nL 320.835863 72.701773 \nL 320.838907 72.834104 \nL 320.848038 69.345719 \nL 320.95761 56.842307 \nL 320.8663 79.072496 \nL 320.966741 66.121403 \nL 321.000221 80.312922 \nL 321.01544 61.306062 \nL 321.0824 76.444138 \nL 321.176754 63.482042 \nL 321.158492 78.284072 \nL 321.210234 65.944838 \nL 321.213278 65.823737 \nL 321.225453 68.631493 \nL 321.310675 100.201606 \nL 321.283282 60.744671 \nL 321.325894 61.738108 \nL 321.331981 55.956393 \nL 321.374592 78.75588 \nL 321.435466 63.598339 \nL 321.532863 76.707682 \nL 321.5633 73.710074 \nL 321.663741 63.972656 \nL 321.611998 77.987958 \nL 321.706352 68.091528 \nL 321.831142 82.651286 \nL 321.727658 65.570282 \nL 321.83723 80.371762 \nL 321.922452 63.583554 \nL 321.949845 74.949881 \nL 321.958976 78.67763 \nL 322.010719 62.619787 \nL 322.056374 75.361964 \nL 322.168989 64.855407 \nL 322.175077 67.570905 \nL 322.187251 76.521931 \nL 322.260299 65.526764 \nL 322.290736 71.934265 \nL 322.308998 68.731771 \nL 322.318129 75.527402 \nL 322.32726 82.973815 \nL 322.403352 63.76181 \nL 322.424657 72.400994 \nL 322.436832 61.741169 \nL 322.500749 74.191789 \nL 322.534229 72.908075 \nL 322.555535 67.094757 \nL 322.63467 73.507825 \nL 322.643801 76.005266 \nL 322.668151 60.826993 \nL 322.729024 69.960575 \nL 322.735111 66.269035 \nL 322.802072 82.095268 \nL 322.826421 76.995583 \nL 322.829465 79.246831 \nL 322.850771 53.613806 \nL 322.926862 71.043617 \nL 322.957299 60.589214 \nL 323.015129 76.396367 \nL 323.039478 69.464811 \nL 323.069915 77.014992 \nL 323.109483 63.550064 \nL 323.155138 74.382466 \nL 323.164269 77.90402 \nL 323.182531 65.539949 \nL 323.252535 66.755242 \nL 323.282972 56.70195 \nL 323.313408 83.518514 \nL 323.346889 84.972448 \nL 323.386456 67.488495 \nL 323.410806 55.11715 \nL 323.456461 74.790022 \nL 323.496028 67.570609 \nL 323.499072 66.366464 \nL 323.566033 79.418227 \nL 323.587338 73.676291 \nL 323.611688 79.079856 \nL 323.632993 62.686196 \nL 323.690823 72.876826 \nL 323.706041 61.245278 \nL 323.803439 70.094508 \nL 323.870399 76.70494 \nL 323.888661 64.517821 \nL 323.894749 61.719768 \nL 323.916054 79.358528 \nL 323.99519 67.220474 \nL 324.001277 66.648344 \nL 324.013452 74.628148 \nL 324.025626 92.23759 \nL 324.116936 63.467614 \nL 324.162591 76.84293 \nL 324.229552 64.547887 \nL 324.241727 57.543389 \nL 324.281294 82.176961 \nL 324.333037 69.178708 \nL 324.403041 64.238604 \nL 324.357386 75.881293 \nL 324.42739 73.989379 \nL 324.436521 74.576058 \nL 324.448696 71.449495 \nL 324.552181 63.842878 \nL 324.503482 80.671297 \nL 324.561312 69.287443 \nL 324.573486 75.464489 \nL 324.664796 66.011551 \nL 324.740888 56.518785 \nL 324.719582 84.045048 \nL 324.771325 67.693052 \nL 324.844373 84.337027 \nL 324.886984 72.657377 \nL 324.963076 63.819659 \nL 324.981338 76.172185 \nL 325.005687 68.008739 \nL 325.078735 75.146006 \nL 325.042211 62.644139 \nL 325.112215 68.43278 \nL 325.203525 62.205552 \nL 325.179176 79.942336 \nL 325.218744 68.13605 \nL 325.237006 77.654881 \nL 325.270486 58.365523 \nL 325.34049 76.217525 \nL 325.346578 78.054567 \nL 325.383102 63.295828 \nL 325.437888 69.530286 \nL 325.480499 63.618624 \nL 325.544416 73.154575 \nL 325.54746 73.33594 \nL 325.574853 68.924103 \nL 325.663119 65.870437 \nL 325.596158 72.152969 \nL 325.67225 69.958209 \nL 325.687468 80.253766 \nL 325.76356 66.564324 \nL 325.778778 66.607793 \nL 325.815302 58.208559 \nL 325.839652 73.269962 \nL 325.860957 70.231003 \nL 325.876176 76.89774 \nL 325.937049 62.640341 \nL 325.964442 66.850183 \nL 326.03749 61.723554 \nL 325.988791 79.125009 \nL 326.058796 71.455526 \nL 326.131844 83.27213 \nL 326.086189 67.451675 \nL 326.171411 72.361456 \nL 326.229241 62.680005 \nL 326.210979 78.860325 \nL 326.280983 72.031091 \nL 326.290114 80.141273 \nL 326.332726 63.274348 \nL 326.393599 75.469809 \nL 326.500127 59.996133 \nL 326.430123 78.696869 \nL 326.515346 66.360263 \nL 326.542739 79.335563 \nL 326.600568 65.704089 \nL 326.664485 73.94378 \nL 326.743621 61.38231 \nL 326.694922 76.318135 \nL 326.783188 65.574881 \nL 326.822756 62.560155 \nL 326.831887 69.094817 \nL 326.868411 74.464814 \nL 326.947546 73.930232 \nL 327.005376 77.034013 \nL 326.974939 59.833136 \nL 327.0419 68.875518 \nL 327.090599 63.05255 \nL 327.066249 72.727397 \nL 327.148428 71.34587 \nL 327.254957 78.378463 \nL 327.197127 62.320419 \nL 327.261044 73.914781 \nL 327.273219 55.541779 \nL 327.291481 80.289864 \nL 327.370616 74.81426 \nL 327.401053 83.887897 \nL 327.495406 64.823479 \nL 327.547149 78.18579 \nL 327.571498 60.321048 \nL 327.629328 71.219862 \nL 327.717594 61.76466 \nL 327.696288 80.597447 \nL 327.748031 67.608856 \nL 327.906301 80.068606 \nL 327.787598 66.987092 \nL 327.915432 73.651881 \nL 328.01283 64.075225 \nL 327.994568 76.287313 \nL 328.031092 65.851012 \nL 328.034135 65.862645 \nL 328.177188 79.939289 \nL 328.085878 58.619249 \nL 328.183275 75.679389 \nL 328.277629 63.432202 \nL 328.250236 80.387306 \nL 328.298934 71.243353 \nL 328.317196 60.772533 \nL 328.365895 80.054094 \nL 328.414594 68.993909 \nL 328.429812 75.931586 \nL 328.463292 66.579227 \nL 328.524166 71.967844 \nL 328.539384 63.847348 \nL 328.588083 76.636976 \nL 328.636781 70.424689 \nL 328.737222 78.447705 \nL 328.746353 71.139997 \nL 328.831576 57.386467 \nL 328.792008 73.420019 \nL 328.855925 69.979985 \nL 328.858969 69.885809 \nL 328.865056 71.754935 \nL 328.886362 82.230605 \nL 328.907668 63.516679 \nL 328.971585 73.203585 \nL 329.056807 59.711178 \nL 329.075069 76.117439 \nL 329.087244 68.964226 \nL 329.102462 80.637827 \nL 329.120724 67.235308 \nL 329.205947 73.521327 \nL 329.272908 55.907129 \nL 329.278995 53.701816 \nL 329.321606 78.805976 \nL 329.367261 64.582092 \nL 329.458571 74.627444 \nL 329.482921 69.226587 \nL 329.495095 63.964201 \nL 329.555969 76.323217 \nL 329.586405 73.643644 \nL 329.677715 62.571443 \nL 329.644235 75.093463 \nL 329.68989 72.320955 \nL 329.702065 83.511803 \nL 329.741632 57.535566 \nL 329.796418 69.793061 \nL 329.817724 62.462203 \nL 329.854248 77.787928 \nL 329.899903 70.117638 \nL 329.933383 84.378106 \nL 329.951645 64.261342 \nL 330.006431 69.759386 \nL 330.097741 62.847355 \nL 330.076436 71.891005 \nL 330.11296 71.771993 \nL 330.143396 76.285757 \nL 330.164702 63.106941 \nL 330.207313 69.483496 \nL 330.283405 59.699522 \nL 330.259056 80.097483 \nL 330.301667 75.124139 \nL 330.313842 82.938784 \nL 330.359497 61.292726 \nL 330.408195 69.656796 \nL 330.432545 68.002323 \nL 330.438632 73.071884 \nL 330.447763 82.275505 \nL 330.493418 61.767188 \nL 330.539073 63.178618 \nL 330.542117 61.537442 \nL 330.557335 72.970033 \nL 330.630383 71.334749 \nL 330.672995 77.001392 \nL 330.691257 64.866744 \nL 330.739955 72.622786 \nL 330.834309 63.745123 \nL 330.806916 78.090323 \nL 330.852571 71.745073 \nL 330.864746 69.238175 \nL 330.925619 68.323315 \nL 330.937794 74.903673 \nL 330.943881 77.778008 \nL 330.986492 61.227215 \nL 331.041278 71.23563 \nL 331.068671 71.96701 \nL 331.056497 70.193436 \nL 331.074759 70.201948 \nL 331.132588 65.903245 \nL 331.169112 74.697904 \nL 331.181287 83.269285 \nL 331.239117 65.727354 \nL 331.275641 70.420796 \nL 331.303034 63.717606 \nL 331.327383 74.565612 \nL 331.3913 65.072593 \nL 331.485654 78.552276 \nL 331.433911 61.304583 \nL 331.500872 66.51526 \nL 331.506959 63.288346 \nL 331.528265 75.400929 \nL 331.6074 69.276006 \nL 331.622619 79.083073 \nL 331.650012 66.310412 \nL 331.720016 72.343919 \nL 331.81437 60.811098 \nL 331.741322 76.715506 \nL 331.832632 70.218087 \nL 331.930029 83.618506 \nL 331.896549 63.481175 \nL 331.942204 74.275213 \nL 332.054819 60.96261 \nL 332.012208 81.828331 \nL 332.060907 65.546742 \nL 332.152217 78.05224 \nL 332.106562 61.368823 \nL 332.170479 66.801363 \nL 332.173522 66.674529 \nL 332.197872 69.946389 \nL 332.231352 76.350722 \nL 332.252658 65.043462 \nL 332.310487 71.241953 \nL 332.353099 65.873151 \nL 332.334837 77.046931 \nL 332.410928 73.817341 \nL 332.496151 78.645369 \nL 332.435278 70.652051 \nL 332.5205 74.355289 \nL 332.633116 63.982433 \nL 332.660509 69.331643 \nL 332.675727 76.595823 \nL 332.712251 64.813408 \nL 332.76095 67.173797 \nL 332.767037 65.299749 \nL 332.812692 76.725564 \nL 332.861391 69.467626 \nL 332.873566 77.6401 \nL 332.922264 62.623218 \nL 332.980094 76.956851 \nL 333.062273 28.760238 \nL 333.074448 81.773715 \nL 333.086622 186.237444 \nL 333.165758 39.373967 \nL 333.180976 42.846762 \nL 333.27533 80.33798 \nL 333.30881 68.595135 \nL 333.311854 68.210326 \nL 333.336203 78.386596 \nL 333.415338 83.138136 \nL 333.390989 62.782724 \nL 333.424469 73.897826 \nL 333.45795 38.291649 \nL 333.49143 152.480587 \nL 333.497517 190.159184 \nL 333.543172 46.594037 \nL 333.588827 53.557343 \nL 333.716661 84.227511 \nL 333.61622 39.357805 \nL 333.725792 75.612969 \nL 333.734923 67.51198 \nL 333.835364 76.46905 \nL 333.838408 76.490053 \nL 333.935805 43.065968 \nL 333.944936 60.226194 \nL 333.963198 163.755775 \nL 334.033203 49.173856 \nL 334.054508 61.716311 \nL 334.087989 68.492928 \nL 334.103207 58.895869 \nL 334.109294 56.444379 \nL 334.167124 79.541144 \nL 334.18843 72.36446 \nL 334.231041 76.539039 \nL 334.246259 67.159399 \nL 334.285827 41.500673 \nL 334.301045 96.566547 \nL 334.31322 149.379953 \nL 334.380181 48.470722 \nL 334.40453 53.52897 \nL 334.407574 52.76935 \nL 334.450185 67.263617 \nL 334.514102 93.629712 \nL 334.474534 56.036082 \nL 334.562801 73.329947 \nL 334.696722 45.107042 \nL 334.699766 44.457339 \nL 334.708897 62.981547 \nL 334.730202 116.787575 \nL 334.809338 56.438831 \nL 334.818469 61.874852 \nL 334.906735 75.832732 \nL 334.845862 51.448554 \nL 334.934128 67.145926 \nL 334.982827 79.622186 \nL 335.025438 65.965514 \nL 335.061962 70.973256 \nL 335.144141 62.198616 \nL 335.119792 75.56673 \nL 335.177621 67.261457 \nL 335.208058 76.383717 \nL 335.2598 65.064763 \nL 335.290237 68.354523 \nL 335.332848 77.857343 \nL 335.372416 62.121874 \nL 335.390678 65.564767 \nL 335.402853 63.784422 \nL 335.424158 78.509807 \nL 335.430246 81.408463 \nL 335.451551 68.020717 \nL 335.518512 69.915043 \nL 335.59156 55.681032 \nL 335.570254 80.240414 \nL 335.62504 69.113109 \nL 335.725481 74.937145 \nL 335.670695 60.138925 \nL 335.734612 71.621064 \nL 335.844184 66.91374 \nL 335.798529 78.851909 \nL 335.847228 68.126447 \nL 335.877665 78.219076 \nL 335.892883 64.121545 \nL 335.953756 68.223803 \nL 335.968975 60.69399 \nL 336.020717 81.941126 \nL 336.063328 68.21039 \nL 336.093765 74.972935 \nL 336.115071 61.507448 \nL 336.148551 66.249891 \nL 336.154638 62.457166 \nL 336.23073 77.565157 \nL 336.248992 75.026373 \nL 336.367695 64.397086 \nL 336.279429 76.767739 \nL 336.37987 68.435824 \nL 336.498573 81.61743 \nL 336.416394 61.604469 \nL 336.50466 77.824835 \nL 336.586839 61.525987 \nL 336.611188 79.020192 \nL 336.62945 64.473449 \nL 336.644669 71.183725 \nL 336.684236 73.448988 \nL 336.726848 66.267914 \nL 336.799896 60.890529 \nL 336.763372 78.817584 \nL 336.81207 70.918388 \nL 336.839463 78.537523 \nL 336.888162 58.619255 \nL 336.918599 68.879481 \nL 336.96121 60.51612 \nL 336.985559 80.0714 \nL 336.988603 80.972464 \nL 337.034258 62.117658 \nL 337.061651 72.101367 \nL 337.073826 64.980239 \nL 337.125568 75.574003 \nL 337.174267 70.385392 \nL 337.277751 79.187261 \nL 337.232096 61.343755 \nL 337.286882 73.947709 \nL 337.396454 65.096743 \nL 337.402542 68.270232 \nL 337.490808 77.939851 \nL 337.45124 62.648645 \nL 337.506026 63.15407 \nL 337.50907 61.257525 \nL 337.576031 82.497903 \nL 337.603424 71.674281 \nL 337.649079 78.156401 \nL 337.685603 60.155096 \nL 337.709952 71.354355 \nL 337.822568 64.461106 \nL 337.804306 73.800544 \nL 337.828655 66.862144 \nL 337.87431 78.675257 \nL 337.919965 62.090571 \nL 337.93214 65.046607 \nL 337.941271 63.688895 \nL 337.956489 76.09642 \nL 337.996057 73.700338 \nL 338.002144 74.247748 \nL 338.02345 76.067884 \nL 338.044755 63.726465 \nL 338.081279 67.197831 \nL 338.087367 67.121585 \nL 338.093454 68.223579 \nL 338.136065 76.235985 \nL 338.178677 60.59791 \nL 338.193895 64.733683 \nL 338.20607 61.978097 \nL 338.248681 79.173522 \nL 338.282162 69.211744 \nL 338.394777 80.261977 \nL 338.373472 63.046377 \nL 338.403908 75.05218 \nL 338.501306 57.832702 \nL 338.458694 76.274293 \nL 338.522611 64.749793 \nL 338.525655 64.649999 \nL 338.534786 66.751513 \nL 338.562179 87.103826 \nL 338.60479 58.216462 \nL 338.62914 59.767366 \nL 338.635227 54.280278 \nL 338.702188 79.039784 \nL 338.729581 69.670867 \nL 338.763061 80.28345 \nL 338.747843 68.034192 \nL 338.848284 75.261638 \nL 338.893939 62.563059 \nL 338.963943 70.643253 \nL 338.997423 76.74346 \nL 339.018729 64.416161 \nL 339.033947 65.037474 \nL 339.046122 59.638025 \nL 339.067428 82.369332 \nL 339.131345 70.533273 \nL 339.213524 76.87881 \nL 339.158738 60.209383 \nL 339.228742 64.225409 \nL 339.234829 62.228349 \nL 339.289615 76.750901 \nL 339.323096 73.678558 \nL 339.35962 78.342083 \nL 339.402231 63.755934 \nL 339.417449 65.039383 \nL 339.435711 78.023327 \nL 339.457017 54.042448 \nL 339.530065 67.46621 \nL 339.627462 63.257034 \nL 339.587895 77.080765 \nL 339.636593 67.782659 \nL 339.691379 80.972885 \nL 339.746165 67.220277 \nL 339.773558 52.486338 \nL 339.843563 77.765019 \nL 339.889218 64.572184 \nL 339.898349 58.892102 \nL 339.944004 80.064793 \nL 340.001833 62.100651 \nL 340.03227 79.536787 \nL 340.117493 64.908582 \nL 340.199672 59.724304 \nL 340.18141 75.019145 \nL 340.227065 63.551668 \nL 340.330549 76.042379 \nL 340.33968 68.94565 \nL 340.424903 55.05256 \nL 340.367073 74.680045 \nL 340.449252 66.744798 \nL 340.482733 81.301303 \nL 340.531431 62.783817 \nL 340.561868 72.945438 \nL 340.641003 61.708928 \nL 340.622741 80.057913 \nL 340.674484 67.000871 \nL 340.768837 74.90853 \nL 340.744488 62.765044 \nL 340.784056 68.517816 \nL 340.790143 66.129929 \nL 340.811449 76.690725 \nL 340.88754 71.903516 \nL 340.896671 72.990267 \nL 340.936239 64.456966 \nL 340.969719 63.779326 \nL 340.954501 72.191699 \nL 340.987981 71.86675 \nL 341.000156 83.545079 \nL 341.021462 58.681625 \nL 341.097553 70.846666 \nL 341.124946 75.690084 \nL 341.164514 62.110911 \nL 341.18582 64.399559 \nL 341.197994 55.233791 \nL 341.2193 78.739905 \nL 341.286261 73.775148 \nL 341.325828 83.59191 \nL 341.31061 65.362278 \nL 341.386702 70.711254 \nL 341.420182 75.077691 \nL 341.511492 62.14943 \nL 341.52671 65.980635 \nL 341.535841 65.536859 \nL 341.639326 92.28857 \nL 341.593671 54.942053 \nL 341.672806 80.695615 \nL 341.688025 61.074291 \nL 341.794553 67.059245 \nL 341.809771 77.710081 \nL 341.879776 66.167258 \nL 341.901081 66.713337 \nL 341.989348 55.47609 \nL 341.919343 75.166364 \nL 342.001522 68.052181 \nL 342.022828 65.137291 \nL 342.117182 73.774455 \nL 342.187186 81.832041 \nL 342.16588 64.372003 \nL 342.226754 74.887563 \nL 342.31502 60.824347 \nL 342.354588 72.048021 \nL 342.385024 80.863268 \nL 342.430679 54.704026 \nL 342.46416 71.274233 \nL 342.482422 81.6111 \nL 342.55547 63.621217 \nL 342.628518 78.273315 \nL 342.689391 73.210546 \nL 342.80505 61.895234 \nL 342.780701 77.399504 \nL 342.817225 64.360356 \nL 342.935928 81.441235 \nL 342.945059 75.568442 \nL 342.966365 60.588739 \nL 343.057675 73.367676 \nL 343.158116 78.826568 \nL 343.094199 69.391676 \nL 343.164203 75.372141 \nL 343.176378 59.423479 \nL 343.279862 67.299129 \nL 343.28595 67.700827 \nL 343.401609 80.160447 \nL 343.358998 66.81782 \nL 343.41074 75.688158 \nL 343.505094 64.848392 \nL 343.520312 76.454369 \nL 343.523356 77.25434 \nL 343.544661 65.858444 \nL 343.605535 69.987222 \nL 343.693801 59.617286 \nL 343.62684 76.081431 \nL 343.724238 68.367451 \nL 343.748587 79.430519 \nL 343.766849 59.744224 \nL 343.83381 68.876887 \nL 343.915989 62.729858 \nL 343.894683 76.54679 \nL 343.937294 74.318003 \nL 343.955556 83.827423 \nL 344.019473 64.753504 \nL 344.065128 54.935103 \nL 344.101652 75.764971 \nL 344.10774 75.212566 \nL 344.214268 62.293466 \nL 344.168613 79.071456 \nL 344.238617 69.753197 \nL 344.250792 80.119567 \nL 344.284272 61.942959 \nL 344.351233 72.890583 \nL 344.363408 67.923647 \nL 344.418194 76.750771 \nL 344.466892 69.112665 \nL 344.482111 73.514557 \nL 344.515591 59.724805 \nL 344.561246 64.132589 \nL 344.582552 59.33912 \nL 344.594726 69.856722 \nL 344.616032 89.077988 \nL 344.682993 63.653755 \nL 344.704298 69.588603 \nL 344.716473 72.10774 \nL 344.734735 61.302444 \nL 344.798652 68.596174 \nL 344.807783 63.76376 \nL 344.868656 80.419902 \nL 344.911268 66.810513 \nL 344.914311 66.587353 \nL 344.932573 73.073794 \nL 345.02084 81.830747 \nL 344.956923 61.167822 \nL 345.029971 70.828027 \nL 345.063451 61.316795 \nL 345.084757 77.850894 \nL 345.139543 71.959832 \nL 345.157805 87.619229 \nL 345.176067 65.764925 \nL 345.233896 70.140263 \nL 345.246071 58.699859 \nL 345.313032 75.969186 \nL 345.343468 69.360811 \nL 345.355643 65.350183 \nL 345.36173 63.680329 \nL 345.383036 80.538218 \nL 345.449997 71.307822 \nL 345.523045 77.520489 \nL 345.547394 63.618259 \nL 345.550438 62.420699 \nL 345.608267 74.228625 \nL 345.63566 70.940467 \nL 345.672184 80.86487 \nL 345.690446 63.617809 \nL 345.75132 77.326317 \nL 345.766538 54.79258 \nL 345.866979 69.979082 \nL 345.958289 86.706459 \nL 345.918722 64.597262 \nL 345.970464 67.642595 \nL 345.976551 64.022254 \nL 345.99177 74.038643 \nL 346.076992 68.168429 \nL 346.195695 84.69036 \nL 346.146997 63.349358 \nL 346.204826 79.257284 \nL 346.247438 58.43924 \nL 346.320486 72.519279 \nL 346.366141 63.922974 \nL 346.405708 79.65717 \nL 346.445276 68.476304 \nL 346.457451 79.250691 \nL 346.518324 59.735275 \nL 346.548761 61.702177 \nL 346.560935 67.594859 \nL 346.57311 78.383178 \nL 346.594416 57.408525 \nL 346.676595 72.820173 \nL 346.679638 72.817436 \nL 346.761817 63.741801 \nL 346.783123 75.369673 \nL 346.792254 78.309938 \nL 346.81356 60.008817 \nL 346.88052 70.410337 \nL 346.953568 61.52831 \nL 346.93835 77.896672 \nL 346.990092 70.310703 \nL 346.999223 75.204181 \nL 347.06314 60.372786 \nL 347.102708 72.30821 \nL 347.160538 63.021946 \nL 347.124014 82.903595 \nL 347.209236 73.594341 \nL 347.260979 65.080102 \nL 347.291415 75.254254 \nL 347.36142 70.417456 \nL 347.467948 75.891809 \nL 347.407075 61.094543 \nL 347.474035 74.575602 \nL 347.513603 62.202389 \nL 347.586651 71.732006 \nL 347.687092 76.869506 \nL 347.632306 63.993174 \nL 347.696223 73.402561 \nL 347.781446 60.428668 \nL 347.76014 77.717552 \nL 347.81797 65.430599 \nL 347.912323 76.886843 \nL 347.891018 61.521545 \nL 347.924498 66.054159 \nL 347.951891 63.374343 \nL 347.994502 76.897463 \nL 348.018852 74.094491 \nL 348.052332 80.959781 \nL 348.094943 65.672889 \nL 348.110162 70.381685 \nL 348.12538 56.887518 \nL 348.195384 80.336101 \nL 348.201472 83.570066 \nL 348.225821 61.640783 \nL 348.286694 67.45346 \nL 348.308 58.990578 \nL 348.335393 74.056065 \nL 348.378004 67.07195 \nL 348.426703 80.109619 \nL 348.402354 65.618683 \nL 348.496707 75.139009 \nL 348.502795 76.129463 \nL 348.5241 64.095309 \nL 348.584974 71.509455 \nL 348.642803 90.854113 \nL 348.709764 61.086659 \nL 348.740201 56.730269 \nL 348.752375 62.608435 \nL 348.858904 78.901342 \nL 348.813249 62.277159 \nL 348.874122 72.110632 \nL 348.91369 77.916629 \nL 348.895428 63.811263 \nL 348.962388 69.596257 \nL 349.044567 60.616483 \nL 349.005 77.047438 \nL 349.068917 71.267629 \nL 349.087179 74.664862 \nL 349.105441 63.275858 \nL 349.111528 59.328362 \nL 349.175445 74.906804 \nL 349.205882 68.483789 \nL 349.248493 73.411358 \nL 349.275886 66.208851 \nL 349.321541 71.912821 \nL 349.425026 66.932265 \nL 349.351978 74.790353 \nL 349.431113 69.215602 \nL 349.446331 81.00494 \nL 349.488943 59.843581 \nL 349.543729 70.889898 \nL 349.561991 59.695501 \nL 349.568078 55.060321 \nL 349.592427 75.760019 \nL 349.659388 71.303144 \nL 349.759829 83.264729 \nL 349.67765 69.680381 \nL 349.772004 74.055995 \nL 349.790266 53.724082 \nL 349.875488 78.361701 \nL 349.881576 75.717664 \nL 349.930274 62.125082 \nL 349.975929 81.900851 \nL 349.988104 78.857693 \nL 350.082458 62.684552 \nL 350.118982 67.966824 \nL 350.216379 81.251246 \nL 350.164637 64.404691 \nL 350.243772 73.727929 \nL 350.307689 65.035557 \nL 350.3503 77.10887 \nL 350.356388 79.24277 \nL 350.380737 60.151692 \nL 350.447698 70.914928 \nL 350.459872 61.895543 \nL 350.481178 78.687185 \nL 350.554226 72.636061 \nL 350.59075 76.143503 \nL 350.62423 66.065811 \nL 350.663798 73.360681 \nL 350.758152 63.468742 \nL 350.73989 80.147778 \nL 350.791632 66.554881 \nL 350.910335 75.041369 \nL 350.825112 61.753704 \nL 350.913379 74.803164 \nL 350.931641 61.333661 \nL 350.962077 80.835848 \nL 351.019907 74.886008 \nL 351.032082 81.387602 \nL 351.083824 63.827278 \nL 351.111217 64.498884 \nL 351.120348 60.003412 \nL 351.150785 82.436667 \nL 351.220789 63.70761 \nL 351.223833 63.250758 \nL 351.242095 76.090376 \nL 351.251226 79.169766 \nL 351.272531 58.868715 \nL 351.339492 70.521243 \nL 351.376016 62.809576 \nL 351.397322 77.001982 \nL 351.458195 65.494685 \nL 351.476457 86.145278 \nL 351.570811 68.396505 \nL 351.619509 76.514856 \nL 351.598204 64.332743 \nL 351.637771 64.397365 \nL 351.646902 58.970109 \nL 351.726038 78.762802 \nL 351.747343 65.225689 \nL 351.805173 77.56061 \nL 351.838653 61.655255 \nL 351.859959 67.367344 \nL 351.939094 56.801153 \nL 351.899527 79.507072 \nL 351.951269 69.317753 \nL 351.972575 64.320046 \nL 352.073016 80.258399 \nL 352.082147 74.126276 \nL 352.112583 58.709875 \nL 352.194762 68.937537 \nL 352.23433 64.027813 \nL 352.252592 77.798148 \nL 352.273898 73.595297 \nL 352.286072 82.078158 \nL 352.316509 62.066516 \nL 352.380426 71.880159 \nL 352.432168 60.533248 \nL 352.477823 78.217263 \nL 352.499129 67.637756 \nL 352.556959 75.191584 \nL 352.581308 63.749225 \nL 352.605657 67.223794 \nL 352.611745 63.646868 \nL 352.663487 78.635219 \nL 352.706098 74.522892 \nL 352.742622 80.186216 \nL 352.785234 70.586191 \nL 352.812627 72.719198 \nL 352.903937 59.915015 \nL 352.836976 74.970569 \nL 352.919155 73.514071 \nL 352.96481 76.143471 \nL 352.943504 65.501103 \nL 353.02264 71.560391 \nL 353.129168 64.571733 \nL 353.107862 79.707052 \nL 353.138299 66.001109 \nL 353.186998 79.455129 \nL 353.20526 64.113878 \nL 353.290482 73.788032 \nL 353.357443 44.903049 \nL 353.375705 83.71599 \nL 353.384836 107.316297 \nL 353.473103 52.846223 \nL 353.476146 53.258323 \nL 353.631373 82.79007 \nL 353.637461 80.53934 \nL 353.728771 62.143058 \nL 353.762251 67.245155 \nL 353.780513 80.208559 \nL 353.84443 64.801754 \nL 353.868779 64.923551 \nL 354.005744 79.44107 \nL 353.947915 59.73823 \nL 354.017919 72.453046 \nL 354.124447 43.700735 \nL 354.06053 74.865497 \nL 354.133578 59.244145 \nL 354.148797 107.187515 \nL 354.24315 55.253657 \nL 354.246194 54.746765 \nL 354.288805 65.887303 \nL 354.361853 81.118687 \nL 354.319242 63.501897 \nL 354.398377 65.708534 \nL 354.401421 65.34415 \nL 354.431858 72.98933 \nL 354.462294 69.811056 \nL 354.553604 79.19887 \nL 354.538386 63.893793 \nL 354.577954 74.551521 \nL 354.651002 61.700189 \nL 354.605347 78.027222 \nL 354.6997 72.463186 \nL 354.760574 76.369028 \nL 354.733181 63.715522 \nL 354.772748 69.335646 \nL 354.784923 61.595846 \nL 354.821447 91.991062 \nL 354.879277 72.49793 \nL 354.918844 61.353031 \nL 354.970587 73.056125 \nL 355.001023 67.880376 \nL 355.007111 66.829821 \nL 355.046678 77.37942 \nL 355.071028 73.181546 \nL 355.159294 111.283653 \nL 355.134945 44.627872 \nL 355.183643 76.222704 \nL 355.277997 57.782885 \nL 355.195818 76.8314 \nL 355.296259 75.169256 \nL 355.335827 83.448015 \nL 355.387569 65.218064 \nL 355.390613 65.233666 \nL 355.561058 75.294161 \nL 355.570189 69.604095 \nL 355.57932 63.4466 \nL 355.63715 79.481162 \nL 355.682805 67.906462 \nL 355.698023 69.730051 \nL 355.713241 78.775691 \nL 355.731503 55.639036 \nL 355.804551 68.247472 \nL 355.813682 73.86987 \nL 355.81977 77.936288 \nL 355.908036 59.61959 \nL 355.917167 53.227181 \nL 355.968909 87.302095 \nL 356.00239 76.826604 \nL 356.096743 61.04744 \nL 356.151529 71.84243 \nL 356.16066 79.111479 \nL 356.178922 60.497666 \nL 356.258058 68.902511 \nL 356.352411 64.590795 \nL 356.3098 77.426597 \nL 356.364586 68.996637 \nL 356.446765 75.341422 \nL 356.40111 60.8875 \nL 356.468071 65.891951 \nL 356.471114 65.372743 \nL 356.519813 72.672579 \nL 356.547206 71.049417 \nL 356.592861 87.367848 \nL 356.614167 67.093406 \nL 356.620254 61.515875 \nL 356.687215 71.379932 \nL 356.723739 68.741148 \nL 356.787656 82.978697 \nL 356.76635 63.443741 \nL 356.833311 69.933119 \nL 356.845485 59.537412 \nL 356.906359 84.097811 \nL 356.952014 63.480323 \nL 356.955057 63.413098 \nL 356.958101 64.458257 \nL 357.049411 81.560926 \nL 357.067673 63.452955 \nL 357.070717 62.067353 \nL 357.119415 83.417425 \nL 357.168114 68.192763 \nL 357.177245 71.630646 \nL 357.216813 59.77858 \nL 357.28073 70.124693 \nL 357.353778 75.87458 \nL 357.326385 68.293429 \nL 357.396389 71.113375 \nL 357.408564 67.288623 \nL 357.423782 74.23082 \nL 357.509005 68.000589 \nL 357.575965 75.47814 \nL 357.539441 65.89234 \nL 357.618577 67.742928 \nL 357.688581 62.70647 \nL 357.649013 76.59411 \nL 357.703799 75.185577 \nL 357.706843 75.827117 \nL 357.746411 65.305401 \nL 357.798153 73.584482 \nL 357.889463 62.584792 \nL 357.868157 84.020442 \nL 357.913812 69.760854 \nL 357.965555 71.991207 \nL 357.98686 66.193397 \nL 357.995991 65.869246 \nL 358.008166 70.523422 \nL 358.017297 76.56356 \nL 358.108607 62.087321 \nL 358.129913 81.39582 \nL 358.184699 61.615842 \nL 358.239485 74.731969 \nL 358.257747 57.41862 \nL 358.276009 79.196153 \nL 358.355144 69.078967 \nL 358.382537 79.754797 \nL 358.400799 66.159897 \nL 358.46776 71.895191 \nL 358.479934 51.876647 \nL 358.568201 75.932021 \nL 358.580375 66.230574 \nL 358.641249 63.41458 \nL 358.616899 79.497294 \nL 358.677773 73.82805 \nL 358.68386 76.927799 \nL 358.702122 63.304468 \nL 358.77517 68.593226 \nL 358.805607 55.46234 \nL 358.829956 84.523102 \nL 358.869524 76.696138 \nL 358.872567 77.373596 \nL 358.918222 63.694183 \nL 358.933441 68.230969 \nL 358.948659 56.653165 \nL 358.979096 77.192093 \nL 359.036925 73.279932 \nL 359.128235 81.044946 \nL 359.094755 68.683102 \nL 359.14041 74.041088 \nL 359.249982 66.542153 \nL 359.234764 77.161224 \nL 359.256069 68.002638 \nL 359.359554 76.860097 \nL 359.298681 61.236242 \nL 359.371729 70.025705 \nL 359.386947 59.771606 \nL 359.411296 74.905148 \nL 359.481301 68.795824 \nL 359.575654 79.541829 \nL 359.508694 67.976376 \nL 359.603047 71.309774 \nL 359.72175 59.963043 \nL 359.651746 78.244349 \nL 359.727838 64.304923 \nL 359.834366 77.783941 \nL 359.743056 61.203939 \nL 359.846541 70.975197 \nL 359.892196 66.131475 \nL 359.873934 77.41638 \nL 359.950025 75.142966 \nL 359.959156 73.177403 \nL 360.071772 56.904465 \nL 360.023073 79.625942 \nL 360.080903 64.408601 \nL 360.096121 76.511246 \nL 360.187431 63.120852 \nL 360.190475 63.567975 \nL 360.242217 77.622085 \nL 360.260479 56.852987 \nL 360.309178 73.212918 \nL 360.41875 62.981281 \nL 360.391357 84.658449 \nL 360.427881 65.966562 \nL 360.467449 77.271853 \nL 360.491798 60.840408 \nL 360.54354 69.283433 \nL 360.637894 62.912862 \nL 360.610501 75.51937 \nL 360.653112 69.52272 \nL 360.765728 80.420205 \nL 360.732248 66.231499 \nL 360.774859 74.872212 \nL 360.787034 64.462511 \nL 360.805296 78.753375 \nL 360.887475 70.165293 \nL 360.948348 82.179597 \nL 360.987916 62.500025 \nL 360.997047 59.408945 \nL 361.064008 78.33685 \nL 361.088357 65.314293 \nL 361.121837 78.675796 \nL 361.210104 72.948782 \nL 361.228366 64.19004 \nL 361.292283 74.999626 \nL 361.313588 71.407354 \nL 361.404898 80.49641 \nL 361.344025 60.622298 \nL 361.420117 71.352548 \nL 361.459684 53.745514 \nL 361.511427 78.482575 \nL 361.535776 67.442993 \nL 361.587518 91.714029 \nL 361.560125 55.953891 \nL 361.657523 79.162462 \nL 361.675785 59.738374 \nL 361.785357 63.665697 \nL 361.87971 78.349584 \nL 361.897972 64.991354 \nL 361.977108 62.671224 \nL 361.93754 78.132985 \nL 361.992326 68.504441 \nL 362.02885 73.48936 \nL 362.080592 61.095846 \nL 362.083636 60.938457 \nL 362.089723 64.219954 \nL 362.107985 87.973864 \nL 362.196252 64.25998 \nL 362.266256 51.250199 \nL 362.24495 75.60375 \nL 362.30278 66.60252 \nL 362.430614 80.337044 \nL 362.366697 61.902555 \nL 362.458007 72.35273 \nL 362.494531 66.999259 \nL 362.54323 74.649489 \nL 362.549317 78.625008 \nL 362.637583 63.680858 \nL 362.640627 62.782436 \nL 362.686282 76.411162 \nL 362.728893 69.315355 \nL 362.820203 63.608485 \nL 362.771505 74.859606 \nL 362.832378 70.414388 \nL 362.920644 84.18768 \nL 362.899339 66.314525 \nL 362.944994 71.405162 \nL 362.972387 58.625822 \nL 362.978474 55.020284 \nL 362.996736 81.571779 \nL 363.081959 58.840985 \nL 363.085002 58.174489 \nL 363.133701 71.215275 \nL 363.21588 73.916453 \nL 363.161094 63.466075 \nL 363.225011 67.822077 \nL 363.24936 77.634286 \nL 363.34067 62.015298 \nL 363.343714 60.701725 \nL 363.36502 78.747319 \nL 363.422849 71.868993 \nL 363.43198 77.391096 \nL 363.471548 63.477438 \nL 363.526334 67.165976 \nL 363.559814 61.824954 \nL 363.587207 77.138096 \nL 363.632862 67.242228 \nL 363.666343 81.346341 \nL 363.742434 69.099916 \nL 363.85505 61.63584 \nL 363.809395 77.632385 \nL 363.861137 62.180476 \nL 363.873312 59.029759 \nL 363.882443 66.524096 \nL 363.918967 89.720212 \nL 363.97984 64.71276 \nL 363.995059 70.912009 \nL 364.019408 58.056244 \nL 364.080281 74.87161 \nL 364.101587 69.665968 \nL 364.183766 85.182539 \nL 364.150286 65.218862 \nL 364.217246 80.402416 \nL 364.348124 63.086617 \nL 364.351168 62.832481 \nL 364.360299 70.547968 \nL 364.466827 81.347306 \nL 364.424216 62.55732 \nL 364.479002 78.570458 \nL 364.530744 61.962675 \nL 364.55205 80.203017 \nL 364.622054 66.376422 \nL 364.661622 79.094184 \nL 364.719451 65.034083 \nL 364.743801 72.508999 \nL 364.838154 62.512264 \nL 364.762063 74.0321 \nL 364.853373 73.310063 \nL 364.862504 69.756383 \nL 364.944683 66.796777 \nL 364.956857 77.897998 \nL 364.959901 79.178779 \nL 364.990338 68.654595 \nL 365.045124 69.307263 \nL 365.072517 74.150071 \nL 365.163827 63.232933 \nL 365.16687 62.807921 \nL 365.188176 74.901201 \nL 365.221656 76.667276 \nL 365.209482 74.623652 \nL 365.227744 74.995372 \nL 365.309923 63.628206 \nL 365.340359 68.515751 \nL 365.446888 81.367873 \nL 365.389058 58.76253 \nL 365.456019 76.365635 \nL 365.544285 61.082837 \nL 365.480368 77.238823 \nL 365.571678 69.630716 \nL 365.626464 73.061751 \nL 365.602115 64.747044 \nL 365.669075 67.89861 \nL 365.687337 62.386311 \nL 365.711687 77.543168 \nL 365.769516 67.928415 \nL 365.781691 79.333336 \nL 365.799953 65.804941 \nL 365.882132 71.952542 \nL 365.924743 77.149884 \nL 365.943005 69.524774 \nL 366.061708 52.491277 \nL 366.022141 88.610089 \nL 366.067796 57.370229 \nL 366.143887 78.163589 \nL 366.095189 57.277594 \nL 366.189542 76.292944 \nL 366.268678 79.962629 \nL 366.244328 65.315706 \nL 366.302158 77.007621 \nL 366.429992 59.612862 \nL 366.439123 61.309471 \nL 366.487822 85.475933 \nL 366.533477 59.076005 \nL 366.570001 67.81524 \nL 366.603481 56.897142 \nL 366.643049 75.407434 \nL 366.667398 69.922747 \nL 366.783057 79.145121 \nL 366.761752 68.709703 \nL 366.786101 79.112171 \nL 366.874367 58.600434 \nL 366.913935 70.523556 \nL 366.971765 83.057436 \nL 366.938284 64.96202 \nL 367.01742 70.067884 \nL 367.026551 59.515924 \nL 367.126992 66.681121 \nL 367.236564 78.699315 \nL 367.242651 74.728288 \nL 367.32483 57.787657 \nL 367.267 77.95249 \nL 367.361354 65.410247 \nL 367.452664 81.769728 \nL 367.428315 62.814425 \nL 367.480057 72.997108 \nL 367.519625 80.086992 \nL 367.543974 63.934922 \nL 367.568323 68.846448 \nL 367.617022 66.876808 \nL 367.601804 73.999124 \nL 367.635284 73.726685 \nL 367.671808 78.186748 \nL 367.653546 70.173962 \nL 367.680939 70.861843 \nL 367.69007 61.50525 \nL 367.753987 83.889678 \nL 367.787467 74.685319 \nL 367.869646 59.418959 \nL 367.887908 75.624284 \nL 367.970087 79.826062 \nL 367.927476 69.130868 \nL 367.982262 70.064309 \nL 368.094878 66.228145 \nL 368.049223 78.269441 \nL 368.097921 66.528705 \nL 368.195319 83.547426 \nL 368.149664 60.056899 \nL 368.225755 71.405803 \nL 368.304891 63.465319 \nL 368.277498 74.926891 \nL 368.338371 70.247739 \nL 368.411419 76.078231 \nL 368.36272 62.985807 \nL 368.450987 73.471129 \nL 368.514904 61.297937 \nL 368.533166 77.656566 \nL 368.554471 76.335602 \nL 368.557515 77.244553 \nL 368.612301 61.353842 \nL 368.642738 71.688412 \nL 368.661 60.362605 \nL 368.71883 79.226841 \nL 368.758397 67.80694 \nL 368.773616 78.719303 \nL 368.81014 62.109059 \nL 368.880144 72.48903 \nL 368.937974 79.134209 \nL 368.916668 64.754361 \nL 368.974498 69.596824 \nL 369.074939 60.084065 \nL 369.023196 81.738804 \nL 369.08407 66.260068 \nL 369.157118 78.36018 \nL 369.187554 63.759077 \nL 369.193642 64.864819 \nL 369.230166 83.22155 \nL 369.251471 61.583467 \nL 369.318432 73.460426 \nL 369.330607 65.160963 \nL 369.431048 71.212531 \nL 369.473659 60.534865 \nL 369.491921 74.853603 \nL 369.528445 71.664417 \nL 369.610624 74.365465 \nL 369.625842 67.456819 \nL 369.69889 58.337489 \nL 369.677585 76.425648 \nL 369.711065 70.597726 \nL 369.802375 77.297935 \nL 369.771938 66.21212 \nL 369.823681 74.735212 \nL 369.826724 75.835689 \nL 369.890641 61.252995 \nL 369.911947 70.555295 \nL 369.924122 59.636972 \nL 369.975864 86.231275 \nL 370.021519 69.794691 \nL 370.128047 82.431099 \nL 370.076305 59.562958 \nL 370.137178 73.36565 \nL 370.21327 57.820895 \nL 370.237619 91.329515 \nL 370.322842 59.423284 \nL 370.398934 60.691984 \nL 370.401977 60.009421 \nL 370.420239 78.113614 \nL 370.423283 80.312348 \nL 370.508506 63.875383 \nL 370.511549 63.058436 \nL 370.535899 86.162159 \nL 370.572423 75.319058 \nL 370.57851 77.212049 \nL 370.663733 65.380584 \nL 370.770261 58.174483 \nL 370.724606 76.033645 \nL 370.776348 62.91366 \nL 370.79461 80.35878 \nL 370.892008 71.450622 \nL 371.007667 81.116708 \nL 370.962012 57.583441 \nL 371.013754 80.888771 \nL 371.1842 58.607098 \nL 371.193331 62.274239 \nL 371.242029 78.881342 \nL 371.318121 72.785709 \nL 371.321165 72.999378 \nL 371.336383 68.208534 \nL 371.360732 68.472329 \nL 371.369863 66.731042 \nL 371.391169 78.974411 \nL 371.45813 70.724934 \nL 371.540309 78.834021 \nL 371.488566 61.048984 \nL 371.567702 69.988321 \nL 371.67423 62.481522 \nL 371.655968 73.433093 \nL 371.680317 65.082405 \nL 371.692492 80.95303 \nL 371.710754 64.506407 \nL 371.79902 80.42304 \nL 371.802064 80.472989 \nL 371.805108 79.275109 \nL 371.884243 60.117617 \nL 371.905549 80.873091 \nL 371.926854 72.357784 \nL 372.00599 62.82486 \nL 371.987728 75.63623 \nL 372.042514 71.036311 \nL 372.13078 79.518682 \nL 372.075994 60.938267 \nL 372.139911 71.757331 \nL 372.228177 64.3297 \nL 372.18861 78.795938 \nL 372.246439 74.12957 \nL 372.377317 62.781695 \nL 372.276876 80.317383 \nL 372.386448 69.96173 \nL 372.492976 78.425179 \nL 372.447321 64.625163 \nL 372.499064 72.062399 \nL 372.508195 61.115532 \nL 372.578199 81.861289 \nL 372.611679 66.413306 \nL 372.724295 76.753253 \nL 372.696902 63.357867 \nL 372.733426 73.3041 \nL 372.821692 60.013875 \nL 372.800387 78.418108 \nL 372.849085 67.65208 \nL 372.870391 69.714737 \nL 372.955614 76.999077 \nL 372.967788 64.758201 \nL 372.973876 59.510089 \nL 373.022574 78.93469 \nL 373.068229 71.729037 \nL 373.10171 82.821716 \nL 373.150408 63.274231 \nL 373.177801 71.795719 \nL 373.211282 62.560715 \nL 373.229544 73.880381 \nL 373.287373 71.516579 \nL 373.342159 67.276455 \nL 373.363465 77.986858 \nL 373.384771 73.016217 \nL 373.476081 78.796394 \nL 373.415207 60.815435 \nL 373.488255 69.144037 \nL 373.594784 63.422426 \nL 373.579565 77.751046 \nL 373.600871 67.879134 \nL 373.613046 81.861487 \nL 373.707399 60.355697 \nL 373.710443 60.072088 \nL 373.728705 67.688649 \nL 373.737836 67.673421 \nL 373.765229 81.675987 \nL 373.789578 66.580689 \nL 373.853495 70.368046 \nL 373.932631 62.714561 \nL 373.95698 75.389006 \nL 373.960024 75.762214 \nL 374.005679 69.059494 \nL 374.030028 60.913012 \nL 374.063508 79.43348 \nL 374.115251 66.559168 \nL 374.179168 78.364799 \nL 374.157862 56.709176 \nL 374.227866 68.031305 \nL 374.243085 57.279871 \nL 374.291783 82.129359 \nL 374.334395 71.484196 \nL 374.364831 79.431914 \nL 374.401355 66.938394 \nL 374.410486 61.58797 \nL 374.434836 74.813151 \nL 374.507884 69.429948 \nL 374.535277 61.081271 \nL 374.583975 74.412188 \nL 374.614412 70.359638 \nL 374.657023 78.688633 \nL 374.672242 65.76822 \nL 374.681373 58.833642 \nL 374.696591 72.687789 \nL 374.77877 70.105052 \nL 374.806163 79.191314 \nL 374.821381 67.103291 \nL 374.827469 64.215361 \nL 374.845731 76.747072 \nL 374.924866 69.671899 \nL 375.031394 78.034531 \nL 374.991827 61.370015 \nL 375.037482 73.847404 \nL 375.122704 65.337025 \nL 375.140966 78.271776 \nL 375.14401 78.096762 \nL 375.271844 64.46156 \nL 375.287062 66.543909 \nL 375.332717 65.322782 \nL 375.314455 79.610161 \nL 375.36011 71.780529 \nL 375.366198 74.106539 \nL 375.427071 63.537191 \nL 375.454464 63.869603 \nL 375.466639 72.838495 \nL 375.579254 80.140391 \nL 375.521425 61.633714 \nL 375.582298 80.118887 \nL 375.743612 63.913532 \nL 375.758831 69.445191 \nL 375.844053 76.186196 \nL 375.795355 59.941242 \nL 375.862315 68.498057 \nL 375.974931 62.637737 \nL 375.889708 77.809368 \nL 375.977975 63.541478 \nL 375.996237 79.995666 \nL 376.02363 62.031886 \nL 376.093634 69.565204 \nL 376.160595 60.61057 \nL 376.11494 79.469172 \nL 376.212337 65.117356 \nL 376.282342 73.922764 \nL 376.23973 58.741573 \nL 376.324953 66.384969 \nL 376.398001 81.975579 \nL 376.413219 61.409754 \nL 376.416263 61.017065 \nL 376.425394 71.41287 \nL 376.434525 76.555392 \nL 376.468005 65.775325 \nL 376.528879 66.931711 \nL 376.565403 63.615116 \nL 376.547141 75.30291 \nL 376.626276 74.535255 \nL 376.635407 72.567154 \nL 376.687149 57.213848 \nL 376.705411 76.359476 \nL 376.760197 65.561729 \nL 376.888031 82.394851 \nL 376.833245 64.273084 \nL 376.894119 80.602114 \nL 377.015865 60.366523 \nL 377.021953 62.031389 \nL 377.113263 80.187576 \nL 377.146743 70.124143 \nL 377.180223 64.231005 \nL 377.195442 74.845491 \nL 377.250228 71.273172 \nL 377.329363 75.339892 \nL 377.350669 66.217591 \nL 377.441979 78.495984 \nL 377.399367 62.755591 \nL 377.454153 66.015869 \nL 377.533289 63.275322 \nL 377.515027 72.528118 \nL 377.557638 69.153689 \nL 377.560682 69.063215 \nL 377.600249 71.000663 \nL 377.63373 74.766085 \nL 377.618511 68.172686 \nL 377.661123 69.621236 \nL 377.670254 68.347155 \nL 377.697647 77.160487 \nL 377.709821 84.821378 \nL 377.752433 57.566454 \nL 377.795044 70.504639 \nL 377.834612 64.101467 \nL 377.852874 77.666707 \nL 377.904616 71.505469 \nL 378.023319 57.318843 \nL 377.968533 78.740673 \nL 378.03245 65.305366 \nL 378.154197 80.513027 \nL 378.050712 59.994512 \nL 378.160284 77.837494 \nL 378.221157 53.888801 \nL 378.260725 87.664165 \nL 378.263769 88.90635 \nL 378.309424 67.526842 \nL 378.324642 69.460248 \nL 378.394646 55.375597 \nL 378.418996 71.479815 \nL 378.449432 76.927782 \nL 378.492044 61.456084 \nL 378.498131 62.325178 \nL 378.525524 77.287743 \nL 378.619878 67.915622 \nL 378.635096 70.621376 \nL 378.656402 65.395877 \nL 378.759886 61.166674 \nL 378.7051 85.198407 \nL 378.769017 62.151658 \nL 378.863371 78.022935 \nL 378.802498 59.430019 \nL 378.902939 68.420127 \nL 378.988161 62.726691 \nL 378.960768 81.637417 \nL 379.00338 74.726637 \nL 379.042947 77.751475 \nL 379.021642 63.949608 \nL 379.079471 66.9025 \nL 379.09469 59.823662 \nL 379.115995 81.047467 \nL 379.155563 72.555126 \nL 379.204262 85.602783 \nL 379.249917 66.143482 \nL 379.25296 66.026712 \nL 379.265135 68.881581 \nL 379.383838 88.321628 \nL 379.289484 56.079102 \nL 379.392969 79.517606 \nL 379.511672 61.56698 \nL 379.514716 61.691661 \nL 379.566458 81.755221 \nL 379.58472 61.205629 \nL 379.636462 66.013172 \nL 379.639506 65.808015 \nL 379.657768 70.553148 \nL 379.739947 78.028709 \nL 379.688205 63.133534 \nL 379.770384 74.290173 \nL 379.809951 62.108687 \nL 379.855606 74.541415 \nL 379.889087 72.78174 \nL 379.922567 78.374039 \nL 379.989528 65.703078 \nL 380.026052 62.622157 \nL 380.00779 71.119215 \nL 380.050401 67.815179 \nL 380.120405 78.780596 \nL 380.102143 64.48618 \nL 380.163017 70.510374 \nL 380.248239 66.703743 \nL 380.229977 75.237115 \nL 380.260414 71.764613 \nL 380.287807 77.732671 \nL 380.303025 60.593598 \nL 380.336506 70.56496 \n\" style=\"fill:none;stroke:#8dd3c7;stroke-linecap:round;stroke-width:1.5;\"/>\n </g>\n <g id=\"patch_3\">\n <path d=\"M 60.754688 224.64 \nL 60.754688 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n </g>\n <g id=\"patch_4\">\n <path d=\"M 395.554688 224.64 \nL 395.554688 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n </g>\n <g id=\"patch_5\">\n <path d=\"M 60.754688 224.64 \nL 395.554688 224.64 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n </g>\n <g id=\"patch_6\">\n <path d=\"M 60.754688 7.2 \nL 395.554688 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n </g>\n </g>\n </g>\n <defs>\n <clipPath id=\"pd56fccd151\">\n <rect height=\"217.44\" width=\"334.8\" x=\"60.754688\" y=\"7.2\"/>\n </clipPath>\n </defs>\n</svg>\n","text/plain":"<Figure size 432x288 with 1 Axes>"},"metadata":{},"output_type":"display_data"}],"source":"plot(x[:100000])"},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[],"source":"from spiketag.base.Binload import _deconvolve"},{"cell_type":"code","execution_count":16,"metadata":{},"outputs":[],"source":"y = _deconvolve(x, kernel)"},{"cell_type":"code","execution_count":18,"metadata":{},"outputs":[{"data":{"text/plain":"torch.Size([11600049])"},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":"y.shape"},{"cell_type":"code","execution_count":19,"metadata":{},"outputs":[{"data":{"text/plain":"[<matplotlib.lines.Line2D at 0x7fbbe41445c0>]"},"execution_count":19,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","image/svg+xml":"<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"248.518125pt\" version=\"1.1\" viewBox=\"0 0 406.627049 248.518125\" width=\"406.627049pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;white-space:pre;}\n </style>\n </defs>\n <g id=\"figure_1\">\n <g id=\"patch_1\">\n <path d=\"M 0 248.518125 \nL 406.627049 248.518125 \nL 406.627049 0 \nL 0 0 \nz\n\" style=\"fill:#ffffff;\"/>\n </g>\n <g id=\"axes_1\">\n <g id=\"patch_2\">\n <path d=\"M 60.754688 224.64 \nL 395.554688 224.64 \nL 395.554688 7.2 \nL 60.754688 7.2 \nz\n\" style=\"fill:#d9d9d9;\"/>\n </g>\n <g id=\"matplotlib.axis_1\">\n <g id=\"xtick_1\">\n <g id=\"line2d_1\">\n <path clip-path=\"url(#p10c26b41e1)\" d=\"M 75.972869 224.64 \nL 75.972869 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_1\">\n <!-- 0 -->\n <defs>\n <path d=\"M 31.78125 66.40625 \nQ 24.171875 66.40625 20.328125 58.90625 \nQ 16.5 51.421875 16.5 36.375 \nQ 16.5 21.390625 20.328125 13.890625 \nQ 24.171875 6.390625 31.78125 6.390625 \nQ 39.453125 6.390625 43.28125 13.890625 \nQ 47.125 21.390625 47.125 36.375 \nQ 47.125 51.421875 43.28125 58.90625 \nQ 39.453125 66.40625 31.78125 66.40625 \nz\nM 31.78125 74.21875 \nQ 44.046875 74.21875 50.515625 64.515625 \nQ 56.984375 54.828125 56.984375 36.375 \nQ 56.984375 17.96875 50.515625 8.265625 \nQ 44.046875 -1.421875 31.78125 -1.421875 \nQ 19.53125 -1.421875 13.0625 8.265625 \nQ 6.59375 17.96875 6.59375 36.375 \nQ 6.59375 54.828125 13.0625 64.515625 \nQ 19.53125 74.21875 31.78125 74.21875 \nz\n\" id=\"DejaVuSans-48\"/>\n </defs>\n <g style=\"fill:#262626;\" transform=\"translate(72.791619 239.238438)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"xtick_2\">\n <g id=\"line2d_2\">\n <path clip-path=\"url(#p10c26b41e1)\" d=\"M 136.846205 224.64 \nL 136.846205 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_2\">\n <!-- 20000 -->\n <defs>\n <path d=\"M 19.1875 8.296875 \nL 53.609375 8.296875 \nL 53.609375 0 \nL 7.328125 0 \nL 7.328125 8.296875 \nQ 12.9375 14.109375 22.625 23.890625 \nQ 32.328125 33.6875 34.8125 36.53125 \nQ 39.546875 41.84375 41.421875 45.53125 \nQ 43.3125 49.21875 43.3125 52.78125 \nQ 43.3125 58.59375 39.234375 62.25 \nQ 35.15625 65.921875 28.609375 65.921875 \nQ 23.96875 65.921875 18.8125 64.3125 \nQ 13.671875 62.703125 7.8125 59.421875 \nL 7.8125 69.390625 \nQ 13.765625 71.78125 18.9375 73 \nQ 24.125 74.21875 28.421875 74.21875 \nQ 39.75 74.21875 46.484375 68.546875 \nQ 53.21875 62.890625 53.21875 53.421875 \nQ 53.21875 48.921875 51.53125 44.890625 \nQ 49.859375 40.875 45.40625 35.40625 \nQ 44.1875 33.984375 37.640625 27.21875 \nQ 31.109375 20.453125 19.1875 8.296875 \nz\n\" id=\"DejaVuSans-50\"/>\n </defs>\n <g style=\"fill:#262626;\" transform=\"translate(120.939955 239.238438)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-50\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"254.492188\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"xtick_3\">\n <g id=\"line2d_3\">\n <path clip-path=\"url(#p10c26b41e1)\" d=\"M 197.719541 224.64 \nL 197.719541 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_3\">\n <!-- 40000 -->\n <defs>\n <path d=\"M 37.796875 64.3125 \nL 12.890625 25.390625 \nL 37.796875 25.390625 \nz\nM 35.203125 72.90625 \nL 47.609375 72.90625 \nL 47.609375 25.390625 \nL 58.015625 25.390625 \nL 58.015625 17.1875 \nL 47.609375 17.1875 \nL 47.609375 0 \nL 37.796875 0 \nL 37.796875 17.1875 \nL 4.890625 17.1875 \nL 4.890625 26.703125 \nz\n\" id=\"DejaVuSans-52\"/>\n </defs>\n <g style=\"fill:#262626;\" transform=\"translate(181.813291 239.238438)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-52\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"254.492188\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"xtick_4\">\n <g id=\"line2d_4\">\n <path clip-path=\"url(#p10c26b41e1)\" d=\"M 258.592877 224.64 \nL 258.592877 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_4\">\n <!-- 60000 -->\n <defs>\n <path d=\"M 33.015625 40.375 \nQ 26.375 40.375 22.484375 35.828125 \nQ 18.609375 31.296875 18.609375 23.390625 \nQ 18.609375 15.53125 22.484375 10.953125 \nQ 26.375 6.390625 33.015625 6.390625 \nQ 39.65625 6.390625 43.53125 10.953125 \nQ 47.40625 15.53125 47.40625 23.390625 \nQ 47.40625 31.296875 43.53125 35.828125 \nQ 39.65625 40.375 33.015625 40.375 \nz\nM 52.59375 71.296875 \nL 52.59375 62.3125 \nQ 48.875 64.0625 45.09375 64.984375 \nQ 41.3125 65.921875 37.59375 65.921875 \nQ 27.828125 65.921875 22.671875 59.328125 \nQ 17.53125 52.734375 16.796875 39.40625 \nQ 19.671875 43.65625 24.015625 45.921875 \nQ 28.375 48.1875 33.59375 48.1875 \nQ 44.578125 48.1875 50.953125 41.515625 \nQ 57.328125 34.859375 57.328125 23.390625 \nQ 57.328125 12.15625 50.6875 5.359375 \nQ 44.046875 -1.421875 33.015625 -1.421875 \nQ 20.359375 -1.421875 13.671875 8.265625 \nQ 6.984375 17.96875 6.984375 36.375 \nQ 6.984375 53.65625 15.1875 63.9375 \nQ 23.390625 74.21875 37.203125 74.21875 \nQ 40.921875 74.21875 44.703125 73.484375 \nQ 48.484375 72.75 52.59375 71.296875 \nz\n\" id=\"DejaVuSans-54\"/>\n </defs>\n <g style=\"fill:#262626;\" transform=\"translate(242.686627 239.238438)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-54\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"254.492188\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"xtick_5\">\n <g id=\"line2d_5\">\n <path clip-path=\"url(#p10c26b41e1)\" d=\"M 319.466213 224.64 \nL 319.466213 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_5\">\n <!-- 80000 -->\n <defs>\n <path d=\"M 31.78125 34.625 \nQ 24.75 34.625 20.71875 30.859375 \nQ 16.703125 27.09375 16.703125 20.515625 \nQ 16.703125 13.921875 20.71875 10.15625 \nQ 24.75 6.390625 31.78125 6.390625 \nQ 38.8125 6.390625 42.859375 10.171875 \nQ 46.921875 13.96875 46.921875 20.515625 \nQ 46.921875 27.09375 42.890625 30.859375 \nQ 38.875 34.625 31.78125 34.625 \nz\nM 21.921875 38.8125 \nQ 15.578125 40.375 12.03125 44.71875 \nQ 8.5 49.078125 8.5 55.328125 \nQ 8.5 64.0625 14.71875 69.140625 \nQ 20.953125 74.21875 31.78125 74.21875 \nQ 42.671875 74.21875 48.875 69.140625 \nQ 55.078125 64.0625 55.078125 55.328125 \nQ 55.078125 49.078125 51.53125 44.71875 \nQ 48 40.375 41.703125 38.8125 \nQ 48.828125 37.15625 52.796875 32.3125 \nQ 56.78125 27.484375 56.78125 20.515625 \nQ 56.78125 9.90625 50.3125 4.234375 \nQ 43.84375 -1.421875 31.78125 -1.421875 \nQ 19.734375 -1.421875 13.25 4.234375 \nQ 6.78125 9.90625 6.78125 20.515625 \nQ 6.78125 27.484375 10.78125 32.3125 \nQ 14.796875 37.15625 21.921875 38.8125 \nz\nM 18.3125 54.390625 \nQ 18.3125 48.734375 21.84375 45.5625 \nQ 25.390625 42.390625 31.78125 42.390625 \nQ 38.140625 42.390625 41.71875 45.5625 \nQ 45.3125 48.734375 45.3125 54.390625 \nQ 45.3125 60.0625 41.71875 63.234375 \nQ 38.140625 66.40625 31.78125 66.40625 \nQ 25.390625 66.40625 21.84375 63.234375 \nQ 18.3125 60.0625 18.3125 54.390625 \nz\n\" id=\"DejaVuSans-56\"/>\n </defs>\n <g style=\"fill:#262626;\" transform=\"translate(303.559963 239.238438)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-56\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"254.492188\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"xtick_6\">\n <g id=\"line2d_6\">\n <path clip-path=\"url(#p10c26b41e1)\" d=\"M 380.339549 224.64 \nL 380.339549 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_6\">\n <!-- 100000 -->\n <defs>\n <path d=\"M 12.40625 8.296875 \nL 28.515625 8.296875 \nL 28.515625 63.921875 \nL 10.984375 60.40625 \nL 10.984375 69.390625 \nL 28.421875 72.90625 \nL 38.28125 72.90625 \nL 38.28125 8.296875 \nL 54.390625 8.296875 \nL 54.390625 0 \nL 12.40625 0 \nz\n\" id=\"DejaVuSans-49\"/>\n </defs>\n <g style=\"fill:#262626;\" transform=\"translate(361.252049 239.238438)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-49\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"254.492188\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"318.115234\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n </g>\n <g id=\"matplotlib.axis_2\">\n <g id=\"ytick_1\">\n <g id=\"line2d_7\">\n <path clip-path=\"url(#p10c26b41e1)\" d=\"M 60.754688 205.359454 \nL 395.554688 205.359454 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_7\">\n <!-- −400000 -->\n <defs>\n <path d=\"M 10.59375 35.5 \nL 73.1875 35.5 \nL 73.1875 27.203125 \nL 10.59375 27.203125 \nz\n\" id=\"DejaVuSans-8722\"/>\n </defs>\n <g style=\"fill:#262626;\" transform=\"translate(7.2 209.158672)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-8722\"/>\n <use x=\"83.789062\" xlink:href=\"#DejaVuSans-52\"/>\n <use x=\"147.412109\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"211.035156\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"274.658203\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"338.28125\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"401.904297\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"ytick_2\">\n <g id=\"line2d_8\">\n <path clip-path=\"url(#p10c26b41e1)\" d=\"M 60.754688 158.827715 \nL 395.554688 158.827715 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_8\">\n <!-- −200000 -->\n <g style=\"fill:#262626;\" transform=\"translate(7.2 162.626934)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-8722\"/>\n <use x=\"83.789062\" xlink:href=\"#DejaVuSans-50\"/>\n <use x=\"147.412109\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"211.035156\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"274.658203\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"338.28125\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"401.904297\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"ytick_3\">\n <g id=\"line2d_9\">\n <path clip-path=\"url(#p10c26b41e1)\" d=\"M 60.754688 112.295977 \nL 395.554688 112.295977 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_9\">\n <!-- 0 -->\n <g style=\"fill:#262626;\" transform=\"translate(47.392188 116.095196)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"ytick_4\">\n <g id=\"line2d_10\">\n <path clip-path=\"url(#p10c26b41e1)\" d=\"M 60.754688 65.764239 \nL 395.554688 65.764239 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_10\">\n <!-- 200000 -->\n <g style=\"fill:#262626;\" transform=\"translate(15.579687 69.563458)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-50\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"254.492188\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"318.115234\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n <g id=\"ytick_5\">\n <g id=\"line2d_11\">\n <path clip-path=\"url(#p10c26b41e1)\" d=\"M 60.754688 19.232501 \nL 395.554688 19.232501 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:round;stroke-width:0.8;\"/>\n </g>\n <g id=\"text_11\">\n <!-- 400000 -->\n <g style=\"fill:#262626;\" transform=\"translate(15.579687 23.03172)scale(0.1 -0.1)\">\n <use xlink:href=\"#DejaVuSans-52\"/>\n <use x=\"63.623047\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"127.246094\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"190.869141\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"254.492188\" xlink:href=\"#DejaVuSans-48\"/>\n <use x=\"318.115234\" xlink:href=\"#DejaVuSans-48\"/>\n </g>\n </g>\n </g>\n </g>\n <g id=\"line2d_12\">\n <path clip-path=\"url(#p10c26b41e1)\" d=\"M 75.972869 112.294957 \nL 75.982 112.326508 \nL 75.985044 112.219649 \nL 75.988088 112.294905 \nL 76.106791 124.749774 \nL 76.012437 111.785619 \nL 76.109834 122.235148 \nL 76.13114 121.886802 \nL 76.122009 123.810549 \nL 76.134184 123.202574 \nL 76.173751 129.693494 \nL 76.192013 116.993066 \nL 76.240712 122.819077 \nL 76.341153 111.153885 \nL 76.249843 127.289925 \nL 76.353328 120.815188 \nL 76.356371 121.019517 \nL 76.374633 115.703885 \nL 76.377677 114.811978 \nL 76.402026 122.842821 \nL 76.4629 120.072134 \nL 76.548122 131.526003 \nL 76.514642 118.621718 \nL 76.578559 126.622416 \nL 76.615083 126.279661 \nL 76.58769 128.152878 \nL 76.618127 128.132037 \nL 76.64552 131.893737 \nL 76.660738 121.363805 \nL 76.718568 123.655343 \nL 76.782485 118.051943 \nL 76.77031 127.494933 \nL 76.831183 121.280448 \nL 76.86162 117.024921 \nL 76.852489 123.822321 \nL 76.864664 119.476003 \nL 76.980323 130.537026 \nL 77.065546 132.569483 \nL 77.038153 126.495404 \nL 77.07772 129.292785 \nL 77.086851 124.524445 \nL 77.141637 133.926125 \nL 77.187292 125.490083 \nL 77.290777 129.517568 \nL 77.257297 121.972149 \nL 77.299908 127.159218 \nL 77.305995 123.336272 \nL 77.406436 131.039267 \nL 77.44296 135.100666 \nL 77.485572 128.291749 \nL 77.516008 130.233742 \nL 77.601231 119.395173 \nL 77.634711 123.846589 \nL 77.637755 123.434947 \nL 77.677323 127.91539 \nL 77.726021 125.08408 \nL 77.796026 129.938117 \nL 77.802113 121.598383 \nL 77.838637 126.50599 \nL 77.841681 126.489113 \nL 77.847768 126.590675 \nL 77.893423 121.339312 \nL 77.884292 129.735277 \nL 77.960384 124.87541 \nL 77.963427 126.799593 \nL 78.04865 116.28722 \nL 78.057781 120.815038 \nL 78.060825 115.884105 \nL 78.155178 123.412662 \nL 78.164309 120.815123 \nL 78.255619 127.915255 \nL 78.273881 120.497392 \nL 78.383453 126.854526 \nL 78.386497 125.515074 \nL 78.462589 120.283349 \nL 78.404759 128.283337 \nL 78.502156 120.678842 \nL 78.639121 129.364561 \nL 78.642165 129.322907 \nL 78.657383 129.238679 \nL 78.660427 129.683568 \nL 78.693907 134.024167 \nL 78.718257 124.293323 \nL 78.760868 126.660182 \nL 78.806523 121.300841 \nL 78.77913 132.891917 \nL 78.876527 123.975137 \nL 78.891746 127.026069 \nL 78.885658 119.960983 \nL 78.983056 123.455863 \nL 79.01958 119.166107 \nL 79.083497 128.544569 \nL 79.092628 123.654971 \nL 79.14437 128.030981 \nL 79.101759 122.108812 \nL 79.171763 125.487891 \nL 79.281335 117.162775 \nL 79.217418 128.209201 \nL 79.284379 117.923406 \nL 79.302641 123.979806 \nL 79.311772 114.895394 \nL 79.396994 120.814911 \nL 79.400038 118.193704 \nL 79.470042 129.046627 \nL 79.500479 125.298766 \nL 79.561352 122.613934 \nL 79.521785 127.969499 \nL 79.570483 127.122332 \nL 79.594833 130.755241 \nL 79.613095 120.962704 \nL 79.677012 125.299382 \nL 79.78354 117.653865 \nL 79.801802 119.210628 \nL 79.810933 123.691229 \nL 79.829195 115.310671 \nL 79.905287 117.894667 \nL 79.90833 117.669632 \nL 79.917461 123.921819 \nL 79.93268 123.416806 \nL 79.96616 125.859795 \nL 79.981378 118.325285 \nL 80.030077 121.82453 \nL 80.130518 110.702514 \nL 80.157911 112.294758 \nL 80.160955 118.811018 \nL 80.209654 109.219762 \nL 80.270527 115.756165 \nL 80.352706 112.108547 \nL 80.364881 116.72531 \nL 80.386186 112.29478 \nL 80.486627 120.958059 \nL 80.471409 111.226537 \nL 80.495758 113.040542 \nL 80.57185 105.129878 \nL 80.51402 113.933841 \nL 80.608374 109.964648 \nL 80.711859 119.079871 \nL 80.650985 104.51026 \nL 80.727077 114.908928 \nL 80.778819 117.073975 \nL 80.766645 109.454752 \nL 80.809256 114.408355 \nL 80.882304 109.161298 \nL 80.860998 115.506637 \nL 80.918828 115.134974 \nL 81.016225 107.526794 \nL 80.931003 115.294402 \nL 81.040575 109.454801 \nL 81.080142 115.880487 \nL 81.089273 105.487735 \nL 81.15319 112.89258 \nL 81.229282 121.40084 \nL 81.168409 110.222114 \nL 81.265806 114.859979 \nL 81.320592 108.975566 \nL 81.299286 117.975116 \nL 81.375378 115.135012 \nL 81.39364 109.160034 \nL 81.381465 116.421608 \nL 81.475819 114.161356 \nL 81.478863 117.435586 \nL 81.481906 110.875036 \nL 81.585391 113.170013 \nL 81.588435 110.875012 \nL 81.676701 118.110904 \nL 81.691919 115.012132 \nL 81.713225 113.460309 \nL 81.749749 118.088907 \nL 81.752793 122.076062 \nL 81.841059 109.108207 \nL 81.853234 110.611109 \nL 81.978024 116.727372 \nL 81.880627 108.091206 \nL 81.981068 116.162986 \nL 81.990199 112.144638 \nL 82.081509 118.17779 \nL 82.087596 118.054754 \nL 82.093683 115.58562 \nL 82.151513 110.875052 \nL 82.127164 118.185738 \nL 82.194124 118.092441 \nL 82.209343 114.543618 \nL 82.282391 107.857094 \nL 82.236736 116.790653 \nL 82.325002 110.447116 \nL 82.468054 121.219217 \nL 82.358482 105.588628 \nL 82.486316 119.395358 \nL 82.571539 110.339408 \nL 82.492404 120.457027 \nL 82.58067 112.867094 \nL 82.687198 102.495098 \nL 82.696329 105.39594 \nL 82.714591 109.455187 \nL 82.757203 102.08925 \nL 82.802858 103.565628 \nL 82.885037 99.206523 \nL 82.909386 105.217267 \nL 82.91243 103.775064 \nL 82.924604 102.237755 \nL 82.918517 103.940189 \nL 82.933735 102.57381 \nL 82.939823 109.053636 \nL 83.028089 98.700572 \nL 83.046351 105.247845 \nL 83.052438 104.169621 \nL 83.155923 93.834941 \nL 83.061569 106.885166 \nL 83.171141 96.674984 \nL 83.241146 102.901749 \nL 83.177229 94.699623 \nL 83.286801 99.527613 \nL 83.289844 99.476401 \nL 83.295932 100.771596 \nL 83.298975 100.672303 \nL 83.335499 104.793279 \nL 83.375067 97.60445 \nL 83.408547 100.056816 \nL 83.46029 106.615309 \nL 83.496814 98.991666 \nL 83.524207 102.320012 \nL 83.530294 102.387411 \nL 83.533338 102.268937 \nL 83.536381 102.355203 \nL 83.539425 105.027559 \nL 83.633779 96.057215 \nL 83.645953 104.420901 \nL 83.652041 96.907361 \nL 83.715958 109.537675 \nL 83.752482 109.424734 \nL 83.85901 101.166992 \nL 83.792049 109.613443 \nL 83.874228 103.742082 \nL 83.895534 100.398479 \nL 83.989888 107.048865 \nL 84.035543 108.496803 \nL 84.087285 102.802997 \nL 84.148158 100.245042 \nL 84.139027 110.399445 \nL 84.181639 106.067472 \nL 84.279036 111.089161 \nL 84.272949 103.953679 \nL 84.297298 109.455769 \nL 84.358171 98.095452 \nL 84.309473 112.414848 \nL 84.409914 100.855192 \nL 84.416001 101.144789 \nL 84.534704 109.367522 \nL 84.44035 99.987598 \nL 84.546879 109.100953 \nL 84.574272 104.909667 \nL 84.565141 110.768641 \nL 84.64732 109.455855 \nL 84.659494 111.169374 \nL 84.689931 105.542975 \nL 84.702106 106.123768 \nL 84.711237 104.971331 \nL 84.784285 112.29602 \nL 84.80559 107.382498 \nL 84.909075 112.896325 \nL 84.851245 104.734052 \nL 84.915162 109.600905 \nL 84.924293 104.958594 \nL 85.009516 117.780833 \nL 85.015603 114.651648 \nL 85.088651 116.556277 \nL 85.076477 111.080055 \nL 85.119088 113.716167 \nL 85.216485 107.619052 \nL 85.231704 107.72586 \nL 85.347363 116.556276 \nL 85.329101 106.240343 \nL 85.359538 111.644325 \nL 85.405193 108.154225 \nL 85.463022 116.753339 \nL 85.46911 116.556351 \nL 85.548245 110.613575 \nL 85.539114 118.424247 \nL 85.581725 113.201884 \nL 85.612162 116.864714 \nL 85.636511 106.616082 \nL 85.669992 108.571725 \nL 85.673035 107.05196 \nL 85.761302 116.657045 \nL 85.764345 115.43701 \nL 85.892179 123.237093 \nL 85.77652 110.504246 \nL 85.90131 119.641346 \nL 86.004795 114.770608 \nL 85.983489 122.588177 \nL 86.010882 118.769254 \nL 86.059581 124.496526 \nL 86.077843 113.716242 \nL 86.10828 119.396426 \nL 86.114367 114.744103 \nL 86.202633 121.701937 \nL 86.220895 116.555984 \nL 86.223939 114.923877 \nL 86.284812 127.644771 \nL 86.318293 122.456647 \nL 86.333511 125.41028 \nL 86.397428 116.556336 \nL 86.421777 118.494244 \nL 86.446127 115.063448 \nL 86.433952 120.884318 \nL 86.503956 119.396354 \nL 86.510044 121.72911 \nL 86.519175 110.876285 \nL 86.610485 117.976434 \nL 86.613528 117.973619 \nL 86.710926 114.787829 \nL 86.65614 119.396527 \nL 86.717013 116.556351 \nL 86.796148 125.619437 \nL 86.811367 113.20536 \nL 86.826585 118.096566 \nL 86.893546 106.731624 \nL 86.948332 109.529918 \nL 86.960506 105.195965 \nL 87.003118 114.147041 \nL 87.030511 112.866471 \nL 87.121821 118.52069 \nL 87.042685 108.262168 \nL 87.140083 117.462585 \nL 87.243567 112.141444 \nL 87.200956 118.115396 \nL 87.252698 112.270613 \nL 87.277048 115.180979 \nL 87.313572 110.748425 \nL 87.32879 111.428199 \nL 87.410969 104.798401 \nL 87.423144 112.765128 \nL 87.441406 109.341004 \nL 87.550978 117.383696 \nL 87.487061 106.423401 \nL 87.557065 110.972384 \nL 87.651419 107.70916 \nL 87.584458 116.556632 \nL 87.663593 110.169976 \nL 87.666637 112.452833 \nL 87.754904 102.289732 \nL 87.770122 109.635659 \nL 87.891869 99.672447 \nL 87.928393 96.659421 \nL 87.916218 105.91808 \nL 87.99231 101.716731 \nL 88.068401 106.228087 \nL 88.08362 93.741679 \nL 88.098838 100.02341 \nL 88.196235 87.655419 \nL 88.20841 93.83621 \nL 88.211454 97.697867 \nL 88.26624 87.988777 \nL 88.317982 94.012269 \nL 88.378855 91.086638 \nL 88.363637 96.60643 \nL 88.430598 92.945918 \nL 88.509733 95.180488 \nL 88.454947 88.540336 \nL 88.524951 90.41508 \nL 88.561475 92.448753 \nL 88.637567 86.602382 \nL 88.72279 97.525346 \nL 88.69844 79.142914 \nL 88.750183 88.597848 \nL 88.859755 96.290326 \nL 88.820187 86.719508 \nL 88.865842 90.844472 \nL 88.874973 92.609148 \nL 88.884104 89.064789 \nL 88.893235 89.576375 \nL 88.999763 82.476098 \nL 88.90541 89.82723 \nL 89.014982 82.476177 \nL 89.027156 85.597804 \nL 89.118466 80.828566 \nL 89.12151 81.056241 \nL 89.142816 78.87749 \nL 89.148903 85.647029 \nL 89.182383 81.056204 \nL 89.279781 85.727987 \nL 89.197602 78.216085 \nL 89.294999 82.998664 \nL 89.431964 72.893567 \nL 89.331523 87.444683 \nL 89.435008 75.170826 \nL 89.438051 77.111716 \nL 89.495881 69.844218 \nL 89.54458 75.666418 \nL 89.559798 71.22965 \nL 89.575016 80.881816 \nL 89.641977 73.252621 \nL 89.70285 79.78644 \nL 89.748505 71.071791 \nL 89.751549 71.84713 \nL 89.848946 78.451623 \nL 89.839815 69.787959 \nL 89.861121 74.786958 \nL 89.9433 68.276262 \nL 89.955475 75.302526 \nL 89.97678 69.356926 \nL 90.06809 78.349708 \nL 89.991999 67.709629 \nL 90.095483 78.216514 \nL 90.208099 69.038111 \nL 90.214186 75.853927 \nL 90.311584 64.043274 \nL 90.317671 69.526846 \nL 90.329846 63.367902 \nL 90.412025 71.203505 \nL 90.415068 71.116451 \nL 90.448549 73.845301 \nL 90.430287 66.85635 \nL 90.472898 67.962315 \nL 90.478985 66.468159 \nL 90.515509 76.722874 \nL 90.561164 73.823013 \nL 90.600732 76.751542 \nL 90.6403 66.220763 \nL 90.664649 69.788028 \nL 90.679867 65.408479 \nL 90.759003 60.925687 \nL 90.743784 69.070105 \nL 90.789439 63.334356 \nL 90.895968 71.361986 \nL 90.829007 62.652073 \nL 90.902055 66.85651 \nL 90.908142 66.471467 \nL 90.911186 68.39775 \nL 90.962928 75.376704 \nL 90.984234 66.443977 \nL 91.020758 69.959697 \nL 91.057282 65.863835 \nL 91.035976 72.910155 \nL 91.105981 70.847174 \nL 91.115112 73.956817 \nL 91.179029 65.141056 \nL 91.212509 68.364764 \nL 91.337299 84.134553 \nL 91.239902 67.009476 \nL 91.343387 82.477031 \nL 91.449915 76.797052 \nL 91.416435 84.992237 \nL 91.456002 78.91176 \nL 91.538181 84.03212 \nL 91.498614 72.618931 \nL 91.568618 80.863225 \nL 91.641666 72.04726 \nL 91.675146 82.427233 \nL 91.67819 82.477185 \nL 91.684277 90.444404 \nL 91.763413 75.012033 \nL 91.784718 78.217121 \nL 91.793849 72.129854 \nL 91.872985 81.675691 \nL 91.89429 76.474918 \nL 91.930814 79.914101 \nL 91.967338 66.856975 \nL 91.994731 72.057337 \nL 92.031255 69.23546 \nL 92.079954 77.574198 \nL 92.098216 72.783941 \nL 92.159089 84.061088 \nL 92.104303 72.537163 \nL 92.213875 80.872458 \nL 92.338666 88.741137 \nL 92.244312 79.211533 \nL 92.353884 85.048451 \nL 92.37519 88.465097 \nL 92.472587 79.495192 \nL 92.545635 89.577867 \nL 92.506067 79.300198 \nL 92.59129 86.737855 \nL 92.603465 92.616289 \nL 92.609552 84.958263 \nL 92.703906 88.277648 \nL 92.737386 84.397647 \nL 92.722168 88.982371 \nL 92.74043 86.724424 \nL 92.743473 93.838013 \nL 92.779997 82.756994 \nL 92.850002 86.737944 \nL 92.938268 96.889797 \nL 92.856089 85.528291 \nL 92.941312 96.678327 \nL 92.953486 103.729063 \nL 93.008272 91.511495 \nL 93.04784 96.678356 \nL 93.130019 84.556963 \nL 93.163499 89.391336 \nL 93.169587 92.41838 \nL 93.233504 82.639812 \nL 93.270028 86.689242 \nL 93.273071 86.8013 \nL 93.279159 84.583421 \nL 93.297421 80.607151 \nL 93.373512 90.121158 \nL 93.385687 88.35845 \nL 93.483084 94.106141 \nL 93.431342 86.396528 \nL 93.501346 91.816594 \nL 93.53787 84.685698 \nL 93.547001 93.633635 \nL 93.613962 86.684638 \nL 93.617006 86.657093 \nL 93.620049 87.129205 \nL 93.626137 89.578583 \nL 93.708316 85.171128 \nL 93.72049 87.203203 \nL 93.726578 80.316317 \nL 93.790495 89.882486 \nL 93.830062 88.178608 \nL 93.845281 87.960056 \nL 93.848324 88.875127 \nL 93.933547 94.475867 \nL 93.854412 86.738704 \nL 93.945722 90.998844 \nL 94.067468 76.798518 \nL 94.155735 83.090299 \nL 94.170953 74.263327 \nL 94.17704 77.218932 \nL 94.189215 75.378623 \nL 94.219652 81.058691 \nL 94.283569 76.550249 \nL 94.38401 88.487968 \nL 94.295743 75.378689 \nL 94.399228 79.586245 \nL 94.402272 79.638826 \nL 94.457058 85.905698 \nL 94.502713 75.023775 \nL 94.5088 75.378743 \nL 94.514887 75.236254 \nL 94.517931 75.896278 \nL 94.56663 79.495268 \nL 94.545324 71.977237 \nL 94.624459 74.560159 \nL 94.627503 74.531102 \nL 94.65794 81.729057 \nL 94.651852 73.737056 \nL 94.737075 78.218921 \nL 94.767512 72.53877 \nL 94.74925 81.243942 \nL 94.849691 75.136327 \nL 94.90752 81.517792 \nL 94.956219 73.525202 \nL 94.962306 76.328765 \nL 94.989699 70.548284 \nL 94.971437 76.621261 \nL 95.074922 70.940819 \nL 95.178407 83.899326 \nL 95.196669 78.402441 \nL 95.315372 69.699058 \nL 95.324503 71.179188 \nL 95.440162 61.524155 \nL 95.357983 73.016916 \nL 95.482774 66.859091 \nL 95.501036 71.862969 \nL 95.507123 61.681901 \nL 95.592346 66.626152 \nL 95.647132 75.088912 \nL 95.66235 65.051209 \nL 95.723223 69.398157 \nL 95.778009 59.468041 \nL 95.826708 66.561968 \nL 95.805402 54.564017 \nL 95.893669 61.179027 \nL 95.896712 61.057899 \nL 95.908887 62.59914 \nL 95.997153 68.53355 \nL 95.960629 61.07396 \nL 96.024546 67.744067 \nL 96.128031 63.62688 \nL 96.088463 73.684639 \nL 96.137162 66.859342 \nL 96.143249 70.168097 \nL 96.231516 63.765719 \nL 96.24369 65.439324 \nL 96.292389 60.758904 \nL 96.30152 68.068255 \nL 96.350219 65.439404 \nL 96.462834 75.543393 \nL 96.475009 71.386636 \nL 96.514577 74.582138 \nL 96.548057 66.85948 \nL 96.569363 70.011787 \nL 96.624149 62.599394 \nL 96.681978 66.893343 \nL 96.688066 65.775418 \nL 96.776332 59.759391 \nL 96.75807 66.024395 \nL 96.806769 61.179485 \nL 96.825031 60.99627 \nL 96.818943 63.696856 \nL 96.831118 63.105074 \nL 96.837205 65.439568 \nL 96.843293 59.413588 \nL 96.934603 59.795755 \nL 96.961996 56.825816 \nL 96.952865 62.827483 \nL 96.968083 61.12055 \nL 96.977214 63.924371 \nL 97.044175 53.867375 \nL 97.068524 55.64443 \nL 97.080699 52.659313 \nL 97.105048 57.067108 \nL 97.129397 56.798584 \nL 97.202445 66.859655 \nL 97.147659 52.945523 \nL 97.257231 62.772536 \nL 97.293755 59.759504 \nL 97.275493 67.344246 \nL 97.299843 66.708437 \nL 97.351585 69.825413 \nL 97.33941 59.759591 \nL 97.394196 61.25571 \nL 97.39724 61.105961 \nL 97.409415 65.53483 \nL 97.418546 65.443239 \nL 97.424633 65.50982 \nL 97.427677 65.272653 \nL 97.518987 58.22452 \nL 97.54638 60.501172 \nL 97.655952 67.13489 \nL 97.607253 58.283932 \nL 97.662039 61.924335 \nL 97.665083 61.605269 \nL 97.698563 68.804474 \nL 97.722912 66.602836 \nL 97.729 69.524207 \nL 97.814222 55.820719 \nL 97.82031 58.723802 \nL 97.890314 55.627871 \nL 97.91162 61.671795 \nL 97.932925 58.339679 \nL 97.935969 58.212127 \nL 97.9451 60.23897 \nL 97.948144 62.599765 \nL 97.984668 56.750235 \nL 98.054672 61.179725 \nL 98.060759 60.845431 \nL 98.063803 62.385475 \nL 98.097283 64.306662 \nL 98.149026 55.408237 \nL 98.167288 59.105617 \nL 98.215986 61.576514 \nL 98.200768 53.402418 \nL 98.264685 55.485396 \nL 98.270772 55.647025 \nL 98.334689 46.746594 \nL 98.389475 46.979442 \nL 98.453392 45.874051 \nL 98.508178 59.964742 \nL 98.559921 48.020888 \nL 98.620794 57.025223 \nL 98.663405 44.139319 \nL 98.736453 52.497644 \nL 98.821676 47.503237 \nL 98.76689 54.239087 \nL 98.836894 53.990779 \nL 98.885593 47.211366 \nL 98.94951 58.415805 \nL 99.056038 50.158716 \nL 99.065169 51.001362 \nL 99.071257 53.025136 \nL 99.119955 44.139312 \nL 99.147348 45.371712 \nL 99.229527 41.070442 \nL 99.159523 46.853595 \nL 99.259964 43.853222 \nL 99.360405 45.608544 \nL 99.302575 38.459095 \nL 99.366492 43.121968 \nL 99.451715 38.254494 \nL 99.40606 46.936226 \nL 99.479108 41.42782 \nL 99.512588 47.10198 \nL 99.524763 34.131048 \nL 99.582593 35.643162 \nL 99.58868 35.67633 \nL 99.594767 33.522456 \nL 99.670859 29.676128 \nL 99.695208 37.194821 \nL 99.707383 31.63257 \nL 99.807824 44.739557 \nL 99.832173 37.603813 \nL 99.926527 34.198984 \nL 99.883916 42.947045 \nL 99.938702 38.875453 \nL 99.956964 44.139385 \nL 100.04523 35.847174 \nL 100.048274 38.45911 \nL 100.063492 35.618994 \nL 100.072623 39.01544 \nL 100.182195 47.749448 \nL 100.118278 36.471507 \nL 100.185239 42.719192 \nL 100.197413 40.245587 \nL 100.212632 47.051043 \nL 100.215675 46.979318 \nL 100.252199 52.972981 \nL 100.279592 44.28223 \nL 100.328291 48.287631 \nL 100.331335 48.513034 \nL 100.35264 44.139188 \nL 100.383077 44.139305 \nL 100.392208 44.047564 \nL 100.398295 45.559388 \nL 100.401339 45.718657 \nL 100.425688 41.393692 \nL 100.440907 42.40242 \nL 100.510911 37.987562 \nL 100.450038 46.870819 \nL 100.544391 45.079806 \nL 100.562653 54.679438 \nL 100.644832 39.780633 \nL 100.653963 43.918119 \nL 100.766579 31.551968 \nL 100.772666 36.644365 \nL 100.800059 37.633172 \nL 100.793972 28.51876 \nL 100.851802 33.109465 \nL 100.873107 26.73301 \nL 100.964417 27.101621 \nL 100.967461 26.829665 \nL 100.997898 32.615858 \nL 101.110513 44.555051 \nL 101.010072 29.6713 \nL 101.122688 41.792578 \nL 101.201823 31.027257 \nL 101.238347 37.818419 \nL 101.293133 42.60439 \nL 101.314439 34.066492 \nL 101.341832 35.618827 \nL 101.350963 35.525938 \nL 101.354007 35.839039 \nL 101.487928 48.874905 \nL 101.390531 34.005964 \nL 101.494015 48.399147 \nL 101.509234 46.979144 \nL 101.503146 50.168582 \nL 101.512277 49.095771 \nL 101.518365 53.249234 \nL 101.600544 44.139043 \nL 101.618806 45.935313 \nL 101.624893 42.423345 \nL 101.685766 47.325645 \nL 101.728378 45.429441 \nL 101.810557 48.571598 \nL 101.752727 39.879004 \nL 101.825775 45.048927 \nL 101.868386 52.073769 \nL 101.938391 42.642517 \nL 101.950565 41.29892 \nL 101.999264 46.554556 \nL 102.002308 47.424634 \nL 102.084487 42.005854 \nL 102.11188 44.84325 \nL 102.200146 33.277103 \nL 102.32798 48.256157 \nL 102.334067 44.005511 \nL 102.404072 45.585184 \nL 102.349286 41.236851 \nL 102.41929 43.91609 \nL 102.498425 38.458645 \nL 102.449727 45.832442 \nL 102.531906 42.653219 \nL 102.534949 42.624937 \nL 102.537993 43.173495 \nL 102.553211 46.433087 \nL 102.607997 35.491403 \nL 102.638434 40.39904 \nL 102.644521 36.247216 \nL 102.684089 62.050359 \nL 102.744962 45.182815 \nL 102.766268 68.725043 \nL 102.833229 38.458485 \nL 102.84236 39.877775 \nL 102.845403 39.776634 \nL 102.848447 39.878437 \nL 102.927583 45.042173 \nL 102.863665 35.618427 \nL 102.958019 41.405718 \nL 102.988456 39.721167 \nL 103.049329 49.243662 \nL 103.076722 68.279192 \nL 103.140639 44.19158 \nL 103.152814 45.558574 \nL 103.164989 38.35421 \nL 103.256299 50.333653 \nL 103.268473 66.673313 \nL 103.353696 36.49438 \nL 103.387176 31.232833 \nL 103.378045 39.316741 \nL 103.466312 33.923785 \nL 103.600233 63.508108 \nL 103.487617 32.778152 \nL 103.636757 46.314707 \nL 103.761547 31.358192 \nL 103.764591 33.664501 \nL 103.861988 37.483267 \nL 103.776766 31.358069 \nL 103.871119 32.968103 \nL 103.886338 32.354488 \nL 103.93808 40.062186 \nL 103.953298 37.550505 \nL 104.044608 53.262081 \nL 103.959386 35.618158 \nL 104.081132 49.818496 \nL 104.08722 49.792707 \nL 104.090263 49.919833 \nL 104.096351 48.398384 \nL 104.126787 58.338694 \nL 104.132875 64.325525 \nL 104.218097 44.13833 \nL 104.230272 48.952155 \nL 104.327669 39.632371 \nL 104.266796 67.732939 \nL 104.342888 41.348811 \nL 104.345931 42.379111 \nL 104.434198 35.36171 \nL 104.446372 39.878124 \nL 104.501158 35.098748 \nL 104.51942 44.406266 \nL 104.552901 38.458063 \nL 104.558988 45.048004 \nL 104.57725 36.420685 \nL 104.665516 42.757974 \nL 104.680735 35.76181 \nL 104.689866 38.458048 \nL 104.723346 31.450238 \nL 104.790307 43.927214 \nL 104.799438 39.221816 \nL 104.875529 36.904223 \nL 104.939446 49.016667 \nL 105.024669 35.617875 \nL 105.061193 38.241909 \nL 105.082499 41.186975 \nL 105.100761 32.777876 \nL 105.143372 36.511575 \nL 105.192071 31.357814 \nL 105.231638 42.804338 \nL 105.2499 39.837831 \nL 105.255988 40.799046 \nL 105.30773 49.485176 \nL 105.368603 48.757362 \nL 105.481219 39.877957 \nL 105.599922 52.837101 \nL 105.618184 41.297997 \nL 105.676014 54.010522 \nL 105.715581 45.300024 \nL 105.794717 54.636698 \nL 105.742974 45.236647 \nL 105.831241 48.398071 \nL 105.858634 50.047643 \nL 105.840372 44.956955 \nL 105.88907 47.852101 \nL 105.989511 37.076812 \nL 106.00473 40.780695 \nL 106.117345 44.506069 \nL 106.059516 37.037594 \nL 106.126476 43.202119 \nL 106.257354 37.037594 \nL 106.193437 48.253728 \nL 106.260398 38.105534 \nL 106.303009 44.137756 \nL 106.290834 36.867223 \nL 106.36997 37.627733 \nL 106.433887 33.913075 \nL 106.449105 42.608839 \nL 106.473454 36.953045 \nL 106.564764 49.173595 \nL 106.488673 36.915652 \nL 106.595201 48.499074 \nL 106.692598 39.440515 \nL 106.707817 46.5184 \nL 106.729122 37.037296 \nL 106.805214 46.97753 \nL 106.820432 44.137516 \nL 106.853913 36.679744 \nL 106.835651 46.977566 \nL 106.930004 42.601074 \nL 106.996965 46.12725 \nL 106.954354 37.204033 \nL 107.045664 44.162912 \nL 107.051751 44.040919 \nL 107.054795 44.867497 \nL 107.097406 47.221451 \nL 107.115668 39.291723 \nL 107.155236 41.297182 \nL 107.164367 43.025153 \nL 107.179585 37.374608 \nL 107.228284 29.741033 \nL 107.188716 39.578111 \nL 107.289157 35.796942 \nL 107.389598 41.381972 \nL 107.325681 32.479273 \nL 107.401773 36.970007 \nL 107.420035 36.558754 \nL 107.423078 38.760716 \nL 107.517432 45.590695 \nL 107.477864 35.148013 \nL 107.53265 38.024504 \nL 107.550912 32.77677 \nL 107.575262 42.677954 \nL 107.642222 37.036838 \nL 107.730489 35.376324 \nL 107.760925 45.323756 \nL 107.776144 39.759737 \nL 107.833973 53.535546 \nL 107.867454 48.239834 \nL 107.907021 49.007026 \nL 107.910065 46.495599 \nL 108.01355 40.941222 \nL 107.961807 51.237096 \nL 108.019637 45.917784 \nL 108.10486 44.234753 \nL 108.053117 48.396901 \nL 108.107903 45.566302 \nL 108.208344 51.635808 \nL 108.193126 45.285672 \nL 108.217475 46.97292 \nL 108.220519 46.976774 \nL 108.281392 54.076983 \nL 108.339222 53.201801 \nL 108.37879 42.653103 \nL 108.451838 47.019052 \nL 108.494449 38.456325 \nL 108.50358 48.654236 \nL 108.567497 44.275984 \nL 108.649676 53.888834 \nL 108.588803 41.940891 \nL 108.689244 51.317694 \nL 108.786641 46.844275 \nL 108.725768 54.022871 \nL 108.795772 53.067971 \nL 108.890126 42.716284 \nL 108.941868 48.198712 \nL 109.054484 53.178273 \nL 108.996654 41.296194 \nL 109.057527 52.656514 \nL 109.069702 46.60745 \nL 109.145794 56.602823 \nL 109.164056 55.496513 \nL 109.170143 57.311324 \nL 109.227973 48.493265 \nL 109.255366 49.816257 \nL 109.301021 45.55616 \nL 109.310152 51.617409 \nL 109.364938 48.232723 \nL 109.422767 55.49643 \nL 109.407549 46.976105 \nL 109.477553 49.257294 \nL 109.559732 46.976054 \nL 109.538427 54.351636 \nL 109.581038 51.282057 \nL 109.675392 49.574539 \nL 109.614518 57.705477 \nL 109.684523 51.037991 \nL 109.69061 54.495855 \nL 109.775833 39.578809 \nL 109.778876 39.388509 \nL 109.803226 44.135946 \nL 109.900623 49.907568 \nL 109.821488 41.530888 \nL 109.924972 49.816097 \nL 109.96454 47.603942 \nL 110.004108 53.535266 \nL 110.028457 49.32667 \nL 110.031501 54.076201 \nL 110.083243 46.730606 \nL 110.141073 53.927518 \nL 110.247601 47.916962 \nL 110.208033 55.036173 \nL 110.250645 51.989812 \nL 110.357173 58.520291 \nL 110.299343 48.309784 \nL 110.366304 58.335967 \nL 110.375435 55.392115 \nL 110.436308 62.399009 \nL 110.463701 59.879156 \nL 110.469789 63.613226 \nL 110.555012 53.866986 \nL 110.567186 56.947336 \nL 110.673715 51.092731 \nL 110.634147 59.487882 \nL 110.679802 53.926231 \nL 110.789374 59.425202 \nL 110.743719 49.45645 \nL 110.795461 56.876725 \nL 110.819811 52.479628 \nL 110.804592 60.100422 \nL 110.871553 57.78514 \nL 110.87764 63.162861 \nL 110.96895 52.536546 \nL 110.981125 56.654994 \nL 111.105915 64.160632 \nL 111.038955 52.392843 \nL 111.112003 61.175886 \nL 111.11809 56.8194 \nL 111.182007 67.398285 \nL 111.215487 64.32438 \nL 111.322016 69.369442 \nL 111.264186 62.595926 \nL 111.331147 67.068378 \nL 111.419413 62.652644 \nL 111.382889 72.258579 \nL 111.440719 66.459198 \nL 111.465068 65.14006 \nL 111.556378 73.093591 \nL 111.571596 66.548466 \nL 111.641601 79.64112 \nL 111.662906 79.251562 \nL 111.69943 81.748902 \nL 111.708561 73.059267 \nL 111.766391 76.79618 \nL 111.857701 71.116029 \nL 111.772478 77.875244 \nL 111.900312 74.251628 \nL 111.936836 72.719604 \nL 112.028146 80.886385 \nL 112.034234 81.229382 \nL 112.052496 77.65063 \nL 112.15598 71.075248 \nL 112.06467 79.864494 \nL 112.165111 73.519866 \nL 112.174242 77.937211 \nL 112.265552 71.087557 \nL 112.268596 71.11576 \nL 112.302076 63.924734 \nL 112.289902 73.364417 \nL 112.378168 69.76679 \nL 112.490784 77.546268 \nL 112.408605 68.509114 \nL 112.496871 73.955901 \nL 112.52122 79.458606 \nL 112.530351 72.424272 \nL 112.609487 74.85814 \nL 112.642967 77.992653 \nL 112.694709 72.535709 \nL 112.719059 75.673533 \nL 112.804281 68.421212 \nL 112.737321 75.784696 \nL 112.828631 72.938608 \nL 112.907766 85.315903 \nL 112.849936 71.800082 \nL 112.947334 81.090899 \nL 112.950377 81.016409 \nL 112.959508 82.558979 \nL 112.968639 81.05579 \nL 113.041687 72.180108 \nL 113.0356 83.047274 \nL 113.081255 76.828047 \nL 113.105604 79.635758 \nL 113.090386 73.955668 \nL 113.148216 77.466477 \nL 113.199958 72.375756 \nL 113.21822 78.514266 \nL 113.257788 78.215594 \nL 113.260831 78.21972 \nL 113.333879 79.635612 \nL 113.27605 72.237538 \nL 113.34301 77.107005 \nL 113.406927 85.376047 \nL 113.455626 72.535363 \nL 113.464757 85.936213 \nL 113.565198 74.172088 \nL 113.589547 72.410226 \nL 113.58346 78.788611 \nL 113.604766 78.692915 \nL 113.705207 88.417067 \nL 113.619984 78.091442 \nL 113.723469 86.783089 \nL 113.756949 85.191004 \nL 113.775211 92.415879 \nL 113.778255 93.807049 \nL 113.869565 86.287413 \nL 113.878696 89.137764 \nL 113.918263 87.412403 \nL 113.924351 95.698221 \nL 113.98218 89.941743 \nL 113.997399 97.488001 \nL 114.079578 85.315478 \nL 114.085665 87.381296 \nL 114.088709 86.104607 \nL 114.149582 95.993372 \nL 114.176975 95.402126 \nL 114.18915 95.108664 \nL 114.204368 98.321595 \nL 114.216543 96.675705 \nL 114.219586 100.377262 \nL 114.304809 88.912089 \nL 114.323071 92.415545 \nL 114.386988 87.843414 \nL 114.329158 94.546242 \nL 114.435687 92.008029 \nL 114.526997 97.405825 \nL 114.46308 87.757892 \nL 114.545259 93.672502 \nL 114.569608 87.652974 \nL 114.651787 95.892178 \nL 114.709617 100.772029 \nL 114.660918 93.515479 \nL 114.746141 96.946895 \nL 114.843538 86.374004 \nL 114.8618 88.874867 \nL 114.904411 88.269076 \nL 114.998765 98.702505 \nL 115.056595 93.214618 \nL 115.062682 100.602363 \nL 115.114424 95.25525 \nL 115.120512 95.547011 \nL 115.123555 94.212466 \nL 115.163123 88.963103 \nL 115.150948 96.782207 \nL 115.236171 92.415216 \nL 115.281826 86.73503 \nL 115.26052 95.353201 \nL 115.330525 93.145161 \nL 115.37618 86.504298 \nL 115.44314 97.736607 \nL 115.50097 90.90778 \nL 115.485752 99.821774 \nL 115.552712 95.596836 \nL 115.564887 99.507004 \nL 115.577062 89.415449 \nL 115.644022 91.109014 \nL 115.647066 83.894849 \nL 115.714027 91.393962 \nL 115.756638 86.377018 \nL 115.790118 93.023215 \nL 115.847948 84.891817 \nL 115.86621 89.529674 \nL 115.948389 81.282759 \nL 115.930127 89.974145 \nL 115.978826 88.072083 \nL 116.10666 74.276879 \nL 116.009262 91.243509 \nL 116.109703 77.215925 \nL 116.225363 85.314849 \nL 116.194926 76.794493 \nL 116.228406 85.292772 \nL 116.310585 90.438141 \nL 116.240581 81.05475 \nL 116.341022 86.7439 \nL 116.344066 86.70437 \nL 116.350153 87.832814 \nL 116.44755 105.056331 \nL 116.407983 82.474752 \nL 116.477987 95.309975 \nL 116.487118 94.974578 \nL 116.523642 98.13 \nL 116.572341 83.634548 \nL 116.584515 87.72215 \nL 116.639301 83.513783 \nL 116.675825 94.216112 \nL 116.681913 92.414754 \nL 116.691044 90.844995 \nL 116.706262 94.091262 \nL 116.751917 100.472624 \nL 116.782354 93.528108 \nL 116.818878 96.674829 \nL 116.864533 88.154597 \nL 116.940624 92.414663 \nL 116.989323 89.093099 \nL 116.99541 95.471266 \nL 117.031934 93.834739 \nL 117.129332 97.085403 \nL 117.135419 91.989156 \nL 117.147594 96.756633 \nL 117.165856 95.04299 \nL 117.211511 92.732399 \nL 117.251078 100.260659 \nL 117.266297 96.805255 \nL 117.272384 100.535795 \nL 117.357607 89.406525 \nL 117.372825 96.853927 \nL 117.397174 88.15445 \nL 117.473266 99.514823 \nL 117.47631 98.090797 \nL 117.588925 107.359812 \nL 117.497615 96.107771 \nL 117.595013 103.774823 \nL 117.652842 99.109653 \nL 117.622406 108.166977 \nL 117.661973 105.182663 \nL 117.710672 108.793195 \nL 117.695454 99.500815 \nL 117.771545 105.294748 \nL 117.777633 105.194729 \nL 117.881117 96.869959 \nL 117.786764 105.502395 \nL 117.893292 100.838714 \nL 118.011995 111.269461 \nL 117.911554 99.288447 \nL 118.060694 107.506084 \nL 118.176353 99.655082 \nL 118.085043 109.588258 \nL 118.185484 102.759154 \nL 118.231139 105.449795 \nL 118.200703 99.327897 \nL 118.2981 105.052332 \nL 118.307231 106.776206 \nL 118.319406 103.805486 \nL 118.325493 99.514531 \nL 118.33158 105.883317 \nL 118.428978 105.177413 \nL 118.441152 105.258286 \nL 118.444196 105.042818 \nL 118.550724 97.831948 \nL 118.456371 108.724941 \nL 118.556812 103.432947 \nL 118.559855 104.053543 \nL 118.596379 99.148864 \nL 118.635947 99.224952 \nL 118.660296 93.834296 \nL 118.684646 103.886423 \nL 118.739432 102.227899 \nL 118.833785 108.54514 \nL 118.769868 97.355163 \nL 118.852047 105.227497 \nL 118.925095 101.882001 \nL 118.867266 108.613957 \nL 118.961619 103.889688 \nL 119.013362 98.627003 \nL 118.973794 107.250792 \nL 119.016405 105.388225 \nL 119.049886 108.242251 \nL 119.113803 99.484346 \nL 119.125977 107.622052 \nL 119.150327 98.20651 \nL 119.199025 109.4937 \nL 119.241637 102.29991 \nL 119.278161 113.866784 \nL 119.354252 107.42378 \nL 119.469912 96.056126 \nL 119.475999 102.137916 \nL 119.48513 94.804508 \nL 119.582527 100.122986 \nL 119.664706 92.413944 \nL 119.603833 100.93423 \nL 119.692099 99.613991 \nL 119.695143 99.514281 \nL 119.774278 94.179823 \nL 119.786453 103.774213 \nL 119.804715 95.081121 \nL 119.838195 100.322765 \nL 119.899069 90.850728 \nL 119.9082 90.993948 \nL 119.911243 88.159361 \nL 120.005597 98.336668 \nL 120.014728 93.834065 \nL 120.112125 100.301199 \nL 120.03299 91.291136 \nL 120.127344 98.078776 \nL 120.130387 98.129029 \nL 120.133431 98.014439 \nL 120.136475 98.094053 \nL 120.243003 105.194272 \nL 120.230828 96.318673 \nL 120.24909 103.159578 \nL 120.258221 96.674079 \nL 120.279527 105.619166 \nL 120.358662 102.882797 \nL 120.376924 99.492024 \nL 120.434754 107.926223 \nL 120.45606 106.614234 \nL 120.541282 115.661011 \nL 120.483453 103.839832 \nL 120.571719 112.264384 \nL 120.63868 105.194305 \nL 120.617374 115.798753 \nL 120.684335 110.874372 \nL 120.72999 100.934145 \nL 120.76347 111.941105 \nL 120.79695 109.421621 \nL 120.836518 112.294433 \nL 120.818256 104.685349 \nL 120.845649 108.835145 \nL 120.924784 102.254477 \nL 120.876086 111.004927 \nL 120.955221 107.811724 \nL 121.055662 113.576095 \nL 121.006963 107.698232 \nL 121.064793 110.874399 \nL 121.174365 102.097375 \nL 121.086099 112.813246 \nL 121.177409 104.114624 \nL 121.283937 113.205271 \nL 121.189583 102.025594 \nL 121.293068 108.034186 \nL 121.296112 107.683732 \nL 121.329592 114.890929 \nL 121.338723 108.034264 \nL 121.433077 119.62671 \nL 121.451339 113.257457 \nL 121.52743 116.807422 \nL 121.533518 110.403089 \nL 121.55178 115.134467 \nL 121.591347 109.239819 \nL 121.664395 110.87753 \nL 121.685701 107.424237 \nL 121.728312 120.559592 \nL 121.731356 121.45108 \nL 121.780055 113.714363 \nL 121.816579 116.21295 \nL 121.920063 113.426764 \nL 121.853103 122.107912 \nL 121.929194 113.79067 \nL 121.953544 118.112227 \nL 121.962675 110.874209 \nL 122.035723 112.870863 \nL 122.038766 108.034208 \nL 122.102683 115.560882 \nL 122.148338 109.649127 \nL 122.245736 114.69616 \nL 122.187906 109.295029 \nL 122.260954 110.442017 \nL 122.337046 103.44907 \nL 122.321827 116.201779 \nL 122.385744 105.412053 \nL 122.394875 107.058663 \nL 122.449661 95.253725 \nL 122.480098 99.513799 \nL 122.492273 96.398618 \nL 122.568364 107.125491 \nL 122.583583 102.154528 \nL 122.668805 108.219586 \nL 122.635325 101.886165 \nL 122.702286 103.437593 \nL 122.802727 99.79412 \nL 122.775334 107.381602 \nL 122.808814 104.555924 \nL 122.842294 107.045767 \nL 122.857513 97.462729 \nL 122.906211 100.933873 \nL 122.948823 97.700991 \nL 122.973172 104.702423 \nL 123.015783 99.253025 \nL 123.067526 108.610292 \nL 123.024914 97.843564 \nL 123.131443 103.568395 \nL 123.140574 106.903877 \nL 123.177098 99.689649 \nL 123.180141 100.933837 \nL 123.244058 96.503125 \nL 123.219709 103.648694 \nL 123.28667 102.353686 \nL 123.289713 102.449124 \nL 123.298844 100.384917 \nL 123.307975 98.002887 \nL 123.399285 105.740507 \nL 123.405373 102.166688 \nL 123.420591 103.656781 \nL 123.457115 99.365964 \nL 123.466246 100.505597 \nL 123.472333 97.666921 \nL 123.554512 108.030267 \nL 123.566687 107.926497 \nL 123.569731 109.797007 \nL 123.65191 96.673461 \nL 123.664084 100.69979 \nL 123.749307 93.765434 \nL 123.779744 97.389576 \nL 123.861923 103.181262 \nL 123.788875 93.833391 \nL 123.892359 100.555906 \nL 123.898447 97.473661 \nL 123.962364 103.676435 \nL 124.001931 99.513468 \nL 124.026281 104.208019 \nL 124.044543 95.367615 \nL 124.111503 99.736172 \nL 124.196726 92.371007 \nL 124.178464 100.817665 \nL 124.23325 96.138057 \nL 124.327604 106.413847 \nL 124.278905 94.997553 \nL 124.348909 103.768426 \nL 124.351953 103.773449 \nL 124.385433 102.094564 \nL 124.361084 106.988675 \nL 124.446307 103.357459 \nL 124.479787 108.565116 \nL 124.455438 99.121632 \nL 124.558922 105.403543 \nL 124.56501 98.093404 \nL 124.665451 105.423708 \nL 124.698931 118.162192 \nL 124.778066 110.873653 \nL 124.805459 100.8094 \nL 124.899813 102.353489 \nL 124.99721 109.932487 \nL 124.972861 102.124456 \nL 125.012429 105.189794 \nL 125.122001 115.740967 \nL 125.030691 105.074587 \nL 125.134175 115.611468 \nL 125.231573 109.31049 \nL 125.225485 117.525312 \nL 125.240704 114.94222 \nL 125.292446 122.052299 \nL 125.310708 109.453634 \nL 125.353319 117.349749 \nL 125.414193 110.981226 \nL 125.359407 118.810301 \nL 125.468979 111.858544 \nL 125.593769 125.290922 \nL 125.596813 123.015975 \nL 125.682036 117.410636 \nL 125.666817 124.423476 \nL 125.709429 120.711032 \nL 125.742909 124.005967 \nL 125.755084 116.352741 \nL 125.819001 121.429276 \nL 125.831175 115.051388 \nL 125.913354 124.070983 \nL 125.925529 123.047626 \nL 126.038145 130.442286 \nL 125.946835 121.270167 \nL 126.050319 126.519245 \nL 126.053363 126.469777 \nL 126.05945 127.425276 \nL 126.153804 131.125949 \nL 126.144673 123.498335 \nL 126.169022 129.502044 \nL 126.272507 118.100362 \nL 126.187284 130.474477 \nL 126.293813 123.47128 \nL 126.351642 121.27035 \nL 126.412516 129.476442 \nL 126.42469 123.45009 \nL 126.485564 132.427336 \nL 126.531219 125.937919 \nL 126.592092 132.964576 \nL 126.576874 125.071385 \nL 126.643834 130.363006 \nL 126.69862 126.25958 \nL 126.753406 130.426433 \nL 126.759494 127.83257 \nL 126.765581 127.820061 \nL 126.774712 130.072519 \nL 126.887328 142.606192 \nL 126.799061 125.453848 \nL 126.890371 138.699843 \nL 126.942114 131.915845 \nL 126.929939 142.043274 \nL 127.015162 132.174445 \nL 127.021249 135.555257 \nL 127.118646 127.68918 \nL 127.146039 134.826194 \nL 127.167345 126.494268 \nL 127.240393 128.868918 \nL 127.313441 125.667065 \nL 127.307354 132.672529 \nL 127.340834 131.524231 \nL 127.39562 136.434593 \nL 127.410838 126.494258 \nL 127.441275 129.334326 \nL 127.535629 119.199432 \nL 127.45345 130.435598 \nL 127.559978 120.828835 \nL 127.566065 120.535823 \nL 127.678681 130.089463 \nL 127.61172 117.251223 \nL 127.684768 127.914235 \nL 127.687812 123.832944 \nL 127.769991 131.199461 \nL 127.79434 129.169603 \nL 127.867388 132.17445 \nL 127.876519 126.108901 \nL 127.903912 128.499419 \nL 127.992179 123.748086 \nL 127.919131 129.993966 \nL 128.010441 128.018103 \nL 128.116969 136.475922 \nL 128.03479 123.654145 \nL 128.123056 132.650554 \nL 128.211323 129.19171 \nL 128.229585 135.245781 \nL 128.357419 125.030208 \nL 128.418292 129.339619 \nL 128.424379 129.440051 \nL 128.427423 129.082111 \nL 128.518733 123.435444 \nL 128.463947 133.141396 \nL 128.540039 128.135506 \nL 128.546126 127.743891 \nL 128.567432 133.594508 \nL 128.570475 133.492949 \nL 128.664829 140.117932 \nL 128.58265 129.334299 \nL 128.670916 133.62932 \nL 128.768314 124.277988 \nL 128.713528 136.753801 \nL 128.783532 130.034698 \nL 128.887017 135.014513 \nL 128.801794 126.209295 \nL 128.896148 132.98325 \nL 128.902235 129.334355 \nL 128.938759 138.08704 \nL 129.00572 132.423866 \nL 129.0392 132.174516 \nL 129.121379 139.279665 \nL 129.182252 142.826625 \nL 129.176165 135.014442 \nL 129.233995 140.674398 \nL 129.240082 140.758137 \nL 129.243126 140.196166 \nL 129.252257 133.594448 \nL 129.307043 145.827613 \nL 129.349654 142.309556 \nL 129.456182 151.105973 \nL 129.37096 141.870961 \nL 129.468357 146.768281 \nL 129.574885 140.463765 \nL 129.52923 149.682415 \nL 129.58706 140.694673 \nL 129.675326 145.009909 \nL 129.635759 138.273434 \nL 129.693588 139.274587 \nL 129.696632 139.263413 \nL 129.699676 139.438709 \nL 129.800117 136.434536 \nL 129.751418 142.502217 \nL 129.812291 136.893759 \nL 129.900558 143.406247 \nL 129.91882 134.69141 \nL 129.930994 142.714771 \nL 130.037523 130.5368 \nL 130.052741 135.149175 \nL 130.064916 124.729643 \nL 130.153182 132.209022 \nL 130.165357 127.914468 \nL 130.195793 136.434613 \nL 130.274929 140.556643 \nL 130.290147 133.280806 \nL 130.305365 137.546456 \nL 130.360151 133.137728 \nL 130.347977 140.69493 \nL 130.417981 137.052282 \nL 130.421025 137.767273 \nL 130.487985 130.751926 \nL 130.506247 132.315451 \nL 130.542771 126.137458 \nL 130.55799 133.550049 \nL 130.62495 129.095205 \nL 130.688867 138.293905 \nL 130.743653 135.014742 \nL 130.761915 134.994332 \nL 130.764959 135.159045 \nL 130.877575 124.410801 \nL 130.792352 140.637007 \nL 130.886706 132.44119 \nL 130.944535 134.695938 \nL 130.968885 128.003967 \nL 130.987147 130.754702 \nL 131.060195 127.577533 \nL 131.01454 136.814984 \nL 131.090631 135.141381 \nL 131.114981 131.724299 \nL 131.133243 136.510158 \nL 131.157592 134.521406 \nL 131.166723 143.299915 \nL 131.251946 130.564789 \nL 131.26412 131.971736 \nL 131.282382 136.24787 \nL 131.300644 129.173613 \nL 131.318906 130.115874 \nL 131.328037 126.252536 \nL 131.422391 133.930475 \nL 131.428478 130.754769 \nL 131.528919 138.708997 \nL 131.440653 127.986141 \nL 131.550225 136.434849 \nL 131.623273 132.834118 \nL 131.608055 139.525585 \nL 131.659797 136.05188 \nL 131.760238 139.173026 \nL 131.702408 133.594939 \nL 131.772413 136.447313 \nL 131.775456 136.450435 \nL 131.7785 136.434836 \nL 131.815024 135.305282 \nL 131.787631 141.412047 \nL 131.827199 140.118521 \nL 131.933727 150.746818 \nL 131.86981 139.275057 \nL 131.942858 150.560022 \nL 131.948945 153.053981 \nL 132.055474 159.684441 \nL 132.037212 150.635341 \nL 132.061561 155.707206 \nL 132.070692 149.578631 \nL 132.128522 159.15567 \nL 132.174177 150.635476 \nL 132.283749 159.374769 \nL 132.219832 149.215382 \nL 132.289836 156.56567 \nL 132.332447 153.152657 \nL 132.353753 159.351125 \nL 132.399408 154.988345 \nL 132.499849 166.439719 \nL 132.411583 154.611511 \nL 132.521155 160.556362 \nL 132.524198 160.575829 \nL 132.527242 160.427971 \nL 132.530286 161.684688 \nL 132.594203 148.981073 \nL 132.60029 149.215462 \nL 132.615508 143.535303 \nL 132.682469 157.969027 \nL 132.706818 152.055475 \nL 132.715949 152.111099 \nL 132.718993 151.925757 \nL 132.722037 152.05561 \nL 132.755517 208.2383 \nL 132.785954 146.192727 \nL 132.831609 153.587333 \nL 132.886395 147.773014 \nL 132.868133 155.206041 \nL 132.953355 147.999965 \nL 133.002054 152.329289 \nL 133.035534 146.154062 \nL 133.059884 146.530875 \nL 133.221198 163.428759 \nL 133.111626 146.068524 \nL 133.224242 161.995963 \nL 133.279028 153.593887 \nL 133.336858 155.541971 \nL 133.342945 161.580733 \nL 133.3886 152.093322 \nL 133.449473 157.824666 \nL 133.461648 156.005667 \nL 133.470779 160.365459 \nL 133.473823 161.123359 \nL 133.47991 156.431044 \nL 133.565133 157.349838 \nL 133.607744 154.632803 \nL 133.595569 162.928619 \nL 133.668617 159.24902 \nL 133.689923 163.969087 \nL 133.759927 154.831144 \nL 133.772102 157.735968 \nL 133.820801 159.406552 \nL 133.805582 152.570084 \nL 133.84515 155.986684 \nL 133.942547 148.718637 \nL 133.915154 157.100814 \nL 133.957766 154.6747 \nL 134.036901 159.437289 \nL 134.003421 152.379696 \nL 134.076469 157.735881 \nL 134.15256 150.635672 \nL 134.100818 160.017063 \nL 134.192128 153.497014 \nL 134.201259 153.309371 \nL 134.21039 160.248631 \nL 134.225608 151.803473 \nL 134.298656 153.017854 \nL 134.3017 147.437015 \nL 134.35953 158.17919 \nL 134.408228 154.766367 \nL 134.472145 160.576029 \nL 134.475189 160.31572 \nL 134.508669 165.159725 \nL 134.48432 158.63759 \nL 134.587805 161.782674 \nL 134.636503 154.903719 \nL 134.606067 162.625054 \nL 134.70042 157.736019 \nL 134.703464 157.749906 \nL 134.712595 157.711103 \nL 134.715639 157.736041 \nL 134.785643 165.229294 \nL 134.749119 156.367997 \nL 134.831298 160.31222 \nL 134.931739 156.505222 \nL 134.88304 163.416225 \nL 134.94087 159.426724 \nL 135.007831 165.93867 \nL 134.9987 156.082696 \nL 135.041311 157.278387 \nL 135.138708 148.965361 \nL 135.059573 159.259679 \nL 135.169145 150.458862 \nL 135.275673 159.587219 \nL 135.205669 149.285757 \nL 135.345678 154.690397 \nL 135.36394 150.234372 \nL 135.449162 156.344622 \nL 135.458293 153.473014 \nL 135.461337 153.475962 \nL 135.52221 166.256224 \nL 135.494817 151.21372 \nL 135.573953 156.886062 \nL 135.640913 141.750835 \nL 135.692656 147.765438 \nL 135.771791 159.029053 \nL 135.808315 154.737216 \nL 135.826577 156.316034 \nL 135.850926 152.811274 \nL 135.869188 154.885783 \nL 135.966586 145.005532 \nL 136.067027 152.45089 \nL 136.079201 148.483136 \nL 136.082245 144.694034 \nL 136.167468 153.927247 \nL 136.18573 153.730647 \nL 136.249647 150.513752 \nL 136.21921 156.56267 \nL 136.267909 153.476071 \nL 136.286171 159.42129 \nL 136.362262 150.415224 \nL 136.374437 150.635977 \nL 136.456616 147.899664 \nL 136.441398 154.170532 \nL 136.480965 150.636112 \nL 136.569232 156.360784 \nL 136.58445 148.396594 \nL 136.593581 153.291773 \nL 136.599668 148.941775 \nL 136.697066 157.461271 \nL 136.706197 149.182846 \nL 136.815769 159.156215 \nL 136.821856 154.328525 \nL 136.913166 149.464955 \nL 136.925341 156.987014 \nL 137.031869 163.45253 \nL 136.943603 152.97624 \nL 137.071437 160.392483 \nL 137.138397 157.902221 \nL 137.107961 165.988139 \nL 137.171878 162.425237 \nL 137.263188 164.400767 \nL 137.269275 158.702934 \nL 137.272319 159.15635 \nL 137.275362 157.457454 \nL 137.290581 166.090927 \nL 137.375803 161.877108 \nL 137.479288 167.309575 \nL 137.418415 160.671605 \nL 137.527987 165.766943 \nL 137.552336 170.75878 \nL 137.643646 159.64342 \nL 137.677126 158.786368 \nL 137.707563 165.076764 \nL 137.731912 163.25131 \nL 137.808004 174.795758 \nL 137.750174 163.228324 \nL 137.847572 167.370764 \nL 137.923663 174.763207 \nL 137.899314 166.53023 \nL 137.938882 166.904117 \nL 137.941925 163.416534 \nL 138.027148 172.740712 \nL 138.04541 168.806803 \nL 138.145851 177.036101 \nL 138.088021 168.549581 \nL 138.176288 172.608802 \nL 138.179331 169.758592 \nL 138.279772 178.943146 \nL 138.361951 184.213265 \nL 138.334558 177.695601 \nL 138.383257 179.036929 \nL 138.46848 174.643024 \nL 138.404563 184.375276 \nL 138.495873 176.316335 \nL 138.511091 173.066355 \nL 138.523266 180.022108 \nL 138.590226 175.880129 \nL 138.596314 183.296939 \nL 138.690667 173.238777 \nL 138.699798 175.01229 \nL 138.748497 181.876899 \nL 138.727191 171.288264 \nL 138.818501 180.646411 \nL 138.915899 170.516506 \nL 138.845894 182.955098 \nL 138.931117 173.356553 \nL 139.046776 180.8401 \nL 138.998078 170.30053 \nL 139.04982 179.370874 \nL 139.052864 173.356611 \nL 139.125912 182.742724 \nL 139.159392 181.876747 \nL 139.165479 181.899249 \nL 139.168523 181.794465 \nL 139.189829 187.556963 \nL 139.19896 179.830688 \nL 139.268964 181.995868 \nL 139.387667 176.196756 \nL 139.314619 190.171044 \nL 139.390711 177.260871 \nL 139.475933 176.486219 \nL 139.512457 183.480943 \nL 139.530719 181.588148 \nL 139.561156 190.108916 \nL 139.612898 186.542862 \nL 139.637248 190.77219 \nL 139.704208 181.603525 \nL 139.716383 181.917396 \nL 139.758994 178.823952 \nL 139.786387 189.241965 \nL 139.816824 186.047205 \nL 139.819868 186.742156 \nL 139.850304 179.739566 \nL 139.902047 181.550785 \nL 139.978138 177.114114 \nL 139.92944 183.372211 \nL 140.002488 182.66647 \nL 140.081623 185.63798 \nL 140.099885 177.072257 \nL 140.115103 184.424287 \nL 140.242937 166.256435 \nL 140.297723 180.466988 \nL 140.370771 179.013598 \nL 140.38599 179.296969 \nL 140.389033 178.415069 \nL 140.395121 174.310323 \nL 140.410339 181.943149 \nL 140.498605 177.616722 \nL 140.504693 177.632434 \nL 140.507736 177.55999 \nL 140.586872 183.313952 \nL 140.547304 175.119459 \nL 140.617308 179.010871 \nL 140.714706 173.338318 \nL 140.684269 182.386487 \nL 140.720793 179.698989 \nL 140.809059 190.140732 \nL 140.766448 175.54987 \nL 140.839496 186.146332 \nL 140.869933 190.681649 \nL 140.930806 185.957333 \nL 140.942981 187.698282 \nL 141.061684 179.036646 \nL 141.159081 186.592629 \nL 141.1743 182.336844 \nL 141.262566 178.221133 \nL 141.195605 187.978061 \nL 141.26561 184.402257 \nL 141.350832 190.396955 \nL 141.363007 180.008061 \nL 141.378225 187.918995 \nL 141.478666 175.643609 \nL 141.536496 181.748712 \nL 141.566933 187.443113 \nL 141.63085 180.219104 \nL 141.652155 184.511068 \nL 141.761727 177.616519 \nL 141.719116 187.394698 \nL 141.767815 182.028346 \nL 141.770858 183.169965 \nL 141.798251 177.334093 \nL 141.871299 179.525411 \nL 141.953478 171.613223 \nL 141.935216 181.096518 \nL 141.990002 173.356458 \nL 142.108705 185.025491 \nL 142.111749 184.716644 \nL 142.215234 178.183915 \nL 142.163491 192.477862 \nL 142.224365 180.706263 \nL 142.236539 185.023383 \nL 142.327849 175.972327 \nL 142.333937 180.018465 \nL 142.382635 185.208724 \nL 142.343068 177.300292 \nL 142.39481 181.107163 \nL 142.397854 177.05973 \nL 142.495251 184.38861 \nL 142.504382 180.693488 \nL 142.607867 188.39833 \nL 142.546993 179.818321 \nL 142.626129 185.638656 \nL 142.717439 174.427517 \nL 142.632216 190.085723 \nL 142.753963 179.532441 \nL 142.833098 182.456518 \nL 142.775268 173.629992 \nL 142.863535 179.170294 \nL 142.866578 177.178219 \nL 142.887884 186.136509 \nL 142.970063 179.666845 \nL 142.979194 184.716484 \nL 143.064417 176.831885 \nL 143.076591 175.716206 \nL 143.082679 180.429304 \nL 143.15877 178.297213 \nL 143.161814 181.876463 \nL 143.253124 171.93619 \nL 143.268342 177.616366 \nL 143.286604 170.897939 \nL 143.27443 180.287739 \nL 143.353565 179.796007 \nL 143.390089 183.296445 \nL 143.368783 176.153576 \nL 143.450962 179.036384 \nL 143.566622 170.343361 \nL 143.572709 171.936215 \nL 143.575753 174.64197 \nL 143.587927 164.836043 \nL 143.676194 168.807723 \nL 143.737067 163.362491 \nL 143.700543 172.169543 \nL 143.782722 167.96114 \nL 143.861857 174.776185 \nL 143.813159 165.992767 \nL 143.898381 172.432927 \nL 143.992735 161.749651 \nL 143.919687 173.085131 \nL 144.017084 164.297947 \nL 144.020128 165.79219 \nL 144.111438 156.761176 \nL 144.117525 164.24991 \nL 144.120569 156.315747 \nL 144.227097 159.15587 \nL 144.245359 162.122628 \nL 144.324495 152.820588 \nL 144.336669 150.424294 \nL 144.354931 156.694388 \nL 144.388412 156.154591 \nL 144.391455 157.771314 \nL 144.455372 144.955488 \nL 144.476678 145.3652 \nL 144.504071 141.59874 \nL 144.540595 151.549061 \nL 144.561901 144.955488 \nL 144.637992 154.895722 \nL 144.631905 144.537306 \nL 144.674516 151.093588 \nL 144.784088 141.153845 \nL 144.683647 152.055704 \nL 144.820612 142.115415 \nL 144.835831 149.215553 \nL 144.854093 137.428742 \nL 144.921053 141.114495 \nL 145.021494 138.771106 \nL 145.012363 143.940431 \nL 145.033669 139.275355 \nL 145.0428 139.184468 \nL 145.045844 139.486973 \nL 145.13411 146.796891 \nL 145.07628 136.605839 \nL 145.164547 143.630555 \nL 145.298468 134.960945 \nL 145.182809 143.940947 \nL 145.328905 138.258944 \nL 145.435433 146.674036 \nL 145.383691 137.855372 \nL 145.444564 140.695439 \nL 145.450651 140.5781 \nL 145.453695 141.203207 \nL 145.508481 149.525912 \nL 145.551092 139.275506 \nL 145.578485 146.235282 \nL 145.611966 139.275499 \nL 145.621097 151.577689 \nL 145.691101 145.1505 \nL 145.785455 150.739547 \nL 145.700232 138.854943 \nL 145.788498 146.34863 \nL 145.791542 141.653347 \nL 145.891983 157.765567 \nL 145.89807 149.938627 \nL 146.007642 153.475933 \nL 146.041123 155.936171 \nL 146.065472 149.165436 \nL 146.111127 150.50229 \nL 146.147651 153.467761 \nL 146.123302 147.923668 \nL 146.172 148.966222 \nL 146.22983 147.211751 \nL 146.238961 155.200803 \nL 146.281572 149.215822 \nL 146.293747 149.14257 \nL 146.290703 149.245416 \nL 146.296791 149.215796 \nL 146.345489 155.204914 \nL 146.315053 148.068887 \nL 146.406363 152.29126 \nL 146.427668 156.1348 \nL 146.525066 142.115491 \nL 146.637681 150.282242 \nL 146.546371 141.852407 \nL 146.640725 147.95459 \nL 146.658987 143.24833 \nL 146.735079 150.615591 \nL 146.753341 146.375764 \nL 146.771603 152.636377 \nL 146.765515 145.949726 \nL 146.865956 149.7456 \nL 146.90248 142.425981 \nL 146.978572 145.542061 \nL 146.981616 149.215865 \nL 147.057707 140.695699 \nL 147.0851 146.945651 \nL 147.088144 143.535591 \nL 147.185541 156.413947 \nL 147.191629 151.683501 \nL 147.23424 156.474827 \nL 147.243371 146.413048 \nL 147.261633 150.43945 \nL 147.304244 141.770396 \nL 147.270764 153.476137 \nL 147.374249 147.957025 \nL 147.386423 141.718388 \nL 147.432078 148.18614 \nL 147.465559 145.834197 \nL 147.47469 149.580627 \nL 147.526432 140.461533 \nL 147.575131 144.955765 \nL 147.590349 149.215927 \nL 147.623829 141.831218 \nL 147.675572 144.701738 \nL 147.693834 148.149798 \nL 147.794275 136.266161 \nL 147.885585 141.391061 \nL 147.824711 131.656321 \nL 147.912978 139.469685 \nL 147.922109 135.107813 \nL 147.96472 143.84053 \nL 148.02255 139.336957 \nL 148.080379 154.855908 \nL 148.141253 145.214503 \nL 148.27213 134.882916 \nL 148.159515 146.993146 \nL 148.275174 135.015417 \nL 148.339091 142.828679 \nL 148.38779 137.787243 \nL 148.390833 136.774669 \nL 148.463882 146.652446 \nL 148.473013 144.955572 \nL 148.4791 151.889713 \nL 148.564323 140.695502 \nL 148.582585 144.962217 \nL 148.60389 145.065183 \nL 148.606934 144.693566 \nL 148.716506 140.695375 \nL 148.62824 149.62702 \nL 148.722593 142.237404 \nL 148.734768 142.019834 \nL 148.743899 144.840875 \nL 148.762161 143.535467 \nL 148.780423 150.500108 \nL 148.835209 140.736296 \nL 148.871733 143.378869 \nL 148.963043 152.634265 \nL 148.911301 143.045488 \nL 148.99348 149.305388 \nL 149.093921 135.985927 \nL 149.112183 139.27521 \nL 149.221755 148.70615 \nL 149.157838 133.59519 \nL 149.236973 145.060217 \nL 149.24306 143.122309 \nL 149.303934 150.893185 \nL 149.328283 150.359743 \nL 149.373938 154.994089 \nL 149.383069 144.729591 \nL 149.413506 146.06466 \nL 149.434811 154.747486 \nL 149.501772 144.66481 \nL 149.526121 148.727035 \nL 149.532209 141.530116 \nL 149.617431 152.677281 \nL 149.635693 147.99818 \nL 149.656999 144.33995 \nL 149.693523 151.678227 \nL 149.717872 150.676427 \nL 149.720916 150.635429 \nL 149.745265 153.60026 \nL 149.809182 145.613473 \nL 149.824401 149.056676 \nL 149.915711 140.177048 \nL 149.842663 150.635472 \nL 149.933973 144.955397 \nL 149.961366 148.147456 \nL 149.976584 141.586511 \nL 150.037457 146.722578 \nL 150.083112 137.997056 \nL 150.186597 148.383125 \nL 150.195728 145.514291 \nL 150.296169 136.321228 \nL 150.229208 147.982629 \nL 150.314431 140.69515 \nL 150.317475 144.675455 \nL 150.384435 131.898426 \nL 150.417916 134.166358 \nL 150.433134 127.976023 \nL 150.484876 136.342782 \nL 150.527488 135.015146 \nL 150.536619 135.115049 \nL 150.542706 133.595124 \nL 150.54575 133.671929 \nL 150.646191 127.583589 \nL 150.606623 138.999977 \nL 150.658365 130.904007 \nL 150.716195 135.266246 \nL 150.755763 127.914939 \nL 150.764894 129.452135 \nL 150.871422 123.335875 \nL 150.856204 134.391711 \nL 150.874466 126.241567 \nL 150.953601 136.43522 \nL 150.987081 134.804392 \nL 151.011431 139.19668 \nL 151.023605 131.670314 \nL 151.06926 133.807367 \nL 151.172745 127.497922 \nL 151.081435 136.916909 \nL 151.187963 129.024882 \nL 151.239706 127.069694 \nL 151.30971 134.222541 \nL 151.315797 128.991031 \nL 151.376671 134.405985 \nL 151.422326 130.641455 \nL 151.528854 137.331576 \nL 151.513636 124.517426 \nL 151.531898 132.175188 \nL 151.650601 118.105256 \nL 151.653644 120.814964 \nL 151.67495 127.175428 \nL 151.757129 120.647983 \nL 151.76626 124.447865 \nL 151.778435 122.34929 \nL 151.82409 129.649736 \nL 151.866701 126.495089 \nL 151.970186 132.689239 \nL 151.954967 121.982325 \nL 151.976273 127.32937 \nL 152.064539 116.554911 \nL 151.997579 128.044444 \nL 152.094976 120.814991 \nL 152.18933 125.910407 \nL 152.134544 114.999924 \nL 152.204548 124.143271 \nL 152.210635 120.61597 \nL 152.268465 131.519716 \nL 152.311076 127.566994 \nL 152.432823 116.656731 \nL 152.435867 117.385561 \nL 152.490653 122.234961 \nL 152.502827 111.598041 \nL 152.536308 117.974779 \nL 152.542395 114.986283 \nL 152.636749 121.336994 \nL 152.642836 119.394888 \nL 152.648923 119.689371 \nL 152.651967 118.379865 \nL 152.655011 116.243304 \nL 152.734146 123.654996 \nL 152.761539 119.356676 \nL 152.767626 124.839246 \nL 152.810238 115.134742 \nL 152.868067 118.540938 \nL 152.928941 112.280184 \nL 152.977639 115.134789 \nL 153.087211 120.940257 \nL 152.992858 113.714687 \nL 153.090255 118.349432 \nL 153.163303 122.130058 \nL 153.202871 114.763795 \nL 153.239395 119.9894 \nL 153.248526 110.753831 \nL 153.288093 112.582302 \nL 153.400709 107.603134 \nL 153.315486 118.081367 \nL 153.403753 108.03467 \nL 153.528543 123.65969 \nL 153.616809 112.29463 \nL 153.644202 115.802497 \nL 153.647246 119.394759 \nL 153.695945 110.791912 \nL 153.750731 112.230451 \nL 153.753774 112.294683 \nL 153.857259 119.454336 \nL 153.765949 108.887457 \nL 153.86639 114.64557 \nL 153.875521 112.294535 \nL 153.92422 120.654154 \nL 153.960744 119.404904 \nL 153.969875 119.279954 \nL 154.036835 125.968118 \nL 154.027704 116.554797 \nL 154.079447 119.564858 \nL 154.167713 115.021119 \nL 154.094665 122.600268 \nL 154.192062 118.13147 \nL 154.268154 125.75967 \nL 154.216412 117.833173 \nL 154.313809 123.653442 \nL 154.319896 123.684285 \nL 154.362508 119.395056 \nL 154.338158 126.940476 \nL 154.392944 126.495124 \nL 154.47208 129.105859 \nL 154.438599 122.234926 \nL 154.478167 125.115564 \nL 154.511647 120.264658 \nL 154.50556 127.06996 \nL 154.590783 120.814964 \nL 154.6547 126.325985 \nL 154.676005 120.533652 \nL 154.703398 122.036178 \nL 154.706442 121.830466 \nL 154.712529 126.49513 \nL 154.715573 126.221326 \nL 154.822101 134.736637 \nL 154.727748 120.815015 \nL 154.825145 130.885641 \nL 154.879931 120.815022 \nL 154.864713 132.175321 \nL 154.940804 123.655096 \nL 154.946892 125.56592 \nL 155.029071 116.159661 \nL 155.035158 117.780959 \nL 155.065595 123.173193 \nL 155.074726 114.599247 \nL 155.135599 120.10033 \nL 155.187341 113.62039 \nL 155.166036 120.746707 \nL 155.245171 119.394911 \nL 155.248215 119.721722 \nL 155.290826 113.714828 \nL 155.321263 116.554936 \nL 155.409529 104.238157 \nL 155.339525 118.237306 \nL 155.439966 105.66198 \nL 155.534319 115.134968 \nL 155.552581 107.093173 \nL 155.613455 115.051912 \nL 155.671284 110.874693 \nL 155.789987 100.978623 \nL 155.689546 113.972931 \nL 155.793031 105.194734 \nL 155.832599 109.804369 \nL 155.853904 99.514597 \nL 155.902603 103.746295 \nL 155.923909 100.179645 \nL 155.917821 105.533752 \nL 155.926952 101.761759 \nL 155.939127 96.075075 \nL 156.039568 109.316584 \nL 156.048699 105.162101 \nL 156.091311 113.643635 \nL 156.130878 109.158327 \nL 156.133922 111.660812 \nL 156.170446 103.998566 \nL 156.234363 106.6148 \nL 156.343935 97.985684 \nL 156.280018 108.034663 \nL 156.350022 100.702702 \nL 156.359153 102.584974 \nL 156.407852 94.472778 \nL 156.453507 99.520601 \nL 156.502206 104.342226 \nL 156.566123 96.736767 \nL 156.575254 105.738321 \nL 156.666564 93.235758 \nL 156.742655 91.925849 \nL 156.733524 99.346755 \nL 156.75483 96.6186 \nL 156.876577 108.128328 \nL 156.809616 92.075105 \nL 156.882664 100.116922 \nL 156.961799 96.334962 \nL 156.907013 108.602557 \nL 156.989192 102.245444 \nL 157.043978 105.844756 \nL 157.019629 98.039066 \nL 157.104852 104.33732 \nL 157.144419 94.720424 \nL 157.220511 97.920965 \nL 157.290515 107.043287 \nL 157.299646 106.614558 \nL 157.394 115.831991 \nL 157.354432 104.580517 \nL 157.406175 106.614538 \nL 157.409218 101.210472 \nL 157.436611 110.874618 \nL 157.515747 106.172305 \nL 157.546183 107.932973 \nL 157.534009 101.873546 \nL 157.625319 107.055083 \nL 157.631406 102.165292 \nL 157.686192 109.953071 \nL 157.737934 105.405407 \nL 157.771415 130.755005 \nL 157.847506 106.614409 \nL 157.94186 95.763142 \nL 157.978384 99.781355 \nL 158.06665 108.212209 \nL 158.005777 96.936687 \nL 158.094043 103.968013 \nL 158.100131 103.654689 \nL 158.109262 109.342777 \nL 158.133611 108.221687 \nL 158.145786 111.809804 \nL 158.154917 104.901489 \nL 158.246227 109.423182 \nL 158.252314 109.53817 \nL 158.255358 109.245364 \nL 158.328406 107.031118 \nL 158.276663 114.988587 \nL 158.355799 109.95163 \nL 158.395366 130.754853 \nL 158.450152 106.176799 \nL 158.462327 107.776797 \nL 158.468414 109.62257 \nL 158.538419 100.515264 \nL 158.568855 107.554836 \nL 158.574943 103.913165 \nL 158.660165 113.286256 \nL 158.675384 110.811496 \nL 158.784956 102.727243 \nL 158.702777 111.339487 \nL 158.803218 108.360994 \nL 158.885397 112.881276 \nL 158.833654 102.042633 \nL 158.91279 109.454147 \nL 158.970619 105.166662 \nL 158.955401 112.359362 \nL 159.019318 109.45422 \nL 159.110628 112.294327 \nL 159.122803 104.853319 \nL 159.138021 111.759848 \nL 159.195851 102.832923 \nL 159.198894 99.364397 \nL 159.232375 120.81455 \nL 159.305423 101.208754 \nL 159.323685 103.773896 \nL 159.378471 95.020817 \nL 159.408907 98.568811 \nL 159.478912 103.875087 \nL 159.49413 96.699686 \nL 159.515436 100.190917 \nL 159.564134 93.074811 \nL 159.609789 102.581788 \nL 159.625008 101.234449 \nL 159.631095 102.864049 \nL 159.713274 96.115818 \nL 159.73458 100.933899 \nL 159.737623 100.896383 \nL 159.740667 101.484404 \nL 159.746754 94.695049 \nL 159.835021 105.164105 \nL 159.841108 103.773849 \nL 159.847195 109.87707 \nL 159.938505 101.443674 \nL 159.953724 107.025959 \nL 160.011553 99.224951 \nL 159.996335 107.760072 \nL 160.066339 104.699751 \nL 160.069383 108.034038 \nL 160.157649 98.048515 \nL 160.178955 106.658215 \nL 160.233741 100.79152 \nL 160.221566 108.034067 \nL 160.288527 107.512439 \nL 160.379837 114.283526 \nL 160.31592 106.375399 \nL 160.395055 107.553089 \nL 160.495496 98.093864 \nL 160.404186 110.874201 \nL 160.516802 104.340726 \nL 160.58985 112.380567 \nL 160.541151 98.093843 \nL 160.629418 106.617907 \nL 160.678116 102.353903 \nL 160.717684 113.942527 \nL 160.726815 110.814823 \nL 160.742033 116.327525 \nL 160.8303 106.613996 \nL 160.833343 111.692869 \nL 160.945959 107.701384 \nL 160.888129 114.941156 \nL 160.949003 108.494233 \nL 161.058575 116.554229 \nL 161.025094 106.840387 \nL 161.061618 113.25692 \nL 161.070749 109.482442 \nL 161.134666 137.854831 \nL 161.13771 138.165094 \nL 161.155972 129.740272 \nL 161.329461 106.414047 \nL 161.350767 107.799375 \nL 161.463382 114.027318 \nL 161.420771 103.484282 \nL 161.466426 113.615581 \nL 161.569911 107.044998 \nL 161.50295 116.21912 \nL 161.575998 110.874181 \nL 161.630784 115.315266 \nL 161.667308 108.034137 \nL 161.68557 110.930355 \nL 161.786011 101.886888 \nL 161.694701 112.449493 \nL 161.840797 107.633105 \nL 161.846884 106.364524 \nL 161.852972 110.231278 \nL 161.913845 112.508433 \nL 161.86819 104.960023 \nL 161.968631 112.174029 \nL 162.056897 118.151156 \nL 162.023417 110.561268 \nL 162.069072 111.898847 \nL 162.096465 109.648266 \nL 162.151251 116.651808 \nL 162.1756 113.041669 \nL 162.187775 115.478864 \nL 162.239517 109.164728 \nL 162.276041 113.714259 \nL 162.315609 104.818828 \nL 162.373439 116.717193 \nL 162.38257 116.55424 \nL 162.419094 121.701677 \nL 162.391701 114.900263 \nL 162.455618 117.297708 \nL 162.522578 119.324928 \nL 162.571277 111.915013 \nL 162.595626 115.134135 \nL 162.580408 109.45398 \nL 162.677805 113.024896 \nL 162.775203 106.508767 \nL 162.735635 116.672485 \nL 162.796508 107.888241 \nL 162.820858 115.26797 \nL 162.887818 107.592469 \nL 162.903037 113.087021 \nL 162.988259 100.923744 \nL 162.918255 113.578922 \nL 163.018696 101.841084 \nL 163.079569 97.021427 \nL 163.040002 106.096387 \nL 163.116093 104.481345 \nL 163.195229 106.802402 \nL 163.140443 99.484773 \nL 163.216534 104.699051 \nL 163.277408 100.432738 \nL 163.27132 107.354231 \nL 163.326106 103.773885 \nL 163.332194 103.467821 \nL 163.335237 104.868354 \nL 163.380892 113.99472 \nL 163.429591 103.696446 \nL 163.444809 105.22919 \nL 163.450897 104.077699 \nL 163.47829 93.833651 \nL 163.566556 100.107168 \nL 163.666997 111.782764 \nL 163.581774 94.46632 \nL 163.682216 107.171936 \nL 163.800919 98.629635 \nL 163.703521 109.236444 \nL 163.81005 101.301777 \nL 163.861792 107.042345 \nL 163.880054 99.328358 \nL 163.922665 103.726461 \nL 164.007888 108.333586 \nL 163.956146 100.554516 \nL 164.032237 103.887724 \nL 164.108329 102.590287 \nL 164.08398 106.629371 \nL 164.126591 104.136942 \nL 164.184421 111.140402 \nL 164.205726 97.434105 \nL 164.230076 102.145781 \nL 164.297036 91.032831 \nL 164.351822 93.980129 \nL 164.367041 89.787143 \nL 164.379215 96.848355 \nL 164.44922 95.651944 \nL 164.458351 99.841016 \nL 164.519224 92.019074 \nL 164.555748 95.145607 \nL 164.634883 86.73318 \nL 164.674451 89.107029 \nL 164.710975 93.833421 \nL 164.768805 80.042405 \nL 164.777936 83.552254 \nL 164.866202 91.160978 \nL 164.902726 91.130629 \nL 164.914901 85.977092 \nL 164.97273 96.318663 \nL 164.99708 93.953548 \nL 165.082302 88.789992 \nL 165.048822 95.378128 \nL 165.118826 91.926865 \nL 165.207093 98.96162 \nL 165.134045 86.474449 \nL 165.231442 95.080454 \nL 165.414062 109.34741 \nL 165.286228 92.331635 \nL 165.441455 103.773424 \nL 165.514503 98.704961 \nL 165.459717 106.224436 \nL 165.554071 102.088241 \nL 165.642337 107.948881 \nL 165.587551 96.426695 \nL 165.651468 100.404157 \nL 165.660599 99.092412 \nL 165.721472 111.300926 \nL 165.736691 106.30393 \nL 165.867568 117.973784 \nL 165.995402 106.855789 \nL 166.083669 111.031147 \nL 166.101931 106.375538 \nL 166.104974 106.133845 \nL 166.111062 112.293655 \nL 166.138455 111.027442 \nL 166.241939 120.679882 \nL 166.254114 117.198056 \nL 166.330206 110.055486 \nL 166.314987 118.485804 \nL 166.36673 112.729159 \nL 166.43369 119.446889 \nL 166.448909 111.869041 \nL 166.479345 115.278518 \nL 166.485433 110.415131 \nL 166.543262 116.931216 \nL 166.591961 113.528886 \nL 166.683271 120.656416 \nL 166.625441 112.388553 \nL 166.704577 115.133584 \nL 166.789799 110.881589 \nL 166.728926 119.098745 \nL 166.811105 112.293495 \nL 166.814149 116.378491 \nL 166.884153 107.889189 \nL 166.917633 112.293415 \nL 166.923721 107.371835 \nL 166.98155 118.262338 \nL 167.024162 115.133465 \nL 167.060686 122.636175 \nL 167.07286 111.972185 \nL 167.139821 117.783005 \nL 167.218956 114.934167 \nL 167.209825 122.572973 \nL 167.246349 120.951384 \nL 167.292004 113.372807 \nL 167.349834 121.199366 \nL 167.374183 116.55359 \nL 167.502017 129.740419 \nL 167.511148 127.913872 \nL 167.605502 119.201363 \nL 167.626808 121.360037 \nL 167.64507 118.492439 \nL 167.721161 124.040571 \nL 167.727249 126.781085 \nL 167.742467 119.367226 \nL 167.821602 120.915634 \nL 167.833777 120.956626 \nL 167.864214 118.758994 \nL 167.949436 126.375467 \nL 167.961611 118.05095 \nL 167.976829 123.609607 \nL 168.037703 113.939217 \nL 168.019441 126.653697 \nL 168.092489 116.725962 \nL 168.189886 109.197857 \nL 168.171624 120.007501 \nL 168.199017 117.555085 \nL 168.250759 122.597284 \nL 168.214235 116.28613 \nL 168.308589 118.134813 \nL 168.311633 117.682954 \nL 168.363375 125.073844 \nL 168.390768 121.06254 \nL 168.46686 123.543581 \nL 168.479034 117.56617 \nL 168.503384 122.246313 \nL 168.53382 119.219401 \nL 168.539908 124.180725 \nL 168.542951 126.583591 \nL 168.55817 115.542587 \nL 168.643392 119.461644 \nL 168.658611 119.276506 \nL 168.670785 122.954572 \nL 168.765139 131.817448 \nL 168.679916 118.905795 \nL 168.789488 126.493829 \nL 168.844274 129.493782 \nL 168.859493 124.293335 \nL 168.896017 125.073861 \nL 168.914279 127.95073 \nL 168.908191 122.468174 \nL 168.92341 124.839711 \nL 169.002545 120.813616 \nL 168.984283 126.748481 \nL 169.029938 124.067206 \nL 169.136466 128.393645 \nL 169.109073 120.81364 \nL 169.13951 127.913792 \nL 169.142554 120.25218 \nL 169.206471 129.584996 \nL 169.249082 126.755193 \nL 169.2643 129.833597 \nL 169.370829 117.890171 \nL 169.373872 117.871855 \nL 169.376916 118.485076 \nL 169.422571 116.16566 \nL 169.50475 126.493772 \nL 169.544318 121.061832 \nL 169.611278 129.3338 \nL 169.678239 124.279539 \nL 169.690414 131.89978 \nL 169.717807 127.913705 \nL 169.72085 131.803947 \nL 169.772593 124.670666 \nL 169.827379 126.267877 \nL 169.845641 131.835221 \nL 169.863903 122.790703 \nL 169.939994 130.599789 \nL 169.970431 127.122939 \nL 170.013042 138.172928 \nL 170.043479 133.108282 \nL 170.058697 138.29001 \nL 170.116527 130.293012 \nL 170.153051 133.077355 \nL 170.262623 126.239662 \nL 170.174357 135.013793 \nL 170.274798 129.482689 \nL 170.338715 126.056228 \nL 170.323496 132.627929 \nL 170.341758 126.493517 \nL 170.414806 121.509321 \nL 170.454374 134.029089 \nL 170.472636 144.307341 \nL 170.612645 120.851405 \nL 170.621776 124.276202 \nL 170.688736 112.332306 \nL 170.703955 114.454744 \nL 170.710042 112.127724 \nL 170.770915 122.423975 \nL 170.804396 121.75541 \nL 170.810483 116.176138 \nL 170.850051 122.504908 \nL 170.917011 120.813114 \nL 170.929186 120.902678 \nL 170.953535 119.247153 \nL 170.996147 125.073395 \nL 170.99919 125.215605 \nL 171.014409 121.235516 \nL 171.087457 115.133102 \nL 171.035714 123.495183 \nL 171.142243 117.801088 \nL 171.273121 123.779644 \nL 171.166592 114.827096 \nL 171.276164 122.662951 \nL 171.327907 122.278616 \nL 171.291383 127.653014 \nL 171.333994 126.191718 \nL 171.364431 129.695932 \nL 171.397911 117.071978 \nL 171.428348 117.49051 \nL 171.528789 112.293139 \nL 171.44661 119.485759 \nL 171.53792 117.157485 \nL 171.5714 119.611661 \nL 171.601837 111.951928 \nL 171.635317 113.713037 \nL 171.665754 116.553027 \nL 171.677928 111.714815 \nL 171.680972 110.87289 \nL 171.732714 117.68624 \nL 171.778369 114.095381 \nL 171.790544 112.634185 \nL 171.784457 115.350163 \nL 171.796631 114.948362 \nL 171.906203 120.126011 \nL 171.842286 109.191604 \nL 171.909247 119.393042 \nL 172.02795 104.125667 \nL 172.113173 111.450835 \nL 172.052299 102.453301 \nL 172.143609 111.190308 \nL 172.247094 105.595478 \nL 172.253181 109.331102 \nL 172.259269 112.292954 \nL 172.341448 106.339325 \nL 172.362753 109.701833 \nL 172.435801 105.330642 \nL 172.454063 111.041432 \nL 172.469282 108.483668 \nL 172.554504 118.839409 \nL 172.496675 108.171764 \nL 172.587985 117.312076 \nL 172.694513 110.463395 \nL 172.627552 119.241465 \nL 172.721906 111.734011 \nL 172.804085 115.533149 \nL 172.813216 110.665971 \nL 172.831478 112.164862 \nL 172.901482 109.389801 \nL 172.928875 117.972965 \nL 172.94105 112.2296 \nL 172.944094 112.292801 \nL 172.947137 111.921412 \nL 172.953225 116.533319 \nL 173.053666 108.460634 \nL 173.071928 115.662629 \nL 173.21498 95.490909 \nL 173.221067 104.798984 \nL 173.327596 101.104834 \nL 173.361076 97.073807 \nL 173.370207 108.032703 \nL 173.428037 102.809156 \nL 173.449342 106.884528 \nL 173.48891 99.617442 \nL 173.537609 102.352497 \nL 173.54674 102.264866 \nL 173.549783 102.560294 \nL 173.583264 106.612675 \nL 173.610657 96.565854 \nL 173.650224 98.760766 \nL 173.701967 97.622635 \nL 173.720229 105.192628 \nL 173.738491 102.922519 \nL 173.814582 111.951651 \nL 173.778058 100.740027 \nL 173.85415 108.089919 \nL 173.869368 106.230621 \nL 173.972853 100.218258 \nL 173.875456 110.333603 \nL 173.994159 100.932462 \nL 174.039814 103.772586 \nL 174.012421 98.090906 \nL 174.051988 99.990224 \nL 174.058076 94.439434 \nL 174.118949 104.158502 \nL 174.164604 95.944119 \nL 174.167648 94.699399 \nL 174.255914 100.668006 \nL 174.262001 100.441585 \nL 174.341137 102.6689 \nL 174.280263 93.954738 \nL 174.36853 98.495497 \nL 174.505495 85.415656 \nL 174.374617 99.512538 \nL 174.548106 91.750054 \nL 174.657678 94.906363 \nL 174.648547 88.152329 \nL 174.660722 93.762046 \nL 174.663765 93.832413 \nL 174.666809 91.32824 \nL 174.706377 99.650533 \nL 174.770294 93.832318 \nL 174.82508 98.536402 \nL 174.873778 92.123653 \nL 174.879866 93.751109 \nL 174.94987 91.357971 \nL 174.965088 98.422569 \nL 175.080748 108.592425 \nL 174.995525 94.102235 \nL 175.089879 105.192694 \nL 175.153796 109.246549 \nL 175.208582 96.842551 \nL 175.226844 106.612741 \nL 175.318154 95.252407 \nL 175.375983 107.007421 \nL 175.418595 94.250904 \nL 175.427726 97.451101 \nL 175.467293 93.788273 \nL 175.525123 102.183058 \nL 175.607302 106.612712 \nL 175.616433 94.591823 \nL 175.619477 95.007837 \nL 175.732092 88.675735 \nL 175.640782 102.902938 \nL 175.741223 89.852915 \nL 175.820359 102.352757 \nL 175.765573 89.181007 \nL 175.856883 94.785426 \nL 175.859926 93.880618 \nL 175.926887 102.352633 \nL 175.945149 100.424715 \nL 176.048634 105.192703 \nL 176.039503 97.505862 \nL 176.054721 99.933414 \nL 176.060808 93.924376 \nL 176.121682 103.884565 \nL 176.164293 101.054387 \nL 176.240385 96.173065 \nL 176.231254 103.772713 \nL 176.270821 98.799555 \nL 176.340826 112.351381 \nL 176.389524 107.568115 \nL 176.508227 100.830571 \nL 176.416917 110.891605 \nL 176.517358 100.872098 \nL 176.569101 105.356548 \nL 176.605625 100.60341 \nL 176.62693 102.352694 \nL 176.730415 97.991401 \nL 176.715197 106.907419 \nL 176.739546 101.356747 \nL 176.760852 99.627426 \nL 176.745633 102.48934 \nL 176.788245 102.313534 \nL 176.794332 105.192913 \nL 176.803463 97.473823 \nL 176.876511 100.580177 \nL 176.989127 87.118186 \nL 176.885642 100.932742 \nL 177.007389 89.572341 \nL 177.086524 94.048571 \nL 177.104786 85.610762 \nL 177.116961 89.429998 \nL 177.23262 97.10798 \nL 177.123048 88.651847 \nL 177.247838 93.190829 \nL 177.32393 83.513636 \nL 177.308712 97.12347 \nL 177.35741 89.572488 \nL 177.387847 95.252607 \nL 177.400022 85.30993 \nL 177.466982 88.014726 \nL 177.470026 83.105111 \nL 177.573511 95.358265 \nL 177.585685 93.832718 \nL 177.594816 96.849151 \nL 177.600904 96.672695 \nL 177.643515 103.52818 \nL 177.622209 95.294458 \nL 177.710476 99.549023 \nL 177.823091 91.049453 \nL 177.734825 100.971945 \nL 177.826135 91.972932 \nL 177.832222 98.752673 \nL 177.92962 89.740284 \nL 177.935707 92.412768 \nL 177.941794 92.280959 \nL 177.947882 94.163704 \nL 177.981362 99.512861 \nL 177.999624 91.086035 \nL 178.060497 96.914262 \nL 178.160938 92.834972 \nL 178.106152 99.781222 \nL 178.170069 96.620625 \nL 178.182244 96.90811 \nL 178.185288 94.899302 \nL 178.307034 83.574553 \nL 178.206593 96.474663 \nL 178.313122 85.602152 \nL 178.425737 96.549077 \nL 178.367908 84.584466 \nL 178.434868 91.704967 \nL 178.520091 86.168659 \nL 178.465305 94.087863 \nL 178.54444 92.412898 \nL 178.553571 92.329334 \nL 178.556615 92.610375 \nL 178.580964 98.69332 \nL 178.617488 89.764301 \nL 178.6601 92.024967 \nL 178.778803 86.536056 \nL 178.669231 94.837787 \nL 178.781846 86.551695 \nL 178.940117 102.353132 \nL 178.943161 100.015486 \nL 179.070995 90.322789 \nL 179.074039 91.693916 \nL 179.095344 97.168312 \nL 179.140999 89.644115 \nL 179.183611 92.413037 \nL 179.244484 96.673159 \nL 179.223178 90.181609 \nL 179.293183 92.441076 \nL 179.347969 89.601716 \nL 179.317532 95.430443 \nL 179.366231 93.832971 \nL 179.442322 98.093175 \nL 179.454497 87.123329 \nL 179.472759 95.253045 \nL 179.570156 85.844375 \nL 179.585375 88.094299 \nL 179.591462 88.063491 \nL 179.60668 91.70627 \nL 179.688859 102.132146 \nL 179.63103 90.745932 \nL 179.72234 95.66988 \nL 179.804519 91.171295 \nL 179.758864 98.398075 \nL 179.828868 95.122062 \nL 179.911047 98.233463 \nL 179.865392 90.033998 \nL 179.929309 90.992908 \nL 179.990182 96.673075 \nL 179.99627 87.790179 \nL 180.044968 92.624603 \nL 180.072361 89.219053 \nL 180.136278 98.844863 \nL 180.151497 92.938304 \nL 180.169759 101.20713 \nL 180.264112 98.093276 \nL 180.346291 90.64798 \nL 180.358466 99.802046 \nL 180.376728 92.948697 \nL 180.407165 89.435818 \nL 180.434558 96.757547 \nL 180.477169 95.253203 \nL 180.483256 95.329255 \nL 180.4863 94.978844 \nL 180.51978 101.269557 \nL 180.574566 90.96344 \nL 180.583697 90.993073 \nL 180.586741 90.774881 \nL 180.595872 95.331545 \nL 180.629352 95.253068 \nL 180.656745 99.217921 \nL 180.66892 90.581233 \nL 180.735881 93.833238 \nL 180.81806 86.280877 \nL 180.751099 98.093207 \nL 180.85154 89.993417 \nL 180.948937 95.253141 \nL 180.897195 87.737684 \nL 180.961112 90.704856 \nL 181.012854 86.806378 \nL 181.028073 93.75768 \nL 181.06764 91.596859 \nL 181.113295 85.342145 \nL 181.180256 95.533758 \nL 181.228955 89.380885 \nL 181.295915 90.10483 \nL 181.402444 104.253753 \nL 181.417662 103.902494 \nL 181.454186 99.861139 \nL 181.469404 105.905852 \nL 181.472448 109.149425 \nL 181.54854 95.937618 \nL 181.572889 100.933407 \nL 181.63985 97.33676 \nL 181.618544 106.613467 \nL 181.682461 100.935498 \nL 181.691592 100.891536 \nL 181.688548 100.951122 \nL 181.694636 100.933354 \nL 181.767684 105.301962 \nL 181.782902 96.445215 \nL 181.801164 100.933372 \nL 181.858994 99.395478 \nL 181.843775 105.576524 \nL 181.904649 103.849789 \nL 181.922911 108.033622 \nL 181.935085 98.645601 \nL 182.00509 100.594475 \nL 182.078138 90.136989 \nL 182.020308 101.091188 \nL 182.120749 96.673377 \nL 182.215103 102.191649 \nL 182.154229 92.814091 \nL 182.230321 96.592176 \nL 182.3125 103.330049 \nL 182.300325 96.184868 \nL 182.34598 99.341471 \nL 182.452509 93.379979 \nL 182.440334 102.353485 \nL 182.458596 97.447333 \nL 182.507295 99.836378 \nL 182.525557 91.93178 \nL 182.571212 98.038288 \nL 182.732526 108.265702 \nL 182.580343 94.091805 \nL 182.747744 102.743464 \nL 182.82688 99.216662 \nL 182.805574 108.033859 \nL 182.872535 99.43745 \nL 182.957757 106.613862 \nL 182.899928 96.622986 \nL 183.003412 102.353664 \nL 183.0095 102.326199 \nL 183.012543 102.454934 \nL 183.018631 100.933619 \nL 183.030805 111.179506 \nL 183.033849 110.873824 \nL 183.036893 114.944856 \nL 183.119072 106.111727 \nL 183.140377 106.613767 \nL 183.207338 98.57696 \nL 183.243862 106.864807 \nL 183.268211 100.957077 \nL 183.344303 91.384665 \nL 183.277342 106.613917 \nL 183.399089 98.093765 \nL 183.405176 98.208768 \nL 183.40822 97.627895 \nL 183.417351 92.451965 \nL 183.478224 102.702255 \nL 183.511705 100.559825 \nL 183.514748 100.556724 \nL 183.542141 95.790214 \nL 183.548229 104.517139 \nL 183.618233 100.450226 \nL 183.62432 105.124809 \nL 183.71563 98.1659 \nL 183.727805 100.407695 \nL 183.788678 94.493672 \nL 183.749111 102.353879 \nL 183.837377 97.338362 \nL 183.916512 103.773932 \nL 183.858683 95.598619 \nL 183.949993 102.503802 \nL 184.059565 97.482991 \nL 183.977386 108.034212 \nL 184.062608 99.701563 \nL 184.17218 108.30575 \nL 184.117394 94.488138 \nL 184.178268 106.750496 \nL 184.181311 106.987528 \nL 184.205661 100.934157 \nL 184.208704 100.934829 \nL 184.260447 90.037584 \nL 184.217835 104.159741 \nL 184.351757 97.457612 \nL 184.449154 103.774155 \nL 184.382193 91.869906 \nL 184.47046 100.780246 \nL 184.607425 89.695201 \nL 184.516115 104.919148 \nL 184.610468 92.80196 \nL 184.662211 100.934264 \nL 184.646992 89.573801 \nL 184.723084 96.674062 \nL 184.790045 91.854917 \nL 184.817438 102.228722 \nL 184.832656 93.451659 \nL 184.890486 100.934187 \nL 184.90266 90.816414 \nL 184.945272 95.786168 \nL 184.951359 98.094211 \nL 185.030494 90.76068 \nL 185.045713 92.029691 \nL 185.124848 86.064573 \nL 185.146154 96.957939 \nL 185.155285 91.647949 \nL 185.261813 102.603475 \nL 185.170503 90.42772 \nL 185.304424 99.530758 \nL 185.307468 99.397793 \nL 185.328774 102.607331 \nL 185.337905 102.085145 \nL 185.362254 109.454529 \nL 185.432258 100.47909 \nL 185.441389 104.418666 \nL 185.505306 91.97448 \nL 185.557049 95.915891 \nL 185.660533 102.405753 \nL 185.569223 93.974413 \nL 185.672708 101.767918 \nL 185.754887 109.605911 \nL 185.7488 99.320629 \nL 185.773149 100.9344 \nL 185.882721 90.431319 \nL 185.78228 105.740848 \nL 185.891852 93.025785 \nL 186.001424 102.354462 \nL 186.007511 100.788018 \nL 186.083603 102.465576 \nL 186.02273 96.102016 \nL 186.104909 100.720061 \nL 186.211437 99.366258 \nL 186.196219 104.023455 \nL 186.217524 99.563262 \nL 186.314922 91.904381 \nL 186.232743 102.696273 \nL 186.345358 97.116269 \nL 186.454931 105.344088 \nL 186.391013 90.257061 \nL 186.464062 101.939721 \nL 186.467105 100.489545 \nL 186.51276 109.56241 \nL 186.564503 105.194559 \nL 186.567546 106.493746 \nL 186.646682 98.892627 \nL 186.6619 99.702614 \nL 186.674075 104.162586 \nL 186.689293 96.423656 \nL 186.768428 100.297996 \nL 186.832345 96.077464 \nL 186.777559 103.77456 \nL 186.878 100.741641 \nL 187.005834 109.454736 \nL 187.008878 108.787632 \nL 187.024096 109.801061 \nL 187.124537 99.392167 \nL 187.197585 104.310769 \nL 187.130625 95.784975 \nL 187.200629 102.890226 \nL 187.212804 96.239641 \nL 187.273677 107.443296 \nL 187.310201 105.194667 \nL 187.319332 105.126769 \nL 187.325419 106.614629 \nL 187.343681 113.405622 \nL 187.428904 106.575538 \nL 187.431948 106.614662 \nL 187.504996 103.330185 \nL 187.492821 113.71477 \nL 187.54152 103.737576 \nL 187.550651 111.905234 \nL 187.578044 103.274749 \nL 187.651092 105.53762 \nL 187.654135 102.637581 \nL 187.736314 112.294947 \nL 187.751533 110.874833 \nL 187.830668 118.439207 \nL 187.763707 109.470177 \nL 187.864148 117.688331 \nL 187.879367 110.189198 \nL 187.903716 117.975062 \nL 187.979808 112.294941 \nL 188.095467 102.594651 \nL 188.101554 105.194856 \nL 188.183733 110.543316 \nL 188.119816 102.689337 \nL 188.217214 110.110046 \nL 188.220257 111.009736 \nL 188.284174 105.194703 \nL 188.314611 106.614864 \nL 188.326786 106.78745 \nL 188.323742 106.543301 \nL 188.329829 106.614839 \nL 188.372441 107.014728 \nL 188.442445 97.449763 \nL 188.558104 106.614932 \nL 188.4881 97.161304 \nL 188.567235 105.924357 \nL 188.570279 106.29627 \nL 188.615934 100.914079 \nL 188.640283 102.589209 \nL 188.643327 100.178171 \nL 188.682895 108.349659 \nL 188.749855 103.795046 \nL 188.755943 108.034927 \nL 188.765074 99.062206 \nL 188.856384 99.625288 \nL 188.929432 94.36856 \nL 188.862471 102.354886 \nL 188.965956 98.056864 \nL 189.054222 108.531374 \nL 189.087702 103.854616 \nL 189.102921 101.962467 \nL 189.096833 106.008725 \nL 189.139445 104.422813 \nL 189.215536 96.49437 \nL 189.148576 107.051703 \nL 189.25206 97.652769 \nL 189.27641 102.606424 \nL 189.288584 93.834721 \nL 189.361632 98.067472 \nL 189.36772 98.215763 \nL 189.370763 97.191147 \nL 189.437724 92.424938 \nL 189.425549 100.934971 \nL 189.480335 95.631106 \nL 189.571645 101.424894 \nL 189.577733 89.574624 \nL 189.586864 93.284103 \nL 189.592951 98.09484 \nL 189.659912 86.461985 \nL 189.687305 93.055862 \nL 189.693392 87.668072 \nL 189.790789 95.254777 \nL 189.796877 90.984872 \nL 189.866881 99.514976 \nL 189.809051 88.186335 \nL 189.909492 92.195243 \nL 189.973409 95.254788 \nL 189.958191 89.574724 \nL 190.003846 90.994742 \nL 190.00689 90.835163 \nL 190.016021 95.566251 \nL 190.025152 94.911875 \nL 190.028195 97.251129 \nL 190.0982 86.307671 \nL 190.125593 88.154703 \nL 190.198641 82.526876 \nL 190.216903 90.994762 \nL 190.235165 87.699468 \nL 190.341693 96.91931 \nL 190.25647 84.669794 \nL 190.34778 90.402389 \nL 190.387348 95.43343 \nL 190.40561 86.088308 \nL 190.448221 89.689394 \nL 190.55475 83.516692 \nL 190.469527 91.115432 \nL 190.563881 88.129261 \nL 190.566924 88.154841 \nL 190.639972 91.234346 \nL 190.67954 83.921632 \nL 190.761719 95.263095 \nL 190.773894 82.806586 \nL 190.792156 86.013501 \nL 190.810418 90.994962 \nL 190.89564 79.429789 \nL 190.904771 77.939966 \nL 190.907815 80.008778 \nL 190.910859 81.412259 \nL 190.935208 72.86608 \nL 191.008256 75.374628 \nL 191.029562 71.306787 \nL 191.105653 79.812742 \nL 191.166527 76.540448 \nL 191.196963 80.515418 \nL 191.203051 80.318124 \nL 191.206094 83.894804 \nL 191.248706 74.047408 \nL 191.309579 76.58365 \nL 191.397845 67.677592 \nL 191.355234 79.84535 \nL 191.425238 74.282677 \nL 191.480024 78.21474 \nL 191.489155 69.973602 \nL 191.543941 78.120284 \nL 191.55916 76.543749 \nL 191.571334 83.894904 \nL 191.647426 79.634831 \nL 191.65047 82.141973 \nL 191.744823 70.925176 \nL 191.747867 71.65138 \nL 191.796566 77.101483 \nL 191.817871 68.102692 \nL 191.860483 73.954716 \nL 191.963967 68.416203 \nL 191.878745 75.595905 \nL 191.976142 71.08763 \nL 192.067452 77.207888 \nL 192.085714 72.917538 \nL 192.186155 65.915911 \nL 192.100932 82.022287 \nL 192.19833 68.383209 \nL 192.262247 60.501903 \nL 192.271378 69.694684 \nL 192.310945 67.724098 \nL 192.335295 65.175951 \nL 192.320076 72.938939 \nL 192.3566 69.779703 \nL 192.396168 75.53413 \nL 192.365731 67.423437 \nL 192.469216 75.374806 \nL 192.551395 64.356683 \nL 192.587919 68.41605 \nL 192.734015 59.560377 \nL 192.59705 68.566729 \nL 192.749233 65.753377 \nL 192.867936 75.520981 \nL 192.764452 65.257803 \nL 192.87098 74.417983 \nL 193.004901 63.03901 \nL 192.913591 75.682697 \nL 193.032294 66.854805 \nL 193.062731 69.694739 \nL 193.080993 62.596678 \nL 193.141866 66.580181 \nL 193.227089 68.818179 \nL 193.221002 63.325787 \nL 193.230133 68.274936 \nL 193.233176 61.972641 \nL 193.284919 70.125586 \nL 193.339705 66.644845 \nL 193.397534 73.955094 \nL 193.437102 63.251954 \nL 193.446233 65.827192 \nL 193.555805 57.196216 \nL 193.461451 66.354974 \nL 193.561892 58.4409 \nL 193.564936 58.334764 \nL 193.577111 61.487958 \nL 193.662333 66.716697 \nL 193.583198 58.886161 \nL 193.689726 62.817562 \nL 193.72625 59.677419 \nL 193.735381 68.142949 \nL 193.774949 61.932151 \nL 193.805386 67.101343 \nL 193.81756 57.935671 \nL 193.884521 62.59509 \nL 193.972787 51.605722 \nL 194.000181 57.084114 \nL 194.048879 52.348667 \nL 194.018443 60.063425 \nL 194.094534 58.7621 \nL 194.179757 62.909241 \nL 194.194975 54.920999 \nL 194.201063 58.308263 \nL 194.268023 52.451909 \nL 194.225412 60.484341 \nL 194.316722 55.902546 \nL 194.322809 58.588598 \nL 194.374552 52.614435 \nL 194.417163 55.495019 \nL 194.462818 52.65494 \nL 194.478036 59.755221 \nL 194.526735 54.151757 \nL 194.590652 61.447962 \nL 194.651525 58.654924 \nL 194.75501 49.532609 \nL 194.767185 51.235082 \nL 194.873713 59.755298 \nL 194.861538 46.949138 \nL 194.8798 53.77405 \nL 194.882844 53.113572 \nL 194.919368 61.17541 \nL 194.961979 59.787078 \nL 194.974154 59.719774 \nL 194.977198 59.839949 \nL 194.980241 59.755458 \nL 195.080682 53.591398 \nL 195.071551 65.435413 \nL 195.089813 53.677929 \nL 195.147643 62.595544 \nL 195.199385 55.830407 \nL 195.290695 63.015224 \nL 195.26939 54.075263 \nL 195.351569 58.654895 \nL 195.357656 55.359506 \nL 195.433748 64.204183 \nL 195.461141 58.17364 \nL 195.497665 61.175541 \nL 195.500708 54.127411 \nL 195.51897 61.177206 \nL 195.613324 49.813806 \nL 195.662023 56.48982 \nL 195.683328 49.643086 \nL 195.722896 49.681198 \nL 195.741158 55.495477 \nL 195.777682 45.382495 \nL 195.835512 50.978961 \nL 195.88421 55.773301 \nL 195.844643 48.599437 \nL 195.938996 49.815312 \nL 195.963346 51.669918 \nL 196.069874 39.335397 \nL 196.185533 52.668343 \nL 196.194664 48.333799 \nL 196.313367 42.593571 \nL 196.246407 50.947552 \nL 196.316411 42.736613 \nL 196.322498 42.632672 \nL 196.325542 43.349792 \nL 196.410765 51.23546 \nL 196.419896 42.293558 \nL 196.438158 45.301966 \nL 196.541642 36.453076 \nL 196.4899 46.092198 \nL 196.550773 42.591797 \nL 196.569035 39.876329 \nL 196.67252 54.831655 \nL 196.748612 43.544156 \nL 196.785136 49.601164 \nL 196.842965 42.35956 \nL 196.903839 44.999051 \nL 196.91297 45.555549 \nL 196.925144 41.475428 \nL 196.934275 43.50563 \nL 196.940363 39.796076 \nL 197.010367 45.983837 \nL 197.043847 42.754833 \nL 197.083415 46.032339 \nL 197.086459 42.165254 \nL 197.089502 38.512272 \nL 197.147332 59.398596 \nL 197.192987 44.123949 \nL 197.205162 51.710102 \nL 197.27821 35.615294 \nL 197.296472 36.769637 \nL 197.451699 47.353114 \nL 197.317777 30.714128 \nL 197.454742 45.909125 \nL 197.539965 40.239792 \nL 197.524747 48.67319 \nL 197.564314 43.551623 \nL 197.667799 50.131756 \nL 197.625188 41.40699 \nL 197.67693 46.194372 \nL 197.716498 48.823139 \nL 197.756065 42.918261 \nL 197.780415 44.135582 \nL 197.807808 41.122848 \nL 197.798677 49.491981 \nL 197.819982 47.984775 \nL 197.91738 56.915884 \nL 197.832157 46.220226 \nL 197.935642 55.761464 \nL 197.981297 52.617896 \nL 197.972166 59.931373 \nL 198.026952 56.677169 \nL 198.045214 59.249552 \nL 198.090869 50.986094 \nL 198.103043 51.642951 \nL 198.127393 47.975338 \nL 198.188266 54.433281 \nL 198.212615 51.161177 \nL 198.285663 54.872715 \nL 198.306969 47.338187 \nL 198.322187 50.998076 \nL 198.431759 45.817421 \nL 198.340449 54.252669 \nL 198.434803 48.395628 \nL 198.480458 54.075896 \nL 198.501764 41.830654 \nL 198.541331 44.135582 \nL 198.547419 44.172204 \nL 198.559593 43.586798 \nL 198.565681 48.579516 \nL 198.638729 36.092048 \nL 198.669165 45.367939 \nL 198.675253 40.211117 \nL 198.754388 49.815646 \nL 198.775694 47.240114 \nL 198.790912 50.274631 \nL 198.873091 45.320033 \nL 198.885266 46.702535 \nL 198.98875 42.043908 \nL 198.906571 48.395635 \nL 198.997881 44.135538 \nL 199.067886 52.972421 \nL 199.025274 41.27785 \nL 199.122672 50.158425 \nL 199.125715 48.387907 \nL 199.201807 60.14073 \nL 199.220069 55.048202 \nL 199.232244 63.191154 \nL 199.329641 55.540246 \nL 199.366165 53.916317 \nL 199.375296 55.683687 \nL 199.454431 59.75564 \nL 199.399645 52.525022 \nL 199.484868 55.49567 \nL 199.493999 55.571284 \nL 199.497043 55.318627 \nL 199.554872 50.399903 \nL 199.600527 58.273284 \nL 199.710099 66.569336 \nL 199.65227 54.075696 \nL 199.716187 60.295626 \nL 199.764885 52.029473 \nL 199.81054 62.896401 \nL 199.825759 59.511711 \nL 199.828802 61.361355 \nL 199.889676 45.043809 \nL 199.917069 50.214779 \nL 199.920112 48.851901 \nL 199.974898 55.899122 \nL 200.01751 54.075496 \nL 200.041859 57.439457 \nL 200.057077 52.185962 \nL 200.081427 49.841333 \nL 200.124038 55.495434 \nL 200.166649 52.621521 \nL 200.172737 53.741583 \nL 200.197086 58.697202 \nL 200.23361 50.909614 \nL 200.279265 52.869906 \nL 200.343182 51.379309 \nL 200.331007 57.204708 \nL 200.346226 53.585229 \nL 200.443623 65.435751 \nL 200.461885 62.473297 \nL 200.547108 66.839493 \nL 200.553195 61.53666 \nL 200.574501 64.281756 \nL 200.659723 53.380279 \nL 200.641461 65.435602 \nL 200.702335 56.915415 \nL 200.790601 64.411085 \nL 200.81495 59.993039 \nL 200.939741 66.740177 \nL 200.845387 58.005523 \nL 200.945828 65.43546 \nL 201.046269 55.656357 \nL 201.009745 68.045839 \nL 201.064531 58.375435 \nL 201.073662 58.447676 \nL 201.076706 57.932893 \nL 201.177147 49.132931 \nL 201.08888 58.934776 \nL 201.189321 55.495186 \nL 201.265413 61.17529 \nL 201.207583 52.329767 \nL 201.298893 55.493521 \nL 201.301937 55.269878 \nL 201.314112 61.3623 \nL 201.347592 59.847263 \nL 201.38716 65.43538 \nL 201.429771 53.52914 \nL 201.451077 57.743247 \nL 201.548474 53.681568 \nL 201.460208 67.386634 \nL 201.560649 56.156424 \nL 201.673265 62.92404 \nL 201.56978 54.075074 \nL 201.682396 62.800982 \nL 201.703701 61.489921 \nL 201.761531 70.865492 \nL 201.779793 66.749411 \nL 201.78588 68.490221 \nL 201.801099 64.172719 \nL 201.941107 57.095889 \nL 201.837623 68.807557 \nL 201.944151 57.699078 \nL 201.950238 61.175188 \nL 202.047636 54.544048 \nL 202.056767 59.755123 \nL 202.163295 52.65494 \nL 202.129815 64.026177 \nL 202.166339 57.286985 \nL 202.224168 65.43534 \nL 202.269823 55.494979 \nL 202.281998 63.296381 \nL 202.367221 49.291161 \nL 202.397657 53.552788 \nL 202.501142 63.7522 \nL 202.440269 49.762941 \nL 202.51636 56.597981 \nL 202.656369 67.377589 \nL 202.586365 55.500145 \nL 202.680718 64.015122 \nL 202.747679 60.555836 \nL 202.720286 68.472921 \nL 202.778116 68.323583 \nL 202.872469 71.528635 \nL 202.835945 64.072949 \nL 202.878557 68.275266 \nL 202.884644 64.968485 \nL 202.97291 76.81556 \nL 202.978998 76.260091 \nL 203.094657 83.550191 \nL 203.097701 83.199165 \nL 203.201185 77.102152 \nL 203.167705 83.895591 \nL 203.21336 81.055477 \nL 203.216404 81.121269 \nL 203.219447 80.460322 \nL 203.231622 74.104057 \nL 203.319888 82.475548 \nL 203.329019 80.821724 \nL 203.423373 82.758721 \nL 203.377718 75.192507 \nL 203.441635 82.47548 \nL 203.532945 75.375373 \nL 203.472072 86.735547 \nL 203.557294 76.768926 \nL 203.581644 81.493057 \nL 203.627299 71.780833 \nL 203.642517 73.45896 \nL 203.645561 70.087503 \nL 203.746002 79.635307 \nL 203.752089 73.971358 \nL 203.846443 82.154755 \nL 203.867748 81.055434 \nL 203.904272 80.363092 \nL 203.986451 88.514613 \nL 204.074718 78.4005 \nL 204.12646 82.475244 \nL 204.25125 95.412747 \nL 204.15994 82.014336 \nL 204.254294 95.21241 \nL 204.257338 95.187742 \nL 204.260381 95.459573 \nL 204.324298 105.195929 \nL 204.34256 95.349373 \nL 204.36691 97.312465 \nL 204.382128 90.984856 \nL 204.397346 102.669546 \nL 204.476482 98.095538 \nL 204.54953 101.104128 \nL 204.52518 96.400316 \nL 204.552573 99.515616 \nL 204.61649 95.544535 \nL 204.579966 103.909509 \nL 204.662145 100.316044 \nL 204.67432 96.675418 \nL 204.686495 103.962001 \nL 204.774761 97.657646 \nL 204.789979 105.575091 \nL 204.863027 94.213408 \nL 204.884333 97.359302 \nL 204.999992 90.165352 \nL 205.003036 90.277924 \nL 205.106521 104.945211 \nL 205.124783 100.541977 \nL 205.133914 96.062099 \nL 205.173481 102.856872 \nL 205.210005 97.556321 \nL 205.216093 102.782032 \nL 205.264791 93.488078 \nL 205.322621 99.967985 \nL 205.416975 91.098122 \nL 205.432193 97.512811 \nL 205.43828 93.430618 \nL 205.550896 106.787786 \nL 205.636119 102.113037 \nL 205.599595 107.064267 \nL 205.657424 105.456927 \nL 205.660468 108.090225 \nL 205.742647 98.340217 \nL 205.766996 105.881733 \nL 205.846132 106.615594 \nL 205.855263 101.060204 \nL 205.873525 104.860903 \nL 205.910049 100.659569 \nL 205.949616 112.948327 \nL 205.964835 111.225532 \nL 206.089625 98.095393 \nL 206.113974 103.985819 \nL 206.117018 106.549638 \nL 206.187022 94.714869 \nL 206.220503 102.692893 \nL 206.232677 95.99254 \nL 206.293551 105.315146 \nL 206.311813 100.705632 \nL 206.421385 109.328589 \nL 206.381817 98.199353 \nL 206.424428 105.195479 \nL 206.451821 102.258387 \nL 206.44269 107.262074 \nL 206.454865 102.355606 \nL 206.503564 96.899487 \nL 206.512695 106.913184 \nL 206.564437 102.052936 \nL 206.576612 99.515345 \nL 206.613136 110.487142 \nL 206.646616 105.854056 \nL 206.661834 107.634284 \nL 206.71662 96.540685 \nL 206.74097 101.350661 \nL 206.747057 96.433482 \nL 206.817061 108.639327 \nL 206.847498 105.518316 \nL 206.890109 108.480006 \nL 206.89924 100.439756 \nL 206.963157 107.223779 \nL 206.978376 101.244599 \nL 207.033162 108.035737 \nL 207.072729 103.443852 \nL 207.142734 108.29082 \nL 207.124472 100.935666 \nL 207.185345 106.615828 \nL 207.255349 114.046844 \nL 207.200563 102.355728 \nL 207.291873 106.615821 \nL 207.422751 94.904197 \nL 207.304048 109.21154 \nL 207.425795 95.69502 \nL 207.431882 98.405548 \nL 207.462319 89.864737 \nL 207.535367 96.675586 \nL 207.541454 96.84607 \nL 207.544498 96.056799 \nL 207.571891 92.088709 \nL 207.559716 100.290763 \nL 207.647982 95.546195 \nL 207.675375 100.853852 \nL 207.72103 92.108952 \nL 207.754511 95.421544 \nL 207.77886 93.835683 \nL 207.83669 100.536532 \nL 207.848864 97.66412 \nL 207.851908 99.850314 \nL 207.861039 92.735231 \nL 207.958436 97.083673 \nL 208.077139 112.768109 \nL 207.967567 96.400868 \nL 208.095401 106.877109 \nL 208.192799 99.255061 \nL 208.125838 107.921829 \nL 208.223235 105.02238 \nL 208.302371 105.70008 \nL 208.238454 102.023628 \nL 208.305414 103.685624 \nL 208.314545 98.97975 \nL 208.369331 108.294074 \nL 208.414986 103.908144 \nL 208.45151 100.010704 \nL 208.469772 105.651288 \nL 208.521515 108.403672 \nL 208.478903 102.356181 \nL 208.576301 104.258341 \nL 208.682829 94.693481 \nL 208.594563 110.353682 \nL 208.734571 96.767866 \nL 208.758921 93.020183 \nL 208.746746 98.639994 \nL 208.761964 96.228337 \nL 208.804576 104.022287 \nL 208.825881 94.295073 \nL 208.877624 100.927776 \nL 208.892842 100.984208 \nL 208.895886 100.597391 \nL 208.947628 104.044472 \nL 208.981108 98.096144 \nL 208.99937 99.909513 \nL 209.078506 96.574953 \nL 209.026763 102.356236 \nL 209.111986 98.751548 \nL 209.215471 99.713649 \nL 209.130248 93.377552 \nL 209.218515 99.348023 \nL 209.224602 92.416076 \nL 209.267213 104.034345 \nL 209.325043 102.831247 \nL 209.42244 99.516248 \nL 209.401135 105.355139 \nL 209.431571 100.602083 \nL 209.434615 104.10382 \nL 209.535056 94.069081 \nL 209.559405 100.936411 \nL 209.550274 91.363989 \nL 209.656803 96.72223 \nL 209.781593 104.673448 \nL 209.665934 93.836153 \nL 209.796811 99.009361 \nL 209.875947 97.24924 \nL 209.860728 108.318453 \nL 209.891165 103.90624 \nL 209.897252 101.671735 \nL 209.988562 91.922848 \nL 209.909427 106.616564 \nL 210.006824 101.215016 \nL 210.022043 94.615856 \nL 210.076829 105.196635 \nL 210.104222 103.397814 \nL 210.189444 107.41575 \nL 210.140746 99.660933 \nL 210.216837 105.178749 \nL 210.247274 102.209539 \nL 210.225968 108.270764 \nL 210.320322 103.776653 \nL 210.381195 110.876829 \nL 210.429894 105.563729 \nL 210.472505 103.776747 \nL 210.530335 110.995571 \nL 210.54251 110.78764 \nL 210.557728 113.307681 \nL 210.6673 125.348345 \nL 210.566859 112.492243 \nL 210.682518 123.372104 \nL 210.72513 118.246013 \nL 210.697737 123.909316 \nL 210.795134 119.297127 \nL 210.880357 126.83808 \nL 210.910793 122.198824 \nL 210.959492 113.717051 \nL 210.922968 123.596212 \nL 211.035584 115.137116 \nL 211.038627 114.882979 \nL 211.075151 119.778318 \nL 211.078195 119.57256 \nL 211.105588 123.127726 \nL 211.136025 115.478003 \nL 211.187767 122.237273 \nL 211.227335 111.854507 \nL 211.30647 115.770602 \nL 211.419086 126.727242 \nL 211.318645 113.2889 \nL 211.428217 125.059409 \nL 211.43126 125.077501 \nL 211.473872 128.758081 \nL 211.525614 120.904121 \nL 211.534745 122.212529 \nL 211.543876 122.100001 \nL 211.54692 122.731113 \nL 211.565182 131.512204 \nL 211.583444 119.397341 \nL 211.659535 123.657528 \nL 211.759976 120.962422 \nL 211.717365 128.814079 \nL 211.772151 122.637324 \nL 211.775195 122.49077 \nL 211.781282 126.497583 \nL 211.884767 135.197474 \nL 211.817806 124.633713 \nL 211.896941 133.957305 \nL 211.899985 135.19542 \nL 211.97912 126.497651 \nL 211.991295 130.694427 \nL 212.103911 121.319261 \nL 212.015644 130.759309 \nL 212.106954 123.108065 \nL 212.210439 127.919277 \nL 212.180002 120.538011 \nL 212.21957 125.050571 \nL 212.222614 125.077589 \nL 212.323055 119.473949 \nL 212.313924 127.917783 \nL 212.335229 122.083784 \nL 212.417408 130.706163 \nL 212.466107 127.91782 \nL 212.514806 119.833642 \nL 212.593941 122.129704 \nL 212.706557 133.717946 \nL 212.621334 121.123867 \nL 212.736993 127.684563 \nL 212.822216 122.323242 \nL 212.755255 130.758061 \nL 212.855696 122.793096 \nL 212.901351 129.707359 \nL 212.965268 123.606202 \nL 212.968312 123.657967 \nL 212.971356 121.871933 \nL 213.080928 132.669257 \nL 212.992661 119.997019 \nL 213.087015 130.693636 \nL 213.102233 131.794395 \nL 213.105277 129.338103 \nL 213.108321 130.763641 \nL 213.111364 128.668529 \nL 213.193543 138.361467 \nL 213.217893 130.376431 \nL 213.275722 136.41373 \nL 213.321377 127.632973 \nL 213.327465 130.787881 \nL 213.44008 122.238134 \nL 213.354858 130.849461 \nL 213.443124 123.09536 \nL 213.49791 129.350132 \nL 213.53139 120.818075 \nL 213.552696 123.592487 \nL 213.650093 131.302022 \nL 213.567914 122.858714 \nL 213.68053 127.677738 \nL 213.780971 117.444067 \nL 213.698792 132.178342 \nL 213.796189 122.137801 \nL 213.811408 118.13585 \nL 213.95446 130.154673 \nL 213.960547 120.968172 \nL 213.994028 131.042571 \nL 214.067076 126.560531 \nL 214.109687 123.658191 \nL 214.158386 130.452772 \nL 214.179691 124.15008 \nL 214.249696 119.978785 \nL 214.188822 129.222726 \nL 214.277089 125.078369 \nL 214.3197 136.698173 \nL 214.359268 124.180998 \nL 214.404923 132.598604 \nL 214.481014 126.812657 \nL 214.499276 135.95155 \nL 214.514495 132.722058 \nL 214.517538 137.921684 \nL 214.557106 127.477556 \nL 214.624067 133.015166 \nL 214.706246 130.569907 \nL 214.68494 135.188885 \nL 214.733639 133.598551 \nL 214.736682 133.602831 \nL 214.739726 133.519294 \nL 214.748857 137.858691 \nL 214.824949 130.758486 \nL 214.827992 130.282681 \nL 214.86756 136.63974 \nL 214.90104 135.018603 \nL 214.934521 139.582218 \nL 214.949739 129.661249 \nL 214.995394 129.693578 \nL 215.071486 125.192759 \nL 215.010612 132.511427 \nL 215.10801 129.240403 \nL 215.229756 139.535671 \nL 215.123228 127.838927 \nL 215.235844 139.278714 \nL 215.238887 137.583441 \nL 215.244975 143.799647 \nL 215.348459 137.945195 \nL 215.430638 146.619547 \nL 215.372809 135.018461 \nL 215.467162 142.376423 \nL 215.570647 132.178515 \nL 215.610215 137.033382 \nL 215.719787 150.474185 \nL 215.619346 134.811628 \nL 215.762398 148.133842 \nL 215.798922 142.118686 \nL 215.805009 150.750046 \nL 215.87197 148.200801 \nL 215.908494 143.364928 \nL 215.948062 150.551308 \nL 215.975455 147.64226 \nL 216.024153 154.050247 \nL 216.033284 144.285489 \nL 216.085027 147.811966 \nL 216.08807 147.798897 \nL 216.188511 144.419606 \nL 216.176337 153.148462 \nL 216.194599 144.95872 \nL 216.206773 154.714332 \nL 216.291996 140.086609 \nL 216.301127 143.538739 \nL 216.334607 137.919482 \nL 216.401568 146.61794 \nL 216.410699 143.44826 \nL 216.413743 141.971624 \nL 216.495922 150.804168 \nL 216.51114 148.126499 \nL 216.572013 150.710316 \nL 216.584188 142.964642 \nL 216.608537 143.98031 \nL 216.651149 142.118657 \nL 216.638974 150.501679 \nL 216.687673 148.75435 \nL 216.690716 150.20256 \nL 216.782027 141.651977 \nL 216.839856 138.489551 \nL 216.861162 146.722102 \nL 216.879424 146.378752 \nL 216.995083 154.354965 \nL 216.885511 143.187777 \nL 216.998127 152.362985 \nL 217.113786 141.660547 \nL 217.147267 145.373947 \nL 217.153354 139.278685 \nL 217.226402 142.985633 \nL 217.305537 136.438653 \nL 217.293363 145.296384 \nL 217.339018 139.243821 \nL 217.439459 144.972749 \nL 217.418153 139.084641 \nL 217.445546 139.968978 \nL 217.488157 136.438561 \nL 217.503376 146.378807 \nL 217.558162 137.678682 \nL 217.591642 139.456922 \nL 217.600773 131.153437 \nL 217.637297 133.529364 \nL 217.649472 136.036083 \nL 217.759044 143.82428 \nL 217.676865 134.426099 \nL 217.768175 142.145093 \nL 217.786437 141.927185 \nL 217.78948 142.576168 \nL 217.88079 145.99235 \nL 217.826004 139.169054 \nL 217.896009 141.480538 \nL 217.902096 138.792042 \nL 217.962969 148.225753 \nL 218.005581 140.698743 \nL 218.020799 139.278664 \nL 218.017755 141.801245 \nL 218.023843 139.926094 \nL 218.02993 146.862733 \nL 218.051236 139.278733 \nL 218.136458 142.16735 \nL 218.139502 141.310401 \nL 218.206463 150.974074 \nL 218.215594 148.301349 \nL 218.267336 145.210221 \nL 218.331253 153.423636 \nL 218.416476 142.118772 \nL 218.449956 146.119069 \nL 218.550397 155.282215 \nL 218.489524 141.778861 \nL 218.559528 147.834803 \nL 218.565615 144.980132 \nL 218.656925 153.701499 \nL 218.666056 149.937685 \nL 218.742148 152.941309 \nL 218.76041 147.458336 \nL 218.778672 150.771421 \nL 218.888244 140.698703 \nL 218.891288 141.609235 \nL 218.997816 152.081191 \nL 219.009991 149.218944 \nL 219.058689 146.42804 \nL 219.022165 155.104595 \nL 219.116519 152.058998 \nL 219.186523 147.779754 \nL 219.21696 155.19202 \nL 219.220004 157.749143 \nL 219.277833 149.423695 \nL 219.323488 153.42333 \nL 219.326532 153.555844 \nL 219.335663 150.46537 \nL 219.344794 150.639017 \nL 219.347838 149.315298 \nL 219.417842 160.505399 \nL 219.436104 157.739258 \nL 219.518283 168.157809 \nL 219.454366 156.814084 \nL 219.54872 161.711076 \nL 219.551763 161.707885 \nL 219.588287 167.679481 \nL 219.603506 156.319157 \nL 219.661335 162.225852 \nL 219.670466 165.155428 \nL 219.682641 158.85975 \nL 219.767864 160.432246 \nL 219.868305 158.10571 \nL 219.813519 166.95131 \nL 219.877436 160.579326 \nL 219.880479 160.615472 \nL 219.883523 160.007622 \nL 219.962658 170.106838 \nL 219.901785 157.459126 \nL 219.999182 164.839325 \nL 220.00527 164.522033 \nL 220.008313 165.975543 \nL 220.03875 169.613656 \nL 220.053968 159.166721 \nL 220.111798 162.885232 \nL 220.114842 163.260874 \nL 220.139191 154.643131 \nL 220.151366 159.159207 \nL 220.157453 150.723497 \nL 220.22137 165.031727 \nL 220.257894 163.419438 \nL 220.260938 163.387848 \nL 220.263981 164.235703 \nL 220.267025 165.16953 \nL 220.324855 158.773561 \nL 220.364422 161.999446 \nL 220.470951 157.739204 \nL 220.443558 164.764427 \nL 220.473994 160.97012 \nL 220.556173 174.976301 \nL 220.504431 157.704836 \nL 220.589654 167.615732 \nL 220.647483 157.514528 \nL 220.635309 168.909933 \nL 220.702269 164.53491 \nL 220.76923 173.738077 \nL 220.720531 162.247751 \nL 220.830103 170.602001 \nL 220.942719 160.579323 \nL 220.845322 170.644683 \nL 220.976199 167.22255 \nL 221.070553 170.834831 \nL 221.055335 162.962057 \nL 221.088815 169.734715 \nL 221.16795 165.032639 \nL 221.152732 173.140595 \nL 221.201431 166.259681 \nL 221.304915 176.123453 \nL 221.292741 164.839587 \nL 221.314046 168.814764 \nL 221.390138 161.86108 \nL 221.371876 169.157547 \nL 221.438837 162.389789 \nL 221.560583 171.285705 \nL 221.46623 159.322711 \nL 221.563627 170.01115 \nL 221.612326 161.999446 \nL 221.654937 170.881446 \nL 221.673199 169.09968 \nL 221.746247 172.858307 \nL 221.776684 165.917858 \nL 221.834513 160.892401 \nL 221.819295 169.805941 \nL 221.892343 163.985162 \nL 222.004959 170.731868 \nL 221.910605 158.667192 \nL 222.008002 167.679699 \nL 222.038439 164.83962 \nL 222.020177 169.291202 \nL 222.041483 168.175731 \nL 222.044526 170.617215 \nL 222.132793 160.14364 \nL 222.144967 163.419627 \nL 222.154098 164.196497 \nL 222.257583 155.735263 \nL 222.26367 162.295497 \nL 222.358024 152.059474 \nL 222.367155 156.022724 \nL 222.397592 164.002586 \nL 222.37933 153.774129 \nL 222.458465 154.434541 \nL 222.534557 148.481602 \nL 222.488902 156.897677 \nL 222.564993 153.428089 \nL 222.568037 156.809267 \nL 222.650216 142.19038 \nL 222.668478 148.08786 \nL 222.726308 142.331391 \nL 222.68674 149.943269 \nL 222.775006 149.347303 \nL 222.884578 156.930453 \nL 222.829792 147.799323 \nL 222.890666 153.47947 \nL 222.92719 156.094096 \nL 222.911971 151.68509 \nL 222.930233 155.415674 \nL 222.939364 175.474423 \nL 223.024587 149.092098 \nL 223.039805 155.61852 \nL 223.082417 148.261456 \nL 223.064155 156.642345 \nL 223.149377 156.319422 \nL 223.182858 152.11528 \nL 223.158508 158.929747 \nL 223.2346 158.3247 \nL 223.335041 166.407568 \nL 223.252862 156.121317 \nL 223.353303 161.198017 \nL 223.356347 159.094666 \nL 223.411133 166.872457 \nL 223.456788 166.071686 \nL 223.535923 168.297015 \nL 223.472006 160.396867 \nL 223.538967 165.421005 \nL 223.54201 161.946283 \nL 223.578534 188.353783 \nL 223.648539 166.128898 \nL 223.65767 164.969894 \nL 223.663757 167.085907 \nL 223.669844 161.821608 \nL 223.755067 174.906699 \nL 223.770285 172.036266 \nL 223.773329 171.93961 \nL 223.776373 174.184134 \nL 223.822028 179.368701 \nL 223.800722 170.070943 \nL 223.888988 175.648343 \nL 223.992473 169.509567 \nL 223.980298 177.86907 \nL 223.995517 173.369691 \nL 224.099001 205.147785 \nL 224.120307 194.114565 \nL 224.169006 177.6199 \nL 224.20553 199.436029 \nL 224.23901 186.932064 \nL 224.242054 186.996118 \nL 224.363801 173.317713 \nL 224.369888 173.264918 \nL 224.372932 174.102108 \nL 224.467285 179.507307 \nL 224.397281 169.099448 \nL 224.482504 175.731751 \nL 224.485547 174.515135 \nL 224.543377 186.146121 \nL 224.567726 185.399388 \nL 224.576857 190.025683 \nL 224.659036 178.874802 \nL 224.671211 181.879793 \nL 224.683386 185.440765 \nL 224.689473 175.569886 \nL 224.759477 179.588396 \nL 224.81122 175.916933 \nL 224.862962 183.893258 \nL 224.869049 180.459826 \nL 224.875137 182.292617 \nL 224.920792 175.977518 \nL 224.981665 181.422859 \nL 225.063844 185.428529 \nL 225.006014 177.619652 \nL 225.091237 181.313276 \nL 225.219071 167.67943 \nL 225.109499 182.11989 \nL 225.234289 171.939527 \nL 225.240377 171.893718 \nL 225.24342 172.122873 \nL 225.258639 166.853939 \nL 225.270813 175.503462 \nL 225.340818 171.939592 \nL 225.39256 178.538378 \nL 225.404735 170.075105 \nL 225.45039 173.633715 \nL 225.477783 166.259321 \nL 225.465608 176.339412 \nL 225.563005 170.39929 \nL 225.572136 168.709006 \nL 225.57518 171.772147 \nL 225.578224 173.935284 \nL 225.629966 164.839354 \nL 225.681708 170.803894 \nL 225.715189 165.668997 \nL 225.733451 177.653432 \nL 225.751713 174.779602 \nL 225.836935 178.173198 \nL 225.776062 167.928938 \nL 225.861285 174.71003 \nL 225.913027 178.580533 \nL 225.925202 171.483512 \nL 225.946507 175.821426 \nL 225.995206 167.679455 \nL 225.979988 176.199693 \nL 226.059123 170.951251 \nL 226.071298 166.259339 \nL 226.129127 176.54149 \nL 226.15652 172.484617 \nL 226.263049 176.511143 \nL 226.223481 169.099579 \nL 226.269136 173.359606 \nL 226.33914 168.712507 \nL 226.311747 174.895939 \nL 226.351315 174.054631 \nL 226.354359 176.950322 \nL 226.439581 163.861533 \nL 226.451756 166.25947 \nL 226.470018 161.700643 \nL 226.476105 168.316777 \nL 226.561328 166.058679 \nL 226.570459 167.892545 \nL 226.576546 164.939274 \nL 226.582634 165.934359 \nL 226.585677 161.472096 \nL 226.652638 172.103729 \nL 226.689162 170.548017 \nL 226.76221 163.221268 \nL 226.725686 171.939574 \nL 226.804821 167.774089 \nL 226.91135 158.135163 \nL 226.82004 169.212669 \nL 226.929612 162.856957 \nL 226.97831 169.994791 \nL 226.996572 161.853863 \nL 227.042227 163.263986 \nL 227.100057 169.950629 \nL 227.163974 166.965978 \nL 227.261371 157.680646 \nL 227.176149 169.160128 \nL 227.282677 157.942264 \nL 227.325288 164.721156 \nL 227.340507 154.436599 \nL 227.395293 159.15929 \nL 227.45921 153.12849 \nL 227.419642 160.811818 \nL 227.517039 154.89915 \nL 227.535301 154.823674 \nL 227.620524 161.765686 \nL 227.608349 152.059082 \nL 227.629655 152.51618 \nL 227.736183 143.769416 \nL 227.654004 159.159218 \nL 227.757489 145.220185 \nL 227.836624 153.47895 \nL 227.775751 142.118712 \nL 227.873148 149.652878 \nL 227.879236 146.577185 \nL 227.970546 152.316889 \nL 227.98272 149.810868 \nL 227.985764 153.531066 \nL 228.067943 143.709256 \nL 228.092292 150.88373 \nL 228.186646 140.698683 \nL 228.207952 147.139008 \nL 228.235345 141.856126 \nL 228.241432 149.262779 \nL 228.244476 151.599101 \nL 228.290131 138.913033 \nL 228.34796 146.331959 \nL 228.433183 153.201359 \nL 228.445358 144.958684 \nL 228.451445 144.563866 \nL 228.454489 146.57941 \nL 228.506231 150.638733 \nL 228.545799 143.418141 \nL 228.561017 143.554236 \nL 228.564061 143.500961 \nL 228.573192 144.902678 \nL 228.661458 148.03517 \nL 228.597541 137.858497 \nL 228.688851 146.378669 \nL 228.722331 143.41391 \nL 228.731462 149.674355 \nL 228.798423 146.613683 \nL 228.819729 143.101693 \nL 228.853209 150.457546 \nL 228.89582 145.447605 \nL 228.898864 149.158675 \nL 228.968868 138.404401 \nL 228.999305 139.292034 \nL 229.01148 139.238813 \nL 229.014523 139.557305 \nL 229.041916 133.614982 \nL 229.029742 144.197803 \nL 229.124095 139.571763 \nL 229.203231 144.906884 \nL 229.218449 137.692673 \nL 229.233667 139.962556 \nL 229.270191 134.976523 \nL 229.324977 143.923247 \nL 229.343239 135.018147 \nL 229.404113 142.118283 \nL 229.452811 135.489211 \nL 229.525859 132.178071 \nL 229.507597 140.775328 \nL 229.547165 139.04264 \nL 229.629344 145.237249 \nL 229.55934 134.865306 \nL 229.656737 141.066527 \nL 229.757178 136.098806 \nL 229.671955 144.156248 \nL 229.766309 138.120425 \nL 229.772396 140.884485 \nL 229.863706 132.052446 \nL 229.875881 139.278155 \nL 229.897187 131.572635 \nL 229.979366 139.42919 \nL 229.988497 136.258897 \nL 230.073719 142.118145 \nL 230.009802 130.675948 \nL 230.08285 136.842333 \nL 230.085894 134.901685 \nL 230.143724 141.703925 \nL 230.186335 138.804096 \nL 230.192422 143.217582 \nL 230.283732 131.927478 \nL 230.28982 132.103793 \nL 230.371999 136.977176 \nL 230.311125 128.590281 \nL 230.393304 130.757773 \nL 230.457221 129.431232 \nL 230.435916 136.702499 \nL 230.466352 132.53961 \nL 230.582012 151.053563 \nL 230.530269 127.917725 \nL 230.591143 146.378109 \nL 230.706802 117.483348 \nL 230.731151 118.906127 \nL 230.737239 125.322628 \nL 230.795068 117.624483 \nL 230.843767 122.924986 \nL 230.965514 129.673753 \nL 230.898553 119.910148 \nL 230.971601 127.917596 \nL 231.05378 116.870254 \nL 231.08726 118.096872 \nL 231.129872 113.2755 \nL 231.11161 119.801333 \nL 231.215094 113.71699 \nL 231.254662 111.705901 \nL 231.330754 118.812968 \nL 231.333797 119.10115 \nL 231.349016 110.795667 \nL 231.412933 116.55713 \nL 231.516417 106.329687 \nL 231.428151 119.397171 \nL 231.534679 109.456753 \nL 231.571203 101.218511 \nL 231.653382 105.91647 \nL 231.705125 109.066351 \nL 231.75078 101.338173 \nL 231.759911 103.649477 \nL 231.772085 107.78514 \nL 231.775129 109.282736 \nL 231.866439 100.108145 \nL 231.872526 102.828317 \nL 231.924269 111.837009 \nL 231.942531 101.466282 \nL 232.042972 96.947638 \nL 231.951662 104.351128 \nL 232.052103 102.356425 \nL 232.189068 109.456603 \nL 232.064278 96.920835 \nL 232.198199 105.494405 \nL 232.228636 116.389817 \nL 232.313858 101.045297 \nL 232.38995 93.760665 \nL 232.469085 97.347001 \nL 232.493435 117.976697 \nL 232.478216 95.256078 \nL 232.575614 96.865849 \nL 232.609094 94.593167 \nL 232.673011 104.05795 \nL 232.685186 96.470344 \nL 232.761277 92.891534 \nL 232.69736 102.668134 \nL 232.803889 93.536829 \nL 232.883024 104.254724 \nL 232.922592 98.785458 \nL 232.925635 97.503631 \nL 232.97129 102.739173 \nL 233.026076 100.935984 \nL 233.056513 99.516075 \nL 233.065644 103.338519 \nL 233.068688 106.25815 \nL 233.132605 99.516051 \nL 233.175216 105.176623 \nL 233.281744 100.91302 \nL 233.196522 107.915431 \nL 233.287832 102.238368 \nL 233.412622 110.941253 \nL 233.296963 101.104762 \nL 233.415666 110.939784 \nL 233.424797 109.364545 \nL 233.464364 115.531037 \nL 233.479583 112.670345 \nL 233.488714 115.37368 \nL 233.573936 105.196177 \nL 233.583067 110.276381 \nL 233.680465 103.776064 \nL 233.640897 112.748112 \nL 233.695683 106.616141 \nL 233.771775 102.356076 \nL 233.786993 109.456263 \nL 233.808299 105.193376 \nL 233.811342 105.213675 \nL 233.814386 105.152936 \nL 233.81743 105.196116 \nL 233.866128 107.113506 \nL 233.872216 100.879793 \nL 233.927002 104.903876 \nL 234.009181 120.727813 \nL 233.981788 98.771089 \nL 234.039617 107.001739 \nL 234.12484 85.159302 \nL 234.176582 89.334929 \nL 234.210063 86.292537 \nL 234.292242 95.208253 \nL 234.395726 88.155599 \nL 234.304416 96.675813 \nL 234.410945 88.155646 \nL 234.417032 88.038982 \nL 234.420076 88.668266 \nL 234.480949 94.7502 \nL 234.502255 85.315572 \nL 234.532691 89.575657 \nL 234.538779 86.002561 \nL 234.587477 93.239727 \nL 234.63922 89.575649 \nL 234.751835 98.487949 \nL 234.687918 87.891382 \nL 234.760966 95.255711 \nL 234.840102 87.018643 \nL 234.824883 97.873171 \nL 234.885757 89.506933 \nL 234.897931 92.415649 \nL 234.940543 84.962756 \nL 234.98011 87.827112 \nL 235.050115 92.415721 \nL 235.098813 82.241175 \nL 235.196211 90.000324 \nL 235.153599 80.17253 \nL 235.208385 83.296393 \nL 235.333176 72.304035 \nL 235.266215 86.576461 \nL 235.339263 72.535207 \nL 235.418398 75.560006 \nL 235.433617 68.067895 \nL 235.451879 74.391627 \nL 235.49449 77.078388 \nL 235.464053 69.375491 \nL 235.55232 73.955265 \nL 235.661892 66.143157 \nL 235.561451 76.017199 \nL 235.664935 71.541391 \nL 235.719721 76.795362 \nL 235.683197 66.522303 \nL 235.774507 70.92534 \nL 235.786682 72.832294 \nL 235.832337 67.069742 \nL 235.877992 68.814046 \nL 235.899298 67.822193 \nL 235.938865 75.802916 \nL 235.966258 74.933078 \nL 236.014957 81.424099 \nL 236.027132 71.607026 \nL 236.07583 81.37531 \nL 236.188446 68.220352 \nL 236.19149 69.695204 \nL 236.200621 72.871893 \nL 236.264538 66.628494 \nL 236.304105 70.595277 \nL 236.371066 72.397964 \nL 236.328455 62.595053 \nL 236.395415 66.712738 \nL 236.489769 63.669973 \nL 236.459332 70.450487 \nL 236.495856 65.435169 \nL 236.504987 70.053615 \nL 236.593254 57.817651 \nL 236.599341 62.737906 \nL 236.696738 52.46625 \nL 236.718044 58.103065 \nL 236.745437 62.898157 \nL 236.727175 55.126091 \nL 236.827616 58.30831 \nL 236.833703 58.455128 \nL 236.836747 57.437181 \nL 236.873271 55.47609 \nL 236.906751 62.595239 \nL 236.949363 56.93578 \nL 236.961537 56.736253 \nL 236.964581 57.341864 \nL 237.083284 69.226844 \nL 237.043716 54.074979 \nL 237.086328 67.276593 \nL 237.189812 55.386113 \nL 237.201987 57.663147 \nL 237.253729 63.831696 \nL 237.214162 54.543663 \nL 237.317646 59.75524 \nL 237.329821 59.768745 \nL 237.332865 59.755352 \nL 237.442437 55.062936 \nL 237.357214 63.8878 \nL 237.44548 58.177079 \nL 237.50331 61.007162 \nL 237.548965 53.96274 \nL 237.555052 57.213229 \nL 237.588533 53.628395 \nL 237.579402 59.860103 \nL 237.670712 55.366617 \nL 237.749847 62.052192 \nL 237.734629 54.95267 \nL 237.77724 59.202199 \nL 237.886812 43.720301 \nL 237.798546 61.334738 \nL 237.895943 45.555047 \nL 237.90203 45.697056 \nL 237.905074 45.051298 \nL 237.990297 42.18551 \nL 237.981166 50.668173 \nL 238.014646 44.694036 \nL 238.102912 49.875628 \nL 238.127262 48.478353 \nL 238.182048 43.623529 \nL 238.169873 51.235264 \nL 238.236834 47.479331 \nL 238.303794 51.467388 \nL 238.261183 45.555149 \nL 238.352493 51.235307 \nL 238.370755 45.728523 \nL 238.395104 55.107722 \nL 238.465109 50.67705 \nL 238.5412 55.860609 \nL 238.480327 47.543836 \nL 238.583812 54.004651 \nL 238.605117 56.151633 \nL 238.623379 49.199028 \nL 238.644685 46.34741 \nL 238.711646 54.230283 \nL 238.735995 48.388336 \nL 238.769475 52.325811 \nL 238.775563 46.230797 \nL 238.78165 43.840491 \nL 238.836436 51.655387 \nL 238.882091 48.391695 \nL 238.885135 48.395425 \nL 238.927746 41.978785 \nL 238.985576 51.622346 \nL 238.988619 51.2036 \nL 238.991663 51.235482 \nL 238.994707 50.26712 \nL 239.064711 51.781583 \nL 239.116453 39.763307 \nL 239.156021 41.625573 \nL 239.14689 35.911083 \nL 239.168196 37.353727 \nL 239.229069 33.982575 \nL 239.210807 43.007064 \nL 239.277768 36.796087 \nL 239.280811 42.715412 \nL 239.378209 35.237042 \nL 239.390383 41.312334 \nL 239.396471 41.27436 \nL 239.399514 41.350207 \nL 239.402558 41.29535 \nL 239.493868 32.775193 \nL 239.429951 45.827091 \nL 239.524305 35.615403 \nL 239.563873 38.329686 \nL 239.573004 35.188583 \nL 239.658226 40.384695 \nL 239.606484 32.542963 \nL 239.685619 36.922101 \nL 239.801279 49.726472 \nL 239.706925 32.775273 \nL 239.853021 47.469014 \nL 239.907807 40.859057 \nL 239.965637 41.295663 \nL 239.96868 41.227152 \nL 239.971724 41.918687 \nL 239.977811 45.932311 \nL 239.99303 34.174402 \nL 240.078252 41.048892 \nL 240.190868 35.625552 \nL 240.129995 43.939683 \nL 240.193912 38.455518 \nL 240.248698 45.441888 \nL 240.309571 45.184102 \nL 240.434361 31.586103 \nL 240.543933 41.295663 \nL 240.501322 31.544377 \nL 240.550021 37.916863 \nL 240.562195 35.497583 \nL 240.647418 40.959675 \nL 240.653505 38.15208 \nL 240.696117 44.135844 \nL 240.708291 35.35561 \nL 240.766121 39.878175 \nL 240.775252 41.779629 \nL 240.778296 37.900206 \nL 240.854387 34.131724 \nL 240.817863 42.71587 \nL 240.887868 37.053902 \nL 240.890911 37.001605 \nL 240.896999 38.181388 \nL 240.979178 41.197008 \nL 240.90613 33.80979 \nL 241.006571 37.87979 \nL 241.125274 49.268425 \nL 241.152667 45.555833 \nL 241.210496 38.877205 \nL 241.173972 47.315896 \nL 241.274413 41.295852 \nL 241.390073 47.185316 \nL 241.307894 38.069777 \nL 241.39616 45.555891 \nL 241.402247 45.681105 \nL 241.408335 43.722969 \nL 241.45399 39.34157 \nL 241.499645 48.521213 \nL 241.508776 46.99844 \nL 241.52095 47.023698 \nL 241.523994 46.581407 \nL 241.587911 49.255411 \nL 241.642697 38.580892 \nL 241.651828 43.651543 \nL 241.712701 33.265906 \nL 241.740094 39.784516 \nL 241.822273 31.355488 \nL 241.797924 40.640678 \nL 241.849666 37.257726 \nL 241.88619 44.165399 \nL 241.895321 33.823582 \nL 241.962282 39.762246 \nL 242.056636 35.192771 \nL 241.986631 41.14181 \nL 242.071854 39.890833 \nL 242.077941 39.938318 \nL 242.084029 38.182471 \nL 242.090116 44.498595 \nL 242.138815 31.25788 \nL 242.193601 37.073039 \nL 242.205775 35.730351 \nL 242.199688 37.078688 \nL 242.208819 36.22246 \nL 242.214906 30.471581 \nL 242.30926 42.715797 \nL 242.312304 42.319376 \nL 242.361002 52.275106 \nL 242.418832 40.505961 \nL 242.424919 44.229438 \nL 242.531448 46.982576 \nL 242.49188 38.455736 \nL 242.534491 44.77154 \nL 242.564928 38.339552 \nL 242.622758 48.80116 \nL 242.644063 44.135858 \nL 242.6745 42.715782 \nL 242.665369 47.650306 \nL 242.692762 46.677895 \nL 242.774941 53.375106 \nL 242.714068 43.888084 \nL 242.805378 51.349641 \nL 242.975823 38.305337 \nL 242.844945 54.883058 \nL 242.98191 39.560974 \nL 243.07322 43.754291 \nL 243.012347 36.798785 \nL 243.085395 38.455678 \nL 243.088439 35.502113 \nL 243.179749 48.416669 \nL 243.191923 42.715688 \nL 243.237578 39.875732 \nL 243.219316 47.080823 \nL 243.301495 42.730876 \nL 243.377587 44.231968 \nL 243.319757 41.148666 \nL 243.383674 41.978516 \nL 243.459766 37.627508 \nL 243.447591 47.54401 \nL 243.484115 44.056718 \nL 243.596731 57.234023 \nL 243.508465 42.343078 \nL 243.633255 54.076295 \nL 243.64543 57.204322 \nL 243.758045 47.847622 \nL 243.867617 57.815717 \nL 243.8037 46.839789 \nL 243.870661 54.812667 \nL 243.882836 47.971077 \nL 243.931534 57.884013 \nL 243.983277 49.816213 \nL 244.041106 57.637231 \nL 244.031975 49.533896 \nL 244.098936 55.138913 \nL 244.211552 44.136142 \nL 244.114154 65.701767 \nL 244.214595 45.85919 \nL 244.220683 51.774083 \nL 244.275469 41.758675 \nL 244.324167 48.037341 \nL 244.348517 44.136149 \nL 244.378953 54.076285 \nL 244.430696 49.708637 \nL 244.552442 57.442543 \nL 244.455045 48.396159 \nL 244.570704 53.091019 \nL 244.668102 49.816468 \nL 244.637665 59.756668 \nL 244.674189 57.470909 \nL 244.719844 49.149362 \nL 244.725931 47.239264 \nL 244.783761 59.955926 \nL 244.811154 55.506129 \nL 244.856809 64.940329 \nL 244.92377 59.652292 \nL 244.935944 61.582334 \nL 244.945075 56.895359 \nL 244.948119 56.971449 \nL 245.021167 49.656187 \nL 244.960294 59.954632 \nL 245.060735 54.527759 \nL 245.152045 59.975051 \nL 245.097259 53.494332 \nL 245.17335 57.025841 \nL 245.261617 52.656609 \nL 245.225093 58.624126 \nL 245.276835 55.496706 \nL 245.38032 63.259781 \nL 245.32249 54.076557 \nL 245.389451 59.127625 \nL 245.511197 45.257055 \nL 245.514241 46.039821 \nL 245.602507 52.361717 \nL 245.581202 39.876256 \nL 245.611638 45.556327 \nL 245.614682 41.214501 \nL 245.672512 51.236551 \nL 245.718167 49.816511 \nL 245.809477 44.136346 \nL 245.754691 52.726221 \nL 245.830782 48.334657 \nL 245.833826 48.331233 \nL 245.849044 49.992932 \nL 245.87035 44.136338 \nL 245.952529 37.981251 \nL 245.90383 48.759754 \nL 245.982966 42.188222 \nL 246.095581 49.686557 \nL 246.025577 41.343554 \nL 246.113843 48.396522 \nL 246.214284 55.756091 \nL 246.132105 48.236955 \nL 246.226459 50.245417 \nL 246.314725 46.959113 \nL 246.241677 54.34859 \nL 246.339075 48.937861 \nL 246.345162 52.681656 \nL 246.354293 45.459563 \nL 246.445603 50.466836 \nL 246.448647 46.97657 \nL 246.472996 55.953382 \nL 246.552131 52.690544 \nL 246.558219 52.400048 \nL 246.649529 57.52674 \nL 246.661703 51.236638 \nL 246.664747 52.589076 \nL 246.728664 45.251428 \nL 246.676922 55.496822 \nL 246.78345 45.556574 \nL 246.859542 51.236587 \nL 246.853454 44.600354 \nL 246.896066 50.432657 \nL 246.99955 45.224745 \nL 246.938677 55.452864 \nL 247.002594 49.114769 \nL 247.09086 52.88152 \nL 247.029987 44.901611 \nL 247.11521 51.172846 \nL 247.212607 41.020624 \nL 247.157821 51.239284 \nL 247.230869 46.580171 \nL 247.267393 50.932684 \nL 247.252175 39.324091 \nL 247.340441 46.688161 \nL 247.343485 45.369262 \nL 247.428708 56.403406 \nL 247.462188 57.24928 \nL 247.495668 50.60891 \nL 247.526105 55.346059 \nL 247.547411 47.918293 \nL 247.602197 55.925158 \nL 247.638721 53.07338 \nL 247.69655 46.976955 \nL 247.684376 60.871256 \nL 247.726987 55.497106 \nL 247.827428 60.071416 \nL 247.775686 49.131688 \nL 247.836559 58.876364 \nL 247.842646 52.790144 \nL 247.921782 75.676197 \nL 247.937 68.528086 \nL 247.940044 72.537657 \nL 248.025266 48.28822 \nL 248.031354 48.397039 \nL 248.125707 58.335829 \nL 248.064834 45.815022 \nL 248.150057 56.07748 \nL 248.1531 49.816904 \nL 248.204843 61.485348 \nL 248.256585 61.243256 \nL 248.259629 61.17738 \nL 248.30224 56.659683 \nL 248.347895 65.849352 \nL 248.369201 60.518026 \nL 248.497035 74.5505 \nL 248.39355 56.869723 \nL 248.503122 68.27748 \nL 248.554864 60.568153 \nL 248.570083 71.765906 \nL 248.615738 63.544188 \nL 248.719222 75.457546 \nL 248.731397 72.537486 \nL 248.810532 63.749709 \nL 248.780096 72.615518 \nL 248.8501 66.84451 \nL 248.853144 66.857571 \nL 248.856187 66.737501 \nL 248.950541 69.283067 \nL 248.88358 62.597445 \nL 248.953585 67.44835 \nL 248.956628 61.853014 \nL 249.011414 72.803088 \nL 249.060113 72.379896 \nL 249.063157 70.855687 \nL 249.09055 82.329825 \nL 249.166641 74.271677 \nL 249.288388 63.776927 \nL 249.194034 74.495829 \nL 249.312737 65.030317 \nL 249.394916 71.54275 \nL 249.330999 61.597293 \nL 249.43144 69.697752 \nL 249.434484 69.676119 \nL 249.437528 70.206119 \nL 249.544056 77.200279 \nL 249.495357 69.679067 \nL 249.550143 72.174826 \nL 249.687108 88.360976 \nL 249.644497 71.117854 \nL 249.708414 79.602596 \nL 249.79668 69.697767 \nL 249.830161 72.224564 \nL 249.942776 81.864362 \nL 249.848423 70.889692 \nL 249.94582 79.350645 \nL 250.015824 82.774812 \nL 250.043217 78.095484 \nL 250.049305 80.005289 \nL 250.143658 73.597643 \nL 250.055392 82.478196 \nL 250.168008 76.746718 \nL 250.180182 77.974843 \nL 250.18627 71.698955 \nL 250.238012 73.958068 \nL 250.292798 69.258562 \nL 250.271492 76.780508 \nL 250.347584 73.265679 \nL 250.353671 79.215154 \nL 250.36889 71.048471 \nL 250.4602 75.811492 \nL 250.518029 69.377054 \nL 250.55151 79.319502 \nL 250.569772 75.237687 \nL 250.572815 75.378154 \nL 250.581946 73.00123 \nL 250.703693 64.701592 \nL 250.804134 75.398196 \nL 250.743261 63.219502 \nL 250.816309 69.698123 \nL 250.819352 68.032022 \nL 250.877182 78.218379 \nL 250.919793 72.466089 \nL 250.938055 75.378354 \nL 250.941099 75.176094 \nL 250.968492 79.63832 \nL 251.038496 72.425447 \nL 251.050671 75.546301 \nL 251.062846 70.17924 \nL 251.102413 76.666538 \nL 251.157199 75.244237 \nL 251.248509 87.730884 \nL 251.303295 82.47854 \nL 251.318514 85.318648 \nL 251.312426 80.005907 \nL 251.330688 81.756525 \nL 251.412867 74.175549 \nL 251.388518 82.696984 \nL 251.443304 79.373766 \nL 251.543745 85.866872 \nL 251.492003 76.213872 \nL 251.549832 83.313312 \nL 251.558963 73.932959 \nL 251.659404 83.421145 \nL 251.662448 85.921192 \nL 251.711147 76.415569 \nL 251.765933 80.464793 \nL 251.775064 82.478758 \nL 251.784195 77.817054 \nL 251.790282 79.638564 \nL 251.857243 68.553261 \nL 251.799413 80.657831 \nL 251.902898 74.558051 \nL 251.918116 79.142699 \nL 251.924203 69.621062 \nL 252.015513 78.063734 \nL 252.122042 72.581721 \nL 252.064212 81.058836 \nL 252.128129 76.940083 \nL 252.152478 76.488842 \nL 252.146391 82.66934 \nL 252.158566 81.941503 \nL 252.167697 86.896977 \nL 252.240745 76.252617 \nL 252.259007 77.798361 \nL 252.338142 68.278367 \nL 252.374666 72.508153 \nL 252.380753 72.589002 \nL 252.383797 72.538711 \nL 252.386841 71.042545 \nL 252.462932 82.984385 \nL 252.478151 80.508406 \nL 252.584679 83.772933 \nL 252.53598 78.218764 \nL 252.587723 82.126258 \nL 252.648596 73.217453 \nL 252.700338 79.57507 \nL 252.80991 88.1592 \nL 252.712513 77.056412 \nL 252.812954 82.191595 \nL 252.843391 79.369036 \nL 252.879915 85.79082 \nL 252.916439 85.319217 \nL 252.92557 85.62603 \nL 252.928613 84.604333 \nL 252.983399 80.793994 \nL 253.026011 87.868603 \nL 253.038185 85.319073 \nL 253.129495 75.378951 \nL 253.162976 79.22835 \nL 253.330377 89.593838 \nL 253.190369 76.798961 \nL 253.339508 86.612539 \nL 253.345596 83.971314 \nL 253.372989 89.579403 \nL 253.44908 86.739413 \nL 253.485604 91.442186 \nL 253.54039 83.899266 \nL 253.567783 81.264677 \nL 253.653006 90.196112 \nL 253.659093 83.277226 \nL 253.729098 94.563412 \nL 253.759534 92.422036 \nL 253.768665 92.419615 \nL 253.777796 99.711518 \nL 253.859975 89.579532 \nL 253.878237 94.309263 \nL 253.933023 89.168818 \nL 253.966504 95.259677 \nL 253.987809 91.597537 \nL 254.069988 98.537454 \nL 254.091294 89.235175 \nL 254.097381 92.693697 \nL 254.136949 91.925269 \nL 254.149124 98.099863 \nL 254.164342 96.679819 \nL 254.261739 105.596004 \nL 254.191735 95.387781 \nL 254.283045 100.572013 \nL 254.374355 97.722969 \nL 254.29522 110.037531 \nL 254.398704 98.016261 \nL 254.523495 106.250833 \nL 254.541757 103.550349 \nL 254.648285 100.388498 \nL 254.596543 110.761437 \nL 254.651329 100.939945 \nL 254.739595 109.761314 \nL 254.709158 99.27712 \nL 254.766988 105.393721 \nL 254.830905 99.279455 \nL 254.788294 108.040107 \nL 254.879604 102.36003 \nL 254.90091 99.075333 \nL 255.004394 113.854751 \nL 255.101792 108.615493 \nL 255.095704 115.694793 \nL 255.110923 113.431824 \nL 255.20832 118.282564 \nL 255.126141 108.14912 \nL 255.223538 116.15652 \nL 255.260062 110.880231 \nL 255.330067 116.732901 \nL 255.412246 122.240431 \nL 255.418333 114.540338 \nL 255.442682 117.98034 \nL 255.445726 117.969486 \nL 255.44877 118.112124 \nL 255.454857 116.361468 \nL 255.48225 124.026857 \nL 255.527905 121.11943 \nL 255.555298 128.313799 \nL 255.549211 115.140179 \nL 255.634433 117.081037 \nL 255.716612 110.880171 \nL 255.707481 120.282056 \nL 255.747049 116.560282 \nL 255.753136 116.744473 \nL 255.75618 115.896107 \nL 255.765311 112.626021 \nL 255.817053 121.136472 \nL 255.838359 120.820349 \nL 255.932713 125.259843 \nL 255.865752 116.803177 \nL 255.941844 121.766092 \nL 255.987499 115.181595 \nL 256.002717 125.364559 \nL 256.051416 122.240431 \nL 256.118376 117.974618 \nL 256.1549 123.815295 \nL 256.167075 118.27439 \nL 256.30404 132.407911 \nL 256.307084 131.580916 \nL 256.389263 122.72612 \nL 256.419699 127.900007 \nL 256.444049 129.501891 \nL 256.459267 124.630417 \nL 256.465354 123.534345 \nL 256.489704 128.923262 \nL 256.559708 126.329379 \nL 256.66928 131.059859 \nL 256.617538 126.206048 \nL 256.678411 131.003118 \nL 256.739284 127.57367 \nL 256.781896 133.600574 \nL 256.791027 129.919186 \nL 256.803201 137.257581 \nL 256.879293 128.971169 \nL 256.897555 129.417303 \nL 256.912773 124.899654 \nL 256.973647 131.936704 \nL 256.991909 129.512044 \nL 257.077131 141.05765 \nL 257.122786 138.263944 \nL 257.177572 129.340528 \nL 257.238446 132.180632 \nL 257.265839 137.82609 \nL 257.326712 130.289444 \nL 257.348018 132.540408 \nL 257.439328 123.419801 \nL 257.463677 127.158401 \nL 257.466721 125.080442 \nL 257.558031 136.440696 \nL 257.561074 137.901111 \nL 257.652384 130.849988 \nL 257.661515 133.40399 \nL 257.725432 125.080368 \nL 257.679777 136.440616 \nL 257.774131 130.554363 \nL 257.886747 136.760146 \nL 257.801524 126.500412 \nL 257.88979 136.229959 \nL 257.999362 127.920497 \nL 257.938489 137.860741 \nL 258.002406 128.222555 \nL 258.069367 138.6939 \nL 258.060236 127.920423 \nL 258.115022 133.605052 \nL 258.127196 133.546869 \nL 258.13024 133.794153 \nL 258.203288 126.026954 \nL 258.236768 133.798328 \nL 258.251987 130.000055 \nL 258.379821 140.700657 \nL 258.315904 124.986287 \nL 258.385908 136.545351 \nL 258.413301 131.755966 \nL 258.449825 138.154508 \nL 258.501567 133.600652 \nL 258.562441 137.860665 \nL 258.544179 127.809135 \nL 258.608096 132.180598 \nL 258.705493 127.705706 \nL 258.653751 135.020744 \nL 258.717668 130.578563 \nL 258.830283 140.16229 \nL 258.732886 130.469588 \nL 258.833327 137.080526 \nL 258.939855 130.916967 \nL 258.900288 144.971545 \nL 258.942899 135.020706 \nL 258.95203 138.154042 \nL 258.988554 130.760624 \nL 259.058558 137.282479 \nL 259.067689 131.931964 \nL 259.149868 141.197033 \nL 259.16813 137.887608 \nL 259.174218 137.747658 \nL 259.177261 138.70857 \nL 259.238135 135.87816 \nL 259.302052 145.695965 \nL 259.381187 136.290528 \nL 259.338576 146.380966 \nL 259.414667 142.120897 \nL 259.420755 142.233316 \nL 259.423798 141.664804 \nL 259.475541 130.760564 \nL 259.545545 136.296766 \nL 259.569894 138.209119 \nL 259.645986 146.212834 \nL 259.585113 134.768426 \nL 259.68251 141.927043 \nL 259.722078 149.349248 \nL 259.706859 139.484134 \nL 259.782951 141.150819 \nL 259.89861 134.94367 \nL 259.86513 147.88974 \nL 259.901654 137.860889 \nL 260.01427 149.373161 \nL 260.023401 142.120931 \nL 260.029488 138.954697 \nL 260.090361 149.326305 \nL 260.126885 147.878638 \nL 260.166453 149.69676 \nL 260.178628 144.092655 \nL 260.254719 141.051488 \nL 260.19689 148.875053 \nL 260.285156 143.262562 \nL 260.309505 148.311143 \nL 260.330811 140.389462 \nL 260.397772 145.074704 \nL 260.44647 138.871578 \nL 260.422121 146.231076 \nL 260.525606 140.700917 \nL 260.632134 146.380966 \nL 260.626047 134.255313 \nL 260.638221 143.137446 \nL 260.738662 129.34058 \nL 260.756924 131.632734 \nL 260.839103 136.311869 \nL 260.845191 126.500507 \nL 260.866496 131.754461 \nL 260.899977 137.088522 \nL 260.969981 133.937185 \nL 261.061291 130.363088 \nL 260.979112 136.74848 \nL 261.079553 132.715553 \nL 261.131295 133.888074 \nL 261.146514 126.50948 \nL 261.161732 128.576275 \nL 261.271304 122.240489 \nL 261.204343 131.548673 \nL 261.274348 125.466146 \nL 261.353483 136.105601 \nL 261.390007 132.291396 \nL 261.426531 134.178107 \nL 261.493492 125.169451 \nL 261.563496 120.367802 \nL 261.499579 127.920564 \nL 261.615238 121.253004 \nL 261.697417 129.340695 \nL 261.730898 125.165985 \nL 261.825251 122.328334 \nL 261.797858 130.049071 \nL 261.837426 126.480502 \nL 261.852644 126.463647 \nL 261.855688 126.760489 \nL 261.983522 115.166464 \nL 261.876994 130.652083 \nL 261.986566 117.98021 \nL 262.038308 111.138017 \nL 262.102225 121.963077 \nL 262.108312 115.140179 \nL 262.175273 124.055397 \nL 262.214841 119.400205 \nL 262.245277 126.500411 \nL 262.324413 117.74579 \nL 262.367024 126.50035 \nL 262.409636 116.413418 \nL 262.433985 117.456773 \nL 262.528339 121.699606 \nL 262.482684 114.060147 \nL 262.546601 118.009295 \nL 262.577037 117.937352 \nL 262.580081 117.980112 \nL 262.692697 125.979814 \nL 262.589212 117.737271 \nL 262.698784 123.322884 \nL 262.777919 113.720006 \nL 262.707915 123.916978 \nL 262.823574 116.560075 \nL 262.896622 122.11976 \nL 262.887491 115.60997 \nL 262.933146 116.455605 \nL 262.951408 121.606702 \nL 263.030544 114.994125 \nL 263.039675 115.047453 \nL 263.06098 121.384725 \nL 263.079242 112.721076 \nL 263.149247 116.726719 \nL 263.258819 109.28609 \nL 263.207076 117.604138 \nL 263.26795 109.456179 \nL 263.274037 109.400748 \nL 263.277081 109.600369 \nL 263.286212 113.928697 \nL 263.362303 103.803074 \nL 263.389696 111.126097 \nL 263.447526 108.039668 \nL 263.432308 116.559983 \nL 263.474919 111.028866 \nL 263.511443 115.338058 \nL 263.538836 106.619742 \nL 263.587535 112.46333 \nL 263.590578 112.472535 \nL 263.672757 104.957863 \nL 263.645364 113.719828 \nL 263.706238 109.459742 \nL 263.767111 115.139775 \nL 263.779286 106.044012 \nL 263.827984 113.71986 \nL 263.840159 113.404307 \nL 263.858421 119.399988 \nL 263.901032 116.95909 \nL 263.952775 122.031432 \nL 263.967993 113.460795 \nL 264.010604 116.55975 \nL 264.074521 105.816081 \nL 264.028866 117.553179 \nL 264.12322 111.08652 \nL 264.211486 109.210782 \nL 264.174962 115.64152 \nL 264.220617 111.210714 \nL 264.324102 121.310231 \nL 264.266272 109.393552 \nL 264.33932 119.16138 \nL 264.372801 113.475668 \nL 264.439761 122.316315 \nL 264.442805 122.623416 \nL 264.458023 114.632657 \nL 264.473242 115.290967 \nL 264.564552 110.496817 \nL 264.485416 119.602534 \nL 264.582814 115.422603 \nL 264.610207 113.426371 \nL 264.658905 118.796374 \nL 264.661949 118.845241 \nL 264.664993 115.139727 \nL 264.731953 124.980006 \nL 264.768477 123.422364 \nL 264.811089 119.933832 \nL 264.829351 126.159029 \nL 264.871962 122.900481 \nL 264.875006 126.972562 \nL 264.960228 119.190345 \nL 264.981534 125.004904 \nL 265.005883 119.66676 \nL 265.088062 128.400901 \nL 265.103281 121.874955 \nL 265.179372 129.471413 \nL 265.18546 129.101307 \nL 265.191547 132.631534 \nL 265.285901 123.130676 \nL 265.361992 119.366607 \nL 265.313294 126.258432 \nL 265.395473 122.239745 \nL 265.462433 126.328248 \nL 265.453302 119.604074 \nL 265.505045 122.437036 \nL 265.5507 120.37493 \nL 265.58418 126.292849 \nL 265.593311 125.079706 \nL 265.681577 129.408037 \nL 265.620704 121.971057 \nL 265.70897 128.297001 \nL 265.833761 117.940245 \nL 265.839848 120.228228 \nL 265.943333 126.499771 \nL 265.952464 123.7125 \nL 265.970726 122.158059 \nL 265.964638 123.808493 \nL 265.973769 122.2397 \nL 266.083341 119.279551 \nL 266.037686 126.747482 \nL 266.086385 121.298706 \nL 266.09856 124.824606 \nL 266.16552 115.950657 \nL 266.195957 120.992801 \nL 266.31466 113.277215 \nL 266.25683 122.152322 \nL 266.326835 116.378888 \nL 266.40597 124.254011 \nL 266.351184 112.082277 \nL 266.448581 122.017271 \nL 266.475974 126.499811 \nL 266.494236 117.576653 \nL 266.597721 112.29937 \nL 266.558153 123.051242 \nL 266.606852 115.111987 \nL 266.609896 115.074379 \nL 266.612939 115.139498 \nL 266.631201 119.545807 \nL 266.71338 110.983724 \nL 266.716424 108.033316 \nL 266.780341 116.559615 \nL 266.819909 112.814433 \nL 266.929481 119.557693 \nL 266.865564 108.093265 \nL 266.935568 115.960403 \nL 267.054271 106.619333 \nL 267.151668 109.816634 \nL 267.087751 104.140353 \nL 267.163843 106.629535 \nL 267.16993 106.568725 \nL 267.172974 106.743098 \nL 267.300808 116.248241 \nL 267.303852 116.111731 \nL 267.401249 109.792144 \nL 267.367769 117.181543 \nL 267.41038 116.28306 \nL 267.462122 126.516297 \nL 267.425598 113.058024 \nL 267.526039 120.819566 \nL 267.583869 108.919054 \nL 267.644742 113.488513 \nL 267.812144 125.019759 \nL 267.830406 123.65963 \nL 267.854755 116.266113 \nL 267.894323 124.926402 \nL 267.943022 117.635692 \nL 268.043463 123.659595 \nL 268.025201 113.954783 \nL 268.052594 118.923252 \nL 268.058681 116.559377 \nL 268.122598 124.053977 \nL 268.153035 122.2471 \nL 268.216952 126.879999 \nL 268.204777 117.310812 \nL 268.262607 122.307223 \nL 268.268694 122.068391 \nL 268.353917 110.296294 \nL 268.390441 115.342871 \nL 268.487838 123.28812 \nL 268.426965 113.581763 \nL 268.509144 119.541679 \nL 268.542624 119.128766 \nL 268.536537 122.488499 \nL 268.560886 122.23933 \nL 268.633934 125.221597 \nL 268.649152 117.948034 \nL 268.667414 120.819304 \nL 268.673502 120.775919 \nL 268.676545 120.970289 \nL 268.767855 117.480961 \nL 268.713069 127.919387 \nL 268.789161 119.399079 \nL 268.862209 123.85654 \nL 268.831772 116.381018 \nL 268.898733 119.847639 \nL 268.901777 118.939334 \nL 268.974825 127.667183 \nL 268.986999 126.499236 \nL 268.990043 126.467446 \nL 268.993087 126.998824 \nL 269.102659 118.511281 \nL 269.005261 132.184569 \nL 269.11179 119.477639 \nL 269.190925 123.190292 \nL 269.197012 117.681644 \nL 269.218318 121.181873 \nL 269.248755 117.831432 \nL 269.309628 126.361409 \nL 269.324846 123.475348 \nL 269.330934 126.974122 \nL 269.394851 116.394552 \nL 269.431375 120.880293 \nL 269.501379 119.108324 \nL 269.455724 126.111718 \nL 269.550078 119.398795 \nL 269.553121 123.235714 \nL 269.620082 111.20502 \nL 269.656606 113.718651 \nL 269.683999 112.161529 \nL 269.772265 121.280892 \nL 269.84227 117.640073 \nL 269.827051 123.261263 \nL 269.884881 120.8186 \nL 269.894012 120.856874 \nL 269.897056 120.727338 \nL 269.900099 120.818713 \nL 269.903143 123.574533 \nL 269.988366 111.67551 \nL 270.009672 119.639815 \nL 270.064458 113.769565 \nL 270.046196 120.787943 \nL 270.131418 116.378002 \nL 270.237947 121.959166 \nL 270.167942 114.467341 \nL 270.247078 121.915602 \nL 270.329257 114.611875 \nL 270.271427 124.216354 \nL 270.362737 116.634296 \nL 270.414479 114.197594 \nL 270.377955 118.212368 \nL 270.417523 117.978386 \nL 270.490571 124.920104 \nL 270.441872 112.173821 \nL 270.521008 115.035326 \nL 270.527095 118.880968 \nL 270.560575 113.89487 \nL 270.578837 114.18489 \nL 270.581881 111.965575 \nL 270.648842 119.750595 \nL 270.682322 117.962151 \nL 270.694497 116.149347 \nL 270.703628 121.623625 \nL 270.724933 134.732171 \nL 270.804069 119.337179 \nL 270.8132 120.818311 \nL 270.816243 121.076467 \nL 270.840593 113.797351 \nL 270.843636 115.138136 \nL 270.886248 107.888809 \nL 270.934946 117.978135 \nL 270.956252 110.986351 \nL 270.962339 109.654908 \nL 271.014082 118.983781 \nL 271.041475 116.558068 \nL 271.074955 125.706739 \nL 271.129741 115.422416 \nL 271.15409 120.344136 \nL 271.160178 114.877509 \nL 271.242357 125.071945 \nL 271.263662 122.014892 \nL 271.327579 112.630655 \nL 271.373234 119.747477 \nL 271.461501 126.883816 \nL 271.400627 116.46489 \nL 271.488894 123.474829 \nL 271.561942 128.934645 \nL 271.598466 121.432406 \nL 271.61064 128.156015 \nL 271.695863 117.977776 \nL 271.704994 119.297248 \nL 271.714125 122.183807 \nL 271.771955 116.557801 \nL 271.796304 118.182532 \nL 271.902832 114.649946 \nL 271.829784 122.779875 \nL 271.905876 116.307832 \nL 271.90892 122.237782 \nL 272.012404 111.882251 \nL 272.048928 126.183657 \nL 272.106758 110.877399 \nL 272.155457 101.055936 \nL 272.225461 102.780576 \nL 272.304596 113.717427 \nL 272.350251 113.71736 \nL 272.353295 114.087906 \nL 272.377644 106.603642 \nL 272.43243 111.545058 \nL 272.441561 105.197108 \nL 272.493304 114.120165 \nL 272.545046 109.775677 \nL 272.651574 115.25154 \nL 272.612007 106.26472 \nL 272.654618 112.297151 \nL 272.660705 104.223194 \nL 272.755059 118.340746 \nL 272.761146 115.137109 \nL 272.812889 114.660794 \nL 272.794627 121.717302 \nL 272.831151 119.39347 \nL 272.834194 119.40111 \nL 272.840282 119.328677 \nL 272.843325 119.88082 \nL 272.895068 111.996829 \nL 272.922461 114.635288 \nL 272.986378 109.756181 \nL 272.968116 116.943559 \nL 273.035076 112.296872 \nL 273.114212 117.643579 \nL 273.123343 111.350126 \nL 273.147692 114.329089 \nL 273.196391 113.2486 \nL 273.223784 119.778472 \nL 273.239002 118.487488 \nL 273.321181 122.40821 \nL 273.257264 111.678114 \nL 273.330312 115.359032 \nL 273.427709 107.019891 \nL 273.388142 117.779434 \nL 273.449015 110.333156 \nL 273.543369 121.130456 \nL 273.567718 117.976685 \nL 273.649897 123.128503 \nL 273.579893 115.349584 \nL 273.683377 119.917238 \nL 273.692508 125.451556 \nL 273.744251 117.524262 \nL 273.792949 120.302019 \nL 273.881216 116.181575 \nL 273.811211 125.076591 \nL 273.902521 119.396434 \nL 273.960351 142.1835 \nL 273.929914 112.586248 \nL 274.030355 127.101048 \nL 274.146015 117.976339 \nL 274.212975 124.702614 \nL 274.222106 115.136064 \nL 274.264718 120.832176 \nL 274.273849 120.671333 \nL 274.276892 121.339916 \nL 274.359071 125.07623 \nL 274.371246 116.326884 \nL 274.505167 122.813662 \nL 274.462556 114.967711 \nL 274.508211 121.506257 \nL 274.520386 115.957404 \nL 274.541691 140.696596 \nL 274.617783 120.816003 \nL 274.620827 121.115421 \nL 274.645176 112.95958 \nL 274.660394 107.452385 \nL 274.654307 116.26218 \nL 274.754748 113.715636 \nL 274.803447 125.219868 \nL 274.867364 116.595992 \nL 274.906931 113.715603 \nL 274.913019 118.43936 \nL 274.940412 117.748346 \nL 275.034765 123.787428 \nL 274.964761 111.813608 \nL 275.040853 117.025789 \nL 275.043896 115.135537 \nL 275.101726 120.96937 \nL 275.150425 116.555578 \nL 275.19608 116.555402 \nL 275.275215 127.779699 \nL 275.32087 135.880091 \nL 275.28739 126.495631 \nL 275.415224 129.445222 \nL 275.451748 127.346917 \nL 275.424355 135.015715 \nL 275.466966 132.074647 \nL 275.47001 139.275786 \nL 275.558276 127.331152 \nL 275.576538 132.175585 \nL 275.603931 149.851758 \nL 275.591756 127.915502 \nL 275.680023 131.865354 \nL 275.746983 123.453336 \nL 275.701328 132.766354 \nL 275.804813 125.075118 \nL 275.829162 129.875328 \nL 275.856555 119.798757 \nL 275.908298 121.447529 \nL 275.987433 116.554851 \nL 275.920472 125.721105 \nL 276.020913 119.423291 \nL 276.027001 117.850731 \nL 276.066568 125.83749 \nL 276.115267 124.284366 \nL 276.124398 119.394791 \nL 276.157878 129.155057 \nL 276.215708 123.654778 \nL 276.218752 126.377385 \nL 276.258319 116.828361 \nL 276.316149 117.14127 \nL 276.440939 110.641922 \nL 276.361804 122.330484 \nL 276.45007 110.781826 \nL 276.587035 121.183646 \nL 276.47442 109.454109 \nL 276.602254 116.712806 \nL 276.678345 110.480952 \nL 276.660083 120.524477 \nL 276.730088 111.187416 \nL 276.736175 113.783425 \nL 276.794005 105.193844 \nL 276.842703 112.570724 \nL 276.900533 105.193748 \nL 276.882271 115.609699 \nL 276.949232 113.445391 \nL 277.028367 118.329193 \nL 276.973581 109.266224 \nL 277.043585 112.367553 \nL 277.119677 109.976796 \nL 277.125764 118.332396 \nL 277.150114 114.648284 \nL 277.18055 116.356099 \nL 277.220118 105.193573 \nL 277.226205 107.839396 \nL 277.232293 102.584175 \nL 277.32969 113.225334 \nL 277.405782 119.768322 \nL 277.414913 107.906038 \nL 277.439262 113.855379 \nL 277.503179 112.069458 \nL 277.481873 116.439248 \nL 277.51231 114.676032 \nL 277.600577 113.713543 \nL 277.62797 119.308246 \nL 277.685799 110.821742 \nL 277.743629 116.221437 \nL 277.886681 124.862241 \nL 277.783197 112.293419 \nL 277.895812 121.837839 \nL 277.950598 116.553524 \nL 277.926249 126.147938 \nL 277.99321 120.495905 \nL 277.996253 125.073765 \nL 278.087563 116.553421 \nL 278.105825 123.859745 \nL 278.166699 118.808351 \nL 278.145393 127.958537 \nL 278.215397 124.477995 \nL 278.288445 127.810459 \nL 278.233659 121.92068 \nL 278.309751 124.087629 \nL 278.385843 116.007216 \nL 278.370624 130.843697 \nL 278.422367 120.813396 \nL 278.443672 124.793261 \nL 278.513677 115.133239 \nL 278.525851 118.77786 \nL 278.565419 114.668918 \nL 278.595856 128.13675 \nL 278.629336 120.754832 \nL 278.635423 126.493458 \nL 278.681078 117.973198 \nL 278.741952 123.653392 \nL 278.84848 126.49346 \nL 278.75717 117.973221 \nL 278.851524 124.315635 \nL 278.951965 116.86983 \nL 278.936746 124.318438 \nL 278.970227 117.973164 \nL 279.189371 134.408846 \nL 278.979358 113.625756 \nL 279.192414 130.9087 \nL 279.27155 124.721924 \nL 279.30503 126.493305 \nL 279.384165 139.024953 \nL 279.317205 125.146126 \nL 279.420689 133.7637 \nL 279.48765 123.652954 \nL 279.432864 135.27062 \nL 279.533305 127.913041 \nL 279.542436 127.84745 \nL 279.554611 130.689924 \nL 279.63679 138.107383 \nL 279.575916 125.128169 \nL 279.645921 128.722555 \nL 279.740274 123.906185 \nL 279.706794 132.646379 \nL 279.752449 127.725807 \nL 279.755493 129.938344 \nL 279.79506 118.61401 \nL 279.858977 126.227854 \nL 279.874196 122.599721 \nL 279.932025 134.654941 \nL 279.965506 127.872802 \nL 280.008117 129.274785 \nL 280.00203 124.837323 \nL 280.011161 125.241008 \nL 280.017248 120.745437 \nL 280.078121 127.771446 \nL 280.120733 124.527176 \nL 280.242479 115.105595 \nL 280.154213 126.902031 \nL 280.248567 116.552584 \nL 280.358139 127.285103 \nL 280.27596 116.438156 \nL 280.36727 123.483011 \nL 280.446405 130.752813 \nL 280.382488 122.192946 \nL 280.495104 126.602577 \nL 280.610763 116.386872 \nL 280.52554 128.847831 \nL 280.613807 116.552515 \nL 280.67468 125.072658 \nL 280.726422 122.525738 \nL 280.762946 116.277786 \nL 280.79947 123.897921 \nL 280.835994 119.2978 \nL 280.905999 123.935448 \nL 280.942523 117.643704 \nL 281.061226 107.38638 \nL 280.969916 120.300031 \nL 281.103837 112.202048 \nL 281.119055 119.58614 \nL 281.13123 109.452209 \nL 281.219496 114.982262 \nL 281.268195 119.392388 \nL 281.246889 113.243394 \nL 281.277326 113.495349 \nL 281.356461 109.024482 \nL 281.292544 116.929382 \nL 281.386898 114.63231 \nL 281.429509 111.757538 \nL 281.493426 117.640497 \nL 281.49647 115.132272 \nL 281.499514 115.193405 \nL 281.502557 114.064989 \nL 281.508645 107.209634 \nL 281.560387 121.585507 \nL 281.612129 116.290429 \nL 281.639522 125.715384 \nL 281.721701 118.402064 \nL 281.80388 112.368688 \nL 281.785618 125.072388 \nL 281.834317 116.310305 \nL 281.940845 120.962039 \nL 281.873885 113.562637 \nL 281.962151 118.887724 \nL 282.023024 111.348518 \nL 281.971282 122.578703 \nL 282.071723 118.424224 \nL 282.144771 122.428621 \nL 282.099116 115.940347 \nL 282.175208 118.884891 \nL 282.275649 110.510228 \nL 282.229994 123.632417 \nL 282.296954 110.524822 \nL 282.427832 119.082012 \nL 282.321304 109.187086 \nL 282.433919 116.647008 \nL 282.436963 115.145139 \nL 282.488705 130.611346 \nL 282.531317 122.232288 \nL 282.53436 123.695622 \nL 282.601321 108.061378 \nL 282.613496 109.240634 \nL 282.631758 103.736244 \nL 282.6835 112.292068 \nL 282.723068 107.225157 \nL 282.823509 116.952399 \nL 282.768723 100.134234 \nL 282.83264 108.856027 \nL 282.860033 106.253513 \nL 282.902644 116.637834 \nL 282.933081 116.461809 \nL 282.978736 119.279291 \nL 282.951343 111.505141 \nL 283.030478 116.545634 \nL 283.143094 107.917383 \nL 283.04874 117.972101 \nL 283.146137 108.92431 \nL 283.203967 113.666748 \nL 283.185705 105.191815 \nL 283.243535 106.359874 \nL 283.301364 104.804574 \nL 283.261797 110.871956 \nL 283.343976 107.549826 \nL 283.401805 114.068542 \nL 283.435286 104.770225 \nL 283.453548 106.847295 \nL 283.547901 100.97467 \nL 283.493115 113.556229 \nL 283.566163 105.191752 \nL 283.569207 105.311887 \nL 283.578338 102.282802 \nL 283.581382 102.351783 \nL 283.666604 98.509527 \nL 283.645299 105.291658 \nL 283.681823 102.899995 \nL 283.684866 107.632292 \nL 283.68791 100.93175 \nL 283.794438 106.61183 \nL 283.797482 106.605014 \nL 283.800526 106.862964 \nL 283.885748 100.931679 \nL 283.843137 110.932962 \nL 283.913141 104.970359 \nL 283.934447 109.197578 \nL 283.983146 102.300794 \nL 283.986189 102.361071 \nL 283.989233 102.362225 \nL 283.992277 102.35185 \nL 284.031844 100.553732 \nL 284.001408 105.480417 \nL 284.083587 100.931803 \nL 284.193159 112.634657 \nL 284.107936 97.799172 \nL 284.199246 109.09089 \nL 284.244901 112.627661 \nL 284.217508 106.43073 \nL 284.302731 108.201504 \nL 284.397084 102.795153 \nL 284.390997 113.027556 \nL 284.409259 106.31077 \nL 284.467089 112.477819 \nL 284.47622 104.851763 \nL 284.521875 108.098688 \nL 284.524918 108.032021 \nL 284.573617 112.774452 \nL 284.549268 106.323075 \nL 284.640578 111.708623 \nL 284.698407 106.985692 \nL 284.689276 116.823544 \nL 284.756237 110.241804 \nL 284.765368 112.360763 \nL 284.84146 102.002781 \nL 284.850591 102.512824 \nL 284.926682 98.690698 \nL 284.947988 105.949815 \nL 284.960163 102.272751 \nL 285.014949 106.754431 \nL 284.969294 102.112692 \nL 285.027123 106.611908 \nL 285.039298 139.144443 \nL 285.124521 96.375754 \nL 285.133652 99.511739 \nL 285.258442 105.388499 \nL 285.142783 94.658067 \nL 285.267573 103.467687 \nL 285.294966 100.743198 \nL 285.358883 109.652851 \nL 285.361927 109.45202 \nL 285.450193 138.158625 \nL 285.374102 102.118909 \nL 285.508023 119.075889 \nL 285.593246 98.697272 \nL 285.623682 106.088918 \nL 285.666294 100.931802 \nL 285.657163 107.681917 \nL 285.72108 105.256768 \nL 285.727167 110.872136 \nL 285.81239 100.058846 \nL 285.830652 106.528273 \nL 285.839783 102.956292 \nL 285.891525 126.768947 \nL 285.897612 130.979604 \nL 285.964573 108.729831 \nL 285.985879 112.292211 \nL 286.08632 94.82381 \nL 286.110669 95.738856 \nL 286.113713 94.644539 \nL 286.168499 105.192076 \nL 286.192848 104.95173 \nL 286.299376 114.248868 \nL 286.205023 100.705647 \nL 286.311551 111.626544 \nL 286.332857 108.259932 \nL 286.354162 130.457716 \nL 286.366337 126.492522 \nL 286.369381 129.049003 \nL 286.424167 114.13441 \nL 286.460691 115.217277 \nL 286.558088 99.265713 \nL 286.603743 104.990929 \nL 286.722446 126.036099 \nL 286.612874 103.307733 \nL 286.746795 122.232474 \nL 286.85028 110.863803 \nL 286.771145 122.443327 \nL 286.880717 112.516445 \nL 286.892891 116.711964 \nL 286.953765 107.617985 \nL 286.987245 110.995228 \nL 287.002463 108.449671 \nL 287.075511 103.671339 \nL 287.069424 113.944508 \nL 287.115079 105.089911 \nL 287.21552 115.271597 \nL 287.236826 108.133205 \nL 287.325092 102.351982 \nL 287.245957 114.177367 \nL 287.355529 105.192178 \nL 287.370747 110.872276 \nL 287.395096 104.537134 \nL 287.465101 106.821108 \nL 287.471188 103.65233 \nL 287.510756 115.172633 \nL 287.565542 113.941695 \nL 287.595978 109.211253 \nL 287.586847 119.283991 \nL 287.620328 116.88584 \nL 287.656852 118.466087 \nL 287.67207 112.019279 \nL 287.732943 118.283607 \nL 287.85469 110.623621 \nL 287.872952 115.132471 \nL 287.985568 123.467691 \nL 287.894258 112.900696 \nL 287.988611 122.482896 \nL 287.991655 121.223179 \nL 288.028179 126.55824 \nL 288.09514 123.665342 \nL 288.098183 123.620742 \nL 288.104271 124.773488 \nL 288.107314 125.768012 \nL 288.1621 116.552415 \nL 288.195581 119.645541 \nL 288.271672 113.467025 \nL 288.235148 121.833526 \nL 288.31124 113.933054 \nL 288.332546 117.958915 \nL 288.34472 112.292456 \nL 288.420812 113.712479 \nL 288.509078 109.586176 \nL 288.515166 119.152366 \nL 288.533428 110.760836 \nL 288.639956 101.691533 \nL 288.548646 115.497467 \nL 288.658218 105.671411 \nL 288.716048 102.991452 \nL 288.783008 114.52275 \nL 288.789096 109.195363 \nL 288.849969 118.217681 \nL 288.889537 118.078391 \nL 288.901711 114.215802 \nL 288.929104 118.56864 \nL 288.932148 115.832566 \nL 288.935192 111.948164 \nL 289.002152 119.746582 \nL 289.044764 113.712517 \nL 289.145205 122.849286 \nL 289.056938 112.557517 \nL 289.154336 113.936954 \nL 289.157379 112.966691 \nL 289.218253 123.775144 \nL 289.236515 120.620615 \nL 289.279126 128.086641 \nL 289.339999 119.734639 \nL 289.352174 123.643847 \nL 289.361305 123.532772 \nL 289.358261 123.701995 \nL 289.364349 123.652851 \nL 289.367392 127.470602 \nL 289.458702 116.382498 \nL 289.470877 120.812748 \nL 289.522619 125.876544 \nL 289.528707 118.559801 \nL 289.53175 117.972737 \nL 289.58958 123.921723 \nL 289.620017 119.840806 \nL 289.711327 124.850621 \nL 289.677846 116.695908 \nL 289.729589 119.392703 \nL 289.787418 124.774422 \nL 289.826986 115.851221 \nL 289.939602 109.479523 \nL 289.839161 120.901718 \nL 289.942645 112.292587 \nL 290.058305 119.532142 \nL 289.982213 108.790682 \nL 290.061348 119.373392 \nL 290.073523 119.234664 \nL 290.076567 119.770307 \nL 290.189182 126.91778 \nL 290.192226 123.767519 \nL 290.216575 120.812798 \nL 290.26223 129.332988 \nL 290.301798 124.263398 \nL 290.335278 126.836906 \nL 290.365715 118.383829 \nL 290.396152 120.35256 \nL 290.423545 117.32207 \nL 290.463112 130.09886 \nL 290.478331 128.703933 \nL 290.554422 136.223182 \nL 290.493549 124.058277 \nL 290.600077 135.441942 \nL 290.697475 127.13218 \nL 290.624427 137.69372 \nL 290.715737 128.945889 \nL 290.770523 125.389041 \nL 290.77661 132.626052 \nL 290.81009 129.332979 \nL 290.813134 134.535416 \nL 290.883138 125.432652 \nL 290.919662 127.821095 \nL 290.928793 133.75662 \nL 290.956186 127.592381 \nL 291.032278 130.942157 \nL 291.093151 124.805026 \nL 291.077933 131.863251 \nL 291.147937 125.05947 \nL 291.150981 125.216898 \nL 291.166199 120.277147 \nL 291.187505 117.814545 \nL 291.251422 126.492859 \nL 291.254466 126.305943 \nL 291.275771 141.380108 \nL 291.373169 135.013175 \nL 291.485784 121.081289 \nL 291.488828 125.479833 \nL 291.491872 131.348447 \nL 291.54057 120.812707 \nL 291.5984 124.181301 \nL 291.677535 127.912939 \nL 291.686666 118.56986 \nL 291.704928 123.633716 \nL 291.756671 121.765651 \nL 291.793195 130.155009 \nL 291.802326 129.135653 \nL 291.820588 133.110538 \nL 291.835806 124.870581 \nL 291.896679 127.663427 \nL 291.978858 119.298416 \nL 291.921029 129.332935 \nL 292.018426 119.675363 \nL 292.039732 125.24756 \nL 292.042775 125.072847 \nL 292.045819 126.854988 \nL 292.051906 118.43188 \nL 292.149304 123.652922 \nL 292.252788 119.07338 \nL 292.231483 123.898138 \nL 292.258876 120.242968 \nL 292.283225 129.566712 \nL 292.341055 120.103722 \nL 292.371491 123.969731 \nL 292.441496 130.930922 \nL 292.45367 119.39285 \nL 292.484107 125.072924 \nL 292.584548 128.329527 \nL 292.548024 119.963699 \nL 292.596723 126.886098 \nL 292.60281 122.304485 \nL 292.678902 131.176728 \nL 292.703251 127.792269 \nL 292.724557 124.432811 \nL 292.821954 133.813651 \nL 292.873697 126.343949 \nL 292.93457 129.463586 \nL 292.958919 126.160901 \nL 292.974138 133.169519 \nL 293.086753 140.174807 \nL 292.989356 126.25213 \nL 293.092841 136.433268 \nL 293.095884 136.432922 \nL 293.144583 134.747328 \nL 293.15067 138.339831 \nL 293.196325 139.401729 \nL 293.171976 131.765218 \nL 293.248068 135.02212 \nL 293.254155 134.925261 \nL 293.257199 135.22652 \nL 293.378945 144.035338 \nL 293.318072 131.903063 \nL 293.385033 143.824207 \nL 293.394164 136.189596 \nL 293.458081 146.373438 \nL 293.491561 144.167332 \nL 293.558522 147.19842 \nL 293.543303 139.164154 \nL 293.601133 145.101602 \nL 293.768535 131.243781 \nL 293.628526 146.452716 \nL 293.795928 137.39716 \nL 293.826364 144.125144 \nL 293.841583 136.140841 \nL 293.911587 141.628649 \nL 293.960286 136.433299 \nL 293.975504 144.953507 \nL 294.027246 137.828416 \nL 294.054639 142.322919 \nL 294.072901 136.172634 \nL 294.106382 138.728553 \nL 294.206823 132.109651 \nL 294.115513 139.363284 \nL 294.218997 135.013272 \nL 294.343788 144.439462 \nL 294.228128 131.535633 \nL 294.346831 144.295454 \nL 294.383355 137.813121 \nL 294.422923 146.284751 \nL 294.465534 141.401263 \nL 294.468578 143.544337 \nL 294.535539 133.695318 \nL 294.572063 139.660564 \nL 294.587281 130.484418 \nL 294.687722 133.493418 \nL 294.724246 136.633512 \nL 294.730333 131.268403 \nL 294.782076 129.333006 \nL 294.800338 136.549568 \nL 294.812512 135.01317 \nL 294.833818 138.837837 \nL 294.912953 127.349832 \nL 294.992089 120.507554 \nL 294.94339 129.648061 \nL 295.043831 121.955797 \nL 295.144272 130.701972 \nL 295.162534 129.332992 \nL 295.238626 132.172978 \nL 295.247757 124.605643 \nL 295.262975 126.517205 \nL 295.290368 123.917023 \nL 295.272106 127.929204 \nL 295.314717 126.492766 \nL 295.320805 130.35412 \nL 295.406027 119.392632 \nL 295.424289 124.898514 \nL 295.463857 126.514027 \nL 295.472988 120.816488 \nL 295.476032 119.229785 \nL 295.561254 128.312776 \nL 295.570385 126.864257 \nL 295.679957 135.012925 \nL 295.692132 133.22427 \nL 295.701263 126.402969 \nL 295.807791 127.21639 \nL 295.908232 135.012805 \nL 295.829097 126.678893 \nL 295.950844 130.40816 \nL 295.959975 127.169781 \nL 295.98128 133.98718 \nL 296.054328 133.211025 \nL 296.13042 139.904017 \nL 296.145638 126.593885 \nL 296.160857 132.510064 \nL 296.25521 120.768261 \nL 296.18825 132.641389 \nL 296.309996 124.987648 \nL 296.334346 123.65254 \nL 296.34652 128.354687 \nL 296.428699 132.440919 \nL 296.440874 123.652486 \nL 296.453049 130.241869 \nL 296.562621 122.23248 \nL 296.565664 124.471068 \nL 296.574795 128.172441 \nL 296.666105 120.827751 \nL 296.672193 123.627064 \nL 296.742197 118.271158 \nL 296.726979 126.952494 \nL 296.790896 120.812223 \nL 296.793939 120.722214 \nL 296.80307 123.537863 \nL 296.818289 122.446378 \nL 296.873075 130.379943 \nL 296.933948 124.852158 \nL 296.940035 125.710682 \nL 296.949166 130.617797 \nL 297.04352 122.527122 \nL 297.049607 125.072517 \nL 297.083088 126.890651 \nL 297.077 121.415636 \nL 297.116568 122.680341 \nL 297.153092 119.703632 \nL 297.213965 128.116557 \nL 297.223096 123.395077 \nL 297.369192 132.172478 \nL 297.25962 120.822897 \nL 297.378323 127.847966 \nL 297.484852 121.611064 \nL 297.457459 130.520124 \nL 297.487895 122.993621 \nL 297.490939 126.492274 \nL 297.554856 118.363337 \nL 297.597467 122.232168 \nL 297.661384 135.255266 \nL 297.71617 131.622151 \nL 297.777044 129.188609 \nL 297.770956 137.805221 \nL 297.822699 134.949483 \nL 297.825742 135.012481 \nL 297.828786 131.196295 \nL 297.910965 139.632875 \nL 297.932271 135.012357 \nL 297.950533 141.20168 \nL 298.032712 134.359234 \nL 298.04793 140.471594 \nL 298.136196 153.051738 \nL 298.060105 140.1926 \nL 298.19707 150.277701 \nL 298.203157 149.258067 \nL 298.212288 153.312425 \nL 298.23055 152.691681 \nL 298.233594 153.638391 \nL 298.300554 143.631984 \nL 298.318816 145.849577 \nL 298.416214 150.751034 \nL 298.382733 143.172378 \nL 298.422301 145.540392 \nL 298.498393 141.764869 \nL 298.483174 148.093476 \nL 298.528829 146.460027 \nL 298.544048 141.356929 \nL 298.598834 138.524671 \nL 298.589703 149.578849 \nL 298.641445 146.822374 \nL 298.665794 149.449916 \nL 298.711449 138.157712 \nL 298.738842 143.532344 \nL 298.821021 132.378614 \nL 298.860589 136.432139 \nL 298.872764 135.57192 \nL 298.875807 140.69237 \nL 298.954943 145.203481 \nL 298.897113 134.378455 \nL 298.982336 139.272305 \nL 298.988423 136.036341 \nL 299.031034 143.701345 \nL 299.094951 137.377309 \nL 299.101039 145.137976 \nL 299.116257 135.898407 \nL 299.204523 136.526746 \nL 299.298877 132.209269 \nL 299.271484 139.272189 \nL 299.314095 136.325925 \nL 299.399318 147.813864 \nL 299.338445 133.630161 \nL 299.441929 146.070981 \nL 299.54237 138.433694 \nL 299.575851 140.69221 \nL 299.584982 140.264155 \nL 299.694554 149.139662 \nL 299.709772 142.322446 \nL 299.773689 150.632353 \nL 299.804126 149.212379 \nL 299.807169 149.251128 \nL 299.8163 149.132301 \nL 299.819344 149.212404 \nL 299.913698 158.170586 \nL 299.93196 152.609745 \nL 300.035444 148.941975 \nL 299.989789 157.41358 \nL 300.050663 150.601188 \nL 300.05675 150.715652 \nL 300.059794 150.424531 \nL 300.084143 148.908927 \nL 300.138929 156.31257 \nL 300.160235 154.463166 \nL 300.294156 172.348926 \nL 300.38851 160.451201 \nL 300.409816 164.909032 \nL 300.415903 162.415505 \nL 300.452427 161.725378 \nL 300.485907 166.65952 \nL 300.507213 162.850312 \nL 300.589392 172.283244 \nL 300.528519 160.562342 \nL 300.622872 171.731799 \nL 300.62896 168.739074 \nL 300.705051 180.713635 \nL 300.726357 177.643006 \nL 300.78723 172.827378 \nL 300.738532 180.760697 \nL 300.805492 175.591159 \nL 300.808536 180.452985 \nL 300.896802 170.63859 \nL 300.915064 174.772884 \nL 300.942457 175.011643 \nL 300.927239 170.247247 \nL 300.948545 170.936146 \nL 300.951588 169.98266 \nL 300.960719 177.612978 \nL 301.048986 172.204183 \nL 301.158558 177.612934 \nL 301.122034 169.76046 \nL 301.167689 175.790221 \nL 301.274217 167.107707 \nL 301.207256 177.93143 \nL 301.292479 169.144565 \nL 301.30161 167.587413 \nL 301.310741 167.672763 \nL 301.347265 159.792795 \nL 301.322916 172.216423 \nL 301.417269 169.092897 \nL 301.505536 178.131044 \nL 301.444662 168.90954 \nL 301.54206 174.844478 \nL 301.581627 170.7969 \nL 301.599889 179.033083 \nL 301.657719 171.159379 \nL 301.675981 179.033032 \nL 301.752073 167.672821 \nL 301.767291 170.512886 \nL 301.79164 163.462113 \nL 301.840339 180.208933 \nL 301.846426 187.778156 \nL 301.919474 163.412669 \nL 301.943824 164.475211 \nL 301.99861 171.86524 \nL 302.062527 167.780073 \nL 302.111225 165.209111 \nL 302.150793 172.377721 \nL 302.162968 169.092908 \nL 302.172099 167.043261 \nL 302.278627 176.656729 \nL 302.293845 173.184327 \nL 302.351675 181.074699 \nL 302.409505 205.844642 \nL 302.473422 189.725975 \nL 302.604299 176.193149 \nL 302.607343 175.979089 \nL 302.616474 178.840929 \nL 302.63778 194.900987 \nL 302.732133 190.08099 \nL 302.783876 180.294682 \nL 302.741264 193.233428 \nL 302.850836 185.736554 \nL 302.893448 193.233472 \nL 302.881273 181.96126 \nL 302.91171 185.394793 \nL 303.024325 172.358829 \nL 302.939103 190.393509 \nL 303.027369 173.020525 \nL 303.124766 180.653173 \nL 303.054762 171.775691 \nL 303.139985 177.308421 \nL 303.225207 190.203347 \nL 303.267819 186.836041 \nL 303.356085 179.443595 \nL 303.337823 192.053805 \nL 303.383478 180.660305 \nL 303.420002 177.07265 \nL 303.410871 184.713307 \nL 303.483919 181.295333 \nL 303.535661 195.091702 \nL 303.572185 170.733093 \nL 303.596535 186.305376 \nL 303.681757 173.540222 \nL 303.727412 178.462248 \nL 303.742631 185.108768 \nL 303.827853 172.235312 \nL 303.91612 185.234819 \nL 303.955687 178.838159 \nL 303.980037 184.780829 \nL 304.022648 176.967823 \nL 304.025692 174.912261 \nL 304.083521 180.602184 \nL 304.13222 177.522925 \nL 304.138307 177.857553 \nL 304.232661 184.713343 \nL 304.241792 176.116925 \nL 304.250923 178.848839 \nL 304.263098 181.873293 \nL 304.284403 175.845841 \nL 304.345277 178.235704 \nL 304.375713 173.280033 \nL 304.415281 183.29334 \nL 304.442674 180.546281 \nL 304.509635 189.282847 \nL 304.55529 183.424778 \nL 304.652687 181.675424 \nL 304.625294 189.387216 \nL 304.658774 183.293514 \nL 304.750084 187.553735 \nL 304.762259 179.853481 \nL 304.768346 184.238618 \nL 304.801827 183.108245 \nL 304.792696 186.326148 \nL 304.823132 185.614983 \nL 304.826176 190.393691 \nL 304.911399 181.135278 \nL 304.932704 187.5538 \nL 304.972272 190.782202 \nL 304.966185 185.781501 \nL 304.993578 186.133681 \nL 305.112281 180.258176 \nL 305.042276 196.245151 \nL 305.115324 180.453588 \nL 305.118368 183.000953 \nL 305.17011 171.205052 \nL 305.218809 177.379752 \nL 305.224896 174.564771 \nL 305.304032 182.232111 \nL 305.325337 179.063183 \nL 305.331425 178.794441 \nL 305.337512 182.167941 \nL 305.431866 174.73688 \nL 305.437953 176.225969 \nL 305.44404 175.75229 \nL 305.447084 177.260275 \nL 305.450128 183.293776 \nL 305.459259 171.860405 \nL 305.553612 173.528996 \nL 305.605355 168.191163 \nL 305.629704 173.770423 \nL 305.663184 173.353724 \nL 305.772756 177.96643 \nL 305.675359 167.58324 \nL 305.791018 175.849105 \nL 305.90059 164.283496 \nL 305.909721 164.830059 \nL 305.918852 164.797396 \nL 305.921896 164.833571 \nL 306.010162 169.783141 \nL 305.976682 161.760167 \nL 306.022337 164.6541 \nL 306.031468 160.447805 \nL 306.086254 168.041879 \nL 306.131909 162.337525 \nL 306.204957 169.54596 \nL 306.177564 160.813668 \nL 306.247568 164.778347 \nL 306.259743 167.778699 \nL 306.26583 163.880208 \nL 306.268874 160.483042 \nL 306.308442 169.728382 \nL 306.378446 161.993888 \nL 306.38149 163.2483 \nL 306.430188 155.79478 \nL 306.4728 156.477139 \nL 306.478887 156.154798 \nL 306.488018 159.226107 \nL 306.503236 157.372984 \nL 306.609765 166.63107 \nL 306.615852 160.347715 \nL 306.618896 159.401714 \nL 306.667594 171.934259 \nL 306.710206 164.799144 \nL 306.716293 165.99569 \nL 306.719337 166.661643 \nL 306.771079 158.982114 \nL 306.792385 160.973617 \nL 306.868476 155.407575 \nL 306.898913 162.154757 \nL 306.901957 161.833077 \nL 307.005441 163.717343 \nL 306.920219 153.674278 \nL 307.008485 161.363804 \nL 307.011529 161.152358 \nL 307.029791 167.580332 \nL 307.051096 166.025594 \nL 307.05414 166.493489 \nL 307.072402 160.198984 \nL 307.118057 161.587724 \nL 307.175887 154.138371 \nL 307.127188 163.774912 \nL 307.227629 159.451484 \nL 307.321983 161.994444 \nL 307.29459 154.91653 \nL 307.340245 160.608412 \nL 307.346332 160.532766 \nL 307.349376 160.683212 \nL 307.358507 167.288029 \nL 307.437642 154.493975 \nL 307.458948 160.574601 \nL 307.465035 155.552314 \nL 307.547214 164.727107 \nL 307.56852 157.326991 \nL 307.595913 161.994593 \nL 307.662873 150.221765 \nL 307.665917 151.229781 \nL 307.781576 159.060749 \nL 307.699397 150.7373 \nL 307.78462 155.389787 \nL 307.815057 151.432659 \nL 307.872887 160.773709 \nL 307.888105 152.693349 \nL 307.894192 159.891191 \nL 307.997677 153.733406 \nL 308.018983 156.461297 \nL 308.119424 148.083912 \nL 308.14986 152.193787 \nL 308.155948 156.199036 \nL 308.180297 143.600804 \nL 308.253345 151.122481 \nL 308.259432 145.704577 \nL 308.299 157.871899 \nL 308.362917 152.240915 \nL 308.439009 146.974221 \nL 308.393354 153.620294 \nL 308.48162 149.328588 \nL 308.612498 156.488979 \nL 308.499882 149.068796 \nL 308.618585 155.216744 \nL 308.649022 151.044922 \nL 308.661196 159.155208 \nL 308.725113 156.561711 \nL 308.731201 156.133193 \nL 308.764681 161.085403 \nL 308.837729 166.067527 \nL 308.855991 152.689376 \nL 308.874253 160.57532 \nL 308.999043 149.767077 \nL 309.002087 148.525222 \nL 309.053829 161.120996 \nL 309.096441 154.711151 \nL 309.099484 158.233014 \nL 309.108615 151.73806 \nL 309.209056 157.735463 \nL 309.260799 150.35441 \nL 309.282104 159.522343 \nL 309.324716 152.571629 \nL 309.346021 154.89546 \nL 309.373414 147.450048 \nL 309.422113 150.635399 \nL 309.446462 146.685157 \nL 309.510379 156.856846 \nL 309.525598 156.667836 \nL 309.537772 151.956675 \nL 309.568209 160.873894 \nL 309.574296 159.155673 \nL 309.653432 167.899056 \nL 309.613864 158.952221 \nL 309.683868 159.125711 \nL 309.769091 162.190531 \nL 309.744742 156.443458 \nL 309.79344 159.340506 \nL 309.857357 151.175622 \nL 309.802571 160.575877 \nL 309.918231 153.971696 \nL 310.052152 214.756364 \nL 310.064327 172.836037 \nL 310.088676 149.231388 \nL 310.179986 152.110666 \nL 310.18303 152.055759 \nL 310.283471 142.303728 \nL 310.265209 155.422955 \nL 310.295645 145.865424 \nL 310.30782 148.178825 \nL 310.362606 138.069444 \nL 310.371737 139.256895 \nL 310.408261 135.136541 \nL 310.39913 142.364418 \nL 310.466091 137.457411 \nL 310.54827 143.535667 \nL 310.52392 136.096376 \nL 310.578706 140.695569 \nL 310.648711 136.259957 \nL 310.624361 143.535696 \nL 310.688278 140.791432 \nL 310.76437 147.679205 \nL 310.709584 138.841698 \nL 310.794807 140.96981 \nL 310.79785 140.170777 \nL 310.840462 146.574218 \nL 310.895248 143.645642 \nL 310.907422 141.623672 \nL 310.910466 142.964791 \nL 310.916553 148.420798 \nL 311.007863 134.932897 \nL 311.013951 135.00221 \nL 311.016994 135.026101 \nL 311.023082 134.843936 \nL 311.032213 142.092934 \nL 311.077868 134.804075 \nL 311.141785 139.275857 \nL 311.144828 139.296669 \nL 311.147872 138.917601 \nL 311.227007 131.650861 \nL 311.172221 140.695926 \nL 311.260488 137.041668 \nL 311.327448 147.295708 \nL 311.37006 137.855932 \nL 311.455282 133.520154 \nL 311.385278 144.955954 \nL 311.485719 133.535613 \nL 311.583116 143.535881 \nL 311.52833 131.961808 \nL 311.668339 140.942864 \nL 311.783998 131.115959 \nL 311.689645 143.53599 \nL 311.793129 133.906878 \nL 311.808348 136.584489 \nL 311.863134 125.22301 \nL 311.881396 126.496403 \nL 311.884439 126.545956 \nL 311.887483 126.495753 \nL 311.972706 116.266004 \nL 312.003142 123.324188 \nL 312.006186 123.402904 \nL 312.00923 120.815582 \nL 312.045754 115.042654 \nL 312.027492 125.385581 \nL 312.118802 122.490653 \nL 312.222286 113.715461 \nL 312.13402 125.527999 \nL 312.231417 118.568825 \nL 312.234461 119.535543 \nL 312.307509 109.193316 \nL 312.313596 110.875267 \nL 312.347077 106.417926 \nL 312.328815 113.7154 \nL 312.429256 108.715306 \nL 312.46578 113.715359 \nL 312.471867 107.194663 \nL 312.538828 109.459115 \nL 312.547959 109.50138 \nL 312.551002 109.28943 \nL 312.621007 115.538865 \nL 312.566221 104.227684 \nL 312.660574 110.452009 \nL 312.742753 101.393264 \nL 312.678836 113.715376 \nL 312.779277 103.570411 \nL 312.83102 108.035154 \nL 312.852325 100.369022 \nL 312.8645 101.68743 \nL 312.940592 96.227109 \nL 312.907111 103.775142 \nL 312.967985 102.35516 \nL 313.022771 105.265158 \nL 313.037989 98.57664 \nL 313.041033 98.631225 \nL 313.083644 96.972273 \nL 313.153648 104.246367 \nL 313.187129 95.77881 \nL 313.266264 99.068492 \nL 313.372792 108.652503 \nL 313.381923 101.526219 \nL 313.458015 95.668973 \nL 313.406273 103.89447 \nL 313.497583 95.930187 \nL 313.591936 100.935175 \nL 313.534107 93.914839 \nL 313.604111 94.472113 \nL 313.610198 92.235689 \nL 313.668028 103.775281 \nL 313.701508 100.583931 \nL 313.808037 99.706496 \nL 313.820211 113.715527 \nL 313.935871 90.653399 \nL 313.83543 115.135463 \nL 313.938914 91.903472 \nL 313.941958 96.675144 \nL 314.008919 74.041399 \nL 314.039355 77.979984 \nL 314.188495 87.094724 \nL 314.054574 74.77133 \nL 314.191539 82.841098 \nL 314.20067 79.634918 \nL 314.215888 102.355425 \nL 314.225019 134.881814 \nL 314.252412 71.791928 \nL 314.322416 85.315013 \nL 314.361984 80.305829 \nL 314.38329 86.734998 \nL 314.431988 84.748883 \nL 314.514167 102.859093 \nL 314.453294 82.076856 \nL 314.556779 95.591149 \nL 314.614608 90.881866 \nL 314.65722 98.095412 \nL 314.666351 95.384681 \nL 314.678525 97.731762 \nL 314.684613 96.783572 \nL 314.775923 105.515263 \nL 314.693744 96.3623 \nL 314.806359 102.147224 \nL 314.934193 110.867368 \nL 314.867233 99.291793 \nL 314.940281 106.103879 \nL 314.943324 103.601609 \nL 315.025503 111.779239 \nL 315.043765 110.265928 \nL 315.159425 117.975994 \nL 315.162468 114.542021 \nL 315.256822 107.619333 \nL 315.229429 120.69824 \nL 315.278128 109.88256 \nL 315.375525 115.377538 \nL 315.317695 105.0812 \nL 315.3877 112.295779 \nL 315.390743 109.53022 \nL 315.448573 120.816124 \nL 315.497272 112.546374 \nL 315.533796 111.88072 \nL 315.521621 119.554215 \nL 315.594669 115.023351 \nL 315.673805 121.348041 \nL 315.667717 110.621468 \nL 315.69511 113.973179 \nL 315.774246 108.712656 \nL 315.75294 116.555938 \nL 315.804682 113.670865 \nL 315.892949 126.395486 \nL 315.838163 111.981545 \nL 315.953822 120.941523 \nL 316.06035 114.844179 \nL 316.039045 125.280007 \nL 316.063394 118.599715 \nL 316.066438 121.624937 \nL 316.090787 112.376955 \nL 316.169922 116.798373 \nL 316.227752 113.989911 \nL 316.185141 120.081363 \nL 316.26732 117.549353 \nL 316.270363 122.236139 \nL 316.279494 115.03364 \nL 316.376892 117.976093 \nL 316.382979 117.833131 \nL 316.386023 118.491292 \nL 316.407328 122.236233 \nL 316.446896 112.557197 \nL 316.489507 118.374185 \nL 316.580817 107.767906 \nL 316.611254 110.705203 \nL 316.717782 122.614295 \nL 316.629516 109.469914 \nL 316.754306 119.381589 \nL 316.75735 119.39619 \nL 316.760394 119.297826 \nL 316.866922 127.053444 \nL 316.812136 117.654823 \nL 316.869966 123.031263 \nL 316.88214 119.406885 \nL 316.93997 126.496423 \nL 316.979538 123.586921 \nL 317.131721 140.883794 \nL 317.019105 121.212564 \nL 317.143896 134.679321 \nL 317.146939 131.52233 \nL 317.192594 139.719463 \nL 317.253468 133.242436 \nL 317.283904 135.30026 \nL 317.299123 127.057552 \nL 317.353909 128.494302 \nL 317.356952 126.576187 \nL 317.45435 133.635334 \nL 317.463481 129.242596 \nL 317.524354 129.768601 \nL 317.493917 124.786245 \nL 317.539572 127.011993 \nL 317.570009 123.255697 \nL 317.597402 130.485585 \nL 317.646101 128.320854 \nL 317.70393 132.621579 \nL 317.713061 123.097981 \nL 317.752629 128.229175 \nL 317.843939 119.954732 \nL 317.871332 123.662724 \nL 317.97786 131.041057 \nL 317.907856 121.994646 \nL 317.983948 126.070542 \nL 317.986991 123.353197 \nL 318.020472 136.436758 \nL 318.090476 126.200811 \nL 318.123956 129.75831 \nL 318.175699 122.54558 \nL 318.200048 127.625495 \nL 318.254834 119.454758 \nL 318.318751 121.58527 \nL 318.428323 133.602633 \nL 318.330926 117.430319 \nL 318.440498 130.055929 \nL 318.498327 138.950797 \nL 318.467891 127.714381 \nL 318.543982 128.022791 \nL 318.556157 133.825362 \nL 318.604856 124.632806 \nL 318.620074 125.818041 \nL 318.623118 124.144732 \nL 318.714428 132.056502 \nL 318.717471 133.80385 \nL 318.726602 123.018796 \nL 318.808781 124.635435 \nL 318.811825 123.656428 \nL 318.894004 132.408749 \nL 318.900091 130.854194 \nL 318.994445 137.856766 \nL 318.954877 128.861825 \nL 319.015751 134.267748 \nL 319.09793 126.116765 \nL 319.024882 136.436698 \nL 319.143585 127.711919 \nL 319.186196 136.234895 \nL 319.225764 141.914916 \nL 319.253157 133.596718 \nL 319.295768 134.891288 \nL 319.353598 132.807318 \nL 319.387078 140.411041 \nL 319.450995 150.637068 \nL 319.493606 136.670345 \nL 319.49665 142.116809 \nL 319.551436 126.895234 \nL 319.636659 133.117374 \nL 319.743187 147.841721 \nL 319.657964 132.123929 \nL 319.755362 144.956921 \nL 319.761449 146.612065 \nL 319.834497 138.690453 \nL 319.846672 140.696758 \nL 319.852759 137.691571 \nL 319.910589 143.52058 \nL 319.956244 140.810207 \nL 319.962331 138.960657 \nL 320.050597 145.902602 \nL 320.053641 144.411896 \nL 320.059728 149.217105 \nL 320.068859 140.399319 \nL 320.163213 144.931463 \nL 320.1693 145.121618 \nL 320.248436 138.6191 \nL 320.263654 148.30071 \nL 320.281916 144.043561 \nL 320.28496 145.115027 \nL 320.364095 136.436698 \nL 320.367139 132.232428 \nL 320.461492 144.270905 \nL 320.470623 142.116769 \nL 320.546715 146.376957 \nL 320.476711 139.990915 \nL 320.589326 143.226652 \nL 320.686724 136.000297 \nL 320.616719 146.286296 \nL 320.708029 140.059519 \nL 320.826732 133.139777 \nL 320.729335 143.536899 \nL 320.829776 135.220118 \nL 320.83282 138.074362 \nL 320.893693 129.242726 \nL 320.939348 136.311529 \nL 320.942392 136.436734 \nL 320.948479 133.161952 \nL 320.960654 130.740637 \nL 321.021527 139.457651 \nL 321.051964 135.22813 \nL 321.094575 146.376946 \nL 321.149361 132.775537 \nL 321.158492 135.604592 \nL 321.164579 133.072088 \nL 321.243715 143.626 \nL 321.249802 151.078792 \nL 321.331981 133.569374 \nL 321.350243 137.734977 \nL 321.411116 131.861448 \nL 321.462859 136.389962 \nL 321.548081 140.907974 \nL 321.484164 132.98842 \nL 321.5633 136.14165 \nL 321.633304 127.969995 \nL 321.578518 136.778175 \nL 321.682003 129.709835 \nL 321.770269 138.084728 \nL 321.694177 129.336538 \nL 321.797662 135.315201 \nL 321.861579 131.922048 \nL 321.83723 138.244572 \nL 321.910278 133.652602 \nL 321.940714 130.581111 \nL 321.919409 133.692574 \nL 321.946802 130.756656 \nL 321.992457 137.856859 \nL 322.059417 126.49075 \nL 322.123334 129.566001 \nL 322.147684 123.480115 \nL 322.165946 126.246436 \nL 322.248125 124.400287 \nL 322.263343 132.29454 \nL 322.299867 126.816944 \nL 322.287692 132.753187 \nL 322.318129 126.922509 \nL 322.372915 115.136249 \nL 322.430745 120.179627 \nL 322.436832 123.976277 \nL 322.500749 115.802269 \nL 322.540317 119.396403 \nL 322.616408 113.71617 \nL 322.585972 120.816408 \nL 322.652932 117.650663 \nL 322.738155 122.236426 \nL 322.674238 115.218972 \nL 322.759461 116.240079 \nL 322.771635 124.227487 \nL 322.789897 110.087888 \nL 322.850771 113.905498 \nL 322.969474 105.990665 \nL 322.862945 115.5743 \nL 322.972517 110.270397 \nL 323.069915 117.566119 \nL 323.015129 110.124819 \nL 323.088177 115.136296 \nL 323.094264 113.938887 \nL 323.100352 119.394464 \nL 323.103395 123.656507 \nL 323.11557 113.677585 \nL 323.209924 119.396377 \nL 323.216011 114.868525 \nL 323.282972 127.946622 \nL 323.319496 120.758044 \nL 323.395587 123.159162 \nL 323.410806 112.284422 \nL 323.413849 111.893419 \nL 323.44733 119.307655 \nL 323.465592 118.52318 \nL 323.547771 126.646465 \nL 323.581251 123.275156 \nL 323.584295 123.307517 \nL 323.639081 116.672 \nL 323.599513 126.755736 \nL 323.693867 121.493888 \nL 323.699954 125.256844 \nL 323.785177 117.801194 \nL 323.803439 123.656591 \nL 323.843006 119.013048 \nL 323.852137 126.91829 \nL 323.916054 120.694085 \nL 323.964753 132.710631 \nL 324.02867 121.813366 \nL 324.040845 125.119301 \nL 324.141286 116.128864 \nL 324.220421 122.478499 \nL 324.17781 113.607999 \nL 324.253901 120.161637 \nL 324.281294 115.220314 \nL 324.351299 123.656689 \nL 324.36043 122.184891 \nL 324.473045 130.756943 \nL 324.491307 125.223237 \nL 324.524788 123.478874 \nL 324.515657 129.559641 \nL 324.603923 124.581549 \nL 324.680015 120.622009 \nL 324.658709 129.398912 \nL 324.710451 125.474624 \nL 324.786543 136.827446 \nL 324.722626 125.471578 \nL 324.835242 135.511025 \nL 324.902202 127.836229 \nL 324.963076 132.152255 \nL 324.966119 132.310143 \nL 324.97525 129.337001 \nL 324.978294 126.53553 \nL 325.042211 134.722694 \nL 325.081779 132.177122 \nL 325.115259 136.374531 \nL 325.154827 128.223515 \nL 325.188307 130.75715 \nL 325.218744 123.656918 \nL 325.279617 132.177077 \nL 325.300923 125.622525 \nL 325.334403 130.105878 \nL 325.386145 116.556659 \nL 325.407451 122.79457 \nL 325.450062 115.490609 \nL 325.4318 123.656888 \nL 325.52311 117.976733 \nL 325.626595 125.354687 \nL 325.544416 117.182019 \nL 325.63877 119.781737 \nL 325.684425 127.768655 \nL 325.714861 114.27294 \nL 325.739211 118.103153 \nL 325.754429 113.543371 \nL 325.815302 120.607644 \nL 325.848783 116.615116 \nL 325.906612 112.702297 \nL 325.961398 122.251119 \nL 325.970529 114.672334 \nL 326.03749 125.367207 \nL 326.061839 123.658536 \nL 326.147062 129.336996 \nL 326.165324 120.975338 \nL 326.171411 124.215774 \nL 326.210979 118.212144 \nL 326.229241 126.722 \nL 326.284027 122.236848 \nL 326.363162 126.038406 \nL 326.308376 118.898773 \nL 326.393599 122.31553 \nL 326.43621 117.976721 \nL 326.439254 116.489807 \nL 326.481865 125.076872 \nL 326.539695 117.411034 \nL 326.576219 124.37993 \nL 326.61883 112.296598 \nL 326.646223 113.308806 \nL 326.761883 103.259806 \nL 326.670573 115.27674 \nL 326.771014 108.036689 \nL 326.783188 106.945873 \nL 326.786232 109.456652 \nL 326.789276 108.61567 \nL 326.792319 105.780562 \nL 326.804494 112.080704 \nL 326.898848 109.558156 \nL 327.026682 100.59399 \nL 326.965808 109.777871 \nL 327.035813 103.635064 \nL 327.063206 106.089745 \nL 327.13321 98.231676 \nL 327.139297 99.557119 \nL 327.163647 99.479254 \nL 327.172778 100.968334 \nL 327.194083 103.902189 \nL 327.206258 97.265552 \nL 327.212345 95.256373 \nL 327.227564 106.616538 \nL 327.291481 103.223194 \nL 327.343223 111.294566 \nL 327.382791 102.299844 \nL 327.410184 110.876799 \nL 327.471057 100.936532 \nL 327.528887 104.935473 \nL 327.553236 101.590797 \nL 327.626284 109.712917 \nL 327.632371 106.678189 \nL 327.638459 112.29678 \nL 327.659764 103.00419 \nL 327.741943 110.539681 \nL 327.766293 107.369359 \nL 327.821079 115.136956 \nL 327.845428 114.387961 \nL 327.848472 115.167365 \nL 327.918476 105.195474 \nL 327.936738 109.403674 \nL 327.939782 109.55835 \nL 327.945869 106.025208 \nL 328.021961 100.259166 \nL 328.034135 109.456746 \nL 328.04631 107.333012 \nL 328.113271 116.880503 \nL 328.161969 116.55073 \nL 328.165013 116.526801 \nL 328.168057 116.638864 \nL 328.1711 116.556913 \nL 328.216755 112.296838 \nL 328.198493 121.040396 \nL 328.280672 116.880416 \nL 328.323284 113.716969 \nL 328.305022 121.328652 \nL 328.37807 118.235704 \nL 328.381113 121.019872 \nL 328.481554 114.865644 \nL 328.484598 115.818246 \nL 328.624607 128.255558 \nL 328.490685 115.137082 \nL 328.630694 124.490539 \nL 328.715917 119.486774 \nL 328.679393 130.165603 \nL 328.740266 122.410731 \nL 328.828532 131.262868 \nL 328.767659 117.851411 \nL 328.858969 126.514283 \nL 328.877231 124.571223 \nL 328.883318 129.68297 \nL 328.895493 129.316855 \nL 328.898537 132.595992 \nL 328.941148 125.022943 \nL 329.005065 129.607662 \nL 329.111593 136.822795 \nL 329.026371 127.703336 \nL 329.123768 133.564808 \nL 329.212034 123.363616 \nL 329.145074 135.017547 \nL 329.218122 123.450502 \nL 329.224209 121.870728 \nL 329.248558 128.149842 \nL 329.32465 124.206914 \nL 329.342912 129.337414 \nL 329.336825 120.765202 \nL 329.431178 121.81112 \nL 329.437266 118.817336 \nL 329.492052 126.516275 \nL 329.54075 120.817108 \nL 329.644235 127.698906 \nL 329.5651 118.861708 \nL 329.650322 124.277943 \nL 329.680759 115.196715 \nL 329.762938 117.680002 \nL 329.869466 132.108581 \nL 329.878597 126.578362 \nL 329.881641 126.599302 \nL 329.884685 125.988099 \nL 329.979038 120.23294 \nL 329.927296 128.012712 \nL 329.9973 122.237186 \nL 330.009475 125.15042 \nL 330.100785 119.344796 \nL 330.103829 119.397128 \nL 330.155571 118.860203 \nL 330.161658 124.186873 \nL 330.182964 122.041008 \nL 330.252968 129.62199 \nL 330.225575 119.397155 \nL 330.292536 122.172225 \nL 330.371671 120.159499 \nL 330.380802 129.751672 \nL 330.383846 131.229342 \nL 330.402108 120.495598 \nL 330.469069 122.237161 \nL 330.484287 116.556931 \nL 330.56951 122.851019 \nL 330.581684 118.942449 \nL 330.618208 124.655732 \nL 330.630383 116.380214 \nL 330.6943 121.733116 \nL 330.779523 119.001465 \nL 330.755174 125.622852 \nL 330.803872 120.81723 \nL 330.80996 120.966731 \nL 330.813003 120.289855 \nL 330.919532 113.655202 \nL 330.87692 122.785853 \nL 330.931706 116.914856 \nL 331.019973 114.83263 \nL 331.044322 120.381628 \nL 331.068671 116.490787 \nL 331.120414 129.342327 \nL 331.147807 127.237856 \nL 331.196505 129.971089 \nL 331.172156 123.66839 \nL 331.217811 125.547276 \nL 331.324339 117.782231 \nL 331.330427 120.524987 \nL 331.42478 125.31282 \nL 331.366951 116.557093 \nL 331.436955 119.594647 \nL 331.439999 116.761486 \nL 331.467392 124.820069 \nL 331.543483 121.710307 \nL 331.650012 130.993588 \nL 331.640881 119.397148 \nL 331.656099 127.917403 \nL 331.750453 117.127596 \nL 331.674361 127.931896 \nL 331.768715 121.707332 \nL 331.872199 125.244522 \nL 331.811326 116.134458 \nL 331.88133 123.056781 \nL 331.887418 118.907931 \nL 331.954378 126.652866 \nL 331.993946 119.086224 \nL 332.018295 126.348132 \nL 332.039601 118.44835 \nL 332.103518 120.846999 \nL 332.106562 119.489479 \nL 332.143086 125.077267 \nL 332.21309 121.491562 \nL 332.277007 126.377846 \nL 332.286138 119.045062 \nL 332.325706 122.237271 \nL 332.334837 122.137069 \nL 332.340924 123.657254 \nL 332.343968 123.482766 \nL 332.441365 132.277636 \nL 332.465714 130.713381 \nL 332.474845 130.830319 \nL 332.477889 130.757543 \nL 332.575286 124.624291 \nL 332.523544 132.177471 \nL 332.593548 126.504921 \nL 332.596592 126.455061 \nL 332.602679 127.840393 \nL 332.608767 126.764904 \nL 332.617898 129.605395 \nL 332.651378 123.201312 \nL 332.718339 127.534744 \nL 332.824867 122.271313 \nL 332.815736 129.674976 \nL 332.837042 124.521019 \nL 332.919221 129.337403 \nL 332.931395 120.612124 \nL 332.946614 123.934429 \nL 333.022705 173.020608 \nL 332.998356 117.7473 \nL 333.062273 125.714703 \nL 333.114015 106.478243 \nL 333.187063 111.856006 \nL 333.281417 121.566973 \nL 333.199238 110.85649 \nL 333.30881 114.936279 \nL 333.369683 109.373291 \nL 333.357509 122.488396 \nL 333.418382 114.983636 \nL 333.424469 117.371867 \nL 333.436644 169.098539 \nL 333.518823 104.81413 \nL 333.530998 106.649349 \nL 333.555347 97.923348 \nL 333.607089 107.882329 \nL 333.643613 105.509255 \nL 333.652744 113.616105 \nL 333.744054 105.300903 \nL 333.756229 109.457177 \nL 333.826233 99.438559 \nL 333.777535 110.233679 \nL 333.871888 102.722392 \nL 333.899281 144.570282 \nL 333.975373 100.86371 \nL 333.993635 101.122686 \nL 333.996679 100.494118 \nL 334.060596 89.576764 \nL 334.109294 98.219167 \nL 334.206692 94.045753 \nL 334.16408 101.353547 \nL 334.209735 97.939976 \nL 334.252347 129.753537 \nL 334.316264 97.568379 \nL 334.413661 86.287102 \nL 334.328438 99.835083 \nL 334.428879 96.322401 \nL 334.43801 99.559275 \nL 334.441054 99.517049 \nL 334.444098 105.915081 \nL 334.538451 93.644174 \nL 334.550626 96.961356 \nL 334.614543 87.683416 \nL 334.651067 101.382708 \nL 334.663242 116.462137 \nL 334.751508 96.136534 \nL 334.757595 100.871705 \nL 334.821512 90.996914 \nL 334.873255 92.845664 \nL 334.924997 101.111738 \nL 334.991958 100.849205 \nL 335.083268 90.665764 \nL 335.13501 92.723912 \nL 335.141097 96.677096 \nL 335.198927 88.272681 \nL 335.238495 88.662346 \nL 335.244582 92.804954 \nL 335.311543 84.11622 \nL 335.345023 88.345983 \nL 335.357198 86.925045 \nL 335.366329 94.072878 \nL 335.448508 87.806176 \nL 335.488075 91.977101 \nL 335.530687 82.834489 \nL 335.551992 83.896703 \nL 335.606778 78.803032 \nL 335.579385 86.396905 \nL 335.631128 84.871714 \nL 335.737656 92.657319 \nL 335.64939 82.366356 \nL 335.749831 86.736737 \nL 335.752874 86.544216 \nL 335.765049 92.416926 \nL 335.810704 95.256969 \nL 335.777224 86.971833 \nL 335.822879 91.767723 \nL 335.905058 84.059015 \nL 335.844184 92.652975 \nL 335.935494 86.76984 \nL 335.938538 86.587376 \nL 335.9568 90.733666 \nL 335.959844 92.704162 \nL 336.04811 83.765973 \nL 336.054197 83.89656 \nL 336.057241 83.773062 \nL 336.072459 86.825613 \nL 336.075503 86.360146 \nL 336.090721 81.77428 \nL 336.191162 92.416888 \nL 336.218555 92.564979 \nL 336.306822 86.611407 \nL 336.331171 91.87032 \nL 336.392044 85.062033 \nL 336.410306 85.668619 \nL 336.422481 83.517043 \nL 336.428568 91.851509 \nL 336.50466 88.093584 \nL 336.507704 88.199855 \nL 336.522922 86.319356 \nL 336.529009 83.577434 \nL 336.553359 92.454277 \nL 336.632494 85.316639 \nL 336.638581 84.197767 \nL 336.644669 87.204768 \nL 336.705542 90.85482 \nL 336.735979 81.656824 \nL 336.83642 73.885638 \nL 336.751197 88.771474 \nL 336.851638 76.908263 \nL 336.860769 75.376497 \nL 336.909468 70.431125 \nL 336.930773 81.478018 \nL 336.973385 73.346848 \nL 337.070782 80.753068 \nL 337.012952 72.536454 \nL 337.086 75.289184 \nL 337.116437 78.447395 \nL 337.104262 73.956483 \nL 337.156005 75.019075 \nL 337.23514 70.38945 \nL 337.189485 78.912691 \nL 337.271664 72.536468 \nL 337.299057 73.730989 \nL 337.289926 69.837609 \nL 337.305144 71.284092 \nL 337.338625 68.02807 \nL 337.375149 76.295346 \nL 337.408629 73.956508 \nL 337.512114 81.015387 \nL 337.448197 70.851758 \nL 337.524288 77.865625 \nL 337.606467 68.276364 \nL 337.54255 79.685786 \nL 337.642991 72.882588 \nL 337.664297 74.22873 \nL 337.65821 70.760737 \nL 337.694734 73.559309 \nL 337.755607 66.922581 \nL 337.740389 74.142519 \nL 337.807349 70.92161 \nL 337.81648 72.77121 \nL 337.828655 67.368065 \nL 337.880397 64.016413 \nL 337.84083 71.91198 \nL 337.935183 68.481722 \nL 337.96562 73.539118 \nL 338.008231 64.564876 \nL 338.044755 70.838333 \nL 338.154327 58.336167 \nL 338.163458 60.843959 \nL 338.184764 66.856394 \nL 338.218244 59.304173 \nL 338.276074 64.016314 \nL 338.333904 69.559455 \nL 338.385646 60.695639 \nL 338.498262 67.929706 \nL 338.434345 59.591099 \nL 338.501306 67.713541 \nL 338.543917 54.088376 \nL 338.616965 61.13452 \nL 338.626096 58.336102 \nL 338.766105 71.277327 \nL 338.875677 62.04278 \nL 338.784367 71.405395 \nL 338.890895 65.834924 \nL 338.933506 68.474538 \nL 338.979161 62.058404 \nL 338.982205 60.532156 \nL 339.003511 69.775777 \nL 339.076559 68.036966 \nL 339.082646 71.116349 \nL 339.170912 61.357011 \nL 339.180043 63.864712 \nL 339.231786 71.229948 \nL 339.253091 60.176959 \nL 339.304834 66.920862 \nL 339.390056 55.233623 \nL 339.3931 53.024547 \nL 339.453973 64.383264 \nL 339.490497 59.93338 \nL 339.603113 68.343893 \nL 339.6092 65.491952 \nL 339.712685 55.335957 \nL 339.627462 71.332452 \nL 339.727903 61.120652 \nL 339.785733 58.599973 \nL 339.794864 66.003649 \nL 339.834432 56.817913 \nL 339.90748 58.958885 \nL 339.910523 56.954225 \nL 339.99879 66.52805 \nL 340.004877 65.951504 \nL 340.047488 69.571819 \nL 340.062707 60.531178 \nL 340.102274 65.436085 \nL 340.132711 54.075925 \nL 340.114449 65.653654 \nL 340.217934 57.51642 \nL 340.266632 60.8223 \nL 340.284894 49.815653 \nL 340.315331 55.495899 \nL 340.357942 49.597957 \nL 340.415772 59.734399 \nL 340.491864 61.719682 \nL 340.467514 52.655751 \nL 340.516213 55.466649 \nL 340.531431 61.799648 \nL 340.54665 52.522677 \nL 340.570999 56.365635 \nL 340.680571 42.715353 \nL 340.683615 44.576289 \nL 340.759706 54.363251 \nL 340.726226 44.13556 \nL 340.799274 52.139979 \nL 340.875366 46.630163 \nL 340.860147 53.820375 \nL 340.91189 48.336816 \nL 340.914933 48.28369 \nL 340.921021 49.930797 \nL 340.930152 49.083571 \nL 340.936239 57.408328 \nL 340.960588 45.920234 \nL 341.042767 52.188361 \nL 341.143208 41.948787 \nL 341.057986 52.835239 \nL 341.152339 48.395635 \nL 341.264955 60.29812 \nL 341.201038 45.118165 \nL 341.271042 57.633956 \nL 341.27713 52.714293 \nL 341.362352 62.542062 \nL 341.380614 58.335767 \nL 341.389745 59.009449 \nL 341.392789 56.744825 \nL 341.395833 48.395439 \nL 341.490186 56.943694 \nL 341.502361 52.655612 \nL 341.511492 52.523917 \nL 341.514536 52.978604 \nL 341.581496 62.850225 \nL 341.523667 49.556086 \nL 341.621064 55.417097 \nL 341.712374 48.146771 \nL 341.64237 55.617877 \nL 341.73368 48.415128 \nL 341.831077 55.863827 \nL 341.840208 46.975152 \nL 341.855426 52.534423 \nL 341.9163 44.44099 \nL 341.925431 46.268851 \nL 341.931518 39.592863 \nL 341.94978 48.928809 \nL 342.031959 46.418487 \nL 342.126313 55.495721 \nL 342.038046 43.514863 \nL 342.144575 48.340633 \nL 342.199361 54.518405 \nL 342.248059 48.395461 \nL 342.251103 47.075603 \nL 342.302845 57.594015 \nL 342.3485 52.568115 \nL 342.427636 58.753629 \nL 342.37285 47.116783 \nL 342.461116 54.075518 \nL 342.46416 54.09906 \nL 342.467203 53.666035 \nL 342.564601 64.480698 \nL 342.585906 59.711827 \nL 342.58895 59.637812 \nL 342.595037 61.293677 \nL 342.604168 60.864462 \nL 342.610256 64.04082 \nL 342.661998 56.417394 \nL 342.704609 56.915589 \nL 342.73809 52.428338 \nL 342.71374 60.14864 \nL 342.832443 55.49828 \nL 342.850705 55.561567 \nL 342.853749 55.33796 \nL 342.905491 51.314197 \nL 342.872011 59.755701 \nL 342.963321 54.075485 \nL 343.066806 62.991918 \nL 343.075937 56.655219 \nL 343.200727 45.614063 \nL 343.097242 59.92122 \nL 343.237251 49.815377 \nL 343.340736 60.071772 \nL 343.258557 47.814621 \nL 343.374216 58.335462 \nL 343.471613 61.641291 \nL 343.404653 55.495415 \nL 343.474657 59.505004 \nL 343.477701 56.812882 \nL 343.569011 63.418251 \nL 343.581185 60.878701 \nL 343.687714 65.677156 \nL 343.629884 56.822591 \nL 343.696845 63.543069 \nL 343.702932 57.446062 \nL 343.803373 69.109577 \nL 343.888596 74.091047 \nL 343.852072 66.573019 \nL 343.912945 70.748483 \nL 344.004255 58.234139 \nL 344.040779 62.594755 \nL 344.059041 62.595355 \nL 344.138176 67.206178 \nL 344.068172 59.478892 \nL 344.165569 62.595308 \nL 344.168613 60.959659 \nL 344.250792 71.271034 \nL 344.259923 71.236488 \nL 344.26601 69.563868 \nL 344.339058 75.722864 \nL 344.35732 74.13335 \nL 344.363408 78.21566 \nL 344.445587 67.890852 \nL 344.460805 71.30939 \nL 344.469936 68.275335 \nL 344.536897 76.139428 \nL 344.573421 86.246747 \nL 344.649512 78.222276 \nL 344.6556 78.1779 \nL 344.658643 78.479618 \nL 344.682993 72.535334 \nL 344.698211 73.955446 \nL 344.743866 68.27519 \nL 344.771259 79.402688 \nL 344.798652 76.301588 \nL 344.804739 83.895691 \nL 344.856482 74.99654 \nL 344.908224 78.343422 \nL 344.914311 78.113377 \nL 344.923442 80.957993 \nL 344.92953 79.272574 \nL 344.944748 76.238599 \nL 345.045189 88.383839 \nL 345.05432 84.87736 \nL 345.096931 97.254111 \nL 345.151717 91.103461 \nL 345.157805 90.689525 \nL 345.160848 93.294738 \nL 345.166936 97.315917 \nL 345.218678 85.51011 \nL 345.264333 86.612027 \nL 345.319119 92.750139 \nL 345.334337 86.412142 \nL 345.410429 89.330062 \nL 345.413473 88.155559 \nL 345.468259 94.329186 \nL 345.516957 89.582849 \nL 345.608267 98.347012 \nL 345.623486 89.473799 \nL 345.632617 92.781659 \nL 345.653922 96.781116 \nL 345.641748 90.995538 \nL 345.69349 91.759066 \nL 345.736101 83.593627 \nL 345.723927 92.978861 \nL 345.806106 89.408275 \nL 345.827411 86.535262 \nL 345.891329 97.751427 \nL 345.897416 100.631719 \nL 345.982639 91.805797 \nL 345.994813 95.692795 \nL 346.080036 87.573165 \nL 346.070905 96.860985 \nL 346.110473 91.254302 \nL 346.137866 97.240815 \nL 346.192652 85.700821 \nL 346.217001 90.157012 \nL 346.223088 84.945102 \nL 346.283962 93.630343 \nL 346.326573 90.995109 \nL 346.341791 98.095395 \nL 346.442232 93.252409 \nL 346.527455 82.044876 \nL 346.512237 94.143483 \nL 346.567023 86.647887 \nL 346.576154 84.647882 \nL 346.579197 87.41377 \nL 346.582241 91.493935 \nL 346.667464 83.595921 \nL 346.688769 87.457494 \nL 346.749643 82.368995 \nL 346.734424 91.342507 \nL 346.798341 86.734934 \nL 346.843996 90.995067 \nL 346.892695 81.28554 \nL 347.002267 76.446723 \nL 346.90487 88.155003 \nL 347.011398 78.214686 \nL 347.06314 85.480607 \nL 347.090533 75.752971 \nL 347.127057 81.987286 \nL 347.200105 80.909033 \nL 347.181843 86.673727 \nL 347.227498 85.014268 \nL 347.346201 73.954461 \nL 347.382725 78.188373 \nL 347.404031 81.32503 \nL 347.458817 69.782371 \nL 347.483166 73.954461 \nL 347.510559 66.763941 \nL 347.586651 77.948306 \nL 347.592738 73.586977 \nL 347.705354 81.332523 \nL 347.708398 80.400478 \nL 347.720572 73.994653 \nL 347.802751 81.054634 \nL 347.821013 75.555273 \nL 347.839275 73.024463 \nL 347.845363 82.518351 \nL 347.897105 76.013502 \nL 347.997546 86.086921 \nL 348.009721 77.710027 \nL 348.12538 82.280594 \nL 348.064507 72.553692 \nL 348.128424 80.896139 \nL 348.247127 72.398444 \nL 348.143642 85.508381 \nL 348.25017 74.268794 \nL 348.36583 82.474408 \nL 348.262345 73.449712 \nL 348.374961 82.268536 \nL 348.381048 75.374326 \nL 348.438878 82.640917 \nL 348.487576 78.21434 \nL 348.575843 88.704677 \nL 348.530188 77.849215 \nL 348.606279 82.485078 \nL 348.609323 82.474357 \nL 348.758463 64.330909 \nL 348.849773 75.685646 \nL 348.776725 64.013962 \nL 348.877166 74.369441 \nL 348.974563 68.274056 \nL 348.941083 77.931987 \nL 348.995869 70.365737 \nL 349.017174 73.902288 \nL 349.050655 64.013912 \nL 349.099353 68.708317 \nL 349.199794 63.907307 \nL 349.111528 73.954145 \nL 349.202838 66.853842 \nL 349.288061 72.362774 \nL 349.218056 62.59385 \nL 349.318497 71.953862 \nL 349.412851 80.408141 \nL 349.348934 67.501778 \nL 349.415895 72.53416 \nL 349.513292 63.630294 \nL 349.443288 73.925194 \nL 349.52851 69.857244 \nL 349.574165 65.271046 \nL 349.558947 70.050012 \nL 349.601558 69.384943 \nL 349.686781 76.912804 \nL 349.610689 68.094974 \nL 349.714174 73.688009 \nL 349.720261 66.853911 \nL 349.808528 74.303173 \nL 349.829833 69.555631 \nL 349.930274 73.826917 \nL 349.866357 66.0934 \nL 349.936362 68.778416 \nL 350.018541 63.771721 \nL 349.945493 69.762512 \nL 350.055065 64.013828 \nL 350.058108 63.628938 \nL 350.064196 69.005324 \nL 350.118982 68.00078 \nL 350.155506 74.133572 \nL 350.137244 67.01414 \nL 350.234641 72.110688 \nL 350.31682 62.068012 \nL 350.292471 72.838485 \nL 350.3503 68.232195 \nL 350.447698 74.590052 \nL 350.362475 65.127427 \nL 350.459872 68.368602 \nL 350.469003 68.206443 \nL 350.475091 71.140073 \nL 350.526833 76.793977 \nL 350.517702 67.565905 \nL 350.587706 72.533971 \nL 350.59075 69.642805 \nL 350.679016 79.634129 \nL 350.697278 73.205638 \nL 350.764239 68.629224 \nL 350.712497 75.25434 \nL 350.812938 69.605972 \nL 350.815981 69.693928 \nL 350.901204 76.981645 \nL 350.837287 67.761134 \nL 350.928597 73.322749 \nL 350.934684 71.058181 \nL 350.965121 76.086397 \nL 350.971208 80.101348 \nL 351.053387 72.12291 \nL 351.077737 77.906035 \nL 351.117304 81.471431 \nL 351.135566 72.53396 \nL 351.187309 77.824364 \nL 351.193396 78.381218 \nL 351.220789 69.840932 \nL 351.269488 76.744439 \nL 351.315143 69.345405 \nL 351.372972 80.007106 \nL 351.376016 79.719649 \nL 351.37906 79.634285 \nL 351.385147 73.669502 \nL 351.415584 85.671291 \nL 351.488632 80.87777 \nL 351.494719 83.591264 \nL 351.582985 73.838554 \nL 351.589073 75.288446 \nL 351.65299 72.534055 \nL 351.662121 79.923455 \nL 351.698645 75.374079 \nL 351.741256 82.262661 \nL 351.77778 73.83478 \nL 351.81126 79.254615 \nL 351.832566 82.686795 \nL 351.878221 71.058191 \nL 351.905614 75.178584 \nL 352.003011 71.113993 \nL 351.9604 78.160414 \nL 352.015186 76.609559 \nL 352.115627 65.015725 \nL 352.149107 66.939253 \nL 352.173457 65.374707 \nL 352.161282 71.04152 \nL 352.182588 68.870076 \nL 352.23433 76.842086 \nL 352.298247 74.570374 \nL 352.304334 78.463736 \nL 352.368251 69.693961 \nL 352.407819 75.910296 \nL 352.468692 69.773436 \nL 352.456518 77.09053 \nL 352.526522 70.970934 \nL 352.651312 84.266574 \nL 352.663487 80.856038 \nL 352.700011 88.217101 \nL 352.745666 86.931242 \nL 352.751753 88.430621 \nL 352.836976 79.436282 \nL 352.903937 86.287768 \nL 352.919155 78.333549 \nL 352.943504 79.285675 \nL 352.946548 78.214446 \nL 352.952635 85.74951 \nL 353.043945 82.145627 \nL 353.123081 85.73962 \nL 353.0896 77.612848 \nL 353.156561 85.585804 \nL 353.162648 79.888741 \nL 353.217434 88.275667 \nL 353.266133 83.894448 \nL 353.320919 100.488844 \nL 353.293526 79.722463 \nL 353.375705 82.836567 \nL 353.442666 74.900866 \nL 353.460928 82.892132 \nL 353.485277 80.050304 \nL 353.567456 91.350661 \nL 353.607024 89.188936 \nL 353.71964 94.18472 \nL 353.622242 84.583646 \nL 353.722683 93.834839 \nL 353.801819 85.941755 \nL 353.832255 91.859167 \nL 353.944871 101.051262 \nL 353.856605 88.281276 \nL 353.954002 100.309102 \nL 353.957046 95.105499 \nL 354.020963 104.632033 \nL 354.063574 95.427659 \nL 354.08488 117.354115 \nL 354.173146 96.456123 \nL 354.252281 90.855701 \nL 354.291849 93.833045 \nL 354.297936 99.591105 \nL 354.355766 93.058775 \nL 354.410552 99.587185 \nL 354.428814 96.240606 \nL 354.437945 100.935159 \nL 354.453163 99.515292 \nL 354.489687 103.957135 \nL 354.474469 94.568488 \nL 354.562735 99.397201 \nL 354.568823 101.167912 \nL 354.587085 96.435934 \nL 354.66622 98.095136 \nL 354.672307 96.273084 \nL 354.751443 107.70144 \nL 354.763617 112.676822 \nL 354.857971 104.691009 \nL 354.888408 103.220684 \nL 354.894495 108.035499 \nL 354.958412 107.742755 \nL 355.071028 102.144186 \nL 355.016242 110.875461 \nL 355.083202 106.403823 \nL 355.095377 122.581226 \nL 355.147119 105.916022 \nL 355.192774 106.085911 \nL 355.220167 103.585194 \nL 355.27191 112.715147 \nL 355.296259 106.181541 \nL 355.354089 110.376134 \nL 355.3967 103.775433 \nL 355.402787 104.886511 \nL 355.427137 108.035542 \nL 355.518447 100.935478 \nL 355.576276 110.746662 \nL 355.542796 100.855954 \nL 355.631062 103.924084 \nL 355.664543 100.496964 \nL 355.649324 110.888198 \nL 355.72846 106.620751 \nL 355.734547 106.58161 \nL 355.755853 109.725425 \nL 355.771071 100.400808 \nL 355.789333 104.584944 \nL 355.856294 95.145678 \nL 355.801508 105.518791 \nL 355.898905 100.935469 \nL 355.904992 107.736012 \nL 355.993259 97.151826 \nL 356.005433 98.095396 \nL 356.115005 93.91519 \nL 356.096743 103.775607 \nL 356.118049 96.395848 \nL 356.169791 103.528356 \nL 356.233708 99.515547 \nL 356.300669 98.197538 \nL 356.242839 104.218191 \nL 356.318931 101.320797 \nL 356.431547 106.615844 \nL 356.334149 96.647754 \nL 356.43459 105.804989 \nL 356.437634 102.970841 \nL 356.535031 115.257852 \nL 356.544162 115.5223 \nL 356.547206 113.751968 \nL 356.626341 104.363995 \nL 356.662865 108.052863 \nL 356.693302 111.51571 \nL 356.699389 105.972572 \nL 356.702433 107.637765 \nL 356.757219 103.203304 \nL 356.717651 111.242916 \nL 356.812005 106.615683 \nL 356.842442 113.716026 \nL 356.82418 105.193087 \nL 356.924621 109.584513 \nL 356.927664 107.648768 \nL 356.988538 116.904214 \nL 357.022018 114.195307 \nL 357.028105 117.138428 \nL 357.04028 106.61579 \nL 357.122459 108.809458 \nL 357.152896 103.477569 \nL 357.134634 110.45351 \nL 357.232031 108.758108 \nL 357.359865 115.136046 \nL 357.25638 104.729228 \nL 357.365952 113.761752 \nL 357.475524 108.485474 \nL 357.411607 117.238423 \nL 357.478568 111.60205 \nL 357.585096 115.746421 \nL 357.566834 107.781205 \nL 357.58814 115.136206 \nL 357.658144 107.346465 \nL 357.700756 112.124871 \nL 357.810328 124.601862 \nL 357.715974 112.066361 \nL 357.825546 115.843674 \nL 357.892507 112.296212 \nL 357.904681 119.521756 \nL 357.941205 115.117282 \nL 357.947293 114.868299 \nL 357.950336 115.773705 \nL 357.965555 113.504713 \nL 358.065996 122.111519 \nL 358.069039 124.318322 \nL 358.117738 115.119422 \nL 358.178611 123.658786 \nL 358.181655 123.656579 \nL 358.19383 116.492064 \nL 358.209048 125.483406 \nL 358.288183 122.23649 \nL 358.324707 126.243214 \nL 358.391668 115.998992 \nL 358.400799 124.482902 \nL 358.422105 110.929596 \nL 358.513415 118.000637 \nL 358.516458 117.976509 \nL 358.522546 113.862312 \nL 358.598637 122.507426 \nL 358.619943 120.464732 \nL 358.622987 125.076704 \nL 358.711253 115.992672 \nL 358.729515 119.396371 \nL 358.74169 113.688535 \nL 358.769083 124.775853 \nL 358.842131 116.856338 \nL 358.845174 118.538385 \nL 358.878655 111.945076 \nL 358.954746 117.827029 \nL 358.960834 113.896312 \nL 359.039969 126.443742 \nL 359.046056 125.161182 \nL 359.122148 133.504896 \nL 359.152585 130.483349 \nL 359.155628 122.236794 \nL 359.170847 133.597011 \nL 359.262157 129.336887 \nL 359.292593 132.176966 \nL 359.329117 124.673971 \nL 359.371729 129.328809 \nL 359.38086 129.321193 \nL 359.386947 129.640321 \nL 359.389991 127.08066 \nL 359.484344 132.331966 \nL 359.493475 129.274916 \nL 359.590873 137.040378 \nL 359.578698 124.908575 \nL 359.59696 129.33697 \nL 359.679139 122.035234 \nL 359.612178 133.597118 \nL 359.709576 124.59486 \nL 359.767405 128.072919 \nL 359.758274 121.991211 \nL 359.782624 126.970663 \nL 359.794798 120.816877 \nL 359.889152 130.63618 \nL 359.974375 123.407258 \nL 359.965244 132.104869 \nL 360.023073 125.077016 \nL 360.11134 130.711144 \nL 360.129602 123.657062 \nL 360.135689 127.599984 \nL 360.184388 132.611334 \nL 360.193519 121.695562 \nL 360.196562 121.040436 \nL 360.217868 132.205474 \nL 360.257436 130.036577 \nL 360.324396 138.235916 \nL 360.26961 129.139303 \nL 360.373095 133.5973 \nL 360.38527 132.200964 \nL 360.379182 133.775949 \nL 360.388313 133.597316 \nL 360.394401 136.878804 \nL 360.430925 128.236215 \nL 360.491798 130.467823 \nL 360.570933 125.077116 \nL 360.552671 130.914595 \nL 360.60137 127.917144 \nL 360.707898 136.437499 \nL 360.631807 126.497148 \nL 360.713986 130.955081 \nL 360.732248 132.845451 \nL 360.738335 127.917256 \nL 360.741379 136.61268 \nL 360.81747 127.677162 \nL 360.850951 132.255452 \nL 360.857038 133.834736 \nL 360.899649 128.608642 \nL 360.939217 121.170027 \nL 361.003134 129.762376 \nL 361.012265 123.65726 \nL 361.018353 123.556217 \nL 361.036615 125.574152 \nL 361.067051 131.795782 \nL 361.045746 124.924624 \nL 361.155318 130.216738 \nL 361.167492 124.701624 \nL 361.26489 129.808232 \nL 361.325763 131.209409 \nL 361.286195 123.657269 \nL 361.362287 125.077347 \nL 361.395767 120.691668 \nL 361.383593 128.473574 \nL 361.444466 125.17364 \nL 361.520558 136.68808 \nL 361.462728 121.948236 \nL 361.563169 133.115253 \nL 361.593606 137.571065 \nL 361.578387 128.982769 \nL 361.672741 134.062281 \nL 361.761007 126.116828 \nL 361.767095 134.485947 \nL 361.7884 129.337416 \nL 361.894929 135.01759 \nL 361.797531 128.921418 \nL 361.901016 131.180527 \nL 361.910147 127.917523 \nL 361.93754 134.052702 \nL 362.004501 133.389467 \nL 362.053199 142.04473 \nL 362.019719 129.620656 \nL 362.132335 135.339201 \nL 362.202339 124.176045 \nL 362.190164 135.738323 \nL 362.251038 129.486377 \nL 362.275387 127.91758 \nL 362.2693 134.160518 \nL 362.324086 131.845295 \nL 362.442789 140.697947 \nL 362.552361 135.893163 \nL 362.494531 144.072752 \nL 362.555404 140.065444 \nL 362.558448 142.67652 \nL 362.57671 133.78762 \nL 362.661933 137.14101 \nL 362.75933 130.114932 \nL 362.744112 137.635564 \nL 362.774548 136.494412 \nL 362.804985 135.074285 \nL 362.814116 137.790984 \nL 362.859771 143.732627 \nL 362.835422 134.729434 \nL 362.911513 137.101385 \nL 362.935863 146.229444 \nL 363.024129 129.344157 \nL 363.078915 133.688799 \nL 363.094133 129.074631 \nL 363.136745 132.158962 \nL 363.139788 132.211854 \nL 363.145876 131.031235 \nL 363.170225 125.830639 \nL 363.231098 137.042294 \nL 363.304146 138.943435 \nL 363.285884 130.590468 \nL 363.337627 136.230984 \nL 363.410675 130.540999 \nL 363.368063 137.979032 \nL 363.45633 133.17549 \nL 363.498941 125.839653 \nL 363.52329 133.597769 \nL 363.571989 131.644652 \nL 363.678517 140.97211 \nL 363.587207 130.220864 \nL 363.687648 134.993332 \nL 363.690692 135.017918 \nL 363.754609 142.238515 \nL 363.803308 141.118596 \nL 363.815482 135.109545 \nL 363.867225 151.126676 \nL 363.891574 146.120163 \nL 363.897661 148.978521 \nL 363.964622 135.017947 \nL 363.992015 139.433065 \nL 364.001146 132.618812 \nL 364.019408 142.797859 \nL 364.098543 141.338654 \nL 364.125936 152.19635 \nL 364.211159 143.237475 \nL 364.226377 149.411273 \nL 364.262901 141.784614 \nL 364.320731 144.03019 \nL 364.323775 143.883993 \nL 364.338993 146.614614 \nL 364.399866 152.326105 \nL 364.363342 141.582294 \nL 364.451609 150.638362 \nL 364.463783 145.893205 \nL 364.488133 152.515038 \nL 364.597705 155.853018 \nL 364.509438 147.678268 \nL 364.600748 153.150876 \nL 364.640316 157.204263 \nL 364.628141 148.91001 \nL 364.679884 152.058529 \nL 364.783368 142.389932 \nL 364.792499 150.319416 \nL 364.807718 143.721739 \nL 364.868591 154.441234 \nL 364.902071 150.639682 \nL 364.905115 150.632335 \nL 364.911202 150.757669 \nL 364.944683 153.875753 \nL 365.029905 146.378353 \nL 365.07556 152.058485 \nL 365.102953 144.442767 \nL 365.145565 150.115542 \nL 365.249049 142.941358 \nL 365.163827 152.219042 \nL 365.25818 146.378269 \nL 365.370796 154.247898 \nL 365.273399 143.538212 \nL 365.37384 154.123019 \nL 365.480368 146.133319 \nL 365.386014 156.676059 \nL 365.492543 147.557761 \nL 365.501674 152.058515 \nL 365.504717 141.713273 \nL 365.599071 146.547012 \nL 365.626464 143.304717 \nL 365.684294 150.638515 \nL 365.702556 144.80446 \nL 365.815171 154.751121 \nL 365.818215 153.116101 \nL 365.894307 149.173289 \nL 365.903438 159.75831 \nL 365.906481 161.132207 \nL 365.9217 147.479097 \nL 365.98866 149.218446 \nL 365.994748 148.549803 \nL 366.037359 144.581573 \nL 366.076927 155.06641 \nL 366.101276 150.619491 \nL 366.10432 150.707146 \nL 366.110407 149.218483 \nL 366.113451 147.728813 \nL 366.201717 157.738786 \nL 366.241285 158.643101 \nL 366.265634 151.967072 \nL 366.277809 152.05862 \nL 366.280852 152.218529 \nL 366.311289 148.292334 \nL 366.372162 144.88524 \nL 366.396512 152.012434 \nL 366.408686 151.321099 \nL 366.423905 153.909674 \nL 366.44521 143.538386 \nL 366.460429 146.378462 \nL 366.539564 134.622196 \nL 366.573044 140.70087 \nL 366.65218 146.68175 \nL 366.600437 140.580651 \nL 366.68566 143.245956 \nL 366.813494 130.155331 \nL 366.71914 146.378672 \nL 366.816538 134.547035 \nL 366.907848 137.987061 \nL 366.883498 132.242132 \nL 366.929153 135.581928 \nL 367.038725 123.658118 \nL 366.947415 139.278422 \nL 367.041769 127.202902 \nL 367.154385 132.686661 \nL 367.056987 123.415222 \nL 367.157428 131.288781 \nL 367.260913 122.135546 \nL 367.172647 137.573364 \nL 367.270044 124.020623 \nL 367.394834 132.721716 \nL 367.297437 122.238078 \nL 367.397878 130.133003 \nL 367.400922 125.945201 \nL 367.467882 135.706078 \nL 367.50745 130.533876 \nL 367.574411 134.872675 \nL 367.553105 127.585776 \nL 367.601804 132.178118 \nL 367.604847 129.063526 \nL 367.693114 140.69846 \nL 367.708332 135.018319 \nL 367.735725 137.245063 \nL 367.741812 131.897198 \nL 367.769205 132.178349 \nL 367.772249 132.068174 \nL 367.796598 135.091338 \nL 367.799642 135.018432 \nL 367.909214 145.195036 \nL 367.808773 129.089709 \nL 367.964 144.902188 \nL 368.043135 136.438413 \nL 367.985306 146.279131 \nL 368.085747 139.257721 \nL 368.08879 139.278355 \nL 368.094878 137.449843 \nL 368.131402 146.240706 \nL 368.198362 139.541581 \nL 368.244017 135.928329 \nL 368.259236 143.856954 \nL 368.301847 137.858539 \nL 368.380982 146.644987 \nL 368.417506 145.250674 \nL 368.548384 135.154039 \nL 368.441856 146.882255 \nL 368.557515 138.208824 \nL 368.657956 142.309566 \nL 368.594039 132.022633 \nL 368.670131 139.470478 \nL 368.673175 137.665314 \nL 368.731004 146.188398 \nL 368.773616 143.5388 \nL 368.78579 140.502194 \nL 368.840576 146.638294 \nL 368.871013 145.022858 \nL 368.874057 148.542344 \nL 368.892319 140.314538 \nL 368.977541 142.266646 \nL 369.014065 136.378366 \nL 369.065808 143.577618 \nL 369.090157 139.57711 \nL 369.17538 148.539839 \nL 369.132768 139.377344 \nL 369.205816 146.81409 \nL 369.224078 148.693067 \nL 369.263646 138.975898 \nL 369.303214 143.165769 \nL 369.415829 127.763018 \nL 369.418873 130.103537 \nL 369.42496 136.552374 \nL 369.491921 128.866013 \nL 369.528445 130.597817 \nL 369.549751 133.598841 \nL 369.580187 126.498564 \nL 369.583231 126.364994 \nL 369.604537 128.993248 \nL 369.610624 127.918718 \nL 369.616711 130.888688 \nL 369.638017 122.371769 \nL 369.717152 126.498736 \nL 369.835855 113.364977 \nL 369.738458 129.109498 \nL 369.841943 121.50902 \nL 369.921078 126.333682 \nL 369.857161 114.846119 \nL 369.945427 120.818531 \nL 370.009344 119.260277 \nL 369.994126 126.383505 \nL 370.048912 122.721431 \nL 370.067174 127.918761 \nL 370.079349 118.944527 \nL 370.152397 122.535132 \nL 370.258925 117.827049 \nL 370.170659 131.880533 \nL 370.265012 117.978538 \nL 370.289362 121.57412 \nL 370.341104 110.878338 \nL 370.344148 109.26077 \nL 370.365453 120.069705 \nL 370.435458 118.442489 \nL 370.517637 125.640569 \nL 370.450676 112.51666 \nL 370.548073 121.566915 \nL 370.639383 113.006927 \nL 370.660689 115.138485 \nL 370.663733 117.949167 \nL 370.709388 107.771929 \nL 370.767217 112.298464 \nL 370.870702 106.490797 \nL 370.858527 115.138509 \nL 370.892008 106.701313 \nL 370.995492 103.778286 \nL 370.958968 114.483382 \nL 370.998536 104.528582 \nL 371.03506 111.283372 \nL 371.105064 101.381845 \nL 371.123326 99.370776 \nL 371.168981 105.610341 \nL 371.1842 105.49567 \nL 371.208549 108.038327 \nL 371.214636 101.932649 \nL 371.290728 103.51374 \nL 371.333339 109.591234 \nL 371.34247 100.51382 \nL 371.412475 107.96867 \nL 371.479435 107.979062 \nL 371.531178 100.681733 \nL 371.628575 106.998446 \nL 371.6164 96.164971 \nL 371.64075 100.713379 \nL 371.774671 111.251272 \nL 371.655968 100.690235 \nL 371.79902 104.961288 \nL 371.820326 102.017378 \nL 371.841632 111.479123 \nL 371.91468 103.145814 \nL 371.996859 108.186945 \nL 372.012077 99.277356 \nL 372.033383 106.381868 \nL 372.069907 110.245757 \nL 372.048601 101.226311 \nL 372.136867 105.198445 \nL 372.139911 105.048328 \nL 372.179479 108.137198 \nL 372.18861 108.041432 \nL 372.194697 108.045875 \nL 372.200784 107.89997 \nL 372.22209 111.060401 \nL 372.286007 102.153427 \nL 372.298182 106.665294 \nL 372.410797 95.258116 \nL 372.413841 97.570987 \nL 372.520369 106.294346 \nL 372.444278 95.153223 \nL 372.547762 100.938316 \nL 372.550806 100.760973 \nL 372.569068 105.978626 \nL 372.58733 104.827145 \nL 372.590374 106.725294 \nL 372.681684 96.229041 \nL 372.690815 101.007738 \nL 372.757775 92.552366 \nL 372.809518 95.174537 \nL 372.897784 99.518205 \nL 372.882566 92.418044 \nL 372.909959 94.587925 \nL 372.913002 90.998017 \nL 372.964745 104.56809 \nL 373.0104 103.412021 \nL 373.04388 107.780952 \nL 373.089535 98.911884 \nL 373.199107 93.385893 \nL 373.107797 104.883332 \nL 373.202151 93.838218 \nL 373.302592 98.202347 \nL 373.256937 93.786163 \nL 373.311723 96.924421 \nL 373.354334 89.57807 \nL 373.333028 100.216263 \nL 373.424338 93.424982 \nL 373.460862 99.518281 \nL 373.506517 90.998041 \nL 373.530867 92.864636 \nL 373.643482 82.477754 \nL 373.549129 98.142422 \nL 373.646526 86.008008 \nL 373.707399 94.035442 \nL 373.765229 90.998068 \nL 373.795666 86.73804 \nL 373.777404 93.913145 \nL 373.874801 87.834579 \nL 373.896107 93.757282 \nL 373.966111 84.216874 \nL 373.984373 88.16418 \nL 373.99046 88.171138 \nL 373.996548 87.912845 \nL 374.008722 92.418137 \nL 374.096989 80.746227 \nL 374.176124 76.79782 \nL 374.112207 88.839561 \nL 374.209604 79.670591 \nL 374.212648 79.45424 \nL 374.227866 85.980049 \nL 374.23091 86.955545 \nL 374.240041 79.031005 \nL 374.331351 83.987542 \nL 374.483534 72.305871 \nL 374.489622 75.842941 \nL 374.571801 69.697629 \nL 374.599194 75.57759 \nL 374.602237 76.797754 \nL 374.620499 68.000082 \nL 374.693547 69.697607 \nL 374.772683 68.133159 \nL 374.748333 75.312694 \nL 374.775726 70.885726 \nL 374.77877 72.994948 \nL 374.827469 66.217771 \nL 374.879211 68.509899 \nL 374.952259 58.337367 \nL 374.99487 65.753602 \nL 375.061831 59.273589 \nL 375.0527 65.795986 \nL 375.119661 64.017601 \nL 375.192709 66.11695 \nL 375.207927 56.637977 \nL 375.220102 61.475667 \nL 375.323586 53.754001 \nL 375.335761 55.491613 \nL 375.350979 55.59487 \nL 375.454464 48.397111 \nL 375.411853 55.664213 \nL 375.472726 51.091321 \nL 375.518381 60.626332 \nL 375.585342 54.675584 \nL 375.679695 46.996332 \nL 375.704045 48.391658 \nL 375.707088 48.415644 \nL 375.710132 48.353364 \nL 375.713176 48.397206 \nL 375.716219 49.740803 \nL 375.728394 41.296979 \nL 375.819704 46.977195 \nL 375.877534 40.134013 \nL 375.828835 50.391803 \nL 375.929276 45.054846 \nL 376.011455 49.094738 \nL 375.968844 41.218856 \nL 376.038848 46.151701 \nL 376.154507 37.037064 \nL 376.054066 52.178669 \nL 376.157551 45.370542 \nL 376.160595 48.329066 \nL 376.248861 35.296304 \nL 376.261036 39.87707 \nL 376.270167 39.844774 \nL 376.273211 39.952997 \nL 376.276254 39.877019 \nL 376.279298 37.924969 \nL 376.334084 49.698306 \nL 376.376695 45.472264 \nL 376.385826 48.85779 \nL 376.431481 41.512137 \nL 376.440612 42.347237 \nL 376.474093 38.457045 \nL 376.455831 45.702575 \nL 376.553228 39.615176 \nL 376.559315 42.469535 \nL 376.623232 31.422646 \nL 376.656713 35.6169 \nL 376.678018 38.861101 \nL 376.690193 32.597085 \nL 376.717586 34.19689 \nL 376.72063 32.788411 \nL 376.805852 39.951695 \nL 376.814983 38.792001 \nL 376.833245 42.618015 \nL 376.888031 34.147777 \nL 376.912381 34.718671 \nL 376.991516 27.096808 \nL 376.930643 35.617126 \nL 377.024996 29.946354 \nL 377.031084 30.018762 \nL 377.034127 29.743177 \nL 377.067608 24.256802 \nL 377.058477 34.868336 \nL 377.149787 26.914738 \nL 377.259359 32.772154 \nL 377.183267 25.706934 \nL 377.274577 31.592988 \nL 377.338494 21.23237 \nL 377.30197 33.46142 \nL 377.423717 25.53697 \nL 377.445022 27.391289 \nL 377.438935 20.881244 \nL 377.527201 24.686399 \nL 377.530245 22.76903 \nL 377.624599 31.4985 \nL 377.627642 31.342015 \nL 377.630686 31.356985 \nL 377.63373 31.256557 \nL 377.648948 37.073242 \nL 377.688516 26.598228 \nL 377.737214 29.936917 \nL 377.752433 28.51695 \nL 377.788957 33.215165 \nL 377.792 34.565407 \nL 377.807219 26.509934 \nL 377.88331 28.832958 \nL 377.956358 22.256759 \nL 377.947227 33.665381 \nL 377.992882 26.122259 \nL 378.093323 36.378734 \nL 378.108542 31.721416 \nL 378.15724 25.824834 \nL 378.202895 39.24695 \nL 378.205939 36.808215 \nL 378.208983 37.037136 \nL 378.239419 31.35692 \nL 378.242463 32.729504 \nL 378.333773 17.083636 \nL 378.376384 19.996661 \nL 378.379428 19.494896 \nL 378.385515 26.396484 \nL 378.45552 22.253073 \nL 378.537699 27.733915 \nL 378.531611 20.834436 \nL 378.568135 25.256885 \nL 378.616834 32.800436 \nL 378.604659 19.99669 \nL 378.674664 23.782949 \nL 378.753799 17.442302 \nL 378.784236 23.038339 \nL 378.790323 20.239659 \nL 378.799454 27.429852 \nL 378.82076 26.566303 \nL 378.896851 34.091663 \nL 378.829891 21.766867 \nL 378.936419 29.711398 \nL 378.972943 33.020255 \nL 378.960768 26.773209 \nL 379.030773 29.936975 \nL 379.03686 25.181766 \nL 379.058166 35.293839 \nL 379.134257 34.334551 \nL 379.143388 37.267556 \nL 379.198174 25.676841 \nL 379.21948 27.562467 \nL 379.231655 22.784974 \nL 379.319921 35.617169 \nL 379.322965 33.157793 \nL 379.329052 34.666337 \nL 379.371663 29.615062 \nL 379.386882 31.084484 \nL 379.490366 21.471972 \nL 379.432537 33.318204 \nL 379.499497 28.898372 \nL 379.505585 30.021081 \nL 379.520803 21.743252 \nL 379.587764 22.191767 \nL 379.621244 19.652719 \nL 379.67603 29.057888 \nL 379.685161 25.676863 \nL 379.70951 27.456571 \nL 379.703423 24.019868 \nL 379.739947 24.66386 \nL 379.742991 22.605979 \nL 379.831257 31.170822 \nL 379.840388 28.273145 \nL 379.861694 31.721685 \nL 379.934742 23.97362 \nL 379.946916 25.638678 \nL 379.953004 25.954592 \nL 379.956047 25.000815 \nL 380.019964 19.99669 \nL 380.059532 26.845399 \nL 380.065619 25.676972 \nL 380.178235 28.959278 \nL 380.093012 21.813355 \nL 380.181279 26.941109 \nL 380.19041 23.758171 \nL 380.22389 33.200994 \nL 380.287807 26.452671 \nL 380.293894 31.356978 \nL 380.312156 22.996308 \nL 380.336506 25.570975 \n\" style=\"fill:none;stroke:#8dd3c7;stroke-linecap:round;stroke-width:1.5;\"/>\n </g>\n <g id=\"patch_3\">\n <path d=\"M 60.754688 224.64 \nL 60.754688 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n </g>\n <g id=\"patch_4\">\n <path d=\"M 395.554688 224.64 \nL 395.554688 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n </g>\n <g id=\"patch_5\">\n <path d=\"M 60.754688 224.64 \nL 395.554688 224.64 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n </g>\n <g id=\"patch_6\">\n <path d=\"M 60.754688 7.2 \nL 395.554688 7.2 \n\" style=\"fill:none;stroke:#ffffff;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\n </g>\n </g>\n </g>\n <defs>\n <clipPath id=\"p10c26b41e1\">\n <rect height=\"217.44\" width=\"334.8\" x=\"60.754688\" y=\"7.2\"/>\n </clipPath>\n </defs>\n</svg>\n","text/plain":"<Figure size 432x288 with 1 Axes>"},"metadata":{},"output_type":"display_data"}],"source":"plot(y[:100000])"},{"cell_type":"code","execution_count":7,"metadata":{"ExecuteTime":{"end_time":"2019-03-20T18:57:04.964690Z","start_time":"2019-03-20T18:57:04.959190Z"}},"outputs":[],"source":"bf2 = bload(nCh=3, fs=20000.)"},{"cell_type":"code","execution_count":10,"metadata":{"ExecuteTime":{"end_time":"2019-03-20T18:57:27.945265Z","start_time":"2019-03-20T18:57:27.921028Z"}},"outputs":[{"ename":"ValueError","evalue":"Size of available data is not a multiple of the data-type size.","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)","\u001b[0;32m<ipython-input-10-6d827bbf8bbc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbf2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./cell_0109_raw.bin'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/disk0/Work/pydev/spiketag/spiketag/base/Binload.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(self, file_name, dtype)\u001b[0m\n\u001b[1;32m 111\u001b[0m '''\n\u001b[1;32m 112\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmemory_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 113\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnpmm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmemmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 114\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_npts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnpmm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nCh\u001b[0m \u001b[0;31m#full #pts/ch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/disk0/anaconda3/lib/python3.6/site-packages/numpy/core/memmap.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(subtype, filename, dtype, mode, offset, shape, order)\u001b[0m\n\u001b[1;32m 234\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mbytes\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0m_dbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 235\u001b[0m \u001b[0mfid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 236\u001b[0;31m raise ValueError(\"Size of available data is not a \"\n\u001b[0m\u001b[1;32m 237\u001b[0m \"multiple of the data-type size.\")\n\u001b[1;32m 238\u001b[0m \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0m_dbytes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: Size of available data is not a multiple of the data-type size."]}],"source":"bf2.load('./cell_0109_raw.bin', dtype=np.int32)"},{"cell_type":"code","execution_count":20,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T05:36:33.043883Z","start_time":"2018-08-08T05:36:33.038627Z"}},"outputs":[],"source":"%gui qt"},{"cell_type":"code","execution_count":21,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T05:36:42.872319Z","start_time":"2018-08-08T05:36:34.976748Z"}},"outputs":[],"source":"bf2.resample(25000.)"},{"cell_type":"code","execution_count":22,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T05:36:42.882971Z","start_time":"2018-08-08T05:36:42.877785Z"}},"outputs":[],"source":"raw_data = bf2.data.numpy().reshape(-1,3)"},{"cell_type":"code","execution_count":23,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T05:36:42.893045Z","start_time":"2018-08-08T05:36:42.888435Z"}},"outputs":[],"source":"raw_x = raw_data[:,2]"},{"cell_type":"code","execution_count":24,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T05:36:42.906401Z","start_time":"2018-08-08T05:36:42.897693Z"}},"outputs":[{"data":{"text/plain":["(11600049,)"]},"execution_count":24,"metadata":{},"output_type":"execute_result"}],"source":"raw_x.shape"},{"cell_type":"code","execution_count":25,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T05:36:43.070388Z","start_time":"2018-08-08T05:36:42.912408Z"}},"outputs":[{"data":{"text/plain":["array([[-5.78660011e+00, 0.00000000e+00],\n"," [-1.22229042e+01, -6.96778419e+00],\n"," [ 3.31405373e+01, 3.83228130e+01],\n"," ...,\n"," [-3.71196133e+04, -3.71140372e+04],\n"," [-3.28710625e+04, -3.28653919e+04],\n"," [-2.95166582e+04, -2.95113722e+04]])"]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":"uu = np.vstack((y, raw_x)).T\nuu"},{"cell_type":"code","execution_count":26,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T05:36:43.871126Z","start_time":"2018-08-08T05:36:43.866583Z"}},"outputs":[],"source":"from spiketag.view import wave_view"},{"cell_type":"code","execution_count":30,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T05:37:33.974626Z","start_time":"2018-08-08T05:37:33.642843Z"}},"outputs":[],"source":"wview = wave_view(data=uu)"},{"cell_type":"code","execution_count":31,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T05:37:34.680919Z","start_time":"2018-08-08T05:37:33.979405Z"}},"outputs":[],"source":"wview.show()"},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":"wview.show()"},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":""},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":""},{"cell_type":"code","execution_count":106,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T03:53:31.668962Z","start_time":"2018-08-08T03:53:31.662428Z"}},"outputs":[],"source":"def fft(x):\n fx = rfft(torch.from_numpy(x), 1, onesided=False)\n return fx[:,0].numpy() + fx[:,1].numpy()*1j"},{"cell_type":"code","execution_count":174,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T04:08:50.489296Z","start_time":"2018-08-08T04:08:50.475705Z"}},"outputs":[{"data":{"text/plain":["array([2592046.19618566 +0. j,\n"," 1949441.08052257 -658612.05064485j,\n"," 4254707.45143443+1419376.36441671j, ...,\n"," 2084225.34648515 +225373.77799279j,\n"," 4254707.45143442-1419376.36441674j,\n"," 1949441.08052257 +658612.05064483j])"]},"execution_count":174,"metadata":{},"output_type":"execute_result"}],"source":"pp = fft(x)\npp"},{"cell_type":"code","execution_count":177,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T04:09:17.718048Z","start_time":"2018-08-08T04:09:17.707040Z"}},"outputs":[{"data":{"text/plain":["array([[ 2592046.19618566, 0. ],\n"," [ 1949441.08052257, -658612.05064485],\n"," [ 4254707.45143443, 1419376.36441671],\n"," ...,\n"," [ 2084225.34648515, 225373.77799279],\n"," [ 4254707.45143442, -1419376.36441674],\n"," [ 1949441.08052257, 658612.05064483]])"]},"execution_count":177,"metadata":{},"output_type":"execute_result"}],"source":"np.vstack((pp.real, pp.imag)).T"},{"cell_type":"code","execution_count":142,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T04:00:15.595196Z","start_time":"2018-08-08T04:00:15.585521Z"}},"outputs":[{"data":{"text/plain":["(100000,)"]},"execution_count":142,"metadata":{},"output_type":"execute_result"}],"source":"x"},{"cell_type":"code","execution_count":163,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T04:04:50.675207Z","start_time":"2018-08-08T04:04:50.668632Z"}},"outputs":[],"source":"y = rfft(torch.from_numpy(x), 1, onesided=False)"},{"cell_type":"code","execution_count":172,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T04:08:05.579413Z","start_time":"2018-08-08T04:08:05.557145Z"}},"outputs":[{"data":{"text/plain":["tensor([[ 2.5920e+06, 0.0000e+00],\n"," [ 1.9494e+06, -6.5861e+05],\n"," [ 4.2547e+06, 1.4194e+06],\n"," ...,\n"," [ 2.0842e+06, 2.2537e+05],\n"," [ 4.2547e+06, -1.4194e+06],\n"," [ 1.9494e+06, 6.5861e+05]])"]},"execution_count":172,"metadata":{},"output_type":"execute_result"}],"source":"y"},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":""},{"cell_type":"code","execution_count":165,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T04:04:53.674095Z","start_time":"2018-08-08T04:04:53.662558Z"}},"outputs":[{"data":{"text/plain":["array([ 0.0000000e+00, 8.7679997e-02, -4.1856000e-01, ...,\n"," -3.0944702e+02, 4.2470558e+01, -2.2157039e+02], dtype=float32)"]},"execution_count":165,"metadata":{},"output_type":"execute_result"}],"source":"irfft(y, 1, onesided=False).numpy()"},{"cell_type":"code","execution_count":166,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T04:04:56.998849Z","start_time":"2018-08-08T04:04:56.984128Z"}},"outputs":[{"data":{"text/plain":["array([ 0.0000000e+00, 8.8957697e-02, -4.1913661e-01, ...,\n"," -3.0944434e+02, 4.2470356e+01, -2.2156749e+02], dtype=float32)"]},"execution_count":166,"metadata":{},"output_type":"execute_result"}],"source":"x"},{"cell_type":"code","execution_count":147,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T04:01:12.439536Z","start_time":"2018-08-08T04:01:12.434356Z"}},"outputs":[],"source":"from numpy.fft import fft, ifft"},{"cell_type":"code","execution_count":153,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T04:01:31.132298Z","start_time":"2018-08-08T04:01:30.784339Z"}},"outputs":[{"data":{"text/plain":["[<matplotlib.lines.Line2D at 0x1c633b7250>]"]},"execution_count":153,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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\n","text/plain":["<matplotlib.figure.Figure at 0x1c5be23350>"]},"metadata":{},"output_type":"display_data"}],"source":"plot(ifft(fft(x)).real - x)"},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":"plot(ifft(fft(x)).real - x)"},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":""},{"cell_type":"code","execution_count":117,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T03:56:28.145787Z","start_time":"2018-08-08T03:56:28.135130Z"},"scrolled":true},"outputs":[],"source":"HH = fft(x)"},{"cell_type":"code","execution_count":122,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T03:56:56.505983Z","start_time":"2018-08-08T03:56:56.496606Z"}},"outputs":[{"data":{"text/plain":["array([2592047.5 +0. j, 1949441. -658611.75j,\n"," 4254708. +1419376.5 j, ..., 2084225.8 +225373.94j,\n"," 4254708. -1419376.5 j, 1949441. +658611.75j], dtype=complex64)"]},"execution_count":122,"metadata":{},"output_type":"execute_result"}],"source":"HH"},{"cell_type":"code","execution_count":159,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T04:03:49.744784Z","start_time":"2018-08-08T04:03:49.714329Z"}},"outputs":[{"ename":"TypeError","evalue":"can't convert np.ndarray of type numpy.complex64. The only supported types are: double, float, float16, int64, int32, and uint8.","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)","\u001b[0;32m<ipython-input-159-069c17864eaa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mirfft\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mHH\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0monesided\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mTypeError\u001b[0m: can't convert np.ndarray of type numpy.complex64. The only supported types are: double, float, float16, int64, int32, and uint8."]}],"source":"irfft(torch.from_numpy(HH), 1, onesided=False)"},{"cell_type":"code","execution_count":120,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T03:56:31.377746Z","start_time":"2018-08-08T03:56:31.362901Z"}},"outputs":[{"data":{"text/plain":["array([-9.32320976e-04+0.00000000e+00j, 8.83269634e-02-3.42126554e-13j,\n"," -4.19410040e-01+2.08412530e-12j, ...,\n"," -3.09444284e+02+4.86635605e-12j, 4.24693514e+01+6.53799467e-12j,\n"," -2.21568396e+02+9.47989261e-12j])"]},"execution_count":120,"metadata":{},"output_type":"execute_result"}],"source":"np.fft.ifft(HH)"},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":""},{"cell_type":"code","execution_count":51,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T03:36:43.443868Z","start_time":"2018-08-08T03:36:43.415210Z"}},"outputs":[{"data":{"text/plain":["tensor([[ 2.5920e+06, 0.0000e+00],\n"," [ 1.9494e+06, -6.5861e+05],\n"," [ 4.2547e+06, 1.4194e+06],\n"," ...,\n"," [ 3.7466e+03, -2.6578e+03],\n"," [ 6.3688e+02, 3.6728e+03],\n"," [ 3.6674e+03, 0.0000e+00]])"]},"execution_count":51,"metadata":{},"output_type":"execute_result"}],"source":"fx"},{"cell_type":"code","execution_count":52,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T03:36:45.034347Z","start_time":"2018-08-08T03:36:44.997814Z"}},"outputs":[],"source":"fp = fft(x)"},{"cell_type":"code","execution_count":55,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T03:36:57.050116Z","start_time":"2018-08-08T03:36:57.023985Z"}},"outputs":[{"data":{"text/plain":["array([[ 2.5920475e+06, 0.0000000e+00],\n"," [ 1.9494410e+06, -6.5861175e+05],\n"," [ 4.2547080e+06, 1.4193765e+06],\n"," ...,\n"," [ 3.7466250e+03, -2.6577500e+03],\n"," [ 6.3687500e+02, 3.6728125e+03],\n"," [ 3.6673750e+03, 0.0000000e+00]], dtype=float32)"]},"execution_count":55,"metadata":{},"output_type":"execute_result"}],"source":"fx.numpy()"},{"cell_type":"code","execution_count":56,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T03:36:59.823681Z","start_time":"2018-08-08T03:36:59.814084Z"}},"outputs":[{"data":{"text/plain":["array([2592046.19618566 +0. j,\n"," 1949441.08052257 -658612.05064485j,\n"," 4254707.45143443+1419376.36441671j, ...,\n"," 2084225.34648515 +225373.77799279j,\n"," 4254707.45143442-1419376.36441674j,\n"," 1949441.08052257 +658612.05064483j])"]},"execution_count":56,"metadata":{},"output_type":"execute_result"}],"source":"fp"},{"cell_type":"code","execution_count":57,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T03:38:48.681343Z","start_time":"2018-08-08T03:38:48.675971Z"}},"outputs":[],"source":"kernel = np.hstack((kernel, np.zeros(len(x) - len(kernel))))"},{"cell_type":"code","execution_count":61,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T03:39:10.178730Z","start_time":"2018-08-08T03:39:10.167175Z"}},"outputs":[{"data":{"text/plain":["(100000,)"]},"execution_count":61,"metadata":{},"output_type":"execute_result"}],"source":"kernel.shape"},{"cell_type":"code","execution_count":79,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T03:45:03.071358Z","start_time":"2018-08-08T03:45:03.048958Z"}},"outputs":[],"source":"torch_H = rfft(torch.from_numpy(kernel), 1, onesided=False)"},{"cell_type":"code","execution_count":80,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T03:45:03.596794Z","start_time":"2018-08-08T03:45:03.571898Z"}},"outputs":[{"data":{"text/plain":["tensor([[ 0.0008, 0.0000],\n"," [ 0.0008, 0.0001],\n"," [ 0.0008, 0.0003],\n"," ...,\n"," [ 0.0008, -0.0004],\n"," [ 0.0008, -0.0003],\n"," [ 0.0008, -0.0001]], dtype=torch.float64)"]},"execution_count":80,"metadata":{},"output_type":"execute_result"}],"source":"torch_H"},{"cell_type":"code","execution_count":87,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T03:48:39.091065Z","start_time":"2018-08-08T03:48:39.071092Z"}},"outputs":[],"source":"HH = torch_H[:,0].numpy() + torch_H[:,1].numpy()*1j"},{"cell_type":"code","execution_count":91,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T03:49:08.777336Z","start_time":"2018-08-08T03:49:08.764334Z"}},"outputs":[{"data":{"text/plain":["array([6.99805622e-07+0.j, 7.21021337e-07+0.j, 7.84669618e-07+0.j, ...,\n"," 8.90753870e-07+0.j, 7.84669618e-07+0.j, 7.21021337e-07+0.j])"]},"execution_count":91,"metadata":{},"output_type":"execute_result"}],"source":"HH*HH.conj()"},{"cell_type":"code","execution_count":88,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T03:48:41.295263Z","start_time":"2018-08-08T03:48:41.277631Z"}},"outputs":[],"source":"H = fft(kernel)"},{"cell_type":"code","execution_count":89,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T03:48:43.142892Z","start_time":"2018-08-08T03:48:43.131941Z"}},"outputs":[{"data":{"text/plain":["array([0.00083654+0. j, 0.000837 +0.00014303j,\n"," 0.00083836+0.00028605j, ..., 0.00084063-0.00042907j,\n"," 0.00083836-0.00028605j, 0.000837 -0.00014303j])"]},"execution_count":89,"metadata":{},"output_type":"execute_result"}],"source":"H"},{"cell_type":"code","execution_count":69,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T03:40:46.998322Z","start_time":"2018-08-08T03:40:46.980515Z"}},"outputs":[{"data":{"text/plain":["array([6.99805622e-07+0.j, 7.21021337e-07+0.j, 7.84669618e-07+0.j, ...,\n"," 8.90753870e-07+0.j, 7.84669618e-07+0.j, 7.21021337e-07+0.j])"]},"execution_count":69,"metadata":{},"output_type":"execute_result"}],"source":"H*np.conj(H)"},{"cell_type":"code","execution_count":75,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T03:41:50.336897Z","start_time":"2018-08-08T03:41:50.327646Z"}},"outputs":[{"data":{"text/plain":["(5+0j)"]},"execution_count":75,"metadata":{},"output_type":"execute_result"}],"source":"(1+2j)*(1-2j)"},{"cell_type":"code","execution_count":81,"metadata":{"ExecuteTime":{"end_time":"2018-08-08T03:45:09.553402Z","start_time":"2018-08-08T03:45:09.522888Z"}},"outputs":[{"data":{"text/plain":["tensor([ 6.9981e-07, 7.2102e-07, 7.8467e-07, ..., 8.9075e-07,\n"," 7.8467e-07, 7.2102e-07], dtype=torch.float64)"]},"execution_count":81,"metadata":{},"output_type":"execute_result"}],"source":"torch_H[:,0]**2 + torch_H[:,1]**2"},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":"torch_H[:,0]**2 + torch_H[:,1]**2"},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":""},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":""},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":""},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":""},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":""},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":""},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":""},{"cell_type":"markdown","metadata":{},"source":["### Ground Truth RAW"]},{"cell_type":"code","execution_count":1,"metadata":{"ExecuteTime":{"end_time":"2019-03-20T19:09:02.794789Z","start_time":"2019-03-20T19:09:02.523306Z"}},"outputs":[{"name":"stdout","output_type":"stream","text":["Populating the interactive namespace from numpy and matplotlib\n"]}],"source":"%pylab inline\n%gui qt"},{"cell_type":"code","execution_count":2,"metadata":{"ExecuteTime":{"end_time":"2019-03-20T19:09:05.438922Z","start_time":"2019-03-20T19:09:03.703100Z"}},"outputs":[],"source":"from spiketag.base import bload"},{"cell_type":"code","execution_count":3,"metadata":{"ExecuteTime":{"end_time":"2019-03-20T19:09:05.449284Z","start_time":"2019-03-20T19:09:05.443856Z"}},"outputs":[],"source":"bf = bload(nCh=3, fs=20000.)"},{"cell_type":"code","execution_count":4,"metadata":{"ExecuteTime":{"end_time":"2019-03-20T19:09:05.598790Z","start_time":"2019-03-20T19:09:05.452702Z"}},"outputs":[{"name":"stdout","output_type":"stream","text":["total 245296\r\n","-rw-rw-r--+ 1 localuser 111360480 May 1 2018 cell_0109.bin\r\n","-rw-rw-r--+ 1 localuser 134 Aug 8 2018 cell_0109_lfp.bin\r\n","-rwxrwxr--+ 1 localuser 139201428 Aug 7 2018 \u001b[0m\u001b[01;32mcell_0109_mua_25000Hz.bin\u001b[0m*\r\n","-rw-rw-r--+ 1 localuser 134 Aug 8 2018 cell_0109_raw.bin\r\n","-rw-rw-r--+ 1 localuser 74472 May 1 2018 cell_0109.spk.bin\r\n","-rw-rw-r--+ 1 localuser 1640 Mar 14 15:12 gt_sort_demo.py\r\n","-rw-rw-r--+ 1 localuser 108314 Mar 20 15:08 kernel.ipynb\r\n","-rw-rw-r--+ 1 localuser 163836 Mar 17 23:14 reverse_filter.ipynb\r\n","-rw-rw-r--+ 1 localuser 9763 Mar 13 15:41 tritrode.json\r\n","-rw-rw-r--+ 1 localuser 19606 Mar 13 15:41 unit_test_controller.ipynb\r\n","-rw-rw-r--+ 1 localuser 120589 Jan 23 15:50 unit_test_spiketag_base.ipynb\r\n","-rw-rw-r--+ 1 localuser 90300 Mar 13 15:41 unit_test_spiketag.ipynb\r\n"]}],"source":"ll"},{"cell_type":"code","execution_count":9,"metadata":{"ExecuteTime":{"end_time":"2019-03-20T19:09:43.130857Z","start_time":"2019-03-20T19:09:43.067074Z"}},"outputs":[{"name":"stderr","output_type":"stream","text":["2019-03-20 15:09:43,119 - spiketag - INFO - ############# load data ###################\n","2019-03-20 15:09:43,121 - spiketag - INFO - ./cell_0109.bin loaded, it contains: \n","2019-03-20 15:09:43,122 - spiketag - INFO - 9280040.0 * 3 points (111360480 bytes) \n","2019-03-20 15:09:43,123 - spiketag - INFO - 3 channels with sampling rate of 20000.0000 \n","2019-03-20 15:09:43,124 - spiketag - INFO - 464.002 secs (7.733 mins) of data\n","2019-03-20 15:09:43,125 - spiketag - INFO - #############################################\n"]}],"source":"bf.load('./cell_0109.bin', dtype=np.int32)"},{"cell_type":"code","execution_count":10,"metadata":{"ExecuteTime":{"end_time":"2019-03-20T19:09:44.752530Z","start_time":"2019-03-20T19:09:44.046210Z"}},"outputs":[],"source":"bf.show(chs=np.array([0,1,2]))"},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":""}],"nbformat":4,"nbformat_minor":2,"metadata":{"language_info":{"name":"python","codemirror_mode":{"name":"ipython","version":3}},"orig_nbformat":2,"file_extension":".py","mimetype":"text/x-python","name":"python","npconvert_exporter":"python","pygments_lexer":"ipython3","version":3}} | bsd-3-clause |
jsaudino/75.06_tp1_acs | old/old.ipynb | 3 | 878546 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# magic function para hacer que los graficos de matplotlib se renderizen en el notebook.\n",
"%matplotlib inline\n",
"\n",
"import datetime as datetime\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.style.use('default') # Make the graphs a bit prettier\n",
"plt.rcParams['figure.figsize'] = (15, 5)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Cargo los datos pero parseando las fechas a DataTime\n",
"trip = pd.read_csv('trip.csv', parse_dates=['start_date','end_date'])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>duration</th>\n",
" <th>start_date</th>\n",
" <th>start_station_name</th>\n",
" <th>start_station_id</th>\n",
" <th>end_date</th>\n",
" <th>end_station_name</th>\n",
" <th>end_station_id</th>\n",
" <th>bike_id</th>\n",
" <th>subscription_type</th>\n",
" <th>zip_code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4576</td>\n",
" <td>63</td>\n",
" <td>2013-08-29 14:13:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 14:14:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>520</td>\n",
" <td>Subscriber</td>\n",
" <td>94127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4607</td>\n",
" <td>70</td>\n",
" <td>2013-08-29 14:42:00</td>\n",
" <td>San Jose City Hall</td>\n",
" <td>10</td>\n",
" <td>2013-08-29 14:43:00</td>\n",
" <td>San Jose City Hall</td>\n",
" <td>10</td>\n",
" <td>661</td>\n",
" <td>Subscriber</td>\n",
" <td>95138</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4130</td>\n",
" <td>71</td>\n",
" <td>2013-08-29 10:16:00</td>\n",
" <td>Mountain View City Hall</td>\n",
" <td>27</td>\n",
" <td>2013-08-29 10:17:00</td>\n",
" <td>Mountain View City Hall</td>\n",
" <td>27</td>\n",
" <td>48</td>\n",
" <td>Subscriber</td>\n",
" <td>97214</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4251</td>\n",
" <td>77</td>\n",
" <td>2013-08-29 11:29:00</td>\n",
" <td>San Jose City Hall</td>\n",
" <td>10</td>\n",
" <td>2013-08-29 11:30:00</td>\n",
" <td>San Jose City Hall</td>\n",
" <td>10</td>\n",
" <td>26</td>\n",
" <td>Subscriber</td>\n",
" <td>95060</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4299</td>\n",
" <td>83</td>\n",
" <td>2013-08-29 12:02:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 12:04:00</td>\n",
" <td>Market at 10th</td>\n",
" <td>67</td>\n",
" <td>319</td>\n",
" <td>Subscriber</td>\n",
" <td>94103</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>4927</td>\n",
" <td>103</td>\n",
" <td>2013-08-29 18:54:00</td>\n",
" <td>Golden Gate at Polk</td>\n",
" <td>59</td>\n",
" <td>2013-08-29 18:56:00</td>\n",
" <td>Golden Gate at Polk</td>\n",
" <td>59</td>\n",
" <td>527</td>\n",
" <td>Subscriber</td>\n",
" <td>94109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>4500</td>\n",
" <td>109</td>\n",
" <td>2013-08-29 13:25:00</td>\n",
" <td>Santa Clara at Almaden</td>\n",
" <td>4</td>\n",
" <td>2013-08-29 13:27:00</td>\n",
" <td>Adobe on Almaden</td>\n",
" <td>5</td>\n",
" <td>679</td>\n",
" <td>Subscriber</td>\n",
" <td>95112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>4563</td>\n",
" <td>111</td>\n",
" <td>2013-08-29 14:02:00</td>\n",
" <td>San Salvador at 1st</td>\n",
" <td>8</td>\n",
" <td>2013-08-29 14:04:00</td>\n",
" <td>San Salvador at 1st</td>\n",
" <td>8</td>\n",
" <td>687</td>\n",
" <td>Subscriber</td>\n",
" <td>95112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>4760</td>\n",
" <td>113</td>\n",
" <td>2013-08-29 17:01:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 17:03:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>553</td>\n",
" <td>Subscriber</td>\n",
" <td>94103</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>4258</td>\n",
" <td>114</td>\n",
" <td>2013-08-29 11:33:00</td>\n",
" <td>San Jose City Hall</td>\n",
" <td>10</td>\n",
" <td>2013-08-29 11:35:00</td>\n",
" <td>MLK Library</td>\n",
" <td>11</td>\n",
" <td>107</td>\n",
" <td>Subscriber</td>\n",
" <td>95060</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id duration start_date start_station_name \\\n",
"0 4576 63 2013-08-29 14:13:00 South Van Ness at Market \n",
"1 4607 70 2013-08-29 14:42:00 San Jose City Hall \n",
"2 4130 71 2013-08-29 10:16:00 Mountain View City Hall \n",
"3 4251 77 2013-08-29 11:29:00 San Jose City Hall \n",
"4 4299 83 2013-08-29 12:02:00 South Van Ness at Market \n",
"5 4927 103 2013-08-29 18:54:00 Golden Gate at Polk \n",
"6 4500 109 2013-08-29 13:25:00 Santa Clara at Almaden \n",
"7 4563 111 2013-08-29 14:02:00 San Salvador at 1st \n",
"8 4760 113 2013-08-29 17:01:00 South Van Ness at Market \n",
"9 4258 114 2013-08-29 11:33:00 San Jose City Hall \n",
"\n",
" start_station_id end_date end_station_name \\\n",
"0 66 2013-08-29 14:14:00 South Van Ness at Market \n",
"1 10 2013-08-29 14:43:00 San Jose City Hall \n",
"2 27 2013-08-29 10:17:00 Mountain View City Hall \n",
"3 10 2013-08-29 11:30:00 San Jose City Hall \n",
"4 66 2013-08-29 12:04:00 Market at 10th \n",
"5 59 2013-08-29 18:56:00 Golden Gate at Polk \n",
"6 4 2013-08-29 13:27:00 Adobe on Almaden \n",
"7 8 2013-08-29 14:04:00 San Salvador at 1st \n",
"8 66 2013-08-29 17:03:00 South Van Ness at Market \n",
"9 10 2013-08-29 11:35:00 MLK Library \n",
"\n",
" end_station_id bike_id subscription_type zip_code \n",
"0 66 520 Subscriber 94127 \n",
"1 10 661 Subscriber 95138 \n",
"2 27 48 Subscriber 97214 \n",
"3 10 26 Subscriber 95060 \n",
"4 67 319 Subscriber 94103 \n",
"5 59 527 Subscriber 94109 \n",
"6 5 679 Subscriber 95112 \n",
"7 8 687 Subscriber 95112 \n",
"8 66 553 Subscriber 94103 \n",
"9 11 107 Subscriber 95060 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Observacion de los datos\n",
"trip.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.text.Text at 0xafd32f0>,\n",
" <matplotlib.text.Text at 0xafd3b30>,\n",
" <matplotlib.text.Text at 0xafc9390>,\n",
" <matplotlib.text.Text at 0xafbc8b0>,\n",
" <matplotlib.text.Text at 0x7c05630>,\n",
" <matplotlib.text.Text at 0x7c05b10>,\n",
" <matplotlib.text.Text at 0x7c05e90>]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABPQAAAIKCAYAAACk8RE8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x3832390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#A cada dato de la columna de comienzo del viaje (start_date) le aplico una funcion para saber en que dia de la semana fueron\n",
"#realizados los viajes\n",
"#Aclaracion: dayofweek nos da los dias ordenados desde 0(lunes) hasta 6(domingo)\n",
"#Realizo un plot de barras para visualizar lo calculado en el paso anterior\n",
"plt = trip['start_date'].apply(lambda x: x.dayofweek).value_counts().plot('bar')\n",
"plt.set_xlabel('Dias de la semana')\n",
"plt.set_ylabel('Cantidad')\n",
"plt.set_title('Cantidad de viajes por dia de la semana')\n",
"plt.set_xticklabels(['Martes','Miercoles','Jueves','Lunes','Viernes','Sabado','Domingo'], fontdict=None, minor=False)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABOsAAAHaCAYAAABRgs3rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzs3X18zvX////7zk8dm2mnzBBlY95qimmZk2VsxNvetFo5\neWO+osIbUchZlMppThLNyZuPt9EZ1bJSvMtIVEQhjSVGmW1Gs9levz/8HG9HY+3Q1o7a7Xq5HJeO\nPZ+P4/V8HK/DX/eer9fLzjAMQwAAAAAAAACqnX11NwAAAAAAAADgCsI6AAAAAAAAwEYQ1gEAAAAA\nAAA2grAOAAAAAAAAsBGEdQAAAAAAAICNIKwDAAAAAAAAbARhHQAAAAAAAGAjCOsAAAAAAAAAG0FY\nBwAAAAAAANgIwjoAAAArtG/fXu3bt//Nuo8//lh2dnb6+OOPK23t/v37q0GDBjf9+QYNGqh///6V\n1k9Vr3vs2DHZ2dlpxYoVld4TAACArSKsAwAANuvo0aMaMmSIGjVqJFdXV5lMJt1zzz2aN2+efvnl\nlypb9+DBg5o8ebKOHTtWZWsAAAAA1+NY3Q0AAABczzvvvKPevXvLxcVFffv2VfPmzVVUVKRPPvlE\nY8aM0YEDB7R06dIqWfvgwYOaMmWK2rdvX2Yn25YtW6pkzb+yQ4cOyd7e+v9HHBISol9++UVOTk5V\n0BUAAIBtIqwDAAA2JzMzU4mJiQoJCdHWrVsVGBhonhs2bJi+++47vfPOO9XSm7Ozc7Ws+2fm4uJy\nU5+zs7OTq6trJXdjGy5evCh3d/fqbgMAANggLoMFAAA2Z9asWSooKNDy5cstgrqrGjdurCeeeML8\nd0pKijp27Cg/Pz+5uLgoLCxMixcvLvO5Bg0aqFu3bvrkk0909913y9XVVY0aNdKqVavMNStWrFDv\n3r0lSR06dJCdnZ3Fveeud8+6EydOqGfPnvLw8JCfn59GjhypS5culVn/v//9r3r37q369evLxcVF\nwcHBGjly5HUv6X3zzTfVvHlzubq6qnnz5nrjjTcqdO4kyTAMTZ8+XfXq1ZO7u7s6dOigAwcOXLc2\nNzdXI0aMUHBwsFxcXNS4cWM9//zzKi0tLXeNbt26qVGjRtedi4yMVKtWrcx///qedTk5ORo9erTC\nw8Pl6ekpk8mkrl276quvvrI4zo3uWfftt9/qH//4h3x8fOTq6qpWrVrp7bfftqgpLi7WlClT1KRJ\nE7m6uqpOnTqKiopSenp6ud9rxYoVsrOz0/bt2zVkyBDVqVNHJpNJffv21blz58rUL1q0SM2aNZOL\ni4uCgoI0bNgw5ebmWtS0b99ezZs31549e9SuXTu5u7vrqaeeumEP/fv3l6enp7KystStWzd5enqq\nbt26WrhwoSRp//796tixozw8PBQSEqK1a9eWOUZFf9d169YpIiJCtWrVkslkUnh4uObNm1fuOQIA\nAFWLnXUAAMDmbNq0SY0aNVLbtm0rVL948WI1a9ZM999/vxwdHbVp0yY9+uijKi0t1bBhwyxqv/vu\nO/3jH//QwIED1a9fP7322mvq37+/IiIi1KxZM7Vr106PP/645s+fr6eeekqhoaGSZP7vr/3yyy/q\n1KmTsrKy9PjjjysoKEirV6/W1q1by9Smpqbq4sWLGjp0qOrUqaPPPvtMCxYs0IkTJ5Sammqu27Jl\nixISEhQWFqaZM2fq7NmzGjBggOrVq1eh8zFp0iRNnz5dcXFxiouL0969e9W5c2cVFRVZ1F28eFHR\n0dH68ccfNWTIENWvX187duzQ+PHjderUKc2dO/eGazzwwAPq27evdu/erbvuuss8fvz4ce3cuVMv\nvPDCDT/7/fff680331Tv3r3VsGFDnT59Wq+88oqio6N18OBBBQUF3fCzBw4c0D333KO6detq3Lhx\n8vDw0Pr169WzZ09t3LhRf//73yVJkydP1syZMzVo0CDdfffdys/P1+eff669e/fqvvvu+81zOHz4\ncHl7e2vy5Mk6dOiQFi9erOPHj5sfHHJ1jSlTpigmJkZDhw411+3evVuffvqpxeW7Z8+eVdeuXZWY\nmKiHH35Y/v7+5a5fUlKirl27ql27dpo1a5bWrFmj4cOHy8PDQ08//bSSkpLUq1cvLVmyRH379lVk\nZKQaNmwoqeK/a3p6uh588EF16tRJzz//vCTpm2++0aeffmoRhgMAgD+YAQAAYEPy8vIMSUaPHj0q\n/JmLFy+WGYuNjTUaNWpkMRYSEmJIMrZv324eO3PmjOHi4mL861//Mo+lpqYakoyPPvqozHGjo6ON\n6Oho899z5841JBnr1683j124cMFo3LhxmWNcr8+ZM2cadnZ2xvHjx81jLVu2NAIDA43c3Fzz2JYt\nWwxJRkhIyHXPwbXfx9nZ2YiPjzdKS0vN40899ZQhyejXr595bNq0aYaHh4dx+PBhi2OMGzfOcHBw\nMLKysm64Tl5eXpnzZhiGMWvWrDLfJyQkxGLdwsJCo6SkxOJzmZmZhouLizF16lSLMUlGSkqKeaxT\np05GeHi4UVhYaB4rLS012rZtazRp0sQ89re//c2Ij4+/Yf83kpKSYkgyIiIijKKiIovvJcl46623\nDMP433nu3LmzxXd5+eWXDUnGa6+9Zh6Ljo42JBlLliypUA/9+vUzJBkzZswwj507d85wc3Mz7Ozs\njHXr1pnHv/32W0OS8cwzz5jHKvq7PvHEE4bJZDIuX75cob4AAMAfg8tgAQCATcnPz5ck1apVq8Kf\ncXNzM7/Py8vTzz//rOjoaH3//ffKy8uzqA0LC9O9995r/tvX11e33367vv/++5vq991331VgYKD+\n8Y9/mMfc3d2VnJxcbp8XLlzQzz//rLZt28owDH3xxReSpFOnTunLL79Uv3795OXlZa6/7777FBYW\n9pv9fPDBByoqKtJjjz1m3gEmSSNGjChTm5qaqnvvvVe1a9fWzz//bH7FxMSopKRE27dvv+E6Vy9d\nXb9+vQzDMI//5z//UZs2bVS/fv0bftbFxcX8wImSkhKdPXtWnp6euv3227V3794bfi4nJ0dbt25V\nnz59dP78eXO/Z8+eVWxsrI4cOaIff/xRkuTt7a0DBw7oyJEjNz5Z5UhOTrbYGTd06FA5Ojrq3Xff\nlfS/8zxixAiLh2cMHjxYJpOpzD0VXVxcNGDAAKt6GDRokPm9t7e3br/9dnl4eKhPnz7m8dtvv13e\n3t4W/34r+rt6e3vrwoULv3lpMAAA+GMR1gEAAJtiMpkkSefPn6/wZz799FPFxMTIw8ND3t7e8vX1\nNd8T7Ndh3fVCpNq1a1/3fmQVcfz4cTVu3NgiGJOuhCi/lpWVpf79+8vHx0eenp7y9fVVdHS0RZ/H\njx+XJDVp0qTM5693zOv1c73P+/r6qnbt2hZjR44cUVpamnx9fS1eMTExkqQzZ86Uu9YDDzygH374\nQRkZGZKko0ePas+ePXrggQfK/VxpaanmzJmjJk2ayMXFRbfccot8fX21b9++Mr/Xtb777jsZhqGJ\nEyeW6fmZZ56x6Hnq1KnKzc3VbbfdpvDwcI0ZM0b79u0rt69r/fr8eXp6KjAwUMeOHZP0v/P869/E\n2dlZjRo1Ms9fVbduXaseTuLq6ipfX1+LMS8vL9WrV6/MvzUvLy+Lf78V/V0fffRR3Xbbberatavq\n1aunf/7zn0pLS6twjwAAoGpwzzoAAGBTTCaTgoKC9PXXX1eo/ujRo+rUqZOaNm2q2bNnKzg4WM7O\nznr33Xc1Z86cMjfUd3BwuO5xrt0dVhVKSkp03333KScnR08++aSaNm0qDw8P/fjjj+rfv/9vPtCh\nKpSWluq+++7T2LFjrzt/2223lfv57t27y93dXevXr1fbtm21fv162dvbmx/QcSMzZszQxIkT9c9/\n/lPTpk2Tj4+P7O3tNWLEiHLPw9W50aNHKzY29ro1jRs3liS1a9dOR48e1VtvvaUtW7Zo2bJlmjNn\njpYsWWKxY+2Pcu2uyoq40b/Tivz7rejv6ufnpy+//FLvv/++3nvvPb333ntKSUlR3759tXLlSqv6\nBQAAlYewDgAA2Jxu3bpp6dKlysjIUGRkZLm1mzZt0qVLl/T2229b7Jr76KOPbnr9X+9cKk9ISIi+\n/vprGYZh8blDhw5Z1O3fv1+HDx/WypUr1bdvX/P4ry9BDAkJkaTrXr7562PeqJ+rn7/2aa0//fRT\nmd2Dt956qwoKCsw7rqzl4eGhbt26KTU1VbNnz9Z//vMf3XvvveU+IEKSNmzYoA4dOmj58uUW47m5\nubrllltu+Lmr38fJyalCPfv4+GjAgAEaMGCACgoK1K5dO02ePLlCYd2RI0fUoUMH898FBQU6deqU\n4uLiJP3vPB86dMjiPBcVFSkzM/Omz2llsOZ3dXZ2Vvfu3dW9e3eVlpbq0Ucf1SuvvKKJEyeag08A\nAPDH4jJYAABgc8aOHSsPDw8NGjRIp0+fLjN/9OhRzZs3T9L/dhpdu7MoLy9PKSkpN72+h4eHpCvh\n0W+Ji4vTyZMntWHDBvPYxYsXtXTpUou66/VpGIb5e1wVGBioli1bauXKlRaXhKanp+vgwYO/2U9M\nTIycnJy0YMECi7Wu92TXPn36KCMjQ++//36ZudzcXF2+fPk313vggQd08uRJLVu2TF999dVvXgIr\nXTkXv97JmJqaar7f3I34+fmpffv2euWVV3Tq1Kky8z/99JP5/dmzZy3mPD091bhxY126dOk3+5Ok\npUuXqri42Pz34sWLdfnyZXXt2lXSlfPs7Oys+fPnW3yX5cuXKy8vT/Hx8RVapypU9Hf99Tmyt7dX\nixYtJKnC5wkAAFQ+dtYBAACbc+utt2rt2rV64IEHFBoaqr59+6p58+YqKirSjh07lJqaqv79+0uS\nOnfubN4dNGTIEBUUFOjVV1+Vn5/fdQOdimjZsqUcHBz0/PPPKy8vTy4uLurYsaP8/PzK1A4ePFgv\nv/yy+vbtqz179igwMFCrV6+Wu7u7RV3Tpk116623avTo0frxxx9lMpm0cePG694rb+bMmYqPj1dU\nVJT++c9/KicnRwsWLFCzZs1UUFBQbu++vr4aPXq0Zs6cqW7duikuLk5ffPGF3nvvvTK71saMGaO3\n335b3bp1U//+/RUREaELFy5o//792rBhg44dO1buTjfpSlhZq1YtjR49Wg4ODkpISCi3Xrqyc3Lq\n1KkaMGCA2rZtq/3792vNmjUWO9RuZOHChYqKilJ4eLgGDx6sRo0a6fTp08rIyNCJEyf01VdfSbry\nIJH27dsrIiJCPj4++vzzz7VhwwYNHz78N9eQruyQ69Spk/r06aNDhw5p0aJFioqK0v333y/pynke\nP368pkyZoi5duuj+++8319111116+OGHK7ROVajo7zpo0CDl5OSoY8eOqlevno4fP64FCxaoZcuW\nCg0Nrbb+AQCo8arnIbQAAAC/7fDhw8bgwYONBg0aGM7Ozoanp6fRpk0bY+7cucYvv/xirnv77beN\nFi1aGK6urkaDBg2M559/3njttdcMSUZmZqa5LiQkxIiPjy+zTnR0tBEdHW0x9uqrrxqNGjUyHBwc\nDEnGRx99dMPa48ePG/fff7/h7u5u3HLLLcYTTzxhpKWlWXzOMAzj4MGDRkxMjOHp6WnccsstxuDB\ng42vvvrKkGSkpKRYHHPjxo1GaGio4eLiYoSFhRmvv/660a9fPyMkJOQ3z1tJSYkxZcoUIzAw0HBz\nczPat29vfP3110ZISIjRr18/i9rz588b48ePNxo3bmw4Ozsbt9xyi9G2bVvjxRdfNIqKin5zLcMw\njKSkJEOSERMTc935X69bWFho/Otf/zL3d8899xgZGRllzm1mZuZ1z83Ro0eNvn37GgEBAYaTk5NR\nt25do1u3bsaGDRvMNdOnTzfuvvtuw9vb23BzczOaNm1qPPvss7/5nVJSUgxJxrZt24zk5GSjdu3a\nhqenp5GUlGScPXu2TP3LL79sNG3a1HBycjL8/f2NoUOHGufOnbOoiY6ONpo1a1buutfq16+f4eHh\nUWb8Rse53r/rivyuGzZsMDp37mz4+fkZzs7ORv369Y0hQ4YYp06dqnCvAACg8tkZRhXfTRkAAAC4\nCUePHlXjxo21evXqP2yn2ooVKzRgwADt3r1brVq1+kPWBAAAuBb3rAMAAIBNunoZ829digsAAPBX\nwj3rAAAAYHNee+01vfbaa3J3d1ebNm2qux0AAIA/DDvrAAAAYHOSk5OVk5Oj1NRUeXt7V3c7AAAA\nfxjuWQcAAAAAAADYCHbWAQAAAAAAADaCsA4AAAAAAACwETxgohKVlpbq5MmTqlWrluzs7Kq7HQAA\nAAAAAFQTwzB0/vx5BQUFyd6+4vvlCOsq0cmTJxUcHFzdbQAAAAAAAMBG/PDDD6pXr16F6wnrKlGt\nWrUkXfkRTCZTNXcDAAAAAACA6pKfn6/g4GBzXlRRhHWV6OqlryaTibAOAAAAAAAAVt8qjQdMAAAA\nAAAAADaCsA4AAAAAAACwEYR1AAAAAAAAgI0grAMAAAAAAABsBGEdAAAAAAAAYCMI6wAAAAAAAAAb\nQVgHAAAAAAAA2AjCOgAAAAAAAMBGENYBAAAAAAAANoKwDgAAAAAAALARhHUAAAAAAACAjSCsAwAA\nAAAAAGwEYR0AAAAAAABgIwjrAAAAAAAAABtBWAcAAAAAAADYCMfqbgDWaTDunepu4Xc59lx8dbcA\nAAAAAABgs9hZBwAAAAAAANgIwjoAAAAAAADARhDWAQAAAAAAADaCsA4AAAAAAACwEYR1AAAAAAAA\ngI0grAMAAAAAAABsBGEdAAAAAAAAYCMI6wAAAAAAAAAb4VjdDQB/Ng3GvVPdLfwux56Lr+4WAAAA\nAADADbCzDgAAAAAAALAR7KwD8KfD7kYAAAAAwF8VO+sAAAAAAAAAG0FYBwAAAAAAANgIwjoAAAAA\nAADARhDWAQAAAAAAADaCsA4AAAAAAACwEYR1AAAAAAAAgI0grAMAAAAAAABshGN1NwAA+HNpMO6d\n6m7hdzv2XHx1twAAAAAA18XOOgAAAAAAAMBGENYBAAAAAAAANoKwDgAAAAAAALARhHUAAAAAAACA\njSCsAwAAAAAAAGwEYR0AAAAAAABgI6o9rPvxxx/18MMPq06dOnJzc1N4eLg+//xz87xhGJo0aZIC\nAwPl5uammJgYHTlyxOIYhYWFGjZsmOrUqSNPT08lJCTo9OnTFjU5OTlKSkqSyWSSt7e3Bg4cqIKC\nAouarKwsxcfHy93dXX5+fhozZowuX75cdV8eAAAAAAAAuEa1hnXnzp3TPffcIycnJ7333ns6ePCg\nXnrpJdWuXdtcM2vWLM2fP19LlizRrl275OHhodjYWBUWFpprRo4cqU2bNik1NVXbtm3TyZMn1atX\nL4u1kpKSdODAAaWnp2vz5s3avn27kpOTzfMlJSWKj49XUVGRduzYoZUrV2rFihWaNGlS1Z8IAAAA\nAAAAQJJjdS7+/PPPKzg4WCkpKeaxhg0bmt8bhqG5c+dqwoQJ6tGjhyRp1apV8vf315tvvqnExETl\n5eVp+fLlWrt2rTp27ChJSklJUWhoqHbu3Kk2bdrom2++UVpamnbv3q1WrVpJkhYsWKC4uDi9+OKL\nCgoK0pYtW3Tw4EF98MEH8vf3V8uWLTVt2jQ9+eSTmjx5spydnf/AMwMAAAAAAICaqFp31r399ttq\n1aqVevfuLT8/P91xxx169dVXzfOZmZnKzs5WTEyMeczLy0utW7dWRkaGJGnPnj0qLi62qGnatKnq\n169vrsnIyJC3t7c5qJOkmJgY2dvba9euXeaa8PBw+fv7m2tiY2OVn5+vAwcOXLf/S5cuKT8/3+IF\nAAAAAAAA3KxqDeu+//57LV68WE2aNNH777+voUOH6vHHH9fKlSslSdnZ2ZJkEaBd/fvqXHZ2tpyd\nneXt7V1ujZ+fn8W8o6OjfHx8LGqut861ffzazJkz5eXlZX4FBwdbfQ4AAAAAAACAq6o1rCstLdWd\nd96pGTNm6I477lBycrIGDx6sJUuWVGdbFTZ+/Hjl5eWZXz/88EN1twQAAAAAAIA/sWoN6wIDAxUW\nFmYxFhoaqqysLElSQECAJJV5suvp06fNcwEBASoqKlJubm65NWfOnLGYv3z5snJycixqrrfOtX38\nmouLi0wmk8ULAAAAAAAAuFnVGtbdc889OnTokMXY4cOHFRISIunKwyYCAgL04Ycfmufz8/O1a9cu\nRUZGSpIiIiLk5ORkUXPo0CFlZWWZayIjI5Wbm6s9e/aYa7Zu3arS0lK1bt3aXLN//36LUC89PV0m\nk6lMoAgAAAAAAABUhWp9GuzIkSPVtm1bzZgxQ3369NFnn32mpUuXaunSpZIkOzs7jRgxQtOnT1eT\nJk3UsGFDTZw4UUFBQerZs6ekKw+cGDhwoEaNGiUfHx+ZTCY99thjioyMVJs2bSRd2a3XpUsX8yW2\nxcXFGj58uBITExUUFCRJ6ty5s8LCwvTII49o1qxZys7O1oQJEzRs2DC5uLhUzwkCAAAAAABAjVKt\nYd1dd92lN954Q+PHj9fUqVPVsGFDzZ07V0lJSeaasWPH6sKFC0pOTlZubq6ioqKUlpYmV1dXc82c\nOXNkb2+vhIQEXbp0SbGxsVq0aJHFWmvWrNHw4cPVqVMnc+38+fPN8w4ODtq8ebOGDh2qyMhIeXh4\nqF+/fpo6dWrVnwgAAAAAAABAkp1hGEZ1N/FXkZ+fLy8vL+Xl5VXZ/esajHunSo77Rzn2XHx1t/C7\n8RtUP36D6vVnP//Sn/83AAAAAGD7bjYnqtZ71gEAAAAAAAD4H8I6AAAAAAAAwEYQ1gEAAAAAAAA2\ngrAOAAAAAAAAsBHV+jRYAABgPR7yAQAAAPx1sbMOAAAAAAAAsBHsrAMAALASuxsBAABQVdhZBwAA\nAAAAANgIwjoAAAAAAADARhDWAQAAAAAAADaCe9YBAADgT4f7BgIAgL8qdtYBAAAAAAAANoKwDgAA\nAAAAALARhHUAAAAAAACAjeCedQAAAACs9me/byD3DAQA2Cp21gEAAAAAAAA2grAOAAAAAAAAsBGE\ndQAAAAAAAICNIKwDAAAAAAAAbARhHQAAAAAAAGAjCOsAAAAAAAAAG0FYBwAAAAAAANgIwjoAAAAA\nAADARhDWAQAAAAAAADaCsA4AAAAAAACwEYR1AAAAAAAAgI0grAMAAAAAAABsBGEdAAAAAAAAYCMI\n6wAAAAAAAAAbQVgHAAAAAAAA2AjCOgAAAAAAAMBGENYBAAAAAAAANoKwDgAAAAAAALARhHUAAAAA\nAACAjSCsAwAAAAAAAGwEYR0AAAAAAABgIxyruwEAAAAAgPUajHunulv4XY49F1/dLQCATWJnHQAA\nAAAAAGAjCOsAAAAAAAAAG0FYBwAAAAAAANgIwjoAAAAAAADARhDWAQAAAAAAADaCsA4AAAAAAACw\nEYR1AAAAAAAAgI0grAMAAAAAAABsRLWGdZMnT5adnZ3Fq2nTpuZ5wzA0adIkBQYGys3NTTExMTpy\n5IjFMQoLCzVs2DDVqVNHnp6eSkhI0OnTpy1qcnJylJSUJJPJJG9vbw0cOFAFBQUWNVlZWYqPj5e7\nu7v8/Pw0ZswYXb58ueq+PAAAAAAAAPAr1b6zrlmzZjp16pT59cknn5jnZs2apfnz52vJkiXatWuX\nPDw8FBsbq8LCQnPNyJEjtWnTJqWmpmrbtm06efKkevXqZbFGUlKSDhw4oPT0dG3evFnbt29XcnKy\neb6kpETx8fEqKirSjh07tHLlSq1YsUKTJk2q+hMAAAAAAAAA/P8cq70BR0cFBASUGTcMQ3PnztWE\nCRPUo0cPSdKqVavk7++vN998U4mJicrLy9Py5cu1du1adezYUZKUkpKi0NBQ7dy5U23atNE333yj\ntLQ07d69W61atZIkLViwQHFxcXrxxRcVFBSkLVu26ODBg/rggw/k7++vli1batq0aXryySc1efJk\nOTs7/3EnBAAAAAAAADVWte+sO3LkiIKCgtSoUSMlJSUpKytLkpSZmans7GzFxMSYa728vNS6dWtl\nZGRIkvbs2aPi4mKLmqZNm6p+/frmmoyMDHl7e5uDOkmKiYmRvb29du3aZa4JDw+Xv7+/uSY2Nlb5\n+fk6cODADXu/dOmS8vPzLV4AAAAAAADAzarWsK5169ZasWKF0tLStHjxYmVmZuree+/V+fPnlZ2d\nLUkWAdrVv6/OZWdny9nZWd7e3uXW+Pn5Wcw7OjrKx8fHouZ661ydu5GZM2fKy8vL/AoODrb2FAAA\nAAAAAABm1XoZbNeuXc3vW7RoodatWyskJETr169XaGhoNXZWMePHj9eoUaPMf+fn5xPYAQAAAEAN\n0WDcO9Xdwu9y7Ln46m4BwHVU+2Ww1/L29tZtt92m7777znwfu18/2fX06dPmuYCAABUVFSk3N7fc\nmjNnzljMX758WTk5ORY111vn6tyNuLi4yGQyWbwAAAAAAACAm2VTYV1BQYG+++47BQYGqmHDhgoI\nCNCHH35ons/Pz9euXbsUGRkpSYqIiJCTk5NFzaFDh5SVlWWuiYyMVG5urvbs2WOu2bp1q0pLS9W6\ndWtzzf79+y1CvfT0dJlMJoWFhVXpdwYAAAAAAACuqtbLYEePHq3u3bsrJCREJ0+e1DPPPCNHR0c9\n+OCDsrOz04gRIzR9+nQ1adJEDRs21MSJExUUFKSePXtKuvLAiYEDB2rUqFHy8fGRyWTSY489psjI\nSLVp00aSFBoaqi5dumjw4MFasmSJiouLNXz4cCUmJiooKEiS1LlzZ4WFhemRRx7RrFmzlJ2drQkT\nJmjYsGFycXGptvMDAAAAAACAmqVaw7oTJ07owQcf1NmzZ+Xr66uoqCjt3LlTvr6+kqSxY8fqwoUL\nSk5OVm5urqKiopSWliZXV1fzMebMmSN7e3slJCTo0qVLio2N1aJFiyzWWbNmjYYPH65OnTqZa+fP\nn2+ed3Bw0ObNmzV06FBFRkbKw8ND/fr109SpU/+YEwEAAAAAAAComsO6devWlTtvZ2enqVOnlhua\nubq6auGznijJAAAgAElEQVTChVq4cOENa3x8fLR27dpy1woJCdG7775bfsMAAAAAAABAFbKpe9YB\nAAAAAAAANRlhHQAAAAAAAGAjCOsAAAAAAAAAG0FYBwAAAAAAANgIwjoAAAAAAADARhDWAQAAAAAA\nADaCsA4AAAAAAACwEYR1AAAAAAAAgI0grAMAAAAAAABsBGEdAAAAAAAAYCMI6wAAAAAAAAAbQVgH\nAAAAAAAA2AjCOgAAAAAAAMBGENYBAAAAAAAANoKwDgAAAAAAALARhHUAAAAAAACAjSCsAwAAAAAA\nAGwEYR0AAAAAAABgIwjrAAAAAAAAABvhaO0H9u7dKycnJ4WHh0uS3nrrLaWkpCgsLEyTJ0+Ws7Nz\npTcJAAAAAADwaw3GvVPdLfwux56Lr+4WYIOs3lk3ZMgQHT58WJL0/fffKzExUe7u7kpNTdXYsWMr\nvUEAAAAAAACgprA6rDt8+LBatmwpSUpNTVW7du20du1arVixQhs3bqz0BgEAAAAAAICawuqwzjAM\nlZaWSpI++OADxcXFSZKCg4P1888/V253AAAAAAAAQA1idVjXqlUrTZ8+XatXr9a2bdsUH3/l+urM\nzEz5+/tXeoMAAAAAAABATWF1WDd37lzt3btXw4cP19NPP63GjRtLkjZs2KC2bdtWeoMAAAAAAABA\nTWH102BbtGih/fv3lxl/4YUX5ODgUClNAQAAAAAAADWR1TvrJCk3N1fLli3T+PHjlZOTI0k6ePCg\nzpw5U6nNAQAAAAAAADWJ1Tvr9u3bp06dOsnb21vHjh3T4MGD5ePjo9dff11ZWVlatWpVVfQJAAAA\nAAAA/OVZvbNu1KhRGjBggI4cOSJXV1fzeFxcnLZv316pzQEAAAAAAAA1idVh3e7duzVkyJAy43Xr\n1lV2dnalNAUAAAAAAADURFaHdS4uLsrPzy8zfvjwYfn6+lZKUwAAAAAAAEBNZHVYd//992vq1Kkq\nLi6WJNnZ2SkrK0tPPvmkEhISKr1BAAAAAAAAoKawOqx76aWXVFBQID8/P/3yyy+Kjo5W48aNVatW\nLT377LNV0SMAAAAAAABQI1j9NFgvLy+lp6frk08+0b59+1RQUKA777xTMTExVdEfAAAAAAAAUGNY\nHdZdFRUVpaioqMrsBQAAAAAAAKjRKhTWzZ8/X8nJyXJ1ddX8+fPLrfX09FSzZs3UunXrSmkQAAAA\nAAAAqCkqFNbNmTNHSUlJcnV11Zw5c8qtvXTpks6cOaORI0fqhRdeqJQmAQAAAAAAgJqgQmFdZmbm\ndd/fSHp6uh566CHCOgAAAAAAAMAKVj8NtiKioqI0YcKEqjg0AAAAAAAA8Jd1Uw+YuHDhgrZt26as\nrCwVFRVZzD3++ONyc3PTE088USkNAgAAAAAAADWF1WHdF198obi4OF28eFEXLlyQj4+Pfv75Z7m7\nu8vPz0+PP/54VfQJAAAAAAAA/OVZfRnsyJEj1b17d507d05ubm7auXOnjh8/roiICL344otV0SMA\nAAAAAABQI1gd1n355Zf617/+JXt7ezk4OOjSpUsKDg7WrFmz9NRTT1VFjwAAAAAAAECNYHVY5+Tk\nJHv7Kx/z8/NTVlaWJMnLy0s//PBD5XYHAAAAAAAA1CBW37Pujjvu0O7du9WkSRNFR0dr0qRJ+vnn\nn7V69Wo1b968KnoEAAAAAAAAagSrd9bNmDFDgYGBkqRnn31WtWvX1tChQ/XTTz9p6dKlN93Ic889\nJzs7O40YMcI8ZhiGJk2apMDAQLm5uSkmJkZHjhyx+FxhYaGGDRumOnXqyNPTUwkJCTp9+rRFTU5O\njpKSkmQymeTt7a2BAweqoKDAoiYrK0vx8fHmB2WMGTNGly9fvunvAwAAAAAAAFjL6rCuVatW6tCh\ng6Qrl8GmpaUpPz9fe/bs0d/+9rebamL37t165ZVX1KJFC4vxWbNmaf78+VqyZIl27dolDw8PxcbG\nqrCw0FwzcuRIbdq0Sampqdq2bZtOnjypXr16WRwnKSlJBw4cUHp6ujZv3qzt27crOTnZPF9SUqL4\n+HgVFRVpx44dWrlypVasWKFJkybd1PcBAAAAAAAAbobVYV1lKygoUFJSkl599VXVrl3bPG4YhubO\nnasJEyaoR48eatGihVatWqWTJ0/qzTfflCTl5eVp+fLlmj17tjp27KiIiAilpKRox44d2rlzpyTp\nm2++UVpampYtW6bWrVsrKipKCxYs0Lp163Ty5ElJ0pYtW3Tw4EH9+9//VsuWLdW1a1dNmzZNCxcu\nVFFR0R9/UgAAAAAAAFAjVSisu/POO3Xu3DlJV+5Zd+edd97wZa1hw4YpPj5eMTExFuOZmZnKzs62\nGPfy8lLr1q2VkZEhSdqzZ4+Ki4stapo2bar69eubazIyMuTt7a1WrVqZa2JiYmRvb69du3aZa8LD\nw+Xv72+uiY2NVX5+vg4cOHDD3i9duqT8/HyLFwAAAAAAAHCzKvSAiR49esjFxcX83s7OrlIWX7du\nnfbu3avdu3eXmcvOzpYkiwDt6t9X57Kzs+Xs7Cxvb+9ya/z8/CzmHR0d5ePjY1FzvXWu7eN6Zs6c\nqSlTpvzm9wQAAAAAAAAqokJh3TPPPGN+P3ny5EpZ+IcfftATTzyh9PR0ubq6Vsox/2jjx4/XqFGj\nzH/n5+crODi4GjsCAAAAAADAn5nV96wbNGiQPv7449+98J49e3TmzBndeeedcnR0lKOjo7Zt26b5\n8+fL0dHRvLPt1092PX36tAICAiRJAQEBKioqUm5ubrk1Z86csZi/fPmycnJyLGqut87VuRtxcXGR\nyWSyeAEAAAAAAAA3q0I76671008/qUuXLvL19VViYqIefvjhm3oKbKdOnbR//36LsQEDBqhp06Z6\n8skn1ahRIwUEBOjDDz9Uy5YtJV3ZubZr1y4NHTpUkhQRESEnJyd9+OGHSkhIkCQdOnRIWVlZioyM\nlCRFRkYqNzdXe/bsUUREhCRp69atKi0tVevWrc01zz77rM6cOWO+ZDY9PV0mk0lhYWFWfzcAAAAA\nAIC/ugbj3qnuFn63Y8/FV3cLZVgd1r311ls6d+6cUlNTtXbtWs2ePVtNmzZVUlKSHnroITVo0KBC\nx6lVq5aaN29uMebh4aE6deqYx0eMGKHp06erSZMmatiwoSZOnKigoCD17NlT0pUHTgwcOFCjRo2S\nj4+PTCaTHnvsMUVGRqpNmzaSpNDQUHXp0kWDBw/WkiVLVFxcrOHDhysxMVFBQUGSpM6dOyssLEyP\nPPKIZs2apezsbE2YMEHDhg0z36sPAAAAAAAAqGpWXwYrSbVr11ZycrI+/vhjHT9+XP3799fq1avV\nuHHjSm1u7Nixeuyxx5ScnKy77rpLBQUFSktLs7jH3Zw5c9StWzclJCSoXbt2CggI0Ouvv25xnDVr\n1qhp06bq1KmT4uLiFBUVpaVLl5rnHRwctHnzZjk4OCgyMlIPP/yw+vbtq6lTp1bq9wEAAAAAAADK\nY/XOumsVFxfr888/165du3Ts2LEyT1S11q/vhWdnZ6epU6eWG5q5urpq4cKFWrhw4Q1rfHx8tHbt\n2nLXDgkJ0bvvvmtVvwAAAAAAAEBluqmddR999JEGDx4sf39/9e/fXyaTSZs3b9aJEycquz8AAAAA\nAACgxrB6Z13dunWVk5OjLl26aOnSperevTv3dQMAAAAAAAAqgdVh3eTJk9W7d295e3tXRT8AAAAA\nAABAjWV1WDd48OCq6AMAAAAAAACo8W7qnnUAAAAAAAAAKh9hHQAAAAAAAGAjCOsAAAAAAAAAG0FY\nBwAAAAAAANiImwrrVq9erXvuuUdBQUE6fvy4JGnu3Ll66623KrU5AAAAAAAAoCaxOqxbvHixRo0a\npbi4OOXm5qqkpESS5O3trblz51Z6gwAAAAAAAEBNYXVYt2DBAr366qt6+umn5eDgYB5v1aqV9u/f\nX6nNAQAAAAAAADWJ1WFdZmam7rjjjjLjLi4uunDhQqU0BQAAAAAAANREVod1DRs21JdffllmPC0t\nTaGhoZXSFAAAAAAAAFATOVr7gVGjRmnYsGEqLCyUYRj67LPP9H//93+aOXOmli1bVhU9AgAAAAAA\nADWC1WHdoEGD5ObmpgkTJujixYt66KGHFBQUpHnz5ikxMbEqegQAAAAAAABqBKvDOklKSkpSUlKS\nLl68qIKCAvn5+VV2XwAAAAAAAECNc1Nh3VXu7u5yd3evrF4AAAAAAACAGq1CYd0dd9whOzu7Ch1w\n7969v6shAAAAAAAAoKaqUFjXs2dP8/vCwkItWrRIYWFhioyMlCTt3LlTBw4c0KOPPlo1XQIAAAAA\nAAA1QIXCumeeecb8ftCgQXr88cc1bdq0MjU//PBD5XYHAAAAAAAA1CD21n4gNTVVffv2LTP+8MMP\na+PGjZXSFAAAAAAAAFATWR3Wubm56dNPPy0z/umnn8rV1bVSmgIAAAAAAABqIqufBjtixAgNHTpU\ne/fu1d133y1J2rVrl1577TVNnDix0hsEAAAAAAAAagqrw7px48apUaNGmjdvnv79739LkkJDQ5WS\nkqI+ffpUeoMAAAAAAABATWF1WCdJffr0IZgDAAAAAAAAKpnV96wDAAAAAAAAUDUI6wAAAAAAAAAb\nQVgHAAAAAAAA2AjCOgAAAAAAAMBGENYBAAAAAAAANqJCT4MdNWpUhQ84e/bsm24GAAAAAAAAqMkq\nFNZ98cUXFn/v3btXly9f1u233y5JOnz4sBwcHBQREVH5HQIAAAAAAAA1RIXCuo8++sj8fvbs2apV\nq5ZWrlyp2rVrS5LOnTunAQMG6N57762aLgEAAAAAAIAawOp71r300kuaOXOmOaiTpNq1a2v69Ol6\n6aWXKrU5AAAAAAAAoCaxOqzLz8/XTz/9VGb8p59+0vnz5yulKQAAAAAAAKAmsjqs+/vf/64BAwbo\n9ddf14kTJ3TixAlt3LhRAwcOVK9evaqiRwAAAAAAAKBGqNA96661ZMkSjR49Wg899JCKi4uvHMTR\nUQMHDtQLL7xQ6Q0CAAAAAAAANYXVYZ27u7sWLVqkF154QUePHpUk3XrrrfLw8Kj05gAAAAAAAICa\nxOqw7ioPDw+1aNGiMnsBAAAAAAAAarSbCus+//xzrV+/XllZWSoqKrKYe/311yulMQAAAAAAAKCm\nsfoBE+vWrVPbtm31zTff6I033lBxcbEOHDigrVu3ysvLqyp6BAAAAAAAAGoEq8O6GTNmaM6cOdq0\naZOcnZ01b948ffvtt+rTp4/q169fFT0CAAAAAAAANYLVYd3Ro0cVHx8vSXJ2dtaFCxdkZ2enkSNH\naunSpZXeIAAAAAAAAFBTWB3W1a5dW+fPn5ck1a1bV19//bUkKTc3VxcvXqzc7gAAAAAAAIAaxOoH\nTLRr107p6ekKDw9X79699cQTT2jr1q1KT09Xp06dqqJHAAAAAAAAoEawemfdyy+/rMTEREnS008/\nrVGjRun06dNKSEjQ8uXLrTrW4sWL1aJFC5lMJplMJkVGRuq9994zzxuGoUmTJikwMFBubm6KiYnR\nkSNHLI5RWFioYcOGqU6dOvL09FRCQoJOnz5tUZOTk6OkpCSZTCZ5e3tr4MCBKigosKjJyspSfHy8\n3N3d5efnpzFjxujy5ctWfR8AAAAAAADg97B6Z52Pj4/5vb29vcaNG3fTi9erV0/PPfecmjRpIsMw\ntHLlSvXo0UNffPGFmjVrplmzZmn+/PlauXKlGjZsqIkTJyo2NlYHDx6Uq6urJGnkyJF65513lJqa\nKi8vLw0fPly9evXSp59+al4nKSlJp06dUnp6uoqLizVgwAAlJydr7dq1kqSSkhLFx8crICBAO3bs\n0KlTp9S3b185OTlpxowZN/39AAAAAAAAAGtUKKzLz8+v8AFNJlOFa7t3727x97PPPqvFixdr586d\nCgsL09y5czVhwgT16NFDkrRq1Sr5+/vrzTffVGJiovLy8rR8+XKtXbtWHTt2lCSlpKQoNDRUO3fu\nVJs2bfTNN98oLS1Nu3fvVqtWrSRJCxYsUFxcnF588UUFBQVpy5YtOnjwoD744AP5+/urZcuWmjZt\nmp588klNnjxZzs7OFf5OAAAAAAAAwM2q0GWw3t7eql27doVeN6ukpETr1q3ThQsXFBkZqczMTGVn\nZysmJsZc4+XlpdatWysjI0OStGfPHhUXF1vUNG3aVPXr1zfXZGRkyNvb2xzUSVJMTIzs7e21a9cu\nc014eLj8/f3NNbGxscrPz9eBAwdu2POlS5eUn59v8QIAAAAAAABuVoV21n300Ufm98eOHdO4cePU\nv39/RUZGSroSdq1cuVIzZ860uoH9+/crMjJShYWF8vT01BtvvKGwsDDt2LFDkiwCtKt/Z2dnS5Ky\ns7Pl7Owsb2/vcmv8/Pws5h0dHeXj42NRc711rs7dyMyZMzVlyhRrvzIAAAAAAABwXRUK66Kjo83v\np06dqtmzZ+vBBx80j91///0KDw/X0qVL1a9fP6sauP322/Xll18qLy9PGzZsUL9+/bRt2zarjlFd\nxo8fr1GjRpn/zs/PV3BwcDV2BAAAAAAAgD8zq58Gm5GRYXFJ6VWtWrXSZ599ZnUDzs7Oaty4sSIi\nIjRz5kz97W9/07x58xQQECBJZZ7sevr0afNcQECAioqKlJubW27NmTNnLOYvX76snJwci5rrrXN1\n7kZcXFzMT7K9+gIAAAAAAABultVhXXBwsF599dUy48uWLauUXWWlpaW6dOmSGjZsqICAAH344Yfm\nufz8fO3atct8+W1ERIScnJwsag4dOqSsrCxzTWRkpHJzc7Vnzx5zzdatW1VaWqrWrVuba/bv328R\n6qWnp8tkMiksLOx3fycAAAAAAACgIip0Gey15syZo4SEBL333nvmsOuzzz7TkSNHtHHjRquONX78\neHXt2lX169fX+fPntXbtWn388cd6//33ZWdnpxEjRmj69Olq0qSJGjZsqIkTJyooKEg9e/aUdOWB\nEwMHDtSoUaPk4+Mjk8mkxx57TJGRkWrTpo0kKTQ0VF26dNHgwYO1ZMkSFRcXa/jw4UpMTFRQUJAk\nqXPnzgoLC9MjjzyiWbNmKTs7WxMmTNCwYcPk4uJi7SkCAAAAAAAAborVYV1cXJwOHz6sxYsX69tv\nv5Ukde/eXf/v//0/q3fWnTlzRn379tWpU6fk5eWlFi1a6P3339d9990nSRo7dqwuXLig5ORk5ebm\nKioqSmlpaXJ1dTUfY86cObK3t1dCQoIuXbqk2NhYLVq0yGKdNWvWaPjw4erUqZO5dv78+eZ5BwcH\nbd68WUOHDlVkZKQ8PDzUr18/TZ061drTAwAAAAAAANw0q8M66cqlsDNmzPjdiy9fvrzceTs7O02d\nOrXc0MzV1VULFy7UwoULb1jj4+OjtWvXlrtWSEiI3n333fIbBgAAAAAAAKpQhcK6ffv2qXnz5rK3\nt9e+ffvKrW3RokWlNAYAAAAAAADUNBUK61q2bKns7Gz5+fmpZcuWsrOzk2EYZers7OxUUlJS6U0C\nAAAAAAAANUGFwrrMzEz5+vqa3wMAAAAAAACofBUK60JCQszvjx8/rrZt28rR0fKjly9f1o4dOyxq\nAQAAAAAAAFScvbUf6NChg3JycsqM5+XlqUOHDpXSFAAAAAAAAFATWR3WGYYhOzu7MuNnz56Vh4dH\npTQFAAAAAAAA1EQVugxWknr16iXpykMk+vfvLxcXF/NcSUmJ9u3bp7Zt21Z+hwAAAAAAAEANUeGw\nzsvLS9KVnXW1atWSm5ubec7Z2Vlt2rTR4MGDK79DAAAAAAAAoIaocFiXkpIiSWrQoIFGjx7NJa8A\nAAAAAABAJatwWHfVM888UxV9AAAAAAAAADWe1Q+YOH36tB555BEFBQXJ0dFRDg4OFi8AAAAAAAAA\nN8fqnXX9+/dXVlaWJk6cqMDAwOs+GRYAAAAAAACA9awO6z755BP997//VcuWLauiHwAAAAAAAKDG\nsvoy2ODgYBmGURW9AAAAAAAAADWa1WHd3LlzNW7cOB07dqwK2gEAAAAAAABqLqsvg33ggQd08eJF\n3XrrrXJ3d5eTk5PFfE5OTqU1BwAAAAAAANQkVod1c+fOrYo+AAAAAAAAgBrP6rCuX79+VdEHAAAA\nAAAAUONZHdZdq7CwUEVFRRZjJpPpdzUEAAAAAAAA1FRWP2DiwoULGj58uPz8/OTh4aHatWtbvAAA\nAAAAAADcHKvDurFjx2rr1q1avHixXFxctGzZMk2ZMkVBQUFatWpVVfQIAAAAAAAA1AhWXwa7adMm\nrVq1Su3bt9eAAQN07733qnHjxgoJCdGaNWuUlJRUFX0CAAAAAAAAf3lW76zLyclRo0aNJF25P11O\nTo4kKSoqStu3b6/c7gAA/1979x4e452wcfyeSSKRkER0M0mUoJSkVYewpLwokTjUUnpgU+fGbope\nZIvaog6xWj0pgt1qHVpWq4vFRRCUloiKNipIqai0JHRJsoLI6f2jr6mpw7Kv5Hma+X6ua67LPL/f\nzNzz/AyTOzPPAwAAAABwIndd1tWvX1+ZmZmSpMaNG+vjjz+W9NMn7nx9fe9tOgAAAAAAAMCJ3HVZ\nN2TIEKWlpUmSXnrpJSUkJMjDw0NjxozR2LFj73lAAAAAAAAAwFnc9THrxowZY/9zRESEjh49qtTU\nVDVo0ECPPPLIPQ0HAAAAAAAAOJO7Lut+KTg4WMHBwfciCwAAAAAAAODU7vhrsNu3b1doaKjy8/Nv\nGMvLy9NDDz2kzZs339NwAAAAAAAAgDO547Ju9uzZiomJkbe39w1jPj4++sMf/qC5c+fe03AAAAAA\nAACAM7njsi4tLU1du3a95XhkZKQOHjx4T0IBAAAAAAAAzuiOy7qcnBy5ubndctzV1VXnzp27J6EA\nAAAAAAAAZ3THZV2tWrV06NChW44fPHhQgYGB9yQUAAAAAAAA4IzuuKzr3r27Jk2apCtXrtwwdvny\nZb3yyit6/PHH72k4AAAAAAAAwJm43unEiRMnavXq1XrwwQc1cuRINWrUSJJ09OhRJSQkqKSkRC+/\n/HK5BQUAAAAAAAAquzsu62w2m/bs2aPY2FhNmDBBZWVlkiSLxaKoqCglJCTIZrOVW1AAAAAAAACg\nsrvjsk6SgoODtXHjRl24cEHHjx9XWVmZGjZsqBo1apRXPgAAAAAAAMBp3FVZd02NGjXUqlWre50F\nAAAAAAAAcGp3fIIJAAAAAAAAAOWLsg4AAAAAAAAwCco6AAAAAAAAwCQo6wAAAAAAAACToKwDAAAA\nAAAATIKyDgAAAAAAADAJyjoAAAAAAADAJCjrAAAAAAAAAJMwtKybOXOmWrVqperVq8vf31+9e/dW\nRkaGw5yysjJNnjxZgYGBqlq1qiIiInTs2DGHOVeuXNGIESNUs2ZNVatWTX379lVOTo7DnPPnzys6\nOlre3t7y9fXVsGHDdPHiRYc5p06dUo8ePeTp6Sl/f3+NHTtWxcXF5fPkAQAAAAAAgF8wtKzbuXOn\nRowYob1792rr1q0qKipSZGSkCgoK7HNmzZqlOXPmaOHChUpJSZGXl5eioqJ05coV+5wxY8Zo/fr1\nWrVqlXbu3KnTp0+rT58+Do8VHR2t9PR0bd26VRs2bNCuXbs0fPhw+3hJSYl69Oihq1evas+ePVq6\ndKmWLFmiyZMnl/+OAAAAAAAAACS5GvngiYmJDteXLFkif39/paamqn379iorK9Ps2bM1ceJE9erV\nS5K0bNky2Ww2rV27Vv369VNeXp7ee+89rVixQp06dZIkLV68WCEhIdq7d6/atGmjI0eOKDExUV98\n8YVatmwpSZo7d666d++uN954Q0FBQdqyZYsOHz6spKQk2Ww2NWvWTNOnT9f48eM1ZcoUValSpWJ3\nDgAAAAAAAJyOqY5Zl5eXJ0ny8/OTJGVmZio7O1sRERH2OT4+PmrdurWSk5MlSampqSoqKnKY07hx\nY9WpU8c+Jzk5Wb6+vvaiTpIiIiJktVqVkpJin9OkSRPZbDb7nKioKOXn5ys9Pb2cnjEAAAAAAADw\nM0M/WXe90tJSjR49Wm3bttXDDz8sScrOzpYkhwLt2vVrY9nZ2apSpYp8fX1vO8ff399h3NXVVX5+\nfg5zbvY41+f4pcLCQhUWFtqv5+fn3/kTBgAAAAAAAH7BNJ+sGzFihA4dOqSVK1caHeWOzZw5Uz4+\nPvZL7dq1jY4EAAAAAACAXzFTlHUjR47Uhg0btGPHDt1///327QEBAZJ0w5ldc3Jy7GMBAQG6evWq\ncnNzbzvn7NmzDuPFxcU6f/68w5ybPc71OX5pwoQJysvLs1+ysrLu6nkDAAAAAAAA1zO0rCsrK9PI\nkSO1Zs0abd++XfXq1XMYr1evngICArRt2zb7tvz8fKWkpCg8PFySFBYWJjc3N4c5GRkZOnXqlH1O\neHi4cnNzlZqaap+zfft2lZaWqnXr1vY5X3/9tUOpt3XrVnl7eys0NPSm+d3d3eXt7e1wAQAAAAAA\nAP5bhh6zbsSIEVqxYoX++c9/qnr16vZjw/n4+Khq1aqyWCwaPXq04uPj1bBhQ9WrV0+TJk1SUFCQ\nevfubZ87bNgwxcXFyc/PT97e3ho1apTCw8PVpk0bSVJISIi6du2qmJgYLVy4UEVFRRo5cqT69eun\noKAgSVJkZKRCQ0M1YMAAzZo1S9nZ2Zo4caJGjBghd3d3Y3YQAAAAAAAAnIqhZd2CBQskSR07dnTY\nvnjxYg0ePFiSNG7cOBUUFGj48OHKzc1Vu3btlJiYKA8PD/v8t99+W1arVX379lVhYaGioqI0f/58\nh/tcvny5Ro4cqc6dO9vnzpkzxz7u4uKiDRs2KDY2VuHh4fLy8tKgQYM0bdq08nnyAAAAAAAAwC8Y\nWtaVlZX9xzkWi0XTpk27bWnm4eGhhIQEJSQk3HKOn5+fVqxYcdvHCg4O1saNG/9jJgAAAAAAAKA8\nmFlmQbEAABqmSURBVOIEEwAAAAAAAAAo6wAAAAAAAADToKwDAAAAAAAATIKyDgAAAAAAADAJyjoA\nAAAAAADAJCjrAAAAAAAAAJOgrAMAAAAAAABMgrIOAAAAAAAAMAnKOgAAAAAAAMAkKOsAAAAAAAAA\nk6CsAwAAAAAAAEyCsg4AAAAAAAAwCco6AAAAAAAAwCQo6wAAAAAAAACToKwDAAAAAAAATIKyDgAA\nAAAAADAJyjoAAAAAAADAJCjrAAAAAAAAAJOgrAMAAAAAAABMgrIOAAAAAAAAMAnKOgAAAAAAAMAk\nKOsAAAAAAAAAk6CsAwAAAAAAAEyCsg4AAAAAAAAwCco6AAAAAAAAwCQo6wAAAAAAAACToKwDAAAA\nAAAATIKyDgAAAAAAADAJyjoAAAAAAADAJCjrAAAAAAAAAJOgrAMAAAAAAABMgrIOAAAAAAAAMAnK\nOgAAAAAAAMAkKOsAAAAAAAAAk6CsAwAAAAAAAEyCsg4AAAAAAAAwCco6AAAAAAAAwCQo6wAAAAAA\nAACToKwDAAAAAAAATIKyDgAAAAAAADAJyjoAAAAAAADAJCjrAAAAAAAAAJOgrAMAAAAAAABMgrIO\nAAAAAAAAMAnKOgAAAAAAAMAkKOsAAAAAAAAAkzC0rNu1a5d69uypoKAgWSwWrV271mG8rKxMkydP\nVmBgoKpWraqIiAgdO3bMYc6VK1c0YsQI1axZU9WqVVPfvn2Vk5PjMOf8+fOKjo6Wt7e3fH19NWzY\nMF28eNFhzqlTp9SjRw95enrK399fY8eOVXFxcfk8cQAAAAAAAOAmDC3rCgoK1LRpUyUkJNx0fNas\nWZozZ44WLlyolJQUeXl5KSoqSleuXLHPGTNmjNavX69Vq1Zp586dOn36tPr06eNwP9HR0UpPT9fW\nrVu1YcMG7dq1S8OHD7ePl5SUqEePHrp69ar27NmjpUuXasmSJZo8eXL5PHEAAAAAAADgJlyNfPBu\n3bqpW7duNx0rKyvT7NmzNXHiRPXq1UuStGzZMtlsNq1du1b9+vVTXl6e3nvvPa1YsUKdOnWSJC1e\nvFghISHau3ev2rRpoyNHjigxMVFffPGFWrZsKUmaO3euunfvrjfeeENBQUHasmWLDh8+rKSkJNls\nNjVr1kzTp0/X+PHjNWXKFFWpUqVidggAAAAAAACcmmmPWZeZmans7GxFRETYt/n4+Kh169ZKTk6W\nJKWmpqqoqMhhTuPGjVWnTh37nOTkZPn6+tqLOkmKiIiQ1WpVSkqKfU6TJk1ks9nsc6KiopSfn6/0\n9PRbZiwsLFR+fr7DBQAAAAAAAPhvmbasy87OliSHAu3a9Wtj2dnZqlKlinx9fW87x9/f32Hc1dVV\nfn5+DnNu9jjX57iZmTNnysfHx36pXbv23T5NAAAAAAAAwM60Zd2vwYQJE5SXl2e/ZGVlGR0JAAAA\nAAAAv2KmLesCAgIk6YYzu+bk5NjHAgICdPXqVeXm5t52ztmzZx3Gi4uLdf78eYc5N3uc63PcjLu7\nu7y9vR0uAAAAAAAAwH/LtGVdvXr1FBAQoG3bttm35efnKyUlReHh4ZKksLAwubm5OczJyMjQqVOn\n7HPCw8OVm5ur1NRU+5zt27ertLRUrVu3ts/5+uuvHUq9rVu3ytvbW6GhoeX6PAEAAAAAAIBrDD0b\n7MWLF3X8+HH79czMTH311Vfy8/NTnTp1NHr0aMXHx6thw4aqV6+eJk2apKCgIPXu3VvSTyecGDZs\nmOLi4uTn5ydvb2+NGjVK4eHhatOmjSQpJCREXbt2VUxMjBYuXKiioiKNHDlS/fr1U1BQkCQpMjJS\noaGhGjBggGbNmqXs7GxNnDhRI0aMkLu7e8XvGAAAAAAAADglQ8u6/fv367HHHrNfj4uLkyQNGjRI\nS5Ys0bhx41RQUKDhw4crNzdX7dq1U2Jiojw8POy3efvtt2W1WtW3b18VFhYqKipK8+fPd3ic5cuX\na+TIkercubN97pw5c+zjLi4u2rBhg2JjYxUeHi4vLy8NGjRI06ZNK+c9AAAAAAAAAPzM0LKuY8eO\nKisru+W4xWLRtGnTbluaeXh4KCEhQQkJCbec4+fnpxUrVtw2S3BwsDZu3PifQwMAAAAAAADlxLTH\nrAMAAAAAAACcDWUdAAAAAAAAYBKUdQAAAAAAAIBJUNYBAAAAAAAAJkFZBwAAAAAAAJgEZR0AAAAA\nAABgEpR1AAAAAAAAgElQ1gEAAAAAAAAmQVkHAAAAAAAAmARlHQAAAAAAAGASlHUAAAAAAACASVDW\nAQAAAAAAACZBWQcAAAAAAACYBGUdAAAAAAAAYBKUdQAAAAAAAIBJUNYBAAAAAAAAJkFZBwAAAAAA\nAJgEZR0AAAAAAABgEpR1AAAAAAAAgElQ1gEAAAAAAAAmQVkHAAAAAAAAmARlHQAAAAAAAGASlHUA\nAAAAAACASVDWAQAAAAAAACZBWQcAAAAAAACYBGUdAAAAAAAAYBKUdQAAAAAAAIBJUNYBAAAAAAAA\nJkFZBwAAAAAAAJgEZR0AAAAAAABgEpR1AAAAAAAAgElQ1gEAAAAAAAAmQVkHAAAAAAAAmARlHQAA\nAAAAAGASlHUAAAAAAACASVDWAQAAAAAAACZBWQcAAAAAAACYBGUdAAAAAAAAYBKUdQAAAAAAAIBJ\nUNYBAAAAAAAAJkFZBwAAAAAAAJgEZR0AAAAAAABgEpR1AAAAAAAAgElQ1gEAAAAAAAAmQVkHAAAA\nAAAAmARl3S8kJCSobt268vDwUOvWrbVv3z6jIwEAAAAAAMBJUNZd56OPPlJcXJxeeeUVHThwQE2b\nNlVUVJTOnj1rdDQAAAAAAAA4Acq667z11luKiYnRkCFDFBoaqoULF8rT01Pvv/++0dEAAAAAAADg\nBFyNDmAWV69eVWpqqiZMmGDfZrVaFRERoeTk5JveprCwUIWFhfbreXl5kqT8/Pxyy1laeKnc7rsi\nlOe+qSisgfFYA2P92ve/xBqYAWtgPNbAeKyBsX7t+19iDcyANTAea2CsX/v+l8p3Da7dd1lZ2V3d\nzlJ2t7eopE6fPq1atWppz549Cg8Pt28fN26cdu7cqZSUlBtuM2XKFE2dOrUiYwIAAAAAAOBXJCsr\nS/fff/8dz+eTdf8PEyZMUFxcnP16aWmpzp8/r5o1a8pisRiY7L+Tn5+v2rVrKysrS97e3kbHcUqs\ngfFYA+OxBsZjDYzF/jcea2A81sB4rIHxWAPjsQbG+7WvQVlZmf79738rKCjorm5HWfd/7rvvPrm4\nuCgnJ8dhe05OjgICAm56G3d3d7m7uzts8/X1LbeMFcXb2/tX+SKoTFgD47EGxmMNjMcaGIv9bzzW\nwHisgfFYA+OxBsZjDYz3a14DHx+fu74NJ5j4P1WqVFFYWJi2bdtm31ZaWqpt27Y5fC0WAAAAAAAA\nKC98su46cXFxGjRokFq2bKnf/va3mj17tgoKCjRkyBCjowEAAAAAAMAJuEyZMmWK0SHM4uGHH5av\nr69mzJihN954Q5K0fPlyNWrUyOBkFcfFxUUdO3aUqys9rlFYA+OxBsZjDYzHGhiL/W881sB4rIHx\nWAPjsQbGYw2M54xrwNlgAQAAAAAAAJPgmHUAAAAAAACASVDWAQAAAAAAACZBWQcAAAAAAACYBGUd\nAAAAAAAAYBKUdQAAACbD+b8AAACcl/Oc9xYAAOBXwt3dXWlpaQoJCTE6ilM4c+aMFixYoM8//1xn\nzpyR1WpV/fr11bt3bw0ePFguLi5GRwQAAE6ET9Y5sXnz5mngwIFauXKlJOmDDz5QaGioGjdurD//\n+c8qLi42OGHlNmrUKH322WdGxwAMdeDAAWVmZtqvf/DBB2rbtq1q166tdu3a2f99QsUpKCjQ4sWL\n9fLLL2vevHn617/+ZXSkSi0uLu6ml5KSEr366qv26yg/+/fvV0hIiDZu3KiioiIdO3ZMYWFh8vLy\n0osvvqj27dvr3//+t9ExK70jR45o8eLFOnr0qCTp6NGjio2N1dChQ7V9+3aD00GSsrKyNHToUKNj\nAOXq8uXL+vzzz3X48OEbxq5cuaJly5YZkArOyFLG9yycUnx8vGbNmqXIyEjt3r1bo0eP1uuvv64x\nY8bIarXq7bffVmxsrKZOnWp01ErLarXKYrHogQce0LBhwzRo0CAFBAQYHcvpXL16VWvXrlVycrKy\ns7MlSQEBAXr00UfVq1cvValSxeCElVvTpk315ptvKiIiQosWLdILL7ygmJgYhYSEKCMjQ4sWLdI7\n77zDDwflKDQ0VJ9//rn8/PyUlZWl9u3b68KFC3rwwQd1/Phxubm5ae/evapXr57RUSslq9Wqpk2b\nytfX12H7zp071bJlS3l5eclisVBWlKN27dqpS5cueuWVVyRJH374oebNm6e9e/fqwoUL6tSpk9q3\nb6933nnH4KSVV2Jionr16qVq1arp0qVLWrNmjQYOHKimTZuqtLRUO3fu1JYtW9SpUyejozq1tLQ0\ntWjRQiUlJUZHcVo5OTn661//qsmTJxsdpVL65ptvFBkZqVOnTslisdh/cRwYGCjpp/0fFBTEa6AC\nfP/99/L19VW1atUcthcVFSk5OVnt27c3KFnFoaxzUg0aNNCsWbPUp08fpaWlKSwsTEuXLlV0dLQk\nac2aNRo3bpyOHTtmcNLKy2q1auvWrVq/fr2WL1+uvLw8devWTTExMerevbusVj74Wt6OHz+uqKgo\nnT59Wq1bt5bNZpP003/EKSkpuv/++7Vp0yY1aNDA4KSVl6enp44cOaLg4GC1aNFCsbGxiomJsY+v\nWLFCM2bMUHp6uoEpKzer1ars7Gz5+/vr2WefVWZmpjZu3CgfHx9dvHhRTzzxhH7zm99oxYoVRket\nlF599VX97W9/06JFixyKCDc3N6WlpSk0NNTAdM7B09NThw4dUv369SVJpaWl8vDwUFZWlmw2m7Zu\n3arBgwfrhx9+MDhp5fXoo4+qU6dOio+P18qVK/X8888rNjZWM2bMkCRNmDBBqamp2rJli8FJK7d1\n69bddvzEiRP605/+RFFhIArT8vXEE0+oqKhIS5YsUW5urkaPHq3Dhw/r008/VZ06dSjrKsCZM2fU\nq1cvpaamymKx6Pe//73mz59vL+2caQ0o65yUp6enjh49qjp16kiSqlSpoi+//FIPPfSQJOm7775T\naGioCgoKjIxZqV3/A3JRUZHWrFmj999/X0lJSbLZbBo8eLCGDBlCUVSOunTpIi8vLy1btkze3t4O\nY/n5+Ro4cKAuX76szZs3G5Sw8rvvvvu0efNmhYWFyWazacuWLWratKl9/Ntvv1WTJk106dIlA1NW\nbtf/W/TAAw9o4cKF6tKli318z5496tevn06dOmVgysrtiy++0LPPPquePXtq5syZcnNzo6yrQHXr\n1tXy5cvVtm1bST/9oFCrVi0VFBSoatWqOnnypEJCQnT58mWDk1ZePj4+Sk1NVYMGDVRaWip3d3ft\n27dPzZs3lyQdOnRIERER9k/Ao3xc+9bH7X48tFgsTvFDslEOHjx42/GjR4+qf//+rEE5sdlsSkpK\nUpMmTST9dLKn559/Xhs3btSOHTvk5eXlNEWRUQYNGqSMjAzNmzdPubm5eumll2SxWLRlyxbVqFFD\nOTk5CgwMVGlpqdFRyx0f3XFSAQEB9u/hHzt2TCUlJQ7fy09PT5e/v79R8ZyOm5ubnn76aSUmJurE\niROKiYnR8uXL1ahRI6OjVWq7d+9WfHz8DUWdJHl7e2v69OkcV7CcdevWTQsWLJAkdejQQZ988onD\n+Mcff0xhXQEsFoukn47Fcu2rHtfUqlVL586dMyKW02jVqpVSU1N17tw5tWzZUocOHbKvCcpf7969\n9cc//lGJiYnasWOHoqOj1aFDB1WtWlWSlJGRoVq1ahmcsvK79nfearXKw8NDPj4+9rHq1asrLy/P\nqGhOIzAwUKtXr1ZpaelNLwcOHDA6YqXXrFkzNW/eXM2aNbvh0rx5c/Xr18/oiJXa5cuX5er68zk4\nLRaLFixYoJ49e6pDhw765ptvDEznHJKSkjRnzhy1bNlSERER2r17twIDA9WpUyedP39ekpzmPRJn\ng3VS0dHRGjhwoHr16qVt27Zp3LhxevHFF/Xjjz/KxcVF8fHxevLJJ42O6ZTq1KmjKVOm6JVXXlFS\nUpLRcSo1X19fnTx5Ug8//PBNx0+ePHnDcaRwb7322mtq27atOnTooJYtW+rNN9/Up59+aj9m3d69\ne7VmzRqjY1Z6nTt3lqurq/Lz85WRkeHwmvjuu+9Us2ZNA9M5h2rVqmnp0qVauXKlIiIi+K19BYqP\nj9eZM2fUs2dPlZSUKDw8XB9++KF93GKxaObMmQYmrPzq1q2rY8eO6YEHHpAkJScn27/9IUmnTp26\n4RcJuPfCwsKUmpqqXr163XT8P33qDv9/fn5+mjVrljp37nzT8fT0dPXs2bOCUzmPxo0b2086dL15\n8+ZJkn73u98ZEcup5OXlqUaNGvbr7u7uWr16tZ566ik99thjDv8/V3aUdU5q6tSpqlq1qpKTkxUT\nE6OXXnpJTZs21bhx43Tp0iX17NlT06dPNzpmpRYcHCwXF5dbjlssFoevouHee+655zRw4EBNmjRJ\nnTt3djhm3bZt2xQfH69Ro0YZnLJyCwoK0pdffqlXX31V69evV1lZmfbt26esrCy1bdtWu3fvVsuW\nLY2OWaldO6j+Nb88kO/69ev1P//zPxUZyan169dP7dq1U2pqqoKDg42O4xSqVaumjz76SFeuXFFx\ncfENr4HIyEiDkjmP2NhYh4L6l79E27RpEyeXqABjx4697SFwGjRooB07dlRgIucTFham06dP3/Lf\n/9zcXArTcvTEE0/o73//uwYMGHDD2Lx581RaWqqFCxcakMx51K9fXwcPHlTDhg3t21xdXbVq1So9\n9dRTevzxxw1MV7E4Zh0Ap/baa6/pnXfeUXZ2tv0j1WVlZQoICNDo0aM1btw4gxMCAADAGaxZs0YF\nBQV69tlnbzp+4cIFrVu3ToMGDargZEDFGD9+vL766qubHjO8uLhYffv21fr1653imHWUdQAgKTMz\n037g6oCAANWrV8/gRAAAAADgPIqLi3Xp0qWbHlP82vgPP/zgFN8+4AQTACCpXr16Cg8PV3h4uL2o\ny8rK0tChQw1OBgAAAPDeFJWfq6vrLYs66acztk+dOrUCExmHT9YBwC2kpaWpRYsWHOgdAAAAhuO9\nKZydM70GOMEEAKe1bt26246fOHGigpIAAADA2fHeFM6O18DP+GQdAKdltVplsVhue1Yti8XiFL+5\nAQAAgLF4bwpnx2vgZxyzDoDTCgwM1OrVq1VaWnrTy4EDB4yOCAAAACfBe1M4O14DP6OsA+C0wsLC\nlJqaesvx//RbHQAAAOBe4b0pnB2vgZ9xzDoATmvs2LEqKCi45XiDBg20Y8eOCkwEAAAAZ8V7Uzg7\nXgM/45h1AAAAAAAAgEnwNVgAAAAAAADAJCjrAAAAAAAAAJOgrAMAAAAAAABMgrIOAAAAAAAAMAnK\nOgAAAAAAAMAkKOsAAAAAAAAAk6CsAwAAcHIdO3bUqFGjNHr0aNWoUUM2m03vvvuuCgoKNGTIEFWv\nXl0NGjTQpk2b7Lc5dOiQunXrpmrVqslms2nAgAH68ccf7eOffPKJmjRpoqpVq6pmzZqKiIhQQUGB\nfXzRokUKCQmRh4eHGjdurPnz59vHrl69qpEjRyowMFAeHh4KDg7WzJkzK2ZnAAAAGIyyDgAAAFq6\ndKnuu+8+7du3T6NGjVJsbKyeeuopPfroozpw4IAiIyM1YMAAXbp0Sbm5uerUqZOaN2+u/fv3KzEx\nUTk5OXr66aclSWfOnFH//v01dOhQHTlyRJ9++qn69OmjsrIySdLy5cs1efJkzZgxQ0eOHNFf/vIX\nTZo0SUuXLpUkzZkzR+vWrdPHH3+sjIwMLV++XHXr1jVq1wAAAFQoS9m1d00AAABwSh07dlRJSYk+\n++wzSVJJSYl8fHzUp08fLVu2TJKUnZ2twMBAJScnKykpSZ999pk2b95sv4/vv/9etWvXVkZGhi5e\nvKiwsDCdPHlSwcHBNzxegwYNNH36dPXv39++LT4+Xhs3btSePXv0wgsvKD09XUlJSbJYLOX87AEA\nAMzF1egAAAAAMN4jjzxi/7OLi4tq1qypJk2a2LfZbDZJ0tmzZ5WWlqYdO3aoWrVqN9zPt99+q8jI\nSHXu3FlNmjRRVFSUIiMj9eSTT6pGjRoqKCjQt99+q2HDhikmJsZ+u+LiYvn4+EiSBg8erC5duqhR\no0bq2rWrHn/8cUVGRpbXUwcAADAVyjoAAADIzc3N4brFYnHYdu0TbqWlpbp48aJ69uyp11577Yb7\nCQwMlIuLi7Zu3ao9e/Zoy5Ytmjt3rl5++WWlpKTI09NTkvTuu++qdevWDrd1cXGRJLVo0UKZmZna\ntGmTkpKS9PTTTysiIkKffPLJPX3OAAAAZkRZBwAAgLvSokUL/eMf/1DdunXl6nrzt5MWi0Vt27ZV\n27ZtNXnyZAUHB2vNmjWKi4tTUFCQTpw4oejo6Fs+hre3t5555hk988wzevLJJ9W1a1edP39efn5+\n5fW0AAAATIGyDgAAAHdlxIgRevfdd9W/f3+NGzdOfn5+On78uFauXKlFixZp//792rZtmyIjI+Xv\n76+UlBSdO3dOISEhkqSpU6fqhRdekI+Pj7p27arCwkLt379fFy5cUFxcnN566y0FBgaqefPmslqt\nWrVqlQICAuTr62vwMwcAACh/lHUAAAC4K0FBQdq9e7fGjx+vyMhIFRYWKjg4WF27dpXVapW3t7d2\n7dql2bNnKz8/X8HBwXrzzTfVrVs3SdJzzz0nT09Pvf766xo7dqy8vLzUpEkTjR49WpJUvXp1zZo1\nS8eOHZOLi4tatWqljRs3ymq1Gvm0AQAAKgRngwUAAAAAAABMgl9PAgAAAAAAACZBWQcAAAAAAACY\nBGUdAAAAAAAAYBKUdQAAAAAAAIBJUNYBAAAAAAAAJkFZBwAAAAAAAJgEZR0AAAAAAABgEpR1AAAA\nAAAAgElQ1gEAAAAAAAAmQVkHAAAAAAAAmARlHQAAAAAAAGASlHUAAAAAAACASfwv8ctIWsr7W9gA\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2f93510>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#A cada dato de la columna de comienzo del viaje (start_date) le aplico una funcion para saber en que mes fueron\n",
"#realizados los viajes\n",
"#Realizo un plot en el cual observamos la cantidad de viajes segun el mes del año\n",
"plt = trip['start_date'].apply(lambda x: x.month).value_counts().plot('bar')\n",
"plt.set_xlabel('meses')\n",
"plt.set_ylabel('Cantidad de viajes')\n",
"plt.set_title('Cantidad de viajes por mes');"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>duration</th>\n",
" <th>start_date</th>\n",
" <th>start_station_name</th>\n",
" <th>start_station_id</th>\n",
" <th>end_date</th>\n",
" <th>end_station_name</th>\n",
" <th>end_station_id</th>\n",
" <th>bike_id</th>\n",
" <th>subscription_type</th>\n",
" <th>zip_code</th>\n",
" <th>start_date_without_time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4576</td>\n",
" <td>63</td>\n",
" <td>2013-08-29 14:13:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 14:14:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>520</td>\n",
" <td>Subscriber</td>\n",
" <td>94127</td>\n",
" <td>2013-08-29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4607</td>\n",
" <td>70</td>\n",
" <td>2013-08-29 14:42:00</td>\n",
" <td>San Jose City Hall</td>\n",
" <td>10</td>\n",
" <td>2013-08-29 14:43:00</td>\n",
" <td>San Jose City Hall</td>\n",
" <td>10</td>\n",
" <td>661</td>\n",
" <td>Subscriber</td>\n",
" <td>95138</td>\n",
" <td>2013-08-29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4130</td>\n",
" <td>71</td>\n",
" <td>2013-08-29 10:16:00</td>\n",
" <td>Mountain View City Hall</td>\n",
" <td>27</td>\n",
" <td>2013-08-29 10:17:00</td>\n",
" <td>Mountain View City Hall</td>\n",
" <td>27</td>\n",
" <td>48</td>\n",
" <td>Subscriber</td>\n",
" <td>97214</td>\n",
" <td>2013-08-29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4251</td>\n",
" <td>77</td>\n",
" <td>2013-08-29 11:29:00</td>\n",
" <td>San Jose City Hall</td>\n",
" <td>10</td>\n",
" <td>2013-08-29 11:30:00</td>\n",
" <td>San Jose City Hall</td>\n",
" <td>10</td>\n",
" <td>26</td>\n",
" <td>Subscriber</td>\n",
" <td>95060</td>\n",
" <td>2013-08-29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4299</td>\n",
" <td>83</td>\n",
" <td>2013-08-29 12:02:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 12:04:00</td>\n",
" <td>Market at 10th</td>\n",
" <td>67</td>\n",
" <td>319</td>\n",
" <td>Subscriber</td>\n",
" <td>94103</td>\n",
" <td>2013-08-29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>4927</td>\n",
" <td>103</td>\n",
" <td>2013-08-29 18:54:00</td>\n",
" <td>Golden Gate at Polk</td>\n",
" <td>59</td>\n",
" <td>2013-08-29 18:56:00</td>\n",
" <td>Golden Gate at Polk</td>\n",
" <td>59</td>\n",
" <td>527</td>\n",
" <td>Subscriber</td>\n",
" <td>94109</td>\n",
" <td>2013-08-29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>4500</td>\n",
" <td>109</td>\n",
" <td>2013-08-29 13:25:00</td>\n",
" <td>Santa Clara at Almaden</td>\n",
" <td>4</td>\n",
" <td>2013-08-29 13:27:00</td>\n",
" <td>Adobe on Almaden</td>\n",
" <td>5</td>\n",
" <td>679</td>\n",
" <td>Subscriber</td>\n",
" <td>95112</td>\n",
" <td>2013-08-29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>4563</td>\n",
" <td>111</td>\n",
" <td>2013-08-29 14:02:00</td>\n",
" <td>San Salvador at 1st</td>\n",
" <td>8</td>\n",
" <td>2013-08-29 14:04:00</td>\n",
" <td>San Salvador at 1st</td>\n",
" <td>8</td>\n",
" <td>687</td>\n",
" <td>Subscriber</td>\n",
" <td>95112</td>\n",
" <td>2013-08-29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>4760</td>\n",
" <td>113</td>\n",
" <td>2013-08-29 17:01:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 17:03:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>553</td>\n",
" <td>Subscriber</td>\n",
" <td>94103</td>\n",
" <td>2013-08-29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>4258</td>\n",
" <td>114</td>\n",
" <td>2013-08-29 11:33:00</td>\n",
" <td>San Jose City Hall</td>\n",
" <td>10</td>\n",
" <td>2013-08-29 11:35:00</td>\n",
" <td>MLK Library</td>\n",
" <td>11</td>\n",
" <td>107</td>\n",
" <td>Subscriber</td>\n",
" <td>95060</td>\n",
" <td>2013-08-29</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id duration start_date start_station_name \\\n",
"0 4576 63 2013-08-29 14:13:00 South Van Ness at Market \n",
"1 4607 70 2013-08-29 14:42:00 San Jose City Hall \n",
"2 4130 71 2013-08-29 10:16:00 Mountain View City Hall \n",
"3 4251 77 2013-08-29 11:29:00 San Jose City Hall \n",
"4 4299 83 2013-08-29 12:02:00 South Van Ness at Market \n",
"5 4927 103 2013-08-29 18:54:00 Golden Gate at Polk \n",
"6 4500 109 2013-08-29 13:25:00 Santa Clara at Almaden \n",
"7 4563 111 2013-08-29 14:02:00 San Salvador at 1st \n",
"8 4760 113 2013-08-29 17:01:00 South Van Ness at Market \n",
"9 4258 114 2013-08-29 11:33:00 San Jose City Hall \n",
"\n",
" start_station_id end_date end_station_name \\\n",
"0 66 2013-08-29 14:14:00 South Van Ness at Market \n",
"1 10 2013-08-29 14:43:00 San Jose City Hall \n",
"2 27 2013-08-29 10:17:00 Mountain View City Hall \n",
"3 10 2013-08-29 11:30:00 San Jose City Hall \n",
"4 66 2013-08-29 12:04:00 Market at 10th \n",
"5 59 2013-08-29 18:56:00 Golden Gate at Polk \n",
"6 4 2013-08-29 13:27:00 Adobe on Almaden \n",
"7 8 2013-08-29 14:04:00 San Salvador at 1st \n",
"8 66 2013-08-29 17:03:00 South Van Ness at Market \n",
"9 10 2013-08-29 11:35:00 MLK Library \n",
"\n",
" end_station_id bike_id subscription_type zip_code start_date_without_time \n",
"0 66 520 Subscriber 94127 2013-08-29 \n",
"1 10 661 Subscriber 95138 2013-08-29 \n",
"2 27 48 Subscriber 97214 2013-08-29 \n",
"3 10 26 Subscriber 95060 2013-08-29 \n",
"4 67 319 Subscriber 94103 2013-08-29 \n",
"5 59 527 Subscriber 94109 2013-08-29 \n",
"6 5 679 Subscriber 95112 2013-08-29 \n",
"7 8 687 Subscriber 95112 2013-08-29 \n",
"8 66 553 Subscriber 94103 2013-08-29 \n",
"9 11 107 Subscriber 95060 2013-08-29 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Ahora para hacer un visualizacion de todos los viajes a traves del tiempo creamos una nueva columa en la cual tendremos\n",
"#la fecha pero sin la hora ni los minutos\n",
"trip['start_date_without_time']=trip.start_date.dt.date\n",
"trip.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABJUAAAGJCAYAAAAzGAKZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCCGEEEIIIYQQQgLz/wONZfFuv4wocgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xb44efd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Realizamos una visualizacion de los viajes a traves del tiempo\n",
"#Quiero aclarar que se realizo una agrupacion dia a dia para realizar este plot\n",
"plt = trip.groupby('start_date_without_time').count()['id'].plot(figsize=(14,4));\n",
"plt.set_xlabel('Fecha')\n",
"plt.set_ylabel('Cantidad de viajes')\n",
"plt.set_title('Cantidad de viajes a traves del tiempo');"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABOsAAAHaCAYAAABRgs3rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nE/8Pbe9jV4YaG04AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xd17d2f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Ahora añadiremos otra columna la cual tendra solo la hora en la que se realiza el viaje\n",
"trip['hora'] = trip['start_date'].apply(lambda x: x.hour)\n",
"#Realizo una visualizacion en base a la hora en que se realiza el viaje\n",
"plt = trip.groupby('hora').count()['id'].plot('bar');\n",
"plt.set_xlabel('Hora del dia')\n",
"plt.set_ylabel('Cantidad de viajes')\n",
"plt.set_title('Cantidad de viajes dependiendo de la hora');"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABOsAAAHaCAYAAABRgs3rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xd157f90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Cambiamos la duracion a minutos\n",
"trip['duration'] = trip['duration'].apply(lambda x: x/60)\n",
"#Cantidad de viajes segun la duracion (en minutos). Visualizacion de la cantidad de viajes segun la duracion del viaje \n",
"plt = trip['duration'].value_counts()[:20].plot('bar')\n",
"plt.set_xlabel('Cantidad de minutos del viajes')\n",
"plt.set_ylabel('Cantidad de viajes')\n",
"plt.set_title('Cantidad de viajes dependiendo de su duracion');"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>start_station_id</th>\n",
" <th>name</th>\n",
" <th>lat</th>\n",
" <th>long</th>\n",
" <th>dock_count</th>\n",
" <th>city</th>\n",
" <th>installation_date</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2</td>\n",
" <td>San Jose Diridon Caltrain Station</td>\n",
" <td>37.329732</td>\n",
" <td>-121.901782</td>\n",
" <td>27</td>\n",
" <td>San Jose</td>\n",
" <td>8/6/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3</td>\n",
" <td>San Jose Civic Center</td>\n",
" <td>37.330698</td>\n",
" <td>-121.888979</td>\n",
" <td>15</td>\n",
" <td>San Jose</td>\n",
" <td>8/5/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4</td>\n",
" <td>Santa Clara at Almaden</td>\n",
" <td>37.333988</td>\n",
" <td>-121.894902</td>\n",
" <td>11</td>\n",
" <td>San Jose</td>\n",
" <td>8/6/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5</td>\n",
" <td>Adobe on Almaden</td>\n",
" <td>37.331415</td>\n",
" <td>-121.893200</td>\n",
" <td>19</td>\n",
" <td>San Jose</td>\n",
" <td>8/5/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>6</td>\n",
" <td>San Pedro Square</td>\n",
" <td>37.336721</td>\n",
" <td>-121.894074</td>\n",
" <td>15</td>\n",
" <td>San Jose</td>\n",
" <td>8/7/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>7</td>\n",
" <td>Paseo de San Antonio</td>\n",
" <td>37.333798</td>\n",
" <td>-121.886943</td>\n",
" <td>15</td>\n",
" <td>San Jose</td>\n",
" <td>8/7/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>8</td>\n",
" <td>San Salvador at 1st</td>\n",
" <td>37.330165</td>\n",
" <td>-121.885831</td>\n",
" <td>15</td>\n",
" <td>San Jose</td>\n",
" <td>8/5/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>9</td>\n",
" <td>Japantown</td>\n",
" <td>37.348742</td>\n",
" <td>-121.894715</td>\n",
" <td>15</td>\n",
" <td>San Jose</td>\n",
" <td>8/5/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>10</td>\n",
" <td>San Jose City Hall</td>\n",
" <td>37.337391</td>\n",
" <td>-121.886995</td>\n",
" <td>15</td>\n",
" <td>San Jose</td>\n",
" <td>8/6/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>11</td>\n",
" <td>MLK Library</td>\n",
" <td>37.335885</td>\n",
" <td>-121.885660</td>\n",
" <td>19</td>\n",
" <td>San Jose</td>\n",
" <td>8/6/2013</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" start_station_id name lat long \\\n",
"0 2 San Jose Diridon Caltrain Station 37.329732 -121.901782 \n",
"1 3 San Jose Civic Center 37.330698 -121.888979 \n",
"2 4 Santa Clara at Almaden 37.333988 -121.894902 \n",
"3 5 Adobe on Almaden 37.331415 -121.893200 \n",
"4 6 San Pedro Square 37.336721 -121.894074 \n",
"5 7 Paseo de San Antonio 37.333798 -121.886943 \n",
"6 8 San Salvador at 1st 37.330165 -121.885831 \n",
"7 9 Japantown 37.348742 -121.894715 \n",
"8 10 San Jose City Hall 37.337391 -121.886995 \n",
"9 11 MLK Library 37.335885 -121.885660 \n",
"\n",
" dock_count city installation_date \n",
"0 27 San Jose 8/6/2013 \n",
"1 15 San Jose 8/5/2013 \n",
"2 11 San Jose 8/6/2013 \n",
"3 19 San Jose 8/5/2013 \n",
"4 15 San Jose 8/7/2013 \n",
"5 15 San Jose 8/7/2013 \n",
"6 15 San Jose 8/5/2013 \n",
"7 15 San Jose 8/5/2013 \n",
"8 15 San Jose 8/6/2013 \n",
"9 19 San Jose 8/6/2013 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Cargamos los datos de station.csv y le cambiamos el nombre a una de sus columnas para un posterior procesamiento\n",
"station = pd.read_csv('station.csv', low_memory=False)\n",
"station.rename(columns={'id': 'start_station_id'}, inplace=True)\n",
"station.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>duration</th>\n",
" <th>start_date</th>\n",
" <th>start_station_name</th>\n",
" <th>start_station_id</th>\n",
" <th>end_date</th>\n",
" <th>end_station_name</th>\n",
" <th>end_station_id</th>\n",
" <th>bike_id</th>\n",
" <th>subscription_type</th>\n",
" <th>zip_code</th>\n",
" <th>start_date_without_time</th>\n",
" <th>hora</th>\n",
" <th>name</th>\n",
" <th>lat</th>\n",
" <th>long</th>\n",
" <th>dock_count</th>\n",
" <th>city</th>\n",
" <th>installation_date</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4576</td>\n",
" <td>1</td>\n",
" <td>2013-08-29 14:13:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 14:14:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>520</td>\n",
" <td>Subscriber</td>\n",
" <td>94127</td>\n",
" <td>2013-08-29</td>\n",
" <td>14</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>37.774814</td>\n",
" <td>-122.418954</td>\n",
" <td>19</td>\n",
" <td>San Francisco</td>\n",
" <td>8/23/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4299</td>\n",
" <td>1</td>\n",
" <td>2013-08-29 12:02:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 12:04:00</td>\n",
" <td>Market at 10th</td>\n",
" <td>67</td>\n",
" <td>319</td>\n",
" <td>Subscriber</td>\n",
" <td>94103</td>\n",
" <td>2013-08-29</td>\n",
" <td>12</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>37.774814</td>\n",
" <td>-122.418954</td>\n",
" <td>19</td>\n",
" <td>San Francisco</td>\n",
" <td>8/23/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4760</td>\n",
" <td>1</td>\n",
" <td>2013-08-29 17:01:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 17:03:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>553</td>\n",
" <td>Subscriber</td>\n",
" <td>94103</td>\n",
" <td>2013-08-29</td>\n",
" <td>17</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>37.774814</td>\n",
" <td>-122.418954</td>\n",
" <td>19</td>\n",
" <td>San Francisco</td>\n",
" <td>8/23/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5070</td>\n",
" <td>2</td>\n",
" <td>2013-08-29 21:43:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 21:46:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>598</td>\n",
" <td>Subscriber</td>\n",
" <td>94115</td>\n",
" <td>2013-08-29</td>\n",
" <td>21</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>37.774814</td>\n",
" <td>-122.418954</td>\n",
" <td>19</td>\n",
" <td>San Francisco</td>\n",
" <td>8/23/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4765</td>\n",
" <td>3</td>\n",
" <td>2013-08-29 17:05:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 17:08:00</td>\n",
" <td>Market at 10th</td>\n",
" <td>67</td>\n",
" <td>553</td>\n",
" <td>Subscriber</td>\n",
" <td>94103</td>\n",
" <td>2013-08-29</td>\n",
" <td>17</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>37.774814</td>\n",
" <td>-122.418954</td>\n",
" <td>19</td>\n",
" <td>San Francisco</td>\n",
" <td>8/23/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>4560</td>\n",
" <td>3</td>\n",
" <td>2013-08-29 13:58:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 14:02:00</td>\n",
" <td>San Francisco City Hall</td>\n",
" <td>58</td>\n",
" <td>438</td>\n",
" <td>Subscriber</td>\n",
" <td>94124</td>\n",
" <td>2013-08-29</td>\n",
" <td>13</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>37.774814</td>\n",
" <td>-122.418954</td>\n",
" <td>19</td>\n",
" <td>San Francisco</td>\n",
" <td>8/23/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>4559</td>\n",
" <td>4</td>\n",
" <td>2013-08-29 13:58:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 14:02:00</td>\n",
" <td>San Francisco City Hall</td>\n",
" <td>58</td>\n",
" <td>554</td>\n",
" <td>Subscriber</td>\n",
" <td>94115</td>\n",
" <td>2013-08-29</td>\n",
" <td>13</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>37.774814</td>\n",
" <td>-122.418954</td>\n",
" <td>19</td>\n",
" <td>San Francisco</td>\n",
" <td>8/23/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>4584</td>\n",
" <td>4</td>\n",
" <td>2013-08-29 14:17:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 14:21:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>587</td>\n",
" <td>Subscriber</td>\n",
" <td>94612</td>\n",
" <td>2013-08-29</td>\n",
" <td>14</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>37.774814</td>\n",
" <td>-122.418954</td>\n",
" <td>19</td>\n",
" <td>San Francisco</td>\n",
" <td>8/23/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>5075</td>\n",
" <td>5</td>\n",
" <td>2013-08-29 21:47:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 21:52:00</td>\n",
" <td>Civic Center BART (7th at Market)</td>\n",
" <td>72</td>\n",
" <td>598</td>\n",
" <td>Subscriber</td>\n",
" <td>94115</td>\n",
" <td>2013-08-29</td>\n",
" <td>21</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>37.774814</td>\n",
" <td>-122.418954</td>\n",
" <td>19</td>\n",
" <td>San Francisco</td>\n",
" <td>8/23/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>4981</td>\n",
" <td>6</td>\n",
" <td>2013-08-29 19:41:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 19:47:00</td>\n",
" <td>Market at 10th</td>\n",
" <td>67</td>\n",
" <td>632</td>\n",
" <td>Subscriber</td>\n",
" <td>94110</td>\n",
" <td>2013-08-29</td>\n",
" <td>19</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>37.774814</td>\n",
" <td>-122.418954</td>\n",
" <td>19</td>\n",
" <td>San Francisco</td>\n",
" <td>8/23/2013</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id duration start_date start_station_name \\\n",
"0 4576 1 2013-08-29 14:13:00 South Van Ness at Market \n",
"1 4299 1 2013-08-29 12:02:00 South Van Ness at Market \n",
"2 4760 1 2013-08-29 17:01:00 South Van Ness at Market \n",
"3 5070 2 2013-08-29 21:43:00 South Van Ness at Market \n",
"4 4765 3 2013-08-29 17:05:00 South Van Ness at Market \n",
"5 4560 3 2013-08-29 13:58:00 South Van Ness at Market \n",
"6 4559 4 2013-08-29 13:58:00 South Van Ness at Market \n",
"7 4584 4 2013-08-29 14:17:00 South Van Ness at Market \n",
"8 5075 5 2013-08-29 21:47:00 South Van Ness at Market \n",
"9 4981 6 2013-08-29 19:41:00 South Van Ness at Market \n",
"\n",
" start_station_id end_date end_station_name \\\n",
"0 66 2013-08-29 14:14:00 South Van Ness at Market \n",
"1 66 2013-08-29 12:04:00 Market at 10th \n",
"2 66 2013-08-29 17:03:00 South Van Ness at Market \n",
"3 66 2013-08-29 21:46:00 South Van Ness at Market \n",
"4 66 2013-08-29 17:08:00 Market at 10th \n",
"5 66 2013-08-29 14:02:00 San Francisco City Hall \n",
"6 66 2013-08-29 14:02:00 San Francisco City Hall \n",
"7 66 2013-08-29 14:21:00 South Van Ness at Market \n",
"8 66 2013-08-29 21:52:00 Civic Center BART (7th at Market) \n",
"9 66 2013-08-29 19:47:00 Market at 10th \n",
"\n",
" end_station_id bike_id subscription_type zip_code start_date_without_time \\\n",
"0 66 520 Subscriber 94127 2013-08-29 \n",
"1 67 319 Subscriber 94103 2013-08-29 \n",
"2 66 553 Subscriber 94103 2013-08-29 \n",
"3 66 598 Subscriber 94115 2013-08-29 \n",
"4 67 553 Subscriber 94103 2013-08-29 \n",
"5 58 438 Subscriber 94124 2013-08-29 \n",
"6 58 554 Subscriber 94115 2013-08-29 \n",
"7 66 587 Subscriber 94612 2013-08-29 \n",
"8 72 598 Subscriber 94115 2013-08-29 \n",
"9 67 632 Subscriber 94110 2013-08-29 \n",
"\n",
" hora name lat long dock_count \\\n",
"0 14 South Van Ness at Market 37.774814 -122.418954 19 \n",
"1 12 South Van Ness at Market 37.774814 -122.418954 19 \n",
"2 17 South Van Ness at Market 37.774814 -122.418954 19 \n",
"3 21 South Van Ness at Market 37.774814 -122.418954 19 \n",
"4 17 South Van Ness at Market 37.774814 -122.418954 19 \n",
"5 13 South Van Ness at Market 37.774814 -122.418954 19 \n",
"6 13 South Van Ness at Market 37.774814 -122.418954 19 \n",
"7 14 South Van Ness at Market 37.774814 -122.418954 19 \n",
"8 21 South Van Ness at Market 37.774814 -122.418954 19 \n",
"9 19 South Van Ness at Market 37.774814 -122.418954 19 \n",
"\n",
" city installation_date \n",
"0 San Francisco 8/23/2013 \n",
"1 San Francisco 8/23/2013 \n",
"2 San Francisco 8/23/2013 \n",
"3 San Francisco 8/23/2013 \n",
"4 San Francisco 8/23/2013 \n",
"5 San Francisco 8/23/2013 \n",
"6 San Francisco 8/23/2013 \n",
"7 San Francisco 8/23/2013 \n",
"8 San Francisco 8/23/2013 \n",
"9 San Francisco 8/23/2013 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Realizamos un join entre trip y station en base a la columna start_station_id\n",
"arch_unidos = pd.merge(trip, station, on='start_station_id', how='inner')\n",
"arch_unidos.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABPQAAAIuCAYAAAA4zFYcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x85be990>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Visulizacion de la cantidad de viajes segun la ciudad\n",
"plt = arch_unidos['city'].value_counts().plot('bar')\n",
"plt.set_xlabel('Ciudad')\n",
"plt.set_ylabel('Cantidad de viajes')\n",
"plt.set_title('Cantidad de viajes dependiendo de la ciudad');"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABOIAAALICAYAAADSYV8sAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nokXwb0NhswEgPooaAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xadfcab0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Visulizacion de la cantidad de viajes segun la estacion\n",
"#Solo mostramos las 20 ciudades con mas cantidad de viajes\n",
"plt = arch_unidos['start_station_name'].value_counts().tail(20).plot('bar')\n",
"plt.set_xlabel('Estacion de bicicleta')\n",
"plt.set_ylabel('Cantidad de viajes')\n",
"plt.set_title('Top20 de estaciones con menos cantidad de viajes');"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABOsAAAMeCAYAAABMZIBgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzs3Xl4Tffe//9XIjLLpBmkUkKVpDU7ktTQgSMIihiiUfSk\nqMasxqrS2zFEW+QUObhbqqWmOoYWzamW+xAxlaqa2iIcYkqTEBIh6/eHX/bXlkGiiew2z8d17eva\n+7M+a33ea9i7zcsarAzDMAQAAAAAAACgzFmXdQEAAAAAAAAA7iKsAwAAAAAAACwEYR0AAAAAAABg\nIQjrAAAAAAAAAAtBWAcAAAAAAABYCMI6AAAAAAAAwEIQ1gEAAAAAAAAWgrAOAAAAAAAAsBCEdQAA\nAAAAAICFIKwDAADFcuzYMVlZWenzzz8v61LKzJYtW2RlZaXdu3eXdSkop4rzPYyIiFCdOnVKbOzM\nzExZWVlpxowZDzV/WX1/fs+448aNk729fSlUBQBAXoR1AAAUk5WVVZFe3333XamMv3XrVvXt21e1\natWSo6OjnnzySb3++uu6ePFivv23b9+uZ599Vo6OjqpSpYpGjhypmzdvlkptZS09PV2TJ0/Wf/7z\nn7IuBSgRn3zyiT788MOyLgMAADxCNmVdAAAAfzTLli0z+/zJJ58oPj4+T3tAQECpjD9q1ChlZWWp\nW7duqlmzpk6ePKl58+Zp06ZNOnjwoB577DFT3z179qhNmzaqX7++Zs+erdOnT+uDDz7QqVOntG7d\nulKpryylp6drypQpsre3V/PmzUttnDZt2ujmzZuys7MrtTEA6e7vy7lz5zR48GCz9tq1a+vmzZuy\ntbUto8r+eH7P93bq1KmaMmVKKVQFAEBehHUAABRT7969zT7v3r1b8fHxedpLy/z589WiRQtZWVmZ\n2lq1aqXQ0FDFxcVp4sSJpvZx48bJx8dH3377rZycnCRJjz/+uIYMGaIdO3aoZcuWj6TmPxtra2su\niUOZsrKy4hgspt/zvbWxsZGNDX86AQAeDS6DBQCglCUnJ6tfv37y9PSUvb29GjRooOXLl5v1yb3/\n1IcffqiYmBj5+fnJwcFBrVq10rFjx8z6tmzZ0iyok+6eMeLk5KSjR4+a2q5cuaLt27erb9++pqBO\nkqKiomRnZ6dVq1Y9sParV6+qd+/ecnFxkYeHh6KionTt2rV8+/7444/q0qWLPDw85ODgoKZNm2rz\n5s0PHEOS7ty5o/fee08BAQGys7OTj4+PoqOjlZ6ebtYvISFBrVu3VuXKleXo6KgaNWro9ddfl3R3\nG/r5+UmSxo8fb7ocOfe+WgcOHNArr7wif39/2dvbq0qVKhowYIBSU1Pz1JOUlKR+/frJx8dH9vb2\nqlmzpgYPHqw7d+5IKvjeV8uXL1eDBg1kb28vT09P9evXT8nJyWZ9IiIi9NhjjykpKUkdOnSQs7Oz\nvLy8NGHCBOXk5JT4dnmQjRs3qkWLFnJ2dparq6uCgoK0evXqUl2v/Pj4+Khbt26Kj49Xo0aN5ODg\noAYNGmjnzp2SpJUrVyowMFD29vZq2rSpfvzxR7P5i7p/U1NTNXjwYFWrVk12dnby8vJSaGioDh8+\n/MAaH3RcXL58WSNGjNDTTz8tJycnubq6qkOHDjpy5IjZcnKPn/Xr12vy5Mny9fWVg4OD2rRpo9On\nT5v6BQcH65tvvtHx48dNx3PuvecKumfdqlWrTNupXr162rRpU77rMn36dIWEhJh9X9evX5+n382b\nNzVkyBBVrlxZlSpVUteuXfPs+8KcPn1aHTt2lJOTk7y9vTV69GhlZ2fn23fnzp3661//KhcXFzk5\nOenFF19UYmJiocs/e/asrK2tNXPmzDzTDh06JCsrKy1evFhS/t/bbdu2KTw8XH5+frKzs1O1atU0\nZswYZWVlmS2roHvWffTRR2rYsKEcHBxUuXJl9e7dWxcuXDDrc/ToUXXu3Fne3t6yt7eXn5+fIiMj\nlZGRUei6AQDKL/55CACAUnT9+nW1aNFCZ8+e1ZAhQ+Tn56eVK1cqMjJS165d08CBA836L1y4UDdu\n3NDQoUOVkZGhOXPm6IUXXtCPP/6oypUrFzhOamqqMjMzzS6BPXTokHJyctSkSROzvg4ODqpbt66+\n//77Qmu/c+eOwsLCtG/fPr3xxhuqVauW1qxZo9deey1P34MHD6pFixby9/fX+PHj5eDgoBUrVqhD\nhw7auHGj2rdvX+hY/fr106pVq/S3v/1Nw4cP1y+//KIPP/xQhw4d0vbt21WhQgX997//Vdu2bfX4\n44/rrbfeUqVKlXTq1Cl9+eWXkiRfX1/FxsZq6NCh6tmzpzp06CBJatiwoSRp8+bN+u9//6uoqCh5\ne3vr8OHDWrhwoY4dO6YdO3aYaklKSlLTpk2VkZGhAQMGqHbt2kpKStKqVauUnZ2tChUq5LsOcXFx\nGjRokIKDgxUTE6Nz584pNjZWu3bt0oEDB+Ts7Gzqe+vWLf31r3/Vc889p1mzZmnLli2aPn26atWq\npVdffbVEt0thcmtu0KCB3nrrLbm4uOjAgQPaunWrunfvXmrrVZCffvpJffv21aBBg/TKK68oJiZG\nHTp00Ny5czV58mS9/vrrunPnjqZNm6aIiAgdPnzYFFwXdf9GRUXpq6++0pAhQ1S7dm1duXJFO3bs\n0PHjx1W3bt0CayvKcXH8+HF99dVX6tatm6pVq6YLFy4oLi5Ozz33nH766Sd5eXmZLXPKlCmytbXV\n2LFjdfXqVc2aNUt9+/bV9u3bTdNHjRqllJQUxcTESJJcXV0LrHHjxo2KiIhQvXr1NH36dF2+fFmR\nkZF6/PHH8/SdM2eOevTooVdeeUVZWVn69NNP1aVLF3399ddq3bq1qV+fPn20Zs0a9enTR02bNtXX\nX3+tl1566YH7UpKuXbumF198URcvXtSwYcPk6emppUuX6uuvv87Td8uWLerUqZOCg4NNl5suXrxY\nzz//vBISEtSgQYN8x/Dz81NISIhWrVqlsWPHmk1buXKlKlasqK5duxZY48qVK5Wdna3o6Gi5u7tr\n9+7dev/993XhwoU8tza439tvv61p06apV69eGjhwoJKTkxUbG6vExER9//33cnZ21s2bN9WmTRtJ\n0vDhw+Xl5aWzZ89qw4YNun79utk/pAAAYGIAAIDfJTo62ijoP6kzZswwJBlr1qwxtWVlZRmNGjUy\n3NzcjIyMDMMwDOPo0aOGJKNSpUpGcnKyqe+OHTsMScb48eMLreGtt94yJBk7d+40tS1btsyQZOzZ\nsydP/44dOxrVq1cvdJmff/65IcmIjY01tWVnZxtBQUGGJGPFihWm9mbNmhmNGzc2bt26ZWq7ffu2\n0bhxY6Nu3bqFjhMfH29IMtauXWvW/q9//cusfcWKFYYk4/DhwwUu6+zZs4YkY/r06Xmm3bhxI0/b\nxx9/nGcb9ejRw7CxsTEOHTpk1jcnJ8f0fvPmzYYkIyEhwTAMw7h586bh7u5uNGrUyMjKyjL1W7Nm\njSHJmDZtmqmtZ8+ehiQjJibGbNmBgYFGs2bNSmW75OfKlSuGg4OD0aJFC7Oa713X0livgnh7extW\nVlbG/v37TW3r1683JBnOzs7G+fPnTe1z58412/6GUbT9m5OTYzg4OBijRo16YD33K8pxcfPmTbPP\nhmEYx48fNypWrGi2XXKPn/r165t9Z2bOnGlIMk6ePGlqa9WqlVG7du089eT+Ztz7PQwICDCqVatm\nXLt2zdS2YcMGQ1KeZdy/vTIzM42nnnrKaN++valt9+7dhiRj5MiRZn3Dw8ML/J7dK/f3b8OGDaa2\na9euGdWqVTPbf7dv3zaqVatmdOrUyWz+a9euGVWrVjU6duxY6Di5x8O9280wDKNmzZpm63P/99Yw\n8j9u3nnnHcPa2trst3js2LGGnZ2d6fPx48cNa2tr4/333zebd//+/WbtCQkJhiRj48aNha4DAAD3\n4jJYAABK0VdffaVq1aqZndlha2urIUOGKDU1Vbt27TLr361bN3l7e5s+t2jRQvXr19dXX31V4Bjx\n8fGaMWOG+vTpo2effdbUnvvE1/xupm5vb//AJ8J+9dVXsre3V//+/U1tNjY2io6ONut34cIF7dy5\nUxEREUpLS9OVK1d05coV/fbbb6bLC69evVrgOKtXr5anp6datmxpmvfKlSsKCQmRra2tvv32W0mS\nm5ubpLtnD92+fbvQ2vPj4OBgen/z5k1duXJFwcHBku5eQilJ2dnZ2rhxo8LDw1WvXj2z+e+/9Phe\nCQkJ+u233zR48GCzG/537dpV/v7++Z7ldu9ZlVZWVmrevLl+/fVXU1tpb5fNmzfr5s2bmjBhQp6H\nFOSua2msV2EaNmyoRo0amT4HBQVJkkJDQ1WlSpU87fcutyj718rKSi4uLkpISCjWpZxFPS7s7e1N\nn2/fvq2rV6/Kw8ND/v7+phruFRUVpYoVK5o+t2jRIs96FdWpU6d09OhRvfrqq2ZnO3bs2FE1a9bM\n0z93exmGod9++03Xrl1Ts2bNzOrM/d0ZOnSo2bzDhg0rUk1fffWVqlevro4dO5ranJ2dFRUVZdZv\nz549OnPmjF5++WWzYz0zM1MvvPCC6VgvSPfu3WVtba2VK1ea2vbt26dffvlFPXv2LHTee4+bjIwM\nXblyRc2aNVNOTo4OHjxY4Hxr166VlZWVwsPDzWp+4oknVL169Tzfzy1btigzM7PQWgAAyEVYBwBA\nKTpz5oyeeuqpPEFP7pNiz5w5Y9Zeq1atPMt46qmnzO5jda/Dhw+re/fuatSokRYsWGA2LfeP0Pvv\nvSRJmZmZZn+kFlS7n59fnvs01a5d2+zzyZMnJUmjR4+Wp6en2WvatGmSpEuXLhU4zsmTJ3X58uU8\n83p7e+vWrVumedu0aaOOHTtqwoQJeuyxx9S1a1d98sknunXrVqHrkevy5cuKjo6Wl5eXHB0d5enp\nadoPaWlpkqTz58/r5s2beuaZZ4q0zFy5+/H+bWNlZaXatWvn2c9ubm5ycXExa3N3d9dvv/1m+lza\n2+WXX36RpELXtTTWqzBPPPGE2efcSz5z70V4f/u9yy3K/pWk9957T/v27VPVqlUVHBysd999t8Dv\nV66iHhd37txRTEyMatSoITs7Oz322GPy9PTUiRMnzGooaH3d3d3zrFdR5e6Lgn5D7rdu3Tr95S9/\nkYODgzw8POTp6amPP/7YrM4zZ87Izs4uT533Hw+F1ZRfPQX9hkREROQ53pctW6br168X+o8LVapU\nUYsWLczCupUrV8rOzu6Bl+yeOnVKvXv3lru7u5ydneXp6Wm6bDW/fXZvzXfu3FH16tXz1Pzrr7+a\nvp916tTRG2+8oXnz5qly5cpq166dFixYUOC9PwEAkLhnHQAAf1inTp1S27Zt5eXlpS+//FKOjo5m\n03PPRLr/Zue5bb6+viVSR+7DAyZMmKAXXngh3z73/7F///xVq1bVxx9/nO/03DMNra2ttWHDBu3c\nuVObNm3S1q1b1bdvX82ZM0c7d+58YPjYpUsXHTp0SKNHj1a9evXk5OSkzMxMderUqUgPQChJBd33\nzjAM0/tHtV1KUlHW62HmL8pyi7p/e/fureeff17r1q0znZU6c+ZMbdiwQa1atSpSnQV555139Pe/\n/10DBgzQiy++KHd3d1lbW+uNN97I9xj7vdvrYcXHx6tr165q1aqV4uLi5OPjIxsbG/3zn//Uxo0b\nS3Xs/ORum7lz5yowMDDfPvef/Xm/iIgIDRo0SMeOHVOdOnW0evVqtW3bttB7/GVnZ6tVq1a6ceOG\nJkyYoNq1a8vR0VGnT59W//79C/1dyMnJUcWKFQs86/ne0HrevHnq37+/NmzYoK+//lrR0dGaOXOm\ndu/eLR8fn0LXCwBQPhHWAQBQiqpVq6YTJ07kac99wmu1atXM2nPPMLnXiRMnVL16dbO2ixcvqk2b\nNjIMQ19//bU8PT3zzFevXj1ZWVlp37596tSpk6n95s2bOnz4cJ5L0fKrPTExUZmZmWZn1x0/ftys\nX+4ldnZ2dmY3pi+qmjVrKjExUS1btnzgH+SS1KxZMzVr1kzTp0/XRx99pKioKK1du1a9e/cu8FLV\n5ORk7dy5UzNnztSYMWNM7fc/ATT3qZz3P2n0QXL34/Hjx80uRc5tu38/F0VJbpeCli/dfYpv1apV\n8+1TGutVGoq6f3NVrVpVQ4YM0ZAhQ3ThwgU1aNBA06dPLzCsK+pxsWbNGrVr107//Oc/zdpTUlJU\no0aNYq7VXYVdfn2v3H1R0G+Ijc3/+9/+tWvXysXFRZs3bza7DPf+s3OrVaumrKwsJSUlme3r+38D\nCqspv3oK+g1xc3N7qN8QSQoPD9fgwYO1cuVKhYaG6syZM6YnQRdk//79OnXqlFauXKkePXqY2osS\nWNasWVPZ2dl66qmnCv3HiFwNGjRQgwYNNGnSJG3btk2tWrXS4sWLNXHixAevHACg3OEyWAAASlH7\n9u115swZrVu3ztSWnZ2tDz/8UG5ubmrWrJlZ/zVr1ujixYumz//3f/+nQ4cOqV27dqa29PR0tW3b\nVleuXNHWrVvzBHm5PD099dxzz2np0qW6ceOGqf2jjz5SVlaW6WmfhdWemZmpRYsWmdpu376tefPm\nmfXz8/NTcHCw5s2bp8uXL+dZTn5t9+rRo4cyMzPz/cM6OzvbdClaSkpKnum5T4jMvdQ398mKqamp\nZv1yz2C6/4ylOXPmmH2uWLGiOnbsqLVr1+qHH34wm1bY2U4hISFyd3fX/PnzlZ2dbWpft26dTp06\npbCwsALnLUhJbpf8tGvXTg4ODpo2bVqeS2Zz17U01qs0FHX/Zmdn57n8sEqVKvL29i50WxX1uKhQ\noUKeGpYtW1boPRsfxMnJKc/xnB9/f3/VqVNHH3/8sa5fv25q37hxo+mS53vrtLa2Njtz7OTJk3nu\nQZj7FOfY2Fiz9rlz5xap9vbt2+v06dNm4df169f1v//7v2b9goOD5efnp5iYGLPfqlwP+g2R7v7e\nvfjii1q5cqVWrlwpR0dHs3vl5Se/4yYnJyfP+uanW7dusrKyMj259l45OTmm72VaWpru3LljNr1+\n/fqSCv9+AgDKN86sAwCgFEVHR2vx4sV6+eWXNWTIEPn5+WnlypXat2+fFixYkOcSxerVq6tZs2Z6\n/fXXlZGRodmzZ8vb21ujRo0y9enRo4cOHjyogQMH6tChQzp06JBpmqurq9kfqNOnT1fLli31/PPP\nKyoqSqdPn9YHH3ygjh076vnnny+09m7duukvf/mLRowYoZ9//lm1atXS6tWr8713VFxcnFq2bKln\nnnlGr732mvz9/U0PnkhJSdGePXsKHCc0NFR9+/bVO++8o3379qlVq1aqUKGCTpw4odWrV2vRokXq\n0KGDFi5cqKVLl6pz586qUaOGUlNTtXDhQrm7uys0NFTS3TNzatSooU8//VTVq1eXm5ub6tevr4CA\nADVt2lRTp07VjRs35O3trc2bN+vs2bN56pk5c6a+/fZbNWvWTAMHDlTt2rX13//+VytXrtT333+f\n5x5+0t0HC0ybNk2DBg3S888/r4iICJ07d06xsbF68sknNWTIkEK3dWlvl/xUrlxZ7733nqKjoxUU\nFKSePXvK1dVVBw8eVE5OjhYtWlQq61UaPD09i7R/r169qqeeekrdu3dX3bp15ejoqK1bt+rw4cN5\nQuj7FeW46NChg2JiYtS/f3/95S9/0aFDh7Ry5crfdQZi48aNtX79eo0dO1YNGjSQq6urKUS734wZ\nM9SlSxc1b95c/fr106VLlzRv3jwFBASYBXMdOnTQ/Pnz1a5dO/Xs2VMXLlzQvHnzVLt2bbOz3oKC\ngtSlSxd98MEHunr1qpo2baqtW7c+8B5/ud544w0tWLBAERERGjZsmLy8vLRkyZI8l6ZWrFhRixYt\nUqdOnVS3bl316dNHvr6+OnfunP7973/L19dXq1evfuB4PXv21GuvvaZz584pLCzMFN4XpG7dunri\niSc0dOhQnTp1Sk5OTlq1alWR7icXEBCgSZMmacqUKfr555/VsWNHOTk56ddff9UXX3yhESNGaPDg\nwdq8ebPGjBmj7t27q1atWsrKytInn3wiOzs7swcPAQBgpkyeQQsAwJ9IdHS0Udh/Us+fP2/06dPH\nqFy5smFra2vUr1/f+PTTT836HD161JBkxMbGGjNmzDCqVq1q2NvbG88//7xx5MgRs77e3t6GpHxf\ntWvXzjP+tm3bjKCgIMPe3t7w9vY2hg0bZmRkZBRp3S5fvmz06tXLcHZ2Ntzc3IxXX33V2LNnjyHJ\nWLFihVnfEydOGJGRkYaXl5dRsWJFo2rVqkanTp2M9evXP3CcnJwcY/78+UbDhg0Ne3t7w8XFxahX\nr54xfvx4Izk52TAMw9izZ4/Ro0cPw8/Pz7C1tTW8vb2Nl156yTh48KDZsrZv3240bNjQsLW1NSQZ\n06dPNwzDMM6cOWN06tTJcHV1Ndzc3IxevXoZZ86cMeuT69dffzUiIyONxx57zLCzszNq1qxpDB06\n1Lh9+7ZhGIaxefNmQ5KRkJBgNt+nn35q1K9f37C1tTUqV65s9OnTx7hw4YJZn549exqVK1fOsw3G\njh1r2NnZldp2KcjatWuN4OBgw8HBwXBxcTGCg4ONNWvWlOp65cfb29sIDw83a7t586YhyRg1apRZ\ne+735R//+IeprSj798aNG8bIkSONunXrGpUqVTKcnZ2Nhg0bGosWLXpgfYbx4OPixo0bxtChQw0f\nHx/D0dHRaNmypbFv3z4jKCjICA0NNS0n9/jZuHFjvut173crLS3N6NGjh+Hq6mr2Hc+vr2EYxooV\nK4zatWsbdnZ2Rt26dY2NGzcaPXv2zPPbEBcXZ9SsWdOws7MzAgMDjU8//TTffZWRkWG88cYbhru7\nu+Hs7Gx06dLF+PXXX/P93hS0zdq3b284ODgYXl5exptvvmls2LAh3+/Pvn37jJdeesnw8PAw7Ozs\njOrVqxu9evUytm/f/sBxDMMwUlJSjIoVKxqS8hzDhpH/9/aHH34wXnjhBcPJycnw9PQ0Bg0aZOzd\nuzfPti3oOP7888+NZ5991nBycjKcnZ2NgIAAY+jQocbPP/9sGMbd38V+/foZ/v7+hr29vVG5cmWj\ndevWxnfffVekdQIAlE9WhlHKd7AFAAAPdOzYMQUEBOgf//iHBg8eXNblAADuMXr0aC1YsMDsEmMA\nAEoL96wDAAAAgEJcuHBBjz32WFmXAQAoJ7hnHQAAAADk4+TJk/rXv/6l9evXmz0xFgCA0sSZdQAA\nAACQj2+++UZTp05V69atNXPmzLIuBwBQTnDPOgAAAAAAAMBCcGYdAAAAAAAAYCEI6wAAAAAAAAAL\nwQMmSlBOTo7Onz+vSpUqycrKqqzLAQAAAAAAQBkxDEPXrl2Tr6+vrK2Lfr4cYV0JOn/+vPz8/Mq6\nDAAAAAAAAFiIs2fPqmrVqkXuT1hXgipVqiTp7k5wcXEp42oAAAAAAABQVtLT0+Xn52fKi4qKsK4E\n5V766uLiQlgHAAAAAACAYt8qjQdMAAAAAAAAABaCsA4AAAAAAACwEIR1AAAAAAAAgIUgrAMAAAAA\nAAAsRJmGdZMnT5aVlZXZq06dOqbphmFo0qRJqlKlihwcHNS6dWudPHnSbBmZmZmKjo5W5cqV5ezs\nrPDwcF28eNGsT0pKiiIjI+Xi4iI3NzdFRUXp+vXrZn2SkpIUFhYmR0dHeXl5afTo0bp9+3bprTwA\nAAAAAABwnzI/s+7pp5/WhQsXTK///Oc/pmkxMTGKjY1VXFycEhMT5eTkpNDQUGVmZpr6jBgxQhs3\nbtTq1au1fft2nT9/Xl27djUbIzIyUkeOHFF8fLw2bdqkHTt2aMCAAabpd+7cUVhYmG7duqVdu3Zp\n6dKlWrJkiSZNmlT6GwAAAAAAAAD4/1kZhmGU1eCTJ0/Wv/71Lx08eDDPNMMw5Ovrq1GjRunNN9+U\nJKWlpcnb21tLlixRRESE0tLS5OnpqeXLl6tbt26SpGPHjikgIEAJCQkKDg7W0aNHFRgYqL1796pJ\nkyaSpC1btqh9+/Y6d+6cfH19tXnzZnXo0EHnz5+Xt7e3JCkuLk5jx47V5cuXZWtrW6T1SU9Pl6ur\nq9LS0uTi4lISmwgAAAAAAAB/QA+bE5X5mXUnT56Ur6+vatSoocjISCUlJUmSTp06peTkZLVu3drU\n19XVVUFBQUpISJAk7d+/X9nZ2WZ96tSpoyeeeMLUJyEhQW5ubqagTpJat24ta2trJSYmmvrUrVvX\nFNRJUmhoqNLT03XkyJHSW3kAAAAAAADgHjZlOXhQUJCWLFmi2rVr68KFC5oyZYpatGihH3/8UcnJ\nyZJkFqDlfs6dlpycLFtbW7m5uRXax8vLy2y6jY2NPDw8zPrkN07utIJkZWUpKyvL9Dk9Pb3I6w4A\nAAAAAADcr0zDunbt2pne16tXT0FBQapWrZpWrVqlgICAMqysaKZPn64pU6aUdRkAAAAAAAD4kyjz\ny2Dv5ebmpqeeeko///yzfHx8JCnPk10vXrxomubj46Nbt24pNTW10D6XLl0ym3779m2lpKSY9clv\nnNxpBRk/frzS0tJMr7NnzxZ3lQEAAAAAAAATiwrrrl+/rp9//llVqlSRv7+/fHx89M0335imp6en\nKzExUSEhIZKkxo0bq2LFimZ9jh8/rqSkJFOfkJAQpaamav/+/aY+27ZtU05OjoKCgkx9Dh8+bBbq\nxcfHy8XFRYGBgQXWa2dnJxcXF7MXAAAAAAAA8LDK9DLYN998Ux07dlS1atV0/vx5vfPOO7KxsVGv\nXr1kZWWl4cOHa+rUqapVq5b8/f319ttvy9fXV507d5Z094ETUVFRGjlypDw8POTi4qIhQ4YoJCRE\nwcHBkqSAgAC1bdtW/fv3V1xcnLKzszV48GBFRETI19dXktSmTRsFBgbqlVdeUUxMjJKTkzVx4kRF\nR0fLzs6uzLYPAAAAAAAAypcyDevOnTunXr166erVq/L09FTz5s21e/dueXp6SpLGjBmjjIwMDRgw\nQKmpqWoI4hMuAAAgAElEQVTevLm2bNkie3t70zJmz54ta2trhYeHKysrS6GhoZo/f77ZOJ999pkG\nDx6sVq1amfrGxsaapleoUEGbNm3SoEGDFBISIicnJ/Xt21fvvvvuo9kQAAAAAAAAgCQrwzCMsi7i\nzyI9PV2urq5KS0vjklgAAAAAAIBy7GFzIou6Zx0AAAAAAABQnpXpZbD4f6qP+/KRj3l6RtgjHxMA\nAAAAAAAF48w6AAAAAAAAwEIQ1gEAAAAAAAAWgrAOAAAAAAAAsBCEdQAAAAAAAICFIKwDAAAAAAAA\nLARhHQAAAAAAAGAhCOsAAAAAAAAAC0FYBwAAAAAAAFgIwjoAAAAAAADAQhDWAQAAAAAAABaCsA4A\nAAAAAACwEIR1AAAAAAAAgIUgrAMAAAAAAAAsBGEdAAAAAAAAYCEI6wAAAAAAAAALQVgHAAAAAAAA\nWAjCOgAAAAAAAMBCENYBAAAAAAAAFoKwDgAAAAAAALAQhHUAAAAAAACAhSCsAwAAAAAAACwEYR0A\nAAAAAABgIQjrAAAAAAAAAAtBWAcAAAAAAABYCMI6AAAAAAAAwEIQ1gEAAAAAAAAWgrAOAAAAAAAA\nsBCEdQAAAAAAAICFIKwDAAAAAAAALARhHQAAAAAAAGAhCOsAAAAAAAAAC0FYBwAAAAAAAFgIwjoA\nAAAAAADAQhDWAQAAAAAAABaCsA4AAAAAAACwEIR1AAAAAAAAgIUgrAMAAAAAAAAsBGEdAAAAAAAA\nYCEI6wAAAAAAAAALQVgHAAAAAAAAWAjCOgAAAAAAAMBCENYBAAAAAAAAFoKwDgAAAAAAALAQhHUA\nAAAAAACAhSCsAwAAAAAAACwEYR0AAAAAAABgIQjrAAAAAAAAAAtBWAcAAAAAAABYCMI6AAAAAAAA\nwEIQ1gEAAAAAAAAWgrAOAAAAAAAAsBCEdQAAAAAAAICFIKwDAAAAAAAALARhHQAAAAAAAGAhCOsA\nAAAAAAAAC0FYBwAAAAAAAFgIwjoAAAAAAADAQhDWAQAAAAAAABaCsA4AAAAAAACwEIR1AAAAAAAA\ngIUgrAMAAAAAAAAsBGEdAAAAAAAAYCEsJqybMWOGrKysNHz4cFObYRiaNGmSqlSpIgcHB7Vu3Von\nT540my8zM1PR0dGqXLmynJ2dFR4erosXL5r1SUlJUWRkpFxcXOTm5qaoqChdv37drE9SUpLCwsLk\n6OgoLy8vjR49Wrdv3y69FQYAAAAAAADuYxFh3d69e/XPf/5T9erVM2uPiYlRbGys4uLilJiYKCcn\nJ4WGhiozM9PUZ8SIEdq4caNWr16t7du36/z58+ratavZciIjI3XkyBHFx8dr06ZN2rFjhwYMGGCa\nfufOHYWFhenWrVvatWuXli5dqiVLlmjSpEmlu+IAAAAAAADAPawMwzDKsoDr16+rUaNGmj9/vqZO\nnaoGDRpozpw5MgxDvr6+GjVqlN58801JUlpamry9vbVkyRJFREQoLS1Nnp6eWr58ubp16yZJOnbs\nmAICApSQkKDg4GAdPXpUgYGB2rt3r5o0aSJJ2rJli9q3b69z587J19dXmzdvVocOHXT+/Hl5e3tL\nkuLi4jR27FhdvnxZtra2RVqX9PR0ubq6Ki0tTS4uLsXaDtXHfVms/iXh9IywRz4mAAAAAABAefCw\nOVGZn1kXHR2tsLAwtW7d2qz91KlTSk5ONmt3dXVVUFCQEhISJEn79+9Xdna2WZ86deroiSeeMPVJ\nSEiQm5ubKaiTpNatW8va2lqJiYmmPnXr1jUFdZIUGhqq9PR0HTlypMDas7KylJ6ebvYCAAAAAAAA\nHpZNWQ7++eef68CBA9q7d2+eacnJyZJkFqDlfs6dlpycLFtbW7m5uRXax8vLy2y6jY2NPDw8zPrk\nN869deRn+vTpmjJlygPXEwAAAAAAACiKMjuz7uzZsxo2bJg+++wz2dvbl1UZv8v48eOVlpZmep09\ne7asSwIAAAAAAMAfWJmFdfv379elS5fUqFEj2djYyMbGRtu3b1dsbKxsbGxMZ7bd/2TXixcvysfH\nR5Lk4+OjW7duKTU1tdA+ly5dMpt++/ZtpaSkmPXJb5zcaQWxs7OTi4uL2QsAAAAAAAB4WGUW1rVq\n1UqHDx/WwYMHTa8mTZooMjJSBw8eVI0aNeTj46NvvvnGNE96eroSExMVEhIiSWrcuLEqVqxo1uf4\n8eNKSkoy9QkJCVFqaqr2799v6rNt2zbl5OQoKCjI1Ofw4cNmoV58fLxcXFwUGBhYqtsBAAAAAAAA\nyFVm96yrVKmSnnnmGbM2JycnVa5c2dQ+fPhwTZ06VbVq1ZK/v7/efvtt+fr6qnPnzpLuPnAiKipK\nI0eOlIeHh1xcXDRkyBCFhIQoODhYkhQQEKC2bduqf//+iouLU3Z2tgYPHqyIiAj5+vpKktq0aaPA\nwEC98soriomJUXJysiZOnKjo6GjZ2dk9wq0CAAAAAACA8qxMHzDxIGPGjFFGRoYGDBig1NRUNW/e\nXFu2bDG7x93s2bNlbW2t8PBwZWVlKTQ0VPPnzzdbzmeffabBgwerVatWpr6xsbGm6RUqVNCmTZs0\naNAghYSEyMnJSX379tW77777yNYVAAAAAAAAsDIMwyjrIv4s0tPT5erqqrS0tGLfv676uC9LqaqC\nnZ4R9sjHBAAAAAAAKA8eNicqs3vWAQAAAAAAADBHWAcAAAAAAABYCMI6AAAAAAAAwEIQ1gEAAAAA\nAAAWgrAOAAAAAAAAsBCEdQAAAAAAAICFIKwDAAAAAAAALARhHQAAAAAAAGAhCOsAAAAAAAAAC0FY\nBwAAAAAAAFgIwjoAAAAAAADAQhDWAQAAAAAAABaCsA4AAAAAAACwEIR1AAAAAAAAgIUgrAMAAAAA\nAAAsBGEdAAAAAAAAYCEI6wAAAAAAAAALQVgHAAAAAAAAWAjCOgAAAAAAAMBCENYBAAAAAAAAFoKw\nDgAAAAAAALAQhHUAAAAAAACAhSCsAwAAAAAAACwEYR0AAAAAAABgIQjrAAAAAAAAAAtBWAcAAAAA\nAABYCMI6AAAAAAAAwEIQ1gEAAAAAAAAWgrAOAAAAAAAAsBCEdQAAAAAAAICFIKwDAAAAAAAALARh\nHQAAAAAAAGAhCOsAAAAAAAAAC0FYBwAAAAAAAFgIwjoAAAAAAADAQhDWAQAAAAAAABaCsA4AAAAA\nAACwEMUO6w4cOKDDhw+bPq9fv16dO3fWhAkTdOvWrRItDgAAAAAAAChPih3WDRw4UCdOnJAk/frr\nr4qIiJCjo6NWr16tMWPGlHiBAAAAAAAAQHlR7LDuxIkTatCggSRp9erVatmypZYvX64lS5Zo7dq1\nJV4gAAAAAAAAUF7YFHcGwzCUk5MjSfr3v/+tDh06SJL8/Px05cqVkq0OfzrVx335yMc8PSPskY8J\nAAAAAADwMIp9Zl2TJk00depULVu2TNu3b1dY2N0g5NSpU/L29i7xAgEAAAAAAIDyothh3Zw5c3Tg\nwAENHjxYb731lp588klJ0po1a/Tss8+WeIEAAAAAAABAeVHsy2Dr1atn9jTYXLNmzVKFChVKpCgA\nAAAAAACgPCr2mXWSlJqaqsWLF2v8+PFKSUmRJP3000+6dOlSiRYHAAAAAAAAlCfFPrPuhx9+UKtW\nreTm5qbTp0+rf//+8vDw0BdffKGkpCR98sknpVEnAAAAAAAA8KdX7LBu5MiRevXVVxUTE6NKlSqZ\n2tu3b6+XX365RIsD/qh46i0AAAAAAHgYxb4Mdu/evRo4cGCe9scff1zJycklUhQAAAAAAABQHhU7\nrLOzs1N6enqe9hMnTsjT07NEigIAAAAAAADKo2KHdZ06ddK7776r7OxsSZKVlZWSkpI0duxYhYeH\nl3iBAAAAAAAAQHlR7LDu/fff1/Xr1+Xl5aWbN2/queee05NPPqlKlSrp73//e2nUCAAAAAAAAJQL\nxX7AhKurq+Lj4/Wf//xHP/zwg65fv65GjRqpdevWpVEfAAAAAAAAUG4UO6zL1bx5czVv3rwkawEA\nAAAAAADKtSKFdbGxsRowYIDs7e0VGxtbaF9nZ2c9/fTTCgoKKpECAQAAAAAAgPKiSGHd7NmzFRkZ\nKXt7e82ePbvQvllZWbp06ZJGjBihWbNmlUiRAAAAAAAAQHlQpLDu1KlT+b4vSHx8vF5++WXCOgAA\nAAAAAKAYiv002KJo3ry5Jk6cWBqLBgAAAAAAAP60HuoBExkZGdq+fbuSkpJ069Yts2lDhw6Vg4OD\nhg0bViIFAgAAAAAAAOVFscO677//Xu3bt9eNGzeUkZEhDw8PXblyRY6OjvLy8tLQoUNLo04AAAAA\nAADgT6/Yl8GOGDFCHTt21G+//SYHBwft3r1bZ86cUePGjfXee++VRo0AAAAAAABAuVDssO7gwYMa\nNWqUrK2tVaFCBWVlZcnPz08xMTGaMGFCadQIAAAAAAAAlAvFDusqVqwoa+u7s3l5eSkpKUmS5Orq\nqrNnz5ZsdQAAAAAAAEA5Uux71jVs2FB79+5VrVq19Nxzz2nSpEm6cuWKli1bpmeeeaY0agQAAAAA\nAADKhWKfWTdt2jRVqVJFkvT3v/9d7u7uGjRokC5fvqyFCxeWeIEAAAAAAABAeVHssK5JkyZ64YUX\nJN29DHbLli1KT0/X/v37Vb9+/WIta8GCBapXr55cXFzk4uKikJAQbd682TTdMAxNmjRJVapUkYOD\ng1q3bq2TJ0+aLSMzM1PR0dGqXLmynJ2dFR4erosXL5r1SUlJUWRkpFxcXOTm5qaoqChdv37drE9S\nUpLCwsJMT7UdPXq0bt++Xaz1AQAAAAAAAH6PYod1Jalq1aqaMWOG9u/fr3379unFF1/USy+9pCNH\njkiSYmJiFBsbq7i4OCUmJsrJyUmhoaHKzMw0LWPEiBHauHGjVq9ere3bt+v8+fPq2rWr2TiRkZE6\ncuSI4uPjtWnTJu3YsUMDBgwwTb9z547CwsJ069Yt7dq1S0uXLtWSJUs0adKkR7MhAAAAAAAAAElW\nhmEYD+rUqFEjffPNN3J3d1fDhg1lZWVVYN8DBw78roI8PDw0a9Ys/e1vf5Ovr69GjRqlN998U5KU\nlpYmb29vLVmyRBEREUpLS5Onp6eWL1+ubt26SZKOHTumgIAAJSQkKDg4WEePHlVgYKD27t2rJk2a\nSJK2bNmi9u3b69y5c/L19dXmzZvVoUMHnT9/Xt7e3pKkuLg4jR07VpcvX5atrW2Rak9PT5erq6vS\n0tLk4uJSrPWuPu7LYvUvCadnhD3yMVnP0lMW6wkAAAAAAPL3sDlRkR4w8dJLL8nOzs70vrCw7mHd\nuXNHq1evVkZGhkJCQnTq1CklJyerdevWpj6urq4KCgpSQkKCIiIitH//fmVnZ5v1qVOnjp544glT\nWJeQkCA3NzdTUCdJrVu3lrW1tRITE9WlSxclJCSobt26pqBOkkJDQzVo0CAdOXJEDRs2zLfmrKws\nZWVlmT6np6eX5CYBAAAAAABAOVOksO6dd94xvZ88eXKJFnD48GGFhIQoMzNTzs7OWrdunQIDA7Vr\n1y5JMgvQcj8nJydLkpKTk2Vrays3N7dC+3h5eZlNt7GxkYeHh1mf/MbJnVaQ6dOna8qUKcVdZQAA\nAAAAACBfxb5n3WuvvabvvvuuxAqoXbu2Dh48qMTERA0aNEh9+/bVTz/9VGLLL03jx49XWlqa6XX2\n7NmyLgkAAAAAAAB/YMUO6y5fvqy2bdvKz89Po0eP1qFDh35XAba2tnryySfVuHFjTZ8+XfXr19fc\nuXPl4+MjSXme7Hrx4kXTNB8fH926dUupqamF9rl06ZLZ9Nu3byslJcWsT37j5E4riJ2dnelJtrkv\nAAAAAAAA4GEVO6xbv369Lly4oLffflt79+5Vo0aN9PTTT2vatGk6ffr07y4oJydHWVlZ8vf3l4+P\nj7755hvTtPT0dCUmJiokJESS1LhxY1WsWNGsz/Hjx5WUlGTqExISotTUVO3fv9/UZ9u2bcrJyVFQ\nUJCpz+HDh81Cvfj4eLm4uCgwMPB3rxMAAAAAAABQFEW6Z9393N3dNWDAAA0YMEDnzp3TihUr9NFH\nH2nSpEm6fft2kZczfvx4tWvXTk888YSuXbum5cuX67vvvtPWrVtlZWWl4cOHa+rUqapVq5b8/f31\n9ttvy9fXV507d5Z094ETUVFRGjlypDw8POTi4qIhQ4YoJCREwcHBkqSAgAC1bdtW/fv3V1xcnLKz\nszV48GBFRETI19dXktSmTRsFBgbqlVdeUUxMjJKTkzVx4kRFR0ebHqwBAAAAAAAAlLaHCutyZWdn\na9++fUpMTNTp06fzPKThQS5duqQ+ffrowoULcnV1Vb169bR161b99a9/lSSNGTNGGRkZGjBggFJT\nU9W8eXNt2bJF9vb2pmXMnj1b1tbWCg8PV1ZWlkJDQzV//nyzcT777DMNHjxYrVq1MvWNjY01Ta9Q\noYI2bdqkQYMGKSQkRE5OTurbt6/efffd37F1AAAAAAAAgOKxMgzDKO5M3377rZYvX661a9cqJydH\nXbt2VWRkpF588UVZWVmVRp1/COnp6XJ1dVVaWlqx719XfdyXpVRVwU7PCHvkY7Kepacs1hMAAAAA\nAOTvYXOiYp9Z9/jjjyslJUVt27bVwoUL1bFjRy4VBQAAAAAAAEpAscO6yZMnq3v37nJzcyuNegAA\nAAAAAIByq9hhXf/+/UujDgAAAAAAAKDcsy7rAgAAAAAAAADcRVgHAAAAAAAAWAjCOgAAAAAAAMBC\nENYBAAAAAAAAFuKhwrply5apWbNm8vX11ZkzZyRJc+bM0fr160u0OAAAAAAAAKA8KXZYt2DBAo0c\nOVLt27dXamqq7ty5I0lyc3PTnDlzSrxAAAAAAAAAoLwodlj3j3/8Q4sWLdJbb72lChUqmNqbNGmi\nw4cPl2hxAAAAAAAAQHliU9wZTp06pYYNG+Zpt7OzU0ZGRokUBeCPo/q4Lx/5mKdnhD3yMQEAAAAA\neBSKfWadv7+/Dh48mKd9y5YtCggIKJGiAAAAAAAAgPKo2GfWjRw5UtHR0crMzJRhGNqzZ49WrFih\n6dOna/HixaVRIwAAAAAAAFAuFDuse+211+Tg4KCJEyfqxo0bevnll+Xr66u5c+cqIiKiNGoEAAAA\nAAAAyoVih3WSFBkZqcjISN24cUPXr1+Xl5dXSdcFAAAAAAAAlDsPFdblcnR0lKOjY0nVAgAAAAAA\nAJRrRQrrGjZsKCsrqyIt8MCBA7+rIAAAAAAAAKC8KlJY17lzZ9P7zMxMzZ8/X4GBgQoJCZEk7d69\nW0eOHNEbb7xROlUCAAAAAAAA5UCRwrp33nnH9P61117T0KFD9T//8z95+pw9e7ZkqwMAAAAAAADK\nEevizrB69Wr16dMnT3vv3r21du3aEikKAAAAAAAAKI+KHdY5ODho586dedp37twpe3v7EikKAAAA\nAAAAKI+K/TTY4cOHa9CgQTpw4ICaNm0qSUpMTNRHH32kt99+u8QLBAAAAAAAAMqLYod148aNU40a\nNTR37lx9+umnkqSAgAB9/PHH6tGjR4kXCACWoPq4Lx/5mKdnhD3yMQEAAAAAZavYYZ0k9ejRg2AO\nAAAAAAAAKGEPFdYBAP6cOIMQAAAAAMpWsR8wAQAAAAAAAKB0ENYBAAAAAAAAFoKwDgAAAAAAALAQ\nhHUAAAAAAACAhSjSAyZGjhxZ5AV+8MEHD10MAAAAAAAAUJ4VKaz7/vvvzT4fOHBAt2/fVu3atSVJ\nJ06cUIUKFdS4ceOSrxAAAAAAAAAoJ4oU1n377bem9x988IEqVaqkpUuXyt3dXZL022+/6dVXX1WL\nFi1Kp0oAAAAAAACgHChSWHev999/X19//bUpqJMkd3d3TZ06VW3atNGoUaNKtEAAAEpa9XFfPvIx\nT88Ie+RjAgAAAPjjKfYDJtLT03X58uU87ZcvX9a1a9dKpCgAAAAAAACgPCp2WNelSxe9+uqr+uKL\nL3Tu3DmdO3dOa9euVVRUlLp27VoaNQIAAAAAAADlQrEvg42Li9Obb76pl19+WdnZ2XcXYmOjqKgo\nzZo1q8QLBAAAAAAAAMqLYod1jo6Omj9/vmbNmqVffvlFklSzZk05OTmVeHEAAAAAAABAeVLssC6X\nk5OT6tWrV5K1AACAEsSDNAAAAIA/nocK6/bt26dVq1YpKSlJt27dMpv2xRdflEhhAAAAAAAAQHlT\n7AdMfP7553r22Wd19OhRrVu3TtnZ2Tpy5Ii2bdsmV1fX0qgRAAAAAAAAKBeKfWbdtGnTNHv2bEVH\nR6tSpUqaO3eu/P39NXDgQFWpUqU0agQAACgQl/sCAADgz6TYZ9b98ssvCgu7+z+otra2ysjIkJWV\nlUaMGKGFCxeWeIEAAAAAAABAeVHssM7d3V3Xrl2TJD3++OP68ccfJUmpqam6ceNGyVYHAAAAAAAA\nlCPFvgy2ZcuWio+PV926ddW9e3cNGzZM27ZtU3x8vFq1alUaNQIAAAAAAADlQrHDug8//FCZmZmS\npLfeeksVK1bUrl27FB4erokTJ5Z4gQAAAAAAAEB5UeywzsPDw/Te2tpa48aNK9GCAAAAAAAAgPKq\nSGFdenp6kRfo4uLy0MUAAAAAAAAA5VmRwjo3NzdZWVkVaYF37tz5XQUBAAAAAAAA5VWRwrpvv/3W\n9P706dMaN26c+vXrp5CQEElSQkKCli5dqunTp5dOlQAAAAAAAEA5UKSw7rnnnjO9f/fdd/XBBx+o\nV69eprZOnTqpbt26Wrhwofr27VvyVQIAAAAAAADlgHVxZ0hISFCTJk3ytDdp0kR79uwpkaIAAAAA\nAACA8qjYYZ2fn58WLVqUp33x4sXy8/MrkaIAAAAAAACA8qhIl8Hea/bs2QoPD9fmzZsVFBQkSdqz\nZ49OnjyptWvXlniBAAAAAAAAQHlR7DPr2rdvrxMnTqhjx45KSUlRSkqKOnbsqBMnTqh9+/alUSMA\nAAAAAABQLhT7zDrp7qWw06ZNK+laAAAAAAAAgHKtSGHdDz/8oGeeeUbW1tb64YcfCu1br169EikM\nAAAAAP4/9u48Pqa7////cyIilizWLJbYl9i3ErR2QWoptZS2lpTqhVraWmqtLlrfiyrShovaqpaq\n7r3sSgna0ialiCjScomS2kKE5Pz+8Mt8TBMqNXEmJ4/77Ta3K3POSef5lmtmzrzmfV5vAABym3sq\n1tWpU0dnzpxRiRIlVKdOHdlsNhmGkeE4m82m1NRUp4cEAAAAAAAAcoN7KtYdP35cxYsXt/8MAAAA\nAAAAwPnuqVgXFBRk//nkyZNq0qSJ3N0df/XmzZuKiopyOBYAAAAAAADAvcvyarAtW7ZUYmJihu0X\nL15Uy5YtnRIKAAAAAAAAyI2yXKwzDEM2my3D9vPnz6tgwYJOCQUAAAAAAADkRvd0GawkdevWTdKt\nRST69++vfPny2felpqYqJiZGTZo0cX5CAAAAAAAAIJe452Kdj4+PpFsz67y8vJQ/f377Pg8PDzVu\n3FiDBg1yfkIAAAAAAAAgl7jny2AXL16sxYsXa8qUKVq0aJH9/uLFizV//nyNHz9exYoVy9KDT58+\nXQ0bNpSXl5dKlCihrl276siRIw7HGIahyZMnKyAgQPnz51ebNm109OhRh2OSk5M1dOhQFS1aVIUK\nFVL37t2VkJDgcExiYqL69u0rb29v+fr6Kjw8XFeuXHE4Jj4+XmFhYSpQoIBKlCihl156STdv3szS\nmAAAAAAAAIB/Kss966ZMmeK03nTbt2/X0KFDtWfPHm3atEk3btxQu3btlJSUZD9mxowZmjNnjiIj\nI7V3714VLFhQoaGhSk5Oth8zatQoffHFF/roo4+0fft2nT592n7Zbrq+ffvq4MGD2rRpk7788kvt\n2LFDgwcPtu9PTU1VWFiYUlJSFBUVpaVLl2rJkiWaPHmyU8YKAAAAAAAA/J0sF+sSEhL01FNPKTAw\nUO7u7sqTJ4/DLSvWr1+v/v37q3r16qpdu7aWLFmi+Ph47du3T9KtWXWzZ8/WxIkT1aVLF9WqVUvL\nli3T6dOn9emnn0q6tQrtokWLNGvWLLVq1Ur169fX4sWLFRUVpT179kiSDh06pPXr12vhwoVq1KiR\nmjVrprlz52rVqlU6ffq0JGnjxo365Zdf9MEHH6hOnTrq0KGDXn31VUVERCglJSWr/0wAAAAAAABA\nlt1zz7p0/fv3V3x8vCZNmqSAgIBMV4b9py5evChJKlKkiCTp+PHjOnPmjNq0aWM/xsfHR40aNdLu\n3bvVu3dv7du3Tzdu3HA4pmrVqipTpox2796txo0ba/fu3fL19VWDBg3sx7Rp00Zubm7au3evHnvs\nMe3evVs1a9aUn5+f/ZjQ0FA999xzOnjwoOrWreu0cQIAAGRV2XFfPfDHPPFm2AN/TAAAgNwuy8W6\nnTt36ttvv1WdOnWcGiQtLU0jR45U06ZNVaNGDUnSmTNnJMmhgJZ+P33fmTNn5OHhIV9f37seU6JE\nCYf97u7uKlKkiMMxmT3O7Tn+6vr167p+/br9/qVLl+59wAAAAMiAoiQAAMjtsnwZbOnSpWUYhtOD\nDB06VAcOHNCqVauc/t/OLtOnT5ePj4/9Vrp0abMjAQAAAAAAIAfLcrFu9uzZGjdunE6cOOG0EMOG\nDdOXX36pbdu2qVSpUvbt/v7+kpRhZdeEhAT7Pn9/f6WkpOjChQt3Pebs2bMO+2/evKnExESHYzJ7\nnNtz/NX48eN18eJF++23337L0rgBAAAAAACA22W5WNerVy998803qlChgry8vFSkSBGHW1YYhqFh\nw4bpk08+0datW1WuXDmH/eXKlZO/v7+2bNli33bp0iXt3btXISEhkqT69esrb968DsccOXJE8fHx\n9mNCQkJ04cIF+8IVkrR161alpaWpUaNG9mN+/vlnh6Lepk2b5O3treDg4Ezz58uXT97e3g43AAAA\nACA6W9sAACAASURBVAAA4J/Kcs+62bNnO+3Bhw4dqg8//FCfffaZvLy87L3hfHx8lD9/ftlsNo0c\nOVKvvfaaKlWqpHLlymnSpEkKDAxU165d7ceGh4dr9OjRKlKkiLy9vTV8+HCFhISocePGkqRq1aqp\nffv2GjRokCIjI3Xjxg0NGzZMvXv3VmBgoCSpXbt2Cg4O1lNPPaUZM2bozJkzmjhxooYOHap8+fI5\nbcwAAAAAAADAnWS5WNevXz+nPfh7770nSWrRooXD9sWLF6t///6SpDFjxigpKUmDBw/WhQsX1KxZ\nM61fv16enp72499++225ubmpe/fuun79ukJDQ/Xuu+86/DdXrFihYcOGqXXr1vZj58yZY9+fJ08e\nffnll3ruuecUEhKiggULql+/fpo2bZrTxgsAAAAAAADcTZaLdbdLTk5WSkqKw7asXAp6LwtV2Gw2\nTZs27a5FM09PT0VERCgiIuKOxxQpUkQffvjhXR8rKChIX3/99d9mAgAAAAAAALJDlnvWJSUladiw\nYSpRooQKFiyowoULO9wAAAAAAAAA/DNZLtaNGTNGW7du1Xvvvad8+fJp4cKFeuWVVxQYGKhly5Zl\nR0YAAAAAAAAgV8jyZbBffPGFli1bphYtWmjAgAF6+OGHVbFiRQUFBWnFihXq27dvduQEAAAAAAAA\nLC/LM+sSExNVvnx5Sbf60yUmJkqSmjVrph07djg3HQAAAAAAAJCLZLlYV758eR0/flySVLVqVa1Z\ns0bSrRl3vr6+zk0HAAAAAAAA5CJZvgx2wIABio6OVvPmzTVu3Dh16tRJ8+bN040bNzRr1qzsyAgA\nAABYTtlxXz3wxzzxZtgDf0wAAJA1WS7WjRo1yv5zmzZtdPjwYe3bt08VK1ZUrVq1nBoOAAAAQM5G\nURIAgKzJcrHur4KCghQUFOSMLAAAAAAAAECuds8967Zu3arg4GBdunQpw76LFy+qevXq2rBhg1PD\nAQAAAAAAALnJPRfrZs+erUGDBsnb2zvDPh8fHz377LOaO3euU8MBAAAAAAAAuck9F+uio6PVvn37\nO+5v166dYmJinBIKAAAAAAAAyI3uuViXkJCgvHnz3nG/u7u7/vjjD6eEAgAAAAAAAHKjey7WlSxZ\nUgcOHLjj/piYGAUEBDglFAAAAAAAAJAb3XOxrmPHjpo0aZKSk5Mz7Lt27ZqmTJmiRx991KnhAAAA\nAAAAgNzE/V4PnDhxotatW6fKlStr2LBhqlKliiTp8OHDioiIUGpqqiZMmJBtQQEAAAAAAACru+di\nnZ+fn6KiovTcc89p/PjxMgxDkmSz2RQaGqqIiAj5+fllW1AAAAAAAADA6u65WCdJQUFB+vrrr/Xn\nn38qLi5OhmGoUqVKKly4cHblAwAAAAAAAHKNLBXr0hUuXFgNGzZ0dhYAAAAAAAAgV7vnBSYAAAAA\nAAAAZC+KdQAAAAAAAICLoFgHAAAAAAAAuIh/1LMOAAAAAPB/yo776oE/5ok3wx74YwIAsh8z6wAA\nAAAAAAAXQbEOAAAAAAAAcBFcBgsAAAAAuCdc7gsA2Y+ZdQAAAAAAAICLYGYdAAAAAAC3YQYhADMx\nsw4AAAAAAABwERTrAAAAAAAAABdBsQ4AAAAAAABwERTrAAAAAAAAABdBsQ4AAAAAAABwERTrAAAA\nAAAAABfhbnYAAAAAAADw4JUd99UDf8wTb4Y98McEchpm1gEAAAAAAAAugmIdAAAAAAAA4CIo1gEA\nAAAAAAAugmIdAAAAAAAA4CJYYAIAAAAAAFhWbllII7eMMzdgZh0AAAAAAADgIijWAQAAAAAAAC6C\nYh0AAAAAAADgIuhZBwAAAAAAgBzD6v35mFkHAAAAAAAAuAiKdQAAAAAAAICLoFgHAAAAAAAAuAiK\ndQAAAAAAAICLoFgHAAAAAAAAuAiKdQAAAAAAAICLoFgHAAAAAAAAuAiKdQAAAAAAAICLoFgHAAAA\nAAAAuAiKdQAAAAAAAICLoFgHAAAAAAAAuAiKdQAAAAAAAICLoFgHAAAAAAAAuAiKdQAAAAAAAICL\noFgHAAAAAAAAuAiKdQAAAAAAAICLoFgHAAAAAAAAuAiKdQAAAAAAAICLoFgHAAAAAAAAuAiKdQAA\nAAAAAICLoFgHAAAAAAAAuAhTi3U7duxQp06dFBgYKJvNpk8//dRhv2EYmjx5sgICApQ/f361adNG\nR48edTgmOTlZQ4cOVdGiRVWoUCF1795dCQkJDsckJiaqb9++8vb2lq+vr8LDw3XlyhWHY+Lj4xUW\nFqYCBQqoRIkSeumll3Tz5s3sGTgAAAAAAACQCVOLdUlJSapdu7YiIiIy3T9jxgzNmTNHkZGR2rt3\nrwoWLKjQ0FAlJyfbjxk1apS++OILffTRR9q+fbtOnz6tbt26Ofx3+vbtq4MHD2rTpk368ssvtWPH\nDg0ePNi+PzU1VWFhYUpJSVFUVJSWLl2qJUuWaPLkydkzcAAAAAAAACAT7mY+eIcOHdShQ4dM9xmG\nodmzZ2vixInq0qWLJGnZsmXy8/PTp59+qt69e+vixYtatGiRPvzwQ7Vq1UqStHjxYlWrVk179uxR\n48aNdejQIa1fv17ff/+9GjRoIEmaO3euOnbsqH//+98KDAzUxo0b9csvv2jz5s3y8/NTnTp19Oqr\nr2rs2LGaOnWqPDw8Hsw/CAAAAAAAAHI1l+1Zd/z4cZ05c0Zt2rSxb/Px8VGjRo20e/duSdK+fft0\n48YNh2OqVq2qMmXK2I/ZvXu3fH197YU6SWrTpo3c3Ny0d+9e+zE1a9aUn5+f/ZjQ0FBdunRJBw8e\nzNZxAgAAAAAAAOlMnVl3N2fOnJEkhwJa+v30fWfOnJGHh4d8fX3vekyJEiUc9ru7u6tIkSIOx2T2\nOLfnyMz169d1/fp1+/1Lly7d8/gAAAAAAACAv3LZmXU5wfTp0+Xj42O/lS5d2uxIAAAAAAAAyMFc\ntljn7+8vSRlWdk1ISLDv8/f3V0pKii5cuHDXY86ePeuw/+bNm0pMTHQ4JrPHuT1HZsaPH6+LFy/a\nb7/99ltWhwkAAAAAAADYuWyxrly5cvL399eWLVvs2y5duqS9e/cqJCREklS/fn3lzZvX4ZgjR44o\nPj7efkxISIguXLigffv22Y/ZunWr0tLS1KhRI/sxP//8s0NRb9OmTfL29lZwcPAdM+bLl0/e3t4O\nNwAAAAAAAOCfMrVn3ZUrVxQXF2e/f/z4cf30008qUqSIypQpo5EjR+q1115TpUqVVK5cOU2aNEmB\ngYHq2rWrpFsLToSHh2v06NEqUqSIvL29NXz4cIWEhKhx48aSpGrVqql9+/YaNGiQIiMjdePGDQ0b\nNky9e/dWYGCgJKldu3YKDg7WU089pRkzZujMmTOaOHGihg4dqnz58j34fxgAAAAAAADkSqYW6374\n4Qe1bNnSfn/06NGSpH79+mnJkiUaM2aMkpKSNHjwYF24cEHNmjXT+vXr5enpaf+dt99+W25ubure\nvbuuX7+u0NBQvfvuuw6Ps2LFCg0bNkytW7e2Hztnzhz7/jx58ujLL7/Uc889p5CQEBUsWFD9+vXT\ntGnTsvlfAAAAAAAAAPg/phbrWrRoIcMw7rjfZrNp2rRpdy2aeXp6KiIiQhEREXc8pkiRIvrwww/v\nmiUoKEhff/3134cGAAAAAAAAsonL9qwDAAAAAAAAchuKdQAAAAAAAICLoFgHAAAAAAAAuAiKdQAA\nAAAAAICLoFgHAAAAAAAAuAiKdQAAAAAAAICLoFgHAAAAAAAAuAiKdQAAAAAAAICLoFgHAAAAAAAA\nuAiKdQAAAAAAAICLoFgHAAAAAAAAuAiKdQAAAAAAAICLoFgHAAAAAAAAuAiKdQAAAAAAAICLoFgH\nAAAAAAAAuAiKdQAAAAAAAICLoFgHAAAAAAAAuAiKdQAAAAAAAICLoFgHAAAAAAAAuAiKdQAAAAAA\nAICLoFgHAAAAAAAAuAiKdQAAAAAAAICLoFgHAAAAAAAAuAiKdQAAAAAAAICLoFgHAAAAAAAAuAiK\ndQAAAAAAAICLoFgHAAAAAAAAuAiKdQAAAAAAAICLoFgHAAAAAAAAuAiKdQAAAAAAAICLoFgHAAAA\nAAAAuAiKdQAAAAAAAICLoFgHAAAAAAAAuAiKdQAAAAAAAICLoFgHAAAAAAAAuAiKdQAAAAAAAICL\noFgHAAAAAAAAuAiKdQAAAAAAAICLoFgHAAAAAAAAuAiKdQAAAAAAAICLoFgHAAAAAAAAuAiKdQAA\nAAAAAICLoFgHAAAAAAAAuAiKdQAAAAAAAICLoFgHAAAAAAAAuAiKdQAAAAAAAICLoFgHAAAAAAAA\nuAiKdQAAAAAAAICLoFgHAAAAAAAAuAiKdQAAAAAAAICLoFgHAAAAAAAAuAiKdQAAAAAAAICLoFgH\nAAAAAAAAuAiKdQAAAAAAAICLoFgHAAAAAAAAuAiKdQAAAAAAAICLoFgHAAAAAAAAuAiKdQAAAAAA\nAICLoFgHAAAAAAAAuAiKdQAAAAAAAICLoFgHAAAAAAAAuAiKdQAAAAAAAICLoFgHAAAAAAAAuAiK\ndQAAAAAAAICLoFj3FxERESpbtqw8PT3VqFEjfffdd2ZHAgAAAAAAQC5Bse42q1ev1ujRozVlyhTt\n379ftWvXVmhoqM6ePWt2NAAAAAAAAOQCFOtuM2vWLA0aNEgDBgxQcHCwIiMjVaBAAb3//vtmRwMA\nAAAAAEAu4G52AFeRkpKiffv2afz48fZtbm5uatOmjXbv3p3p71y/fl3Xr1+337948aIk6dKlS1l+\n/LTrV7P8O/frn+S8X4wz+5gxTin3jJVxZh/GmX0YZ/ZhnNknt4xTyj1jZZzZh3FmH8aZfRhn9skt\n45RyzljTf8cwjCz9ns3I6m9Y1OnTp1WyZElFRUUpJCTEvn3MmDHavn279u7dm+F3pk6dqldeeeVB\nxgQAAAAAAEAO8ttvv6lUqVL3fDwz6+7D+PHjNXr0aPv9tLQ0JSYmqmjRorLZbA8kw6VLl1S6dGn9\n9ttv8vb2fiCPaQbGaS2M01oYp/XklrEyTmthnNbCOK0lt4xTyj1jZZzWwjizj2EYunz5sgIDA7P0\nexTr/n/FihVTnjx5lJCQ4LA9ISFB/v7+mf5Ovnz5lC9fPodtvr6+2Zbxbry9vS39pErHOK2FcVoL\n47Se3DJWxmktjNNaGKe15JZxSrlnrIzTWhhn9vDx8cny77DAxP/Pw8ND9evX15YtW+zb0tLStGXL\nFofLYgEAAAAAAIDswsy624wePVr9+vVTgwYN9NBDD2n27NlKSkrSgAEDzI4GAAAAAACAXCDP1KlT\np5odwlXUqFFDvr6+ev311/Xvf/9bkrRixQpVqVLF5GR3lydPHrVo0ULu7tauvTJOa2Gc1sI4rSe3\njJVxWgvjtBbGaS25ZZxS7hkr47QWxulaWA0WAAAAAAAAcBH0rAMAAAAAAABcBMU6AAAAAAAAwEVQ\nrAMAAAAAAABcBMU6AAAAAAAAwEVQrINL+/PPP3Xq1Cldv37d7CjZ5saNG/rtt9905MgRJSYmmh0n\nW8XFxWnDhg26du2aJIn1beDKli1blulrT0pKipYtW2ZCoux18+ZNbd68WfPnz9fly5clSadPn9aV\nK1dMTuZ88fHx+uGHH/Tdd9853KwkLS1NsbGx2rlzp3bs2OFwQ85y4MCBO+779NNPH2CS7OHh4aE/\n/vjD7BjAPxYfH5/pOa1hGIqPjzchUfbIbedFgNlYDTYHiY+P1+rVq/Xtt9/q5MmTunr1qooXL666\ndesqNDRUYWFhypMnj9kx78v169e1Zs0arVq1Srt27bJ/YJSkqlWrql27dho0aJCCg4NNTHn/Ll++\nrA8++ECrVq3Sd999p5SUFBmGIZvNplKlSqldu3YaPHiwGjZsaHZUpzh//rx69eqlrVu3ymaz6ejR\noypfvrwGDhyowoULa+bMmWZHdJrY2FiH52etWrVUqFAhs2PdtwULFtzzsYMHD87GJA9Onjx59L//\n/U8lSpRw2H7+/HmVKFFCqampJiVzvpMnT6p9+/aKj4/X9evXFRsbq/Lly2vEiBG6fv26IiMjzY7o\nFPv27VPfvn119OjRDB+sbDabZf6me/bsUZ8+fXTy5ElLj1OSLly4oO+++05nz55VWlqaw76nn37a\npFTOVbJkSe3cuVPlypVz2P7xxx/r6aefVlJSkknJnMPNzU1nzpzJ8FprZcuXL1dkZKSOHz+u3bt3\nKygoSLNnz1a5cuXUpUsXs+M5hWEYWrt2rbZt25bp83PdunUmJXO+3HK+kBvGaRiGdu7cmenn7dat\nWyswMNDsiE71/fff3/E5OmvWLJNS3b+YmJh7PrZWrVrZmOT+uJsdAH/v8OHDGjdunL7++mvVrVtX\nDz30kEJCQpQ/f34lJibqwIEDGjx4sPLkyaOXX35ZQ4YMyZFFuwULFmjKlCkqXLiwOnXqpIEDByow\nMNBhnN9++60aNmyoDh06aObMmQoKCjI7dpbNmjVLr7/+uipUqKBOnTrp5ZdfznSc7dq1U6NGjTR3\n7lxVqlTJ7Nj3ZdSoUXJ3d1d8fLyqVatm396rVy+NHj06xxfrjhw5onfffVerV6/W2bNnJd06oUlN\nTVWePHnUuHFjDR48WE888YTc3XPmy+6UKVMc7l+8eFHJycnKnz+/JOnatWvy9PSUr6+vZYp16QX0\nv/r999/l4+NjQqLsM2LECDVo0EDR0dEqWrSofftjjz2mQYMGmZjMuQYNGqSqVatq5cqVCggIyPTv\nawVDhgxRgwYN9NVXX1l6nF988YX69u2rK1euyNvb22GcNpvNMsW6Z555Rm3atNGuXbvk7+8vSVq9\nerUGDhyoJUuWmBsOWfbee+9p8uTJGjlypF5//XV7gcPX11ezZ8+2TLFu5MiRmj9/vlq2bCk/Pz/L\nvg5Jdz5fuHLlijw9PU1IlD2sfF6UnJysd955RxEREUpISFCtWrXsn88OHDhgP8fv0KGDJk+erAYN\nGpgd+b698cYbmjhxoqpUqZLhOZrTn6916tSRzWa74/9nb+fKRWZm1uUAJUuW1IgRI/TUU08pICAg\n02PS0tK0ZcsWvfPOO2ratKnGjx//gFPev06dOmnixIlq1KjRXY+7dOmSFixYoEKFCmnIkCEPKJ3z\nPPHEE5o4caKqV69+1+OuX7+uxYsXy8PDQwMHDnxA6bKHv7+/NmzYoNq1a8vLy0vR0dEqX768fv31\nV9WqVStHX2Y3ePBgffTRR+rUqZM6deqkhx56SIGBgcqbN68uXbpkL76uXLlSV69e1dKlSxUSEmJ2\n7Puydu1azZw5U5GRkapdu7YkKTo6Wv/61780cuRI9ejRw+SE96du3bqy2WyKjo5W9erVHQqsqamp\nOn78uNq3b681a9aYmNK5ihYtqqioKFWpUsXhOXrixAkFBwfr6tWrZkd0ioIFCyo6OloVK1Y0O0q2\nyi3jrFy5sjp27Kg33nhDBQoUMDtOtho+fLi2bdumHTt2aP369XrmmWe0fPlyde/e3exo983NzU1v\nvvnm385C/9e//vWAEmWv4OBgvfHGG+ratavD6+2BAwfUokULnTt3zuyITlGkSBF98MEH6tixo9lR\nss3o0aMlSe+8844GDRrk8DqUmpqqvXv3Kk+ePNq1a5dZEZ0iN5wXlSlTRg0aNFD//v0VGhqqfPny\nZTgmLi5OK1eu1IIFCzR16lSFh4ebkNR5/Pz89NZbb6l///5mR3G6kydP2n/+8ccf9eKLL+qll16y\nfwbbvXu3Zs6cqRkzZqhr165mxfxbOXOKRy4TFxdnn71yJ25ubmrbtq3atm1r7weW03zxxRf3dJy3\nt7defPHFbE6TfVauXHlPx+XLly9HFiMzk5SUlOkHqcTExEzfDHOSkiVL6sSJE5l+o+jt7a0mTZqo\nSZMmGjt2rL755htL9OUZP368Vq5caS/USVLt2rU1e/Zs9e7dO8cX69LftH/66SeFhoY6fID08PBQ\n2bJlLfEB+XZpaWmZfrP4+++/y8vLy4RE2aN+/fo6ceKE5YtYjRo1UlxcnOXHeerUKT3//POWL9RJ\n0ty5c9W3b181btxYp06d0sqVKy0zA0u6Vexwc7tzK22bzWaZYt3x48dVt27dDNvz5cuX4y9pvp2P\nj4/Kly9vdoxs9eOPP0q6NePs559/loeHh32fh4eHateunaM/s6TLDedFX3/9tWrUqHHXYypWrKhJ\nkyZp7NixDsWgnMrNzU1NmzY1O0a2uP3qux49emjOnDkOXxzUqlVLpUuX1qRJk1y6WMfMOgDZrmPH\njqpfv75effVVeXl5KSYmRkFBQerdu7fS0tK0du1asyMiC/Lnz69du3apXr16Dtt/+OEHPfLII5aZ\nhbV06VL16tXLUpew3EmvXr3k4+OjBQsW2J+jxYsXV5cuXVSmTBktXrzY7Ij/WGxsrP3nI0eOaOLE\niRo/frxq1qypvHnzOhxbuXLlBx3PaW7vz3Ls2DFNnDhRL730UqbjdOX+LFnRrVs39e7dWz179jQ7\nitN9/vnnGbbduHFDo0aNUrt27dS5c2f79tt/zolyW8+64OBgTZ8+XV26dHGYWTd37lwtXrxY+/fv\nNzuiUyxdulTr16/X+++//7eTDnK6AQMG6J133pG3t7fZUbJVbjkvOn36dKa96QzD0P/+9z/L9K2b\nMWOGTp8+rdmzZ5sdJVvlz59f+/fvd2jFJEmHDh1SvXr1XHqiE8W6HGjv3r3asmVLpo0g58yZY1Kq\n+5eV3jJWWXHosccey/Q6epvNJk9PT1WsWFF9+vRRlSpVTEjnPAcOHFDr1q1Vr149bd26VZ07d9bB\ngweVmJioXbt2qUKFCmZHdJqUlBRt3LhRx44d04ABA+Tt7a0TJ07I19dXvr6+Zsdzio4dO+rcuXNa\nsmSJfbGXgwcPauDAgSpatKi+/vprkxM6z4ULF7R27VodO3ZML730kooUKaL9+/fLz89PJUuWNDue\n0/z+++8KDQ2VYRg6evSoGjRooKNHj6pYsWLasWNHjv4Q7ebmZn+dvf2U5/bX3vSeJq7ct+TvpI/z\nTqd1t/duycnjvL2I9ccff2jatGkaMGBApkXJnFzEutsss9vl9L+ndOem9Va1cOFCTZ06VTNnzlR4\neLgWLlyoY8eOafr06Vq4cKF69+5tdkSnuHbtmh577DHt2rVLZcuWzfD8tEpR8nZxcXE6duyYHnnk\nEeXPn/+e+mXlRCkpKZl+Di1TpoxJiZwrNyykId26qiIsLEyxsbEKDg7O8By1yiIw9erVU40aNbRw\n4UL77NeUlBQ988wzOnDggEu/FnEZbA7z//7f/9PYsWNVrVq1DE2jc/qbwV+r2hs3blS+fPlUp04d\nSbemXqekpKhdu3ZmxMsWPj4++vTTT+Xr66v69etLunXycuHCBbVr106rV6/WW2+9pS1btuToaco1\natRQbGys5s2bJy8vL125ckXdunXT0KFD79iHMSc6duyYQkNDdf78eV2+fFmdOnWSt7e35syZoytX\nrmRpRVVXtmjRIvXt21c1atRQwYIFJUlXr15V8+bNtWjRIpPTOU9MTIzatGkjHx8fnThxQoMGDVKR\nIkW0bt06xcfHW+ZLA0kqVaqUoqOjtWrVKsXExOjKlSsKDw9X3759c/yMiEOHDpkd4YE4fvy42REe\niMwuV5k2bVqGbTm9iPXXD8FWltvmDTzzzDPKnz+/Jk6cqKtXr6pPnz4KDAzUO++8Y5lCnST169dP\n+/bt05NPPmn5BSYSExPVo0cPbdu2TTabTUePHlX58uUVHh6uwoUL5/iF1NIdPXpUAwcOVFRUlMN2\nK3wRdLs7vSYlJSVZalbh888/r23btqlly5YqWrSoZZ+jkZGR6tSpk0qVKmW/siAmJkY2m+2e23CZ\nhZl1OUypUqU0efJky6y2eCdTp05VXFycFi1aZO9plpycrMGDB6ts2bKZnpjnRBMmTNCff/6pefPm\n2b9FT0tL04gRI1SoUCG98cYbGjJkiA4ePKidO3eanBZ/59FHH5Wfn5/mz5+vwoUL2y9t2bFjhwYO\nHKi4uDizIzpVTEyMDh8+LEmqVq2aatasaXIi52rdurXq16+vGTNmOFyqFBUVpT59+ujEiRNmR0QW\nfffdd6pfv36GFdNTU1O1b98+PfTQQyYlc64dO3aoSZMmGVafvnnzpqKiovTII4+YlAxZdePGDbVv\n316RkZE5fmX4O5k0aZLGjx+fK3oP/tXVq1d15coVS84qLFiwoDZs2KBmzZqZHSXbPf300zp79qwW\nLlyoatWq2c8XNmzYoNGjR+vgwYNmR3SKpk2byt3dXePGjct0pfHbexnnRGPGjJEkzZw5U88991yG\nBUP27NkjwzAyFCtzKi8vL61atUphYWFmR8l2SUlJWrFihcPnlj59+tgnHbgqinU5TOHChfXDDz9Y\n6rLBzPj5+Wn79u2qWrWqw/bDhw/rkUce0dmzZ01K5lwlSpTQzp07M/RJio2NVZMmTXTu3Dn9/PPP\nevjhh3XhwgWTUjpHcnKyYmJiMp02n5MvVbpdkSJFtGfPHlWuXNnSK2rmFj4+Ptq/f78qVKjg8Pc8\nefKkqlSpouTkZLMjOtXp06e1c+fOTJ+jzz//vEmpnCu3XNqSW8a5bNky9erVK8NCRSkpKVq1alWW\n2mu4suLFiysqKsqyxbq/s27dOk2dOtWhLyNcX9WqVbVmzRrL9Mi8G39/f23YsEG1a9d2OF/49ddf\nVatWLV25csXsiE5RsGBB7du3L8PnM6t4+OGHJUm7du3SQw895HBZaPpCGmPGjMnx7YnSBQUFacOG\nDZb9e0q3vvB69tlnNWnSJJUrV87sOFnGZbA5zNNPP61PPvnEEisL3U1ycrKOHz+e4cXj119/1fXr\n101K5Xw3btzQ4cOHMxTrDh8+bP8w5enpmeOnJa9fv15PP/20zp07l2GflabN22w2paSkZNge/QoD\nCAAAIABJREFUHx+f6WqxOZVhGPrwww/v2DvTKj3r8uXLp0uXLmXYHhsbq+LFi5uQKPssWbJEzz77\nrDw8PDJcCmGz2SxTrLtT/6A///zTUrN67jTO8+fPu/y3yFkxYMAAtW/fPkNR8vLlyxowYIBlinVP\nPvmkFi1apDfffNPsKNlm/vz52rRpkzw8PDRixAg1atRIW7du1QsvvKDY2FjL/C0lqVy5cnc9r/v1\n118fYJrsM3PmTI0ZM0aRkZEqW7as2XGyVVJSUqbvIYmJiRm+TMjJgoODMz2Xt4pvv/1WkvTUU08p\nIiLC8guGTJ06VVOmTNHixYstdQ50u7x58+rjjz/WpEmTzI7yj1CsywEmT55s/zl//vx64403tG3b\nNtWqVStDI0irXB7at29fDRgwQNOmTbNflrR371698sor6tu3r8npnOepp55SeHi4Xn75ZTVs2FCS\n9P333+uNN96wn5hu375d1atXNzPmfRs+fLh69OihyZMny8/Pz+w42SY0NFQzZ860r5xps9l04cIF\nTZo0SY8++qjJ6Zxn9OjRioyMVLt27VSqVKkcX0y+k86dO2vatGlas2aNpFt/z/j4eI0dO1bdu3c3\nOZ1zTZo0SZMnT9b48ePvubF9TtKnTx9Jt/6Gzz77rEPPmdTUVP30009q3LixWfGcplu3bpJujbN/\n//4OHxJTU1MVExOjJk2amBXP6e5UlPz9998t9QXJzZs39f7772vz5s2qX79+hoLrrFmzTErmHG++\n+aYmT56s2rVr69ChQ/rss880YcIEzZ07VyNGjNCzzz6rwoULmx3TaUaOHOlw/8aNG/rxxx+1fv16\nvfTSSyalcr4nn3xSV69eVYUKFVSgQIEMn1kSExNNSuZ8Dz/8sJYtW6ZXX31V0q3X4LS0NM2YMUMt\nW7Y0OZ3zvPXWWxozZozeeOONTBf1sUpxa/ny5ZKkEydO6NixY2ratKmletWlmzNnjo4dOyY/Pz9L\nLwLTtWtXffrppxo1apTZUbKMYl0O8N///tfhfoUKFXT27Flt3rzZYbvNZrNMsW7OnDkqVqyYxo4d\nq4sXL0q69Qbw/PPP59jKeGbefvtt+fn5acaMGUpISJB06xLgUaNGaezYsZKkdu3aqX379mbGvG8J\nCQkaPXq0pQt10q1vkTt27KgKFSooOTlZjz/+uI4dO6YyZcpYakbEihUrtHr1astcvnwnM2fO1OOP\nP64SJUro2rVrat68uc6cOaOQkBC9/vrrZsdzqqtXr6p3796WLNRJ/9cs2jAM+y2dp6enevfureee\ne86seE6TXqAyDENeXl4Oi4N4eHiocePGGjRokFnxnKZu3bqy2Wyy2Wxq3bq1Q2++1NRUHT9+PMe/\nb97uwIEDqlevnqRbM3tvZ4UvSxYvXqz//Oc/6tevn7799ls1b95cUVFRiouLs9RM0HQjRozIdHtE\nRIR++OGHB5wm+8yePdvsCA/MjBkz1Lp1a/3www9KSUnRmDFjdPDgQSUmJmrXrl1mx3OaNm3aSLrV\n0/d2Vltg4s8//1Tv3r21adMmhwVD+vfvr2LFiunf//632RGdIrMFm6yoUqVKmjZtmnbt2pXpF16u\nfPUIPevg8k6fPi1JCgwMNDlJ9kq/3M4q30rdbuDAgWratKnCw8PNjpLt0tLStG7dOvuKmvXq1VPP\nnj3tS4Vbgb+/v7799ttc0z9p165dio6Otv89009WrWTMmDEqUqSIxo0bZ3aUbDV+/HhNnDjRkgWA\n273yyit68cUXLTvOV155xf6/L7zwggoVKmTfl95XqHv37pZ63bWy/PnzKzY2VqVLl5Z0qwVBVFSU\n6tevb3KyB+vXX39VnTp1Mm2/ANd38eJFzZs3z+F8YejQoQoICDA7mtNs3779rvubN2/+gJJkr/79\n++vUqVP6z3/+o5o1a9p7EK5fv14vvPCCZRYMyS3u1qvOZrO5dOsBinU5zPPPP6/p06dnOAG/evWq\nxo0bpzlz5piUDLizq1evqkePHipevHim0+Zd+RsNZDR9+nSdPXtWb7/9ttlRstXvv/+uUqVKZbpv\nz549lrhsMl1qaqoeffRRXbt2LdPnaE6/zC63Su/xaqWeSbdbunSpevXqZcnLk3ITNzc3JSQk2HuB\nenl5KSYmJkc2A78fM2bM0LvvvmuplcZTU1P16aef6tChQ5Kk6tWrq3PnzhlW5M7pbty4keF9M925\nc+dUrFixB5wI9yMgIED//e9/VadOHUsvGJJu3759Ds/RunXrmpwI6SjW5TB3WuHt3Llz8vf3182b\nN01K5lyJiYmaMGHCHRvYW+Vbx4SEBL344ov2cf716WiV6eSLFi3SkCFD5OnpmWnzelf+RuPvpPcz\nuxc9e/bMxiQPzhNPPKH169fLz88v08LOhx9+aFIy5woODtbOnTtVpEgRh+27du1SWFhYjl+h+Xav\nvfaaJk+erCpVqsjPzy/Dc3Tr1q0mprt/DRs2VM+ePdWvX78M759Ws2nTJr399tvavXu3w4ztkJAQ\njR492pIzQ9MtWbJEjz32mKX61aX74YcftGbNGsXHx2dYyGjdunUmpXIONzc3DR482N7gPCIiQk8+\n+WSGv6NVvjRIv4w7nWEYOnPmjP744w+9++67Gjx4sInpnCcuLk4dO3bUqVOn7KtnHjlyRKVLl9ZX\nX32lChUqmJzQebp37661a9dmuCw9ISFBrVu31oEDB0xKlj2uXr2a6WuRVVb+LVSokH766SdVrFjR\noVi3b98+tW3b1jL9Fs+ePavevXvrm2++ka+vryTpwoULatmypVatWmW5xdRyIop1OURaWpoMw1De\nvHl1+vRphw8bqamp+vzzzzV06FCdOXPGxJTO06VLFx08eFDPPvusAgICMrz5WWWRiQ4dOig+Pl7D\nhg3LdJxdunQxKZlz+fv76/nnn9e4ceMs1xPr9p5Qd2Oz2XT16tVsTvNgPPHEE3fdv3LlygeUJHsN\nHDhQMTEx2rZtm7y8vCRJO3bsUKdOnTR16tQc2aj2TgoXLqy3335b/fv3NztKtnBzc5OXl5euXbum\njh07atCgQerQoYPlXo+WLl2qZ555Ro8//rhCQ0PtfUITEhK0ceNGrV27VosWLdJTTz1lctLs4eHh\noejoaFWrVs3sKE61atUqPf300woNDdXGjRvVrl07xcbGKiEhQY899ph9UaOcqkWLFvfUe2/btm0P\nIE32S7+MO52bm5uKFy+uFi1aqGrVqialcr6OHTvKMAytWLHC/qXX+fPn9eSTT8rNzU1fffWVyQmd\np2HDhqpVq5YWLVpk3/a///1PrVq1UvXq1bV27VoT0znPH3/8oQEDBmTop57OKpMM2rdvr8aNG2vq\n1Kn2mb5ly5ZVnz59lJKSoo8//tjsiE7Rq1cv/frrr1q2bJn9ffOXX35Rv379VLFiRcucz0u3rpb5\n/PPPMy0yu/QXQQZyBJvNZri5ud319vLLL5sd02m8vLyM77//3uwY2a5QoULGjz/+aHaMbFe4cGEj\nLi7O7BhAlqSmphqPPfaY0bx5cyM5OdnYunWrUahQIWP27NlmR3M6Pz8/IzY21uwY2cZmsxknT540\nVq5cabRt29Zwc3MzSpYsaUyYMMFSr02VKlUy5s2bd8f9ERERRsWKFR9gouxRuHDhTG82m83w8fGx\n37eKmjVr2v+uhQoVMo4dO2akpaUZgwYNMiZPnmxyOiBzBQoUMGJiYjJs/+mnn4yCBQuakCj7nD17\n1qhataoxatQowzAM49SpU0blypWNHj16GKmpqSanc54+ffoYTZs2Nb7//nujYMGCxsaNG43ly5cb\nVapUMb788kuz4zlNTEyMUaxYMePRRx81PDw8jF69ehk1atQwihcvbqlzJW9vb+O7777LsH3v3r2G\nj4+PCYmyx+bNm40CBQoYNWrUMNzd3Y06deoYvr6+ho+Pj9GyZUuz490Vq8HmEF988YUMw1Dnzp21\nfPly+1RV6f+aKVup2XtAQECuaAxdunTpDJe+WlG/fv20evVqvfzyy2ZHgZMYhqFdu3bp2LFj6t69\nuwoVKqRz586pYMGC9zzb0NW5ublp1apVCgsLU6tWrRQTE6Pp06dr2LBhZkdzuhEjRmju3LmW7nua\nvupr7969dfLkSb3//vtasmSJpk+frubNm+uZZ55Rnz59zI55X+Lj4+96mWvr1q31wgsvPMBE2ePG\njRtq3ry5evToYd9mGIaeeeYZjRkzRiVLljQxnfMdO3ZMYWFhkm6d8yUlJclms2nUqFFq1apVhpla\nVnPo0CEtWrTIMiswSreumImLi8u01csjjzxiUirnypcvny5fvpxh+5UrVyx3jl+8eHFt3LhRzZo1\nkyR9+eWXqlevnlasWGGpGdxbt27VZ599pgYNGsjNzU1BQUFq27atvL29NX36dPvrVE5Xs2ZNxcbG\nas6cOcqbN68SExMVFham4cOHW+r9JS0tLdNei3nz5s3wupSTjR8/Xi+++KJeeeUVeXl56eOPP1aJ\nEiXUt29fl185nstgc5iDBw+qWrVqlnrhz8znn3+u+fPna8mSJZa+Xn7jxo2aOXOm5s+fr7Jly5od\nJ9s8//zzWrZsmWrXrq1atWpZunn9pUuXtHnz5kynWY8ZM8akVM71+++/KywsTIcPH1ZqaqpiY2NV\nvnx5DRs2TIZhKCIiwuyI/1hMTEyGbZcvX9YTTzyhsLAwPffcc/btVunNIkmPPfaYtm7dqqJFi6p6\n9eoZnqNW6Il15syZDP3qDMPQhg0btGjRIn355Ze6du2aSQmdo379+mrdurVmzJiR6f6xY8dq8+bN\n2rdv3wNO5lxxcXHq06ePqlWrpoiICPtqsHnz5lV0dLSCg4NNTuhcpUqV0n//+1/VrFlTtWrV0vjx\n4/XEE09o9+7dat++vS5evGh2RKdLSkrSqlWrtGjRIu3Zs0fBwcGW6fu1Z88e9enTRydPnszwha3N\nZrPMpYRPP/209u/fr0WLFumhhx6SJO3du1eDBg1S/fr1tWTJEnMDZoPY2Fg9/PDDatu2rZYvX35P\nl3fnJN7e3vZLQoOCgvThhx+qadOmOn78uKpXr26Zdi83b96Uu3vmc5rOnz+vokWLPuBE2aNLly66\ncOGCVq5cqcDAQEnSqVOn1LdvXxUuXFiffPKJyQmdw8vLSz/99JMqVKigwoULa+fOnapevbqio6PV\npUsXl17Uh5l1OUz16tUd7g8fPlwTJkyQv7+/SYmyx9ChQ3X+/HkFBASoePHiGT44xsfHm5TMuXr1\n6qWrV6+qQoUKKlCgQIZxWqWB6c8//2xfWeivJ9tWOpH57rvvFBYWJpvNpsTERAUEBOjMmTPy9PRU\n6dKlLVOsGzFihKpVq6Y9e/Y4FD+6deumIUOGmJjs/tWpU0c2m83hA1T6/fnz52vBggUyDMNSH6gk\nydfXV926dTM7xgNns9nUvn17tW/fXufPnzc7zn2bOXOmHn30Ua1fv15t2rRx6Fm3ZcsW/frrr5bo\nE1WxYkVFRUVpwoQJqlOnjpYuXaqmTZuaHSvbPPLII9q0aZNq1qypHj16aMSIEdq6das2bdqk1q1b\nmx3PqXbt2qVFixZpzZo1unbtmkaNGqX333/fUr3chgwZogYNGuirr77KtF+xVcyZM0f9+vVTSEiI\n/fz25s2b6ty5s9555x2T092/woULZ/q3u3r1qr744guHgo5VzuerVKmiI0eOqGzZsqpdu7Z9skFk\nZKQCAgLMjuc0ffr00erVqzP8ff/44w+1bt060y92c6J58+apc+fOKlu2rEqXLi1J+u2331SjRg19\n8MEHJqdznoIFC9onUAQEBOjYsWP2msq5c+fMjPa3KNblEHdaLXP+/Pnq1KmT/ZuM8uXLP8hY2Wbc\nuHFmR3ggZs+ebXaEB8IqTaH/zosvvqhevXpp7ty58vb21vbt21WgQAH16dPHMqu7SdL27dv17bff\nZrjctXz58vr9999NSuUcx48fNzuCKXJ6g/q/06tXr7+9PNsK35S3aNFCBw4c0Hvvvac9e/bYF53y\n9/dXhw4dNGTIEMvM4nZ3d9dbb72l0NBQ9enTR3379rVs0WPevHlKTk6WJE2YMEF58+ZVVFSUunfv\nrokTJ5qc7v6dPXtWS5Ys0fvvv6+LFy/qiSee0DfffKOQkBANHDjQUoU6STp69KjWrl2rihUrmh0l\nW/n6+uqzzz5TXFycDh06JEmqVq2aZcadW87hbzdixAj973//kyRNmTJF7du314oVK+Th4WGpmZLH\njh3Ts88+qwULFti3JSQkqFWrVqpcubKJyZyrdOnS2r9/vzZv3qzDhw9LuvUctdqq8Y0bN9bOnTtV\nrVo1dezYUS+88IJ+/vlnrVu3To0bNzY73l1xGWwO4ebmZp/d8dfl3m/fbqWZHrCm9GJOqVKlTE7i\nfD4+Pvr+++9VuXJl+fr6KioqSsHBwfr+++/Vt29fxcbGmh3RKXx9fbV7925Vq1bNYUn7qKgode3a\nVWfPnjU74n27ceOGnn32WU2aNEnlypUzO84D88cff+jIkSOSbn2DbuU2BLCO8+fPa9CgQdq2bZv2\n7NmjKlWqmB0JWZA/f349/vjjevLJJ9W2bVt7qxerXtbcqlUrjRkzxuV7JTlbamqqfv75ZwUFBalw\n4cJmx3Gamzdv6sMPP3RYgTu3uHr1qg4fPqwyZcqoWLFiZsdxmrNnz+rhhx9Wly5dNGPGDPvKvsHB\nwVq9evUdL5G1ggsXLjj0xreCX3/9VVeuXFGtWrWUlJSkF154QVFRUapUqZJmzZqloKAgsyPekbUb\nn1lI5cqV1aFDB+3fv18xMTGKiYlRdHS08uTJo3Xr1unnn3+2zJTcdKdOndKbb76pQYMG6Y8//pAk\nffPNNzp69KjJye7PpUuXHH6+280q0tLSNG3aNPn4+CgoKEhBQUHy9fXVq6++aqkGpvny5bP/7Ofn\np5MnT0qSChUqpFOnTpkVy+natGnj0JfOZrPp2rVreuWVVyzz4SNv3rz6+OOPzY7xwCQlJWngwIEK\nCAjQI488okceeUSBgYEKDw+3TA8aWFfRokW1bt06/fnnn5Yq1KWlpWnGjBlq2rSpGjZsqHHjxuX4\nvoqZCQoK0s6dO7Vjxw7LfKl1N8OHD9cLL7ygJUuWaN++ffbz+vSbVYwcOVKLFi2SdKtQ17x5c9Wr\nV0+lS5fWN998Y244J3J3d9eQIUPss1+t7K9XyhQoUED16tWzVKFOkkqUKKGNGzdq1apVGjNmjFq2\nbKkaNWpozZo1lirUvfXWW1q9erX9fs+ePVW0aFGVLFlS0dHRJiZzrvLly9v7TBcsWFCRkZGKiYnR\nxx9/7NKFOknSA19/Fv9IUlKSER4ebtStW9c4dOiQfbu7u7tx8OBBE5Nlj927dxuFChUyGjdubOTN\nm9c4duyYYRiGMW3aNKNnz54mp7s/bm5uRkJCgmEYhmGz2Qw3N7cMt/TtVjFu3DijePHixrvvvmtE\nR0cb0dHRRkREhFG8eHHj5ZdfNjue07Rv395YtmyZYRiGMWTIEKNu3brGwoULjZYtWxohISEmp3Oe\nEydOGBUrVjTq1q1r5M2b12jevLnh7+9vVKhQwTh9+rTZ8Zzm6aefNmbNmmV2jAdi8ODBRvny5Y2v\nv/7auHjxonHx4kXjq6++MipUqGAMGTLE7Hhwkp9++slS7y1WN23aNCNPnjxGaGio0aVLF8PT09MY\nMGCA2bGyxc6dO40BAwYYhQoVMurVq2fMmjXLcHd3N3755RezozmdzWbLcLPiuV/JkiWN77//3jAM\nw/jkk0+MgIAA48iRI8bEiRONJk2amJzOuZo3b2588sknZsfIdh4eHkb58uWNV1991YiPjzc7TrY7\ndOiQUaxYMaN3795Gamqq2XGcrmzZssauXbsMwzCMjRs3Gr6+vsaGDRuM8PBwo23btianc55JkyYZ\nW7duNa5du2Z2lCzjMtgc5qOPPtLIkSM1btw4DR8+3LKXCDz88MNq3769JkyY4HCZ3d69e9WjR48c\nvcDE9u3b1bRpU7m7u2v79u13PbZ58+YPKFX2CgwMVGRkpDp37uyw/bPPPtO//vUvy8w6i46O1uXL\nl9WsWTOdP39e4eHh9mnW8+fPV40aNcyO6DQpKSlavny5oqOjdeXKFdWrV0/9+vWTl5eX2dGc5rXX\nXtPMmTPVunVr1a9fXwULFnTY//zzz5uUzPmKFSumtWvXqkWLFg7bt23bpp49e9pnNyNni46OVt26\ndS01o9nKKlWqpJdeesne83Tz5s0KCwvTtWvX7JeKWs2VK1e0cuVKLV68WHv27FHz5s3Vp08fde3a\n1TKX5afPur8Tl5/pcY88PT0VFxenUqVKafDgwSpQoIBmz56t48ePq3bt2pa6gmTNmjUaP368Ro0a\nlen5glVWjz937pyWL1+upUuX6uDBg2rVqpXCw8PVtWtXeXh4mB3vvhQvXjzTvqdXrlxR/vz5lSdP\nHvs2K7R7kW61IIiNjVXp0qU1YsQIJScna/78+YqNjVWjRo30559/mh3RKdq2bavdu3fr5s2batiw\noZo3b64WLVqoadOmf9vP2GwU63Kg3377TU899ZQ8PT21ZcsWSxbrbi/Q3f7ziRMnVLVq1Vwx1dxK\nPD09FRMTk6Ep65EjR1SnTh1LXtaDnO9uvepsNtsdF/7JiQoUKKB9+/apWrVqDtsPHjyohx56SElJ\nSSYlQ1b83Yq+Fy9e1DfffEN/2xwiX758iouLs6/SJzkWQKzu0KFDWrhwoT744AMlJibqxo0bZkdC\nFgQFBek///mPWrdurXLlyum9995TWFiYDh48qGbNmlmmECAp0+K51XuK79+/X4sXL9bKlSsl3VpB\nNTw8XLVr1zY52T+Tfsn2vQgPD8/GJA9OYGCg1q5dqyZNmqhKlSp67bXX1KNHDx05ckQNGza0VEH9\n5s2b2rt3r3bs2KHt27crKipK169fV8OGDbVz506z492RdS66zkVKly6tbdu2afr06UpNTVWhQoXM\njuR0Xl5eOnfuXIbVbQ8cOJDjlwbPSj8Sq3wTV7t2bc2bN09z5sxx2D5v3rwc+6Z+JykpKdq4caOO\nHTumAQMGyNvbWydOnJCvr69lGrauXLlSRYoUUWhoqCRp8uTJWrBggYKDg7Vs2TLLfIjMTSvDhoSE\naMqUKVq2bJk8PT0lyd6HMCQkxOR0zjNjxgwNHz48wzepycnJmjNnjsaMGWNSMuf44osv1LZt2zs2\nObfiB0Yru3nzpv35mC5v3ry5pmhVrVo1zZw5U2+99ZY+//xzs+M41fLlyxUZGanjx49r9+7dCgoK\n0uzZs1WuXDl16dLF7HhOMWDAAPXs2VMBAQGy2Wz2FSb37t1ruRV+c9P5Qrp69erJ399fRYsW1Ztv\nvqn3339f7777rkJCQhQZGanq1aubHTFL0gtwN2/e1Jo1a9SmTRuVKFHC5FTZq1u3burTp48qVaqk\n8+fPq0OHDpKkH/8/9s48rqb8/+Ove8tNpY0yRN0KLaiUJJKSYawxtkZZKiISUVm+1iwZRkaWsSVl\nLGUnW0wroWxtlBZRFG2DVlLn90ePe36uWzOMk497us/Ho8eje05/PO/jdLb35708eMCaqc0CpKWl\nYWlpCTU1NbRt2xYKCgo4e/YsPQX3e0WSWSfhu2T+/Pl49OgRTp06hc6dOyMlJQVv376Fvb09xo0b\nBz8/P9KK/5mmJvs2BlterGJjYzFy5EhoamrSL/63bt1Cfn4+Ll26BCsrK8KGzJCTk4OhQ4eirKwM\n5eXlyMzMhI6ODhYtWoSKigqhEfDijIGBAXbs2IEff/wRd+7cwcCBA7Fp0yZcvnwZbdq0wYkTJ0gr\nSvhC0tLS8NNPP+Hdu3d0AD05ORmtW7dGRESE2D10N4WUlBQKCwtFHsBLS0vRvn17sb/mGhkZYcGC\nBU2u+iclJaF3795i/z0FuLi4ICAgQKT8vrKyEh4eHggKCiJkxgxcLhfDhw8XGl4UHh4OW1tboTK7\n06dPk9BrViiKQnR0NKqrq9G/f39WTQ/dvXs3Vq1aBU9PT2zYsAFpaWnQ0dFBcHAwQkJCRJr4izMn\nT55Efn4+Jk6cSC/khYSEQFlZmTVByZZGbW0tzp07h6CgIFy7dg1mZmaYMWMGJk+ejOLiYqxYsQL3\n79/Ho0ePSKv+Z+Tk5JCens6akvSmqK2tRUBAAPLz8+Hk5AQTExMAwO+//w4FBQXMnDmTsCEz7Nu3\nDzExMYiNjcW7d+9gZWUFGxsb2NjYwMjI6F/fx0kiCdZJ+C6prq6Gi4sLTp06hbq6OsjJyaGqqgp2\ndnYICwsT674IH/cqefDgAby9veHj4yMUxPL398fmzZsxduxYUpqMU1BQgF27dtErGAYGBpg7dy7U\n1dUJmzHHqFGj8MMPP2Dv3r1QUVGhy7fj4uLg4uKC7Oxs0oqMICcnh4yMDGhqamLZsmXIz8/H4cOH\nkZqaisGDB7OmlwcAPH/+HOfPn0deXh7ev38vtG/r1q2ErJqHqqoqHDlyROgcdXR0/O77eXwJXC4X\nr169Eul9dePGDYwdOxYlJSWEzJjB2dkZcnJyQtOaPyY9PR0jRoxgTRZIU8HXkpISdOjQAR8+fCBk\nxgzOzs6f9XcHDx5sZpPm5fXr11iwYAHu378PCwsL+Pv7Y8SIEbh58yaA/5/MyJZqg+7du8PPzw9j\nx44VavWSlpYGGxsbsb8OtWQePXrU6PPCpz2bxRUPDw8cO3YMFEVh6tSpmDlzpkg/5pcvX0JdXV2s\ne6MOHDgQ3t7erDluLR0ulws1NTV4eXlh7ty5YlWVKCmDlfBdIisri2PHjiEjIwMpKSl0A/tevXqR\nVvtqPl6lmThxIrZv344RI0bQ24yMjKChoYGVK1eyKlinrq6ODRs2kNZoVm7evInbt2+LjHXX1NRE\nQUEBISvmkZeXR1lZGTQ1NXHt2jXMmzeP3s6m3maRkZGws7ODjo4OMjIy0LNnTzx9+hQRWyVVAAAg\nAElEQVQURcHU1JS0HuPIycnB1dWVtEazICjD4nA4MDQ0FOovVFdXh7KyMjg5OZETZIg9e/b8Y9ac\ngYEBKwJ1b9++BUVRoCgK5eXlQqWidXV1uHTpEivKl8Q9CPe5eHt749atW5g+fTrCw8MxbNgwUBSF\n27dvg8PhYPHixVi+fDnCw8NJqzJCbm4uncHyMTIyMmJ/D/203ck/waYhTU+ePMHPP/+M1NRUunoG\nAJ2xw5Zs5kePHmHHjh0YN26cUMbvx6iqqop9dqiHhwe8vLxQUFDQ6MAQce4V/yUtBdgSrDx9+jTi\n4uIQGhqK1atXw8TEhM6sGzBgAOTk5EgrNokkWCfhu0ZfX5/ua5GdnY2amhqR/i3iTGpqaqNN7LW1\ntcU6ffxTrly5gjZt2mDAgAEAgF27dmH//v3o3r07du3axZryFg6HI7KaCgB5eXlQUlIiYNQ8DB48\nGG5ubujduzcePnyIkSNHAmh4iGNTycCyZcvg7e0NX19fKCgo4NSpU2jfvj0cHR0xbNgw0nqMEhIS\nAlVVVfpYLl68mO5DeOzYMbE/rmvWrAFFUZg7dy68vLygqKhI7+PxeNDS0sKgQYMIGjJDUy9PbENZ\nWZkOvn46uAhouBb7+voSMJPwX7h8+TKOHj0Ka2trODk5QUNDA1FRUTA3NwcAbNq0iTUvjUDDM15S\nUpLIdfXKlSsiQ37Ejd9///2z/o7D4bAqWLdgwQJoa2sjMjIS2traSExMRGlpKby8vLBlyxbSeoxQ\nW1sLPp8PCwuLf7zXSEtLw9ra+huaMY+9vT0AYO7cufQ2tgwM+dxEEHH/nh8zduxY+nu/efMG169f\nx4kTJzBq1ChwudzvenClpAxWwnfJsmXLYGBggGnTpoGiKAwdOhSRkZFQVFTE5cuXWdPw3NTUFD17\n9kRgYCBd2vv+/XvMnDkTaWlpuH//PmFDZjA0NMSmTZswYsQIpKamwszMDF5eXoiOjoa+vj5rsgcc\nHBwgIyODgwcPQkFBASkpKVBRUcGYMWOgq6uL/fv3k1ZkhNLSUixZsgT5+fmYO3cu3Xdm+fLlkJaW\nZs1LsoKCApKSktClSxeoqKjgxo0b6NGjB5KTkzFmzBg8ffqUtCJj6OnpYffu3bC1tcWtW7cwePBg\nbNu2DRcuXIC0tDRremJFRETA1tYWrVq1Iq0i4SuIjY0FRVGwtbXFqVOn0LZtW3ofj8cDn89nVYsF\ntiMtLY38/Hx6gJicnBxSU1PRpUsXAA1ldZ06dWLNi2NgYCDWrFkDf39/zJgxA4GBgcjJycHGjRsR\nGBiIX375hbSihC9EVVUVUVFRMDIygpKSEhITE6Gnp4eoqCh4eXnhwYMHpBUZQUlJCUlJSY0mGrCJ\nnJycf9wvuDZJEB9KS0sRGxuLmJgYxMTE4OHDh1BRUYGVlRXOnDlDWq9JJJl1YoaRkRFiY2NFMpHe\nvHkDKyurL5o0+j1z5MgRnDx5EgBw8eJFpKSk4O7duzh69CiWLl2K2NhYwobMsGfPHowePRqdO3em\ne7GkpKSAw+GwptwDaCj5EKSMnzp1CqNHj4afnx/u378vVAIs7gj67HTp0gU1NTWYMGECcnJyoKmp\niV9//ZW0HmO0a9cOgYGBItvZVuYsLy9PZ0p27NgROTk59KAFtvUUys/Ppyd/nT17FhMmTMCsWbNg\naWkJGxsbsnIMIphgDAD19fUiPc3EuR9qS0KQtZGbmwsNDQ2hsmYJ4kd9fT2kpKToz1JSUkINv7/n\n5t//hZkzZ0JWVhYrVqxAVVUVHBwcoK6ujoCAANYG6j4tC2UbdXV19KAbVVVVFBQUQE9PD3w+H48f\nPyZsxxxjx47F2bNnsXDhQtIqzYokGMcuDA0NkZ6eDhUVFQwcOBCurq6wtrYWiz6okmCdmJGWloba\n2lqR7TU1Nd/96OEvoaioiF4Vv3jxIiZNmgRTU1MoKSmxJjsJAMzNzfHkyROhpu729vZwcHAQ6Y8g\nzvB4PFRVVQEA/vrrL0ybNg0A0LZtW7x9+5akGqN07NgR9+7dw5kzZ5CcnIyKigosXLgQkyZNYl0Q\noKKiAvfv30dRUZFQE2EOh4OJEycSNGMOCwsL3LhxAwYGBhgxYgS8vLyQmpqK06dPw8LCgrQeo7Rp\n0walpaXQ1NTE1atXsWjRIgBA69atUV1dTdiOOWpqarBixQocP34cBQUF+LS4gC2ZOy0FQRlhVVVV\no03dxeFBXEIDgYGBdNPvDx8+IDg4GKqqqgCA8vJykmrNgqOjIxwdHVFVVYWKigpW9FhsjEOHDuG3\n335DVlYWAEBXVxc+Pj6YOnUqYTNm6dmzJ5KTk6GtrY2+ffti8+bN4PF42LdvH3R0dEjrMUa3bt2w\ndu1axMfHN9rLjU2lzQCQmZnZ6L2FTYkGsbGx2LJlC9LT0wE09OPz8fGBlZUVYTPmcHNzg7W1tcgw\nFHFAUgYrJkRFRQEAfvzxR5w8eRLKysr0vrq6Oly7dg1nzpyhb4bijoaGBg4dOgRra2t06dIF27Zt\nw5gxY5Ceno5+/frh9evXpBUlfAF2dnZ4//49LC0tsW7dOuTm5qJTp064evUq5s2bh8zMTNKKEr6A\nK1euwMHBAa9fvwaPxxPJgBAEZsWdJ0+eoKKiAkZGRqisrISXlxdu3ryJbt26YevWrWLfx+1jHB0d\nkZGRARMTExw7dgx5eXlo164dzp8/j//9739IS0sjrcgInp6euHTpElavXg1XV1ds3boVz58/R1BQ\nEDZu3Ijp06eTVpTwBRQXF8PZ2RmXL19udL8k+CoeaGlpfVbGFRuGowDA+vXr4ejoyPpSwq1bt2Ll\nypWYN28eLC0tATRM3t61axfWr1/PquysiIgIVFZWYty4ccjOzsaoUaOQmZmJdu3aISwsDLa2tqQV\nGeGf/mc5HA6ePHnyDW2aj9zcXIwfPx5JSUlCveoEsOXecvjwYTg7O2PcuHH0ORofH48zZ84gODgY\nDg4OhA2ZR9yyfCXBOjFBUOLx8YShj+nYsSO2b9+O8ePHf2u1ZmHp0qUICgqChoYGCgsLkZOTA1lZ\nWRw6dAg7d+5EYmIiacX/TEucwpOXl4e5c+ciPz8f8+fPx4wZMwAACxcuRF1d3RdND/ueefHiBTp2\n7Agul4snT55gz549qK6uxpgxY/Djjz+S1mMMfX19DBo0CH5+fqwZDtLSef36NVasWIH8/HzMmTOH\nHqCxevVq8Hg8LF++nLAhM/D5fAQFBWHw4MFQUFDAgwcP0LVrVxw8eBBnzpz5ouvz94yLiwsCAgLo\nsiwBlZWV8PDwQFBQECEzZnF0dMSzZ8+wbds22NjY4MyZM3j16hXWr18Pf39/emAKG8jKykJ0dLRI\nNjMArFq1ipCVhP+CsbEx0tLS0LdvX0yZMgWTJk2iswjZhLa2Nnx9felqCgEhISFYs2YNa4KvTVFW\nVgYVFRWxCQpI+H/s7OxAURT279+Pbt264ebNmygtLYWPjw+2bNki9gM0BBgYGGDWrFkigfOtW7di\n//79dLYdGxDXLF9JsE5MqKysBEVR0NbWxp07d6Cmpkbva9WqFetK7CiKQkhICPLz8+Hg4ED3Dti3\nbx+UlJToKT3iyOf21mHTFB628+DBA4wcORKvXr0Cn89HWFgYRowYQa/ElZWVITQ0lDXlofLy8khN\nTWVVaYeEloG8vDzS09OhqamJTp064ezZs+jTpw9yc3NhZGTEmnI7KSkpFBYWipTWlZSUoEOHDiK9\n+sSVjh074ty5czA3N4eioiLu3r0LXV1dnD9/Hps3b8aNGzdIKzLC/v37MWfOHKiqqqJDhw4i2cxs\nGUbVknj48CGOHDmC0NBQPH/+HEOGDIGjoyPGjh0LOTk50nqM0Lp1a6SlpdH9UAVkZWXB0NDwu57A\nKKFlo6qqisjISBgbG0NRURF37tyBnp4eIiMj4ePjw5prroyMDB4+fChyjmZnZ6Nnz56sOUfFOctX\n0rNOTBD0BCguLiZs8m3gcDhwcnIS2T5r1qxvL8Mwn66ItxTq6+uRnZ3daFbAwIEDCVkxw5IlS9Cv\nXz/873//Q0hICOzs7DB+/Hjs3r0bHA4HCxcuxG+//caaYJ2trS2SkpJYG6z73O/FlnIPAa9fv0Zi\nYmKjfQi/95XHz0VbWxt5eXnQ1NSEnp4eTp8+jT59+iAiIgKKioqk9b6at2/fgqIoUBSF8vJytG7d\nmt5XV1eHS5cusao3VmVlJf19VFRUUFxcDF1dXRgaGrLmZQpoKJvcsGEDlixZQlpFAkP06NEDfn5+\n8PPzQ3x8PI4ePQpPT0+4ubmxppdv165dcfz4cfzvf/8T2h4WFoZu3boRsmIWFxeXz/o7NmQzZ2Vl\nISUlBaamptDW1sbFixexadMmVFdXY+zYsfjf//7HmizCuro6+plAVVUVhYWF0NPTg7a2Nqt6xGto\naCAyMlIkWPfXX39BQ0ODkBXz7NixA7t37xbK8rWzs0OPHj2wZs0aSbBOArO8f/8et2/fbrTh5efe\nNMSBhIQEREZGNhrcYUvZZEvh9u3bcHBwwLNnz0TKuNmQQXjv3j3ExMTA0NAQBgYG2LlzJ2bNmkU/\ntLi5ubHiQU3AxIkT4e3tjczMTBgaGqJVq1ZC+4cOHUrIjBmePn0KPp8PBwcHVgU2/onw8HA4Ojqi\noqICioqKIpk7bAnWTZ06FXfu3MGAAQPg4+ODsWPHYteuXaisrMTGjRtJ6301ysrK4HA44HA40NXV\nFdnP4XDg6+tLwKx50NPTw+PHj6GlpQVjY2Ps3bsXWlpa2LNnDzp27EhajzH+/vtv1iz2SBBFXl4e\nsrKy4PF4rMnuBQBfX1/Y29sjLi5OqB9WZGQkjh8/TtiOGYKDg8Hn82FiYtJomyK2cObMGUyaNAlc\nLhccDgf79u3D7NmzMWjQICgqKmLNmjWQlpZmzYJCjx49kJKSAm1tbZibm2PLli2QlZXF3r17WdVr\n0svLC/Pnz0dSUhL69+8PoOEcDQ4ORkBAAGE75igsLKS/38f0798fhYWFBIw+H0kZrJjx8OFDjBw5\nEi9fvkRtbS1kZWVRVVUFGRkZKCgooKioiLQiI/z2229YsmQJDAwM0LFjR5EXx6tXrxK0+zq2b9+O\nWbNmoXXr1v8adGTLVKVevXpBV1cXvr6+IscTAJSUlAiZMQOXy8XLly/pwI6CggKSk5PpDK1Xr15B\nXV1d7IOSAv6plJsNwdcTJ04gKCgIMTExGD58OFxcXDBixIjPLmEXR3R1dTFixAj4+fmxpgTrc8jK\nysKdO3fQtWtXmJubk9b5amJjY0FRFGxtbXHq1Cm0bduW3sfj8cDn8+lJ62zg8OHD+PDhA5ycnHDv\n3j0MGzYMZWVl4PF4CA4OFuuWGR8zY8YM9OnTB25ubqRVJDBEbm4ujh49iqNHj+Lx48ewtraGg4MD\nJkyYIPbPRB9z7949/P7773TvKwMDA3h5ecHExISwGTO4u7vj2LFj4PP5cHZ2xpQpU4Suu2zBzMwM\nP/30E9avX4/g4GC4u7vDz88Pnp6eABraFH18nMWdS5cuobq6GuPHj0dWVhZGjhyJ7OxsqKioICws\njFV9qM+cOQN/f3+hc9THxwdjxowhbMYcPXv2hIODg0iW7/r16xEWFobU1FRCZv+OJFgnZgwZMgQd\nO3bE/v37oaqqiuTkZLx//x4zZszA8uXLWTNKunPnzli1ahUryl4/RVtbG3fv3kW7du1azFQleXl5\nJCcni6RZswUpKSm8fPmS7iWpoKBAr8gB7AvWvXv37h/3y8jIfCOT5uXFixcIDg5GcHAwqqqqMHXq\nVMyYMYM15TsfI+lDyC6ePXsGDQ0NVgeYG6OqqgoZGRnQ1NQU+4b9Hy/mVVZWYuvWrRg5cmSj2cxs\nWdhrKVhYWODOnTswMjKCo6MjJk+ejE6dOpHWkvAfeffuHU6fPo2goCDcvHkTI0eOxIwZMzB06FDW\nlIUqKCggKSkJXbp0QX19PXg8HpKSktCzZ08ADRUJ3bt3R1VVFWHT5qOoqAjt2rWDlJQUaRUJX8ip\nU6dgb2+PH3/8sdEs359//pmwYdNIgnVihoqKCuLj49G9e3coKSkhISEB+vr6iI+Px6xZs/Dw4UPS\nioygoqKCu3fv0oMlJIg3tra2WLx4MT1hkm1wuVzY2trSg16uXbsGS0tLOkPp/fv3iI6OZk2w7sOH\nD5CWblldFGJjY7FmzRrExcWhpKSEdVNwx40bh19++QWTJk0irSKBQaqqqhptmWFkZETISMLn8rml\nVmxa2GtqMEppaSnat2/Pmnvo8uXL4ejoiO7du5NWaRZWrVqFpUuX0s9Af//9N+vumU3x7NkzBAcH\n49ChQ/jw4QMePnyINm3akNb6alpaBQnbCQoKgqOjI2sW1z8Hcc3ybVlvWyyAy+XSJ1b79u2Rn58P\nfX19qKmpsWoE+rRp03DmzBl4e3uTVpHAAB4eHvDy8sLLly8bzQoQ9xfH2bNnC32eOXOmyN+wKRtL\nWVkZ/fv3h7W1NWxsbGBubi5yTNlCTU0NTp48iaCgICQkJGDixImsLBMdOXIkfHx88OjRo0bPUTs7\nO0JmEv4LxcXFcHZ2xuXLlxvdL3mh+v5h0zPd59JU/sC7d+/oxTA2sGHDhka3p6en48CBA9iyZcs3\nNmKWDRs2YN68efS9ks/ns3oo1ccIerpRFMWq66ygF2pTn9nC51Z07du3r5lNmhdXV1eMGjWKDr6q\nq6vj5s2b0NLSIivWjPTu3RuHDx8mrfHFSIJ1YkavXr1w7949dOnSBZaWlli7di2qqqpw8OBBGBgY\nkNZjDFlZWfj5+SE6OhpGRkYiL45r164lZMYs/zYQhC1DCcaPHw9A+PsKHmbY0ONs9+7dpBW+KWfP\nnkVcXBwiIiKwbt06SEtLw8LCgg7eWVlZkVb8ahISEnDgwAEcP34cOjo6cHFxwalTp1ibHeDq6gqg\n8WsrG87Rloanpydev36NhIQE2NjY4MyZM3j16hXWr18Pf39/0noSvpC1a9fC29tbZKGguroav/32\nG1atWkXIjBkEJb8cDgeBgYFCmUh1dXWIi4uDvr4+Kb1mpbKyEqGhoThw4ABu376N7t27i32w7tOg\nK9uLuD4ug71x4wZGjRqFnTt3YtiwYaxpRUBRFHR1dekAXUVFBUxMTOjvx5ZjHBgYCE1NTRgZGbHm\nOzXGp9+tvLxcZJgjG/jcydqCyb/fI5IyWDEjPj4eFRUV+Omnn/DixQvY29sjISEBOjo6+PPPP1nR\nIBsA+vTp0+Q+DoeDxMTEb2jTfHxaI19bW4u0tDS8fv0atra2OH36NCEzZnn27Nk/7ufz+d/IRALT\n1NTU4Pbt2wgODsaRI0dQX18v9oGdHj16oKioCA4ODnBxcYGxsTFpJQkSvoiOHTvi3LlzMDc3h6Ki\nIu7evQtdXV2cP38emzdvxo0bN0grSvgC2F4eKij5ffbsGTp37izUE4rH40FLSwtr165F3759SSky\nTnx8PL0gVF1djYULF2LmzJmsCEr+W8kkm5g7dy5CQ0OhoaEBFxcXODo6in2/zMYICQn5rL+bPn16\nM5s0L7Nnz0ZYWBi6du1KH082DXwR0FLOUUGma1OIQ9KIJFjHAurr61mzciOh4XjOmTMHXbp0weLF\ni0nrSJDQKHl5eYiJiUFMTAyio6NRWloKS0tL2NjYYMmSJaT1vgoulwt5eXlIS0v/402+rKzsG1pJ\n+K982q/tn2BLqZ2ioiJSUlKgpaUFPp+Po0ePwtLSErm5uejRowdrmoDn5eVBQ0ND5DylKAr5+fnQ\n1NQkZMYsXC4Xr169oocYCYiKioK9vT2Ki4sJmTHLoEGDcPr0adZmMBcVFSE4OBhBQUF48+YNJk+e\nDAcHB/Tr1w/Jycms6WEnJSWFzMxMqKmpgaIoaGho4MaNGyIldt9zNsvnwuVyoampCRMTk398XmDL\n4ntLoLq6mm5/kpiYiDFjxmDGjBkYPHgwaTXG+HQwnqKiIpKTkz+7V6q4EBsbS/9OURRGjBiBwMBA\nkYE+1tbW31rts5EE6yR8lyQmJsLMzKzFBiEfP34MGxsbFBYWklZhlEePHjXa7FzSD0u80NHREQrO\nWVtbw8zMjDUTslrKCvKnVFZWIjY2ttFzVJynTf7byurHfM+rq19Cnz59sH79evz000+ws7ODsrIy\nNm7ciO3bt+PkyZPIyckhrcgIbM84U1FRAYfDwZs3b6CoqCj0f1xXV4eKigq4ublh165dBC2Z5/37\n98jNzUWXLl1YNcxIVlYWEyZMwJQpUzBkyBD6GbdVq1asCtZ9es0VZK98+lncz08AcHJy+qz7y8GD\nB7+BjQSmycnJQXBwMP78809wuVykpqZCXl6etNZXw+VyoaSkRP/vvn79GoqKiiLv3WxblBbHDEL2\n3AElsIoBAwZAVlYW/fv3p4MBffr0YU0w4N/IycnBhw8fSGswxpMnT/Dzzz8jNTWV7lUHgL5JsOGB\nrSXB4/FQW1uLmpoa1NTU4N27d6irq2PN+cm2INzn8ODBA4wYMQJVVVWorKxE27ZtUVJSAjk5ObRv\n316sg3UfD1nIz8/H8uXL6WwWALh16xaOHTvWZNN3cWTBggX0Ys/q1asxbNgwHDlyBDweD8HBwWTl\nGOTTIICAiooKtG7dmoARs2zbtg0URcHFxQW+vr5C5ViC8lDB/zEbqK6uxrx58+gFk8zMTOjo6MDD\nwwOdOnXC0qVLCRt+HXw+Hzdu3ICmpib4fD4rSl4bIzo6mrTCN4NN11MJosjKykJWVhatWrVCRUUF\na/rYSYLH4oMks07Cd0llZSVu3LiB2NhYxMbG4u7du+DxeOjfvz8GDRok9g9sAhYtWiT0maIoFBYW\n4uLFi5g+fTp27txJyIxZRo8eDSkpKQQGBkJbWxuJiYkoLS2Fl5cXtmzZwoqBBABw9epV2NraimQC\nfPjwAVFRURg6dCghM+YpKipCTEwMfY7m5uaib9++GDRoEFauXElaT8IXYmNjA11dXezZswdKSkpI\nTk5Gq1atMGXKFCxYsADjxo0jrcgIP/30ExwdHTFt2jSh7YcOHcKhQ4fw119/ETJrXqqqqpCRkQFN\nTU1W9FMS3DsDAgLg6uoqNHihrq4OCQkJkJKSQnx8PClFRomNjUX//v1ZO3VbwIIFCxAfH49t27Zh\n2LBhSElJgY6ODs6dO4c1a9bgwYMHpBW/GkGvuhMnTkBXVxdTpkzB4sWLkZKSwqpBcRIkiCu1tbU4\ne/YsgoKCEBMTg2HDhsHZ2RkjR45kzaJ0S0UcM+skwToJYkFWVhY2bNjAmgb2AgYNGiT0mcvlQk1N\nDba2tnBxcWFN+YeqqiqioqJgZGQEJSUlJCYmQk9PD1FRUfDy8mLFAzjA/pKsxigvL0dcXBxOnDjB\nuvOzJaGsrIyEhATo6elBWVkZt27dgoGBARISEjB9+nRkZGSQVmQEOTk5JCcno1u3bkLbs7KyYGxs\nzJpebmxHcO+MjY1Fv379hHoNCjLOvL29RY6zuJKXl/eP+9nSm4/P5yMsLAwWFhZCL1XZ2dkwNTX9\n7Ml+4kBFRQWOHTuGgwcP4vbt27C2toaDgwPGjh0r0ptQggQJ34b58+fj6NGj6NChA5ydnTF16lSR\nZ3oJ4ouCggJSUlLEqjcfOyIBElhHQUEB3bw+JiYGhYWFsLCwwJo1a2BjY0NajzFaSqlAXV0dFBQU\nADQE7goKCqCnpwc+n4/Hjx8TtmOOpkqyiouL0aZNGwJGzcOlS5foczMpKQlycnLo378/NmzY8F03\naZXQNK1ataJ7lbRv3x55eXkwMDCAkpIS8vPzCdsxR6dOnRASEoL169cLbQ8JCRFpOCzh+0Vw73R2\ndkZAQAArGtX/E1paWv/YF4stCyTFxcWNvhhXVlZ+dt9JcaFNmzZwdXWFq6sr0tPTceDAAaxYsQJz\n585FbW0taT0JEhpl7dq18Pb2FspmBhpK2H/77TesWrWKkBkz7Ny5E5qamvRiZUJCQqN/d/z48W9s\nJuG/8GlVSE1NDdzc3ET6Dn7PA2AkwToxICgo6LP/1sXFpRlNvh2dO3eGmpoa5syZg+DgYPTp04f1\n5R9AQ1Pl9+/fsyqwAwA9e/akpwz17dsXmzdvBo/Hw759+8QqFbkpRowYAaChB9/kyZOFsjzq6urw\n8OFDDBgwgJQe4zg4OGDAgAGYNGkSdu3ahd69e7fYYTBswcTEBHfu3EG3bt1gbW2NVatWoaSkBH/+\n+Sd69uxJWo8xtmzZgkmTJiEiIgJ9+/YFACQkJCAlJUXy8C2GCPruZGdnIycnBwMHDoSsrGyTCyfi\nyqfZ57W1tXjw4AG2bt3Kql6LZmZmuHjxIjw8PAD8f1/bwMBAVvXm+xQDAwNs2bIFv/76K86fP09a\nR4KEJvH19YWbm5tIsK6qqgq+vr5iH6xzcHBg1b2jpfNxn1cAmDJlCiGT/46kDFYM+DQdvqKiAu/e\nvaMDAu/fv4eMjAwUFBRQVFREQpFxZs6cibi4OBQUFNBDJmxsbGBubs6a0tCDBw/i/v37sLCwgKOj\nI5YtW4atW7fiw4cPsLW1RWhoKNq1a0dakxEiIiJQWVmJcePGITs7G6NGjUJmZibatWuHsLAw2Nra\nklb8KubMmQMA2Lt3L6ZNmwZZWVl6n6Aky8nJCSoqKqQUGaW+vr5FBOfYvoL8MXfv3kV5eTkGDRqE\noqIiTJs2DTdv3kS3bt0QFBQEY2Nj0oqMkZOTgz/++APp6ekAGl6U586diy5duhA2k/CllJWVYeLE\niYiOjgaHw0FWVhZ0dHTg4uICFRUV+Pv7k1ZsVi5evIjffvsNMTExpFUY4caNGxg+fDimTJmC4OBg\nzJ49G48ePcLNmzcRGxuL3r17k1aUIKFR4uLi0L9//0Z7Ft+8eRMDBw4kZMYsXC4Xr169Enk3jYqK\ngr29PYqLiwmZSZDATiTBOjHj9OnT2Lx5M53NAgD37t3DvHnz4OPjw5om4AIKCgBlkTgAACAASURB\nVAroBvaxsbF4/vw5+vfvj4iICNJqX8WGDRuwYcMGWFpa4v79+5g0aRLOnj2LhQsXgsPhYPv27Rg1\nahR2795NWrXZKCsrg4qKCqtWsDZt2gQPDw+R4A4boSgKly5dogMe3bt3x/Dhw1l1PFtiD0IJ7CAv\nLw8aGhoi5yNFUcjPz2dNj7Np06ahqKgIgYGBMDAwoHucRUREYNGiRXj48CFpxWYlOzsbxsbGqKys\nJK3CGDk5Ofj111+RnJyMiooKmJqaYsmSJTA0NCStJuELcXFxQUBAAN0GRUBlZSU8PDy+qHLoe4ft\nzwuC5/U3b95AUVFR6N5SV1eHiooKuLm5YdeuXQQtJXwpLWlRWlyRBOvEDF1dXRw5cgR9+vQR2p6Y\nmAgHBwdkZ2cTMmseqqqqcOPGDURHRyMmJgZ3795F69atUV5eTlrtq+jWrRvWrl2LyZMn4+7du+jb\nty+OHz+O8ePHAwAuX74MNzc3PHv2jLBp8/Ds2TNUVlZCX1+/RWRosY2nT59i1KhRyMrKorORcnJy\noKenh/DwcPD5fMKGzNCSV5BjY2NRVVUFCwsL1mSECqioqMD9+/dRVFSE+vp6oX2TJk0iZMUsbH9x\nFNChQwdERETA2NhYaCDBkydPYGRkhIqKCtKKjPDpYAXB5Pg1a9YgIyMDSUlJhMwkSGiapq5DJSUl\n6NChAz58+EDIjHmael7IzMyEmZmZ2A9HCQkJAUVRcHFxwbZt24TKCwUVJGwuVWcrLeVZQZxhRz1h\nCyI/P7/RMlBpaWkUFBQQMGoe/ve//yEmJgb37t2DjIwM+vXrh9GjR8Pf318kUCmO5OXl0T3MzMzM\nIC0tLdQXysjICIWFhaT0GCMoKAivX7/GokWL6G2zZs3CgQMHAAB6enqIiIiAhoYGKUXGOXz4MI4f\nP468vDy8f/9eaN+jR48IWTGLh4cHOnTogMjISPzwww8AgJcvX8LR0RHz58/HuXPnCBt+HYIVZA6H\nA11d3SZXkNnApk2bUFFRgXXr1gFoCAIMHz4cV69eBdAwbCIyMhI9evQgqckYV65cgYODA16/fg0e\njyd0bDkcDmuCdU31bKuoqEDr1q0JGDUPlZWVjWYyl5WVQUZGhoBR86CsrNxolqSGhgZCQ0MJWTUP\nOTk5OHjwIJ48eYJt27ahffv2uHz5MjQ1NVlzHWI7b9++BUVRoCgK5eXlQtecuro6XLp0iTUTNgUV\nTRwOB05OTkLXnbq6OqSkpKB///6k9Bhj+vTpAABtbW3079+/RfQRbwk09ayQnJyMtm3bEjCS8CmS\nYJ2YYW1tjblz5yIkJAS6uroAgMePH2PevHms6YcANDRTtrOzw9atW+lgFpuora0VuqHzeDyhG5+0\ntDQrVjP27duH2bNn05+vXLmCgwcP4tChQzAwMMC8efPg6+uLwMBAgpbM4e/vj3Xr1sHV1RVXr17F\n7NmzkZOTgxs3bmDhwoWk9RgjOjoaN2/epAN1QEOGi7+/P6ysrAiaMcO2bdvoFWRfX19WryCHhYVh\nyZIl9OeTJ08iLi4O169fh4GBAaZNmwZfX1/WDF/w9PSEvb09/Pz8WJcxCIBeGOFwOFi5cqVQIKuu\nrg4JCQno1asXKT3GsbKywqFDh+hgM4fDQX19PTZv3oxBgwYRtmOOTyfHc7lcqKmpoWvXrqx6PoqN\njcXw4cNhaWmJuLg4rF+/Hu3bt0dycjIOHDiAkydPklZsFt6+fYuoqCjo6enBwMCAtM5XIwguCxa8\nPoXD4cDX15eAGfMIng8oioKCgoJIz2ILCwu4urqS0mMca2tr+veamhqRRWm2T+ZmCy1pUVrcYc8d\nvoUQFBSEyZMnw8DAgO4BUV5eDktLSzpbiQ1cvnyZtEKz8+jRI7x8+RJAw00+IyODLtkpKSkhqcYY\nWVlZMDMzoz+fO3cOY8aMgaOjIwDAz88Pzs7OpPQYZ+/evdi/fz8mTpyIPXv2YMGCBdDR0cG6devw\n/Plz0nqMIS0tjerqapHtNTU1rHhxbEkryLm5uTAyMqI/X7p0CRMmTIClpSUAYMWKFZg4cSIpPcbJ\nz8+Hj48PKwN1wP9PDaUoCqmpqUKTqXk8HoyNjeHt7U1Kj3E2b96MwYMH4+7du3j//j0WL16Mhw8f\noqysDPHx8aT1GOPjF2Q2s3TpUqxfvx6LFi0S6nNma2uLnTt3EjRjlkmTJmHgwIGYN28eqqurYWZm\nhqdPn4KiKISGhtItUcSV6OhoUBQFW1tbnDp1SihDh8fjgc/nQ11dnaAhcwgmUmtpacHb2xvy8vKE\njZqXqqoqLF68GMePH0dpaanIfjYkGgDAzZs30bdvX0hJSQltFyx6iXu2ZEtalAbEewCMpGedmHL/\n/n1kZGQAaJhkZ2JiQtiIeV68eIGdO3cKNbB3d3dHp06dCJt9PVwuFxwOB42dfoLtHA5H7G96cnJy\nSE9Pp3uYGRsbY8aMGZg/fz6AhnJgPT29RgM/4oicnBwyMjKgqamJH374AREREejVqxeys7Nhbm6O\nsrIy0oqM4ODggPT0dAQHB9NTQpOSkuDs7IwePXrg8OHDhA2Zh60ryB/3+QIAfX19eHp60iuqbDtH\nR48eDWdnZ9YNY/oUZ2dnBAQEsOJ/9N948+YNdu7cKTSQwN3dHR07diStxig5OTnYtm2b0DPRggUL\nWDXFuE2bNkhNTYW2trbQtenp06fQ19dHTU0NaUVG+LjX4tGjR7F69WokJycjJCQE+/bto4Pu4s6z\nZ8+goaEh6U3MItzd3REdHY1169Zh6tSp2LVrF168eIG9e/fi119/pRfjxZ2W0sstNjaW9YvSgHgf\nT/FPgWihmJqawtTUlLRGsxETE4MRI0ZAR0eHXr04f/48AgICcPny5e86Av455Obmklb4JvD5fNy7\ndw98Ph8lJSV4+PAhnbEDNPQ5+3g1R9zp2LEjSkpKoKmpCT6fj/j4ePTq1QuZmZmsmpK6Y8cOODg4\nwMTEhC6zq66uxtChQ7F9+3bCdszRElaQu3Tpgri4OOjo6CAvLw+ZmZlC19fnz5+jXbt2BA2ZZeLE\nifD29kZmZiYMDQ1FHlCHDh1KyIxZBNke2dnZyMnJwcCBAyErK9tkfxpxpba2FkpKSli+fLnIvpKS\nEqiqqhKwYp6IiAjY2dmhV69e9D00Pj4ePXr0QHh4OIYMGULYkBmUlZVRWFgIbW1toe0PHjxgxUKt\ngDdv3tDZZleuXMH48eMhJyeHkSNHwsfHh7AdcwgWaquqqhrt4/txVjcbOHnyZJM9i+/fv0/IilnC\nw8Nx6NAh2NjYwNnZGVZWVujatSv4fD6OHDnCmmBdU/fKsrIyVmVPtpSy5qaOZ2lp6Xd/PCXBOjEk\nLCwMkZGRjU6yO3/+PCErZlm8eDFcXV0REBAgtH3+/Pnw9vZGYmIiITNmYMu0zH9j+vTpcHd3x8OH\nDxEVFQV9fX307t2b3n/z5k2hwRrizuDBg3H27FmYmppi5syZmD9/PkJDQ5GcnIzJkyeT1mOMdu3a\nISIiAmlpaXSWh4GBAauOJQD4+PggOjoau3fvbnQFmQ24u7tj3rx5uH79Om7fvo1+/fqhe/fu9P6o\nqChWZW47OTkBaBhi9ClsyGYWUFZWhokTJyI6OhocDgdZWVnQ0dHBjBkzoKKiAn9/f9KKjPDLL7/g\n5MmTIg/hr169wuDBg5GWlkbIjFmWLl2KhQsXilx3li5diiVLlrAmWPfLL79gyZIlOHHiBN1/MD4+\nHt7e3pg2bRppPcbQ0NDArVu30LZtW1y5coUeEvL333+zagBMcXExnJ2dm2xtw5brLQBs374dy5cv\nh5OTE86dOwdnZ2fk5OTgzp07cHd3J63HGGVlZXQmvqKiIl0xMmDAAMyZM4ekGiMIhkxxOBzMnDlT\nZGBIcnIyLCwsSOkxDtsXpdkwAEaSlyxmCB5YXrx4AVVVVfzwww9CP2whJSWl0Zubu7s7UlNTCRhJ\n+C8Igq6nT59G69atceLECaH98fHxrApi7du3D2vXrgXQMPU2NDQU/fr1g7+/P/744w/Cdl+Pjo6O\n0M28Z8+emDhxIiZOnMi6QB3QsIL8xx9/YPz48ZCWloaVlRVWrFgBPz8/HDlyhLQeI7i6umL79u0o\nKyvDwIEDcerUKaH9BQUFcHFxIWTHPNXV1U3+VFVVkdZjDE9PT7Rq1Qp5eXlCQybs7e1x5coVgmbM\nkpeXh5kzZwptKywshI2NDfT19QlZMU96ejpmzJghst3FxYU1U8aBhj62+vr60NDQQEVFBbp3746B\nAweif//+WLFiBWk9xvD09ISjoyM6d+4MdXV12NjYAGjoq2RoaEhWjkE8PT3x+vVrJCQkQFZWFleu\nXEFISAi6devGmuQCAX/88Qf27duHHTt2gMfjYfHixbh27Rrmz5+PN2/ekNZjDB0dHbo6SF9fnx4+\nFR4eDmVlZZJqjCAjIwMZGRlQFAUej0d/lpGRgbKyMqZPn86qVi8+Pj6IiorC7t27ISMjg8DAQPj6\n+kJdXR2HDh0irffVKCkpQUlJiR4AI/ispKSEDh06YNasWd//8aQkiBVqamrUmTNnSGs0O+rq6tTZ\ns2dFtp85c4ZSV1cnYCRBggQOh0O9evWKtMY3Q15ennr27BlFURTVqVMnKiEhgaIoinry5AklLy9P\nUk0CA9TV1ZFWaDZ++OEHKikpiaIoimrTpg2Vk5NDURRF5eTksOp/t6ioiNLX16cWLlxIURRFvXjx\ngtLV1aUmTpzIquPbuXNn6vjx4yLbw8LCKA0NDQJGzUteXh518eJFKiwsjMrMzCSt0yzcuXOHOn36\nNFVeXk5vu3DhAnXjxg2CVszSoUMH+r6poKBAPX78mKIoijp37hxlaWlJUo1xZGVlqadPn1IU1fCu\nJrj+ZmZmUm3btiWpxihbt26lAgICKIqiqGvXrlGtW7emZGRkKC6XS23bto2wHXOsWLGCqqioIK3R\n7GhoaFDR0dEURTWco1lZWRRFUdShQ4eo4cOHEzRjljVr1ojt8ZSUwYoZHA4HPXr0IK3R7EyfPh0z\nZ87E8+fP6fTU+Ph4+Pr6Yvbs2YTtJEiQ0BIQrCBramrSK8jm5uasWUFuidTX18Pf3x979uxBfn4+\nMjIyoKOjA19fX2hra7Om1K6yslIoo05AWVmZUBmIuKOmpoarV69iwIABAIALFy7A1NQUR44cYVVT\ne1dXV8yaNQtPnjwReibatGkTFi1aRNiOGWpra6Gvr48LFy7AwMAAGhoapJWaFTMzM5iZmQltGzly\nJCGb5qGyspJu6K6iooLi4mLo6urC0NCQNT3cBHTo0AFlZWXg8/nQ1NTE7du3YWxsjNzc3EaHyYkr\nCxcupH//8ccfkZGRgXv37qFr166s6kG4bt060grfBLaXNQtYvXo1aYX/jCRYJ2Z4eHhg79692LJl\nC2mVZmX9+vWQlZXFihUr6PRxJSUleHt7Y+nSpYTtJEhouURERPzrUBA7O7tvZNO8ODs7Izk5GdbW\n1li6dClGjx6NnTt3ora2Flu3biWtJ+E/sGnTJuzduxcrV66Eh4cHvV1XVxc7duxgTbDOysoKhw4d\nol84BP2/Nm/ejEGDBhG2YxYNDQ1cu3YNVlZWGDJkCP78809WDdEAgJUrV0JBQQH+/v5YtmwZAEBd\nXR1r1qyhp6uLO61atWLNtNd/499aCwQFBX0jk+ZFT08Pjx8/hpaWFoyNjbF3715oaWlhz549rJvW\nbGtri/Pnz8PExATOzs5YuHAhTp48ibt377J6+jifz2dtH+6zZ882OTBE3HunC2hJi9LiOgCGQ7Ep\n3N8CmDp1Ki5cuIBOnTo1OslO3OvLCwoKoK6uLrStsLAQAFh3YwcaIv0uLi6svdFJYBefk6nCpib9\nn/Ls2TNWriC3JHR1dbFz504MHToUCgoKSE5Oho6ODtLT02FpaUmvKos7aWlpGDx4MExNTREVFQU7\nOzs8fPgQZWVliI+PR5cuXUgr/mdUVFQaDcZVVVVBRkYGUlJS9Da2HM+PKS8vBwAoKCgQNmEePz8/\nZGZmIjAwENLS7M0n+Pnnn4U+19bWIi0tDa9fv4atrS1Onz5NyIxZDh8+jA8fPsDJyQn37t3DsGHD\nUFZWBh6Ph+DgYNjb25NWZIz6+nrU19fT/7ehoaG4efMmunXrhtmzZ4PH4xE2lPAl7Ny5E0uXLsXU\nqVMRFBSEadOmITs7Gw8ePICbmxtrhoz9/vvvkJKSwvz58/HXX39h9OjRoCiKXpResGABaUVG+HgA\nzL59+0QGwGzYsIG0YpNIgnVixsSJE/9x/6cN/MUNKSkpFBYW0mnzbKdXr15IS0uDtbU1ZsyYgfHj\nx7OqREkCu+ByuXj58mWLOT8lsA9ZWVmkp6dDS0tLJFhnZmaGyspK0oqM8ebNG+zcuRPJycmoqKiA\nqakp3N3dxX7hKyQk5LP/dvr06c1oQo7Y2FhUVVXBwsICKioqpHUY4+eff0ZkZCTatGkDQ0NDyMvL\nC+1nSxCrMerr6zFnzhx06dIFixcvJq3TLFRVVSEjIwOamppQVVUlrSNBQpPo6+tj5cqVcHR0FHpW\nWL58OcrLy7F9+3bSis0CWxel9fX1sXr1akyePFnoeK5atQplZWXYuXMnacUmkQTrJHxXtMRgwIMH\nD3Dw4EEcO3YMHz58wC+//AIXFxf06dOHtBpjrF27Ft7e3iI9lKqrq/Hbb79h1apVhMwkfAktLZje\nknBxcUFAQIBItk5lZSU8PDxYU5bVq1cvLFmyROSBzc/PD+Hh4bh16xZpRUaora0VybwXUFJSwooX\n5Q8fPuDo0aP46aef8MMPP5DWaRY2bdqEiooKupyZoigMHz4cV69eBQC0b98ekZGRrOll7Ozs/I/7\nDx48+I1MyPD48WPY2NjQFSUSJEggg5ycHNLT08Hn86Gmpoa//voLxsbGyMrKQr9+/VBSUkJaUcIX\n8PHxbN++Pa5du0YfTwsLC5SWlpJWbBL25pizGIqikJCQgJycHIwZMwZt2rTB33//DTk5OVZkZbGt\n18y/YWJiAhMTE/j7+yM8PBwHDx6EpaUl9PX1MWPGDDg5Of1rj7DvHV9fX7i5uYkE66qqquDr6yvW\nwToDA4PP/p999OhRM9s0L5K1HfYSEhKCX3/9VSRYV11djUOHDrEmWLdixQrMnj0bRUVFqK+vx6VL\nl/D48WPs378fZ86cIa3HGL/88gtOnjwpcm169eoVBg8ejLS0NEJmzCEtLQ03Nzekp6eTVmk2wsLC\nsGTJEvrzyZMnERcXh+vXr8PAwADTpk2Dr68vjh8/TtCSOdgejPs3cnJy8OHDB9IaEiS0eH744Qd6\nYAifz0diYiKMjY3x7Nkz1NfXk9aT8IWI8wAYSbBOzCgoKMCoUaOQmpqK+vp6ZGVloU2bNli+fDm4\nXO53ncb5ufj6+jY6xe5jNm/e/I1svh2CHgHv378HRVFQUVHBzp07sXLlSuzfv1+se3tQFNVoQCs5\nORlt27YlYMQcTk5O9O+VlZUICAiAqakp+vXrBwC4ffs27t27B09PT0KGzDF9+nTIysqS1pDAIG/f\nvgVFUaAoCuXl5WjdujW9r66uDpcuXWJVJuWECROgrKwMX19fSEtLw9PTE7169cKJEycwfPhw0nqM\nkZeXh5kzZ+LAgQP0tsLCQtja2rImCwsAzM3N8eDBA9b2fc3NzRUqRbp06RImTJgAS0tLAA3B539r\njyJOCPq1fdrY/O3btxg7diyioqIImTHLpxN8KYpCYWEhLl68yNrSbQnsoa6uDmfPnqUXSnr06AE7\nOzuhfqHijq2tLcLDw2FiYoLp06fD09MTp0+fRkJCAmuGqLUkxHkAjKQMVswQPJQdPHgQHTt2pEt4\noqKiMGfOHDx+/Jiw4dfB5XJhbm7eZPkO0JB5FxcX9w2tmpd79+7RZbAyMjKYNm0aZs6cia5duwIA\nduzYgfXr1+PVq1eETb8cQSPwN2/eQFFRUShgV1dXh4qKCri5uWHXrl0ELZlj+vTp6Nq1K1auXCm0\nfcOGDcjIyMCff/5JyEyChMbhcrn/mBnK4XDg6+uL5cuXf0Orb0dTCwniTnFxMQYOHIjhw4dj69at\nKCgowKBBg2BsbIzQ0NDPGhYjDhw/fhzLli3DwoUL0bt3b5EeZ+Lec+fjUm2goe+Op6cn3NzcADQE\nZfX09FBdXU1SkzGaaoVSVFSETp06oba2lpAZs3w6kZnL5UJNTQ22trZwcXFh9XANCeJNdnY2Ro4c\niefPn0NPTw9AQ/m2hoYGLl68KNbDiz7mw4cPqKuroyvWDh8+TA8MmTt3Lisq2VoS4jwARhKsEzPU\n1NQQGxuL7t27Cz3EPX36FN27d0dVVRVpxa+ipfWsMzQ0REZGBoYOHQpXV1eMHj1aZGWqpKQE7du3\nF8u065CQEFAUBRcXF2zbtk2onJfH40FLS4vOQGMDioqKuH//Ph1oFZCdnQ1TU1O8ffuWkJmE/0JT\nPfpKS0vRvn17Vky9jY2NBUVRsLW1xalTp4QyXXk8Hvh8vsiEbnHm5cuXABpKIgAgKSkJoaGh6N69\nO6ZNm0ZSjXHy8/MxYMAAjB8/HhcuXICpqSmOHDnCquyHxoKOHA6HDsKK+znaq1cveHp6wsnJCXl5\nedDS0kJaWhq6d+8OALh58yYmTZqE58+fEzb9OlJSUgA0fN+oqCih61BdXR2uXLmCvXv34unTp4QM\nJUj4dz58+ICYmBjk5OTAwcEBCgoKKCgogKKiItq0aUNajxFGjBgBiqJw5MgR+jwtLS3FlClTwOVy\ncfHiRcKGEiSwC8nSjZhRW1vb6MNpYWEhK24EbMxw+CcmTZoEFxcXdOrUqcm/UVVVFctAHfD/k/i0\ntbXRv3//f8yYZAPy8vKIiYkRCdbFxMSIZHxI+P5pai3r3bt33/Uq3JdgbW0NoKHcTkNDgzUZV01h\nb28PZ2dnODk5oaioCDY2NtDR0cG+ffvw4sULLFu2jLQiY2hoaODatWuwsrLCkCFD8Oeff7LuHpub\nm0taoVlxd3fHvHnzcP36ddy+fRv9+vWjA3UAEBUVBRMTE4KGzNCrVy9wOBxwOBzY2tqK7JeVlcWO\nHTsImDUvRUVFdEWMnp4eKxaqBYHXz0HcM18/5tmzZxg2bBjy8vLw7t07DBkyBAoKCti0aRPevXuH\nPXv2kFZkhNjYWNy+fVsooN6uXTv8+uuvdHm+BPGiJZQ1izOSYJ2YYWtri927dyMgIABAQ3CrpqYG\na9euxdChQwnbfT0tKdGztrYWwcHBmDBhwj8G69iAICAAADU1NXj//r3QfkVFxW+t1Cz4+PjA3d0d\nCQkJ6Nu3LwAgISEBhw8fxvr16wnbSfhctm/fDqDh+hoYGCi0EFJXV4e4uDjo6+uT0msWBD2/qqqq\nkJeXJ3KOsuWlKjU1FRYWFgAaSij19fVx+/ZtXL58GR4eHmIdrBO0HfiUqqoqhIeHo127dvS2srKy\nb6nWbLC1V50AV1dXSElJITw8HAMHDsTq1auF9hcUFMDFxYWQHXMImnzr6OggMTERampq9D4ej4f2\n7duz6sXx7du3cHd3x7Fjx+jFWCkpKdjb22PXrl1iPVRMEHj9nBYD4p75+jELFiyAmZkZkpOTha61\nP//8M1xdXQmaMYuMjAzKy8tFtldUVLBmEbMl0VhZ88aNG1lX1izOSMpgxYzc3FwMHjwY7dq1Q3Jy\nMgYOHIiMjAzweDxcv35d7IM+Bw4cwJQpU1pML4BOnTrhr7/+goGBAWmVZqWqqgqLFy/G8ePHGx2P\nzaYHtvPnzyMgIIBeoTIwMMCCBQtY1ZDWxcUFAQEBIpNDKysr4eHhIfaTQ7W1tQE0rJR37txZ6CVR\nUL69du1aOiDLBoqLi+Hs7IzLly83up8t56i8vDwePXoEPp+PsWPHom/fvli2bBny8/Ohq6sr1r2/\nQkJCPvtv2dbE/tGjR40Gmdl03ZXAHuzt7fHgwQPs2LGDbgVy69YtLFiwAL169UJoaChhw//Os2fP\n6N8fPHgAb29v+Pj4CH1Pf39/bN68GWPHjiWlyTjt2rXDzZs3oaenx8o2RQKmTZuG+/fv48CBAzA3\nNwfQsCjt6uqK3r17Izg4mKyghC9CUtYsBlASxI6amhpqz5491Jw5c6ipU6dSv//+O/XmzRvSWhL+\nAxs2bKCmT59O1dbWklZpVubOnUsZGBhQJ0+epGRlZamgoCBq3bp1VOfOnanDhw+T1mOE2tpaKiIi\ngiotLSWt0uxwuVzq1atXItuLi4spKSkpAkbNg42NDfX333+T1vgmODg4UJaWltSdO3coeXl56urV\nq9Sff/5J6enpURcuXCCtxxhmZmbUqlWrqMTEREpOTo66f/8+RVEUdfv2bUpdXZ2wHTPU1tZSISEh\n1MuXL0mrNDs5OTmUkZERxeFwKC6XS3E4HPp3LpdLWk/CZ/L48WMqISFBaNtff/1F2djYUH369KE2\nbNhAyKx5kJOTo65fvy6yPS4ujpKTkyNg1Dz06dOHunjxosj2ixcvUqampgSMmg9lZWXq4cOHFEVR\nVJs2baicnByKoijq+vXrVPv27UmqMcrff/9N2dnZURwOh+LxeBSPx6O4XC41duxY6vXr16T1JHwh\ncnJyVEpKisj2pKQkSl5enoCRhE+RlMGKITIyMpg9ezZpDQkMcOfOHURGRuLq1aswNDQU6Wt2+vRp\nQmbMEh4ejkOHDsHGxgbOzs6wsrJC165dwefzceTIETg6OpJW/GqkpaVhZ2eHjIwMoV4ebOLt27eg\nKAoURaG8vBytW7em99XV1eHSpUus6LkDNJSp5+XlobCwEMrKyqR1mp2oqCicO3cOZmZm4HK54PP5\nGDJkCBQVFbFx40aMHDmStCIj+Pn5Ydy4cVi/fj3s7e3pfl8XLlxAnz59CNsxg7S0NNzc3OjsXjaz\nYMECaGtrIzIyEtra2khMTERpaSm8vLywZcsW0noSPpMlS5bA0NCQztTJfVY5WAAAIABJREFUzc3F\n6NGjYWVlBSMjI2zcuBFycnLw9PQkbMoM7dq1a7TUVUlJCSoqKgSMmofU1FQ6U/1jtLW18ejRIwJG\nzcfQoUOxbds27Nu3D0BDG42KigqsXr0aI0aMIGzHHMrKyjh37hyys7OFKkg+7dUsQTyQlDV//7C7\nkzQLOX78OK5du0Z/Xrt2LTp37oyhQ4eioKCAoJmE/4KysjLGjx+Pn376Cerq6lBSUhL6YQtlZWXQ\n0dEB0NCfTtAvacCAAYiLiyOpxijGxsbIysoirdFsKCsro23btuBwONDV1YWKigr9o6qqChcXF7i7\nu5PWZIRWrVqhpqaGtMY3o7Kykg60qqiooLi4GEDDxOr79++TVGOUIUOGoKSkBM+fP8fRo0fp7VOn\nTsUff/xB0IxZzM3N8eDBA9Iazc6tW7ewdu1aqKqqgsvlgsvlYsCAAdi4cSPmz59PWk/CZ3L37l0M\nHz6c/nzkyBHo6uoiIiICAQEB2LZtG6vK61asWIFFixbR06mBhknVPj4+WLlyJUEzZjEwMMDGjRuF\nytPfv3+PjRs3sq79i7+/P+Lj49G9e3fU1NTAwcEBWlpaePHiBTZt2kRajxFqa2vRpUsXpKeno2vX\nrhg9ejRGjx7NykCdoDWIpqYmWrduDR6PJ/TDFkaNGoVZs2YhISGBXoy/ffs23NzcWNVG4tWrV5g6\ndSrU1dUhLS0NKSkpoZ/vGUlmnZixZs0aerjE3bt3sXHjRmzYsAFXrlzBwoULERYWRthQwudCURR8\nfX2hpqYGWVlZ0jrNio6ODnJzc6GpqQl9fX0cP34c5ubmCA8PZ1XW0sqVK7Fo0SKsXr0avXv3FsmU\nFPess+joaFAUBVtbW5w6dUoog5DH44HP50NdXZ2gIbO4u7tj06ZNCAwMhLQ0u2+Xenp6ePz4MbS0\ntGBsbIy9e/dCS0sLe/bsQceOHUnrMYqMjIzId9LV1SVk0zzMnTsXXl5eeP78eaPXIrYMDKmrq6N7\nZ6qqqqKgoAB6enrg8/n0lE0J3z8lJSXo3Lkz/Tk6OhqjR4+mP9vY2MDLy4uEGmOYmJgIDVzIysqC\npqYmNDU1AQB5eXmQkZFBcXExa6pn9uzZg9GjR6Nz5870NSclJQUcDgfh4eGE7Zilc+fOSE5ORmho\nKFJSUlBRUfF/7N15XI35+z/w1zlpV0kLoZVKFFKW0KIoW8gMkbTZsyTL4GMbITONbciMQclajJ2x\nhVYlJK3SMqmQkhCtTp3fH/3O/XUU04xTd/d93s/HYx6jc84frx5t97nu93VdmDlzJqZPn86aa3xx\nuonp4eGB3NxcrFixAhoaGqzbpC6we/duuLu7w8LCApKSkgAAHo+H8ePHU/UGNvDw8EBBQQHWrVvH\nuK8nWTDBMJ8Ox/7f//6Hp0+f4sSJE0hJScGIESNQUlJCd0SR+fDhA4qLiwEAnTp1EtrIyAb19fWQ\nkZFBeno69PX16Y7Tonbu3AkJCQksXrwYN2/ehKOjI/h8Pj5+/IgdO3bAx8eH7ogiweX+32HlT/8Q\n8P//VjS2DOnPz8+Hpqam0OfLRk5OTrh16xbat2/P6jZ1ADh27Bh4PB48PDyQmJiIUaNGoaysDFJS\nUggJCYGzszPdEUXi9evXWLVqFW7duoWSkhJqE6MAW4aAN/Wz+emGRrb8LrK0tMSyZcswceJEuLi4\n4M2bN1i7di3279+PxMREpKWl0R1RJNi+1Kdr1644d+4cBg4ciPr6eigrK+PEiRNU+/3jx48xePBg\nvHv3juak/93GjRub/drPt/4yWUVFBY4fP47MzEwADaftXFxcGv09JZjB398fWVlZrL+JqaCggOjo\naGpUBtuxva1ZQUEBMTEx6NevH91R/jX2/pSxlJycHN6+fQttbW2Eh4fD29sbANC+fXtUVFTQnE40\nQkJCsGPHDqSnpws93rt3byxbtow1W+y4XC709fXx+vVr1hfrfH19qX+PGDECmZmZSExMRI8ePVhz\nwgNoaMkSB9ra2gAaChtNbWBky9dU0KYuDlxdXal/m5mZIT8/H5mZmdDS0oKqqiqNyUTLw8MDT548\nwaJFixh3d/XfyMvLoztCq1i7di117ePn54dx48bB0tISKioqrOo0OHz4MH766adGxbqqqiocOXKE\n8cU6GxsbbNq0Cb/99hv+/PNP1NfXw8bGhno+IyMDOjo6tOUTBTYV4P4NeXl5zJkzh+4YLeLixYvN\nfi1bWgrFZdZ2t27dWHt9IPDx40f07NkTly9fZmWB7lOamppg6vk0crKOYaZMmYLnz5/D3Nwc+/fv\nR35+PtTV1fHXX39hxYoVjB/Yun37dqxduxYLFy6Eg4MDOnXqBKCh1/zGjRvYu3cvNm/eLFT8YbJL\nly4hICAAv//+O4yNjemOQxDNIpjlcfXq1SafZ8upHYJ9FBUVERkZif79+9MdhWghZWVlUFZWZsUb\nLcFSH2VlZWRnZ0NNTY16rq6uDpcuXcKqVasYP7P46dOnGDlyJHJzcyEhIYHdu3dj/vz51PMTJ06E\nrq4udu7cSWPKlvHhw4dGJ3wVFRVpSiN62dnZiIiIaPIk8/r162lKJRrN7S5g02lmT0/Prz5/6NCh\nVkrSsq5du4Zff/0VBw4cEGrRZ5uuXbvi5s2brJsh+bkbN25g+/bt1IgXJiHFOoZ59eoVVqxYgcLC\nQixYsACTJk0CAPzvf/+DhIQENm3aRHPCb6Ojo4OtW7di2rRpTT4fGhqKlStXoqCgoJWTtQxlZWVU\nVlaCx+NBSkqq0VwLwSIGglnKysqaPHEm2HTHdNOnT0d+fj527doFGxsbnDt3DsXFxdi8eTO2b9/O\nms2hBPv07NkTJ0+eRN++femO0ioyMjKa/F3EllMebMflcr9adORwONi4cSPWrFnTiqlaBo/HQ3p6\nOtTU1BrNPk1OTka3bt2goqJCUzrRysvLw8KFCxEZGSk0A4xtbeoHDhzA/Pnzoaqqis6dOwt9L3M4\nHFYtLyKYT01NTeh7tLy8HB8/foSioiI1z02ALWOn2NzW/PlNu4qKCvB4PMjJyTX6erbl99ukWEe0\nKbKysnj48OEXK/yPHz+GmZkZa+YKHT58+KvPs6XlV1wUFRXBzc0Nt2/fbvJ5tlyAa2ho4MKFCxg4\ncCAUFRXx4MEDGBgY4OLFiwgICEBsbCzdEUXm9OnTOHXqVJMFD/JGg3n++usv7N27F8HBwejcuTPd\ncVrM33//DScnJ6SmplKz6oD/m6XJ9N9FXl5ezXod09tDo6KixGqpj7gYOnQo+Hw+fHx80KlTp0YF\nWWtra5qSiZa2tja8vb2xcuVKuqMQIsTj8RAZGYnc3Fy4uLhAQUEBL168gKKiIqPniwcFBTX7tTNn\nzmzBJK2HzbOZ/+k99qfa8vttUqxjoMrKSjx69KjRkXIOhwMnJycak307S0tL6Ovr4+DBg42Ol9fX\n12PWrFnIzs5GTEwMTQkJ4sucnZ1RUlKCPXv2YPDgwbhy5QpKSkrw448/Yvv27XBwcKA7okgoKioi\nJSUFOjo60NbWxokTJzB06FDk5eWhd+/erCmm7969G2vWrIGHhwf2798PT09P5Obm4v79+1iwYAG2\nbNlCd0TiX9LQ0MDbt29RW1uLjh07Nrq7yvR2QgFHR0dISEjg4MGD0NXVxb179/D69WssW7YM27Zt\ng6WlJd0RvwmXy4W2tjZMTU2/Oofm3LlzrZiq5YjLUh9x0b59eyQmJsLQ0JDuKC1KUVERjx49gp6e\nHt1RWkVFRQWioqKavLm3ePFimlKJVn5+PkaNGoWCggLU1NQgKysLenp68PHxQU1NDfbt20d3ROJf\nEJe2ZiZj13lHMRAeHo5p06ahrKwMEhISQs9xOJxGfxyYZs+ePXBwcEDnzp1hY2MjNLMuKioKHA4H\n169fpzmlaOXm5uLQoUPIzc3Fr7/+CnV1dVy9ehVaWlro3bs33fGIfyEyMhJ//fUXjI2NweVy0blz\nZ1hZWUFWVhY//vgja4p1hoaGePLkCXR0dNC3b19qBsS+ffugoaFBdzyR+e2337B//35MmzYNISEh\n+OGHH6Cnp4f169e36SPz/0VBQQE0NTUbnfDg8/koLCyElpYWTclEa8OGDayYZfZP4uPjcfv2baiq\nqoLL5YLL5WLYsGHYunUrFi9ejKSkJLojfpP58+cjNDQUeXl58PT0hKurq9CpM7YRl6U+4mLAgAEo\nLCxkfbFu8uTJuHHjBubNm0d3lBaXlJSEMWPGoLKyEhUVFejYsSNKS0shJycHdXV11hTrfHx8YG5u\njuTkZKG2dCcnJ8yePZvGZKIlJSWF58+fC80JBRraJTt37sz499sC4lKMk5CQQFFREdTV1YUef/36\nNdTV1dt0twEp1jGMj48PJk2ahC1btjT6BcIG/fr1Q1ZWFo4cOYK7d+9SCzM6d+6MdevWwdXVFR06\ndKA5pehERUVh9OjRGDp0KKKjo7Flyxaoq6sjOTkZQUFBOH36NN0RRaaurg7nz5+nVoP37t0b48eP\nb1R0ZrLq6mrq57Jjx44oKSmBgYEBevXqheTkZJrTiY6Pjw+KiooANBQ/Ro0ahePHj0NKSgohISH0\nhhOhgoICDBkyBEBDi/779+8BADNmzMDgwYMRGBhIZzyR0tXVbfJCpqysDLq6um36QubfEIc3jUDD\n71vB5lBVVVW8ePEChoaG0NbWxpMnT2hO9+327t2LHTt24OzZswgODsbq1asxduxYzJw5E/b29qwr\nyJKlPuxy8OBBzJs3D8+fP4exsXGjE75sKb726NED69atw927d2FiYtLo82RLAQsAfH194ejoiH37\n9kFJSQl3796FpKQkXF1d4ePjQ3c8kYmJiUFcXBykpKSEHtfR0cHz589pSiV6PB6vyVPb1dXVrDvh\nzNa25k996QR+TU1No+/ltoYU6xgmPz8fly5dYmWhTkBJSQmLFi3CokWL6I7S4latWoXNmzdj6dKl\n1BsrALC1tWVVISAnJwdjx47Fs2fPqDvJW7duhaamJv766y90796d5oSi0bNnT2RkZFDtWXv37oWG\nhgYCAwPRtWtXuuOJjKurK/VvMzMz5OfnIzMzE1paWlBVVaUxmWh17twZZWVl0NbWhpaWFu7evYu+\nffsiLy+PsSvgv0Qw2PxzHz58gIyMDA2JWsacOXMwfPhwWFlZsepn8nPGxsZITk6Grq4uBg0ahICA\nAEhJSWH//v2saUmTlpbGtGnTMG3aNOTn5yMkJATe3t7UogK2vMkAgCVLluDt27dISEhocqkPwSyv\nXr1Cbm6uUAuaYLYkmxZM7N+/H+3bt0dUVBSioqKEnuNwOKwq1j169Ah//PEHuFwuJCQkUFNTAz09\nPQQEBMDd3Z1aCMh09fX1TX5/Pnv2TOh9DFP99ttvABq+P0NCQoT+jtTV1SEqKopVJ2I/b2seOXIk\nFBQU8PPPP7OirXn37t0AGr6eBw8ebPT1jI6ORs+ePemK1yykWMcwNjY2SE1NZU1xo7lKS0tRW1vL\nukHKqampOHHiRKPH1dXVUVpaSkOilrF48WLo6ekhPj6ealV6/fo1XF1dsXjxYvz11180JxSNZcuW\nUV+3DRs2YPTo0dDX14ecnByOHDlCc7qWIycnh/79+9MdQ+RsbW1x8eJFmJqawtPTE76+vjh9+jQe\nPHjAmgvvpUuXAmi4kFm3bh3k5OSo5+rq6pCQkIB+/frRFU/kKisrsXLlSjx//hx6enqwtraGjY0N\nrK2toampSXc8kVm7di0qKioAAH5+fhg3bhwsLS2hoqKCkydP0pxO9ARbU/l8PmsKHZ+6ffs2Lly4\nAHNzc2pe38iRI6GoqIitW7cyegN3SkpKs1/LlhNnXl5eMDU1RWhoaJMLJtgiLy+P7gitRlJSkjpx\npa6ujoKCAhgZGUFJSQmFhYU0pxMde3t77Nq1C/v37wfQcO3w4cMHbNiwAWPGjKE53bfbunUrgIYb\nmL/++qvQKTopKSno6Ojg999/pyueyLG9rXnnzp0AGr6e+/btE+rmEnw923pBkiyYYJhjx45hw4YN\nmD9/fpNHym1tbWlKJhofPnzAwoULERMTAxsbG+zbtw/Lly/Hnj17wOFwYGNjg/Pnz7Pi7g0AdOvW\nDadOncKQIUOgoKCA5ORk6Onp4dy5c1i+fDlyc3PpjigS8vLyVBvEp5KTkzF06FB8+PCBpmQtq7a2\nFjk5OejWrRsUFRXpjkP8S/X19aivr6fW2YeFhSEuLg76+vqYO3dumz863xzDhw8H0NCSb2FhIfQ5\nCS5kli9fDn19fboitojc3FxER0cjKioK0dHRyM/Ph66uLnJycuiO1mLKysqgrKzMmsJATU0N1QYb\nGxuLcePGwdPTE6NGjWJdmxKbl/p8WmhtChtPnMnLyyM5ORk9evSgOwohIvb29vDw8ICLiwtmz56N\nlJQULF68GEePHsWbN2+QkJBAd0SRePbsGRwcHMDn85GdnQ1zc3NkZ2dDVVUV0dHRjUZpMJWlpSUu\nXrwIZWVluqO0KBUVFcTFxcHQ0FDofejTp0/Rq1cvRv9t+dTw4cNx9uxZRn49yck6hnFzcwMA/PDD\nD42eY8OFzJo1axAXF4eFCxfi3LlzmDp1KjIzMxEREYG6ujosWLAAAQEB2LRpE91RRWLq1KlYuXIl\n/vzzT3A4HNTX1+POnTtYvnw59bVmA2lpaWre16c+fPjAioKHQEZGBnr16kV9LCUlJfQxwSyCwfwC\nU6dOxdSpU2lMJHoREREAGjaC/frrr2JTVNbU1ET37t1RUFCA/Px8PHv2rNHNL7Zh0wIGb29vhIWF\nQVNTE15eXggNDWVVC/7n2LzUR5xOXwnY2tqyvlhXVVWFxMREdOzYsdF1UHV1NU6dOsWq61x/f3/q\nOnfLli1wc3PD/Pnzoa+vj+DgYJrTiU63bt2QnJyMsLAwpKSk4MOHD5g5cyamT58OWVlZuuOJTExM\nDN0RWgXb25oFBNe6TERO1jGMoK3lS+Tl5VspScvQ1tbGoUOHYGtri+fPn0NTUxMXLlyAo6MjAODS\npUtYsWIFMjMzaU4qGrW1tViwYAFCQkJQV1eHdu3aoa6uDi4uLggJCWHN8gU3Nzc8fPgQQUFBGDhw\nIAAgISEBs2fPhpmZGWuWEkhISEBVVRVWVlZUa52xsTHdsYj/KDo6+qvPW1lZtVKS1pOTk4Pc3Fxq\ni/GXZtkxlZ+fHyIjI5GQkABtbW2qDfbT7eNM5uXl1azXMf3NI5fLhZaWFkxNTb/6/Xn27NlWTNVy\njh07Bh6PBw8PDyQmJmLUqFEoKyujlvo4OzvTHZH4F/bv34/NmzfDy8uryS6Z8ePH05RMNLKysmBv\nb4+CggJwOBwMGzYMYWFhVGG5uLgYXbp0YfwBA4J9fH19MWXKFFhYWNAdpVU4OztDSUkJ+/fvh4KC\nAlJSUqCmpoYJEyZAS0uLddtiKyoqcOrUKeTk5EBDQwPTpk0Tav9ti0ixjmHq6+tZ197xKRkZGWRn\nZ1Ozg+Tl5ZGUlAQDAwMADYMwe/fuzbq2yYKCAqSlpeHDhw8wNTVlXcvZ27dv4e7ujkuXLlEXpTwe\nD+PHj0dISAiUlJRoTigapaWl1CDlqKgopKWlQUVFBZaWlhg+fDgWLlxId0TiX2jqd+2nhQE2vdEo\nKyvD5MmTERERAQ6Hg+zsbOjp6cHLywvKysqsGWLP5XKhpqaGZcuWYerUqdDS0qI7kkgJ5pmZmpp+\ndQnKuXPnWjGV6Hl4eDSriMy2NxoClZWVrFnqc/HixWa/lulFLIGvXcezoUvGyckJHz9+REhICN6+\nfYslS5YgIyMDkZGR0NLSYm2xjq1bNcXpZ1TQlm9oaIiZM2fCzc2N1Usd2d7W3KtXL8TGxqJjx44o\nLCyElZUV3rx5AwMDA+Tk5EBSUhJ3796Frq4u3VG/iBTrGEZRURHDhg2jTgOYm5uz5vQVAHTt2hWX\nL1+GqakpAGDKlCnYs2cPdeIhPT0dlpaWKCsrozPmN2N70fVLcnJy8PjxYwCAkZERq1tAgIbPd/Pm\nzTh+/PgXj5ozhTgOAX/37p3Qxx8/fkRSUhLWrVuHLVu2wM7OjqZkoufm5oaSkhIcPHgQRkZG1NyS\n69evY+nSpUhPT6c7okgkJCQgKioKkZGRuHPnDlRUVKiTdTY2Nowv3i1YsAChoaHQ1taGp6cnXF1d\nWdX+SrBPc6+F2FDEEhedOnXCzZs3qTnFfD4f3t7euHLlCiIiIiAvL8+6Yt3nWzWzsrKgp6cHHx8f\nxm/V/PxntKkZk4KbJ0z/mnK5XFy9ehWXLl3CiRMnUFFRgXHjxmH27NlwcHBgVaeBAI/HE2pr7t+/\nP2vamrlcLl6+fAl1dXW4uroiLy8PV65cgZKSEj58+AAnJyeoqak1ueyxzeATjHL16lX+6tWr+RYW\nFnxJSUm+goIC38HBge/v78+Pi4ujO943c3Bw4P/xxx9ffP7w4cN8CwuLVkzUMrhcLr+4uJj6ePny\n5fzXr1/TmKjl1NbW8vX09PgZGRl0R2lxb9684V+4cIHv6+vLNzU15cvIyPAHDx7MX7VqFf/atWt0\nx/smHA6Hz+Vyqf9/7T+2i4yM5Pfv35/uGCLVqVMn/qNHj/h8Pp/fvn17fm5uLp/P5/Nzc3P58vLy\ndEZrMXV1dfx79+7x3d3d+e3ateNLSEjQHUkkqqur+SdOnOCPGDGCLycnx588eTL/2rVr/Pr6erqj\nEQQhBhQUFJq85luwYAG/W7du/OjoaNZdK0yYMIHv6urKr6mpEfobGhERwe/RowfN6UQnPDyc379/\nf/61a9f479694797945/7do1vrm5Of/GjRt0x/tmHA6Hen9WXV3NP378ON/Ozo7P5XL5mpqa/PXr\n1/Pz8vLoDUk026dfTz09vUbfo3fu3OFramrSEa3ZyIIJhhk1ahRGjRoFoKENIi4uDseOHcP69esZ\nf3IHaJjL8rW7rCoqKqxYLsH/7I7UH3/8gfnz57PyBISkpCSqq6vpjtEqVFRUoKqqCk9PT/z0008Y\nOnQo4+dICnw6BDwpKQnLly/HihUrqLke8fHx2L59OwICAuiK2Go6deqEJ0+e0B1DpCoqKiAnJ9fo\n8bKyMkhLS9OQqOUI2rEiIyMRHR2NkpIS9OzZE9bW1nRHEwlpaWlMmzYN06ZNQ35+PkJCQuDt7Q0e\nj4f09HRGt2MR4qO6uhoyMjJ0x2gRfn5+X31+/fr1rZSkZfTs2RMPHjyAkZGR0OOBgYEAmN8q2ZSY\nmBjExcU1Wpqmo6OD58+f05RK9JYsWYJ9+/Zh2LBh1GMODg6Qk5PDnDlzqO4ZNpCWloaLiwtcXFzw\n999/Izg4GMHBwdiyZQt4PB7d8f4zcWprBv7v1Gd1dXWjhUxdu3bFq1ev6IjVbKRYx0AvXryg3mhE\nRESgqKiIauFhun+avTJ27NhWStK6Pi/esc2CBQvw888/4+DBg2jXjr2/diZPnozo6GgcOHAAWVlZ\nyMzMhI2NDSvaQrW1tal/T548Gbt378aYMWOox/r06QNNTU2sW7cOEydOpCOiyH3e+svn81FUVISf\nfvoJ/fr1oylVy7C0tMSRI0eomyGC7dQBAQEYPnw4zelEp1OnTigtLYWRkRGsra2xZ88eWFtbM34u\ny5cI5u/w+XzG38wj2K+urg7+/v7Yt28fiouLqVbCdevWQUdHBzNnzqQ7okh8PjPy48ePyMvLQ7t2\n7dC9e3fGF+ucnJwQGhqKGTNmNHouMDAQ9fX1jG4LbYq4bNXMzc1Fhw4dGj2upKSEp0+ftn4gEftS\nm6uenh42b94MPz8/XL9+vZVTidbn1+hsbmsGADs7O7Rr1w7l5eV48uSJ0OK//Px8smCCEC0DAwMU\nFRXBwsKC2jY5cODARpuk2CYxMRE1NTUYOHAgK4o9n/bQA4CCggI1I4qNnJyccOvWLbRv3x4mJiaN\nTpuxZWOfQFZWFrVkIjo6GpWVlbC2tsaZM2fojiYSsrKyePjwYaO75o8fP0b//v1RVVVFUzLR+rTQ\n8anBgwcjODgYPXv2pCmZ6KWlpcHOzg79+/fH7du3MX78eKSnp6OsrAx37txB9+7d6Y4oEmfOnIG1\ntTXjh/J/TU1NDc6ePYvg4GDExsZi3Lhx8PT0xKhRo8RyVirBHH5+fjh8+DD8/Pwwe/ZspKWlQU9P\nDydPnsSuXbsQHx9Pd8QWU15eDg8PDzg5OTVZ5CLaNnHZqmllZQUZGRkcPXqUmideXFwMNzc3VFdX\nIyoqiuaE30ZTUxOPHj1q8wUcUbl58yZWrlwJf39/oU6ZtWvXwt/fHyNHjqQ54bfZuHGj0MeDBw+G\ng4MD9fGKFSvw7NkzhIaGtna0ZiPFOobp0aMHXr58iSFDhlCn6dhSwAIafuE7OzsjLi4OlpaWOHPm\nDDw8PKgju4aGhoiMjKT+QDAVl8vFnDlzqLazvXv3wtXVtdFW1B07dtART+Q8PT2/+jxbLmIEeDwe\n7t27h4iICOoULJfLRU1NDd3RRKJ///4wNjbGwYMHqZaP2tpazJo1C2lpaXj48CHNCUUjPz9f6GPB\nJlG2tma9e/cOgYGBSE5OpoYML1iwoFHbAFuUlpYC+OcT3Uzi7e2NsLAwaGpqwsvLC9OnT2fV5ydO\nxHGpT48ePfDHH3/Azs5O6CZmZmYmLCws8ObNG7ojtqjU1FQ4Ojqy4oSSuGH7Vk2BnJwcODk5ISsr\nC5qamgCAwsJC6Ovr4/z586xfHMc2xsbGjdqagYa2bra1NTMVKdYxkKANVnBy5/nz57CwsMDw4cOx\nevVquuN9Ew8PD2RmZmLlypU4ceIEiouLUVdXhxMnTqCurg6urq4wNzfH7t276Y76TWxsbP5xoxCH\nw8Ht27dbKREhCv7+/oiMjER8fDw+fvwIMzMzanMzm+bX3bt3D46OjuDz+dSbxJSUFHA4HFy6dAkD\nBw6kOaHosXl+EtDQhvWlE9qlpaWsKfjw+Xz88ssv2LZtG16/fg33MzIDAAAgAElEQVSgYdbkihUr\nsHz5csZveuNyudDS0oKpqelXPxe2nWZmo09P9v7T9yUbWpWAhlPbmZmZ0NbWFirWZWRkYODAgfjw\n4QPdEVtUbGwsHB0dWV+UZCsej4eTJ08K3fBiy1bNT/H5fISHhyMzMxMAYGRkhBEjRjD+76c4kpWV\nxf3794VaQ4GGa/pBgwaxplOGyUixjsEqKysRExOD0NBQHD9+nBULJrp27YrTp0/DwsICr1+/hpqa\nGq5fv04dw7116xbmzp2LnJwcmpMS/xaPx0NkZCRyc3Ph4uICBQUFvHjxAoqKiqwZeG5paUkV54YM\nGdLkwH62qKiowPHjx4Uu1lxcXFhTkATEZ34SAHz33Xc4ffp0o4vt4uJi2NnZIS0tjaZkorV+/Xrs\n3bsXa9euxdChQwE0vEHesmULFi1ahB9//JHegN/Iw8OjWW+Y2HaamY0+Pdn7T0t92DIn1MzMDL6+\nvnB1dRUq1vn5+SE8PBwxMTF0RxSJz284C+ahHj16FNbW1jhx4gRNyQhCfC1cuBBr1qxpVjfBmTNn\nUFtbi2nTprVCspbD9rZmNmBH76QYuXHjBtVWl5iYCCkpKVhYWGDjxo2s2GRXVlaGbt26AWg47SAn\nJwddXV3qeX19fbx48YKueMR/lJ+fj1GjRqGgoAA1NTUYOXIkFBQU8PPPP6OmpoY1g4bZ8kaiOeTl\n5TFnzhy6Y7SoLVu24PDhwwgICMDs2bOpx42NjbFr1y5WFesKCgowa9YsBAUFUY8VFRXB1tYWvXv3\npjGZaAUFBeHgwYNwcnKiHhs4cCC0tbXh4+PD+GJdSEgI3REIERHHpT7r16+Hu7s7nj9/jvr6epw9\nexZPnjzBkSNHcPnyZbrjiczOnTuFPhaMWHB3d2d8h4y4ev36NTXnrLCwEAcOHEBVVRUcHR1hZWVF\nczrRqqioQFRUFAoKClBbWyv03OLFi2lK9e0UFBSozfCOjo4wNzdHly5dICMjgzdv3iAjIwOxsbEI\nDQ2FqqoqDhw4QHfkbxYcHAwnJydoaWk12dZM0I+crGMYRUVFal6dtbU1BgwYwJp5dUDDxemff/5J\ntdEtX74c//vf/9CxY0cADcdy7ezs2vya5a/56aefsHjx4madukpISEBpaSnjt+BOnDgRCgoKCAoK\ngoqKCnW3PDIyErNnz0Z2djbdEUWmtLQUf/zxBzXnoVevXpg7dy7rhtVmZ2cjIiICJSUlqK+vF3qO\n6ZvsBMRpftKrV69gZWWF0aNHY8eOHXjx4gWGDx+Ovn37IiwsjDWLCWRkZJCSkgIDAwOhx7OystC3\nb1/S8kG0SeKy1AdouOnl5+cn1Eq4fv162Nvb0x2NIBoRzBkUFDjCwsIwatQoVFRUgMPhoLKyEqdP\nn2ZNQT0pKQljxoxBZWUlKioq0LFjR5SWlkJOTg7q6ur4+++/6Y74TV68eIEDBw4gLCwMT548ETqp\nLicnBzs7O8yaNQvjxo2jMaVokbbmto0U6ximrq4OEhISdMdoMRMmTICdnd0X78z89ttvOH36NKNn\nubm5ueHq1auYPHkydedGTU0NQEOrqODOzbFjx/DixQscOXKE8XflVFRUEBcXB0NDQ6Gix9OnT9Gr\nVy9UVlbSHVEk4uPjMWrUKKioqGDw4MEAgLt376KsrAzXr1/HoEGDaE4oGgcOHMD8+fOhqqqKzp07\nC/1B53A4rFkwIW7zkwoLCzFs2DB89913uHz5Mvr374/jx4+z6m+Oubk5bGxssG3bNqHHly1bhujo\naNy/f5+mZATxZeKy1IcgmGb06NFo164dVq1ahaNHj+Ly5ctwcHCgTl0tWrQIiYmJuHv3Ls1JRcPG\nxgYGBgbYt28flJSUkJycDElJSbi6usLHxweTJk2iO6LIlJaWIj8/H1VVVVBVVYW+vj6rrocIZiDF\nOoa6fv260MkdttxxFHw7fqmaf/fuXcjKyqJv376tGUvkkpOTERgYiNOnT6O8vBwSEhKQlpamilam\npqaYNWsWPDw8WDHUXllZGXfu3EGvXr2Eih6xsbH47rvvUFxcTHdEkRgyZAi1WUlwEqm+vh7z5s1D\neno67ty5Q3NC0dDW1oa3tzdWrlxJd5QWJS7zkz6VlZUFS0tLjBw5EkePHmXdndWbN2/C0dERPXv2\nxJAhQwAAcXFxePLkCS5fvgxbW1uaExJEY+Ky1Gf9+vUYPnw4LCwsWHHt86l/U8QgC2CYQ1VVFbdv\n30afPn3w4cMHKCoq4v79+zAzMwMAZGZmYvDgwXj79i3NSUWjQ4cOSEhIgKGhITp06ID4+HgYGRkh\nISEB7u7u1OksgjnY2tbMFqRYxzAFBQVwdHTE48ePoaOjAwB4+vQpjI2NceHCBarfnGCG+vp6pKSk\nCN256devH2s2Lwo4OztDSUkJ+/fvh4KCAlJSUqCmpoYJEyZAS0uLNcPOZWVl8ejRIxgaGgo9npmZ\nCVNTU9a0KikqKuLRo0fQ09OjO0qLunDhAjVDyM/PDxs3bhSanyRYfMNUysrKTRbjKisrIS0tLXQH\nuaysrDWjtaj8/Hzs2bOHuuFlZGSERYsWCc0II4i2RhyW+owcORLx8fHg8XgYMGCA0DZ1pm/U9PT0\nbPZr2XJNJA64XC5evnwJdXV1ABC6sQc0DOvv0qUL4xcACqipqSEuLg76+vowMDDAnj174ODggMzM\nTJiZmaGiooLuiMS/wPa2ZjYgxTqGmTBhAsrLy3H8+HF06dIFAPD8+XPMmDEDioqKjB4Gef/+fQwY\nMKBZr62qqsLTp08bzW8h2qZnz57BwcEBfD4f2dnZMDc3R3Z2NlRVVREdHU1d5DCdhoYGgoKChIaA\nA8CVK1cwc+ZMFBUV0ZRMtGbOnIkBAwZg3rx5dEdpcWyen3T48OFmv9bd3b0Fk7Q8Pz8/LF++nNUb\nmgmCDXg8HhISEhAdHY2oqCjExcWhpqYGAwYMQGxsLN3xCEIIl8tFcXExNc5GcENasByPbcU6e3t7\neHh4wMXFBbNnz0ZKSgoWL16Mo0eP4s2bN0hISKA7IvEviFNbM1ORYh3DKCgoIDY2tlEbaFJSEqys\nrPD+/Xuakn07PT09GBkZYdasWRg9enSTLRBZWVk4duwYgoKCsGXLFnh4eLR+UOI/4fF4CAsLQ0pK\nClX0mD59OuPvln9q2bJlCA0NxdatW6kWuzt37uB///sfpk+fjl9++YXmhKKxdetW7NixA2PHjoWJ\niQkkJSWFnifH5pmFx+PhxIkTcHBwQKdOneiO0yIkJCRQVFTEmhsDhPgRh6U+n8rKykJERARu3ryJ\n8+fPQ0lJCaWlpXTHEqlXr17hyZMnAABDQ0Oq4EMwB5fLxejRoyEtLQ0AuHTpEmxtbakTrzU1Nbh2\n7RprinUPHjzA+/fvMXz4cJSUlMDNzY06aRcUFIR+/frRHZH4F0hbc9tHinUM06FDB9y4caPRfJKE\nhAQ4ODgweiZCbW0tAgMDsXfvXhQWFsLIyEhoZfbjx4/x9u1bjB8/HmvWrCF/EIg25+PHj1i7di32\n7NmDmpoa8Pl8yMjIwMfHB35+fo2KWkwluGPcFA6Hw5pj83p6erh//36jTb5v375F//79WfN5Ag1b\nzh4/fszaVtDPW5UIgknEZanP/v37ERkZiaioKNTU1MDS0hI2NjawsbFBnz59WDNDs6KiAosWLcKR\nI0eowquEhATc3NywZ88ecgKYQZrb3kxam5nhxYsXVOeaOCBtzW0fKdYxzNSpU5GdnY0jR46gd+/e\nAIC0tDR4eHjAwMAAJ06coDmhaNy9exexsbFCs9xMTU1ha2tL7jwyxMWLF5v92vHjx7dgktb38eNH\nPH36FACgo6PDmiKduPlSgae4uBhaWlqoqamhKZno2djYYMmSJZg4cSLdUVrE561KBMEk4rLUh8vl\nQk1NDcuWLYO3tzfat29Pd6QWMXfuXNy8eROBgYEYOnQoACA2NhaLFy/GyJEj8fvvv9OckCCalpeX\nBx6PB319faHHs7OzISkpSc1TZypxO4VP2prbPlKsY5hXr15h6tSpiIiIgKKiIgDg/fv3sLW1RWho\nKOsWExDMJdiGKsDhcPD5rxvBXXK2tAcQ7CAoNE+cOBGHDx+GkpIS9VxdXR1u3bqF8PBwqn2JDU6d\nOoXVq1fD19cXZmZmjYbWCzZQMhWXy4WSktI/nsxh0yINgj3EZanP+fPnER0djcjISDx+/BimpqbU\nybphw4ax5sSZqqoqTp8+DRsbG6HHIyIiMGXKFLx69YqeYATxD6ytrTF79my4uroKPX7s2DEcPHgQ\nkZGR9AQTEXE7hU/amts+UqxjqEePHgltsiM/TERbdvPmTaxcuRL+/v6wsLAAAMTHx2Pt2rXw9/dn\n/FbNzxdKfMmVK1daOEnLq6qqQmJiIjp27IhevXoJPVddXY1Tp07Bzc2NpnSiISg0N1VgFtw53r59\nO8aNG0dHvBbxeXEd+L/Pn8PhML6gzuVysWvXLqHCa1OYvkiDYCdxWuoj8O7dO8TExODPP/9EaGgo\nuFwuqqur6Y4lEnJyckhMTGy0JC09PR0DBw4krWdEm6WoqIikpCR0795d6PGcnByYm5szehwTQE7h\nE21PO7oDEM1jYGCAu3fvomPHjgCAfv36kQIdSzx48ACnTp1CQUEBamtrhZ47e/YsTalEa8mSJdi3\nbx+GDRtGPebg4AA5OTnMmTOHKjwz1edzvoKCgjB58mTq9CtbZGVlwd7eHgUFBeBwOBg2bBjCwsKg\noaEBoOHNlaenJ+OLdYIZQrq6urh//75YnFjOy8ujO0KLmzp1qtjcLSfYpUePHli3bh3u3r3L+qU+\nr1+/RlRUFCIjIxEZGYn09HQoKyvD0tKS7mgiY2FhgQ0bNuDIkSPUMrWqqips3LiRuqFJEG0Rh8NB\neXl5o8ffvXvH+Jt6Ahs3bvzHU7wBAQGtlKZlsb2tmQ3IyTqGELdjueIiLCwMbm5ucHBwwI0bN2Bv\nb4+srCwUFxfDycmJNQNpZWVlcf/+fRgbGws9npKSgkGDBqGqqoqmZC1DQUEBycnJrGtZcnJywseP\nHxESEoK3b99iyZIlyMjIQGRkJLS0tFBcXIwuXbqw5oKNYA9xm0NDsIu4LPUxMTHB48ePoaysDCsr\nK9jY2MDa2prxbfifS0tLg4ODA2pqatC3b18AQHJyMmRkZHD9+nVqJjVBtDWOjo6QlZVFaGgoJCQk\nADSMB3F2dkZFRQWuXr1Kc8Jvw+VyMXDgwK/OmuZwOIiOjm7FVC2H7W3NbECKdQxBinXs1KdPH8yd\nOxcLFiygCjy6urqYO3cuNDQ0sHHjRrojioSVlRVkZGRw9OhRdOrUCUDDkH43NzdUV1cjKiqK5oSi\nxdZiXadOnXDz5k2YmJgAAPh8Pry9vXHlyhVERERAXl6eFcW6+Ph4vH79WqjN9ciRI9iwYQMqKiow\nceJE7NmzB9LS0jSmbBkZGRlNnvJl+hIY8jeUINq+vXv3wtrautGNPTaqrKzE8ePHkZmZCaBhpM30\n6dMhKytLczKC+LL09HRYW1ujQ4cO1GnXmJgYlJeX4/bt24z/2RW3awW2tzWzAWmDZZBbt27947yd\n5s7OItqG3NxcjB07FgAgJSWFiooKcDgc+Pr6wtbWljXFuuDgYDg5OUFLSwuampoAgMLCQujr6+P8\n+fM0pyOaq6qqCu3a/d+fDQ6Hg99//x0LFy6EtbU1a7ZR+/n5wcbGhirWpaamYubMmfDw8ICRkRF+\n+eUXdOnSBT/++CO9QUXo77//hpOTE1JTU4Vm9bFlCYygtZkgiLZrwYIF1L8//x3ENnJycpg9ezbd\nMQjiX+nduzdSUlIQGBiI5ORkyMrKws3NDQsXLqRGNTEZW3/ffIk4tDUzHSnWMcj06dO/+jwbhoAL\nvHz5EkpKSo3uMPJ4PNy7dw9DhgyhKZloKSsr4/379wCArl27Ii0tDSYmJnj79i0qKytpTic6PXr0\nQEpKCsLDw4XuIo8YMULs/jAyWc+ePfHgwYNGQ7EDAwMBMP/0lcCjR4+wadMm6uOwsDAMGjQIBw4c\nAABoampiw4YNrCrW+fj4QFdXF7du3YKuri7u3buH169fY9myZdi2bRvd8QhCbInDUp9PHTlyBL/8\n8guys7MBNMxsXrFiBWbMmEFzsm+XmJiI5cuX48KFC41m2r579w4TJ07ETz/9hEGDBtGUkCC+jMfj\nwd/fH15eXvD396c7TosQt4ZDKysrbN26tVFb89atW4XmjBP0IcU6Bnn+/Dnrj+UKZrUlJCRAQkIC\nrq6u2LNnD+Tl5QE0DB62tLRkTVHSysoK4eHhMDExweTJk+Hj44Pbt28jPDwcdnZ2dMcTKQ6HA3t7\ne9jb29MdReROnTol9HFdXR2uXLnS6Od1ypQprRlL5JycnBAaGtrkm6bAwEDU19dj3759NCQTrTdv\n3lDt2gAQFRWF0aNHUx8PGDAAhYWFdERrMfHx8bh9+zZUVVXB5XLB5XIxbNgwbN26FYsXL0ZSUhLd\nEQlC7IjLUh+BHTt2YN26dVi4cCGGDh0KAIiNjcW8efNQWloKX19fmhN+m+3bt8PW1rbJ5VNKSkoY\nMWIEtm/f3uiagiDagnbt2iEgIIA1v2+acuDAgX/sYquqqmJNu/pPP/0Ea2trGBoaNtnWTNCPzKxj\nCHHpoffy8kJaWhp2796Nt2/fYvXq1ZCWlsb169ehpKSE4uJiaGhosKalqaysDNXV1ejSpQvq6+sR\nEBCAuLg46OvrY+3atVBWVqY7oshUVFQgKiqqyXlYTN9k15w/2hwOh1WnJdlMW1sbR48ehZWVFWpr\na9GhQwdcunSJKqCnpqbC2toaZWVlNCcVHWVlZTx8+BC6urro3r07Dh48iOHDhyM3NxcmJibke5cg\naCBuS310dXWxcePGRsWAw4cP48cff2T81uru3bvj3LlzX1yYkZqaigkTJrBmYQjBPhMmTMCkSZPg\n7u5Od5RWV1NTg8DAQPzyyy94+fIl3XFE5sWLF0JtzX369GFNWzMbkJN1RJty48YNnDlzhmoBsLa2\nxvfffw9bW1vcvHkTALvmCXz6i5DL5WLVqlU0pmk5SUlJGDNmDCorK1FRUYGOHTuitLQUcnJyUFdX\nZ3yxjm3bbMXdmDFjsGrVKvz88884f/485OTkqDuOQMMW48+H8TKdsbExteBm0KBBCAgIgJSUFPbv\n38+6RSkEwRRxcXG4efMmVFVVoaqqikuXLsHb2xuWlpbUUh82KSoqanLMyZAhQ1BUVERDItF6/vw5\nFBQUvvh8+/btWfF5Euw1evRorFq1CqmpqTAzM2v0O4jp41Bqa2uxYcMGhIeHQ0pKCj/88AMmTpyI\nQ4cOYc2aNZCQkGD8CV8BcWhrZgMu3QGI5hGXDVFv376FiooK9bGsrCzOnz+Prl27wtbWFqWlpTSm\nE70vLZF48+YNbG1taUjUMnx9feHo6Ig3b95AVlYWd+/eRX5+PszMzMg8LKLN2bRpE9q1awdra2sc\nOHAABw4cgJSUFPV8cHAw69q5165dS51Y9vPzQ15eHiwtLXHlyhXs3r2b5nQEIZ6+tNTH0dER1tbW\nyMrKojGd6PXo0aPJFtCTJ09CX1+fhkSipaamhidPnnzx+czMTKiqqrZiIoL4d7y9vVFcXIwdO3Zg\n+vTpmDhxIvWfk5MT3fG+2bp16/D7779DV1cXT58+xeTJkzFnzhzs3LkTO3bswNOnT7Fy5Uq6Y4qE\noK2Zx+PRHYX4CtIGywCVlZWQk5Nr9uuZ3Evfp08f/Pjjj5g0aZLQ4x8/fsSkSZOQlpaGgoIC1rR8\ncLlcqKioYOjQoTh+/Dh1h4ptrS0dOnRAQkICDA0N0aFDB8THx8PIyAgJCQlwd3enlk4wUWRkJGxs\nbJr12vLycuTl5aFv374tG4oQiXfv3qF9+/bU0F2BsrIytG/fXqiAx0ZlZWVQVlZm1WlmgmCSgQMH\nYtGiRU3OCV24cCGOHz+O8vJy1lwrnDlzBs7OzhgxYgQ1s+7OnTu4desWTp06xfhigKenJ3JychAT\nE9PoOT6fD0tLS+jr6+PQoUM0pCMIQk9PD7t27cL48eORlpaGPn36wMPDA0FBQay8FhLntmamICfr\nGEBfXx/btm1DSUnJV18XEREBR0dH7Ny5s5WSid6oUaOojYufkpSUxNmzZ9G7d28aUrWsmzdv4uXL\nlxg8eDCePn1Kd5wWISkpCS634deNuro6CgoKADQMVGb6oH5vb29YW1vj6NGjePPmTaPn+Xw+Hj58\niKVLl0JfXx/p6ek0pCT+CyUlpUaFOqChfZ3thTqg4fNk48UpQTCFYKlPUwIDAzFt2jRWbS/87rvv\nkJCQAFVVVZw/fx7nz5+Hqqoq7t27x/hCHdBwgjk1NRWDBg3CqVOnkJycjOTkZJw8eRKDBg1CWloa\n1qxZQ3dMghBbz549g5mZGYCG8SDS0tLw9fVl7bWQoK15+fLlCA0NxcWLF4X+I+hHTtYxQEZGBlav\nXo3r16/DzMwM5ubm6NKlC2RkZPDmzRtkZGQgLi4O9fX1WLVqFby9vYXaJpiktrYWHz58+OJQSx6P\nh/z8fNbMixIsDlFSUoKnpyfCw8Px559/wsjIiFUn6+zt7eHh4QEXFxfMnj0bKSkpWLx4MVXgSkhI\noDvif1ZXV4egoCAEBgYiPT0dOjo6Qj+fWVlZqKurw5QpU7Bq1SoYGhrSHZkgKF5eXs16XXBwcAsn\nIQiCYL8HDx7Aw8MDGRkZVAGAz+ejV69eOHToEAYMGEBzQoL4OjYvjJOQkMDLly+hpqYGAFBQUEBK\nSgp0dXVpTtYyBAcpmsLhcFjzPpTJSLGOQfLy8nDq1CnExMQgPz8fVVVVUFVVhampKRwcHDBu3DjG\nFunElYSEBIqKiqgtv5s3b8bmzZuxcuVKbN68mTW/JB88eID3799j+PDhKCkpgZubG7X1NigoCP36\n9aM7okikpaUhNja20c+nlZXVV4dKEwRduFwutLW1YWpq+tUTOufOnWvFVARBiJP6+nps27YNFy5c\nQG1tLezs7LBhwwbGjnRpjkePHiE7Oxt8Ph8GBgasuQ4i2O2fFsYxfZMxl8vF6NGjIS0tDQC4dOkS\nbG1tGy3SOHv2LB3xCDFEinUEQSPByTpBsQ5omNni7u6Oqqoq1hTrCIJomxYsWIDQ0FBoa2vD09MT\nrq6uXzzZTBAE0RI2bdqEjRs3YsSIEZCRkcH169cxbdo0cqKXINoYGxsbGBgYYN++fVBSUkJycjIk\nJSXh6uoKHx+fRjPHmcbT07NZryNzJYnWQop1BEGj/Px8aGlpNZqFkJaWhsTERNYM/MzLywOPx2u0\nzS07OxuSkpLQ0dGhJxhBEKipqcHZs2cRHByMuLg4jB07FjNnzoS9vT1r57QQBNF26OvrY8WKFZgz\nZw6Ahlm+Y8eORVVV1VfbtAiCaF1sXhgnrtjc1swGpFhHEESLs7a2xuzZs+Hq6ir0+LFjx3Dw4EFE\nRkbSE4wgCCH5+fkICQnBkSNHwOPxkJ6ejvbt29MdiyAIFpOWlkZOTg40NTWpx2RkZJCTk4Nu3brR\nmIwgiE+pqalRY2wMDAywZ88eODg4IDMzE2ZmZqioqKA7Yovh8/m4du0agoKCcPr0abrjiATb25rZ\ngAw4IwiaPXjwAKdOnWryjgZbZiIkJSXBwsKi0eODBw/GwoULaUhEEERTuFwuOBwO+Hw+acMnCKJV\n8Hg8yMjICD0mKSmJjx8/0pSIIIimmJqa4v79+9DX14e1tTXWr1+P0tJSHD16FMbGxnTHaxF5eXkI\nDg5GSEgIXr16hREjRtAdSWR8fX3h6OhItTXfvXtXqK2ZoB8p1hFtVnl5OR48eICSkhLU19cLPefi\n4kJTKtEKCwuDm5sbHBwccOPGDdjb2yMrKwvFxcVwcnKiO57IcDgclJeXN3r83bt3pCBAEDT7tA02\nNjYW48aNQ2BgIEaNGkVa0AiCaHF8Ph8eHh7UUHcAqK6uxrx584QGu7PlBmZBQQE0NTUbjRng8/ko\nLCyElpYWTckI4uv8/f3x/v17AMCWLVvg5uaG+fPnQ19fn1UzJmtqanD69GkEBQUhNjYWdXV12LZt\nG2bOnAlFRUW644nMo0eP8Mcff4DL5UJCQgI1NTXQ09NDQEAA3N3dGT+DkA1IGyzRJl25cgUuLi4o\nLy+HnJyc0AXNlwo/TNSnTx/MnTsXCxYsgIKCApKTk6Grq4u5c+dCQ0MDGzdupDuiSDg6OkJWVhah\noaGQkJAAANTV1cHZ2RkVFRW4evUqzQkJQjx5e3sjLCwMmpqa8PLywvTp06Gqqkp3LIIgxIi4DXWX\nkJBAUVGR0HIxAHj9+jXU1dXJTUyCoEliYiKCgoIQGhqKHj16YMaMGXB2dka3bt2QnJyMXr160R1R\npMS5rZkpSLGOgcThxJmhoSFGjhyJrVu3QkFBge44LUZeXh7p6enQ0dGBiooKIiMjYWJigsePH8PW\n1hZFRUV0RxSJ9PR0WFtbo0OHDrC0tAQAxMTEoLy8HLdv32b00fmAgIBmv/aHH35owSQE8e9xuVxo\naWnB1NT0q8sk2HKihSAIgm5cLhfFxcVQU1MTejw/Px+9evUib5CJNq+kpARPnjwBAPTs2bPR9zJT\ntWvXDosWLcK8efNgaGhIPS4pKcnKYp29vT08PDzg4uKC2bNnIyUlBYsXL8bRo0fx5s0bJCQk0B1R\n7JE2WIb5pxNnbCnWPXv2DEuXLmV1oQ4AlJWVqePkXbt2RVpaGkxMTPD27VtUVlbSnE50evfujZSU\nFAQGBiI5ORmysrJwc3PDwoUL0bFjR7rjfZPm3unncDikWEe0OW5ubmTjK0EQRCtYunQpgIbrgXXr\n1kFOTo56rq6uDgkJCejXrx9d8QjiH71//546kS84ASohIQFnZ2fs3bsXSkpKNCf8NnZ2dggKCkJJ\nSQlmzJgBBwcHVl8jiUtbM5ORYh3D+Pr6wtXVlfUnzkaMGOYhBYMAACAASURBVIGHDx9CT0+P7igt\nysrKCuHh4TAxMcHkyZPh4+OD27dvIzw8HHZ2dnTHEwkejwd/f394eXnB39+f7jgi9/jxY7ojEMR/\nFhISQncEgiAIsZCUlASgYTZdamoqpKSkqOekpKTQt29fLF++nK54BPGPZs2ahaSkJFy+fJlaHBcf\nHw8fHx/MnTsXYWFhNCf8NtevX0dhYSEOHTqE+fPno6qqCs7OzgDAyqKdubk59W91dXVcu3aNxjRE\nU0gbLMPIy8sjNTWV9UWsQ4cOYePGjZg1axZMTEwgKSkp9PyYMWNoSiZaZWVlqK6uRpcuXVBfX4+A\ngABqdsDatWuhrKxMd0SRaN++PdLS0qCjo0N3FIIgCIIgCNp4enri119/ZdWgekI8yMvL4/r16xg2\nbJjQ4zExMRg1ahTrWrjDw8Nx6NAhnDt3Dpqamvj+++/x/fffo3///nRHEym2tjWzASnWMcyECRMw\nY8YMfP/993RHaVFf20DI4XDI8F2GmTBhAiZNmgR3d3e6o7S4vLw8nDt3DgUFBaitrRV67rfffqMp\nFUEQBEEQBEH8d1paWvjrr79gYmIi9HhKSgrGjBmDZ8+e0ZSsZb158wbHjh1DcHAwUlJSWPM+lO1t\nzWxA2mAZZuLEiVi+fDkyMzNZfeLs48ePdEdoMf9mky1b7rqOHj0aq1atQmpqKszMzCAvLy/0/Pjx\n42lKJlrXrl2Dk5MTjI2NkZycjP79++Pvv/9GbW0thgwZQnc8giAIgiDagAcPHuDUqVNN3tgjC32I\ntmrt2rVYunQpjh49is6dOwMAXr58iRUrVmDdunU0p2s5ysrKWLRoERYtWoSHDx/SHUdk2N7WzAbk\nZB3DkBNnzMflcv9x7gGfz2fV11Ncvm/Nzc3h5OSENWvWQEFBAcnJydDQ0ICbmxuGDRsGHx8fuiMS\nBEEQBEGjsLAwuLm5wcHBATdu3IC9vT2ysrJQXFwMJyenZi+uIojW8Pm2+OzsbNTU1EBLSwsAUFBQ\nAGlpaejr67OqkCVQUVGBkydPoqqqCg4ODujRowfdkURG3NqamYicrGMYNp84++233+Dl5QUZGZl/\nbBf09vZupVSiFxERQXeEVldfX093hFaRmZmJqVOnAmhY815ZWQlZWVmsX78eY8eOJcU6giAIghBz\n/v7+2LlzJxYsWAAFBQX8+uuv0NXVxdy5c6GhoUF3PIIQMnHiRLojtJqCggLMmDEDDx8+xODBgxEU\nFISRI0ciOzsbACArK4urV6/CysqK5qSioaKi0mSrq5KSEmvmpjMdOVlHtBmampp49OgRVFRUoKmp\n+cXXcTgcFBQUtGIygmgeDQ0N3Lp1C7169ULv3r2xefNmODk54eHDh7C2tqbWoxMEQRAEIZ7k5eWR\nnp4OHR0dqKioIDIyEiYmJnj8+DFsbW1RVFREd0SCEEtTpkxBYWEhFi5ciFOnTiErKws9evRAUFAQ\nOBwO5s+fj7KyMty+fZvuqCKxf/9+/Pnnn43amt3d3TFp0iTMnTuX5oQEOVnHAOJy4qywsLDJf7NR\nRUUFVqxYgQsXLqC2thZ2dnbYs2cPq7fvVFRUICoqqsn5LIsXL6YplWhZWFggOjoavXr1wrhx47B0\n6VI8fPgQFy5cIDPrCIIgCIKAsrIydfOua9euSEtLg4mJCd6+fYvKykqa0xFE83z48KFR5wzTZ21H\nR0fj4sWLGDhwIEaPHg1VVVUEBwdDXV0dALBu3TrY2dnRnPLbNNXWrKWl1ait+dWrV6RY1waQk3UM\nQE6csc/SpUuxf/9+uLq6QlpaGqGhoRg6dCjOnTtHd7QWkZSUhDFjxqCyshIVFRXo2LEjSktLIScn\nB3V1dfz99990RxSJgoICVFRUwMjICFVVVVi9ejXi4uKgr6+Pn3/+Gd26daM7IkEQBEEQNHJxcYG5\nuTmWLl2KTZs2Yc+ePZgwYQLCw8PRv39/smCCaLPy8vKwcOFCREZGorq6mnqcLbO2uVwuioqK0KlT\nJwBA+/btkZKSAj09PQBAcXExunTpwujPc+PGjc1+7YYNG1owCdEcpFhHtFlFRUW4dOlSkyexAgIC\naEolGrq6uggICMDkyZMBAImJiRg8eDCqqqrQrh37Drza2NjAwMAA+/btg5KSEpKTkyEpKQlXV1f4\n+Phg0qRJdEckCIIgCIJocWVlZaiurkaXLl1QX1+PgIAA6sbe2rVryawoos0aOnQo+Hw+fHx80KlT\np0YL86ytrWlKJhpcLhcvX76kTtIJlsWxqVhHMAsp1hFtUkREBBwdHaGpqYmcnBwYGRkhPz8fHA4H\nffr0QXR0NN0Rv4mkpCTy8/PRpUsX6jE5OTlkZmZSx5DZpEOHDkhISIChoSE6dOiA+Ph4GBkZISEh\nAe7u7sjMzKQ7okiVl5ejpKSkUXuAgYEBTYkIgiAIgiAI4r9r3749EhMTYWhoSHeUFsHlcjFnzhzI\nyckBAPbu3QtXV1dqCUNlZSUOHDjAymIdG9ua2YB9R3jEAJtPnAmsWrUKS5YswebNm6GgoIDz589D\nVVUV06dPh6OjI93xvll9fT0kJSWFHmvXrh0rf/kDDcVJLpcLAFBXV0dBQQGMjIygpKTEqvmE6enp\n8PDwaLS6ni3tAQRBEARBEIR4GjBgAAoLC1lbrLOyssKTJ0+oj4cMGdJoVA9bNsEC7G9rZgNSrGOY\nfzpxxhYZGRk4fvw4gIYiVlVVFRQVFbFp0yY4OTlhzpw5NCf8Nnw+H3Z2dkItr5WVlXB0dISUlBT1\n2OdFH6YyNTXF/fv3oa+vD2tra6xfvx6lpaU4evQojI2N6Y4nMjNnzoSSkhJu3rwJDQ2NRu0BBEEQ\nBEEQBMFEBw8exLx58/D8+XMYGxs3OnjA9PeikZGRdEdoVa6uruDz+QgODm6yrZmgHynWMQzbT5wJ\nyMvL4+PHjwCAzp07Izc3F7179waXy8WrV69oTvftmhrYOWHCBBqStA5/f39q89mWLVvg5uaG+fPn\nQ19fH8HBwTSnE53U1FQkJSWRdleCIAiCIAiCVV69eoXc3Fx4enpSj3E4HHISi6GSk5NZ3dbMBqRY\nxzBsP3EmMGjQINy5cwdGRkYYPXo0VqxYgcePH+PMmTMYOHAg3fG+mbht1zE3N6f+ra6ujmvXrtGY\npuWYmJjg5cuXpFhHEARBEARBsIqXlxdMTU0RGhpKTmKxANvbmtmAFOsYhu0nzgS2b99OncTy8/ND\neXk5Dh8+DH19fezatYvmdMR/VVJSQs2C6NmzJ9TU1GhOJFqbNm3C8uXL4efnBxMTk0btAYLtUgRB\nEARBiLecnBzk5ubCysoKsrKy1Okkgmir8vPzcfHiRfTo0YPuKIQIsL2tmQ1IsY5h2H7iDADq6upQ\nUlKC3r17A2jYPHTw4EGaUxHf4v379/D29kZYWBh1RF5CQgLOzs7Yu3cvtWWJ6RwcHAAAY8aMEbrg\nJu0BBEEQBEEAwOvXr+Hs7Izbt2+Dw+EgOzsbenp6mDlzJpSVlbF9+3a6IxJEk2xtbZGcnEyKdSxB\n2prbPlKsYxhxOHEmISGB4cOHIzMzkzVFHHE3a9YsJCUl4fLly7CwsAAAxMfHw8fHB3PnzkVYWBjN\nCUUjPj6e7ggEQRAEQbRhvr6+aNeuHQoKCmBkZEQ97uzsjKVLl5JiHdFmOTo6wtfXF6mpqU12kIwf\nP56mZKLD4/Hg7+8PLy8vdOvWje44LYq0Nbd9HD6fz6c7BNE8dXV1SEhIQO/evVlfxDIzM8O2bdvw\n/9i79/ie6///4/f3Zht2sBFDDttYc5yz1CcKJWdRkZytgyJF0iRyjOT0kUolh+WUJDmU86nMWeaQ\n05hDDJ9Mzmts+/3h5/1tbUTe9ny/3u/b9XLZ5bL38/mO+zvh3eP9eD6etWvXNh0FDuDr66slS5bo\nkUceybD+008/qX79+rp06ZKhZAAAANmnYMGCWrJkiSpUqCB/f3/FxcUpLCxMhw4dUmRkpC5evGg6\nIpAlDw+Pm+65UieWv7+/du7cqZCQENNR7ilfX186JZ0cnXUW4k4dZ++//7569eqloUOHqkqVKvL1\n9c2wnzt3bkPJ7r0//vhDgYGBpmM4VL58+bL8bzZPnjwKCgoykMhxNm3apCpVqsjT01ObNm265XNd\n5ag6AAD4dy5dupTl+9ikpCT5+PgYSATcnrS0NNMRskWdOnW0Zs0aly/WcazZ+VGss5hy5crp8OHD\nCg0NNR3lnmrQoIGkzLO/bnCVT24++OADhYSEqFWrVpKkli1b6ttvv1XBggX1ww8/qEKFCoYTOsa7\n776rnj176quvvlLBggUlSSdPntRbb72lfv36GU53d2rUqKGTJ0+qQIECqlGjhn3Ww9+50ieOAADg\n36lZs6ZiYmI0ePBgSdffH6SlpWnEiBGcKAGcQIMGDRQdHa2dO3dm2TTiCsd9Jfc41mx1HIO1mCVL\nluidd95x+Y6zFStW3HK/bt262ZTk3goNDdX06dP18MMPa9myZWrZsqW+/vprzZ49W0ePHtXSpUtN\nR/zXKlWqlKHQeuDAAf35558qVqyYJOno0aPy8fFReHi4tm3bZirmXTt16pSCg4Pt39/KjecBAAD3\ntGvXLtWtW1eVK1fWypUr1bRpU+3evVtJSUlat26dSpQoYToikEHDhg01c+ZM+ymZ4cOHq0uXLvaT\nQGfOnFHNmjX166+/mozpMO5y3NddXqeVUayzmL/+pnLljrMTJ06ocOHCmdbT09OVmJiY5Z4V5cqV\nS/v371fRokX1+uuvKzk5WZ999pn279+vBx98UGfPnjUd8V8bOHDgbT/3vffeu4dJAAAAnMe5c+c0\nfvx4xcXF6eLFi6pcubK6du2qQoUKmY4GZOLp6anExEQVKFBAkhQQEKDt27crLCxM0vUPqwsXLuwy\n/x8KOAuOwVrMsmXLTEfIFkWLFs3wl8INSUlJKlq0qMv8ZRAUFKRjx46paNGiWrx4sYYMGSLpelHS\n6q/RXQtwhw8f1tq1a3X69OlMsz169+5tKBUAAHAWefLkUd++fU3HAG7L33t73KnXJzk5WTlz5jQd\nA26KYp3FlC5d+pYdZ67iZn8JXLp0yaX+wGzRooWef/55hYeH68yZM/ZZfb/88ovLDvu8ePFipiJW\nQECAoTSO9fnnn6tr164KCAhQ/vz5M3S/2mw2inUAALi5tWvX3nK/Vq1a2ZQEQFZSU1P1/vvva8KE\nCTp16pT279+vsLAw9evXTyEhIYqKijId8a6427FmK6NYZzGu3nF2o5hhs9k0aNCgDDP4UlNTtWHD\nBpe5dEGSxowZo5CQEB07dkwjRoyQn5+fJCkxMVGvvvqq4XSOk5CQoG7dumn16tVKTk62r6enp7vU\nTIRBgwZp2LBh6tWrl+koAADACT322GOZ1v764Z6rvCeC67DZbJnGL2U1jslVDB06VFOnTtWIESP0\n4osv2tfLlSunsWPHWr5Yt2TJEv3555/2x++//75atmxpL9Zdu3ZN+/btMxUPf0GxzmJcveNs/fr1\nkq6/zi1btmS4lcbb21ulSpVyqe4kLy+vLAs7PXr0MJDm3mnbtq3S09M1adIkBQcHu+xf8MnJyWre\nvLnpGAAAwEn9fR7x1atX9csvv6hfv34aOnSooVTAzaWnp6tjx47y8fGRdP39bpcuXewXHf618OMK\nYmJi9Pnnn6tu3brq0qWLfb1ChQrau3evwWSO4c7Hmq2GYp1FuEvH2U8//SRJateunT7++GOXOR55\nK1999ZU+++wzHTp0SOvXr1fx4sU1duxYhYaGqlmzZqbjOURcXJy2bt2qiIgI01HuqZdeeknTp09X\n//79TUcBAABO6MbRs7964okn5O3trZ49e2rr1q0GUgE316FDhwyP27Ztm+k57du3z64499zx48ez\nHEeUlpamq1evGkgEd0WxziLcrePsq6++Mh0hW3z66afq37+/3njjDQ0dOtR+9CEwMFBjx451mWJd\ntWrVdOzYMZcv1g0ZMkSNGjVSjRo1VL58+Qy/TyXpk08+MZQMAAA4s+DgYI6ewSlNnjzZdIRsVaZM\nGf30008qXrx4hvU5c+aoUqVKhlI5jrsda7YyinUW4Q4dZy1btrzt586ePfseJsk+H330kb744gs9\n9dRTGj58uH29atWqLjX3bOLEierSpYuOHz+ucuXKZSpiRUZGGkrmWAMGDNCSJUtUoUIFHTt2LNMF\nEwAAwL3t2LEjw+Mbl8QNHz5cFStWNJQKwA39+/dXhw4ddPz4caWlpWnu3Lnat2+fYmJitHDhQtPx\n7pq7HWu2Mls6h5ThJNq1a3fbz3WVzrtcuXJp7969Kl68uPz9/RUXF6ewsDAdOHBAkZGRunLliumI\nDrFhwwY9//zzOnz4sH3NZrO53AUTgYGB+vTTT9W6dWvTUQAAgBPy8PCwvwf6qxo1amjSpEkqVaqU\noWQAbvjpp580aNAgxcXF6eLFi6pcubL69++vevXqmY521zp16nRbz3O3jkpnRGedBbhLx5mrFODu\nRGhoqLZv356pzXrx4sUqXbq0oVSO17lzZ1WqVEkzZ8506QsmcufOrSpVqpiOAQAAnFRCQkKGxx4e\nHsqfP79LXBQHuIqaNWtq2bJlpmPcExThrINinQXcaFGF6+nZs6e6du2q5ORkpaena9OmTZo5c6aG\nDRumiRMnmo7nMEeOHNH8+fOzHNbqSt58802NHj1an3zyiTw8PEzHAQAATubvH9ACcC5hYWHavHmz\n8uXLl2H9jz/+UOXKlXXo0CFDyeBuOAYLpzVv3jzNnj1bR48eVUpKSoa9TZs2GUrleNOnT9eAAQN0\n8OBBSVLhwoU1cOBARUVFGU7mOE2aNFHHjh319NNPm45yTzVo0ECxsbHy9/dX6dKlM83m++GHHwwl\nAwAAzmLFihVasWKFTp8+rbS0tAx7kyZNMpQKgHS92/XkyZMqUKBAhvVTp06pWLFizHRDtqGzDk5p\n/Pjxio6OVrt27bR582a1b99e8fHx+uWXX9SlSxfT8RyqTZs2atOmjS5fvqyLFy9m+ovBFTRp0kQ9\nevTQzp07s7wltWnTpoaSOVZISIhCQkJMxwAAAE5q4MCBGjRokKpWrapChQq57GgQwGrmz59v/37J\nkiXKkyeP/XFqaqpWrFjB+3xkKzrrLMgdOs5KlSqlfv36qU2bNhkuXujbt68uXLigcePGmY6IO3Cr\nI6GucsFEWlqa4uPjdf/999tvUwIAAPirQoUKacSIEXd0sRqAe+/G/69kdQGMl5eXQkJCNGrUKDVu\n3NhEPLghOussxl06zo4ePapHHnlEkpQzZ05duHBBktSxY0c99NBDLlOsq1SpUpafqNpsNuXMmVMl\nS5ZUx44dVbt2bQPpHOfvRzxckc1mU7ly5fTrr7+6/Gw+AADw76SkpOjhhx82HQPA39z4/5XQ0FBt\n3rxZ9913n+FEcHdMQLeY8ePH67PPPtOnn34qb29v9enTR6tWrVLXrl11+fJl0/EcJjg4WElJSZKu\nD+K90TF45MgRlyr8NGjQQIcOHZKvr69q166t2rVry8/PTwcPHlS1atWUmJioxx9/XN9//73pqPgH\nNptNEREROn36tOkoAADASb3wwguaMWOG6RgAbiIhIYFCHZwCnXUW4y4dZ3Xq1NGCBQtUqVIldejQ\nQW+88Ybmzp2rjRs3usx8M0lKSkrSm2++qX79+mVYHzJkiI4cOaKlS5fqvffe0+DBg9WsWTNDKf+9\nhg0baubMmfaZD8OHD1eXLl0UGBgoSTpz5oxq1qypX3/91WRMhxk5cqTeeust/fe//1WlSpXk6elp\nOhIAAHAiycnJ+vzzz7V8+XJFRkZmmuM7evRoQ8kA3MAlMHAGzKyzmNDQUM2dO1eVKlVS1apV9fLL\nL+vFF1/U8uXL1bJlS3s3mtVdu3ZNqamp8vHxkSRNmzZNsbGxCg8P16uvvmpft7rAwEBt2bIl07HJ\n+Ph4ValSRefOndPevXtVrVo1e2HWSjw9PZWYmGi/NCMgIEDbt29XWFiYpOu3KhUuXNglZtZJUq5c\nuXT16lWlp6fLw8Mj0xtwV+p+BQAAd+5Wo01sNptWrlyZjWkA/N0/XQLz3XffGUoGd0NnncW4S8dZ\njhw5lCPH//3n2bZtW7Vt29ZgonvDx8dHsbGxmYp1sbGxypkzp6Tr8xNufG81f/8swNU/G5gyZYrp\nCAAAwImtWrXKdAQAtzBhwgRNmTKFS2BgHMU6i/nss8/sXUivvfaagoKCFBsbq3r16unVV181nO7u\nxcfHa8CAAfrkk08UEBCQYe/cuXPq1q2b+vXrpwceeMBQQsd67bXX1KVLF23dulXVqlWTJG3evFkT\nJ07UO++8I+n61eEVK1Y0GRO3qVWrVqYjAAAAC4iPj9fBgwdVq1Yt5cqVS+np6VleOgYge3EJDJwF\nx2DhVLp06SI/Pz+NHDkyy/3evXvr999/d6lZAdOnT9f48eO1b98+SVJERIRee+01Pf/885KkK1eu\n2G+HtRpPT0+dPHlS+fPnlyT5+/trx44dCg0NleR6x2Alac+ePfryyy918OBBTZgwQcHBwZo/f76K\nFy+uChUqmI4HAAAMOnPmjFq2bKlVq1bJZrPpwIEDCgsLU+fOnRUUFKRRo0aZjgi4tbffflt+fn6Z\nZooD2Y3OOotwl46z1atX66uvvrrpfsuWLdWmTZtsTHTvtWnT5pavKVeuXNmYxrHS09PVsWNH+4zB\n5ORkdenSRb6+vpKkP//802Q8h1u+fLmaNGmixx9/XEuXLtWlS5ckSfv27dPkyZOZcQEAgJvr0aOH\nvLy8dPToUZUuXdq+3qpVK/Xs2ZNiHWAYl8DAWVCss4iRI0eqYMGCmQp1kpQnTx4VKlRIw4cPt3zH\n2ZEjRxQcHHzT/fz58+vYsWPZmAh3o0OHDhkeZzV3sH379tkV557r27evRowYoddee03+/v729dq1\na2vs2LEGkwEAAGewdOlSLVmyREWKFMmwHh4eriNHjhhKBeCGHTt22EcQ7dq1K8MeR9WRnSjWWYS7\ndJwFBATo4MGDKlasWJb7Bw8ezFAEsaKgoKDb/oPe6rf7Tp482XSEbLVr1y41adIk03q+fPl05swZ\nA4kAAIAzuXTpknLnzp1pPSkpyX4SAYA5XAIDZ0GxziLcpeOsVq1aGj9+/E2vtR8/frxq1qyZzakc\n668dVmfOnNGQIUP05JNP6qGHHpIkrV+/XkuWLGFOggXly5dPv/32m0JCQjKsb926VUWLFjUTCgAA\nOI2aNWsqJiZGgwcPlnS9UyctLU0jRoy46ftfANmPS2BgGsU6i3CHjjNJio6O1sMPP6znnntOvXv3\nVkREhCRp7969GjFihBYtWqR169YZTnl3/no09Omnn9agQYPUrVs3+1r37t01fvx4LV++XD169DAR\nEf9SmzZt1KtXL3399dey2WxKSUnRihUr1KtXL7344oum4wEAAMNGjBihunXrasuWLUpJSVHv3r21\ne/duJSUlWf49LuAKbnYJTFRUFJfAIFtxG6xFPPvss0pLS9O3336b5X6LFi3k4eGhOXPmZHMyx/v+\n++8VFRWls2fPZlgPDAzUxIkT1bx5c0PJHM/Pz0/bt29XyZIlM6zHx8erYsWKunjxoqFk+DeuXr2q\nnj17asKECUpNTZXNZpPNZlOnTp302WefycPDw3REAABg2Llz5zR+/HjFxcXp4sWLqly5srp27apC\nhQqZjga4vfbt2+v06dOaOHGiSpcurbi4OIWFhWnJkiXq2bOndu/ebToi3ATFOovYunWrHn74YTVv\n3jzLjrN58+Zp3bp1qlq1quGkjnHp0iX98MMPio+PV3p6uh544AHVr19ffn5+pqM5VPHixdW9e3e9\n+eabGdZHjRqlcePGMWjYok6dOqXdu3fr4sWLqlix4k07YgEAAAA4j4IFC2rJkiWqUKGC/P397cW6\nQ4cOKTIykmYKZBuOwVpElSpVNHv2bEVFRembb77JsBcYGKhZs2a5TKFOknx9ffXss8+ajnHPDRw4\nUC+88IJWr16tBx98UJK0ceNGLV68WF988YXhdLhdefPm1f79+3XfffdJkoKDg285YxIAALinkiVL\nqm3btmrTpo3Cw8NNxwHwN1wCA2dBZ53FuEvHmTvZuHGjxo0bpz179kiSSpcure7du9uLd3B+Hh4e\nOnnypAoUKGA6CgAAcGJjxozRjBkztHXrVlWpUkVt27ZVq1atVLBgQdPRAEhq2LChqlSposGDB8vf\n3187duxQ8eLF9dxzzyktLc0lxk7BGijWAcBdolgHAADuxP79+zV9+nTNnDlTCQkJql27ttq2bav2\n7dubjga4tV27dqlu3bqqXLmyVq5cqaZNm2a4BKZEiRKmI8JNUKwDstn58+cVEBBg//5WbjwPzs3D\nw0Pjxo37x18v3oADAIC/27Bhg1555RXt2LFDqamppuMAbo9LYOAMKNYB2czT01OJiYkqUKCAPDw8\nZLPZMj0nPT1dNpuNN2wW4eHhoYCAgCx/LW+w2WxKSkrKxlQAAMCZbdq0STNmzNDXX3+t8+fPq0mT\nJpo1a5bpWAAAJ8AFE3BaaWlpWrBggX2WW5kyZdS4cWN5eHgYTnZ3Vq5cqbx580qSVq1aZTgNHGX/\n/v0cgwUAALf09+OvderU0QcffKAWLVowgxow6MCBA+rfv78+++yzTKdlzp07p1deeUX9+/dXqVKl\nDCWEu6FYB6d06NAhNW7cWAkJCfabsg4cOKCwsDAtXLhQoaGhhhP+e48++qgk6dq1a1qzZo06d+6s\nIkWKGE6Fu3GrjjoAAIAbSpUqpWrVqqlr16567rnnuD0ecBIffvihihYtmuVYmzx58qho0aIaPny4\npkyZkv3h4JY4BmtBrtpx9leNGzfW1atXNW3aNOXPn1+SdPr0abVt21Y+Pj5asGCB4YSO4e/vr507\ndyokJMR0FNwFLpgAAAC348CBA/YPov8uNTVVnp6eyaxo3gAAIABJREFU2ZwIgCRFRERo2rRpqlat\nWpb7W7du1fPPP699+/ZlczK4KzrrLMaVO87+avXq1YqNjbUX6iSpQIEC+vDDD/XII48YTOZYderU\n0Zo1ayjWWdzrr78uX19f0zEAAICTy6pQt3//fk2cOFFfffWVEhMTDaQCcPTo0Vt+8H7ffffp2LFj\n2ZgI7o5incV0795dRYsW1Zo1azJ1nHXv3t1lOs68vLx0+fLlTOuXL1+Wl5eXgUT3RoMGDRQdHa2d\nO3eqSpUqmQo+TZs2NZQMd2LMmDGmIwAAAAu5fPmyvv76a02aNEnr169X1apV1bNnT9OxALeVJ08e\nHTx4UMWLF89yPz4+PssjssC9wjFYi/Hz81NsbKwiIyMzrMfFxemRRx7RhQsXDCVzrLZt22rHjh2a\nPHmyqlSpIknasmWLoqKiVKFCBcXExBhO6Bi3OrrMbbAAAACuZcOGDZo4caK++eYbFStWTHv27NGq\nVatUs2ZN09EAt9ayZUtdvXpV3333XZb7zZo1k7e3t7755ptsTgZ35TpDztyEu3ScffTRRypatKiq\nVaumXLlyKVeuXHrwwQdVrFgx/fe//zUdz2HS0tJu+kWhDgAAwDWMGjVKZcuW1TPPPKOgoCCtXbtW\nO3fulM1mU758+UzHA9xenz599OOPP+qZZ57Rpk2bdO7cOZ07d04bN27U008/rSVLlqhPnz6mY8KN\n0FlnMe7ScXbD3r177RdplC5dmquyAQAAYDk5cuTQ22+/rUGDBmW4RMLLy0txcXEqU6aMwXQAJGnh\nwoXq3Lmzzpw5k2E9X758mjhxIiOKkK0o1lnM2bNn1bZtW/3444/y8fGRJKWkpKhhw4aKiYlRUFCQ\n4YS4XRcuXND+/fsVEREhPz8/bdu2TWPHjtWVK1f01FNPqU2bNqYj4g6NGTNG7du35xNyAACQwbBh\nwzR58mQlJyerdevWateuncqVK0exDnAyV65c0eLFixUfH6/09HQ98MADqlevnnLnzm06GtwMxTqL\ncvWOs1atWqlatWrq1atXhvUPP/xQ27Zt08yZMw0lc4y1a9eqcePGunjxooKCgjRz5kw988wzKlKk\niDw8PLRnzx5NmDBBL774oumouAMlSpTQ8ePH1ahRI0VFRal+/fq3nEsIAADcy5o1azRp0iTNmTNH\nJUuW1O7du7VmzRr95z//MR0NAOBEKNbBKRUoUEArV65UuXLlMqzv3LlTTzzxhE6ePGkomWPUqlVL\n4eHhGjRokCZNmqTRo0frlVde0fvvvy9JGjJkiObMmaPt27cbToo7tWrVKk2ZMkXffvut8uTJo/bt\n26tz584KDw83HQ0AADiJCxcuaMaMGZo0aZK2bt2q6tWr65lnnuFGWACAJIp1luPqHWc35MqVS9u3\nb1dERESG9X379qlixYq6cuWKoWSOERgYqA0bNqhUqVJKSUlRrly5tG3bNlWoUEHS9avBK1Wq5DK3\n+7qjCxcuaPbs2ZoyZYrWr1+vhx56SC+88IKee+45+xF2AACAnTt36ssvv9SMGTN0+vRp03EAAE6A\n81kWs2rVKtWvXz/Tev369bVq1SoDie6NsmXLZnkt9uzZs13iyO/58+eVN29eSZK3t7dy584tf39/\n+76/v3+Wt/7COvz8/FSkSBEVLlxYnp6eOn/+vKKjo1WsWDEtWrTIdDwAAOAkypcvr7Fjx+r48eOm\nowAAnEQO0wFwZy5cuCAvL69M697e3jp37pyBRPfGu+++q2effVYJCQmqU6eOJGnFihWaNm2aZs2a\nZTjd3bPZbLLZbDd9DOs6ePCgJk+erJiYGJ0/f16tW7fWunXrVLVqVV27dk0ffPCBXnrpJd6QAwCA\nDLJ6jw8AcE8cg7WYqlWr6qmnntK7776bYX3w4MGaO3eufvnlF0PJHG/+/PkaOnSotm/frty5c6t8\n+fJ67733VLduXdPR7pqHh4fKlSunHDmu18t37NihUqVKydvbW5J07do17d69W6mpqSZj4g7VrFlT\nsbGxqlGjhl588UW1bNky081R//vf/xQcHKy0tDRDKQEAAABk5YcffpCnp6eefPLJDOtLlixRWlqa\nGjRoYCgZ3A2ddRbj6h1nf9W0aVM1bdrUdIx74r333svwuFmzZpme8/TTT2dXHDhI9erV9fnnn6t0\n6dI3fU7+/Pl19uzZbEwFAAAA4HZER0frww8/zLSenp6u6OhoinXINnTWWZArd5zdcOLECUlS4cKF\nJUlbt27VzJkzVaZMGXXu3NlkNAAAAACAC8qVK5f27NmjkJCQDOuHDx9W2bJldenSJTPB4HborLMg\nV+44u+G5557TCy+8oPbt2+vUqVOqU6eOIiIiNHnyZCUmJqpv376mIwJZSkpK0uLFi3X06FGlpKRk\n2Ovfv7+hVAAAwBl4enoqMTFRBQoUyLB+5swZFShQgBEogGF58uTRoUOHMhXr4uPj5evrayYU3BLF\nOotxl46zXbt26cEHH5R0/QbYsmXLKjY2VosXL1bXrl0p1sEpxcbGqlGjRvLz89PJkydVrFgxnThx\nQt7e3ipRogTFOgAA3NzNDjX9+eef9tnFAMxp1qyZ3njjDX333XcqUaKEpOuFujfffNPlG2bgXCjW\nWYy7dJz9+eefypkzpyRp+fLl9j8Yy5Ytay9YAs7mrbfeUqdOnTR69Gj5+/tr2bJlCggI0PPPP6+2\nbduajgcAAAwZN26cJMlms2nixIny8/Oz76Wmpmrt2rUqVaqUqXgA/r8RI0aofv36KlWqlIoUKSJJ\n+u2331SzZk2NHDnScDq4E2bWWUzevHm1fv16RURE6KOPPtLMmTMzdJwdPHjQdESHqF69up544gk1\nbtxYdevWVWxsrCpWrKgNGzbo6aef1vHjx01HBDIJCAjQ1q1bFR4ersDAQMXGxqpMmTLaunWrWrVq\npfj4eNMRAQCAAaGhoZKkI0eOqEiRIvL09LTveXt7KyQkRIMGDbKfLAFgTnp6upYtW6a4uDjlypVL\nkZGRqlWrlulYcDN01lmMu3ScDRs2TC1atNDw4cPVpk0bVaxYUZK0YMECVa9e3XA6xzl06JDCwsJM\nx4CD5MqVS2lpaZKkggULKiEhQWXKlFHOnDmVmJhoOB0AADAlISFBklS7dm3NnTtXQUFBhhMBuBmb\nzaZ69eqpXr16pqPAjVGss5iyZcvq888/V+PGjbVs2TINHDhQknT8+HHlzZvXcDrHqVu3rs6cOaOz\nZ88qf/789vXOnTu71GDPkiVL6tFHH1VUVJSeeeYZeyEW1lS1alVt2LBBERERevzxx9WnTx8dPnxY\ns2bNUuXKlU3HAwAAhq1atcp0BAB/M27cOL300kvKmTOn/cj6zXTv3j2bUsHdcQzWYlasWKEWLVro\n4sWLatOmjWJiYiRJffv21a+//qrvvvvOcELcie3bt2vy5MmaOXOmUlJS1KpVK0VFRblU96A72b17\nty5cuKAaNWrojz/+UJcuXRQbG6vw8HB98sknioiIMB0RAAAYlJqaqilTpmjFihU6ffq0vSP/hpUr\nVxpKBriv0NBQbdmyRfny5bMfWc+KzWbToUOHsjEZ3BnFOgu6du1apo6zgwcPytfXVwULFjSY7O5U\nr15dS5YsUVBQkKpVqyabzXbT527atCkbk917165d0/z58zVlyhQtXrxYDzzwgDp37qx27dpl+HUG\nAACAdXXr1k1TpkxRo0aNVKhQoUzvd8eMGWMoGQDAmVCsg9Po16+f+vTpo9y5c6tfv363fO7gwYOz\nKVX2+vPPP/XJJ5+oT58+SklJkbe3t1q2bKkPPvhAhQoVMh0Ptyk+Pl579uyRJJUpU8Z+7TsAAHBv\n9913n2JiYtSwYUPTUQBk4eeff9YjjzxiOgZAsc4K3LnjzF1s2bJFkyZN0qxZs+Tr66sOHTooKipK\nv/32mwYOHKjz58/za2sB//vf/9S5c2ctWrRIOXJcHwmampqqBg0aaPLkyXRJAgDg5goXLqzVq1fr\ngQceMB0FQBa8vb11//33q3Xr1mrTpo3Kli1rOhLcFBdMWMCTTz4pHx8fSVL9+vUNp8keW7duVVpa\nmqpVq5ZhfcuWLfL09FSlSpUMJXOs0aNHa/Lkydq3b58aNmxo/6TVw8ND0vX5CVOmTFFISIjZoLgt\nL7zwgo4ePar169fb/9vdvHmzunTpohdeeEHff/+94YQAAMCkN998U//97381fvz4W34AD8CMEydO\naNasWZo5c6aGDx+uyMhItWnTRq1bt1aRIkVMx4MbobMOTunBBx/U22+/rRYtWmRY//bbb/Xhhx9q\nw4YNhpI5Vnh4uDp37qyOHTve9JhrSkqKZs6cqQ4dOmRzOtyp3Llza/Xq1ZkuCNm4caNq166ty5cv\nG0oGAACcQfPmzbVq1SrlzZtXZcuWlZeXV4b9uXPnGkoG4O8SEhI0Y8YMzZw5U3v37lWtWrW4BAbZ\nhs46i3GXjrPdu3dn+VoqV66s3bt3G0jkeNeuXVObNm3Url27W86j8/b2plBnEQULFpS3t3emdS8v\nLxUoUMBAIgAA4EwCAwPVvHlz0zEA3IbQ0FBFR0erQoUK6tevn9asWWM6EtwIxTqLefXVV/X2229n\nKtYdOXLEpTrOfHx8dOrUqUxXZ588eVKenp6GUjlWjhw5NGrUKHXs2NF0FDjIkCFD9Nprr2nSpEkK\nDw+XJB04cEA9evTQ0KFDDacDAACmTZ482XQEALdh3bp1mj59uubMmaPk5GQ1a9ZMw4YNMx0LboRj\nsBbj5+ennTt3ZipiJSQkKDIyUhcuXDCUzLFatWql33//XfPmzZO/v78k6fz582revLmCgoI0Z84c\nwwkdo1mzZmrRogWdcxYWGhqaYebMqVOnlJycrKCgIEnS2bNnlTNnTgUHB+vQoUOmYgIAAAD4B336\n9NGsWbN04sQJPfHEE2rTpo2aNWum3Llzm44GN0NnncW4Q8eZJI0cOVK1atVS8eLFVaVKFUnStm3b\nlC9fPpf6RLJBgwaKjo7Wzp07VaVKFfn6+mbYb9q0qaFkuF1vvPGG6QgAAMBC5syZo9mzZ+vo0aNK\nSUnJsLdt2zZDqQBI0tq1a/XWW2+pZcuWuu+++0zHgRujs85i3KXjTJIuXryomJgYxcXFKVeuXIqM\njFTbtm2znAlmVTdufc2KzWZTampqNqYBAADAvTRu3Dj17dtXHTt21Oeff65OnTrp4MGD2rx5s7p2\n7crYDACAJIp1lnPs2DHVqlVL586dy9Rxtnz5chUrVsxwQsB97du3Tx4eHvZ5dStXrlRMTIzKlCmj\nXr163bI4CwAAXF+pUqX03nvvqXXr1vL391dcXJzCwsLUv39/JSUlafz48aYjAm5n/vz5atCggby8\nvDR//vxbPpeTT8guFOssyB06ziTp0KFDWr16tU6fPq20tLQMe++8846hVI5z9epV1a9fXxMmTLAX\nd2BtDz30kF5//XU999xzOnbsmCIiIvTYY49px44dateuHUNpAQBwc7lz59aePXtUvHhxFShQQMuW\nLVOFChV04MAB1ahRQ2fOnDEdEXA7Hh4eOnnypAoUKMDJJzgNZtZZkJ+fn1599VXTMe6pSZMm6eWX\nX1ZgYKCCg4MzDPC32WwuUazz8vLSjh07TMeAA+3Zs8fe8Tp79mw9+OCD+uGHH7R69Wp17NiRYh0A\nAG6uYMGCSkpKUvHixVWsWDFt2LBBFSpUUEJCguihAMz4a2PI35tEAFMo1lmQq3ecSdKgQYM0cOBA\nl3k9N9O2bVt9+eWXGj58uOkocIDU1FT7RS/Lly9X48aNJUlhYWE6deqUyWgAAMAJ1KlTR/Pnz1el\nSpXUqVMn9ejRQ3PmzNGWLVvUokUL0/EAAE6CY7AW808dZ67SqRUQEKDt27crLCzMdJR76rXXXlNM\nTIzCw8OzvA129OjRhpLh33jkkUdUsWJFPfnkk3rmmWf0yy+/qEyZMlq3bp1at26to0ePmo4IAAAM\nSktLU1pamnLkuN4zMWvWLMXGxio8PFwvv/yyy421Aaxi5cqV6tatmzZs2KCAgIAMe+fOndPDDz+s\n0aNH68knnzSUEO6GYp3FhISE6KWXXnL5jrNOnTrp4Ycf1osvvmg6yj1Vu3btm+7ZbDatXLkyG9Pg\nbm3atElPP/20Tp48qW7dumnMmDGSpF69euno0aOaPXu24YQAAMCUa9eu6f3331fnzp1VpEgR03EA\n/EXTpk1Vu3Zt9ejRI8v9cePGaenSpVq4cGE2J4O7olhnMe7ScTZixAiNHDlSTZs2Vfny5eXl5ZVh\n39Vn9sHarl27Zv/EXJJ+//13+fj4yN/f32AqAABgmp+fn3bt2qWQkBDTUQD8RfHixbV48WKVLl06\ny/29e/eqXr16nJRBtqFYZzHu0nFWtGjRm+7ZbDb+kITTu3jxYqaZkn9vqQcAAO6lWbNmatGihTp0\n6GA6CoC/yJkzp3bt2qWSJUtmuR8fH6/y5cvrypUr2ZwM7ooLJiymdOnS6tu3rzZu3OjSHWfHjh0z\nHSHbbNmyRbNnz9bRo0eVkpKSYW/u3LmGUuHfOHr0qLp27aqVK1cqOTnZvp6ens5V7wAAQA0aNFB0\ndLR27tyZ5bzipk2bGkoGuLf777//lsW6HTt2qFChQtmcCu6MzjqLoePMtcyaNUvt27fXk08+qaVL\nl6pevXrav3+/Tp06pebNm2vy5MmmI+IO1KlTR+fPn9ebb76pQoUKZbgARpIeffRRQ8kAAIAz8PDw\nuOkeH+wB5rz22mtavXq1Nm/erJw5c2bYu3LliqpXr67atWtr3LhxhhLC3VCsg9NKTEzUggULsuw4\nGzFihKFUjhUZGamXX35ZXbt2lb+/v+Li4hQaGqqXX35ZhQoV0sCBA01HxB3w8/PTxo0bVbZsWdNR\nAAAAANymU6dOqXLlyvL09FS3bt0UEREh6fqsuo8//lipqanatm2bgoODDSeFu+AYLJzSqlWr1KRJ\nExUtWlTx8fEqXbq0jhw5IpvNpsjISNPxHObgwYNq1KiRJMnb21uXLl2SzWZTjx49VKdOHYp1FlOi\nRAldunTJdAwAAAAAdyA4OFixsbF65ZVX1KdPH93oabLZbHryySf18ccfU6hDtqJYZ0Hu0HEWHR2t\nN954Q0OGDJG/v7/mzZun++67T23atFGTJk1Mx3OYoKAgXbhwQdL/zUkoX768/vjjD12+fNlwOtyp\nTz75RH369NGoUaNUvnx5eXp6mo4EAACcwJUrV7RixQo1btxYktSnTx/9+eef9n1PT08NHjw40/E7\nANmnePHi+uGHH3T27FnFx8crPT1d4eHhCgoKMh0NbohjsBbzTx1na9euNR3RIfz9/fXLL7+oZMmS\nCgoK0s8//6yyZctq+/btat68uRISEkxHdIjnn39eVatWVc+ePTV48GB99NFHatasmZYtW6bKlStz\nwYTF3JhD8/dZdTcwhwYAAPc0YcIELVq0SAsWLJB0/b1u2bJllStXLknXj9r17t1bPXr0MBkTAOAk\n6KyzGHfpOPP19dXVq1clSQULFtTBgwdVtmxZeXh46H//+5/hdI4zfvx4+62hffv2lZeXl2JjY/X0\n00/r3XffNZwOd+q7774zHQEAADih6dOnq3fv3hnWZsyYobCwMEnStGnT9PHHH1OsAwBIorPOctyl\n46xZs2Zq0qSJXnjhBfXs2VOLFi1S586d9e2338rPz08rV640HREAAAC4LYUKFdL69esVEhIiScqf\nP782b95sf7x//35Vq1ZN586dMxcSAOA06KyzGHfpOBs1apR9ltugQYN0/vx5TZ06VeHh4Ro7dqzh\ndHfv/Pnzt/W8gICAe5wE98rJkyczzZQsVqyYoTQAAMCkP/74I8OMur+/b09LS8uwDwBwbxTrLObB\nBx/UunXrVLp0aTVo0EBvvfWW9uzZo2+//VbVq1c3Hc8hUlNTdfr0aZUtW1aS5Ofnp4kTJxpO5ViB\ngYE3nWsmSenp6bLZbMw4s5g//vhD3bp107fffpupUCcxsw4AAHdVpEgR7dq1SxEREVnu79ixQ0WK\nFMnmVAAAZ0WxzmJcveNMun4bVu3atbV3717lyZPHdJx7YtWqVfbv09PT1bBhQ02cOFH333+/wVS4\nW2+++ab27NmjhQsXqmnTpoqJiVFiYqJGjx6tkSNHmo4HAAAMadiwofr3769GjRpluvH1ypUrGjhw\noBo1amQoHQDA2TCzzkJSU1O1ceNGlS1b1mWLWDdUqVJFI0eOVO3atU1HyRb+/v6Ki4uzDxmGNd1/\n//2aPXu2/vOf/yggIEBbt25VeHi4vvnmG02YMEErVqwwHREAABhw6tQpVaxYUd7e3urWrZseeOAB\nSdK+ffs0fvx4Xbt2Tb/88ouCg4MNJwUAOAM66yzEHTrObnj//ffVq1cvDR06VFWqVJGvr2+G/dy5\ncxtKBtzc+fPn7UdYAgMD9fvvvys8PFxVq1bVhg0bDKcDAACmBAcHKzY2Vq+88oqio6N1o1/CZrPp\niSee0CeffEKhDgBgR7HOYsqVK6fDhw8rNDTUdJR7qkGDBpKuHxnIarYbs7/gjMLDwxUfH6/ixYur\nbNmyiomJUWRkpKZPn678+fObjgcAAAwKDQ3V4sWLlZSUpPj4eElSyZIllTdvXsPJAADOhmOwFrNk\nyRK98847Lt9x9k/HBevWrZtNSbKHv7+/duzY4fJFWFf3+eefKz09XS+//LJ+/vlnNWzYUJcuXZKH\nh4cmTJigqKgo0xEBAAAAAE6OYp3FeHh42L+n48y6WrRokeHxggULVKdOnUzF17lz52ZnLDjY77//\nrh07digsLEwhISGm4wAAAAAALIBjsBazbNky0xHuqfbt2+vjjz+Wv7+/JCkuLk5lypSRl5eX4WSO\n9feZg23btjWUBI5y9epV1ahRQzNmzFBERIQk6b777lOdOnUMJwMAAAAAWAmddXAqnp6eSkxMVIEC\nBSRJAQEB2r59O7ekwhIKFiyoNWvW2It1AAAAAADcKY9/fgqcQfv27XXhwgX747i4OF29etVgonvj\n77VjasmwkqioKH300UemYwAAAAAALIzOOotwl44zDw8PnTx50v46/f39FRcX53KvE66pY8eOmjt3\nrgoWLKhKlSplmkE4adIkQ8kAAAAAAFbBzDqLcKeOs19//VUnT56UdP117t27VxcvXszwnMjISBPR\ngFs6d+6c/abilJQUpaSkGE4EAAAAALAaOusswl06zjw8PGSz2bIsRt5Yt9ls3HoLp3L69Gn7700A\nAAAAAO4GnXUW4g4dZwkJCaYjAHesUKFCGY6pAwAAAADwb9FZZxF0nAHO6++drwAAAAAA/Ft01lkE\nHWeAc7PZbKYjAAAAAABcAJ11AHCXPDw89Nprr2W6/fXv3n///WxKBAAAAACwKjrrAMAB1q1bJy8v\nr5vu03kHAAAAALgddNYBwF1iZh0AAAAAwFE8TAcAAKujaw4AAAAA4CgU6+CUTp06pXbt2qlw4cLK\nkSOHPD09M3wBzoQGZQAAAACAozCzDk6pY8eOOnr0qPr166dChQrRuQSn9umnnypPnjymYwAAAAAA\nXAAz6yzm1KlT6tWrl1asWKHTp09n6uhJTU01lMyx/P399dNPP6lixYqmowAAAAAAAGQbOussxl06\nzooWLcrRQgAAAAAA4HborLMYd+k4W7p0qUaNGqXPPvtMISEhpuMAAAAAAABkCzrrLMZdOs5atWql\ny5cvq0SJEsqdO7e8vLwy7CclJRlKBgAAAAAAcO9QrLOYsWPHKjo62uU7zsaOHWs6AnDHXn75ZUVF\nRal69eqmowAAAAAALIpjsBYTFBSky5cv69q1a3ScAU6mfv36Wr58uUqVKqXOnTurXbt2yp8/v+lY\nAAAAAAALoVhnMVOnTr3lfocOHbIpSfZJTk5WSkpKhrWAgABDaYBbO378uKZOnaqYmBglJCSoYcOG\n6ty5sxo1aiQPDw/T8QAAAAAATo5iHZzSpUuX9Pbbb2v27Nk6c+ZMpv3U1FQDqYA7Exsbq8mTJ2va\ntGkKCgpShw4d1KVLFxUvXtx0NAAAAACAk6LNw8KSk5N1/vz5DF+uonfv3lq5cqU+/fRT+fj4aOLE\niRo4cKAKFy6smJgY0/GAf3TmzBlt3rxZmzZtUnp6umrWrKmffvpJ4eHh+uSTT0zHAwAAAAA4KTrr\nLMZdOs6KFSummJgYPfbYYwoICNC2bdtUsmRJffXVV5o5c6Z++OEH0xGBTNLS0vTjjz9q8uTJWrhw\nocLDwxUVFaX27dsrb968kqSvv/5ar7zyCvMlAQAAAABZorPOYtyl4ywpKUlhYWGSrs+nu1HYeOSR\nR7R27VqT0YCbuv/++9W6dWvlyZNHq1ev1s6dO/XGG2/YC3WSVK9ePfn4+BhMCQAAAABwZjlMB8Cd\nWbBggb3jrFOnTqpZs6ZKliyp4sWLa/r06WrTpo3piA4RFhamhIQEFStWTKVKldLs2bNVvXp1LViw\nQIGBgabjAVkaNGiQWrduLT8/v5s+JygoSImJidmYCgAAAABgJXTWWYy7dJx16tRJcXFxkqTo6Gh9\n/PHHypkzp3r06KG33nrLcDogs6tXr6pHjx46cuSI6SgAAAAAAAujs85i3KXjrEePHvbvH3/8ce3Z\ns8c+ty4yMtJgMiBrXl5eyp8/v9LS0kxHAQAAAABYGBdMWMyYMWPk6emp7t27a/ny5WrSpInS09N1\n9epVjR49Wq+//rrpiIDb+vTTT7V48WJNmzZN/v7+puMAAAAAACyIYp3FHT582KU6ztavX68zZ86o\ncePG9rWYmBi99957unTpkp566il99NFHDOiHU3rooYe0e/dupaWlqUSJEvL19c2wHxsbaygZAAAA\nAMAqOAZrcSEhIQoJCTEdw2EGDRqkxx57zF6s27lzp6KiotSxY0eVLl1aH374oQoXLqwBAwaYDQpk\n4bHHHtNjjz1mOgYAAAAAwMLorLMId+k4K1QivY1mAAATL0lEQVSokBYsWKCqVatKkvr27as1a9bo\n559/liR98803eu+99/Trr7+ajAkAAAAAAHBPcBusRQwaNEi7d++2P77Rcfb4448rOjpaCxYs0LBh\nwwwmdIyzZ88qODjY/njNmjVq0KCB/XG1atV07NgxE9EAAAAAAADuOYp1FrF9+3bVrVvX/njWrFl6\n8MEH9cUXX6hnz54aN26cZs+ebTChYwQHByshIUGSlJKSom3btqlGjRr2/QsXLsjLy8tUPOCW0tLS\nNH78eNWqVUshISEqXLhwhi8AAAAAAP4JxTqLcJeOs4YNGyo6Olo//fST+vTpo9y5c6tmzZr2/R07\ndqhEiRIGEwI3N3ToUA0ePFhPPvmkTp06paioKNWpU0fJycnq2bOn6XgAAAAAAAugWGcR7tJxNnjw\nYOXIkUOPPvqovvjiC33xxRfy9va270+aNEn16tUzmBC4ualTp+qLL75Q3759lSNHDnXs2FHTpk1T\n3759tWPHDtPxAAAAAAAWwG2wFnGj4+yDDz7QvHnzXLbj7L777tPatWt17tw5+fn5ydPTM8P+N998\nIz8/P0PpgFs7ceKEKlasKEny9fXV+fPnJUnNmzfX4MGDTUYDAAAAAFgEnXUW4W4dZ3ny5MlUqJOk\nvHnzZnjdgDMpUqSITp06JUkKCwvTypUrJV2fOekKna8AAAAAgHvPlp6enm46BG7fzTrOkpKS5Ofn\nRyELMKhnz54KCgpSv379NG3aNHXq1EmlSpVSfHy8XnnlFY0ePdp0RAAAAACAk6NYBwD3yKpVq7R+\n/XqFh4fr2WefNR0HAAAAAGABFOsAwAGuXr2q119/Xb1791ZISIjpOAAAAAAAi6JYBwAOEhAQoLi4\nOIWGhpqOAgAAAACwKC6YAAAHadKkiRYuXGg6BgAAAADAwnKYDgAAriIyMlIDBgzQxo0bVaVKFfn6\n+mbYf+mllwwlAwAAAABYBcdgAcBBChUqdNM9m82mEydOZGMaAAAAAIAVUawDAAAAAAAAnAQz6wAA\nAAAAAAAnQbEOABxo9uzZqlatmvz9/eXv76/q1avrm2++MR0LAAAAAGARFOsAwEE++ugjdejQQQ8/\n/LC+/PJLffnll6pRo4Y6dOigjz/+2HQ8AAAAAIAFMLMOABykRIkSeueddxQVFZVhfeLEiRo2bJgO\nHjxoKBkAAAAAwCoo1gGAg/j4+Gj37t0qWbJkhvX4+HiVK1dOycnJhpIBAAAAAKyCY7AA4CAlSpTQ\nd999l2l97ty5KlGihIFEAAAAAACryWE6AAC4iv79+6tt27Zat26d/vOf/0iS1q1bp0WLFmn69OmG\n0wEAAAAArIBjsADgQLGxsRo9erT27NkjSSpdurR69eqlGjVqGE4GAAAAALACinUAAAAAAACAk+AY\nLAA4UHp6uhYtWmTvrCtTpowaNGggDw9GhAIAAAAA/hnFOgBwkH379umpp55SQkKCwsLCJEmHDh1S\nSEiI5s2bp1KlShlOCAAAAABwdhyDBQAHeeSRR+Tv76+pU6eqQIECkqTTp0+rQ4cOunjxon766SfD\nCQEAAAAAzo5iHQA4SK5cubR582aVK1cuw/rOnTtVvXp1XblyxVAyAAAAAIBVMEQJABykZMmSOnPm\nTKb1pKQkhYaGGkgEAAAAALAainUA4CAjR47U66+/roULF+r333/X77//roULF6pHjx4aM2aMUlJS\n7F8AAAAAAGSFY7AA4CB/vfHVZrNJun477F8f35Campp9wQAAAAAAlsFtsADgID/++KPpCAAAAAAA\ni6OzDgAAAAAAAHASdNYBgANdvXpVe/bs0enTp5WWlpZhr169eoZSAQAAAACsgmIdADjIypUr1a5d\nOyUmJmbas9lszKkDAAAAAPwjjsECgINERESoVq1aevfddxUcHJzpUgkfHx9DyQAAAAAAVkGxDgAc\nJCAgQL/88otKlChhOgoAAAAAwKI8TAcAAFfRrFkz/fzzz6ZjAAAAAAAsjM46AHCQixcv6rnnnlOR\nIkVUvnx5eXl5Zdh/6aWXDCUDAAAAAFgFxToAcJCvvvpKL7zwgiQpKCgow8w6m82mEydOmIoGAAAA\nALAIinUA4CCFCxfWiy++qH79+ilHDi7bBgAAAADcOYp1AOAggYGB2rp1KxdMAAAAAAD+NS6YAAAH\nadeunebNm2c6BgAAAADAwjinBQAO4uPjoyFDhmjp0qWKjIzMdMHE+++/bygZAAAAAMAqOAYLAA7y\n0EMP3XTPZrMpNjY2G9MAAAAAAKyIYh0AAAAAAADgJJhZBwAO9ttvv2nNmjVKTk42HQUAAAAAYDEU\n6wDAQf744w81atRIxYoVU506dXTixAlJUlRUlN5++23D6QAAAAAAVkCxDgAc5M0339SVK1e0f/9+\n5c6d277+zDPPaNGiRQaTAQAAAACsgttgAcBBfvzxRy1atEglS5bMsB4REaHDhw+bCQUAAAAAsBQ6\n6wDAQc6fPy9/f/9M62fPnpW3t7eBRAAAAAAAq6FYBwAO8p///EczZ860P7bZbJKkMWPG6NFHHzUV\nCwAAAABgIRyDBQAHGTFihOrUqaNt27YpJSVF/fr1065du/Tbb79p3bp1puMBAAAAACzAlp6enm46\nBAC4ijNnzmjs2LGKi4vTxYsXVblyZb3++usqWrSo6WgAAAAAAAugWAcAd2nQoEHq1atXhhtgAQAA\nAAD4NyjWAcBd8vT0VGJiogoUKGA6CgAAAADA4rhgAgDuEp95AAAAAAAchWIdADjAjZtfAQAAAAC4\nGxyDBYC75OHhoTx58vxjwS4pKSmbEgEAAAAArCqH6QAA4AoGDhyoPHnymI4BAAAAALA4OusA4C55\neHjo5MmTXDABAAAAALhrzKwDgLvEvDoAAAAAgKNQrAOAu0SDMgAAAADAUTgGCwAAAAAAADgJOusA\nAAAAAAAAJ0GxDgAAAAAAAHASFOsAAAAAAAAAJ0GxDgAAwIUcPnxYNptN27dvz/afu2PHjnrqqafu\n6scICQnR2LFjb/kcm82mefPm3daPN2DAAFWsWPG2f/47+bEBAADuBYp1AAAA/1LHjh1ls9kyfdWv\nX/+2/vnbKUzdqaJFiyoxMVHlypVz6I/rTBITE9WgQYPbem6vXr20YsWKe5bFEQVKAACAv8phOgAA\nAICV1a9fX5MnT86w5uPjYyiN5OnpqYIFCxr7+bPDnbw+Pz8/+fn53cM0AAAAjkVnHQAAwF3w8fFR\nwYIFM3wFBQVJktLT0zVgwAAVK1ZMPj4+Kly4sLp37y5Jeuyxx3TkyBH16NHD3pEnSWfOnFHr1q11\n//33K3fu3CpfvrxmzpyZ4edMS0vTiBEjVLJkSfn4+KhYsWIaOnSopKyPwa5Zs0bVq1eXj4+PChUq\npOjoaF27ds2+/9hjj6l79+7q3bu38ubNq4IFC2rAgAG3fN2pqanq2bOnAgMDlS9fPvXu3Vvp6emZ\ncg4bNkyhoaHKlSuXKlSooDlz5vzjv9MLFy6odevW8vX11f3336+PP/44w/7fj6r+9ttvat26tfLm\nzStfX19VrVpVGzdulJT1MdhJkyapbNmy9n8f3bp1u2mWY8eOqWXLlgoMDFTevHnVrFkzHT582P5j\nT506Vd9//73913D16tWSpLffflsPPPCAcufOrbCwMPXr109Xr179x9cOAABAsQ4AAOAe+fbbbzVm\nzBh99tlnOnDggObNm6fy5ctLkubOnasiRYpo0KBBSkxMVGJioiQpOTlZVapU0aJFi7Rr1y699NJL\nateunTZt2mT/cfv06aPhw4erX79++vXXX/X111/ftNvs+PHjatiwoapVq6a4uDh9+umn+vLLLzVk\nyJAMz5s6dap8fX21ceNGjRgxQoMGDdKyZctu+tpGjRqlKVOmaNKkSfr555+VlJSk7777LsNzhg0b\nppiYGE2YMEG7d+/+f+3daUiUXRsH8H9l2qiTYhpNaoq5oKDiEmEFEhKaYlq0IApCkoYt5lKWS2pR\niWLqhEiIoC1gRbRhVBa5MOqHSjPJdZTMpcQiawhnXM7z4eEdnnkdzep9eIX+P7g/nHNfZ73nw3DB\nuW8kJiYiKioKdXV18+5bfn4+PD090dLSgpMnTyIhIWHOuahUKvj7+2NoaAj3799HW1sbTp06hZmZ\nGb3xpaWlOHToEGJjY9He3o7q6mo4OzvrjZ2cnERgYCCkUikaGhqgUChgamqKoKAgaDQapKSkYO/e\nvQgKCtI+w02bNgEApFIpKioq8PbtWxQXF6OsrAyFhYXzrpuIiIgIACCIiIiI6JdER0eLZcuWCRMT\nE53r3LlzQgghCgoKhLOzs9BoNHrb29nZicLCwh+OExISIpKTk4UQQnz9+lUYGRmJsrIyvbH9/f0C\ngGhpaRFCCJGWliZcXFzEzMyMNqakpESYmpqK6elpIYQQ/v7+YsuWLTr9bNiwQaSmps45J5lMJvLy\n8rTlyclJYWNjI8LCwoQQQkxMTAhjY2PR2Nio0y4mJkZERETM2a+dnZ0ICgrSqdu3b5/Yvn27tgxA\n3LlzRwghxOXLl4VUKhWfPn3S219WVpbw9PTUlteuXSvS09PnHP+ffV+9enXW3qnVaiGRSMTjx4+F\nEH//Bv6z5vnk5+cLHx+fH8YRERER8Z11RERERL9h69atKC0t1amzsLAAAOzZswdFRUVwcHBAUFAQ\ngoODERoaCgODuf+CTU9P4/z587h58yaGhoag0WigVqthbGwMAOjo6IBarUZAQMCC5tfR0QE/Pz/t\nMVsA2Lx5M1QqFQYHB7Fu3ToAgIeHh047mUyG0dFRvX2Oj49jZGQEGzdu1NYZGBjA19dXexS2t7cX\n379/x7Zt23TaajQaeHl5zTtnPz+/WeW5PsTR2toKLy8v7Z7PZ3R0FMPDwwveu9evX6O3txdSqVSn\nfmJiAkqlct62N27cgFwuh1KphEqlwtTUFFauXLmgcYmIiOjPxmQdERER0W8wMTGBo6Oj3nu2trbo\n6urC06dPUVNTg/j4eOTn56Ourg7Lly/X2yY/Px/FxcUoKiqCu7s7TExMcOzYMWg0GgCARCL5V9bx\n3/NZsmTJnEdJF0KlUgEAqqurYW1trXPvf/kBjp/Zj5/dO5VKBR8fH1y/fn3WPSsrqznbNTU1ITIy\nEjk5OQgMDISZmRmqqqpQUFDwU+MTERHRn4nvrCMiIiL6F0kkEoSGhkIul6O2thZNTU148+YNAMDQ\n0BDT09M68QqFAmFhYYiKioKnpyccHBzQ3d2tve/k5ASJRIJnz54taHxXV1c0NTXpfPxBoVBAKpXC\nxsbml9ZkZmYGmUym/YgDAExNTeHly5faspubG4yMjDAwMABHR0edy9bWdt7+m5ubZ5VdXV31xnp4\neKC1tRWfP3/+4bylUins7e0XvHfe3t7o6enB6tWrZ63BzMwMgP5n2NjYCDs7O6Snp8PX1xdOTk54\n9+7dgsYkIiIiYrKOiIiI6Deo1Wp8+PBB5xobGwMAVFRUoLy8HO3t7ejr68O1a9cgkUhgZ2cHALC3\nt0d9fT2Ghoa0bZycnFBTU4PGxkZ0dHQgLi4OHz9+1I63YsUKpKam4sSJE7hy5QqUSiWam5tRXl6u\nd37x8fF4//49jhw5gs7OTty7dw9ZWVlISkrC0qW//lcwISEBubm5uHv3Ljo7OxEfH48vX75o70ul\nUqSkpCAxMRGVlZVQKpV49eoVLl26hMrKynn7VigUyMvLQ3d3N0pKSnDr1i0kJCTojY2IiMCaNWsQ\nHh4OhUKBvr4+3L59G01NTXrjs7OzUVBQALlcjp6eHu2c9ImMjISlpSXCwsLQ0NCA/v5+1NbW4ujR\noxgcHATw9zNsa2tDV1cXxsbGMDk5CScnJwwMDKCqqgpKpRJyuXzWxzeIiIiI5sJjsERERES/4dGj\nR5DJZDp1Li4u6OzshLm5OXJzc5GUlITp6Wm4u7vjwYMHWLVqFQDgzJkziIuLw/r166FWqyGEQEZG\nBvr6+hAYGAhjY2PExsYiPDwc4+Pj2v4zMzNhYGCA06dPY3h4GDKZDAcPHtQ7P2trazx8+BDHjx+H\np6cnLCwsEBMTg4yMjN9ad3JyMkZGRhAdHY2lS5di//792Llzp848z549CysrK1y4cAF9fX0wNzeH\nt7c30tLSftj3ixcvkJOTg5UrV+LixYsIDAzUG2toaIgnT54gOTkZwcHBmJqagpubG0pKSvTGR0dH\nY2JiAoWFhUhJSYGlpSV2796tN9bY2Bj19fVITU3Frl278O3bN1hbWyMgIED7/rkDBw6gtrYWvr6+\nUKlUeP78OXbs2IHExEQcPnwYarUaISEhyMzMRHZ29gJ2loiIiP50S8Q/z0QQERERERERERHR/w2P\nwRIRERERERERES0STNYREREREREREREtEkzWERERERERERERLRJM1hERERERERERES0STNYRERER\nEREREREtEkzWERERERERERERLRJM1hERERERERERES0STNYREREREREREREtEkzWERERERERERER\nLRJM1hERERERERERES0STNYREREREREREREtEkzWERERERERERERLRJ/ATwnstm20BB2AAAAAElF\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xadfc090>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Visulizacion de la cantidad de viajes segun la estacion\n",
"#Solo mostramos las 20 ciudades con mas menos de viajes\n",
"plt = arch_unidos['start_station_name'].value_counts().head(20).plot('bar')\n",
"plt.set_xlabel('Estacion de bicicleta')\n",
"plt.set_ylabel('Cantidad de viajes')\n",
"plt.set_title('Top20 de estaciones con mas cantidad de viajes');"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABKcAAAGJCAYAAABfHlCcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nERERGQyDU0REREREREREZDD/DxtlgThyELelAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7d3ed70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#ACLARO QUE ESTE GRAFICO SOLO NOS MUESRTA DATOS INCORRECTOS, HABRIA QUE DECIDIR SI PONERLO O NO\n",
"# Duracion de viajes por bicicleta.\n",
"plt = trip.groupby('bike_id').sum()['duration'].plot(figsize=(14,4));\n",
"plt.set_xlabel('ID de Bicicleta')\n",
"plt.set_ylabel('Duracion')\n",
"plt.set_title('Cantidad de viajes por bicicleta');"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABJ4AAAGJCAYAAADL+PluAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xaa4c190>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Filtramos los viajes para que solo queden los de duracion menor a 5000\n",
"trip_con_duracion_filtrada = trip[trip['duration'] < 5000 ]\n",
"# Duracion de viajes por bicicleta.\n",
"plt = trip_con_duracion_filtrada.groupby('bike_id').sum()['duration'].plot(figsize=(14,4));\n",
"plt.set_xlabel('ID de Bicicleta')\n",
"plt.set_ylabel('Duracion')\n",
"plt.set_title('Cantidad de viajes por bicicleta');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
ThunderShiviah/code_guild | wk9/notebooks/Ch.2-Extending our functional test using the unittest module.ipynb | 2 | 9971 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Ch. 2: Extending our functional test using the unittest module"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using a functional test to scope out a miniumum viable app\n",
"\n",
"We'll use selenium to simulate a user visiting our website in a real web browser. We call our tests with selenium *functional tests* because they let us see how the app functions from the user's point of view.\n",
"\n",
"Functional tests tend to track what we might call the *User Story*, i.e. how a user might work with a particular feature and how the app should respond to them.\n",
"\n",
"### Functional Test == Acceptance Test == End-to-End Test\n",
"\n",
"### Minimum viable app\n",
"What is the simplest thing that we can build that is still useful?"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/thunder/Documents/work/codeguild2015/code_guild/wk9/examples/superlists\n",
"db.sqlite3 \u001b[0m\u001b[01;32mmanage.py\u001b[0m* \u001b[01;34msuperlists\u001b[0m/\r\n"
]
}
],
"source": [
"%cd ../examples/superlists/\n",
"%ls"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Writing functional_tests.py\n"
]
}
],
"source": [
"%%writefile functional_tests.py\n",
"\n",
"from selenium import webdriver\n",
"\n",
"browser = webdriver.Firefox()\n",
"\n",
"# Edith has heard about a cool new online to-do app. She goes\n",
"# to check out its homepage\n",
"browser.get('http://localhost:8000')\n",
"\n",
"# She notices the page title and header mention to-do lists\n",
"assert 'To-Do' in browser.title\n",
"\n",
"# She is invited to enter a to-do item straight away\n",
"\n",
"# She types \"Buy peacock feathers\" into a text box (Edith's hobby\n",
"# is tying fly-fishing lures)\n",
"\n",
"# When she hits enter, the page updates, and now the page lists\n",
"# \"1: Buy peacock feathers\" as an item in a to-do list\n",
"\n",
"# There is still a text box inviting her to add another item. She\n",
"# enters \"Use peacock feathers to make a fly\" (Edith is very methodical)\n",
"\n",
"# The page updates again, and now shows both items on her list\n",
"\n",
"# Edith wonders whether the site will remember her list. Then she sees\n",
"# that the site has generated a unique URL for her -- there is some\n",
"# explanatory text to that effect.\n",
"\n",
"# She visits that URL - her to-do list is still there.\n",
"\n",
"# Satisfied, she goes back to sleep\n",
"\n",
"browser.quit()\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that I've updated the assert to include the word \"To-Do\" instead of \"Django\". Now our test should fail. Let's check that it fails.\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Traceback (most recent call last):\n",
" File \"functional_tests.py\", line 11, in <module>\n",
" assert 'To-Do' in browser.title\n",
"AssertionError\n"
]
}
],
"source": [
"# First start up the server:\n",
"#!python3 manage.py runserver\n",
"\n",
"# Run test\n",
"!python3 functional_tests.py"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We got what was called an *expected fail* which is what we wanted!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Python Standard Library's unittest Module\n",
"\n",
"There are a couple of little annoyances we should probably deal with. Firstly, the message \"AssertionError\" isn’t very helpful—it would be nice if the test told us what it actually found as the browser title. Also, it’s left a Firefox window hanging around the desktop, it would be nice if this would clear up for us automatically.\n",
"\n",
"One option would be to use the second parameter to the assert keyword, something like:\n",
"\n",
"```python\n",
"assert 'To-Do' in browser.title, \"Browser title was \" + browser.title\n",
"```\n",
"And we could also use a try/finally to clean up the old Firefox window. But these sorts of problems are quite common in testing, and there are some ready-made solutions for us in the standard library’s unittest module. Let’s use that! In functional_tests.py:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting functional_tests.py\n"
]
}
],
"source": [
"%%writefile functional_tests.py\n",
"\n",
"from selenium import webdriver\n",
"import unittest\n",
"\n",
"class NewVisitorTest(unittest.TestCase): #1\n",
"\n",
" def setUp(self): #2\n",
" self.browser = webdriver.Firefox()\n",
" self.browser.implicitly_wait(3) # Wait three seconds before trying anything.\n",
"\n",
" def tearDown(self): #3\n",
" self.browser.quit()\n",
"\n",
" def test_can_start_a_list_and_retrieve_it_later(self): #4\n",
" # Edith has heard about a cool new online to-do app. She goes\n",
" # to check out its homepage\n",
" self.browser.get('http://localhost:8000')\n",
"\n",
" # She notices the page title and header mention to-do lists\n",
" self.assertIn('To-Do', self.browser.title) #5\n",
" self.fail('Finish the test!') #6\n",
"\n",
" # She is invited to enter a to-do item straight away\n",
" # [...rest of comments as before]\n",
"\n",
"if __name__ == '__main__': #7\n",
" unittest.main(warnings='ignore') #8"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some things to notice about our new test file:\n",
"\n",
"1. Tests are organised into classes, which inherit from unittest.TestCase.\n",
"\n",
"2. and\n",
"3. setUp and tearDown are special methods which get run before and after each test. I’m using them to start and stop our browser—note that they’re a bit like a try/except, in that tearDown will run even if there’s an error during the test itself.[4] No more Firefox windows left lying around!\n",
"\n",
"4. The main body of the test is in a method called test_can_start_a_list_and_retrieve_it_later. Any method whose name starts with test is a test method, and will be run by the test runner. You can have more than one test_ method per class. Nice descriptive names for our test methods are a good idea too.\n",
"\n",
"5. We use self.assertIn instead of just assert to make our test assertions. unittest provides lots of helper functions like this to make test assertions, like assertEqual, assertTrue, assertFalse, and so on. You can find more in the unittest documentation.\n",
"\n",
"6. self.fail just fails no matter what, producing the error message given. I’m using it as a reminder to finish the test.\n",
"\n",
"7. Finally, we have the if __name__ == '__main__' clause (if you’ve not seen it before, that’s how a Python script checks if it’s been executed from the command line, rather than just imported by another script). We call unittest.main(), which launches the unittest test runner, which will automatically find test classes and methods in the file and run them.\n",
"\n",
"8. warnings='ignore' suppresses a superfluous ResourceWarning which was being emitted at the time of writing. It may have disappeared by the time you read this; feel free to try removing it!\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Running our new test"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"F\r\n",
"======================================================================\r\n",
"FAIL: test_can_start_a_list_and_retrieve_it_later (__main__.NewVisitorTest)\r\n",
"----------------------------------------------------------------------\r\n",
"Traceback (most recent call last):\r\n",
" File \"functional_tests.py\", line 20, in test_can_start_a_list_and_retrieve_it_later\r\n",
" self.assertIn('To-Do', self.browser.title) #5\r\n",
"AssertionError: 'To-Do' not found in 'Welcome to Django'\r\n",
"\r\n",
"----------------------------------------------------------------------\r\n",
"Ran 1 test in 2.297s\r\n",
"\r\n",
"FAILED (failures=1)\r\n"
]
}
],
"source": [
"!python3 functional_tests.py"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We got the same expected failure but now it looks nice!"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
weixi2008/lab | DevTools/Python闭包与装饰器.ipynb | 1 | 4049 | {
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Called:\n",
" function: <function hello at 0x109239b18>\n",
" args: (('World!',), {})\n",
" kargs: {}\n",
"time delta: 0.00108003616333\n"
]
},
{
"ename": "TypeError",
"evalue": "hello() takes exactly 1 argument (2 given)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-87691b3c8a9d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m\"Hello\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m \u001b[0mhello\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"World!\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-5-87691b3c8a9d>\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kargs)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mnow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 21\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: hello() takes exactly 1 argument (2 given)"
]
}
],
"source": [
"from time import time\n",
"\n",
"def logged(when):\n",
" def log(f, *args, **kargs):\n",
" print '''Called:\n",
" function: %s\n",
" args: %r\n",
" kargs: %r''' % (f, args, kargs)\n",
" \n",
" def pre_logged(f):\n",
" def wrapper(*args, **kargs):\n",
" log(f, args, kargs)\n",
" return f(args, kargs)\n",
" return wrapper\n",
" \n",
" def post_logged(f):\n",
" def wrapper(*args, **kargs):\n",
" now = time()\n",
" try:\n",
" return f(args, kargs)\n",
" finally:\n",
" log(f, args, kargs)\n",
" print \"time delta: %s\" % (time() - now)\n",
" return wrapper\n",
" \n",
" try:\n",
" return {\"pre\": pre_logged, \"post\": post_logged}[when]\n",
" except KeyError, e:\n",
" raise ValueError(e), 'must be \"pre\" or \"post\"'\n",
"\n",
"@logged(\"post\")\n",
"def hello(name):\n",
" print \"Hello\", name\n",
"\n",
"hello(\"World!\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
JaeGyu/PythonEx_1 | 토요_파이썬/대소니2.ipynb | 1 | 107081 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import numpy as np\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"np.random.seed(9074)\n",
"x_train = [1,2,3,4]\n",
"y_train = [2,4,6,8]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = Sequential()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"model.add(Dense(1, input_dim=1))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model.compile(loss='mse', optimizer='adam')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/1000\n",
"4/4 [==============================] - 0s 799us/step - loss: 5.3383\n",
"Epoch 2/1000\n",
"4/4 [==============================] - 0s 576us/step - loss: 5.3225\n",
"Epoch 3/1000\n",
"4/4 [==============================] - 0s 637us/step - loss: 5.3067\n",
"Epoch 4/1000\n",
"4/4 [==============================] - 0s 709us/step - loss: 5.2909\n",
"Epoch 5/1000\n",
"4/4 [==============================] - 0s 639us/step - loss: 5.2751\n",
"Epoch 6/1000\n",
"4/4 [==============================] - 0s 604us/step - loss: 5.2594\n",
"Epoch 7/1000\n",
"4/4 [==============================] - 0s 579us/step - loss: 5.2437\n",
"Epoch 8/1000\n",
"4/4 [==============================] - 0s 644us/step - loss: 5.2281\n",
"Epoch 9/1000\n",
"4/4 [==============================] - 0s 746us/step - loss: 5.2125\n",
"Epoch 10/1000\n",
"4/4 [==============================] - 0s 589us/step - loss: 5.1969\n",
"Epoch 11/1000\n",
"4/4 [==============================] - 0s 843us/step - loss: 5.1814\n",
"Epoch 12/1000\n",
"4/4 [==============================] - 0s 624us/step - loss: 5.1659\n",
"Epoch 13/1000\n",
"4/4 [==============================] - 0s 736us/step - loss: 5.1505\n",
"Epoch 14/1000\n",
"4/4 [==============================] - 0s 630us/step - loss: 5.1351\n",
"Epoch 15/1000\n",
"4/4 [==============================] - 0s 852us/step - loss: 5.1197\n",
"Epoch 16/1000\n",
"4/4 [==============================] - 0s 847us/step - loss: 5.1043\n",
"Epoch 17/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 5.0890\n",
"Epoch 18/1000\n",
"4/4 [==============================] - 0s 731us/step - loss: 5.0738\n",
"Epoch 19/1000\n",
"4/4 [==============================] - 0s 626us/step - loss: 5.0585\n",
"Epoch 20/1000\n",
"4/4 [==============================] - 0s 778us/step - loss: 5.0433\n",
"Epoch 21/1000\n",
"4/4 [==============================] - 0s 878us/step - loss: 5.0281\n",
"Epoch 22/1000\n",
"4/4 [==============================] - 0s 719us/step - loss: 5.0130\n",
"Epoch 23/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 4.9979\n",
"Epoch 24/1000\n",
"4/4 [==============================] - 0s 953us/step - loss: 4.9829\n",
"Epoch 25/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 4.9678\n",
"Epoch 26/1000\n",
"4/4 [==============================] - 0s 617us/step - loss: 4.9528\n",
"Epoch 27/1000\n",
"4/4 [==============================] - 0s 836us/step - loss: 4.9378\n",
"Epoch 28/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 4.9229\n",
"Epoch 29/1000\n",
"4/4 [==============================] - 0s 692us/step - loss: 4.9080\n",
"Epoch 30/1000\n",
"4/4 [==============================] - 0s 668us/step - loss: 4.8932\n",
"Epoch 31/1000\n",
"4/4 [==============================] - 0s 884us/step - loss: 4.8784\n",
"Epoch 32/1000\n",
"4/4 [==============================] - 0s 914us/step - loss: 4.8636\n",
"Epoch 33/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 4.8489\n",
"Epoch 34/1000\n",
"4/4 [==============================] - 0s 967us/step - loss: 4.8341\n",
"Epoch 35/1000\n",
"4/4 [==============================] - 0s 704us/step - loss: 4.8195\n",
"Epoch 36/1000\n",
"4/4 [==============================] - 0s 625us/step - loss: 4.8048\n",
"Epoch 37/1000\n",
"4/4 [==============================] - 0s 683us/step - loss: 4.7902\n",
"Epoch 38/1000\n",
"4/4 [==============================] - 0s 782us/step - loss: 4.7757\n",
"Epoch 39/1000\n",
"4/4 [==============================] - 0s 553us/step - loss: 4.7611\n",
"Epoch 40/1000\n",
"4/4 [==============================] - 0s 684us/step - loss: 4.7466\n",
"Epoch 41/1000\n",
"4/4 [==============================] - 0s 688us/step - loss: 4.7321\n",
"Epoch 42/1000\n",
"4/4 [==============================] - 0s 846us/step - loss: 4.7177\n",
"Epoch 43/1000\n",
"4/4 [==============================] - 0s 803us/step - loss: 4.7033\n",
"Epoch 44/1000\n",
"4/4 [==============================] - 0s 593us/step - loss: 4.6889\n",
"Epoch 45/1000\n",
"4/4 [==============================] - 0s 547us/step - loss: 4.6746\n",
"Epoch 46/1000\n",
"4/4 [==============================] - 0s 641us/step - loss: 4.6603\n",
"Epoch 47/1000\n",
"4/4 [==============================] - 0s 823us/step - loss: 4.6461\n",
"Epoch 48/1000\n",
"4/4 [==============================] - 0s 981us/step - loss: 4.6318\n",
"Epoch 49/1000\n",
"4/4 [==============================] - 0s 830us/step - loss: 4.6176\n",
"Epoch 50/1000\n",
"4/4 [==============================] - 0s 566us/step - loss: 4.6035\n",
"Epoch 51/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 4.5893\n",
"Epoch 52/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 4.5752\n",
"Epoch 53/1000\n",
"4/4 [==============================] - 0s 723us/step - loss: 4.5612\n",
"Epoch 54/1000\n",
"4/4 [==============================] - 0s 746us/step - loss: 4.5472\n",
"Epoch 55/1000\n",
"4/4 [==============================] - 0s 874us/step - loss: 4.5331\n",
"Epoch 56/1000\n",
"4/4 [==============================] - 0s 882us/step - loss: 4.5192\n",
"Epoch 57/1000\n",
"4/4 [==============================] - 0s 714us/step - loss: 4.5053\n",
"Epoch 58/1000\n",
"4/4 [==============================] - 0s 885us/step - loss: 4.4914\n",
"Epoch 59/1000\n",
"4/4 [==============================] - 0s 766us/step - loss: 4.4775\n",
"Epoch 60/1000\n",
"4/4 [==============================] - 0s 664us/step - loss: 4.4637\n",
"Epoch 61/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 4.4499\n",
"Epoch 62/1000\n",
"4/4 [==============================] - 0s 997us/step - loss: 4.4362\n",
"Epoch 63/1000\n",
"4/4 [==============================] - 0s 677us/step - loss: 4.4224\n",
"Epoch 64/1000\n",
"4/4 [==============================] - 0s 679us/step - loss: 4.4087\n",
"Epoch 65/1000\n",
"4/4 [==============================] - 0s 862us/step - loss: 4.3951\n",
"Epoch 66/1000\n",
"4/4 [==============================] - 0s 715us/step - loss: 4.3815\n",
"Epoch 67/1000\n",
"4/4 [==============================] - 0s 615us/step - loss: 4.3679\n",
"Epoch 68/1000\n",
"4/4 [==============================] - 0s 818us/step - loss: 4.3543\n",
"Epoch 69/1000\n",
"4/4 [==============================] - 0s 772us/step - loss: 4.3408\n",
"Epoch 70/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 4.3273\n",
"Epoch 71/1000\n",
"4/4 [==============================] - 0s 962us/step - loss: 4.3139\n",
"Epoch 72/1000\n",
"4/4 [==============================] - 0s 862us/step - loss: 4.3004\n",
"Epoch 73/1000\n",
"4/4 [==============================] - 0s 736us/step - loss: 4.2870\n",
"Epoch 74/1000\n",
"4/4 [==============================] - 0s 765us/step - loss: 4.2737\n",
"Epoch 75/1000\n",
"4/4 [==============================] - 0s 640us/step - loss: 4.2603\n",
"Epoch 76/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 4.2470\n",
"Epoch 77/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 4.2338\n",
"Epoch 78/1000\n",
"4/4 [==============================] - 0s 833us/step - loss: 4.2206\n",
"Epoch 79/1000\n",
"4/4 [==============================] - 0s 581us/step - loss: 4.2074\n",
"Epoch 80/1000\n",
"4/4 [==============================] - 0s 966us/step - loss: 4.1942\n",
"Epoch 81/1000\n",
"4/4 [==============================] - 0s 933us/step - loss: 4.1811\n",
"Epoch 82/1000\n",
"4/4 [==============================] - 0s 853us/step - loss: 4.1679\n",
"Epoch 83/1000\n",
"4/4 [==============================] - 0s 691us/step - loss: 4.1549\n",
"Epoch 84/1000\n",
"4/4 [==============================] - 0s 956us/step - loss: 4.1418\n",
"Epoch 85/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 4.1288\n",
"Epoch 86/1000\n",
"4/4 [==============================] - 0s 924us/step - loss: 4.1159\n",
"Epoch 87/1000\n",
"4/4 [==============================] - 0s 945us/step - loss: 4.1029\n",
"Epoch 88/1000\n",
"4/4 [==============================] - 0s 842us/step - loss: 4.0900\n",
"Epoch 89/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 4.0771\n",
"Epoch 90/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 4.0643\n",
"Epoch 91/1000\n",
"4/4 [==============================] - 0s 793us/step - loss: 4.0515\n",
"Epoch 92/1000\n",
"4/4 [==============================] - 0s 690us/step - loss: 4.0387\n",
"Epoch 93/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 4.0259\n",
"Epoch 94/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 4.0132\n",
"Epoch 95/1000\n",
"4/4 [==============================] - 0s 866us/step - loss: 4.0005\n",
"Epoch 96/1000\n",
"4/4 [==============================] - 0s 2ms/step - loss: 3.9879\n",
"Epoch 97/1000\n",
"4/4 [==============================] - 0s 774us/step - loss: 3.9752\n",
"Epoch 98/1000\n",
"4/4 [==============================] - 0s 772us/step - loss: 3.9626\n",
"Epoch 99/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 3.9501\n",
"Epoch 100/1000\n",
"4/4 [==============================] - 0s 903us/step - loss: 3.9376\n",
"Epoch 101/1000\n",
"4/4 [==============================] - 0s 809us/step - loss: 3.9251\n",
"Epoch 102/1000\n",
"4/4 [==============================] - 0s 883us/step - loss: 3.9126\n",
"Epoch 103/1000\n",
"4/4 [==============================] - 0s 672us/step - loss: 3.9001\n",
"Epoch 104/1000\n",
"4/4 [==============================] - 0s 795us/step - loss: 3.8877\n",
"Epoch 105/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 3.8754\n",
"Epoch 106/1000\n",
"4/4 [==============================] - 0s 857us/step - loss: 3.8630\n",
"Epoch 107/1000\n",
"4/4 [==============================] - 0s 717us/step - loss: 3.8507\n",
"Epoch 108/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 3.8384\n",
"Epoch 109/1000\n",
"4/4 [==============================] - 0s 865us/step - loss: 3.8262\n",
"Epoch 110/1000\n",
"4/4 [==============================] - 0s 777us/step - loss: 3.8139\n",
"Epoch 111/1000\n",
"4/4 [==============================] - 0s 801us/step - loss: 3.8018\n",
"Epoch 112/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 3.7896\n",
"Epoch 113/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 3.7775\n",
"Epoch 114/1000\n",
"4/4 [==============================] - 0s 830us/step - loss: 3.7654\n",
"Epoch 115/1000\n",
"4/4 [==============================] - 0s 725us/step - loss: 3.7533\n",
"Epoch 116/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 3.7413\n",
"Epoch 117/1000\n",
"4/4 [==============================] - 0s 863us/step - loss: 3.7293\n",
"Epoch 118/1000\n",
"4/4 [==============================] - 0s 968us/step - loss: 3.7173\n",
"Epoch 119/1000\n",
"4/4 [==============================] - 0s 751us/step - loss: 3.7053\n",
"Epoch 120/1000\n",
"4/4 [==============================] - 0s 733us/step - loss: 3.6934\n",
"Epoch 121/1000\n",
"4/4 [==============================] - 0s 857us/step - loss: 3.6815\n",
"Epoch 122/1000\n",
"4/4 [==============================] - 0s 978us/step - loss: 3.6697\n",
"Epoch 123/1000\n",
"4/4 [==============================] - 0s 765us/step - loss: 3.6578\n",
"Epoch 124/1000\n",
"4/4 [==============================] - 0s 592us/step - loss: 3.6460\n",
"Epoch 125/1000\n",
"4/4 [==============================] - 0s 804us/step - loss: 3.6343\n",
"Epoch 126/1000\n",
"4/4 [==============================] - 0s 618us/step - loss: 3.6225\n",
"Epoch 127/1000\n",
"4/4 [==============================] - 0s 962us/step - loss: 3.6108\n",
"Epoch 128/1000\n",
"4/4 [==============================] - 0s 732us/step - loss: 3.5991\n",
"Epoch 129/1000\n",
"4/4 [==============================] - 0s 842us/step - loss: 3.5875\n",
"Epoch 130/1000\n",
"4/4 [==============================] - 0s 661us/step - loss: 3.5759\n",
"Epoch 131/1000\n",
"4/4 [==============================] - 0s 862us/step - loss: 3.5643\n",
"Epoch 132/1000\n",
"4/4 [==============================] - 0s 623us/step - loss: 3.5527\n",
"Epoch 133/1000\n",
"4/4 [==============================] - 0s 973us/step - loss: 3.5412\n",
"Epoch 134/1000\n",
"4/4 [==============================] - 0s 959us/step - loss: 3.5297\n",
"Epoch 135/1000\n",
"4/4 [==============================] - 0s 667us/step - loss: 3.5182\n",
"Epoch 136/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 3.5068\n",
"Epoch 137/1000\n",
"4/4 [==============================] - 0s 391us/step - loss: 3.4954\n",
"Epoch 138/1000\n",
"4/4 [==============================] - 0s 742us/step - loss: 3.4840\n",
"Epoch 139/1000\n",
"4/4 [==============================] - 0s 829us/step - loss: 3.4727\n",
"Epoch 140/1000\n",
"4/4 [==============================] - 0s 725us/step - loss: 3.4613\n",
"Epoch 141/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 3.4500\n",
"Epoch 142/1000\n",
"4/4 [==============================] - 0s 886us/step - loss: 3.4388\n",
"Epoch 143/1000\n",
"4/4 [==============================] - 0s 804us/step - loss: 3.4275\n",
"Epoch 144/1000\n",
"4/4 [==============================] - 0s 812us/step - loss: 3.4163\n",
"Epoch 145/1000\n",
"4/4 [==============================] - 0s 739us/step - loss: 3.4052\n",
"Epoch 146/1000\n",
"4/4 [==============================] - 0s 992us/step - loss: 3.3940\n",
"Epoch 147/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 3.3829\n",
"Epoch 148/1000\n",
"4/4 [==============================] - 0s 688us/step - loss: 3.3718\n",
"Epoch 149/1000\n",
"4/4 [==============================] - 0s 709us/step - loss: 3.3607\n",
"Epoch 150/1000\n",
"4/4 [==============================] - 0s 836us/step - loss: 3.3497\n",
"Epoch 151/1000\n",
"4/4 [==============================] - 0s 367us/step - loss: 3.3387\n",
"Epoch 152/1000\n",
"4/4 [==============================] - 0s 631us/step - loss: 3.3277\n",
"Epoch 153/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 3.3168\n",
"Epoch 154/1000\n",
"4/4 [==============================] - 0s 494us/step - loss: 3.3059\n",
"Epoch 155/1000\n",
"4/4 [==============================] - 0s 746us/step - loss: 3.2950\n",
"Epoch 156/1000\n",
"4/4 [==============================] - 0s 745us/step - loss: 3.2841\n",
"Epoch 157/1000\n",
"4/4 [==============================] - 0s 795us/step - loss: 3.2733\n",
"Epoch 158/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 3.2625\n",
"Epoch 159/1000\n",
"4/4 [==============================] - 0s 2ms/step - loss: 3.2517\n",
"Epoch 160/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 3.2409\n",
"Epoch 161/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 3.2302\n",
"Epoch 162/1000\n",
"4/4 [==============================] - 0s 915us/step - loss: 3.2195\n",
"Epoch 163/1000\n",
"4/4 [==============================] - 0s 964us/step - loss: 3.2089\n",
"Epoch 164/1000\n",
"4/4 [==============================] - 0s 959us/step - loss: 3.1982\n",
"Epoch 165/1000\n",
"4/4 [==============================] - 0s 910us/step - loss: 3.1876\n",
"Epoch 166/1000\n",
"4/4 [==============================] - 0s 853us/step - loss: 3.1771\n",
"Epoch 167/1000\n",
"4/4 [==============================] - 0s 700us/step - loss: 3.1665\n",
"Epoch 168/1000\n",
"4/4 [==============================] - 0s 718us/step - loss: 3.1560\n",
"Epoch 169/1000\n",
"4/4 [==============================] - 0s 664us/step - loss: 3.1455\n",
"Epoch 170/1000\n",
"4/4 [==============================] - 0s 748us/step - loss: 3.1350\n",
"Epoch 171/1000\n",
"4/4 [==============================] - 0s 835us/step - loss: 3.1246\n",
"Epoch 172/1000\n",
"4/4 [==============================] - 0s 592us/step - loss: 3.1142\n",
"Epoch 173/1000\n",
"4/4 [==============================] - 0s 749us/step - loss: 3.1038\n",
"Epoch 174/1000\n",
"4/4 [==============================] - 0s 737us/step - loss: 3.0934\n",
"Epoch 175/1000\n",
"4/4 [==============================] - 0s 666us/step - loss: 3.0831\n",
"Epoch 176/1000\n",
"4/4 [==============================] - 0s 832us/step - loss: 3.0728\n",
"Epoch 177/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 3.0625\n",
"Epoch 178/1000\n",
"4/4 [==============================] - 0s 843us/step - loss: 3.0523\n",
"Epoch 179/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 3.0421\n",
"Epoch 180/1000\n",
"4/4 [==============================] - 0s 696us/step - loss: 3.0319\n",
"Epoch 181/1000\n",
"4/4 [==============================] - 0s 682us/step - loss: 3.0217\n",
"Epoch 182/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 3.0116\n",
"Epoch 183/1000\n",
"4/4 [==============================] - 0s 900us/step - loss: 3.0015\n",
"Epoch 184/1000\n",
"4/4 [==============================] - 0s 813us/step - loss: 2.9914\n",
"Epoch 185/1000\n",
"4/4 [==============================] - 0s 895us/step - loss: 2.9813\n",
"Epoch 186/1000\n",
"4/4 [==============================] - 0s 888us/step - loss: 2.9713\n",
"Epoch 187/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.9613\n",
"Epoch 188/1000\n",
"4/4 [==============================] - 0s 903us/step - loss: 2.9513\n",
"Epoch 189/1000\n",
"4/4 [==============================] - 0s 953us/step - loss: 2.9414\n",
"Epoch 190/1000\n",
"4/4 [==============================] - 0s 863us/step - loss: 2.9315\n",
"Epoch 191/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.9216\n",
"Epoch 192/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.9117\n",
"Epoch 193/1000\n",
"4/4 [==============================] - 0s 841us/step - loss: 2.9019\n",
"Epoch 194/1000\n",
"4/4 [==============================] - 0s 663us/step - loss: 2.8921\n",
"Epoch 195/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.8823\n",
"Epoch 196/1000\n",
"4/4 [==============================] - 0s 889us/step - loss: 2.8725\n",
"Epoch 197/1000\n",
"4/4 [==============================] - 0s 791us/step - loss: 2.8628\n",
"Epoch 198/1000\n",
"4/4 [==============================] - 0s 870us/step - loss: 2.8531\n",
"Epoch 199/1000\n",
"4/4 [==============================] - 0s 731us/step - loss: 2.8434\n",
"Epoch 200/1000\n",
"4/4 [==============================] - 0s 830us/step - loss: 2.8337\n",
"Epoch 201/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.8241\n",
"Epoch 202/1000\n",
"4/4 [==============================] - 0s 847us/step - loss: 2.8145\n",
"Epoch 203/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.8049\n",
"Epoch 204/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.7954\n",
"Epoch 205/1000\n",
"4/4 [==============================] - 0s 694us/step - loss: 2.7859\n",
"Epoch 206/1000\n",
"4/4 [==============================] - 0s 718us/step - loss: 2.7764\n",
"Epoch 207/1000\n",
"4/4 [==============================] - 0s 835us/step - loss: 2.7669\n",
"Epoch 208/1000\n",
"4/4 [==============================] - 0s 901us/step - loss: 2.7575\n",
"Epoch 209/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.7481\n",
"Epoch 210/1000\n",
"4/4 [==============================] - 0s 888us/step - loss: 2.7387\n",
"Epoch 211/1000\n",
"4/4 [==============================] - 0s 875us/step - loss: 2.7293\n",
"Epoch 212/1000\n",
"4/4 [==============================] - 0s 910us/step - loss: 2.7200\n",
"Epoch 213/1000\n",
"4/4 [==============================] - 0s 831us/step - loss: 2.7106\n",
"Epoch 214/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.7014\n",
"Epoch 215/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.6921\n",
"Epoch 216/1000\n",
"4/4 [==============================] - 0s 758us/step - loss: 2.6829\n",
"Epoch 217/1000\n",
"4/4 [==============================] - 0s 794us/step - loss: 2.6737\n",
"Epoch 218/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.6645\n",
"Epoch 219/1000\n",
"4/4 [==============================] - 0s 924us/step - loss: 2.6553\n",
"Epoch 220/1000\n",
"4/4 [==============================] - 0s 941us/step - loss: 2.6462\n",
"Epoch 221/1000\n",
"4/4 [==============================] - 0s 755us/step - loss: 2.6371\n",
"Epoch 222/1000\n",
"4/4 [==============================] - 0s 736us/step - loss: 2.6280\n",
"Epoch 223/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.6189\n",
"Epoch 224/1000\n",
"4/4 [==============================] - 0s 998us/step - loss: 2.6099\n",
"Epoch 225/1000\n",
"4/4 [==============================] - 0s 927us/step - loss: 2.6009\n",
"Epoch 226/1000\n",
"4/4 [==============================] - 0s 782us/step - loss: 2.5919\n",
"Epoch 227/1000\n",
"4/4 [==============================] - 0s 709us/step - loss: 2.5829\n",
"Epoch 228/1000\n",
"4/4 [==============================] - 0s 2ms/step - loss: 2.5740\n",
"Epoch 229/1000\n",
"4/4 [==============================] - 0s 848us/step - loss: 2.5651\n",
"Epoch 230/1000\n",
"4/4 [==============================] - 0s 962us/step - loss: 2.5562\n",
"Epoch 231/1000\n",
"4/4 [==============================] - 0s 786us/step - loss: 2.5474\n",
"Epoch 232/1000\n",
"4/4 [==============================] - 0s 941us/step - loss: 2.5385\n",
"Epoch 233/1000\n",
"4/4 [==============================] - 0s 985us/step - loss: 2.5297\n",
"Epoch 234/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.5209\n",
"Epoch 235/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.5122\n",
"Epoch 236/1000\n",
"4/4 [==============================] - 0s 795us/step - loss: 2.5034\n",
"Epoch 237/1000\n",
"4/4 [==============================] - 0s 911us/step - loss: 2.4947\n",
"Epoch 238/1000\n",
"4/4 [==============================] - 0s 776us/step - loss: 2.4860\n",
"Epoch 239/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.4774\n",
"Epoch 240/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.4687\n",
"Epoch 241/1000\n",
"4/4 [==============================] - 0s 740us/step - loss: 2.4601\n",
"Epoch 242/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.4515\n",
"Epoch 243/1000\n",
"4/4 [==============================] - 0s 462us/step - loss: 2.4430\n",
"Epoch 244/1000\n",
"4/4 [==============================] - 0s 642us/step - loss: 2.4344\n",
"Epoch 245/1000\n",
"4/4 [==============================] - 0s 665us/step - loss: 2.4259\n",
"Epoch 246/1000\n",
"4/4 [==============================] - 0s 796us/step - loss: 2.4174\n",
"Epoch 247/1000\n",
"4/4 [==============================] - 0s 937us/step - loss: 2.4090\n",
"Epoch 248/1000\n",
"4/4 [==============================] - 0s 780us/step - loss: 2.4005\n",
"Epoch 249/1000\n",
"4/4 [==============================] - 0s 660us/step - loss: 2.3921\n",
"Epoch 250/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.3837\n",
"Epoch 251/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.3753\n",
"Epoch 252/1000\n",
"4/4 [==============================] - 0s 667us/step - loss: 2.3670\n",
"Epoch 253/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.3587\n",
"Epoch 254/1000\n",
"4/4 [==============================] - 0s 594us/step - loss: 2.3504\n",
"Epoch 255/1000\n",
"4/4 [==============================] - 0s 835us/step - loss: 2.3421\n",
"Epoch 256/1000\n",
"4/4 [==============================] - 0s 710us/step - loss: 2.3339\n",
"Epoch 257/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.3256\n",
"Epoch 258/1000\n",
"4/4 [==============================] - 0s 954us/step - loss: 2.3174\n",
"Epoch 259/1000\n",
"4/4 [==============================] - 0s 832us/step - loss: 2.3093\n",
"Epoch 260/1000\n",
"4/4 [==============================] - 0s 940us/step - loss: 2.3011\n",
"Epoch 261/1000\n",
"4/4 [==============================] - 0s 683us/step - loss: 2.2930\n",
"Epoch 262/1000\n",
"4/4 [==============================] - 0s 2ms/step - loss: 2.2849\n",
"Epoch 263/1000\n",
"4/4 [==============================] - 0s 698us/step - loss: 2.2768\n",
"Epoch 264/1000\n",
"4/4 [==============================] - 0s 737us/step - loss: 2.2687\n",
"Epoch 265/1000\n",
"4/4 [==============================] - 0s 663us/step - loss: 2.2607\n",
"Epoch 266/1000\n",
"4/4 [==============================] - 0s 500us/step - loss: 2.2527\n",
"Epoch 267/1000\n",
"4/4 [==============================] - 0s 665us/step - loss: 2.2447\n",
"Epoch 268/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.2367\n",
"Epoch 269/1000\n",
"4/4 [==============================] - 0s 959us/step - loss: 2.2288\n",
"Epoch 270/1000\n",
"4/4 [==============================] - 0s 905us/step - loss: 2.2208\n",
"Epoch 271/1000\n",
"4/4 [==============================] - 0s 936us/step - loss: 2.2129\n",
"Epoch 272/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.2051\n",
"Epoch 273/1000\n",
"4/4 [==============================] - 0s 863us/step - loss: 2.1972\n",
"Epoch 274/1000\n",
"4/4 [==============================] - 0s 703us/step - loss: 2.1894\n",
"Epoch 275/1000\n",
"4/4 [==============================] - 0s 749us/step - loss: 2.1816\n",
"Epoch 276/1000\n",
"4/4 [==============================] - 0s 771us/step - loss: 2.1738\n",
"Epoch 277/1000\n",
"4/4 [==============================] - 0s 639us/step - loss: 2.1660\n",
"Epoch 278/1000\n",
"4/4 [==============================] - 0s 748us/step - loss: 2.1583\n",
"Epoch 279/1000\n",
"4/4 [==============================] - 0s 747us/step - loss: 2.1506\n",
"Epoch 280/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.1429\n",
"Epoch 281/1000\n",
"4/4 [==============================] - 0s 886us/step - loss: 2.1352\n",
"Epoch 282/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.1276\n",
"Epoch 283/1000\n",
"4/4 [==============================] - 0s 925us/step - loss: 2.1199\n",
"Epoch 284/1000\n",
"4/4 [==============================] - 0s 867us/step - loss: 2.1123\n",
"Epoch 285/1000\n",
"4/4 [==============================] - 0s 781us/step - loss: 2.1048\n",
"Epoch 286/1000\n",
"4/4 [==============================] - 0s 635us/step - loss: 2.0972\n",
"Epoch 287/1000\n",
"4/4 [==============================] - 0s 732us/step - loss: 2.0897\n",
"Epoch 288/1000\n",
"4/4 [==============================] - 0s 716us/step - loss: 2.0822\n",
"Epoch 289/1000\n",
"4/4 [==============================] - 0s 605us/step - loss: 2.0747\n",
"Epoch 290/1000\n",
"4/4 [==============================] - 0s 846us/step - loss: 2.0672\n",
"Epoch 291/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.0598\n",
"Epoch 292/1000\n",
"4/4 [==============================] - 0s 842us/step - loss: 2.0523\n",
"Epoch 293/1000\n",
"4/4 [==============================] - 0s 722us/step - loss: 2.0449\n",
"Epoch 294/1000\n",
"4/4 [==============================] - 0s 717us/step - loss: 2.0376\n",
"Epoch 295/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.0302\n",
"Epoch 296/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.0229\n",
"Epoch 297/1000\n",
"4/4 [==============================] - 0s 834us/step - loss: 2.0156\n",
"Epoch 298/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 2.0083\n",
"Epoch 299/1000\n",
"4/4 [==============================] - 0s 753us/step - loss: 2.0010\n",
"Epoch 300/1000\n",
"4/4 [==============================] - 0s 869us/step - loss: 1.9937\n",
"Epoch 301/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.9865\n",
"Epoch 302/1000\n",
"4/4 [==============================] - 0s 790us/step - loss: 1.9793\n",
"Epoch 303/1000\n",
"4/4 [==============================] - 0s 697us/step - loss: 1.9721\n",
"Epoch 304/1000\n",
"4/4 [==============================] - 0s 859us/step - loss: 1.9650\n",
"Epoch 305/1000\n",
"4/4 [==============================] - 0s 909us/step - loss: 1.9578\n",
"Epoch 306/1000\n",
"4/4 [==============================] - 0s 763us/step - loss: 1.9507\n",
"Epoch 307/1000\n",
"4/4 [==============================] - 0s 665us/step - loss: 1.9436\n",
"Epoch 308/1000\n",
"4/4 [==============================] - 0s 750us/step - loss: 1.9365\n",
"Epoch 309/1000\n",
"4/4 [==============================] - 0s 752us/step - loss: 1.9295\n",
"Epoch 310/1000\n",
"4/4 [==============================] - 0s 828us/step - loss: 1.9225\n",
"Epoch 311/1000\n",
"4/4 [==============================] - 0s 823us/step - loss: 1.9154\n",
"Epoch 312/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.9085\n",
"Epoch 313/1000\n",
"4/4 [==============================] - 0s 630us/step - loss: 1.9015\n",
"Epoch 314/1000\n",
"4/4 [==============================] - 0s 845us/step - loss: 1.8945\n",
"Epoch 315/1000\n",
"4/4 [==============================] - 0s 589us/step - loss: 1.8876\n",
"Epoch 316/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.8807\n",
"Epoch 317/1000\n",
"4/4 [==============================] - 0s 882us/step - loss: 1.8738\n",
"Epoch 318/1000\n",
"4/4 [==============================] - 0s 703us/step - loss: 1.8670\n",
"Epoch 319/1000\n",
"4/4 [==============================] - 0s 799us/step - loss: 1.8601\n",
"Epoch 320/1000\n",
"4/4 [==============================] - 0s 951us/step - loss: 1.8533\n",
"Epoch 321/1000\n",
"4/4 [==============================] - 0s 775us/step - loss: 1.8465\n",
"Epoch 322/1000\n",
"4/4 [==============================] - 0s 853us/step - loss: 1.8397\n",
"Epoch 323/1000\n",
"4/4 [==============================] - 0s 858us/step - loss: 1.8330\n",
"Epoch 324/1000\n",
"4/4 [==============================] - 0s 701us/step - loss: 1.8262\n",
"Epoch 325/1000\n",
"4/4 [==============================] - 0s 784us/step - loss: 1.8195\n",
"Epoch 326/1000\n",
"4/4 [==============================] - 0s 963us/step - loss: 1.8128\n",
"Epoch 327/1000\n",
"4/4 [==============================] - 0s 767us/step - loss: 1.8061\n",
"Epoch 328/1000\n",
"4/4 [==============================] - 0s 845us/step - loss: 1.7995\n",
"Epoch 329/1000\n",
"4/4 [==============================] - 0s 409us/step - loss: 1.7928\n",
"Epoch 330/1000\n",
"4/4 [==============================] - 0s 862us/step - loss: 1.7862\n",
"Epoch 331/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.7796\n",
"Epoch 332/1000\n",
"4/4 [==============================] - 0s 987us/step - loss: 1.7730\n",
"Epoch 333/1000\n",
"4/4 [==============================] - 0s 736us/step - loss: 1.7665\n",
"Epoch 334/1000\n",
"4/4 [==============================] - 0s 704us/step - loss: 1.7600\n",
"Epoch 335/1000\n",
"4/4 [==============================] - 0s 554us/step - loss: 1.7534\n",
"Epoch 336/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.7470\n",
"Epoch 337/1000\n",
"4/4 [==============================] - 0s 680us/step - loss: 1.7405\n",
"Epoch 338/1000\n",
"4/4 [==============================] - 0s 858us/step - loss: 1.7340\n",
"Epoch 339/1000\n",
"4/4 [==============================] - 0s 867us/step - loss: 1.7276\n",
"Epoch 340/1000\n",
"4/4 [==============================] - 0s 815us/step - loss: 1.7212\n",
"Epoch 341/1000\n",
"4/4 [==============================] - 0s 640us/step - loss: 1.7148\n",
"Epoch 342/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.7084\n",
"Epoch 343/1000\n",
"4/4 [==============================] - 0s 660us/step - loss: 1.7021\n",
"Epoch 344/1000\n",
"4/4 [==============================] - 0s 935us/step - loss: 1.6957\n",
"Epoch 345/1000\n",
"4/4 [==============================] - 0s 960us/step - loss: 1.6894\n",
"Epoch 346/1000\n",
"4/4 [==============================] - 0s 679us/step - loss: 1.6831\n",
"Epoch 347/1000\n",
"4/4 [==============================] - 0s 733us/step - loss: 1.6769\n",
"Epoch 348/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.6706\n",
"Epoch 349/1000\n",
"4/4 [==============================] - 0s 578us/step - loss: 1.6644\n",
"Epoch 350/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.6582\n",
"Epoch 351/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.6520\n",
"Epoch 352/1000\n",
"4/4 [==============================] - 0s 939us/step - loss: 1.6458\n",
"Epoch 353/1000\n",
"4/4 [==============================] - 0s 796us/step - loss: 1.6396\n",
"Epoch 354/1000\n",
"4/4 [==============================] - 0s 823us/step - loss: 1.6335\n",
"Epoch 355/1000\n",
"4/4 [==============================] - 0s 717us/step - loss: 1.6274\n",
"Epoch 356/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.6213\n",
"Epoch 357/1000\n",
"4/4 [==============================] - 0s 746us/step - loss: 1.6152\n",
"Epoch 358/1000\n",
"4/4 [==============================] - 0s 808us/step - loss: 1.6091\n",
"Epoch 359/1000\n",
"4/4 [==============================] - 0s 812us/step - loss: 1.6031\n",
"Epoch 360/1000\n",
"4/4 [==============================] - 0s 940us/step - loss: 1.5971\n",
"Epoch 361/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.5911\n",
"Epoch 362/1000\n",
"4/4 [==============================] - 0s 987us/step - loss: 1.5851\n",
"Epoch 363/1000\n",
"4/4 [==============================] - 0s 905us/step - loss: 1.5791\n",
"Epoch 364/1000\n",
"4/4 [==============================] - 0s 899us/step - loss: 1.5732\n",
"Epoch 365/1000\n",
"4/4 [==============================] - 0s 725us/step - loss: 1.5673\n",
"Epoch 366/1000\n",
"4/4 [==============================] - 0s 831us/step - loss: 1.5614\n",
"Epoch 367/1000\n",
"4/4 [==============================] - 0s 578us/step - loss: 1.5555\n",
"Epoch 368/1000\n",
"4/4 [==============================] - 0s 731us/step - loss: 1.5496\n",
"Epoch 369/1000\n",
"4/4 [==============================] - 0s 756us/step - loss: 1.5437\n",
"Epoch 370/1000\n",
"4/4 [==============================] - 0s 937us/step - loss: 1.5379\n",
"Epoch 371/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.5321\n",
"Epoch 372/1000\n",
"4/4 [==============================] - 0s 2ms/step - loss: 1.5263\n",
"Epoch 373/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.5205\n",
"Epoch 374/1000\n",
"4/4 [==============================] - 0s 814us/step - loss: 1.5148\n",
"Epoch 375/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.5090\n",
"Epoch 376/1000\n",
"4/4 [==============================] - 0s 709us/step - loss: 1.5033\n",
"Epoch 377/1000\n",
"4/4 [==============================] - 0s 681us/step - loss: 1.4976\n",
"Epoch 378/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.4919\n",
"Epoch 379/1000\n",
"4/4 [==============================] - 0s 870us/step - loss: 1.4863\n",
"Epoch 380/1000\n",
"4/4 [==============================] - 0s 723us/step - loss: 1.4806\n",
"Epoch 381/1000\n",
"4/4 [==============================] - 0s 769us/step - loss: 1.4750\n",
"Epoch 382/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.4694\n",
"Epoch 383/1000\n",
"4/4 [==============================] - 0s 970us/step - loss: 1.4638\n",
"Epoch 384/1000\n",
"4/4 [==============================] - 0s 698us/step - loss: 1.4582\n",
"Epoch 385/1000\n",
"4/4 [==============================] - 0s 919us/step - loss: 1.4527\n",
"Epoch 386/1000\n",
"4/4 [==============================] - 0s 844us/step - loss: 1.4471\n",
"Epoch 387/1000\n",
"4/4 [==============================] - 0s 687us/step - loss: 1.4416\n",
"Epoch 388/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.4361\n",
"Epoch 389/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.4306\n",
"Epoch 390/1000\n",
"4/4 [==============================] - 0s 761us/step - loss: 1.4252\n",
"Epoch 391/1000\n",
"4/4 [==============================] - 0s 784us/step - loss: 1.4197\n",
"Epoch 392/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.4143\n",
"Epoch 393/1000\n",
"4/4 [==============================] - 0s 684us/step - loss: 1.4089\n",
"Epoch 394/1000\n",
"4/4 [==============================] - 0s 793us/step - loss: 1.4035\n",
"Epoch 395/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.3981\n",
"Epoch 396/1000\n",
"4/4 [==============================] - 0s 792us/step - loss: 1.3927\n",
"Epoch 397/1000\n",
"4/4 [==============================] - 0s 677us/step - loss: 1.3874\n",
"Epoch 398/1000\n",
"4/4 [==============================] - 0s 654us/step - loss: 1.3821\n",
"Epoch 399/1000\n",
"4/4 [==============================] - 0s 840us/step - loss: 1.3768\n",
"Epoch 400/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.3715\n",
"Epoch 401/1000\n",
"4/4 [==============================] - 0s 871us/step - loss: 1.3662\n",
"Epoch 402/1000\n",
"4/4 [==============================] - 0s 798us/step - loss: 1.3610\n",
"Epoch 403/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.3557\n",
"Epoch 404/1000\n",
"4/4 [==============================] - 0s 2ms/step - loss: 1.3505\n",
"Epoch 405/1000\n",
"4/4 [==============================] - 0s 791us/step - loss: 1.3453\n",
"Epoch 406/1000\n",
"4/4 [==============================] - 0s 734us/step - loss: 1.3401\n",
"Epoch 407/1000\n",
"4/4 [==============================] - 0s 817us/step - loss: 1.3349\n",
"Epoch 408/1000\n",
"4/4 [==============================] - 0s 713us/step - loss: 1.3298\n",
"Epoch 409/1000\n",
"4/4 [==============================] - 0s 940us/step - loss: 1.3247\n",
"Epoch 410/1000\n",
"4/4 [==============================] - 0s 939us/step - loss: 1.3195\n",
"Epoch 411/1000\n",
"4/4 [==============================] - 0s 897us/step - loss: 1.3145\n",
"Epoch 412/1000\n",
"4/4 [==============================] - 0s 898us/step - loss: 1.3094\n",
"Epoch 413/1000\n",
"4/4 [==============================] - 0s 839us/step - loss: 1.3043\n",
"Epoch 414/1000\n",
"4/4 [==============================] - 0s 498us/step - loss: 1.2993\n",
"Epoch 415/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.2942\n",
"Epoch 416/1000\n",
"4/4 [==============================] - 0s 895us/step - loss: 1.2892\n",
"Epoch 417/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.2842\n",
"Epoch 418/1000\n",
"4/4 [==============================] - 0s 719us/step - loss: 1.2792\n",
"Epoch 419/1000\n",
"4/4 [==============================] - 0s 825us/step - loss: 1.2743\n",
"Epoch 420/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.2693\n",
"Epoch 421/1000\n",
"4/4 [==============================] - 0s 552us/step - loss: 1.2644\n",
"Epoch 422/1000\n",
"4/4 [==============================] - 0s 735us/step - loss: 1.2595\n",
"Epoch 423/1000\n",
"4/4 [==============================] - 0s 902us/step - loss: 1.2546\n",
"Epoch 424/1000\n",
"4/4 [==============================] - 0s 831us/step - loss: 1.2497\n",
"Epoch 425/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.2448\n",
"Epoch 426/1000\n",
"4/4 [==============================] - 0s 788us/step - loss: 1.2400\n",
"Epoch 427/1000\n",
"4/4 [==============================] - 0s 737us/step - loss: 1.2352\n",
"Epoch 428/1000\n",
"4/4 [==============================] - 0s 651us/step - loss: 1.2303\n",
"Epoch 429/1000\n",
"4/4 [==============================] - 0s 451us/step - loss: 1.2255\n",
"Epoch 430/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.2208\n",
"Epoch 431/1000\n",
"4/4 [==============================] - 0s 883us/step - loss: 1.2160\n",
"Epoch 432/1000\n",
"4/4 [==============================] - 0s 799us/step - loss: 1.2112\n",
"Epoch 433/1000\n",
"4/4 [==============================] - 0s 989us/step - loss: 1.2065\n",
"Epoch 434/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.2018\n",
"Epoch 435/1000\n",
"4/4 [==============================] - 0s 771us/step - loss: 1.1971\n",
"Epoch 436/1000\n",
"4/4 [==============================] - 0s 862us/step - loss: 1.1924\n",
"Epoch 437/1000\n",
"4/4 [==============================] - 0s 953us/step - loss: 1.1877\n",
"Epoch 438/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.1831\n",
"Epoch 439/1000\n",
"4/4 [==============================] - 0s 777us/step - loss: 1.1784\n",
"Epoch 440/1000\n",
"4/4 [==============================] - 0s 793us/step - loss: 1.1738\n",
"Epoch 441/1000\n",
"4/4 [==============================] - 0s 590us/step - loss: 1.1692\n",
"Epoch 442/1000\n",
"4/4 [==============================] - 0s 701us/step - loss: 1.1646\n",
"Epoch 443/1000\n",
"4/4 [==============================] - 0s 609us/step - loss: 1.1601\n",
"Epoch 444/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.1555\n",
"Epoch 445/1000\n",
"4/4 [==============================] - 0s 770us/step - loss: 1.1510\n",
"Epoch 446/1000\n",
"4/4 [==============================] - 0s 789us/step - loss: 1.1464\n",
"Epoch 447/1000\n",
"4/4 [==============================] - 0s 675us/step - loss: 1.1419\n",
"Epoch 448/1000\n",
"4/4 [==============================] - 0s 945us/step - loss: 1.1374\n",
"Epoch 449/1000\n",
"4/4 [==============================] - 0s 863us/step - loss: 1.1329\n",
"Epoch 450/1000\n",
"4/4 [==============================] - 0s 732us/step - loss: 1.1285\n",
"Epoch 451/1000\n",
"4/4 [==============================] - 0s 825us/step - loss: 1.1240\n",
"Epoch 452/1000\n",
"4/4 [==============================] - 0s 971us/step - loss: 1.1196\n",
"Epoch 453/1000\n",
"4/4 [==============================] - 0s 873us/step - loss: 1.1152\n",
"Epoch 454/1000\n",
"4/4 [==============================] - 0s 859us/step - loss: 1.1108\n",
"Epoch 455/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.1064\n",
"Epoch 456/1000\n",
"4/4 [==============================] - 0s 796us/step - loss: 1.1020\n",
"Epoch 457/1000\n",
"4/4 [==============================] - 0s 577us/step - loss: 1.0976\n",
"Epoch 458/1000\n",
"4/4 [==============================] - 0s 534us/step - loss: 1.0933\n",
"Epoch 459/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.0890\n",
"Epoch 460/1000\n",
"4/4 [==============================] - 0s 623us/step - loss: 1.0847\n",
"Epoch 461/1000\n",
"4/4 [==============================] - 0s 721us/step - loss: 1.0804\n",
"Epoch 462/1000\n",
"4/4 [==============================] - 0s 743us/step - loss: 1.0761\n",
"Epoch 463/1000\n",
"4/4 [==============================] - 0s 960us/step - loss: 1.0718\n",
"Epoch 464/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 1.0676\n",
"Epoch 465/1000\n",
"4/4 [==============================] - 0s 678us/step - loss: 1.0633\n",
"Epoch 466/1000\n",
"4/4 [==============================] - 0s 735us/step - loss: 1.0591\n",
"Epoch 467/1000\n",
"4/4 [==============================] - 0s 796us/step - loss: 1.0549\n",
"Epoch 468/1000\n",
"4/4 [==============================] - 0s 758us/step - loss: 1.0507\n",
"Epoch 469/1000\n",
"4/4 [==============================] - 0s 728us/step - loss: 1.0465\n",
"Epoch 470/1000\n",
"4/4 [==============================] - 0s 570us/step - loss: 1.0424\n",
"Epoch 471/1000\n",
"4/4 [==============================] - 0s 965us/step - loss: 1.0382\n",
"Epoch 472/1000\n",
"4/4 [==============================] - 0s 813us/step - loss: 1.0341\n",
"Epoch 473/1000\n",
"4/4 [==============================] - 0s 768us/step - loss: 1.0300\n",
"Epoch 474/1000\n",
"4/4 [==============================] - 0s 734us/step - loss: 1.0259\n",
"Epoch 475/1000\n",
"4/4 [==============================] - 0s 801us/step - loss: 1.0218\n",
"Epoch 476/1000\n",
"4/4 [==============================] - 0s 795us/step - loss: 1.0177\n",
"Epoch 477/1000\n",
"4/4 [==============================] - 0s 702us/step - loss: 1.0136\n",
"Epoch 478/1000\n",
"4/4 [==============================] - 0s 801us/step - loss: 1.0096\n",
"Epoch 479/1000\n",
"4/4 [==============================] - 0s 574us/step - loss: 1.0056\n",
"Epoch 480/1000\n",
"4/4 [==============================] - 0s 767us/step - loss: 1.0015\n",
"Epoch 481/1000\n",
"4/4 [==============================] - 0s 810us/step - loss: 0.9975\n",
"Epoch 482/1000\n",
"4/4 [==============================] - 0s 921us/step - loss: 0.9935\n",
"Epoch 483/1000\n",
"4/4 [==============================] - 0s 692us/step - loss: 0.9896\n",
"Epoch 484/1000\n",
"4/4 [==============================] - 0s 658us/step - loss: 0.9856\n",
"Epoch 485/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.9817\n",
"Epoch 486/1000\n",
"4/4 [==============================] - 0s 982us/step - loss: 0.9777\n",
"Epoch 487/1000\n",
"4/4 [==============================] - 0s 707us/step - loss: 0.9738\n",
"Epoch 488/1000\n",
"4/4 [==============================] - 0s 748us/step - loss: 0.9699\n",
"Epoch 489/1000\n",
"4/4 [==============================] - 0s 686us/step - loss: 0.9660\n",
"Epoch 490/1000\n",
"4/4 [==============================] - 0s 856us/step - loss: 0.9621\n",
"Epoch 491/1000\n",
"4/4 [==============================] - 0s 979us/step - loss: 0.9583\n",
"Epoch 492/1000\n",
"4/4 [==============================] - 0s 690us/step - loss: 0.9544\n",
"Epoch 493/1000\n",
"4/4 [==============================] - 0s 804us/step - loss: 0.9506\n",
"Epoch 494/1000\n",
"4/4 [==============================] - 0s 676us/step - loss: 0.9468\n",
"Epoch 495/1000\n",
"4/4 [==============================] - 0s 776us/step - loss: 0.9430\n",
"Epoch 496/1000\n",
"4/4 [==============================] - 0s 706us/step - loss: 0.9392\n",
"Epoch 497/1000\n",
"4/4 [==============================] - 0s 711us/step - loss: 0.9354\n",
"Epoch 498/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.9316\n",
"Epoch 499/1000\n",
"4/4 [==============================] - 0s 856us/step - loss: 0.9279\n",
"Epoch 500/1000\n",
"4/4 [==============================] - 0s 867us/step - loss: 0.9241\n",
"Epoch 501/1000\n",
"4/4 [==============================] - 0s 640us/step - loss: 0.9204\n",
"Epoch 502/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.9167\n",
"Epoch 503/1000\n",
"4/4 [==============================] - 0s 821us/step - loss: 0.9130\n",
"Epoch 504/1000\n",
"4/4 [==============================] - 0s 634us/step - loss: 0.9093\n",
"Epoch 505/1000\n",
"4/4 [==============================] - 0s 800us/step - loss: 0.9057\n",
"Epoch 506/1000\n",
"4/4 [==============================] - 0s 967us/step - loss: 0.9020\n",
"Epoch 507/1000\n",
"4/4 [==============================] - 0s 800us/step - loss: 0.8984\n",
"Epoch 508/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.8947\n",
"Epoch 509/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.8911\n",
"Epoch 510/1000\n",
"4/4 [==============================] - 0s 793us/step - loss: 0.8875\n",
"Epoch 511/1000\n",
"4/4 [==============================] - 0s 723us/step - loss: 0.8839\n",
"Epoch 512/1000\n",
"4/4 [==============================] - 0s 781us/step - loss: 0.8803\n",
"Epoch 513/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.8768\n",
"Epoch 514/1000\n",
"4/4 [==============================] - 0s 923us/step - loss: 0.8732\n",
"Epoch 515/1000\n",
"4/4 [==============================] - 0s 783us/step - loss: 0.8697\n",
"Epoch 516/1000\n",
"4/4 [==============================] - 0s 913us/step - loss: 0.8662\n",
"Epoch 517/1000\n",
"4/4 [==============================] - 0s 766us/step - loss: 0.8626\n",
"Epoch 518/1000\n",
"4/4 [==============================] - 0s 701us/step - loss: 0.8591\n",
"Epoch 519/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.8557\n",
"Epoch 520/1000\n",
"4/4 [==============================] - 0s 963us/step - loss: 0.8522\n",
"Epoch 521/1000\n",
"4/4 [==============================] - 0s 730us/step - loss: 0.8487\n",
"Epoch 522/1000\n",
"4/4 [==============================] - 0s 938us/step - loss: 0.8453\n",
"Epoch 523/1000\n",
"4/4 [==============================] - 0s 665us/step - loss: 0.8418\n",
"Epoch 524/1000\n",
"4/4 [==============================] - 0s 521us/step - loss: 0.8384\n",
"Epoch 525/1000\n",
"4/4 [==============================] - 0s 756us/step - loss: 0.8350\n",
"Epoch 526/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.8316\n",
"Epoch 527/1000\n",
"4/4 [==============================] - 0s 806us/step - loss: 0.8282\n",
"Epoch 528/1000\n",
"4/4 [==============================] - 0s 871us/step - loss: 0.8249\n",
"Epoch 529/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.8215\n",
"Epoch 530/1000\n",
"4/4 [==============================] - 0s 2ms/step - loss: 0.8181\n",
"Epoch 531/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.8148\n",
"Epoch 532/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.8115\n",
"Epoch 533/1000\n",
"4/4 [==============================] - 0s 2ms/step - loss: 0.8082\n",
"Epoch 534/1000\n",
"4/4 [==============================] - 0s 953us/step - loss: 0.8049\n",
"Epoch 535/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.8016\n",
"Epoch 536/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.7983\n",
"Epoch 537/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.7951\n",
"Epoch 538/1000\n",
"4/4 [==============================] - 0s 883us/step - loss: 0.7918\n",
"Epoch 539/1000\n",
"4/4 [==============================] - 0s 1000us/step - loss: 0.7886\n",
"Epoch 540/1000\n",
"4/4 [==============================] - 0s 762us/step - loss: 0.7854\n",
"Epoch 541/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.7822\n",
"Epoch 542/1000\n",
"4/4 [==============================] - 0s 942us/step - loss: 0.7790\n",
"Epoch 543/1000\n",
"4/4 [==============================] - 0s 712us/step - loss: 0.7758\n",
"Epoch 544/1000\n",
"4/4 [==============================] - 0s 579us/step - loss: 0.7726\n",
"Epoch 545/1000\n",
"4/4 [==============================] - 0s 790us/step - loss: 0.7694\n",
"Epoch 546/1000\n",
"4/4 [==============================] - 0s 886us/step - loss: 0.7663\n",
"Epoch 547/1000\n",
"4/4 [==============================] - 0s 771us/step - loss: 0.7631\n",
"Epoch 548/1000\n",
"4/4 [==============================] - 0s 633us/step - loss: 0.7600\n",
"Epoch 549/1000\n",
"4/4 [==============================] - 0s 531us/step - loss: 0.7569\n",
"Epoch 550/1000\n",
"4/4 [==============================] - 0s 628us/step - loss: 0.7538\n",
"Epoch 551/1000\n",
"4/4 [==============================] - 0s 506us/step - loss: 0.7507\n",
"Epoch 552/1000\n",
"4/4 [==============================] - 0s 665us/step - loss: 0.7476\n",
"Epoch 553/1000\n",
"4/4 [==============================] - 0s 940us/step - loss: 0.7446\n",
"Epoch 554/1000\n",
"4/4 [==============================] - 0s 836us/step - loss: 0.7415\n",
"Epoch 555/1000\n",
"4/4 [==============================] - 0s 624us/step - loss: 0.7385\n",
"Epoch 556/1000\n",
"4/4 [==============================] - 0s 826us/step - loss: 0.7354\n",
"Epoch 557/1000\n",
"4/4 [==============================] - 0s 768us/step - loss: 0.7324\n",
"Epoch 558/1000\n",
"4/4 [==============================] - 0s 848us/step - loss: 0.7294\n",
"Epoch 559/1000\n",
"4/4 [==============================] - 0s 804us/step - loss: 0.7264\n",
"Epoch 560/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.7234\n",
"Epoch 561/1000\n",
"4/4 [==============================] - 0s 889us/step - loss: 0.7204\n",
"Epoch 562/1000\n",
"4/4 [==============================] - 0s 873us/step - loss: 0.7175\n",
"Epoch 563/1000\n",
"4/4 [==============================] - 0s 643us/step - loss: 0.7145\n",
"Epoch 564/1000\n",
"4/4 [==============================] - 0s 798us/step - loss: 0.7116\n",
"Epoch 565/1000\n",
"4/4 [==============================] - 0s 458us/step - loss: 0.7087\n",
"Epoch 566/1000\n",
"4/4 [==============================] - 0s 499us/step - loss: 0.7057\n",
"Epoch 567/1000\n",
"4/4 [==============================] - 0s 577us/step - loss: 0.7028\n",
"Epoch 568/1000\n",
"4/4 [==============================] - 0s 990us/step - loss: 0.6999\n",
"Epoch 569/1000\n",
"4/4 [==============================] - 0s 873us/step - loss: 0.6971\n",
"Epoch 570/1000\n",
"4/4 [==============================] - 0s 733us/step - loss: 0.6942\n",
"Epoch 571/1000\n",
"4/4 [==============================] - 0s 687us/step - loss: 0.6913\n",
"Epoch 572/1000\n",
"4/4 [==============================] - 0s 611us/step - loss: 0.6885\n",
"Epoch 573/1000\n",
"4/4 [==============================] - 0s 832us/step - loss: 0.6856\n",
"Epoch 574/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.6828\n",
"Epoch 575/1000\n",
"4/4 [==============================] - 0s 849us/step - loss: 0.6800\n",
"Epoch 576/1000\n",
"4/4 [==============================] - 0s 769us/step - loss: 0.6772\n",
"Epoch 577/1000\n",
"4/4 [==============================] - 0s 751us/step - loss: 0.6744\n",
"Epoch 578/1000\n",
"4/4 [==============================] - 0s 768us/step - loss: 0.6716\n",
"Epoch 579/1000\n",
"4/4 [==============================] - 0s 879us/step - loss: 0.6688\n",
"Epoch 580/1000\n",
"4/4 [==============================] - 0s 614us/step - loss: 0.6661\n",
"Epoch 581/1000\n",
"4/4 [==============================] - 0s 978us/step - loss: 0.6633\n",
"Epoch 582/1000\n",
"4/4 [==============================] - 0s 739us/step - loss: 0.6606\n",
"Epoch 583/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.6578\n",
"Epoch 584/1000\n",
"4/4 [==============================] - 0s 806us/step - loss: 0.6551\n",
"Epoch 585/1000\n",
"4/4 [==============================] - 0s 723us/step - loss: 0.6524\n",
"Epoch 586/1000\n",
"4/4 [==============================] - 0s 816us/step - loss: 0.6497\n",
"Epoch 587/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.6470\n",
"Epoch 588/1000\n",
"4/4 [==============================] - 0s 892us/step - loss: 0.6443\n",
"Epoch 589/1000\n",
"4/4 [==============================] - 0s 828us/step - loss: 0.6417\n",
"Epoch 590/1000\n",
"4/4 [==============================] - 0s 954us/step - loss: 0.6390\n",
"Epoch 591/1000\n",
"4/4 [==============================] - 0s 614us/step - loss: 0.6364\n",
"Epoch 592/1000\n",
"4/4 [==============================] - 0s 596us/step - loss: 0.6337\n",
"Epoch 593/1000\n",
"4/4 [==============================] - 0s 731us/step - loss: 0.6311\n",
"Epoch 594/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.6285\n",
"Epoch 595/1000\n",
"4/4 [==============================] - 0s 903us/step - loss: 0.6259\n",
"Epoch 596/1000\n",
"4/4 [==============================] - 0s 796us/step - loss: 0.6233\n",
"Epoch 597/1000\n",
"4/4 [==============================] - 0s 757us/step - loss: 0.6207\n",
"Epoch 598/1000\n",
"4/4 [==============================] - 0s 797us/step - loss: 0.6181\n",
"Epoch 599/1000\n",
"4/4 [==============================] - 0s 763us/step - loss: 0.6156\n",
"Epoch 600/1000\n",
"4/4 [==============================] - 0s 713us/step - loss: 0.6130\n",
"Epoch 601/1000\n",
"4/4 [==============================] - 0s 810us/step - loss: 0.6105\n",
"Epoch 602/1000\n",
"4/4 [==============================] - 0s 2ms/step - loss: 0.6080\n",
"Epoch 603/1000\n",
"4/4 [==============================] - 0s 854us/step - loss: 0.6054\n",
"Epoch 604/1000\n",
"4/4 [==============================] - 0s 728us/step - loss: 0.6029\n",
"Epoch 605/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.6004\n",
"Epoch 606/1000\n",
"4/4 [==============================] - 0s 926us/step - loss: 0.5979\n",
"Epoch 607/1000\n",
"4/4 [==============================] - 0s 685us/step - loss: 0.5955\n",
"Epoch 608/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.5930\n",
"Epoch 609/1000\n",
"4/4 [==============================] - 0s 626us/step - loss: 0.5905\n",
"Epoch 610/1000\n",
"4/4 [==============================] - 0s 694us/step - loss: 0.5881\n",
"Epoch 611/1000\n",
"4/4 [==============================] - 0s 814us/step - loss: 0.5856\n",
"Epoch 612/1000\n",
"4/4 [==============================] - 0s 377us/step - loss: 0.5832\n",
"Epoch 613/1000\n",
"4/4 [==============================] - 0s 611us/step - loss: 0.5808\n",
"Epoch 614/1000\n",
"4/4 [==============================] - 0s 534us/step - loss: 0.5784\n",
"Epoch 615/1000\n",
"4/4 [==============================] - 0s 883us/step - loss: 0.5760\n",
"Epoch 616/1000\n",
"4/4 [==============================] - 0s 805us/step - loss: 0.5736\n",
"Epoch 617/1000\n",
"4/4 [==============================] - 0s 645us/step - loss: 0.5712\n",
"Epoch 618/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.5688\n",
"Epoch 619/1000\n",
"4/4 [==============================] - 0s 376us/step - loss: 0.5664\n",
"Epoch 620/1000\n",
"4/4 [==============================] - 0s 873us/step - loss: 0.5641\n",
"Epoch 621/1000\n",
"4/4 [==============================] - 0s 811us/step - loss: 0.5617\n",
"Epoch 622/1000\n",
"4/4 [==============================] - 0s 768us/step - loss: 0.5594\n",
"Epoch 623/1000\n",
"4/4 [==============================] - 0s 830us/step - loss: 0.5571\n",
"Epoch 624/1000\n",
"4/4 [==============================] - 0s 540us/step - loss: 0.5548\n",
"Epoch 625/1000\n",
"4/4 [==============================] - 0s 856us/step - loss: 0.5525\n",
"Epoch 626/1000\n",
"4/4 [==============================] - 0s 827us/step - loss: 0.5502\n",
"Epoch 627/1000\n",
"4/4 [==============================] - 0s 544us/step - loss: 0.5479\n",
"Epoch 628/1000\n",
"4/4 [==============================] - 0s 714us/step - loss: 0.5456\n",
"Epoch 629/1000\n",
"4/4 [==============================] - 0s 668us/step - loss: 0.5433\n",
"Epoch 630/1000\n",
"4/4 [==============================] - 0s 695us/step - loss: 0.5411\n",
"Epoch 631/1000\n",
"4/4 [==============================] - 0s 873us/step - loss: 0.5388\n",
"Epoch 632/1000\n",
"4/4 [==============================] - 0s 735us/step - loss: 0.5366\n",
"Epoch 633/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.5343\n",
"Epoch 634/1000\n",
"4/4 [==============================] - 0s 705us/step - loss: 0.5321\n",
"Epoch 635/1000\n",
"4/4 [==============================] - 0s 473us/step - loss: 0.5299\n",
"Epoch 636/1000\n",
"4/4 [==============================] - 0s 816us/step - loss: 0.5277\n",
"Epoch 637/1000\n",
"4/4 [==============================] - 0s 908us/step - loss: 0.5255\n",
"Epoch 638/1000\n",
"4/4 [==============================] - 0s 596us/step - loss: 0.5233\n",
"Epoch 639/1000\n",
"4/4 [==============================] - 0s 818us/step - loss: 0.5211\n",
"Epoch 640/1000\n",
"4/4 [==============================] - 0s 422us/step - loss: 0.5190\n",
"Epoch 641/1000\n",
"4/4 [==============================] - 0s 929us/step - loss: 0.5168\n",
"Epoch 642/1000\n",
"4/4 [==============================] - 0s 885us/step - loss: 0.5147\n",
"Epoch 643/1000\n",
"4/4 [==============================] - 0s 351us/step - loss: 0.5125\n",
"Epoch 644/1000\n",
"4/4 [==============================] - 0s 692us/step - loss: 0.5104\n",
"Epoch 645/1000\n",
"4/4 [==============================] - 0s 737us/step - loss: 0.5083\n",
"Epoch 646/1000\n",
"4/4 [==============================] - 0s 922us/step - loss: 0.5062\n",
"Epoch 647/1000\n",
"4/4 [==============================] - 0s 721us/step - loss: 0.5040\n",
"Epoch 648/1000\n",
"4/4 [==============================] - 0s 634us/step - loss: 0.5020\n",
"Epoch 649/1000\n",
"4/4 [==============================] - 0s 721us/step - loss: 0.4999\n",
"Epoch 650/1000\n",
"4/4 [==============================] - 0s 895us/step - loss: 0.4978\n",
"Epoch 651/1000\n",
"4/4 [==============================] - 0s 707us/step - loss: 0.4957\n",
"Epoch 652/1000\n",
"4/4 [==============================] - 0s 717us/step - loss: 0.4937\n",
"Epoch 653/1000\n",
"4/4 [==============================] - 0s 661us/step - loss: 0.4916\n",
"Epoch 654/1000\n",
"4/4 [==============================] - 0s 820us/step - loss: 0.4896\n",
"Epoch 655/1000\n",
"4/4 [==============================] - 0s 837us/step - loss: 0.4875\n",
"Epoch 656/1000\n",
"4/4 [==============================] - 0s 617us/step - loss: 0.4855\n",
"Epoch 657/1000\n",
"4/4 [==============================] - 0s 733us/step - loss: 0.4835\n",
"Epoch 658/1000\n",
"4/4 [==============================] - 0s 814us/step - loss: 0.4815\n",
"Epoch 659/1000\n",
"4/4 [==============================] - 0s 560us/step - loss: 0.4795\n",
"Epoch 660/1000\n",
"4/4 [==============================] - 0s 605us/step - loss: 0.4775\n",
"Epoch 661/1000\n",
"4/4 [==============================] - 0s 664us/step - loss: 0.4755\n",
"Epoch 662/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.4735\n",
"Epoch 663/1000\n",
"4/4 [==============================] - 0s 817us/step - loss: 0.4715\n",
"Epoch 664/1000\n",
"4/4 [==============================] - 0s 710us/step - loss: 0.4696\n",
"Epoch 665/1000\n",
"4/4 [==============================] - 0s 598us/step - loss: 0.4676\n",
"Epoch 666/1000\n",
"4/4 [==============================] - 0s 802us/step - loss: 0.4657\n",
"Epoch 667/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.4637\n",
"Epoch 668/1000\n",
"4/4 [==============================] - 0s 737us/step - loss: 0.4618\n",
"Epoch 669/1000\n",
"4/4 [==============================] - 0s 621us/step - loss: 0.4599\n",
"Epoch 670/1000\n",
"4/4 [==============================] - 0s 698us/step - loss: 0.4580\n",
"Epoch 671/1000\n",
"4/4 [==============================] - 0s 794us/step - loss: 0.4561\n",
"Epoch 672/1000\n",
"4/4 [==============================] - 0s 614us/step - loss: 0.4542\n",
"Epoch 673/1000\n",
"4/4 [==============================] - 0s 744us/step - loss: 0.4523\n",
"Epoch 674/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.4504\n",
"Epoch 675/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.4485\n",
"Epoch 676/1000\n",
"4/4 [==============================] - 0s 752us/step - loss: 0.4467\n",
"Epoch 677/1000\n",
"4/4 [==============================] - 0s 726us/step - loss: 0.4448\n",
"Epoch 678/1000\n",
"4/4 [==============================] - 0s 758us/step - loss: 0.4430\n",
"Epoch 679/1000\n",
"4/4 [==============================] - 0s 488us/step - loss: 0.4411\n",
"Epoch 680/1000\n",
"4/4 [==============================] - 0s 632us/step - loss: 0.4393\n",
"Epoch 681/1000\n",
"4/4 [==============================] - 0s 684us/step - loss: 0.4375\n",
"Epoch 682/1000\n",
"4/4 [==============================] - 0s 616us/step - loss: 0.4357\n",
"Epoch 683/1000\n",
"4/4 [==============================] - 0s 657us/step - loss: 0.4339\n",
"Epoch 684/1000\n",
"4/4 [==============================] - 0s 855us/step - loss: 0.4321\n",
"Epoch 685/1000\n",
"4/4 [==============================] - 0s 875us/step - loss: 0.4303\n",
"Epoch 686/1000\n",
"4/4 [==============================] - 0s 812us/step - loss: 0.4285\n",
"Epoch 687/1000\n",
"4/4 [==============================] - 0s 785us/step - loss: 0.4267\n",
"Epoch 688/1000\n",
"4/4 [==============================] - 0s 702us/step - loss: 0.4250\n",
"Epoch 689/1000\n",
"4/4 [==============================] - 0s 691us/step - loss: 0.4232\n",
"Epoch 690/1000\n",
"4/4 [==============================] - 0s 887us/step - loss: 0.4214\n",
"Epoch 691/1000\n",
"4/4 [==============================] - 0s 735us/step - loss: 0.4197\n",
"Epoch 692/1000\n",
"4/4 [==============================] - 0s 807us/step - loss: 0.4180\n",
"Epoch 693/1000\n",
"4/4 [==============================] - 0s 665us/step - loss: 0.4162\n",
"Epoch 694/1000\n",
"4/4 [==============================] - 0s 923us/step - loss: 0.4145\n",
"Epoch 695/1000\n",
"4/4 [==============================] - 0s 660us/step - loss: 0.4128\n",
"Epoch 696/1000\n",
"4/4 [==============================] - 0s 750us/step - loss: 0.4111\n",
"Epoch 697/1000\n",
"4/4 [==============================] - 0s 856us/step - loss: 0.4094\n",
"Epoch 698/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.4077\n",
"Epoch 699/1000\n",
"4/4 [==============================] - 0s 745us/step - loss: 0.4060\n",
"Epoch 700/1000\n",
"4/4 [==============================] - 0s 964us/step - loss: 0.4043\n",
"Epoch 701/1000\n",
"4/4 [==============================] - 0s 953us/step - loss: 0.4026\n",
"Epoch 702/1000\n",
"4/4 [==============================] - 0s 786us/step - loss: 0.4010\n",
"Epoch 703/1000\n",
"4/4 [==============================] - 0s 2ms/step - loss: 0.3993\n",
"Epoch 704/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.3977\n",
"Epoch 705/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.3960\n",
"Epoch 706/1000\n",
"4/4 [==============================] - 0s 720us/step - loss: 0.3944\n",
"Epoch 707/1000\n",
"4/4 [==============================] - 0s 662us/step - loss: 0.3928\n",
"Epoch 708/1000\n",
"4/4 [==============================] - 0s 872us/step - loss: 0.3912\n",
"Epoch 709/1000\n",
"4/4 [==============================] - 0s 621us/step - loss: 0.3895\n",
"Epoch 710/1000\n",
"4/4 [==============================] - 0s 733us/step - loss: 0.3879\n",
"Epoch 711/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.3863\n",
"Epoch 712/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.3847\n",
"Epoch 713/1000\n",
"4/4 [==============================] - 0s 709us/step - loss: 0.3832\n",
"Epoch 714/1000\n",
"4/4 [==============================] - 0s 742us/step - loss: 0.3816\n",
"Epoch 715/1000\n",
"4/4 [==============================] - 0s 713us/step - loss: 0.3800\n",
"Epoch 716/1000\n",
"4/4 [==============================] - 0s 617us/step - loss: 0.3784\n",
"Epoch 717/1000\n",
"4/4 [==============================] - 0s 778us/step - loss: 0.3769\n",
"Epoch 718/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.3753\n",
"Epoch 719/1000\n",
"4/4 [==============================] - 0s 971us/step - loss: 0.3738\n",
"Epoch 720/1000\n",
"4/4 [==============================] - 0s 811us/step - loss: 0.3723\n",
"Epoch 721/1000\n",
"4/4 [==============================] - 0s 857us/step - loss: 0.3707\n",
"Epoch 722/1000\n",
"4/4 [==============================] - 0s 754us/step - loss: 0.3692\n",
"Epoch 723/1000\n",
"4/4 [==============================] - 0s 975us/step - loss: 0.3677\n",
"Epoch 724/1000\n",
"4/4 [==============================] - 0s 778us/step - loss: 0.3662\n",
"Epoch 725/1000\n",
"4/4 [==============================] - 0s 745us/step - loss: 0.3647\n",
"Epoch 726/1000\n",
"4/4 [==============================] - 0s 679us/step - loss: 0.3632\n",
"Epoch 727/1000\n",
"4/4 [==============================] - 0s 648us/step - loss: 0.3617\n",
"Epoch 728/1000\n",
"4/4 [==============================] - 0s 849us/step - loss: 0.3602\n",
"Epoch 729/1000\n",
"4/4 [==============================] - 0s 887us/step - loss: 0.3587\n",
"Epoch 730/1000\n",
"4/4 [==============================] - 0s 678us/step - loss: 0.3573\n",
"Epoch 731/1000\n",
"4/4 [==============================] - 0s 820us/step - loss: 0.3558\n",
"Epoch 732/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.3544\n",
"Epoch 733/1000\n",
"4/4 [==============================] - 0s 972us/step - loss: 0.3529\n",
"Epoch 734/1000\n",
"4/4 [==============================] - 0s 680us/step - loss: 0.3515\n",
"Epoch 735/1000\n",
"4/4 [==============================] - 0s 870us/step - loss: 0.3500\n",
"Epoch 736/1000\n",
"4/4 [==============================] - 0s 844us/step - loss: 0.3486\n",
"Epoch 737/1000\n",
"4/4 [==============================] - 0s 826us/step - loss: 0.3472\n",
"Epoch 738/1000\n",
"4/4 [==============================] - 0s 866us/step - loss: 0.3458\n",
"Epoch 739/1000\n",
"4/4 [==============================] - 0s 745us/step - loss: 0.3443\n",
"Epoch 740/1000\n",
"4/4 [==============================] - 0s 709us/step - loss: 0.3429\n",
"Epoch 741/1000\n",
"4/4 [==============================] - 0s 666us/step - loss: 0.3415\n",
"Epoch 742/1000\n",
"4/4 [==============================] - 0s 680us/step - loss: 0.3402\n",
"Epoch 743/1000\n",
"4/4 [==============================] - 0s 820us/step - loss: 0.3388\n",
"Epoch 744/1000\n",
"4/4 [==============================] - 0s 704us/step - loss: 0.3374\n",
"Epoch 745/1000\n",
"4/4 [==============================] - 0s 655us/step - loss: 0.3360\n",
"Epoch 746/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.3347\n",
"Epoch 747/1000\n",
"4/4 [==============================] - 0s 931us/step - loss: 0.3333\n",
"Epoch 748/1000\n",
"4/4 [==============================] - 0s 812us/step - loss: 0.3319\n",
"Epoch 749/1000\n",
"4/4 [==============================] - 0s 633us/step - loss: 0.3306\n",
"Epoch 750/1000\n",
"4/4 [==============================] - 0s 933us/step - loss: 0.3292\n",
"Epoch 751/1000\n",
"4/4 [==============================] - 0s 799us/step - loss: 0.3279\n",
"Epoch 752/1000\n",
"4/4 [==============================] - 0s 745us/step - loss: 0.3266\n",
"Epoch 753/1000\n",
"4/4 [==============================] - 0s 677us/step - loss: 0.3253\n",
"Epoch 754/1000\n",
"4/4 [==============================] - 0s 888us/step - loss: 0.3239\n",
"Epoch 755/1000\n",
"4/4 [==============================] - 0s 700us/step - loss: 0.3226\n",
"Epoch 756/1000\n",
"4/4 [==============================] - 0s 800us/step - loss: 0.3213\n",
"Epoch 757/1000\n",
"4/4 [==============================] - 0s 854us/step - loss: 0.3200\n",
"Epoch 758/1000\n",
"4/4 [==============================] - 0s 848us/step - loss: 0.3187\n",
"Epoch 759/1000\n",
"4/4 [==============================] - 0s 759us/step - loss: 0.3175\n",
"Epoch 760/1000\n",
"4/4 [==============================] - 0s 436us/step - loss: 0.3162\n",
"Epoch 761/1000\n",
"4/4 [==============================] - 0s 539us/step - loss: 0.3149\n",
"Epoch 762/1000\n",
"4/4 [==============================] - 0s 588us/step - loss: 0.3136\n",
"Epoch 763/1000\n",
"4/4 [==============================] - 0s 835us/step - loss: 0.3124\n",
"Epoch 764/1000\n",
"4/4 [==============================] - 0s 773us/step - loss: 0.3111\n",
"Epoch 765/1000\n",
"4/4 [==============================] - 0s 749us/step - loss: 0.3099\n",
"Epoch 766/1000\n",
"4/4 [==============================] - 0s 748us/step - loss: 0.3086\n",
"Epoch 767/1000\n",
"4/4 [==============================] - 0s 716us/step - loss: 0.3074\n",
"Epoch 768/1000\n",
"4/4 [==============================] - 0s 657us/step - loss: 0.3061\n",
"Epoch 769/1000\n",
"4/4 [==============================] - 0s 717us/step - loss: 0.3049\n",
"Epoch 770/1000\n",
"4/4 [==============================] - 0s 618us/step - loss: 0.3037\n",
"Epoch 771/1000\n",
"4/4 [==============================] - 0s 618us/step - loss: 0.3025\n",
"Epoch 772/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.3013\n",
"Epoch 773/1000\n",
"4/4 [==============================] - 0s 845us/step - loss: 0.3001\n",
"Epoch 774/1000\n",
"4/4 [==============================] - 0s 901us/step - loss: 0.2989\n",
"Epoch 775/1000\n",
"4/4 [==============================] - 0s 866us/step - loss: 0.2977\n",
"Epoch 776/1000\n",
"4/4 [==============================] - 0s 554us/step - loss: 0.2965\n",
"Epoch 777/1000\n",
"4/4 [==============================] - 0s 909us/step - loss: 0.2953\n",
"Epoch 778/1000\n",
"4/4 [==============================] - 0s 562us/step - loss: 0.2941\n",
"Epoch 779/1000\n",
"4/4 [==============================] - 0s 860us/step - loss: 0.2929\n",
"Epoch 780/1000\n",
"4/4 [==============================] - 0s 826us/step - loss: 0.2918\n",
"Epoch 781/1000\n",
"4/4 [==============================] - 0s 903us/step - loss: 0.2906\n",
"Epoch 782/1000\n",
"4/4 [==============================] - 0s 765us/step - loss: 0.2895\n",
"Epoch 783/1000\n",
"4/4 [==============================] - 0s 739us/step - loss: 0.2883\n",
"Epoch 784/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.2872\n",
"Epoch 785/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.2860\n",
"Epoch 786/1000\n",
"4/4 [==============================] - 0s 737us/step - loss: 0.2849\n",
"Epoch 787/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.2838\n",
"Epoch 788/1000\n",
"4/4 [==============================] - 0s 875us/step - loss: 0.2826\n",
"Epoch 789/1000\n",
"4/4 [==============================] - 0s 855us/step - loss: 0.2815\n",
"Epoch 790/1000\n",
"4/4 [==============================] - 0s 809us/step - loss: 0.2804\n",
"Epoch 791/1000\n",
"4/4 [==============================] - 0s 800us/step - loss: 0.2793\n",
"Epoch 792/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.2782\n",
"Epoch 793/1000\n",
"4/4 [==============================] - 0s 764us/step - loss: 0.2771\n",
"Epoch 794/1000\n",
"4/4 [==============================] - 0s 901us/step - loss: 0.2760\n",
"Epoch 795/1000\n",
"4/4 [==============================] - 0s 759us/step - loss: 0.2749\n",
"Epoch 796/1000\n",
"4/4 [==============================] - 0s 931us/step - loss: 0.2738\n",
"Epoch 797/1000\n",
"4/4 [==============================] - 0s 996us/step - loss: 0.2728\n",
"Epoch 798/1000\n",
"4/4 [==============================] - 0s 606us/step - loss: 0.2717\n",
"Epoch 799/1000\n",
"4/4 [==============================] - 0s 869us/step - loss: 0.2706\n",
"Epoch 800/1000\n",
"4/4 [==============================] - 0s 852us/step - loss: 0.2696\n",
"Epoch 801/1000\n",
"4/4 [==============================] - 0s 900us/step - loss: 0.2685\n",
"Epoch 802/1000\n",
"4/4 [==============================] - 0s 753us/step - loss: 0.2675\n",
"Epoch 803/1000\n",
"4/4 [==============================] - 0s 678us/step - loss: 0.2664\n",
"Epoch 804/1000\n",
"4/4 [==============================] - 0s 962us/step - loss: 0.2654\n",
"Epoch 805/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.2643\n",
"Epoch 806/1000\n",
"4/4 [==============================] - 0s 684us/step - loss: 0.2633\n",
"Epoch 807/1000\n",
"4/4 [==============================] - 0s 812us/step - loss: 0.2623\n",
"Epoch 808/1000\n",
"4/4 [==============================] - 0s 838us/step - loss: 0.2613\n",
"Epoch 809/1000\n",
"4/4 [==============================] - 0s 792us/step - loss: 0.2602\n",
"Epoch 810/1000\n",
"4/4 [==============================] - 0s 760us/step - loss: 0.2592\n",
"Epoch 811/1000\n",
"4/4 [==============================] - 0s 709us/step - loss: 0.2582\n",
"Epoch 812/1000\n",
"4/4 [==============================] - 0s 799us/step - loss: 0.2572\n",
"Epoch 813/1000\n",
"4/4 [==============================] - 0s 662us/step - loss: 0.2562\n",
"Epoch 814/1000\n",
"4/4 [==============================] - 0s 766us/step - loss: 0.2552\n",
"Epoch 815/1000\n",
"4/4 [==============================] - 0s 735us/step - loss: 0.2542\n",
"Epoch 816/1000\n",
"4/4 [==============================] - 0s 760us/step - loss: 0.2533\n",
"Epoch 817/1000\n",
"4/4 [==============================] - 0s 730us/step - loss: 0.2523\n",
"Epoch 818/1000\n",
"4/4 [==============================] - 0s 939us/step - loss: 0.2513\n",
"Epoch 819/1000\n",
"4/4 [==============================] - 0s 639us/step - loss: 0.2503\n",
"Epoch 820/1000\n",
"4/4 [==============================] - 0s 984us/step - loss: 0.2494\n",
"Epoch 821/1000\n",
"4/4 [==============================] - 0s 661us/step - loss: 0.2484\n",
"Epoch 822/1000\n",
"4/4 [==============================] - 0s 929us/step - loss: 0.2475\n",
"Epoch 823/1000\n",
"4/4 [==============================] - 0s 706us/step - loss: 0.2465\n",
"Epoch 824/1000\n",
"4/4 [==============================] - 0s 855us/step - loss: 0.2456\n",
"Epoch 825/1000\n",
"4/4 [==============================] - 0s 648us/step - loss: 0.2446\n",
"Epoch 826/1000\n",
"4/4 [==============================] - 0s 979us/step - loss: 0.2437\n",
"Epoch 827/1000\n",
"4/4 [==============================] - 0s 905us/step - loss: 0.2428\n",
"Epoch 828/1000\n",
"4/4 [==============================] - 0s 710us/step - loss: 0.2418\n",
"Epoch 829/1000\n",
"4/4 [==============================] - 0s 755us/step - loss: 0.2409\n",
"Epoch 830/1000\n",
"4/4 [==============================] - 0s 725us/step - loss: 0.2400\n",
"Epoch 831/1000\n",
"4/4 [==============================] - 0s 735us/step - loss: 0.2391\n",
"Epoch 832/1000\n",
"4/4 [==============================] - 0s 898us/step - loss: 0.2382\n",
"Epoch 833/1000\n",
"4/4 [==============================] - 0s 798us/step - loss: 0.2373\n",
"Epoch 834/1000\n",
"4/4 [==============================] - 0s 869us/step - loss: 0.2364\n",
"Epoch 835/1000\n",
"4/4 [==============================] - 0s 737us/step - loss: 0.2355\n",
"Epoch 836/1000\n",
"4/4 [==============================] - 0s 788us/step - loss: 0.2346\n",
"Epoch 837/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.2337\n",
"Epoch 838/1000\n",
"4/4 [==============================] - 0s 785us/step - loss: 0.2328\n",
"Epoch 839/1000\n",
"4/4 [==============================] - 0s 768us/step - loss: 0.2319\n",
"Epoch 840/1000\n",
"4/4 [==============================] - 0s 797us/step - loss: 0.2311\n",
"Epoch 841/1000\n",
"4/4 [==============================] - 0s 926us/step - loss: 0.2302\n",
"Epoch 842/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.2293\n",
"Epoch 843/1000\n",
"4/4 [==============================] - 0s 764us/step - loss: 0.2285\n",
"Epoch 844/1000\n",
"4/4 [==============================] - 0s 620us/step - loss: 0.2276\n",
"Epoch 845/1000\n",
"4/4 [==============================] - 0s 778us/step - loss: 0.2268\n",
"Epoch 846/1000\n",
"4/4 [==============================] - 0s 645us/step - loss: 0.2259\n",
"Epoch 847/1000\n",
"4/4 [==============================] - 0s 812us/step - loss: 0.2251\n",
"Epoch 848/1000\n",
"4/4 [==============================] - 0s 659us/step - loss: 0.2242\n",
"Epoch 849/1000\n",
"4/4 [==============================] - 0s 878us/step - loss: 0.2234\n",
"Epoch 850/1000\n",
"4/4 [==============================] - 0s 912us/step - loss: 0.2226\n",
"Epoch 851/1000\n",
"4/4 [==============================] - 0s 778us/step - loss: 0.2217\n",
"Epoch 852/1000\n",
"4/4 [==============================] - 0s 808us/step - loss: 0.2209\n",
"Epoch 853/1000\n",
"4/4 [==============================] - 0s 541us/step - loss: 0.2201\n",
"Epoch 854/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.2193\n",
"Epoch 855/1000\n",
"4/4 [==============================] - 0s 684us/step - loss: 0.2185\n",
"Epoch 856/1000\n",
"4/4 [==============================] - 0s 737us/step - loss: 0.2177\n",
"Epoch 857/1000\n",
"4/4 [==============================] - 0s 2ms/step - loss: 0.2169\n",
"Epoch 858/1000\n",
"4/4 [==============================] - 0s 804us/step - loss: 0.2161\n",
"Epoch 859/1000\n",
"4/4 [==============================] - 0s 971us/step - loss: 0.2153\n",
"Epoch 860/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.2145\n",
"Epoch 861/1000\n",
"4/4 [==============================] - 0s 899us/step - loss: 0.2137\n",
"Epoch 862/1000\n",
"4/4 [==============================] - 0s 964us/step - loss: 0.2129\n",
"Epoch 863/1000\n",
"4/4 [==============================] - 0s 861us/step - loss: 0.2121\n",
"Epoch 864/1000\n",
"4/4 [==============================] - 0s 566us/step - loss: 0.2114\n",
"Epoch 865/1000\n",
"4/4 [==============================] - 0s 959us/step - loss: 0.2106\n",
"Epoch 866/1000\n",
"4/4 [==============================] - 0s 624us/step - loss: 0.2098\n",
"Epoch 867/1000\n",
"4/4 [==============================] - 0s 631us/step - loss: 0.2091\n",
"Epoch 868/1000\n",
"4/4 [==============================] - 0s 650us/step - loss: 0.2083\n",
"Epoch 869/1000\n",
"4/4 [==============================] - 0s 629us/step - loss: 0.2075\n",
"Epoch 870/1000\n",
"4/4 [==============================] - 0s 561us/step - loss: 0.2068\n",
"Epoch 871/1000\n",
"4/4 [==============================] - 0s 892us/step - loss: 0.2060\n",
"Epoch 872/1000\n",
"4/4 [==============================] - 0s 856us/step - loss: 0.2053\n",
"Epoch 873/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.2046\n",
"Epoch 874/1000\n",
"4/4 [==============================] - 0s 781us/step - loss: 0.2038\n",
"Epoch 875/1000\n",
"4/4 [==============================] - 0s 654us/step - loss: 0.2031\n",
"Epoch 876/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.2024\n",
"Epoch 877/1000\n",
"4/4 [==============================] - 0s 942us/step - loss: 0.2016\n",
"Epoch 878/1000\n",
"4/4 [==============================] - 0s 712us/step - loss: 0.2009\n",
"Epoch 879/1000\n",
"4/4 [==============================] - 0s 834us/step - loss: 0.2002\n",
"Epoch 880/1000\n",
"4/4 [==============================] - 0s 812us/step - loss: 0.1995\n",
"Epoch 881/1000\n",
"4/4 [==============================] - 0s 784us/step - loss: 0.1988\n",
"Epoch 882/1000\n",
"4/4 [==============================] - 0s 721us/step - loss: 0.1981\n",
"Epoch 883/1000\n",
"4/4 [==============================] - 0s 849us/step - loss: 0.1974\n",
"Epoch 884/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.1967\n",
"Epoch 885/1000\n",
"4/4 [==============================] - 0s 740us/step - loss: 0.1960\n",
"Epoch 886/1000\n",
"4/4 [==============================] - 0s 593us/step - loss: 0.1953\n",
"Epoch 887/1000\n",
"4/4 [==============================] - 0s 735us/step - loss: 0.1946\n",
"Epoch 888/1000\n",
"4/4 [==============================] - 0s 802us/step - loss: 0.1939\n",
"Epoch 889/1000\n",
"4/4 [==============================] - 0s 817us/step - loss: 0.1932\n",
"Epoch 890/1000\n",
"4/4 [==============================] - 0s 848us/step - loss: 0.1925\n",
"Epoch 891/1000\n",
"4/4 [==============================] - 0s 822us/step - loss: 0.1919\n",
"Epoch 892/1000\n",
"4/4 [==============================] - 0s 973us/step - loss: 0.1912\n",
"Epoch 893/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.1905\n",
"Epoch 894/1000\n",
"4/4 [==============================] - 0s 691us/step - loss: 0.1899\n",
"Epoch 895/1000\n",
"4/4 [==============================] - 0s 633us/step - loss: 0.1892\n",
"Epoch 896/1000\n",
"4/4 [==============================] - 0s 678us/step - loss: 0.1885\n",
"Epoch 897/1000\n",
"4/4 [==============================] - 0s 700us/step - loss: 0.1879\n",
"Epoch 898/1000\n",
"4/4 [==============================] - 0s 723us/step - loss: 0.1872\n",
"Epoch 899/1000\n",
"4/4 [==============================] - 0s 769us/step - loss: 0.1866\n",
"Epoch 900/1000\n",
"4/4 [==============================] - 0s 739us/step - loss: 0.1859\n",
"Epoch 901/1000\n",
"4/4 [==============================] - 0s 923us/step - loss: 0.1853\n",
"Epoch 902/1000\n",
"4/4 [==============================] - 0s 478us/step - loss: 0.1847\n",
"Epoch 903/1000\n",
"4/4 [==============================] - 0s 561us/step - loss: 0.1840\n",
"Epoch 904/1000\n",
"4/4 [==============================] - 0s 610us/step - loss: 0.1834\n",
"Epoch 905/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.1828\n",
"Epoch 906/1000\n",
"4/4 [==============================] - 0s 916us/step - loss: 0.1821\n",
"Epoch 907/1000\n",
"4/4 [==============================] - 0s 854us/step - loss: 0.1815\n",
"Epoch 908/1000\n",
"4/4 [==============================] - 0s 529us/step - loss: 0.1809\n",
"Epoch 909/1000\n",
"4/4 [==============================] - 0s 868us/step - loss: 0.1803\n",
"Epoch 910/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.1797\n",
"Epoch 911/1000\n",
"4/4 [==============================] - 0s 974us/step - loss: 0.1791\n",
"Epoch 912/1000\n",
"4/4 [==============================] - 0s 731us/step - loss: 0.1785\n",
"Epoch 913/1000\n",
"4/4 [==============================] - 0s 637us/step - loss: 0.1778\n",
"Epoch 914/1000\n",
"4/4 [==============================] - 0s 762us/step - loss: 0.1772\n",
"Epoch 915/1000\n",
"4/4 [==============================] - 0s 690us/step - loss: 0.1767\n",
"Epoch 916/1000\n",
"4/4 [==============================] - 0s 856us/step - loss: 0.1761\n",
"Epoch 917/1000\n",
"4/4 [==============================] - 0s 742us/step - loss: 0.1755\n",
"Epoch 918/1000\n",
"4/4 [==============================] - 0s 732us/step - loss: 0.1749\n",
"Epoch 919/1000\n",
"4/4 [==============================] - 0s 600us/step - loss: 0.1743\n",
"Epoch 920/1000\n",
"4/4 [==============================] - 0s 696us/step - loss: 0.1737\n",
"Epoch 921/1000\n",
"4/4 [==============================] - 0s 884us/step - loss: 0.1731\n",
"Epoch 922/1000\n",
"4/4 [==============================] - 0s 561us/step - loss: 0.1726\n",
"Epoch 923/1000\n",
"4/4 [==============================] - 0s 849us/step - loss: 0.1720\n",
"Epoch 924/1000\n",
"4/4 [==============================] - 0s 422us/step - loss: 0.1714\n",
"Epoch 925/1000\n",
"4/4 [==============================] - 0s 564us/step - loss: 0.1709\n",
"Epoch 926/1000\n",
"4/4 [==============================] - 0s 559us/step - loss: 0.1703\n",
"Epoch 927/1000\n",
"4/4 [==============================] - 0s 537us/step - loss: 0.1697\n",
"Epoch 928/1000\n",
"4/4 [==============================] - 0s 578us/step - loss: 0.1692\n",
"Epoch 929/1000\n",
"4/4 [==============================] - 0s 909us/step - loss: 0.1686\n",
"Epoch 930/1000\n",
"4/4 [==============================] - 0s 851us/step - loss: 0.1681\n",
"Epoch 931/1000\n",
"4/4 [==============================] - 0s 584us/step - loss: 0.1675\n",
"Epoch 932/1000\n",
"4/4 [==============================] - 0s 491us/step - loss: 0.1670\n",
"Epoch 933/1000\n",
"4/4 [==============================] - 0s 645us/step - loss: 0.1664\n",
"Epoch 934/1000\n",
"4/4 [==============================] - 0s 688us/step - loss: 0.1659\n",
"Epoch 935/1000\n",
"4/4 [==============================] - 0s 821us/step - loss: 0.1654\n",
"Epoch 936/1000\n",
"4/4 [==============================] - 0s 720us/step - loss: 0.1648\n",
"Epoch 937/1000\n",
"4/4 [==============================] - 0s 749us/step - loss: 0.1643\n",
"Epoch 938/1000\n",
"4/4 [==============================] - 0s 729us/step - loss: 0.1638\n",
"Epoch 939/1000\n",
"4/4 [==============================] - 0s 659us/step - loss: 0.1632\n",
"Epoch 940/1000\n",
"4/4 [==============================] - 0s 882us/step - loss: 0.1627\n",
"Epoch 941/1000\n",
"4/4 [==============================] - 0s 630us/step - loss: 0.1622\n",
"Epoch 942/1000\n",
"4/4 [==============================] - 0s 617us/step - loss: 0.1617\n",
"Epoch 943/1000\n",
"4/4 [==============================] - 0s 749us/step - loss: 0.1612\n",
"Epoch 944/1000\n",
"4/4 [==============================] - 0s 659us/step - loss: 0.1607\n",
"Epoch 945/1000\n",
"4/4 [==============================] - 0s 661us/step - loss: 0.1602\n",
"Epoch 946/1000\n",
"4/4 [==============================] - 0s 732us/step - loss: 0.1596\n",
"Epoch 947/1000\n",
"4/4 [==============================] - 0s 769us/step - loss: 0.1591\n",
"Epoch 948/1000\n",
"4/4 [==============================] - 0s 977us/step - loss: 0.1586\n",
"Epoch 949/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.1581\n",
"Epoch 950/1000\n",
"4/4 [==============================] - 0s 454us/step - loss: 0.1576\n",
"Epoch 951/1000\n",
"4/4 [==============================] - 0s 569us/step - loss: 0.1572\n",
"Epoch 952/1000\n",
"4/4 [==============================] - 0s 658us/step - loss: 0.1567\n",
"Epoch 953/1000\n",
"4/4 [==============================] - 0s 621us/step - loss: 0.1562\n",
"Epoch 954/1000\n",
"4/4 [==============================] - 0s 522us/step - loss: 0.1557\n",
"Epoch 955/1000\n",
"4/4 [==============================] - 0s 856us/step - loss: 0.1552\n",
"Epoch 956/1000\n",
"4/4 [==============================] - 0s 773us/step - loss: 0.1547\n",
"Epoch 957/1000\n",
"4/4 [==============================] - 0s 472us/step - loss: 0.1543\n",
"Epoch 958/1000\n",
"4/4 [==============================] - 0s 727us/step - loss: 0.1538\n",
"Epoch 959/1000\n",
"4/4 [==============================] - 0s 714us/step - loss: 0.1533\n",
"Epoch 960/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.1528\n",
"Epoch 961/1000\n",
"4/4 [==============================] - 0s 808us/step - loss: 0.1524\n",
"Epoch 962/1000\n",
"4/4 [==============================] - 0s 728us/step - loss: 0.1519\n",
"Epoch 963/1000\n",
"4/4 [==============================] - 0s 527us/step - loss: 0.1514\n",
"Epoch 964/1000\n",
"4/4 [==============================] - 0s 701us/step - loss: 0.1510\n",
"Epoch 965/1000\n",
"4/4 [==============================] - 0s 880us/step - loss: 0.1505\n",
"Epoch 966/1000\n",
"4/4 [==============================] - 0s 424us/step - loss: 0.1501\n",
"Epoch 967/1000\n",
"4/4 [==============================] - 0s 691us/step - loss: 0.1496\n",
"Epoch 968/1000\n",
"4/4 [==============================] - 0s 759us/step - loss: 0.1492\n",
"Epoch 969/1000\n",
"4/4 [==============================] - 0s 627us/step - loss: 0.1487\n",
"Epoch 970/1000\n",
"4/4 [==============================] - 0s 707us/step - loss: 0.1483\n",
"Epoch 971/1000\n",
"4/4 [==============================] - 0s 729us/step - loss: 0.1478\n",
"Epoch 972/1000\n",
"4/4 [==============================] - 0s 822us/step - loss: 0.1474\n",
"Epoch 973/1000\n",
"4/4 [==============================] - 0s 678us/step - loss: 0.1470\n",
"Epoch 974/1000\n",
"4/4 [==============================] - 0s 618us/step - loss: 0.1465\n",
"Epoch 975/1000\n",
"4/4 [==============================] - 0s 757us/step - loss: 0.1461\n",
"Epoch 976/1000\n",
"4/4 [==============================] - 0s 399us/step - loss: 0.1457\n",
"Epoch 977/1000\n",
"4/4 [==============================] - 0s 886us/step - loss: 0.1452\n",
"Epoch 978/1000\n",
"4/4 [==============================] - 0s 601us/step - loss: 0.1448\n",
"Epoch 979/1000\n",
"4/4 [==============================] - 0s 657us/step - loss: 0.1444\n",
"Epoch 980/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.1440\n",
"Epoch 981/1000\n",
"4/4 [==============================] - 0s 617us/step - loss: 0.1435\n",
"Epoch 982/1000\n",
"4/4 [==============================] - 0s 648us/step - loss: 0.1431\n",
"Epoch 983/1000\n",
"4/4 [==============================] - 0s 573us/step - loss: 0.1427\n",
"Epoch 984/1000\n",
"4/4 [==============================] - 0s 479us/step - loss: 0.1423\n",
"Epoch 985/1000\n",
"4/4 [==============================] - 0s 552us/step - loss: 0.1419\n",
"Epoch 986/1000\n",
"4/4 [==============================] - 0s 758us/step - loss: 0.1415\n",
"Epoch 987/1000\n",
"4/4 [==============================] - 0s 322us/step - loss: 0.1411\n",
"Epoch 988/1000\n",
"4/4 [==============================] - 0s 703us/step - loss: 0.1407\n",
"Epoch 989/1000\n",
"4/4 [==============================] - 0s 667us/step - loss: 0.1403\n",
"Epoch 990/1000\n",
"4/4 [==============================] - 0s 483us/step - loss: 0.1399\n",
"Epoch 991/1000\n",
"4/4 [==============================] - 0s 829us/step - loss: 0.1395\n",
"Epoch 992/1000\n",
"4/4 [==============================] - 0s 751us/step - loss: 0.1391\n",
"Epoch 993/1000\n",
"4/4 [==============================] - 0s 527us/step - loss: 0.1387\n",
"Epoch 994/1000\n",
"4/4 [==============================] - 0s 832us/step - loss: 0.1383\n",
"Epoch 995/1000\n",
"4/4 [==============================] - 0s 828us/step - loss: 0.1379\n",
"Epoch 996/1000\n",
"4/4 [==============================] - 0s 732us/step - loss: 0.1375\n",
"Epoch 997/1000\n",
"4/4 [==============================] - 0s 795us/step - loss: 0.1371\n",
"Epoch 998/1000\n",
"4/4 [==============================] - 0s 900us/step - loss: 0.1368\n",
"Epoch 999/1000\n",
"4/4 [==============================] - 0s 1ms/step - loss: 0.1364\n",
"Epoch 1000/1000\n",
"4/4 [==============================] - 0s 757us/step - loss: 0.1360\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x11baa52e8>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(x_train, y_train, epochs=1000)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y_predict = model.predict(np.array([1,2,3,4]))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 2.3216579 ]\n",
" [ 4.01589918]\n",
" [ 5.71014023]\n",
" [ 7.40438128]]\n"
]
}
],
"source": [
"print(y_predict)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
jingr1/SelfDrivingCar | matrix/kalman_filter_demo.ipynb | 1 | 157885 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Kalman Filter and your Matrix Class\n",
"\n",
"Once you have a working matrix class, you can use the class to run a Kalman filter! \n",
"\n",
"You will need to put your matrix class into the workspace:\n",
"* Click above on the \"JUPYTER\" logo. \n",
"* Then open the matrix.py file, and copy in your code there. \n",
"* Make sure to save the matrix.py file. \n",
"* Then click again on the \"JUPYTER\" logo and open this file again.\n",
"\n",
"You can also download this file kalman_filter_demo.ipynb and run the demo locally on your own computer.\n",
"\n",
"Once you have our matrix class loaded, you are ready to go through the demo. Read through this file and run each cell one by one. You do not need to write any code in this Ipython notebook.\n",
"\n",
"The demonstration has two different sections. The first section creates simulated data. The second section runs a Kalman filter on the data and visualizes the results."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Kalman Filters - Why are they useful?\n",
"\n",
"Kalman filters are really good at taking noisy sensor data and smoothing out the data to make more accurate predictions. For autonomous vehicles, Kalman filters can be used in object tracking. \n",
"\n",
"\n",
"### Kalman Filters and Sensors\n",
"Object tracking is often done with radar and lidar sensors placed around the vehicle. A radar sensor can directly measure the distance and velocity of objects moving around the vehicle. A lidar sensor only measures distance.\n",
"\n",
"Put aside a Kalman filter for a minute and think about how you could use lidar data to track an object. Let's say there is a bicyclist riding around in front of you. You send out a lidar signal and receive the signal back. The lidar sensor tells you that the bicycle is 10 meters directly ahead of you but gives you no velocity information.\n",
"\n",
"By the time your lidar device sends out another signal, maybe 0.05 seconds will have passed. But during those 0.05 seconds, your vehicle still needs to keep track of the bicycle. So your vehicle will predict where it thinks the bycicle will be. But your vehicle has no bicycle velocity information.\n",
"\n",
"After 0.05 seconds, the lidar device sends out and receives another signal. This time, the bicycle is 9.95 meters ahead of you. Now you know that the bicycle is traveling -1 meter per second towards you. For the next -.05 seconds, your vehicle will assume the bicycle is traveling -1 m/s towards you. Then another lidar signal goes out and comes back, and you can update the position and velocity again.\n",
"\n",
"### Sensor Noise\n",
"Unfortunately, lidar and radar signals are noisy. In other words, they are somewhat inacurrate. A Kalman filter helps to smooth out the noise so that you get a better fix on the bicycle's true position and velocity. \n",
"\n",
"A Kalman filter does this by weighing the uncertainty in your belief about the location versus the uncertainty in the lidar or radar measurement. If your belief is very uncertain, the Kalman filter gives more weight to the sensor. If the sensor measurement has more uncertainty, your belief about the location gets more weight than the sensor mearuement. \n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part 1 - Generate Data\n",
"\n",
"The next few cells in the Ipython notebook generate simulation data. Imagine you are in a vehicle and tracking another car in front of you. All of the data you track will be relative to your position. \n",
"\n",
"In this simulation, you are on a one-dimensional road where the car you are tracking can only move forwards or backwards. For this simulated data, the tracked vehicle starts 5 meters ahead of you traveling at 100 km/h. The vehicle is accelerating at -10 m/s^2. In other words, the vehicle is slowing down. \n",
"\n",
"Once the vehicle stops at 0 km/h, the car stays idle for 5 seconds. Then the vehicle continues accelerating towards you until the vehicle is traveling at -10 km/h. The vehicle travels at -10 km/h for 5 seconds. Don't worry too much about the trajectory of the other vehicle; this will be displayed for you in a visualization\n",
"\n",
"\n",
"You have a single lidar sensor on your vehicle that is tracking the other car. The lidar sensor takes a measurment once every 50 milliseconds.\n",
"\n",
"Run the code cell below to start the simulator and collect data about the tracked car. Noticed the line \n",
"`import matrix as m`, which imports your matrix code from the final project. You will not see any output yet when running this cell."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import pandas as pd\n",
"import math\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"import datagenerator\n",
"import matrix as m\n",
"\n",
"matplotlib.rcParams.update({'font.size': 16})\n",
"\n",
"# data_groundtruth() has the following inputs:\n",
"# Generates Data\n",
"# Input variables are:\n",
"# initial position meters\n",
"# initial velocity km/h\n",
"# final velocity (should be a negative number) km/h\n",
"# acceleration (should be a negative number) m/s^2\n",
"# how long the vehicle should idle \n",
"# how long the vehicle should drive in reverse at constant velocity\n",
"# time between lidar measurements in milliseconds\n",
"\n",
"time_groundtruth, distance_groundtruth, velocity_groundtruth, acceleration_groundtruth = datagenerator.generate_data(5, 100, -10, -10,\n",
" 5000, 5000, 50)\n",
"data_groundtruth = pd.DataFrame(\n",
" {'time': time_groundtruth,\n",
" 'distance': distance_groundtruth,\n",
" 'velocity': velocity_groundtruth,\n",
" 'acceleration': acceleration_groundtruth\n",
" })"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualizing the Tracked Object Distance\n",
"\n",
"The next cell visualizes the simulating data. The first visualization shows the object distance over time. You can see that the car is moving forward although decelerating. Then the car stops for 5 seconds and then drives backwards for 5 seconds."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'data_groundtruth' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-15a9d30dc577>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0max1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata_groundtruth\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'line'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'time'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'distance'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Object Distance Versus Time'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'time (milliseconds)'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mylabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'distance (meters)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'data_groundtruth' is not defined"
]
}
],
"source": [
"ax1 = data_groundtruth.plot(kind='line', x='time', y='distance', title='Object Distance Versus Time')\n",
"ax1.set(xlabel='time (milliseconds)', ylabel='distance (meters)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualizing Velocity Over Time\n",
"\n",
"The next cell outputs a visualization of the velocity over time. The tracked car starts at 100 km/h and decelerates to 0 km/h. Then the car idles and eventually starts to decelerate again until reaching -10 km/h. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Text(0,0.5,'velocity (km/h)'), Text(0.5,0,'time (milliseconds)')]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAaMAAAElCAYAAABJfI0xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCJmLYBRCCCFzEYxCCCFk7idYRxn0j8Q7HgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f49c15b6438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax2 = data_groundtruth.plot(kind='line', x='time', y='velocity', title='Object Velocity Versus Time')\n",
"ax2.set(xlabel='time (milliseconds)', ylabel='velocity (km/h)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualizing Acceleration Over Time\n",
"\n",
"This cell visualizes the tracked cars acceleration. The vehicle declerates at 10 m/s^2. Then the vehicle stops for 5 seconds and briefly accelerates again. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Text(0,0.5,'acceleration (m/s^2)'), Text(0.5,0,'time (milliseconds)')]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAaYAAAElCAYAAACvVUZ1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnKrCDZPjOI5TVbhhchzHcaqK/w8wnS+KLRKdzgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f49c1527630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data_groundtruth['acceleration'] = data_groundtruth['acceleration'] * 1000 / math.pow(60 * 60, 2)\n",
"ax3 = data_groundtruth.plot(kind='line', x='time', y='acceleration', title='Object Acceleration Versus Time')\n",
"ax3.set(xlabel='time (milliseconds)', ylabel='acceleration (m/s^2)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Simulate Lidar Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following code cell creates simulated lidar data. Lidar data is noisy, so the simulator takes ground truth measurements every 0.05 seconds and then adds random noise."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'datagenerator' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-2-2c2ef11cc1c5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# make lidar measurements\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mlidar_standard_deviation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.15\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mlidar_measurements\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatagenerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate_lidar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdistance_groundtruth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlidar_standard_deviation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mlidar_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime_groundtruth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'datagenerator' is not defined"
]
}
],
"source": [
"# make lidar measurements\n",
"lidar_standard_deviation = 0.15\n",
"lidar_measurements = datagenerator.generate_lidar(distance_groundtruth, lidar_standard_deviation)\n",
"lidar_time = time_groundtruth"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualize Lidar Meausrements\n",
"\n",
"Run the following cell to visualize the lidar measurements versus the ground truth. The ground truth is shown in red, and you can see that the lidar measurements are a bit noisy."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABKkAAAOKCAYAAABQ3AOoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n45RUAAAAAIxTUgEAAAAwTkkFAAAAwLg7X74hICOFi5UAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f49c1451828>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data_lidar = pd.DataFrame(\n",
" {'time': time_groundtruth,\n",
" 'distance': distance_groundtruth,\n",
" 'lidar': lidar_measurements\n",
" })\n",
"\n",
"matplotlib.rcParams.update({'font.size': 22})\n",
"\n",
"ax4 = data_lidar.plot(kind='line', x='time', y ='distance', label='ground truth', figsize=(20, 15), alpha=0.8,\n",
" title = 'Lidar Measurements Versus Ground Truth', color='red')\n",
"ax5 = data_lidar.plot(kind='scatter', x ='time', y ='lidar', label='lidar measurements', ax=ax4, alpha=0.6, color='g')\n",
"ax5.set(xlabel='time (milliseconds)', ylabel='distance (meters)')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part 2 - Using a Kalman Filter\n",
"\n",
"The next part of the demonstration will use your matrix class to run a Kalman filter. This first cell initializes variables and defines a few functions.\n",
"\n",
"The following cell runs the Kalman filter using the lidar data.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Kalman Filter Initialization\n",
"\n",
"initial_distance = 0\n",
"initial_velocity = 0\n",
"\n",
"x_initial = m.Matrix([[initial_distance], [initial_velocity * 1e-3 / (60 * 60)]])\n",
"P_initial = m.Matrix([[5, 0],[0, 5]])\n",
"\n",
"acceleration_variance = 50\n",
"lidar_variance = math.pow(lidar_standard_deviation, 2)\n",
"\n",
"H = m.Matrix([[1, 0]])\n",
"R = m.Matrix([[lidar_variance]])\n",
"I = m.identity(2)\n",
"\n",
"def F_matrix(delta_t):\n",
" return m.Matrix([[1, delta_t], [0, 1]])\n",
"\n",
"def Q_matrix(delta_t, variance):\n",
" t4 = math.pow(delta_t, 4)\n",
" t3 = math.pow(delta_t, 3)\n",
" t2 = math.pow(delta_t, 2)\n",
" \n",
" return variance * m.Matrix([[(1/4)*t4, (1/2)*t3], [(1/2)*t3, t2]])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run the Kalman filter\n",
"\n",
"The next code cell runs the Kalman filter. In this demonstration, the prediction step starts with the second lidar measurement. When the first lidar signal arrives, there is no previous lidar measurement with which to calculate velocity. In other words, the Kalman filter predicts where the vehicle is going to be, but it can't make a prediction until time has passed between the first and second lidar reading. \n",
"\n",
"The Kalman filter has two steps: a prediction step and an update step. In the prediction step, the filter uses a motion model to figure out where the object has traveled in between sensor measurements. The update step uses the sensor measurement to adjust the belief about where the object is."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "unsupported operand type(s) for *: 'NoneType' and 'NoneType'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-f7676cf8ed07>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0mx_prime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mP_prime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mP\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mQ\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;31m# Measurement Update Step - updates belief based on lidar measurement\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for *: 'NoneType' and 'NoneType'"
]
}
],
"source": [
"# Kalman Filter Implementation\n",
"\n",
"x = x_initial\n",
"P = P_initial\n",
"\n",
"x_result = []\n",
"time_result = []\n",
"v_result = []\n",
"\n",
"\n",
"for i in range(len(lidar_measurements) - 1):\n",
" \n",
" # calculate time that has passed between lidar measurements\n",
" delta_t = (lidar_time[i + 1] - lidar_time[i]) / 1000.0\n",
"\n",
" # Prediction Step - estimates how far the object traveled during the time interval\n",
" F = F_matrix(delta_t)\n",
" Q = Q_matrix(delta_t, acceleration_variance)\n",
" \n",
" x_prime = F * x\n",
" P_prime = F * P * F.T() + Q\n",
" \n",
" # Measurement Update Step - updates belief based on lidar measurement\n",
" y = m.Matrix([[lidar_measurements[i + 1]]]) - H * x_prime\n",
" S = H * P_prime * H.T() + R\n",
" K = P_prime * H.T() * S.inverse()\n",
" x = x_prime + K * y\n",
" P = (I - K * H) * P_prime\n",
"\n",
" # Store distance and velocity belief and current time\n",
" x_result.append(x[0][0])\n",
" v_result.append(3600.0/1000 * x[1][0])\n",
" time_result.append(lidar_time[i+1])\n",
" \n",
"result = pd.DataFrame(\n",
" {'time': time_result,\n",
" 'distance': x_result,\n",
" 'velocity': v_result\n",
" })"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualize the Results\n",
"\n",
"The following code cell outputs a visualization of the Kalman filter. The chart contains ground turth, the lidar measurements, and the Kalman filter belief. Notice that the Kalman filter tends to smooth out the information obtained from the lidar measurement.\n",
"\n",
"It turns out that using multiple sensors like radar and lidar at the same time, will give even better results. Using more than one type of sensor at once is called sensor fusion, which you will learn about in the Self-Driving Car Engineer Nanodegree"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ax6 = data_lidar.plot(kind='line', x='time', y ='distance', label='ground truth', figsize=(22, 18), alpha=.3, title='Lidar versus Kalman Filter versus Ground Truth')\n",
"ax7 = data_lidar.plot(kind='scatter', x ='time', y ='lidar', label='lidar sensor', ax=ax6)\n",
"ax8 = result.plot(kind='scatter', x = 'time', y = 'distance', label='kalman', ax=ax7, color='r')\n",
"ax8.set(xlabel='time (milliseconds)', ylabel='distance (meters)')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualize the Velocity\n",
"\n",
"One of the most interesting benefits of Kalman filters is that they can give you insights into variables that you\n",
"cannot directly measured. Although lidar does not directly give velocity information, the Kalman filter can infer velocity from the lidar measurements.\n",
"\n",
"This visualization shows the Kalman filter velocity estimation versus the ground truth. The motion model used in this Kalman filter is relatively simple; it assumes velocity is constant and that acceleration a random noise. You can see that this motion model might be too simplistic because the Kalman filter has trouble predicting velocity as the object decelerates."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ax1 = data_groundtruth.plot(kind='line', x='time', y ='velocity', label='ground truth', figsize=(22, 18), alpha=.8, title='Kalman Filter versus Ground Truth Velocity')\n",
"ax2 = result.plot(kind='scatter', x = 'time', y = 'velocity', label='kalman', ax=ax1, color='r')\n",
"ax2.set(xlabel='time (milliseconds)', ylabel='velocity (km/h)')\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
huizhuzhao/jupyter_notebook | examples/keras_input_output.ipynb | 1 | 13650 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### input_shape, output_shape, reinstantiate model\n",
"Dense, LSTM, Embedding layers, Convoluation"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import numpy as np\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Activation, Convolution2D, Convolution1D\n",
"from keras.layers import Embedding, LSTM, SimpleRNN, TimeDistributed\n",
"from keras.layers import MaxPooling2D, MaxPooling1D"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dense layer"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"((None, 784), (None, 20))\n",
"((None, 784), (None, 20))\n",
"(100, 20)\n",
"(22, 20)\n"
]
}
],
"source": [
"model_1 = Sequential([Dense(output_dim = 20, input_shape = (784, ))\n",
" ])\n",
"model_2 = Sequential([Dense(output_dim = 20, input_dim = 784)\n",
" ])\n",
"\n",
"x = np.random.uniform(size=(22, 784))\n",
"# Dense layer: \n",
"## input_shape (nb_samples, input_dim)\n",
"## output_shape (nb_samples, output_dim)\n",
"print (model_1.input_shape, model_1.output_shape)\n",
"print (model_2.input_shape, model_2.output_shape)\n",
"print model_1.layers[0].get_output_shape_for((100, 784))\n",
"output = model_1.predict(x)\n",
"print (output.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Embedding layer"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"((None, None), (None, None, 150))\n",
"(100, 3, 150)\n"
]
}
],
"source": [
"word_vec_dim = 150\n",
"voca_size = 2000\n",
"n_samples = 100\n",
"seq_length = 3\n",
"model_1 = Sequential([Embedding(output_dim = word_vec_dim, input_dim = voca_size)])\n",
"# Embedding layer:\n",
"## input_shape (nb_samples, sequence_length)\n",
"## output_shape (nb_samples, sequence_length, output_dim)\n",
"print (model_1.layers[0].input_shape, model_1.layers[0].output_shape)\n",
"x = np.random.randint(low=0, high=2000, size=(n_samples, seq_length))\n",
"output = model_1.predict(x)\n",
"print (output.shape) # (n_samples, seq_length, word_vec_dim)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### SimpleRNN"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"((None, 10, 50), (None, 10, 150))\n"
]
}
],
"source": [
"input_dim = 50\n",
"output_dim = 150\n",
"seq_length = 10\n",
"model = Sequential()\n",
"#model.add(SimpleRNN(output_dim=output_dim, input_dim=input_dim, input_length=seq_length, return_sequences=True))\n",
"model.add(SimpleRNN(output_dim=output_dim, input_shape=(seq_length, input_dim), return_sequences=True))\n",
"# SimpleRNN\n",
"## input_shape (nb_samples, timesteps, input_dim)\n",
"## output_shape (nb_samples, timesteps, output_dim)\n",
"print (model.input_shape, model.output_shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### LSTM layer"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(None, 10, 150)\n",
"(None, 300)\n",
"(20, 300)\n",
"(20, 10, 300)\n"
]
}
],
"source": [
"input_dim = 150\n",
"hidden_dim = 300\n",
"seq_length = 10\n",
"n_samples = 20\n",
"\n",
"model_1 = Sequential([LSTM(output_dim = hidden_dim, input_dim = input_dim, input_length = seq_length)])\n",
"# LSTM layer\n",
"## input_shape (nb_samples, timesteps, input_dim)\n",
"## output_shape: \n",
"#### return_sequences==True: (nb_samples, timesteps, input_dim) \n",
"#### return_sequences==False: (nb_samples, input_dim) ### only the last output returned\n",
"print (model_1.layers[0].input_shape)\n",
"print (model_1.layers[0].output_shape)\n",
"x = np.random.uniform(size=(n_samples, seq_length, input_dim))\n",
"output = model_1.predict(x)\n",
"print (output.shape)\n",
"\n",
"model_2 = Sequential([LSTM(output_dim = hidden_dim, input_dim = input_dim, input_length = seq_length, \n",
" return_sequences=True)]) ## return sequences\n",
"output = model_2.predict(x)\n",
"print (output.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### wrapper: TimeDistributed"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"((None, 10), (None, 10, 200))\n"
]
}
],
"source": [
"voca_size = 1000\n",
"seq_length = 10\n",
"word_vec_dim = 100\n",
"hidden_dim = 300\n",
"output_dim = 200\n",
"model = Sequential()\n",
"model.add(Embedding(output_dim=word_vec_dim, input_dim=voca_size, input_length=seq_length))\n",
"model.add(SimpleRNN(output_dim=hidden_dim, activation='sigmoid', return_sequences=True))\n",
"model.add(TimeDistributed(Dense(output_dim=output_dim, activation='softmax')))\n",
"print (model.input_shape, model.output_shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Convolution2D"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"input_shape: (None, 3, 256, 256)\n",
"output_shape: (1, 1, 128, 64)\n",
"conv layer: weight_shape: (5, 5, 256, 64), bias_shape: (64,)\n"
]
}
],
"source": [
"nb_filter = 64\n",
"rf_size = (5, 5) # receptive field size\n",
"input_shape = (3, 256, 256) # 256x256 RGB picture\n",
"strides = (2, 2)\n",
"model = Sequential()\n",
"model.add(Convolution2D(nb_filter=nb_filter, nb_row=rf_size[0], nb_col=rf_size[1], input_shape=input_shape, \n",
" border_mode='same'))\n",
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
"# Convolution2D layer\n",
"## dim_ordering == 'th'\n",
"#### input_shape (nb_samples, channels, rows, cols)\n",
"#### output_shape (nb_samples, nb_filter, new_rows, new_cols)\n",
"\n",
"## dim_ordering == 'tf'\n",
"#### input_shape (nb_samples, rows, cols, channels)\n",
"#### output_shape (nb_samples, new_rows, new_cols, nb_filter)\n",
"\n",
"print ('input_shape: {0}'.format(model.layers[0].input_shape))\n",
"x = np.random.uniform(size=(1, 3, 256, 256))\n",
"\n",
"output = model.predict(x)\n",
"print ('output_shape: {0}'.format(output.shape))\n",
"ws = model.layers[0].get_weights()\n",
"print ('conv layer: weight_shape: {0}, bias_shape: {1}'.format(ws[0].shape, ws[1].shape)) # kernel shape (nb_filters, nb)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Convolution1D"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"input_shape: (None, None, 32)\n",
"output_shape: (1, 8, 64)\n",
"weight_shape: (3, 1, 32, 64), bias_shape: (64,)\n"
]
}
],
"source": [
"nb_filter = 64\n",
"rf_size = 3\n",
"input_dim = 32 ## channels to Convolution2D\n",
"model = Sequential([Convolution1D(nb_filter=nb_filter, filter_length=rf_size, input_dim=32)])\n",
"# Convolution1D\n",
"## input_shape (nb_samples, timesteps, channels)\n",
"## output_shape (nb_samples, new_timesteps, nb_filter)\n",
"print ('input_shape: {0}'.format(model.input_shape))\n",
"x = np.random.uniform(size=(1, 10, input_dim))\n",
"output = model.predict(x)\n",
"print ('output_shape: {0}'.format(output.shape))\n",
"ws = model.layers[0].get_weights()\n",
"print ('weight_shape: {0}, bias_shape: {1}'.format(ws[0].shape, ws[1].shape))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## reinstantiate model"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"from keras.layers import Dense, LSTM, Embedding, Merge\n",
"from keras.models import Sequential, Model, model_from_yaml, model_from_json\n",
"from jupyter_notebook.datasets.importer.mnist_importer import MnistImporter"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"test test sequential_10 sequential_11\n"
]
}
],
"source": [
"def build_model():\n",
" model = Sequential(name='test')\n",
" model.add(Dense(output_dim=100, input_dim=784))\n",
" model.add(Dense(output_dim=10, activation='softmax'))\n",
" return model\n",
"\n",
"def reinstantiate_model(model):\n",
" config = model.get_config()\n",
" config[0]['model_name'] = model.name\n",
" json = model.to_json()\n",
" yaml = model.to_yaml()\n",
" \n",
" model_config = Sequential.from_config(config)\n",
" model_config.name = config[0]['model_name']\n",
" model_json = model_from_json(json)\n",
" model_yaml = model_from_yaml(yaml)\n",
" \n",
" print model.name, model_config.name, model_json.name, model_yaml.name\n",
"\n",
"model = build_model()\n",
"reinstantiate_model(model)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\"class_name\": \"Sequential\", \"keras_version\": \"1.2.2\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"W_constraint\": null, \"b_constraint\": null, \"name\": \"dense_3\", \"output_dim\": 100, \"activity_regularizer\": null, \"trainable\": true, \"init\": \"glorot_uniform\", \"bias\": true, \"input_dtype\": \"float32\", \"input_dim\": 784, \"b_regularizer\": null, \"W_regularizer\": null, \"activation\": \"linear\", \"batch_input_shape\": [null, 784]}}, {\"class_name\": \"Dense\", \"config\": {\"W_constraint\": null, \"b_constraint\": null, \"name\": \"dense_4\", \"activity_regularizer\": null, \"trainable\": true, \"init\": \"glorot_uniform\", \"bias\": true, \"input_dim\": 100, \"b_regularizer\": null, \"W_regularizer\": null, \"activation\": \"softmax\", \"output_dim\": 10}}]}\n"
]
}
],
"source": [
"import json\n",
"json_str = model.to_json()\n",
"config = json.loads(json_str)\n",
"print json_str"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## get layers"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tensor(\"dense_input_14:0\", shape=(?, 784), dtype=float32)\n",
"[<keras.layers.core.Dense object at 0x7f7eb8706910>, <keras.layers.core.Dense object at 0x7f7eb8706d10>]\n",
"<keras.layers.core.Dense object at 0x7f7eb8706910>\n"
]
}
],
"source": [
"def mlp_mnist():\n",
" model = Sequential()\n",
" model.add(Dense(input_shape=(784, ), output_dim=64, name='hidden_1', activation='relu'))\n",
" model.add(Dense(output_dim=10, name='output', activation='softmax'))\n",
" return model\n",
"\n",
"model = mlp_mnist()\n",
"input = model.input\n",
"hidden_1 = model.get_layer('hidden_1')\n",
"layers = model.layers\n",
"print input\n",
"print layers\n",
"print hidden_1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
tylerwmarrs/billboard-hot-100-lyric-analysis | notebooks/exploratory/02-raw-lyric-analysis.ipynb | 1 | 87367 | {
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"nb_dir = os.path.split(os.getcwd())[0]\n",
"project_dir = os.path.join(nb_dir, os.pardir)\n",
"\n",
"if project_dir not in sys.path:\n",
" sys.path.append(project_dir)\n",
"\n",
"from src import webscrapers\n",
"from src import corpus"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Scrape swear word list\n",
"\n",
"We scrape swear words from the web from the site:\n",
"http://www.noswearing.com/\n",
"\n",
"It is a community driven list of swear words."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['niggas', 'anus', 'arse', 'arsehole', 'ass', 'ass-hat', 'ass-jabber', 'ass-pirate', 'assbag', 'assbandit', 'assbanger', 'assbite', 'assclown', 'asscock', 'asscracker', 'asses', 'assface', 'assfuck', 'assfucker', 'assgoblin', 'asshat', 'asshead', 'asshole', 'asshopper', 'assjacker', 'asslick', 'asslicker', 'assmonkey', 'assmunch', 'assmuncher', 'assnigger', 'asspirate', 'assshit', 'assshole', 'asssucker', 'asswad', 'asswipe', 'axwound', 'bampot', 'bastard', 'beaner', 'bitch', 'bitchass', 'bitches', 'bitchtits', 'bitchy', 'blow job', 'blowjob', 'bollocks', 'bollox', 'boner', 'brotherfucker', 'bullshit', 'bumblefuck', 'butt plug', 'butt-pirate', 'buttfucka', 'buttfucker', 'camel toe', 'carpetmuncher', 'chesticle', 'chinc', 'chink', 'choad', 'chode', 'clit', 'clitface', 'clitfuck', 'clusterfuck', 'cock', 'cockass', 'cockbite', 'cockburger', 'cockface', 'cockfucker', 'cockhead', 'cockjockey', 'cockknoker', 'cockmaster', 'cockmongler', 'cockmongruel', 'cockmonkey', 'cockmuncher', 'cocknose', 'cocknugget', 'cockshit', 'cocksmith', 'cocksmoke', 'cocksmoker', 'cocksniffer', 'cocksucker', 'cockwaffle', 'coochie', 'coochy', 'coon', 'cooter', 'cracker', 'cum', 'cumbubble', 'cumdumpster', 'cumguzzler', 'cumjockey', 'cumslut', 'cumtart', 'cunnie', 'cunnilingus', 'cunt', 'cuntass', 'cuntface', 'cunthole', 'cuntlicker', 'cuntrag', 'cuntslut', 'dago', 'damn', 'deggo', 'dick', 'dick-sneeze', 'dickbag', 'dickbeaters', 'dickface', 'dickfuck', 'dickfucker', 'dickhead', 'dickhole', 'dickjuice', 'dickmilk', 'dickmonger', 'dicks', 'dickslap', 'dicksucker', 'dicksucking', 'dicktickler', 'dickwad', 'dickweasel', 'dickweed', 'dickwod', 'dike', 'dildo', 'dipshit', 'doochbag', 'dookie', 'douche', 'douche-fag', 'douchebag', 'douchewaffle', 'dumass', 'dumb ass', 'dumbass', 'dumbfuck', 'dumbshit', 'dumshit', 'dyke', 'fag', 'fagbag', 'fagfucker', 'faggit', 'faggot', 'faggotcock', 'fagtard', 'fatass', 'fellatio', 'feltch', 'flamer', 'fuck', 'fuckass', 'fuckbag', 'fuckboy', 'fuckbrain', 'fuckbutt', 'fuckbutter', 'fucked', 'fucker', 'fuckersucker', 'fuckface', 'fuckhead', 'fuckhole', 'fuckin', 'fucking', 'fucknut', 'fucknutt', 'fuckoff', 'fucks', 'fuckstick', 'fucktard', 'fucktart', 'fuckup', 'fuckwad', 'fuckwit', 'fuckwitt', 'fudgepacker', 'gay', 'gayass', 'gaybob', 'gaydo', 'gayfuck', 'gayfuckist', 'gaylord', 'gaytard', 'gaywad', 'goddamn', 'goddamnit', 'gooch', 'gook', 'gringo', 'guido', 'handjob', 'hard on', 'heeb', 'hell', 'ho', 'hoe', 'homo', 'homodumbshit', 'honkey', 'humping', 'jackass', 'jagoff', 'jap', 'jerk off', 'jerkass', 'jigaboo', 'jizz', 'jungle bunny', 'junglebunny', 'kike', 'kooch', 'kootch', 'kraut', 'kunt', 'kyke', 'lameass', 'lardass', 'lesbian', 'lesbo', 'lezzie', 'mcfagget', 'mick', 'minge', 'mothafucka', \"mothafuckin\\\\'\", 'motherfucker', 'motherfucking', 'muff', 'muffdiver', 'munging', 'negro', 'nigaboo', 'nigga', 'nigger', 'niggers', 'niglet', 'nut sack', 'nutsack', 'paki', 'panooch', 'pecker', 'peckerhead', 'penis', 'penisbanger', 'penisfucker', 'penispuffer', 'piss', 'pissed', 'pissed off', 'pissflaps', 'polesmoker', 'pollock', 'poon', 'poonani', 'poonany', 'poontang', 'porch monkey', 'porchmonkey', 'prick', 'punanny', 'punta', 'pussies', 'pussy', 'pussylicking', 'puto', 'queef', 'queer', 'queerbait', 'queerhole', 'renob', 'rimjob', 'ruski', 'sand nigger', 'sandnigger', 'schlong', 'scrote', 'shit', 'shitass', 'shitbag', 'shitbagger', 'shitbrains', 'shitbreath', 'shitcanned', 'shitcunt', 'shitdick', 'shitface', 'shitfaced', 'shithead', 'shithole', 'shithouse', 'shitspitter', 'shitstain', 'shitter', 'shittiest', 'shitting', 'shitty', 'shiz', 'shiznit', 'skank', 'skeet', 'skullfuck', 'slut', 'slutbag', 'smeg', 'snatch', 'spic', 'spick', 'splooge', 'spook', 'suckass', 'tard', 'testicle', 'thundercunt', 'tit', 'titfuck', 'tits', 'tittyfuck', 'twat', 'twatlips', 'twats', 'twatwaffle', 'unclefucker', 'va-j-j', 'vag', 'vagina', 'vajayjay', 'vjayjay', 'wank', 'wankjob', 'wetback', 'whore', 'whorebag', 'whoreface', 'wop']\n"
]
}
],
"source": [
"import string\n",
"import os\n",
"import requests\n",
"from fake_useragent import UserAgent\n",
"from lxml import html\n",
"\n",
"def requests_get(url): \n",
" ua = UserAgent().random \n",
" return requests.get(url, headers={'User-Agent': ua})\n",
"\n",
"def get_swear_words(save_file='swear-words.txt'): \n",
" \"\"\"\n",
" Scrapes a comprehensive list of swear words from noswearing.com\n",
" \"\"\"\n",
" words = ['niggas']\n",
" if os.path.isfile(save_file):\n",
" with open(save_file, 'rt') as f:\n",
" for line in f:\n",
" words.append(line.strip())\n",
" \n",
" return words\n",
" \n",
" base_url = 'http://www.noswearing.com/dictionary/'\n",
" letters = '1' + string.ascii_lowercase\n",
" \n",
" for letter in letters:\n",
" full_url = base_url + letter\n",
" result = requests_get(full_url)\n",
" tree = html.fromstring(result.text)\n",
" search = tree.xpath(\"//td[@valign='top']/a[@name and string-length(@name) != 0]\")\n",
" \n",
" if search is None:\n",
" continue\n",
" \n",
" for result in search:\n",
" words.append(result.get('name').lower())\n",
" \n",
" with open(save_file, 'wt') as f:\n",
" for word in words:\n",
" f.write(word)\n",
" f.write('\\n')\n",
" \n",
" return words\n",
"\n",
"print(get_swear_words())\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Testing TextBlob\n",
"\n",
"I don't really like TextBlob as it tries to be \"nice\", but lacks a lot of basic functionality.\n",
"\n",
"1. Stop words not included\n",
"2. Tokenizer is pretty meh.\n",
"3. No built in way to obtain word frequency"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>word</th>\n",
" <th>frequency</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>n't</td>\n",
" <td>819</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>'m</td>\n",
" <td>569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>'s</td>\n",
" <td>405</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>know</td>\n",
" <td>376</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>got</td>\n",
" <td>339</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>like</td>\n",
" <td>335</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>yeah</td>\n",
" <td>225</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>low</td>\n",
" <td>214</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>love</td>\n",
" <td>212</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>na</td>\n",
" <td>210</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>oh</td>\n",
" <td>200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>baby</td>\n",
" <td>194</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>get</td>\n",
" <td>185</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>'re</td>\n",
" <td>183</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>let</td>\n",
" <td>178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>go</td>\n",
" <td>178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>need</td>\n",
" <td>151</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>want</td>\n",
" <td>137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>give</td>\n",
" <td>134</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>ca</td>\n",
" <td>134</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>wan</td>\n",
" <td>133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>back</td>\n",
" <td>133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>make</td>\n",
" <td>128</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>come</td>\n",
" <td>127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>gon</td>\n",
" <td>120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>girl</td>\n",
" <td>120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>ai</td>\n",
" <td>116</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>say</td>\n",
" <td>110</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>bitch</td>\n",
" <td>109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>one</td>\n",
" <td>104</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3347</th>\n",
" <td>serve</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3348</th>\n",
" <td>blinds</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3349</th>\n",
" <td>tatted</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3350</th>\n",
" <td>named</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3351</th>\n",
" <td>gap</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3352</th>\n",
" <td>shadows</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3353</th>\n",
" <td>picked</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3354</th>\n",
" <td>yeah…</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3355</th>\n",
" <td>oh…</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3356</th>\n",
" <td>fact</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3357</th>\n",
" <td>unconditional</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3358</th>\n",
" <td>outer</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3359</th>\n",
" <td>laflare</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3360</th>\n",
" <td>ruler</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3361</th>\n",
" <td>marie</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3362</th>\n",
" <td>twelve</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3363</th>\n",
" <td>killer</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3364</th>\n",
" <td>wealth</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3365</th>\n",
" <td>shift</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3366</th>\n",
" <td>envy</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3367</th>\n",
" <td>reaper</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3368</th>\n",
" <td>layaway</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3369</th>\n",
" <td>throbbin</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3370</th>\n",
" <td>built</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3371</th>\n",
" <td>core</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3372</th>\n",
" <td>texts</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3373</th>\n",
" <td>ben</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3374</th>\n",
" <td>justin</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3375</th>\n",
" <td>advantage</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3376</th>\n",
" <td>crimes</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3377 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" word frequency\n",
"0 n't 819\n",
"1 'm 569\n",
"2 's 405\n",
"3 know 376\n",
"4 got 339\n",
"5 like 335\n",
"6 yeah 225\n",
"7 low 214\n",
"8 love 212\n",
"9 na 210\n",
"10 oh 200\n",
"11 baby 194\n",
"12 get 185\n",
"13 're 183\n",
"14 let 178\n",
"15 go 178\n",
"16 need 151\n",
"17 want 137\n",
"18 give 134\n",
"19 ca 134\n",
"20 wan 133\n",
"21 back 133\n",
"22 make 128\n",
"23 come 127\n",
"24 gon 120\n",
"25 girl 120\n",
"26 ai 116\n",
"27 say 110\n",
"28 bitch 109\n",
"29 one 104\n",
"... ... ...\n",
"3347 serve 1\n",
"3348 blinds 1\n",
"3349 tatted 1\n",
"3350 named 1\n",
"3351 gap 1\n",
"3352 shadows 1\n",
"3353 picked 1\n",
"3354 yeah… 1\n",
"3355 oh… 1\n",
"3356 fact 1\n",
"3357 unconditional 1\n",
"3358 outer 1\n",
"3359 laflare 1\n",
"3360 ruler 1\n",
"3361 marie 1\n",
"3362 twelve 1\n",
"3363 killer 1\n",
"3364 wealth 1\n",
"3365 shift 1\n",
"3366 envy 1\n",
"3367 reaper 1\n",
"3368 layaway 1\n",
"3369 throbbin 1\n",
"3370 built 1\n",
"3371 core 1\n",
"3372 texts 1\n",
"3373 ben 1\n",
"3374 justin 1\n",
"3375 advantage 1\n",
"3376 crimes 1\n",
"\n",
"[3377 rows x 2 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"import operator\n",
"\n",
"import pandas as pd\n",
"from textblob import TextBlob, WordList\n",
"from nltk.corpus import stopwords\n",
"\n",
"def get_data_paths():\n",
" dir_path = os.path.dirname(os.path.realpath('.'))\n",
" data_dir = os.path.join(dir_path, 'billboard-hot-100-data')\n",
" dirs = [os.path.join(data_dir, d, 'songs.csv') for d in os.listdir(data_dir) \n",
" if os.path.isdir(os.path.join(data_dir, d))]\n",
" \n",
" return dirs\n",
"\n",
"def lyric_file_to_text_blob(row):\n",
" \"\"\"\n",
" Transform lyrics column to TextBlob instances.\n",
" \"\"\"\n",
" return TextBlob(row['lyrics'])\n",
"\n",
"def remove_stop_words(word_list):\n",
" wl = WordList([])\n",
" \n",
" stop_words = stopwords.words('english')\n",
" for word in word_list:\n",
" if word.lower() not in stop_words:\n",
" wl.append(word)\n",
" \n",
" return wl\n",
"\n",
"def word_freq(words, sort='desc'):\n",
" \"\"\"\n",
" Returns frequency table for all words provided in the list.\n",
" \"\"\"\n",
" \n",
" reverse = sort == 'desc'\n",
" \n",
" freq = {}\n",
" for word in words:\n",
" if word in freq:\n",
" freq[word] = freq[word] + 1\n",
" else:\n",
" freq[word] = 1\n",
" \n",
" return sorted(freq.items(), key=operator.itemgetter(1), reverse=reverse)\n",
"\n",
"data_paths = corpus.raw_data_dirs()\n",
"songs = corpus.load_songs(data_paths[0])\n",
"\n",
"songs = pd.DataFrame.from_dict(songs)\n",
"songs[\"lyrics\"] = songs.apply(lyric_file_to_text_blob, axis=1)\n",
"\n",
"all_words = WordList([])\n",
"\n",
"for i, row in songs.iterrows():\n",
" all_words.extend(row['lyrics'].words)\n",
"\n",
"cleaned_all_words = remove_stop_words(all_words)\n",
"cleaned_all_words = pd.DataFrame(word_freq(cleaned_all_words.lower()), columns=['word', 'frequency'])\n",
"cleaned_all_words"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEiCAYAAADjxEWuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXl4lcX1+D8nCwkJIWEn7IiIArIYwLW1btW6a9Vq61Kr\ntf3pt9pqW2tbq7a1q121tnXfrYgboLgjIoqQIIRdQfYlyBYCIYEk5/fHzE1ubt6bjdyE3Hs+z/M+\nyZ133jnzbnNmzjkzr6gqhmEYhhFJUltXwDAMwzg4MQVhGIZhBGIKwjAMwwjEFIRhGIYRiCkIwzAM\nIxBTEIZhGEYgpiAMwzCMQExBGIZhGIGYgjAMwzACSWnrChwI3bt310GDBjX7+L1799KxY8dWT493\n2a0hw2S3vuzWkGGyD1x2YygoKNiqqj0azKiq7XbLy8vTAyE/P79N0uNddmvIMNnxKcNkt8wxDQHk\nayPaWDMxGYZhGIGYgjAMwzACMQVhGIZhBGIKwjAMwwjEFIRhGIYRiCkIwzAMIxBTEIZhGEYgCakg\nXpm/gfP/NYv3Vu9t66oYhmEctLTrmdTNZceefcxft5POkt7WVTEMwzhoScgRxHGHdgegcMs+3KRC\nwzAMI5KEVBBDe3aie6c0dpZVsfKL3W1dHcMwjIOShFQQIsJxQ7oBMGvFtjaujWEYxsFJQioIgOMP\ndQriw5Vb27gmhmEYBycJqyCOG+L8EB+t3EZllfkhDMMwIklYBdG/awY9M5PZVVbBko272ro6hmEY\nBx0JqyAAjuzZAYBZZmYyDMOogykI4MOV5qg2DMOIJKEVxEivIOau2s6+iqo2ro1hGMbBRUIriC7p\nyRzWqxN791cyf93Otq6OYRjGQUVCKwioiWaatcL8EIZhGOGYghhi8yEMwzCCiJmCEJF0EZkjIgtE\nZLGI3OXT7xSRDSIy329nhh1zm4isEJHlInJ6rOoWztGHdCNJ4JO1OyndV9EaIg3DMNoFsRxBlAMn\nq+poYAxwhogc4/f9TVXH+O01ABEZDlwKjADOAO4XkeQY1g+A7I6pHNk3m4oqZc6q7bEWZxiG0W6I\nmYJQR2glvFS/1Tdl+Tzgf6parqqrgBXAhFjVL5xjw2ZVG4ZhGI6Y+iBEJFlE5gNbgLdU9WO/6wci\nUigij4hIF5/WF1gXdvh6nxZzQusy2YQ5wzCMGqQ1vocgIjnAS8APgC+ArbjRxG+AXFX9jojcB8xW\n1af8MQ8D01R1UkRZ1wHXAeTm5uZNmTKl2fUqLS0lIyOD8grlyleKqKyCR8/rSXJFGRkZGVHzH2h6\nS5Z1MMpuDRkmu/Vlt4YMk33gshvDuHHjClR1XIMZVbVVNuBXwI8j0gYBi/z/twG3he17Azi2vjLz\n8vL0QMjPz6/+/5L/fKgDb52q0xZurJUeLf+BpLdkWQej7NaQYbLjU4bJbpljGgLI10a027GMYurh\nRw6ISEfgNGCZiOSGZbsAWOT/nwxcKiJpIjIYGArMiVX9IgnNh7BlNwzDMByx9EHkAtNFpBCYi/NB\nTAX+JCILffpJwI8AVHUxMBFYArwO3KCqlTGsXy2q/RA2Yc4wDAOAlFgVrKqFwNiA9CvqOeZu4O5Y\n1ak+RvXLIaNDMiu/2MP2vc2z6xmGYcQTCT+TOkSHlCQmDO4KwMIt+9q4NoZhGG2PKYgwQstuTFtR\nyidrd4Sc5YZhGAmJKYgwTh/Rm05pKXy2fT8X3P8h5943i4lz17F3X6u5QgzDMA4aTEGEMbBbJtNu\n+hLnDcskJyOVhRuK+ekLhRzz+3f47dQlbNljisIwjMTBFEQE/btmcOWoLGbfdgp/uXg0o/vnULx3\nPw99sIrbp1sIrGEYiYMpiCikpybz9bx+vHLD8Uz+v+PpkJzE1r1VZm4yDCNhMAXRCEb1y6F7J/d5\n0m17ytu4NoZhGK2DKYhG0q1TGgBbd1sIrGEYiYEpiEbSLTSC2G0jCMMwEgNTEI2kux9BbLMRhGEY\nCYIpiEYSGkF8YSMIwzASBFMQjaR7po0gDMNILExBNJLuWW4EsdVGEIZhJAimIBpJt9AIwsJcDcNI\nEExBNJKaKCYzMRmGkRiYgmgkParnQdgIwjCMxMAURCPpkulGENv37KOyypYBNwwj/jEF0UhSk5PI\n6iBUKewsNTOTYRjxjymIJpCd5i6XLbdhGEYiYAqiCWSnu8tly20YhpEImIJoAtlpyYDNpjYMIzEw\nBdEEakYQZmIyDCP+MQXRBEI+CJssZxhGImAKogmERhBbS2wEYRhG/BMzBSEi6SIyR0QWiMhiEbnL\np3cVkbdE5DP/t0vYMbeJyAoRWS4ip8eqbs3FRhCGYSQSsRxBlAMnq+poYAxwhogcA/wMeEdVhwLv\n+N+IyHDgUmAEcAZwv4gkx7B+TSbHjyC+MB+EYRgJQMwUhDp2+5+pflPgPOBxn/44cL7//zzgf6pa\nrqqrgBXAhFjVrzlUjyAsiskwjARAVGO3bIQfARQAhwL/UtVbRWSnqub4/QLsUNUcEbkPmK2qT/l9\nDwPTVHVSRJnXAdcB5Obm5k2ZMqXZ9SstLSUjI6PR6dt27eG6N0pISxaeubBXs8tpzjEtlR4vMkx2\n68tuDRkm+8BlN4Zx48YVqOq4BjOqasw3IAeYDowEdkbs2+H/3gdcHpb+MHBRfeXm5eXpgZCfn9+k\n9Llz5+qwX76mA2+dqrvL9je7nOYc01Lp8SLDZMenDJPdMsc0BJCvjWi7WyWKSVV3egVxBlAkIrkA\n/u8Wn20D0D/ssH4+7aBBRGq+C2F+CMMw4pxYRjH1EJGQKakjcBqwDJgMXOWzXQW84v+fDFwqImki\nMhgYCsyJVf2aS3f/XYitFslkGEackxLDsnOBx70fIgmYqKpTReQjYKKIXAOsAS4BUNXFIjIRWAJU\nADeoamUM69csuoe+C1FiCsIwjPgmZgpCVQuBsQHp24BTohxzN3B3rOrUElR/WW6PmZgMw4hvbCZ1\nE+lmIwjDMBIEUxBNJGRishGEYRjxjimIJlLtpLbJcoZhxDmmIJpItZPaFIRhGHGOKYgmUu2ktnkQ\nhmHEOaYgmkj1RDnzQRiGEeeYgmgiXTM7IAI7SvdRUVnV1tUxDMOIGaYgmkhyktA1owOqsL3URhGG\nYcQvpiCaQcgPYV+WMwwjnjEF0Qxq5kJYJJNhGPGLKYhmEJpNbZFMhmHEM6YgmoFNljMMIxEwBdEM\naibL2QjCMIz4xRREM+iWGZosZyMIwzDiF1MQzcCW2zAMIxEwBdEM7JsQhmEkAqYgmoF9Vc4wjETA\nFEQzqJ4ot2cfqtrGtTEMw4gNpiCaQUaHFDI6JLOvoord5RVtXR3DMIyYYAqimVSPIizU1TCMOMUU\nRDOpXm7DIpkMw4hTTEE0k9B3ISzU1TCMeMUURDPpkWUmJsMw4puYKQgR6S8i00VkiYgsFpGbfPqd\nIrJBROb77cywY24TkRUislxETo9V3VqC6i/LmYIwDCNOSYlh2RXALao6T0SygAIRecvv+5uq3hOe\nWUSGA5cCI4A+wNsicpiqVsawjs2mW/iCfV3auDKGYRgxIGYjCFXdpKrz/P8lwFKgbz2HnAf8T1XL\nVXUVsAKYEKv6HSj2TQjDMOKdVvFBiMggYCzwsU/6gYgUisgjIhLqf/cF1oUdtp76FUqbYmGuhmHE\nOxLrmcAi0gmYAdytqi+KSC9gK6DAb4BcVf2OiNwHzFbVp/xxDwPTVHVSRHnXAdcB5Obm5k2ZMqXZ\ndSstLSUjI6NZ6WuL9/OjN7fRJyuZP345s0nlHKjsA0mPFxkmu/Vlt4YMk33gshvDuHHjClR1XIMZ\nVTVmG5AKvAHcHGX/IGCR//824LawfW8Ax9ZXfl5enh4I+fn5zU7ftrtcB946VUfd+UaTyzlQ2QeS\nHi8yTHZ8yjDZLXNMQwD52og2vMkmJhHpIiKjGpFPgIeBpar617D03LBsFwCL/P+TgUtFJE1EBgND\ngTlNrV9rkdMxleQkoXjvfvZX2XpMhmHEH42KYhKR94Bzff4CYIuIzFLVm+s57HjgCmChiMz3aT8H\nLhORMTgT02rgewCqulhEJgJLcBFQN+hBGsEEkJQkdM3swBcl5ewqr2rr6hiGYbQ4jQ1zzVbVXSJy\nLfCEqt4hIoX1HaCqHwASsOu1eo65G7i7kXVqc7p5BVFcZgrCMIz4o7EmphRvGroEmBrD+rQrQqGu\nxTaCMAwjDmmsgrgL5zReoapzReQQ4LPYVat90N2HutoIwjCMeKSxJqZNqlrtmFbVz0Xkr/UdkAh0\nsxGEYRhxTGNHEPc2Mi2hCE2W21l20PrSDcMwmk29IwgRORY4DughIuERS52B5FhWrD0Q8kFYFJNh\nGPFIQyamDkAnny8rLH0XcFGsKtVe6F49gjAFYRhG/FGvglDVGcAMEXlMVde0Up3aDaElv80HYRhG\nPNJYJ3WaiDyAWxqj+hhVPTkWlWovdM8yBWEYRvzSWAXxPPAf4CHAPLKebpnOxLSrrApVxa0uYhiG\nER80VkFUqOq/Y1qTdkh6ajJZaSmUlFewa28F2RmpbV0lwzCMFqOxYa5TROR6EckVka6hLaY1ayeE\nzEwfr9rWxjUxDMNoWRqrIK4CfgJ8iFusrwDIj1Wl2hMXjnXfNLp54gKWbNzVxrUxDMNoORqlIFR1\ncMB2SKwr1x644aRDOb5/OrvLK/jOY3PZVLy3ratkGIbRIjR2ue8rg9JV9YmWrU77IylJ+MH4bPan\nZDBn1XaufnQuE79/LJ3TzR9hGEb7prEmpvFh25eAO3HfhzCA1GThgSvyGNIjk2WbS7j+qXnsr7TQ\nV8Mw2jeNNTH9IGz7LnAUboa14cnJ6MBjV0+ge6c0PlixldteXBj6dKphGEa7pMmfHPXsAQa3ZEXi\ngf5dM3jk2+PomJrMpIL1TFyyu62rZBiG0Wwa64OYgvtEKLhF+o4AJsaqUu2ZUf1yuPeysVz3ZD4T\nl+zhvbvfZlS/HEb3y2Z0/xxG9csmJ6NDW1fTMAyjQRo7Ue6esP8rgDWquj4G9YkLTh3eiz9+fRR3\nvbKQLSXlvL20iLeXFlXvH9gtg34ZVZxU+jmj++cwok9nMjo09lYYhmG0Do1qlVR1hoj0wjmpwb4m\n1yAXj+vPYIroNugICtfvZMG6Yhas38nijcWs2VbKmm0wa91SAJIEDuuVxeh+OYzpvI+8Nq67YRgG\nNN7EdAnwZ+A9QIB7ReQnqjophnVr94gIg7tnMrh7JueNcRPqKiqrWF5UwpRZhRQnZ7NgXTHLi0pY\nttltLyXBwEO2ctyh3du49oZhJDqNtWv8AhivqlsARKQH8DZgCqKJpCQnMaJPNmWHZJCX577iundf\nJUs2FfP07LW8+MkGrn0inye+M4Fxg2w1E8Mw2o7GRjElhZSDZ1sTjjUaoGOHZPIGduWei0fzlYHp\nlO6r5OpH51K4fmdbV80wjASmsY386yLyhoh8W0S+DbwKvFbfASLSX0Smi8gSEVksIjf59K4i8paI\nfOb/dgk75jYRWSEiy0Xk9OaeVHslKUm4flw2Z43KpaS8gisensPSTba+k2EYbUO9CkJEDhWR41X1\nJ8B/gVF++wh4oIGyK4BbVHU4cAxwg4gMB34GvKOqQ4F3/G/8vkuBEcAZwP0iknDfvU5OEv7+jTGc\nekQvivfu5/KHPmbFFptPYRhG69PQCOLvuO9Po6ovqurNqnoz8JLfFxVV3aSq8/z/JcBSoC9wHvC4\nz/Y4cL7//zzgf6parqqrgBXAhKafUvsnNTmJ+745li8N7c62Pfv41kOz2by7oq2rZRhGgtGQguil\nqgsjE33aoMYKEZFBwFjgY1/mJr9rM9DL/98XWBd22HqflpCkpybzwBXjOHpwV4p2lfOL6dt5fdEm\nW77DMIxWQ+prcETkM28KCtq3QlUPbVCASCdgBnC3qr4oIjtVNSds/w5V7SIi9wGzVfUpn/4wMC0y\nlFZErgOuA8jNzc2bMmVKw2cZhdLSUjIyMlo9vSnH7N1fxe9n7WDxF/sByMtN49qxnemZmRxz2a1x\nfia7/chuDRkm+8BlN4Zx48YVqOq4BjOqatQNeBb4bkD6tcBz9R3r86UCbwA3h6UtB3L9/7nAcv//\nbcBtYfneAI6tr/y8vDw9EPLz89skvanHVFRW6W+fe19H/up1HXjrVD38l9P0gRkrdX9FZcxlNye9\nNWSY7PiUYbJb5piGAPK1gfZbVRs0Mf0QuFpE3hORv/htBnANcFN9B4qIAA8DS1X1r2G7JuO+UIf/\n+0pY+qUikiYig4GhwJwG6pcQJCcJZwzJ4J1bTuSsUbns3V/J3a8t5Zz7ZvHptn1tXT3DMOKUeifK\nqWoRcJyInASM9Mmvquq7jSj7eOAKYKGIzPdpPwf+AEwUkWuANcAlXtZiEZkILMFFQN2gqpVNPaF4\npmfndP71zaO4KG8Lt7+8iKWbdvGLTZDcdR2XjOvf1tUzDCPOaOxaTNOB6U0pWFU/wC3LEcQpUY65\nG7i7KXISkZOG9eStH53IPW8u5+EPVvHTSYWUlFVwzQm2ArthGC2HzYZup3TskMztZw/n6jFZAPxm\n6hL+9tanFuVkGEaLYQqinXP20Ez+fNEokgT+8c5n3DVlCVVVpiQMwzhw7CMEccDF4/qTlZ7Cjc/O\n57EPV1NSVsE3BpuSMAzjwLARRJxwxshcHvafO31h3nr+MnsnyzbvotJGE4ZhNBMbQcQRXxrag6eu\nncDVj87l4w3lnPH3mXRMTWZk387us6f93adPzU9hGEZjMAURZ+QN7Mrz3z+OX78wh7V7hHXb9zJ3\n9Q7mrt5RnWdYt1Tu6LKV44bYR4kMw4iOKYg4ZFjvLG4+Joe8vDy279nHgvU7KVxXTOH6ncxdvZ3l\n2/bzzQc/5vhDu/Hjrw5j7IAuDRdqGEbCYQoizuma2YGThvXkpGE9AdhdXsHdE2cxdWUZs1ZsY9aK\nDzn1iF7c8tXD2rimhmEcbJiCSDA6paVw0fBO3Pr1Y3ng/c95dNZq3l5axDvLiuiVmUzH6e/VOaas\nrIz0gPQ03ceTh5XTIyst9hU3DKPVMQWRoORkdOCnZxzO1ccP5v73VvD07LVs3l0Ju/cEHxAl/bm5\na/m/kwMX/DUMo51jCiLB6ZGVxh3njOCHpxzG+3PmMWLEiDp5Fi9eXCe9YM0OfjKpkJfnb+SGkw7F\nrc1oGEY8YQrCACA7I5U+WSkc0qNTnX07AtL7d83gN5MXsmLLbhZv3MXIvtmtVVXDMFoJmyhnNIvU\n5CSO698RgFfmb2jj2hiGEQtMQRjN5ssD0wGYvGCjzdg2jDjEFITRbA7rmsqArhkU7Srn48+3tXV1\nDMNoYUxBGM1GRDhvTB8AXvrEzEyGEW+YgjAOiPPG9AXg9UWbKdtvHwA0jHjCFIRxQBzasxNH9s2m\npLyCd5dtaevqGIbRgpiCMA6YkJnpZTMzGUZcYQrCOGDOHd2HJIH3ln9Bcen+tq6OYRgthCkI44Dp\n2Tmd44Z0Z19lFa8t2tTW1TEMo4UwBWG0CBbNZBjxhykIo0U4Y2Rv0lKSmLNqOxt27m3r6hiG0QKY\ngjBahKz0VE4d3guAyfM3tnFtDMNoCWKmIETkERHZIiKLwtLuFJENIjLfb2eG7btNRFaIyHIROT1W\n9TJix/l+ToStzWQY8UEsRxCPAWcEpP9NVcf47TUAERkOXAqM8MfcLyLJMaybEQNOPKwHORmpLNtc\nwppii2YyjPZOzBSEqr4PbG9k9vOA/6lquaquAlYAE2JVNyM2dEhJ4swjcwGYtCTKh4cMw2g3iGrs\nVuEUkUHAVFUd6X/fCVwNFAP5wC2qukNE7gNmq+pTPt/DwDRVnRRQ5nXAdQC5ubl5U6ZMaXb9SktL\nycjIaPX0eJa9eXcFt7y5jbJK5UdHZ3PCgI6tJru1ZCSq7NaQYbIPXHZjGDduXIGqjmswo6rGbAMG\nAYvCfvcCknEjl7uBR3z6fcDlYfkeBi5qqPy8vDw9EPLz89skPd5lPz17jQ68daqOuvMN3Vy8t1Vl\nt4aMRJXdGjJMdssc0xBAvjaiDW/VKCZVLVLVSlWtAh6kxoy0AegflrWfTzPaIZdN6M/Y3h0o3ruf\nn04qDCl9wzDaGa2qIEQkN+znBUAowmkycKmIpInIYGAoMKc162a0HCLC9eOyye6YyoxPv+DZOeva\nukqGYTSDWIa5Pgt8BAwTkfUicg3wJxFZKCKFwEnAjwBUdTEwEVgCvA7coKq2dnQ7pmvHZH57/kgA\nfvvqEtZsM6e1YbQ3UmJVsKpeFpD8cD3578b5JYw44ZzRfXhzSRFTFmzklokLeO57x7Z1lQzDaAI2\nk9qIKb85bwQ9s9LIX7ODB2d+3tbVMQyjCZiCMGJKTkYH/njRKAD++uanLN26z5zWhtFOMAVhxJyT\nhvXkm0cPYF9lFb+cvp2T7nmP3722lPzV26msMmVhGAcrMfNBGEY4vzzrCFKShJcL1rJ6WykPvP85\nD7z/Od07deCUw3uRWbGHhWWr6hy3eWMpxZlF9MxKp3d2Ol0zOpCUJG1wBoaReJiCMFqFjA4p/Pq8\nkZzdtwy6HcKbizfz5pIi1m4v5bl8HwY7f0ngsf8pyK/+PzVZ6JmVTrrsJ2v2rDp5U5OFUTkVjBxd\nSVqKLedlGAeCKQijVUkWIW9wVyYM7sovzjqCT4t28/bSIhatXEfPHj3q5F+3aQsVHTpRVFzG5l1l\nFO/dX/O9iR07A2XMBV5fPYObThnKhUf1JSXZLKmG0RxMQRhthogwrHcWw3pnUdC5mLy8kXXyFBQU\nkJeXV/27bH8lRbvK+LCgkGGHH14n//ode/nzqwtZt3MvP32hkP/MWMkPTzuMs4/MrZPXMIz6MQVh\ntCvSU5MZ2C2Trd06cNSALnX2HzWgC733b2Rjci5/e/tTPt+6hxuf/YT7p69gTDdlZdU6emWn07uz\n2zp3tFfAMKJhb4cRdySLcP7Yvpw1KpdJBev55zufsWxzCcs2w/8WF9bKm56axBHdUvjXkL30yekY\npUTDSExMQRhxS2pyEpdNGMAFY/syZcFGZi1ciWTkULTL+TO27Cpnd3kFn2zex9f+MZM/fn0UZ4zs\n3dbVNoyDBlMQRtyTnprMxeP6c4hsIS9vTK19W3aV8f1HPmDe5nK+/1QBlx8zgF+eNZz0VIuAMgwL\n7zASmp6d0/n5CTncfvZwUpOFp2av5bz7ZvFpUUlbV80w2hwbQRgJj4hwzQmDOXpwV2589hOWF5Vw\nzr0fcOGwDFZW1V2qfNP6vQwdsZ/O6altUFvDaD1MQRiGZ2TfbKb84ATunLyY5wvW8+zi3Twb4dQO\n8Z9573D+2L5cccxAhvfp3Mo1NYzWwRSEYYSRmZbCny8ezSlH9GLiB0vo1q1bnTxL1hax+Iv9PDtn\nLc/OWcu4gV244tiB5uA24g5TEIYRwBkje9OjfAN5eaPr7CsoKCC7/2E8NXstLxSsJ3/NDvLX7KB7\npw6ce2gaY8YqybZelBEHmJPaMJrBoT2zuPPcEcz++SncfcFIDu+dxdbd+3hkfgnn/2sWC9cXt3UV\nDeOAMQVhGAdAZloK3zp6INNu+hIPXJFHt45JLNxQzHn/+oBfT1nC7vKKtq6iYTQbMzEZRgsgInx1\nRG8ySrozfWsmj85axSOzVjFt0SbuOncEdT0ZhnHwYwrCMFqQjqlJ3H72cC4Y25efv7SQwvXFXPdk\nAT0zkxk09yN6dk6jd+d0enVOp1d2OlJSQV7DxRpGm2AKwjBiwMi+2bx0/fE8+dFq/vLmp2zZU8GW\nPdvr5BPgrU2f8KPTDmNw98xWr6dh1IcpCMOIEclJwrePH8ylEwbw5qx8uvcf4taBKi6naFcZG3bu\nZfqyIiYv2MirCzdx0VH9uPHUofS1RQONgwRTEIYRY9JTk+mblULekO519r0+cw7Ti9KZNG89z+Wv\n46VPNvDNowdwbJdK9gQ4uMsr7BveRusRMwUhIo8AZwNbVHWkT+sKPAcMAlYDl6jqDr/vNuAaoBK4\nUVXfiFXdDONgoUdGMn+8aBTfO/EQ/v72Z0wp3MhjH67mMYBXg1+BzFdfp1d2Or38d7p7dU6nd+c0\ndm8tQ3rsoFfndHpmpZFqX9IzDpBYjiAeA+4DnghL+xnwjqr+QUR+5n/fKiLDgUuBEUAf4G0ROUxV\nK2NYP8M4aDikRyf+edlYrj9pCH9981NmfroFSarbwO+rqGTPvko+/2IPn3+xp25BH30IgAh0y0yj\nV+c0+nesoFPfEob1zor1aRhxRswUhKq+LyKDIpLPA77i/38ceA+41af/T1XLgVUisgKYAHwUq/oZ\nxsHI4b0788CV4+p8ajVEfn4+Q4ePpqikjM3Foe9auL/L1xZRnpRO0a4yvigpZ+tuty0GXv/7+0wY\n1JUrjh3I6SN60yHFRhdGw7S2D6KXqm7y/28Gevn/+wKzw/Kt92mGYYQhImRnpJKdkcphvWqPCAoK\n9lUrlYrKKrbu3sf6HaU89NYCZq4rZ87q7cxZvZ0eWWlcNr4/h6dXoKqI2LIgRjCiGjunlx9BTA3z\nQexU1Zyw/TtUtYuI3AfMVtWnfPrDwDRVnRRQ5nXAdQC5ubl5U6ZMaXb9SktLycjIaPX0eJfdGjJM\ndtNkS2o6M9aUMW1lKet31Ti/c9KSGNI1laFdUzm0ayqHdkklubKs3Z2fyW4a48aNK1DVcQ1mVNWY\nbThn9KKw38uBXP9/LrDc/38bcFtYvjeAYxsqPy8vTw+E/Pz8NkmPd9mtIcNkN++Yqqoq/WjlVr3+\n6QId/stXdeCtU+ts4389TW94ukAfmLFSZ6/cqrvL9rfaebTna3uwyG4MQL42og1vbRPTZOAq4A/+\n7yth6c+IyF9xTuqhwJxWrpthxD0iwjGHdOOYQ7qRn59P90FHsGD9ThasK6Zw/U4WbSxmy55KphZu\nYmqhswYnCQztmcXo/tn0Tylj2MgKOqVZhHwiEMsw12dxDunuIrIeuAOnGCaKyDXAGuASAFVdLCIT\ngSVABXCDWgSTYcQUEWFQ90wGdc/kvDHO5VdRWcUr781hf1YfFqwvZsG6nSwvKqneAO6d+xbHH9qN\nr47ozSmFrUq+AAAgAElEQVRH9KRnVnpbnoYRQ2IZxXRZlF2nRMl/N3B3rOpjGEbDpCQnMSgnlby8\nAVw6waWV7a9k8cZdzF29nZfnrGT59v1MX/4F05d/gQiM7Z9Dz9R9zN61ws/JSKdX5zR6ZaeHTMZG\nO8XGiYZh1Et6ajJ5A7uQN7AL4zvtZMBhI3l3WRFvLi5i5oqtzFu7E4DXVy6vc2xastDnvfecwqhW\nHumUbiuD7tv9pL50C7s9SDEFYRhGk+iRlcY3xg/gG+MHsKe8go9WbmNW4XI6dO7B5l1lFO0qo2hX\nOZuLy9i7v5JVW/ewamvQpL6aaU7dMjvQs3M6HbWcYWsK6elniVevfNs5zUYjbYApCMMwmk1mWgqn\nDu9Fl73rycs7otY+VWXmx/nkDj7cKYxq5VHGsjWbKU/uSFFxGV/sLmfbnn1s27MPgHmb1wXKSkmC\n3u+8Wz0SCS2dzq4yBhxWTo+stJifb6JhCsIwjJggImSmJjG0VxZD60zqK6+e1FdZpWzbXU7RrnJm\nfbKIzB79KCp2iiSkVDYXl7GrrIL1O/ayfsfeOrJ+P+tt+mSnM7p/DqP65TC6XzY7dlewbntpnby7\n91XF5oTjEFMQhmG0KclJQs/O6fTsnM6+onTy8gYG5vvw43z6DDmiluLYuLOMuZ9tYHVxFRuLy9hY\nvJlpizbXHDRtep1ykgROW5HPlccO4rgh3WwmeT2YgjAMo12QllITlhtOQUEZY8Yexedf7K4OzS1c\nv5NN23fTIa1DnXI27dzLG4uLeGNxEYf0yOSKYwZy4VH9Wus02hWmIAzDaPckJ0m1KeuiPNfYR1vw\n8O0P5rCkvAvPfLyWz7/Yw11TlvCn15dzbN8OHFf6eS3neM/OaaSlJLf26Rw0mIIwDCOh6NIxmRtP\nGMr1XxnC20uLeOKjNXy4chvvrt7Lu6uX1snfLbMDuRlwwpZljO6Xzaj+OfTJTk8I05QpCMMwEpKU\n5CTOGJnLGSNzWbGlhMffmkdyVvfqZdSLdpWxpSQUYQWLZqysPrZ7pw6M6pdDp6rdfFL6uYus8h9x\n6tk5fqKpTEEYhpHwHNozi/MP70Re3oha6ZVVypaSMl55fx570rpX+zi27t7Hu8u2ADD507qjjg5J\nkPTytDrpqaKM/uRjRvfP9tFWOfTOPniXKjEFYRiGEYXkJCE3uyPj+6STlzcMcPM71m4vpXB9MbMX\nfUZq2Khjix917KtSqKobTlsGfLBiKx+s2Fqd1jMrjYFZ8OWdn/kw3WxyMuo619sCUxCGYRhNQEQY\n2C2Tgd0y6VOxqc6oo6pKmT23gLFjx9Y59v2PC9AuAyhcv5MF63dSuL6YLSXlbCmBuRs/rc43sFsG\no/s5ZZFeuo/h+yrp2KH1neWmIAzDMFqQpCQhLUUCG/TuGcnkjezNGSN7A06ZrN62h5dnzmdXShcK\n1+9k8cZdrNlWypptpUxesBGAO2a8wdCenZzS6J/N6H45VFTFfukRUxCGYRhtRFKScEiPTpw4sGP1\nSGR/ZRWfFpVQ6P0dsz/dxLqSSpZtLmHZ5hKey3dLkXRMERaMrYrpQoemIAzDMA4iUpOTGNEnmxF9\nsrlswgAKCvYz/MgxLNlUzIJ1xdWmKSrKY74KrikIwzCMg5yOHZLJG9iVvIFdq9Nmz8mPuVxbhN0w\nDKMdkpoc+4l6piAMwzCMQExBGIZhGIGYgjAMwzACMQVhGIZhBGIKwjAMwwjEFIRhGIYRiCkIwzAM\nIxBRjf16HrFCRL4A1hxAEd2BrW2QHu+yW0OGyY5PGSa7ZY5piIGq2qPBXKqasBuQ3xbp8S473s8v\nUWXH+/nFi+yW3MzEZBiGYQRiCsIwDMMIJNEVxANtlB7vsltDhsmOTxkmu2WOaRHatZPaMAzDiB2J\nPoIwDMMwomAKwjAMwwjEFAQgImmNSYsnRKSriPxcRG4Wkc5tXR/DaA+ISJ2PMMRzW5FQCkJEPhCR\nu0XkDBHJCtv1UUD2WmkiktFIGb8RkdNEJDPK/oEicqr/v2NEPRqFiHSLkj44Wpo4LheRX/ldU4Gh\nQF/gIxE5JOK4PwaUFZTWtYHt+IBjjheRDBG5XUQe9GlDReTshs++0ec9PuJ3Rvj/jZUtIhdG2b7l\n/0Y77wIRuUFEujTxXHrX9zsg/8UBaR+LyFki0qT3u6nPpoik+w7GiyLygoj8SETSmyKznrJ7Rz6z\nIjJARCaISLKI3NPE8pKjpDfqOQ/j4Yi8nYDXwn739PUcICIDGqjTX0RkRD37+4rIcSLy5dBWX3mx\nIKGc1L4h+ZLfjgEqgcXAUcA3gVDvoDPwH1U9XESOAx4COqnqABEZDdwErAAGEfbZVlX9johc7cs/\nFigBZgLvq+orIvJd4Dqgq6oOEZGhwH+As4BrgBFA+Av2/4CvR8oBrgDmA48C09TfRBGZp6pHRZxz\ngarmici/gSrgZFU9QkQWA6WqOl5ETvfnuBO4BbgWODSgrEJ/bnf6v+CUzHb//wBgh7+OOcBaYEdA\nOfOAz4AC4EpVHekb8A9VdYyI9ABuBYZHXI/vAz8BBkZcjxzgHFXd4Ms/EbhPVY+Mcv8mAQ9GkX28\nP7+QjNBs00nU5lTgLeAkIPwlEv/7q8DVwDeAfNy9elNVVUQuBP4I9PT5BVBV7Swir6rqWWHX6lXg\nR8C/gV6+vqOAc1X1t1Hu+WfAHNwz/jzwqKou9/tu8nUp8ddlLPAzf75Bz+b/q0f2RF/OU170Y0Cq\nP98gfhytrMiM/rzXUvuZ7eKv4XgRma2qx4TlXxhxHyLpBLzgr8WSsOOCrl+hqo4SkcMC6vsPYKmq\nXu/r8yruWdoG/AXoA2zx13Mf8Gk9dfon7hlJwd2TZ1W12Nfhj7hnZwmunQIIdXIavH4tRUIpCAAR\nyQVOxDVw5+MaoA7UfqhLgMdU9UUR+Ri4CJisqmN9GaXAvbgGLnTzUNUXwuT0Bi7BvRRdVDVLROYD\nE4CPw8paCCzz2zeBXwPfApYCw4DiSDnAX3EN1HeA8cA7wEJcQ/KTsHydgZ+o6ojQiyAin6jqWBGZ\n5es13NdDcA/313GNwgBgZVhZWcAsoCOwCHjcp18BjMa9IC+p6mu+vB/58+8H/C2iThcA+1V1XKg+\n/pgFqjpaRN4EnvPX7vvAVcAXwBm4RivyeiQB9wPn4JT974GzVXVdlPu3V1U7RpG9zF/HcBmCa6Qm\nEoCIdMUpymplpqoz/L4k4GxcQ1OJawiuAM5S1aVB5QWUPwN3X/8bVt/VwBR/jZ+LuL7DVXWCiGQD\nlwG/ANbhGrIf+vM8HfgecDvwpL+GQc/m9gDZi3wDtST0/Pj0E3HPxVVRTuXX0cqKct61nlmfFrpP\n/8aNfp8H9lCjyIf4v0/6v9/yf+8GLsU1yEm4Z3sMrvNV5zlX1cujXPdFuBFDZyAP+IOqviAiC4CT\ngbf9+3WSv763AjcE1UlVf+bLHObrdRnuHXsQ95yPUtXysOsRWJ9o169FiPVU7YNpwz0IH+NGAEcB\nST796/Uc87H/+0lY2t568j8EfAi8BNyMe+lSgsrC9RwKw34X+r+pwGxgUSPO6SRc41wO7MeZjh71\n2z+B40KygWRgnv99DLAkoLxs3EvzLK4XFNq6+v3zA46ZDyyMSDsRKAI2AXeEbTfjGtMPccomVJ8h\nwBz/f0H49fD/zw2lR7kOx/prOQfo0cD921OP7I+jlB9tuYNrccp5BzAd2Au84/eNwinH5f5eHI0b\noe1u4nM7N+AcluEa4jX+b2i7EOgCdMM95/nAZFxv9N6QbFxP+IJQufU8m0Gy5/u/TwHHhKUfDTzR\nxPOo8zyF37uIZ7ZHWP0eDdgeCS87rJx5Ac/mRqAU+Bz4MhHPeWR9/XW9EFiF60TNx81DCKXn+7wL\nqGlXFkSeb2Sd/PmdB7yM65TcilP8G3Gj3mZfv5bYwofpicA/gRNwmnosMENE3gfeEZG/4h4UgBnA\nr9UN99Z5M4WKSCrupftcRM5U31uOoBvupu/E9b62qmpFqFwR+TnQUUROA67HPQyn+f07RWQksBln\nfnhbRI5U1YXhAsT5IC7H9USLgO/iGoErgF+pah2bvD/3l4CeInI3rlf9y8hM/pyLgctE5ARgqKo+\nKiLdvYlur4icoKof+Locj2sUd4nIL6kxN5yAe4muU9U6CyqKyJ3A60B/EXkaOB74tt+93//dJCJn\n4V6WrsCTInK9P49y4BmcWSGUP8PX/WERQVXPJfj+zalH9nQR+TPwopcR4m0R+TGut74nLP0m3Chu\ntqqeJCKHA78TkQLcM/Aw8DNVLfempVXAZhF5DtcoVMtQ1Rcjr5Nnq4gM8eeKiFwErFLVx0XkGVXd\nH55ZRF7CjT6fxJneNvldz4nIZX6ENhi4TZyfoYroz+ZRAbJD5eUBH4rIWv97IFAuNX6RzyLOY0M9\nZQUR9ZlV1auDDhCR+SJyvKrO8r+PA5LE+SDOwvXUBwH3AE/jLAkPAV/BKcVOItJJVddS+7qf488v\nFTci/MT/f47fv1OcP2Im8LSIbKHmOZEodfqbP/4d4HeqOsfn/6OIlADzReQdap6RHk28fgdMwpmY\noNqxdDXOhNEP96LWMZuo6oUi0h3X2zoV14OpwN2wTGp67dU25DAZRwCn48wVyaraz5sbrsHZpwV4\nA/dwXoOzjx6Js+MOwCmJXbje9udeVsi+nY57+R9V1fVhMg/DDX/LNMBG6RuvU3w572g9Jg4RuQMY\nBwxT1cNEpA9uOH898ARupAGu53wVsB43Qviyr+P7OJNCd3+dB1HbX3OyV3TH+PrMVtWtXvbZuBet\nP67X2xnnF/hnRDXTvazLgs5BVWdE3D8B3sQ16kSRPT2oKFyDGpS+TZ1NfD5wtFcEi3EN8+fhmUXk\n0aB61lRXvxO0Q1wAwQPAcbjrvQq4XFVXS12fiQBpqtonSllJONPK56q609+Dvrjnv9azqaoPNiB7\nYETxfSN+b4j4nRytrGgXJdozK8H+gXOBabiRRLY/ZgfOFDsJN8J7WFU/DCv//4A/496xKp+s6nwQ\nUc89oJ434kbdO3AmpGzgaVXdJiJ5Ueo0GpioqnsCyvs+ruMVTg+cmbXR1+9ASSgFISJ/wfVsO+Gi\nlGb67UVVHRORd746p2VXVd0esW+wqq6KIuNsXK/kyzjn6Wxgpqo+4nuQr2qYXdEfk0aNMzoV9yAp\nrkHsQo1D+H1cr3Stqqq48FRV1RJfTjSb6Whgsaoe3oRrNR83ypoXVlYhToGBu4YAu/F+ElWdLyKZ\n4Q+8t80G+Q3uxI0AJke+ICLyOHCTqu70v7sC99TTgA4GNqlqmf/dEddwrI52/3C25kHUVlrRevBR\n8b31q4Ef4mzQO4BUVT3Tj35qBR6o6q+bKiNMVibOfFESlhbkMwHIJcLJr6pP+GO6EOEzAfaoakGE\nvLNVdWo02T691igTyIr2btR3HvXk7YLrKITfp3nRnnX19nhx/pfQiBg/KtgdUP4KnGLf1pj6Bjyb\nXXDO6Y04H8c8nDJ4QyMa14A6JeH8joeo6q/FRT31DhtJNFif+vK1BIlmYvoI+JOqFoUnikg0swnA\nFBH5mqru8vuOAJ4XkS9R1zH5Pk7DzwT+oaobI+SfA/xNnFnrOeB1b356hRpndDmu0QXnRL8WZ+4Q\n3KjhQVxY6qO4Rk5EZCeuR5KhqnOkdqh2hapWishyERngh86NYZ9XQqHhbChsd5zfJvs6fQtnq/6x\nb8jLgVC00Pe8/H9HFi4uTPEbwB9EZC7wP2Cqb+RHhV5Af123i0ioERhJ3eimG3G9qhCVuNHOeILv\n34c4f9RiwnqNwIv+JQ6NhMD1LL+L68XWQVUv8P/e6Ucf2cDrIvIfnMnrJNwo8SKcaStIAXYB/lKP\nAgzvQKSE7q9XNsWqOi0i/x04k8lw3Ijya8AHwBMici1uBNUPZwI8Bvde5IjIlaq6yJdxGfBDEfkA\nuDJA9o3ho0ycD2Aezhy0QFWPDjiPXsDvgD6q+jURGQ4cq6oPR+b1+X+DM/2tpCZCSXGKOPBZj7x/\nXpH8GuglzrEdOeJYh3v3wuXeHKU+AKdEPJs7RGSsugjG26mJXrtPXJTXw6q6MryzEFbnXHyUlq9j\nCc6SMF5cFNnvqf2sdwEGRzw3t6hqHVNxS5FQCkJVJ4nIuVITTzxDVafgImWeCGl4aswm4B7oKf4G\nD8OZVybhevORL9nJqvp//kUYLyJH4ZyfW7z8q8XZwb+GM4v8S0TeAvqp6hmR9fU99mNCPWxxoW8f\n4Rqs61V1pk8/AfeC1mfj7QIsFpE5hNnQ1dnpg5goIv/FNRzfxSmgB4GLgaNCvTHfSLyKe7iTQ2Wr\n6gJ/nSdJbb9BSO4MnN07GfeCfBfX8+qMs892UdUdXkZXXOMUreFLUdV9YWXvE5EO/mfQ/duuquOi\nnPcjOHPLJWG/H8U1IkqNmS/0t3rU4c8JX+fjvJmiUFXv8qPXUEMeqQB3hBRgFCI7EAB9/fMV5DO5\n3J/rJ/6Z60WNbyjQZ4LriEwSkW/iRqxX4hq7abhR8EJqlGmIC/CjTH8eA/z51lEOnsdw1/IX/ven\nuI5SoILA3YMh4fc2jCC/zCbq3r8rvMxu+BGHTz8VF130GvCeuLDa0PX7KsFzo8A9h3WeTX/+KiKb\ncebhCtw7N0mcRiikbmdhrPooLX/8jrDn9lGcovubP+5q4HsBz82ZBPgSW4qEUhAi8ntcVNHTPulG\nETkW91Wmx6ltNhkvIkmq+qpv1N/E9dgvwGt56r5kiHPQ3QO8h2tE7hWRn6jqJABV3S8i03APdkfc\nKOFFCXBG++PDzQaVPq0ipBx8mR+ISAXugX8AOFxENuBslKEwv9ubcq1U9R5xzspduMbmV6r6lojc\nQG3n7X6gF64Bi7SlVlKjaMPDbxU4RJwp6BzcSOIoanxAf8GNkp73vy/GhSn+FGcui2z4vhCRc1V1\nMoCInIf/0laU+3eriAzXsHj4MIao6tfDfn9FnLntSWoUQ+gcikVkjKrODyinzP8tFee/2Y7rMUIU\nBRhQRog6HQg/WglXKuEKr5eqVolIhTgz5BacmQacf6pMRBCRNFVdJiLDVPVzEbkU549bC3xVVfeK\nSLqqBvaoiT7KjEZ3VZ0oIrcBqGqFiFTWk38Rzky7JWBftGf9lYj7d5e/f/sjRhyhSYBr/dbBbwAf\neaVe7VgOISIp1Dybgmvs7xY3v+RK3HP3EC68fL84M9JedebqyM7Cft9BCl2/HtQo4Y6q+o6IiLog\njztF5Kf+npX7/B2BmM7iTigFgYtiGKOqVVA91P8E9yBGmk2SgM7iJh3Nw5kOVgL/B/QMesm8jF8C\n40OjBn/T38b1JL6Gawy/glMgD+F6OoXAt0VkFbWd0Y8CH4uzc4MbdUzB9WL+i3OKqS/zPZyyeQ3n\njEvCNdinipssV927bSyq+hZuMlg4T/s6veJ/n4PzJYzFmVR6SE200FJVvTSobD/8noCLJroPN5qr\n8nKfEJF83MgC4EJVXSIiN0Zp+M7CRY7c56/dOmCFiIQ7tcPvXw7uJd9M2PVW1VFEj9LKo/Yzcjbu\nvn1fRJ5X1T9FnOIUEcnBOUDn4e7Tg35fkAL8XdB18nwY2YFQ1ZOiZRaR+73sB3Gjjt3U9IjX+30v\nA2+JyBh/TKHf3xU3EvzYN6aP+hHkVGqPALcTfZQZjT3inOKhBvEYIsw7Efwe+EScHy1c9rnAGlU9\nVSLs8RLdXLwzfMSBMy/OV9W76pF/L67jEs5ZuLDWyGfzLv9/rYg9/7wu8z9DnYVtuM7CHUSPLCz3\nyuUzcY70Dbj3+R2pCXa4mppOVUxINCd1IfAV/3CHem7v4Ry/Z4aZTTrh4sfvAe4Cfh5R1I04m3SQ\nY3Khqh4ZJjMJFw99pIg8ixtST9PaE2Aio0EAUNU14swIJ/ikK6jxT9TJjhvaRjZi/XAv/BZVrXfq\nv69LCcEzUsNn+47DhYaCm1SUL1GihdRFcQT5DYpwk4rq60FG1u1+3L24FD+fAPeSX+33d8JVcreI\nXBW1IPgTzqRVy2zir/dogqO07qPuM/IqzudUoGETxvz+i3E+phJxtumjgN+o6jy/fzg1jcy7UUYz\nobKWEBDN5k1YQb37i3GK/RncSKazqhZGZhI3se1QXFj3/sj9nrNxo7edhPkBVPUQX8Zp1I58iuxQ\nhMs7CtfojsR1ynoAFwXVzedfjDMJRd6nGeJCa1/HvU/vqm/IvMJ7nJqIoe24+7ebuhFJ38Ld18jn\nPRV3nfOoO8nzEtw9r4XW49vz9/9e3P3+l09+SFVvl+hRWuNxk2VzgN942X/CKfCQP+wtVX0jmtyW\nINEUxGXAH3A9bME5sn6GUwJHqo8nF+cUXKBuqY3qWZxRyjwR75j0tu8/4yZIPeuzfAM34etWn78X\nzjwFYf6JFjq/92lCI9YaSF2/wXnAu7jGqw5aTySRiDyFa8xmEtHwSRMihkTkI1U9Nsq+UIMbGaX1\nS+Cwxj4jUrNcwwm4F/wenJnuaBF5UlWviMhfJy1s30ACotm8QnsG1ymY4vedjbPFD8X1OFNwo+T3\nVfUfvlMUSUnYefWktiJ/D5igPgw4St2Gqurb4pYsSdYo0TVeab6BG/V9HTex7vaQ0gzIP1dVx0fZ\nl+HP9VKc8p0K/C9s5NAZQGuCE9JwPfRBuEZ2F04xdMMpqvD3tRNuVYGRuCVRQpTgAi9CnbuOwGBg\nuarWt6ZSR9zqBF/yMmcC//ZWiGSciTY8Smut74T9gpq5F36XjoomJxYklIlJVZ8VkfeoaaBvVdXN\n4mKqI80mFX74Ojhs+B3O9dSE9/XAxYCvUtWfiMjXqelhP6CqL0HD/ommENQg4ibX1fEPeFtyrdDa\nlkaix6VfRG2/wT3+d2iCUVSnbwAP416ye3Gznz/xSvEIokcMBUWDdPMN6xTqTlSLFqWVCqwWkdBX\nvM4BnvHPSFDvPzQyOgt4UJ0vJLRmTq3GxDcSefWcd7RotntxI8SgoIHDcL30B3BBGCNwI7x5uAY6\nfM2szSJS5s+xGzVrCS3FrTlWGlQpCVtbDHc/+uJCmgMjvnDK4Hlx0Tcn4d6Ff+MURRAzxfkNJ1P7\nPs1T1VJgIs7M1cWf2wyvAIOimJ7DjYLm4UJSQ5wToYSmhBSTiGyONEGJyDpVfT7s91G4tqA+Hscp\nl5DJ85u4oJiZvq5F1PgXFdfBfBrntwsfPZ0hzuRdZw2vBuQ3m4QaQQCISF8iFntT1fcjzSbABlXd\nFMX8cxOuh1ZrEpmq1lm5NEL2AuC0SP+Eqo5u4jlEC6FcjXPChiu6yTib9wOq+q06hbUQEn0Oxl7/\nshX4+pbgGp4HCXD64udT1CMnGafgT8I1fHtxztJQxNAoP3KapqpfEhemGYoGOQdntz0f16sOR9WF\nKtY3CluMa4jAm9bqqedUnN34NFwPdy/u/nTC9TxLw859H+7+3BalrEJcOGgomi0T50gdJc6+HTn6\n3Y5rWA7x1+iDsGfuQWBSyDQhIl/F9eZPxvWqk7RmLaHLcY3/CNyoO7yRvlGirC0WbmKNOI/QOmC/\nxy3N8kx9I3SJMmlRVU/2+0/E9fjPwJmEn8M1vkFrhR2mAWsWichS4PSQiUjcXIQ31C0OWKqqGRH5\ngxb3i3rOfn+tNatCaTineOAcDBH5QFVPiEhbgVNojVrDqyVIqBGE1KyQGBn//r5/2eu88Bq8TMTJ\n1A7v2ygix4jIriCx1Gj5pAiT0jaat+R6YAilql4jLkIqpKi+H9aIxUw5eALj0oG5EuwwbarTF3HL\nDmRSM8lxvKpuEbcgH9R1AkJwNMg5GmWpBuofhRWr6j+iHBdJyFZ9j7oZy7nAVar6poj8PpoyiEK0\naDYIDhpYgHu+K3G90Z3erLYXFzb93VBBvj734JRzCiDiovemi8jfcQtDvhylXuXerOoq6SJ86utx\nbhDn1D4Nt5xEGvU8/1q/I341TslPxEUMhZTn7RocxVTH0e+5BfhARFbirulg3FIt/wYqpXagQ2eg\nu9SYIZNwz3HkfKdI5onIMao629fxaFxb05/oTvo7ROQh3DIcoedxf2sqB0gwBYHrOQ7TiJnMQUj9\nztoM1TrhfYsbYR98XUTeoLa9M2g9p4YITeKr0yBGU3StQLS49M44p+l7OKdiZ1Ut9D31INPIl3GK\npI6CwCmQPJxtuBjf8AFTvRL6kz8W3MgKgqNBcsRFhoUU6UycQ309UaK06jElBeJNIOFzJDZJzTyb\n571pIvKYQFs8daPZzsfPHVDV30TrFIhbZ+nb/vjeuJDITSJyK25iIrhnsAj3XHfH+TdCawntVtX6\nomRmSN31mybXkz9Iaf4kWmapO2lxBjVrpI0K+RciiIxiWunPvQNwtYgEOfqHAqFVBpbjwrrHAGdS\n8zxdhxuJX0BNiGwFzvdRvYpzRP1DS5CnUrNmleIsGMtwHZ3IORio6l9xI93D/bGhzmyVNG0NrwMm\noUxM/kW6WAOm3DexnB/jTEyn4ezb3wGeUdV7G3FsuH9iZsg/0UT5UaMimlpWSyHB69Z8C2fmCH2D\nYwjeYYpz2jU3MCDU8P0Y9/LnEN0JGBQN0g9nKw8tv3w58C1VPc2XXydK64AuTk29H1DV67zpJPzF\nCzVWJ0c5NGTrDpkcZgIrVXWXBDudr8Up0jycWWsm7ll7V1y02R1hZc2iJlLvfmru2024ezWBuh0l\nVbfk9nhcQxq+ttgm9ctzHCgi8gLR10iL5vOaQu0otN24UcIyanMcbkZ9oO9H3VL/KeoX2vQmoVNx\nnZyvBOTfHpkmUaITw/h2FNl3ichyVR0Wni7Ba3mpRpmB3xIkmoJ4AWePDB+2oao3NrGcG3G94wk0\nIryvpZF6oiJaqw4BdQqMElG3xkyQ3+Bpmugv8SOALxHR8Pkywz9c800gW1UvkeBokENVtWNE2fM1\nYj2uWOHv3/W4RrpZ90+cj+McnBlpdfgunBI8E+fPqah7dGB5tWzrvnf/Bq5hDe/lC265mkvEffjp\nquWUe6EAAAf9SURBVJDZRvzyHBp9JnWTCLonUrNGWjSf1yM+a+BaYWHl3KWqd4Q1uqGGUHCh8IOl\n9keIuvmtA3Wvt6oP+23meVaHZ4elPQr8WesJf24NEk1B/Bj34ZlwslT1viaW81saWJgrIn+Dcwua\nKD/yS17VDWJTymlJROR1aqJEwu3lZ1LbbxDuMG1ST93fv5lENHwSxQmoqsNFZDl1o0GewfU+Q6a+\ny4CrVTVa9E2L4u/fLmpm9Df7/kkTPxgjIlOo/SwOxK0enIkzr4T2hT4QNVyjf3HtENyyM5fhzEBX\n4j7UVN/kt0bjzYc/0dqT3u5R1WOlJtIo/GNC83FmwCDf1iBcIMmfImSkU/erjZmq+tMoI4DfapRw\n5Gac30jcKDY0CtyK+8rhYnHO8yG4EV3IJAZu5BsZzh2zEUTMPjRxMG64xmtk2O/LiPKBmEaUJbjl\nvP+HCwX8HW6ZhtY4j6AP/dRJa+VrG/hxI1z00Pu4iVt34sxiHVtYdtQP1+AUUmT+gbgG5AtcSOfL\nQP9WvFYtdv9w5pfxTcj/D5yCPMdvz+EaqU9xPpOBfrsFp1T34BrY0LYKeCqsvMNwjfLrMbivY3AO\n99V++wTnewC3VMUQaj68c5FPe5+wD+3gRhIzcJFjQdf9df8O/9Sf8y3Aza30HHwInBT2+yu4T9+G\nntHI7VWcmXQlbvLfm7hFQWNXx9a4EAfLhrOHz8M5f76L641mH0B5o4G/44bh//YP8J9a4Tya9CWv\nVrq2D+B8CtH2ZwE/wH0BrbyFZC70jdZS3OhgtW/AqkKNAS4m/yFcZyD09a/puM+thsrpCjzSiteq\nxe6ff/YqfKNRGLom9eSfGy0NF2gRSov6ZcGw6x7aNuNGH4X1yW7GuaXh/CG/wkVT3YGbbBh6l9/G\nhQtvwC3aGHL+pkaUscz/H/RltzodG9zofFfAVgLsasHzW9CYtLB9gV+ejOWzmlBRTBplQbKmliP1\nL8z1Ga430uI0IiqiLTmB4PWkHqC23+ARnGJuCc5uRJ6gaJBR6hfKg9rLiceSGN2/05uYv5OELfsu\nLu4/ZK8PXxG3+suCkQWI++ZJa/AKNWbLyI8PbcBFZ02nxud1FU2PQgta5yorIF8s+NwHnIQHS3xe\nT/7QciiRX56MGQnhg4hwNoG7qMV4R7U2cfq6uIW5HtHgORJHaIxilRuKigiqT2tRT90uJsBv0FpE\niQZZgHNEhq+mOkPrmezUQnVp8/snbnno/+BGHKG4/+txYcjfVdW/x7oOjaU+/0o0n5eq/qUpvi0f\nnXQotW392tQ2oTmImwF+F7XDre/UsCW9I/JfS+0vT3bCzU7/b1D+FqljgiiINn8xjbYhKBpERK7E\nhXXWWk5cVZ8MKCLu8BFn1XH/2obRb/UhblmTe7Xu5LYmO+frkRHYNrSSsg5F2A2ixkFeRzlJ8IKM\nEpb/r7GqY0KYmEwBJDTH4D7+Hmn6qrNkcxvVr1UQkZPVzYO4MGLXEBFBYzjZqqmEjfhTiDK5jegz\no5tEG7cNT+Pm8iyi7seYwgmZvIbhwsVDkxHPwa85FisSYgRhJC5t2UM8mGgg7l81lqGSTaQxI/62\nNA21FBKw3lID+d8HztKab19k4b5x/+X6jzyAOpqCMIzEIUrcv2qUpdEPVuJB8YvIKbgggMiJu4Gj\nOT+nZ5TWfFEuDRfRNCwof0uQECYmwzCqeZka527I99DueontSRHUQ1CEnRJ9yfsngDlSe02ux2JZ\nQRtBGEYC0VLOXePACYqwa8QxRxH24ShVjVy2vkWxEYRhJBYt4tw1WoQPRWR4UwIk1K34G23V3xbH\nRhCGkUDEg3M3Xoiy3tJBdS9MQRhGAhEPzt14oT3cC1MQhmEYRiDN+dylYRiGkQCYgjAMwzACMQVh\nGB4R+YWILBaRQhGZL+7j8rGS9Z5fi8cwDloszNUwABE5Frd8+FGqWi7u280d2rhahtGm2AjCMBy5\nwNbQMgaqulVVN4rIr0RkrogsEpEHRESgegTwNxHJF5GlIjJeRF4Ukc/8J2kRkUEiskxEnvZ5JolI\nRqRgEfmqiHwkIvNE5Hnx3ygWkT+IyBI/ornn/7d3Py82xWEcx9/PjijKerDA5EdYDCkLkX9AzcrK\nijI2rC0s/Ac0VqyULKdkwcbCFLFQSo1kMwuKhSJJeCy+z+G4naTLnVLv1+r8uPd877mbp+/53vt8\nVvC7kAALhNS5A0xFxPOImI+IQ3X8cmbuq38fr+bXkKLPmTlDy1dYAOaAXbTgpA31mmlgPjO300Jt\nTvcHrZnKeeBotuznx8C5ev8xYGf9Lv7iBO5Z+i0LhARk5gda6t1JWlb1zYg4ARyOiIfVgvoILTC+\n07VdfkqL63xVM5CXwFSdW87Mxdq+Tkve6zsA7AAWI+IJLRVtEy3Q6hNwtVp0f/xnNyv9IdcgpJKZ\nX2nJaveqIJwCdgMzmbkcEReAVb23dB04v/W2u/0fnVJHhxnZD+BuZg5Fe+6nZWrPAmf4mV8hrQhn\nEBIQEdMRsbV3aC+wVNtva11gdoxLb6wFcIDjwP2R8w+AgxGxpT7HmojYVuOty8zbwFlgzxhjS3/F\nGYTUrAUuRcR64Avwgva46R0t8es18GiM6y4BcxFxDXgGXOmfzMw39SjrRvX3h7Ym8R5YqPyGAIZi\nJ6WJstWGNCERsRm4ZXtt/a98xCRJGuQMQpI0yBmEJGmQBUKSNMgCIUkaZIGQJA2yQEiSBlkgJEmD\nvgMjmBDB9BBnjgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2f9c453390>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"[('know', 376),\n",
" ('got', 339),\n",
" ('like', 335),\n",
" ('yeah', 225),\n",
" ('low', 214),\n",
" ('love', 212),\n",
" ('na', 210),\n",
" ('oh', 200),\n",
" ('baby', 194),\n",
" ('get', 185),\n",
" (\"'re\", 183),\n",
" ('?', 178),\n",
" ('let', 178),\n",
" ('go', 178),\n",
" ('need', 151),\n",
" ('want', 137),\n",
" ('give', 134),\n",
" ('ca', 134),\n",
" ('wan', 133),\n",
" ('back', 133),\n",
" ('make', 128),\n",
" ('come', 127),\n",
" ('gon', 120),\n",
" ('girl', 120),\n",
" ('ai', 116),\n",
" ('say', 110),\n",
" ('bitch', 109),\n",
" (':', 104),\n",
" ('one', 104),\n",
" ('right', 103),\n",
" ('way', 102),\n",
" ('niggas', 98),\n",
" ('take', 96),\n",
" ('time', 94),\n",
" ('keep', 91),\n",
" (\"'ll\", 90),\n",
" ('see', 89),\n",
" ('.', 88),\n",
" ('home', 87),\n",
" ('never', 86),\n",
" ('good', 84),\n",
" ('hey', 84),\n",
" ('night', 80),\n",
" ('little', 79),\n",
" ('fuck', 77),\n",
" ('ya', 76),\n",
" ('bad', 76),\n",
" ('money', 74),\n",
" ('man', 72),\n",
" ('dance', 70),\n",
" ('nigga', 69),\n",
" (\"'ve\", 69),\n",
" ('cause', 68),\n",
" (\"''\", 66),\n",
" ('even', 66),\n",
" ('better', 65),\n",
" ('verse', 63),\n",
" ('shit', 62),\n",
" ('look', 62),\n",
" ('hold', 61),\n",
" ('party', 60),\n",
" ('feel', 60),\n",
" ('tell', 59),\n",
" ('put', 58),\n",
" ('every', 57),\n",
" ('could', 57),\n",
" ('stop', 57),\n",
" ('heart', 57),\n",
" ('round', 57),\n",
" ('wo', 56),\n",
" ('call', 55),\n",
" ('show', 55),\n",
" ('name', 54),\n",
" ('``', 52),\n",
" ('think', 52),\n",
" ('thing', 50),\n",
" ('everything', 50),\n",
" ('body', 50),\n",
" ('day', 49),\n",
" ('ever', 48),\n",
" (\"'cause\", 48),\n",
" ('hit', 48),\n",
" ('real', 47),\n",
" (\"'d\", 46),\n",
" ('chorus', 46),\n",
" ('used', 46),\n",
" ('stay', 45),\n",
" ('still', 45),\n",
" (\"'em\", 44),\n",
" ('tonight', 44),\n",
" ('would', 44),\n",
" ('eyes', 44),\n",
" ('said', 44),\n",
" ('getting', 42),\n",
" ('new', 42),\n",
" ('world', 42),\n",
" ('away', 42),\n",
" ('uhh', 42),\n",
" ('feeling', 42),\n",
" ('long', 41)]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import nltk\n",
"\n",
"def remove_extra_junk(word_list):\n",
" words = []\n",
" remove = [\",\", \"n't\", \"'m\", \")\", \"(\", \"'s\", \"'\", \"]\", \"[\"]\n",
" \n",
" for word in word_list:\n",
" if word not in remove:\n",
" words.append(word)\n",
" \n",
" return words\n",
" \n",
" \n",
"data_paths = corpus.raw_data_dirs()\n",
"songs = corpus.load_songs(data_paths[0])\n",
"songs = pd.DataFrame.from_dict(songs)\n",
"\n",
"all_words = []\n",
"\n",
"for i, row in songs.iterrows():\n",
" all_words.extend(nltk.tokenize.word_tokenize(row['lyrics']))\n",
"\n",
"cleaned_all_words = [w.lower() for w in remove_extra_junk(remove_stop_words(all_words))]\n",
"freq_dist = nltk.FreqDist(cleaned_all_words)\n",
"\n",
"freq_dist.plot(50)\n",
"freq_dist.most_common(100)\n",
"#cleaned_all_words = pd.DataFrame(word_freq(cleaned_all_words), columns=['word', 'frequency'])\n",
"#cleaned_all_words"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Repetitive songs skewing data?\n",
"\n",
"Some songs may be super reptitive. Lets look at a couple of songs that have the word in the title. These songs probably repeat the title a decent amount in their song. Hence treating all lyrics as one group of text less reliable in analyzing frequency.\n",
"\n",
"To simplify this process, we can look at only single word titles. This will at least give us a general idea if the data could be skewed by a single song or not."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Chris Brown Featuring Usher & Gucci Mane Party\n",
"Marshmello Alone\n",
"Dierks Bentley Black\n",
"Ugly God Water\n"
]
}
],
"source": [
"for i, song in songs.iterrows():\n",
" title = song['title']\n",
" title_words = title.split(' ')\n",
" \n",
" if len(title_words) > 1:\n",
" continue\n",
" \n",
" lyrics = song['lyrics']\n",
" words = nltk.tokenize.word_tokenize(lyrics)\n",
" clean_words = [w.lower() for w in remove_extra_junk(remove_stop_words(words))]\n",
" \n",
" dist = nltk.FreqDist(clean_words)\n",
" freq = dist.freq(title_words[0].lower())\n",
" \n",
" if freq > .1:\n",
" print(song['artist'], title)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Seems pretty reptitive\n",
"\n",
"There are a handful of single word song titles that repeat the title within the song at least 10% of the time. This gives us a general idea that there is most likely a skew to the data. I think it is safe to assume that if a single word is repeated many times, the song is most likely reptitive.\n",
"\n",
"Lets look at the song \"water\" by Ugly God to confirm."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"I drip on your bitch like water\n",
"I splash on your bitch with the water\n",
"I feel like I'm 21 Savage\n",
"I pull up and fuck on your daughter\n",
"I pull up and fuck on your daughter\n",
"Water water water water water water water water\n",
"I drip on your bitch like water\n",
"I splash on your bitch with the water\n",
"Water water water water\n",
"\n",
"Water on my bitch I keep her wet like my cellphone\n",
"Bitches on me dark skins and the redbones\n",
"Ugly God wrap your bitch up like some headphones\n",
"You say I ain't shit bitch I'm ugly god i'm well known\n",
"I ain't got time for no wife, yeah\n",
"Lmfao you kiss bitches I pipe, yeah\n",
"Bitch I feel like Yachty just give me one night, yeah\n",
"And if she gay I tell her i'm a fucking dike yeah\n",
"Yeah bitch i'm a dike\n",
"Strapped up like a dike\n",
"Man made like a dike\n",
"Boosie faded like a dike\n",
"Niggas trynna join the wave\n",
"Pussy nigga take a hike\n",
"Your bitch my slave\n",
"Bitch I'm balling like Mike\n",
"Bitch I'm balling like Mike\n",
"\n",
"I drip on your bitch like water\n",
"I splash on your bitch with the water\n",
"I feel like I'm 21 Savage\n",
"I pull up and fuck on your daughter\n",
"I pull up and fuck on your daughter\n",
"Water water water water water water water water\n",
"I drip on your bitch like water\n",
"I splash on your bitch with the water\n",
"Water water water water\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEuCAYAAACDJBUcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XecXGXZ//HPtembHgJhSSCREEInsEtHOgooAkqTqihB\nKfKIqPD4gKiPUkT4ISiKtCCCgoqQ0J5QEgg1u0kgCRACoYZASEhfUvf6/XGfSWZnp2en7Mz3/XrN\na3dmzn3Otbuz5zp3PebuiIhI9aopdQAiIlJaSgQiIlVOiUBEpMopEYiIVDklAhGRKqdEICJS5ZQI\nRESqnBKBiEiVUyIQEalySgQiIlWuc6kDyMbAgQN92LBheZX9/PPP6dGjR9mVUVyKS3GVT5lyjSvf\nMjFNTU0L3H3TjBu6e9k/6uvrPV+NjY1lWUZxKa5CllFclRFXvmVigEbP4hyrpiERkSqnRCAiUuWU\nCEREqpwSgYhIlVMiEBGpckoEIiJVrqITwd0vvscd05ayfNXaUociIlK2KjoR3P7cO4yb3cyHi5pL\nHYqISNmq6EQwuF+YjTd30ecljkREpHxVdCIY0j9KBIuVCEREUqnoRKAagYhIZpWdCKIawYeqEYiI\npFTZiaBfLaAagYhIOpWdCNRHICKSUUUngkG9u1Fj8OmyVaxcs67U4YiIlKWKTgSdO9WwSY/wI85b\nsrLE0YiIlKeKTgQAA2s7AeonEBFJpeITwaaxRLBYs4tFRJKp/ETQUzUCEZF0Kj8RrK8RqI9ARCSZ\nKkoEahoSEUmm8hNBrGlIcwlERJIqWCIws+5m9rKZvWJmM83sF9HrA8xsvJnNjr72L1QMsGHU0LzF\nK1nX4oU8lIhIh1TIGsEq4BB33xUYBRxhZnsDlwBPuvsI4MnoecF062QM7NWVtS3O/GXqJxARSVSw\nRODB8uhpl+jhwDHAmOj1McCxhYohRquQioikVtA+AjPrZGbTgPnAeHd/CRjk7vOiTT4GBhUyBtCa\nQyIi6Zh74dvNzawf8ABwATDJ3fvFvbfI3dv0E5jZaGA0QF1dXf3YsWPzOnZzczP3z17LQ282c+pO\nvfj69r2yKlNbW5vzcXIpU4xjKC7FpbjK5xjFLBPT0NDQ5O4NGTd096I8gMuBi4FZQF30Wh0wK1PZ\n+vp6z1djY6PfPmmOD/3pOL/0369mXSaf4xRy+2KVUVyKq5BlFFdxysQAjZ7F+bmQo4Y2jWoCmFkP\n4HDgDeAh4MxoszOBBwsVQ4z6CEREUutcwH3XAWPMrBOhL+I+dx9nZi8A95nZd4D3gBMLGAOgPgIR\nkXQKlgjc/VVgtySvLwQOLdRxkxkS3anso8Wf4+6YWTEPLyJS1ip+ZjFAnx6d6dWtM82r17G4eU2p\nwxERKStVkQjMbEM/gZqHRERaqYpEABv6CT5Uh7GISCvVkwhUIxARSap6EkF/DSEVEUmmehLB+hqB\n7ksgIhKvehKB5hKIiCRVNYlgiGYXi4gkVTWJYGCvbnTtVMOi5jU0r15b6nBERMpG1SSCmhqjrl93\nQLUCEZF4VZMIQENIRUSSUSIQEaly1ZUINJdARKSN6koEqhGIiLRRXYlANQIRkTaqKhHE7kugGoGI\nyAZVlQg279sdM/hk6UrWrGspdTgiImWhqhJB1841DOrdnRaHj5esLHU4IiJloaoSAei+BCIiiaou\nEWyhkUMiIq1UXSIYrMXnRERaqb5E0F/3JRARiVewRGBmW5rZ02b2mpnNNLMLo9evMLO5ZjYtehxV\nqBiSiS1H/dFidRaLiAB0LuC+1wI/cvcpZtYbaDKz8dF717v7tQU8dkq6QY2ISGsFSwTuPg+YF32/\nzMxeBwYX6njZil9moqXFqamxEkckIlJaRekjMLNhwG7AS9FLF5jZq2Z2u5n1L0YMMT27daZfbRdW\nr21hwYpVxTy0iEhZMncv7AHMegETgV+7+7/NbBCwAHDgV0Cdu5+VpNxoYDRAXV1d/dixY/M6fnNz\nM7W1ta1eu3j8At5ZvJYrDxnAtpt0zapMPsdpz+0Vl+JSXNUXV75lYhoaGprcvSHjhu5esAfQBXgc\nuCjF+8OAGZn2U19f7/lqbGxs89rZYyb70J+O87GvzM26TD7Hac/ti1VGcSmuQpZRXMUpEwM0ehbn\n6kKOGjLgNuB1d78u7vW6uM2OA2YUKoZUtAqpiMgGhRw1tB9wOjDdzKZFr/038E0zG0VoGnoXOKeA\nMSSl+xKIiGxQyFFDk4BkQ3IeKdQxszVENQIRkfWqbmYxaL0hEZF4VZkItN6QiMgGVZkIBvTsSvcu\nNSxbtZalK9eUOhwRkZKqykRgZqoViIhEqjIRAAzuH92/WIlARKpc9SYCdRiLiABVnAiGaBVSERGg\nihOB+ghERILqTQSxm9irRiAiVa56E4FqBCIiQBUngkF9utOpxliwfBUr16wrdTgiIiVTtYmgU42x\neZ/uAHyk5iERqWJVmwhA9y8WEYEqTwRDon4C1QhEpJpVdSLQDWpERKo9EfTTEFIRkepOBKoRiIhU\neSLQekMiItWdCGJ3Kvt4yUrWtXiJoxERKY2qTgTdu3RiYK9urG1xPlm6stThiIiURFUnAtBcAhGR\nqk8EQ7TmkIhUuYIlAjPb0syeNrPXzGymmV0YvT7AzMab2ezoa/9CxZCNLfqFZSZUIxCRalXIGsFa\n4EfuvgOwN3Ceme0AXAI86e4jgCej5yWzfi6BagQiUqUKlgjcfZ67T4m+Xwa8DgwGjgHGRJuNAY4t\nVAzZWH/vYtUIRKRKFaWPwMyGAbsBLwGD3H1e9NbHwKBixJDKYK03JCJVztwLO37ezHoBE4Ffu/u/\nzWyxu/eLe3+Ru7fpJzCz0cBogLq6uvqxY8fmdfzm5mZqa2tTvr9idQtnPDifbp2Mvx23GWaWsUw+\nx9nY7YtVRnEpLsXV8cvENDQ0NLl7Q8YN3b1gD6AL8DhwUdxrs4C66Ps6YFam/dTX13u+GhsbM26z\n0+WP+dCfjvOFy1dlXSaf42zM9sUqo7gUVyHLKK7ilIkBGj2Lc3UhRw0ZcBvwurtfF/fWQ8CZ0fdn\nAg8WKoZsac0hEalmOScCM+tvZrtksel+wOnAIWY2LXocBVwFHG5ms4HDoucltWHNoeYSRyIiUnyd\ns9nIzCYAX4u2bwLmm9lz7n5RqjLuPgmwFG8fmmOcBRWrEWgIqYhUo2xrBH3dfSnwdeAud9+LcDVf\nEbQKqYhUs2wTQWczqwNOBMYVMJ6SUB+BiFSzbBPBLwijf95y98lmtjUwu3BhFZdqBCJSzbLqIwDm\nufv6DmJ3n2Nm16Ur0JEoEYhINcu2RnBjlq91SAN7daNrpxoWN69hxaq1pQ5HRKSo0tYIzGwfYF9g\nUzOLHyHUB+hUyMCKqabG2KJfd95d2KylJkSk6mSqEXQFehESRu+4x1Lg+MKGVlzrh5AqEYhIlUlb\nI3D3icBEM7vT3d8rUkwlMTjuBjV9upQ4GBGRIsq2s7ibmd0CDIsv4+6HFCKoUhjcb8Ny1NtvWuJg\nRESKKNtEcD/wJ+BWYF3hwimdVnMJlAhEpIpkmwjWuvvNBY2kxFoPIe1W2mBERIoo2+GjY83sXDOr\ni+45PMDMBhQ0siIbotnFIlKlsq0RxJaN/nHcaw5s3b7hlM7mfbtTY/DJspWsaSnszXpERMpJVonA\n3b9Q6EBKrUunGgb16c68JStZ2FyR3SAiIklluwz1Gcled/e72jec0tqiXw/mLVnJp0oEIlJFsm0a\n2iPu++6E+wlMASoqEQzu14Om9xYpEYhIVcm2aeiC+Odm1g/4e0EiKqHYENIFzS0ljkREpHjyvWfx\nCqDi+g1iQ0g/XaEagYhUj2z7CMYSRglBWGxue+C+QgVVKrEagZqGRKSaZNtHcG3c92uB99z9wwLE\nU1JbD+wJwOufrmb8a59w+A6DShyRiEjhZdU0FC0+9wZh5dH+wOpCBlUqQzfpyXf3/wJrHb5/dxOP\nTp9X6pBERAouq0RgZicCLwMnEO5b/JKZVdQy1DE/+8r2HDOyJ2tbnPPvncqD0+aWOiQRkYLKtrP4\nZ8Ae7n6mu58B7Alclq6Amd1uZvPNbEbca1eY2VwzmxY9jso/9MIwM07fuRcXHLIN61qcH/5jGv9s\nqrhWMBGR9bJNBDXuPj/u+cIsyt4JHJHk9evdfVT0eCTL4xeVmfGjL43kosO3pcXhx/98hb+//H6p\nwxIRKYhsO4sfM7PHgXuj5ycBaU/i7v6MmQ3LP7TS+8GhI+jauYarHn2DS/49nTXrWjh9n2GlDktE\npF2lvao3s23MbD93/zHwZ2CX6PECcEuex7zAzF6Nmo7657mPovnegcO57Ks7AHDZgzO5bdI7JY5I\nRKR9mXvqlTbNbBxwqbtPT3h9Z+A37n502p2HGsE4d98pej4IWECYk/AroM7dz0pRdjQwGqCurq5+\n7NixWf5IrTU3N1NbW7vRZR57q5m/TF0KwGk79+K47Xpt1HHaK672LqO4FJfi6vhlYhoaGprcvSHj\nhu6e8gFMTvPe9HRlo22GATNyfS/xUV9f7/lqbGxstzL3vvSeD7tknA/96Ti/4Yk3N+o47RlXe5ZR\nXIqrkGUUV3HKxACNnsU5NlOHb7807/XImGUSmFld3NPjgBmpti1HJ++5Fdcevys1BteNf5NrH58V\nS2oiIh1Wps7iRjM7293/Ev+imX0XaEpX0MzuBQ4CBprZh8DPgYPMbBShaehd4Jw84y6Zb9QPoXMn\n46L7XuGmp99i9boWLj1yu1KHJSKSt0yJ4L+AB8zsVDac+BuAroQr+pTc/ZtJXr4t5wjL0DGjBtO1\nUw0X3DuVW56Zw+q1LXx1C9UMRKRjSts05O6fuPu+wC8IV/DvAr9w933c/ePCh1e+jty5jptPq6dr\npxrufP5dxs/RvY5FpGPKdq2hp939xujxVKGD6igO32EQlx8dhpY2zltV4mhERPKT7/0IJHLgtpsC\nMGvhalp003sR6YCUCDbSkP49GNSnG8tXO3MWLC91OCIiOVMi2EhmRv3QMEG66b1FJY5GRCR3SgTt\nYPetlAhEpONSImgHDcMGANCoRCAiHZASQTvYoa4PXWtgzqcr+GxFRd68TUQqmBJBO+jauYZtBnQB\nYIpqBSLSwSgRtJORA7sC0PS+EoGIdCxKBO1k5CahRqAOYxHpaJQI2snITUKN4JUPFrN6bUuJoxER\nyZ4SQTvp062GrTftyaq1Lbw2b2mpwxERyZoSQTtqiCaWNb77WYkjERHJnhJBO4rNMJ6iDmMR6UCU\nCNpR/FITunOZiHQUSgTtaOuBvehX24VPlq7iw0W6P4GIdAxKBO2opsbWrzuk5iER6SiUCNpZ/foO\nYyUCEekYlAjamZakFpGORomgne06pB+da4w3Pl7K8lVrSx2OiEhGSgTtrEfXTuy4RR9aHKa9v7jU\n4YiIZFSwRGBmt5vZfDObEffaADMbb2azo6/9C3X8UtpdzUMi0oEUskZwJ3BEwmuXAE+6+wjgyeh5\nxWkYGrtRjWYYi0j5K1gicPdngMQz4THAmOj7McCxhTp+Ke0+tB8QmobWtWhimYiUt2L3EQxy93nR\n9x8Dg4p8/KKo69uDwf16sGzVWmbPX1bqcERE0rJCLoVgZsOAce6+U/R8sbv3i3t/kbsn7Scws9HA\naIC6urr6sWPH5hVDc3MztbW1RS9z/YuLmfTBSkbv3ocvD2+7r1LFVQ7HUFyKS3EVtkxMQ0NDk7s3\nZNzQ3Qv2AIYBM+KezwLqou/rgFnZ7Ke+vt7z1djYWJIydz73jg/96Tj/4d+nllVc5XCMfMooLsVV\nyDLlGle+ZWKARs/iHFvspqGHgDOj788EHizy8Ytm/QxjjRwSkTJXyOGj9wIvACPN7EMz+w5wFXC4\nmc0GDoueV6TtNu9NbddOvP9ZM/OXrSx1OCIiKXUu1I7d/Zsp3jq0UMcsJ5071TBqy348//ZCpry3\nmCN22rzUIYmIJKWZxQXUsH5imeYTiEj5UiIoIM0wFpGOQImggHbbqj9mMGPuUlauWVfqcEREklIi\nKKC+Pbqw7Wa9Wb2uhRlzl5Q6HBGRpJQICkzNQyJS7pQICqxB8wlEpMwpERRYbGLZlPcWxWZXi4iU\nFSWCAhu6SS0De3Vl4YrVvLewudThiIi0oURQYGbG7lupeUhEypcSQRHohvYiUs6UCIqgYZhmGItI\n+VIiKIIdt+hL1041vPnJcpZ8vqbU4YiItKJEUATdu3Ri5yF9AZj6vpqHRKS8KBEUifoJRKRcKREU\nSWzkkBKBiJQbJYIiidUIpn2wmLXrWkocjYjIBkoERbJp724M3aSW5tXreOPjZaUOR0RkPSWCIlI/\ngYiUIyWCItIN7UWkHCkRFFH8AnQiIuVCiaCItt2sN727dWbu4s9Z2Kw7lolIeVAiKKKaGmO3qFbw\nxsLVJY5GRCQoSSIws3fNbLqZTTOzxlLEUCqxG9XMWqilJkSkPHQu4bEPdvcFJTx+ScT6Cd5YoBqB\niJSHUiaCqrTrlv2oMXhn8Vp+/fBrmFnWZWtXfc6uo1ro3EkteiLSfqwUt080s3eAJcA64M/ufkuS\nbUYDowHq6urqx44dm9exmpubqa2tLasylzy5kNmf5dc0tHnPThy3fU8OHNqDLjWZk0iuP0s5/r4U\nl+Kq1rjyLRPT0NDQ5O4NmbYrVSIY7O5zzWwzYDxwgbs/k2r7hoYGb2zMryuhqamJ+vr6sioz59Pl\njBk/hS0GD856/6vXtnDPC28zb3kYbTS4Xw++d+DWnNCwJd27dGqXuPLZvlhlFJfiqsa48i0TY2ZZ\nJYKSNA25+9zo63wzewDYE0iZCCrN1pv24msje1JfPzyncnv1WcK8LnXc9NRbzJ6/nMsenMmNT73F\n6AO25tS9htKja+qEICKSStEbm82sp5n1jn0PfAmYUew4OqJONcYxowbz+H8dwB9P3Z3t6/owf9kq\n/vfh19n/6qf444S3WL5qbanDFJEOphS9joOASWb2CvAy8LC7P1aCODqsmhrjqJ3reOQH+3PrGQ3s\nOqQvC1es5prHZrHfVU9xwxOzWdKs4akikp2iNw25+xxg12IftxKZGYftMIhDt9+MZ2cv4ManZjP5\n3UVc/8Sb3PrsHE7bZyidV3zOu3yY9T7ffTe37YtV5tOPVrLzri107awRUyLtTcNHK4CZccC2m/LF\nEQN5cc5n3PjUbJ5/eyE3T3g7bDD5ldx2mOv2RSrz15kT+N5BwzmhfkjaDnIRyY0SQQUxM/YZvgn7\nDN+Epvc+419T5jL34/lsMmCTrPex8LOFOW1frDIvv/UxHy7+nMv+M4ObnprN6AOGc8qeW6mDXKQd\nKBFUqPqhA6gfOiAaejYq63K5bl+sMpMbG/m022BufOotXp+3lF+Ne42bJ7zFd7+4NaftPZRe3fRR\nFsmXGlylQ6ixth3kC5av5qpH32D/q5/i90/OZsnn6iAXyYcSgXQosQ7y/5y3H2PO2pOGof1Z3LyG\n68a/yf5XPcW1j89i0Qqt4ySSC9WnpUMyMw7cdlMOGDGQF+Ys5Kan3uL5txdy09Nvcftz73D63kPZ\ns6/u+SCSDSUC6dDMjH2HD2Tf4QNpeu8zfv/kW0x881P+/Mwc7qiBUxbO5HsHDmfzvt1LHapI2VLT\nkFSM+qEDGHPWnjx0/n4cvsMgVrfAnc+/ywHXPM3PHpjOB581lzpEkbKkRCAVZ5ch/fjLGQ387vBN\n+MrOdaxpaeFvL73PwddO4Mf3v8I7C1aUOkSRsqJEIBVrWL8u/OHU3Rn/wwM4brfBtLhzf9OHHPq7\nCVz496nM/mRZqUMUKQtKBFLxttmsN9efNIqnfnQQJzVsSY0ZD077iC/9v2c4929NzPxoSalDFCkp\ndRZL1Rg2sCdXH78LFxy6DX+eOId/TP6AR6Z/zCPTP+aw7Tfj/ENGsM6ddS3Z36Mj1+2LVaZc42op\nwf1PJDMlAqk6Q/rX8qtjd+L8Q0JCuOfl93ji9fk88fr8sME/H8lth7luX6wyZRhXlxr4+ruv8v2D\nhjNsYM/cjyUFoUQgVWtQn+5cfvQOnHvwcG599h3uffl9ln2+BrK/jTQ4uW1frDJlGteaFvhH4wfc\n3/QBx4wazHkHb8M2m/XK8aDS3pQIpOoN7NWNS47cjkuO3K5ibnFYrnGNnfASz3zanQemzuWBqXP5\nz7S5HLVzHRccsg3bbd4np2NL+1FnsYgUzRa9O/PbE3bl6YsP4pS9tqJLTQ0PvzqPI/7fs5x9VyPT\nP1THfSkoEYhI0W05oJbfHLczE39yEN/adxjdOtcw/rVPOPqmSXzrjpdpem9RqUOsKmoaEpGSqevb\ngyu+tiPnHjyc2559h7+++B4TZn3KhFmfsu/wTbjgkBF00UijglMiEJGS26x3dy49anvOOXA4t096\nhzHPv8vzby/k+bcXMqhnJ/o8MzGn/X2+ciU9JmZfJtfti1lm3zojx66bnCkRiEjZGNCzKxd/eSRn\nH7A1Y55/l9smvcMnK9bwyYrlue9saY5lct2+SGV27F/4YbZKBCJSdvr26MIPDh3B2V/cmscmTWbH\nHXfMqfzMmTNzKpPr9sUs88Fbr+e0fT5KkgjM7AjgBqATcKu7X1WKOESkvPXo2omt+nZh20G9cyq3\n7MPcyuS6fXHLFP6+3EUfNWRmnYA/AEcCOwDfNLMdih2HiIgEpRg+uifwlrvPcffVwN+BY0oQh4iI\nUJpEMBj4IO75h9FrIiJSAuZFHqNrZscDR7j7d6PnpwN7ufv5CduNBkYD1NXV1Y8dOzav4zU3N1Nb\nW1t2ZRSX4lJc5VOmXOPKt0xMQ0NDk7s3ZNzQ3Yv6APYBHo97filwaboy9fX1nq/GxsayLKO4FFch\nyyiuyogr3zIxQKNncV4uRdPQZGCEmX3BzLoCJwMPlSAOERGhBMNH3X2tmZ0PPE4YPnq7u88sdhwi\nIhIUvY8gH2b2KfBensUHAgvKsIziUlyFLKO4KiOufMvEDHX3TTNulU37UUd+kGUbWbHLKC7FpbjK\np0y5xpVvmVwfWoZaRKTKKRGIiFS5akgEt5RpGcVVfsfIp4ziKr9j5FOmXOPKt0xOOkRnsYiIFE41\n1AhERCQNJQIRkSqnRFBhzMySvNatFLGISMdQcYnAzGrM7MQiHzOrFaEsOM3MLo+eb2Vme7ZzOLcl\nHLMX8EgWsQ02s33N7IDYI8V2nczsb/kGZ2Z9zCy3O3NUADM7IfZzm9n/mNm/zWz3MohrkzzKDDWz\nw6Lve6T6e5pZn+jrgGSPjYu8zbFqzewyM/tL9HyEmX01y7JZfybN7HdmltstxjqAirtVpbu3mNlP\ngPtyKWdmXweuBjYDLHq4u/dJU2Zf4FagF7CVme0KnOPu56Yo8kegBTgE+CWwDPgXsEeK/W8KnA0M\nI+5v5e5npflRPjSzP7r7uWbWH3gY+Eua7TGzq4GTgNeAdbHDAM8kbuvu66ITQVcP95PIipntAdwO\n9A5PbTFwlrs3Jdn2onT7cvfrErafHsXbZldhc98lyTFSlYkdo02ZuLKDgN8AW7j7kdGNlfZx99tS\nlQEuc/f7zWx/4DDgt8DNwF5pjpMsxiVAI/C/7r4wzfFi+3jU3Y9Ms8mLZjYNuAN41DOMHjGzswmr\nAg8AhgNDgD8BhybZ/B7gq0BTws9h0fOt0xxnW+DHwFBaf/YPSVHkjug4+0TP5wL3A+PSHCPrz2Sc\n14FbzKxzdMx73X1Jkn3n/JmMKzsCuJJw467usdfdPeXva2NVXCKIPGFmFwP/AFbEXnT3z9KUuQY4\n2t1zuUHo9cCXiRbNc/dXUl1JR/Zy993NbGq0/aJo4b1UHgSeBZ5gwwk6LXe/3MyuMbM/AfXAVe7+\nrwzFjgVGuvuqbI4BzAGeM7OHaP37vS51EW4DznX3ZwGiE+IdQLJ/iFxrDFld+aUoc1709a/R11Oz\nKHsnIfafRc/fJHzW0iWC2N/vK8At7v6wmf1vhuM8GpW7J3p+MlALfBzFcDRAmpqFAaMyHGNbQmI6\nC/i9md0H3Onub6bY/jzCzaVeAnD32Wa2WbIN3f2r0dcvRDWAEcSd2DK4n5Bg/kJ2n/3h7n6SmX0z\nOmZzsmbSBLl8Jon2eytwq5mNBL4NvGpmzwF/cfen4zbN5zMZcwfwc8L55eDoOAVtvanURHBS9PW8\nuNfSXoEAn+SYBMJO3T9I+Lyl+9CuiW7V6bD+ir8lzfa17v7TbOKIajQxLwGXAS8DbmZfd/d/pyk+\nB+gCZJsI3o4eNWR/0l4X+4cDcPdJZrY22Ybu/oss9xnbfv06VGY2FBjh7k+YWQ9SfMZjZczscHff\nLe6tS8xsCnBJmkMOdPf7zOzSaF9rzSzTyWqumf0ZOBy4Ouq3yfTPfZi7x5/kp5vZlOhi4rS41ycD\nEwkn/kT90h0gqgGMB8ab2cHA3cC5ZvYKcIm7v5BQZJW7r4595qMr40y1iO8CFxJqD9OAvYHnSV6L\niFnr7jen22+C1dHfO/a/NZzMn+esP5Pxov/h7aLHAuAV4CIzO8fdT472le/aaAA93P1JM7NoP1eY\nWRNw+UbsM62KTATu/oVst407gTaa2T+A/xD3AcpwAv0gah5yM+tC+LCnSya/Bx4ANjOzXwPHE07Y\nqYwzs6PcPWMbP9HVYZyphJP70YR/jjY/h5ndGL3XDEwzsydp/bP/INmBYidqM6t19+YsYgOYGJ0I\n742OeRIwIXY16+5T4uL6fbodpYorx2aLuGK2n7s/Fz3Zl8wn6BVR23rspLM3ockmnROBI4Br3X2x\nmdURmj7S6WRme7r7y9Fx9iCs2AsQf8J6ndAkOTtxB2b2QeJrCe9vApwGnA58AlxAqOGOIlyVJ/4v\nTTSz/wZ6mNnhwLlAprtGXUho/nzR3Q82s+0ITWvpjDWzcwn/L/GfyVS1+iuAx4AtLfRh7Qd8K8Mx\nsv5MxpjZ9YSr/aeA38T+NoTkPituu2VsSJCxBO1k0eQMrDKzGmC2hZWa5xKanwumIieUWei8vQjY\nyt1HR21uI929TXuhmd2RZleerj3ezAYCNxCq1gb8H3Bhurbb6J/g0Gj7J5PVQuI+RAb0JPwjrCG7\nD1HWzOwZIzUyAAAWsklEQVTMdO+7+5gU5fYhVKt7uXs2fSOY2dOp3guH2tD2uxFxTSNqtohd5ZvZ\ndHffOU1c9YR24r6E3+8iQjtxm5NAXJndgRuBnYAZwKbA8e7+arq4o6aHEe5+R1Qb7OXu76TZPtaG\n3SuKbSnwXWAm8BV3vy/a7nhgurvPSrKPY939P2mO8SahWewOd/8w4b2fuvvVCa/VAN8BvhTF9Dhw\na7q+BTOb7O57RH+fvdx9lZnNdPeUna5mluz34unayaOktncU14vunnbFzlw+k3Flvg3c5+4rkrzX\nN1l/Qa6iv/vrhNrcrwifzWvc/cWN3XdKXuBV7UrxILTX/gSYET2vBaYV4DgDkrz2hTTbfyfJa1e1\nc0xjgH5xz/sT7vmQbfn+wC4ZtnkJ2BKYGvfajDL4u78UfZ0afe0MvJpl2b5A3yy3tWjfOxKSQReg\nW4YyPydcOb8ZPd8CeK69YiM0UxxKSC7xrx+R6Wcpwt/lAcJJ7QrCAIQHgUfa+RhjgW8CPQv8s+wX\nOwahJnUdYanndGX2B74dfT8w3TmiVI+KbBoij44jMxtDuJpfHD3vD/zO04/QGWtmR7r70qjM9oTq\n9E4ptv+Gma10979F2/+BDJ1nURytOtncvc1onji7xH6GaNtFZrZbmu0xswnA1wgntyZgvpk95+4p\nR+94ln0jZnaau99tKUYCeZoO5uiKrc2VpqceOZJzs4WZ9SWcpA+Ink8Efunpr+xuiz4XM6MyPQnN\nKemaoI4DdgOmRD/DR5ZhyGLUj/ANolFjsd+3u/8yYbsfEPrDXgduM7ML3f3B6O3fEJpMEvc9lg1N\nW22O7e5fSxFTziOZ3P246Nsror9p32QxJRxnEqHf41lCwlyWbnvgWkLTzlVmNhn4OzDO3VdmOM5X\nCAk9/v/rl6lLcDOwa1QL/hFh1OBdwIEp9v9zoAEYSegE7kroh9kvTUy5jpjaaJWaCPLpOMr5BEr4\nJxsbfZhGEj4Q6UadfAN4yMxaCO3Fi939O6k2TtHJ9gJh+GkqNWbW390XRfsYQOa/c193Xxod7y53\n/7mZpWvmyKVvpGf0NZ+5AxfHfd+d8PtL15l3CaHZYjpwDmH+xK0ZjnE7oXknNvfkdMI/7NdTlggd\nvzkN0QVWu7ubWewz2TPD9hCunJcQknO6z+/ZQL27LzezYcA/zWyYu99A8g5kCCfOfGQ1kikVd5+Y\n5XFOB75I+Jv/1sxWAc+6+w/T7HeihY7cQwi/k9uBdMO//xTFfjDhc3I8YYBFOmujv+MxwE3ufpuZ\npfwfJo8LAHIfMbXxSl0lKcSD0H45EfgU+BvwLnBwhjKvAP3jng8gtLtmOtaxhBEQ04FtU2wzIO4x\nlNCRe1PstTT7nk44AU6Lnm8H/DtDPGcAbxDaFv83+v70DGWmA3WEPo49otdSNqkQqrd/I3Quzidc\n4aT7OToBP2ynv+3LGd7vQegPynZ/bZoMk72WZJtrCP+sk4FvZLH9xcCfCSO0ziYk9AsylMmquQ2Y\nmfC8F+GK+7psfpYcf/9TUr2Wzf9LjseqIySaPxDmuDyWxd/+RMLcnHeAGzNs/2rC116EZJOuzETg\nUsKQ4c0JAwtS/tyxz2vc76hnuv+taJum9vw9ZvOoyBqBu/+fheFWsY6jCz1DxxHwO+AFM7s/en4C\nKUY22IbRNjF9CcMpzzczvO2oltiEGov7+pXokW5Y60p3X2lmmFk3d3/DwvjllNz9LjNrZEOt4evu\n/lq6MoTJbY8Dk9x9spltDbQZgRJnpLu3qvmY2X7AcyliWhc1012fIY5WrPXs0xpCFbtvmu2/Rpio\n1RX4gpmNIjTzJG3miHxuZvu7+6S4n+PzFPvPe4iuu18bNVctJdQeL3f38WniAnjezHZ29+kZtvvE\nzEa5+7ToWMstzKq9HUjZUR79TLk29WQ7kmmjmNnbhKGZ9xAGJlzg7imHWluY/7AnIQHeBExMt30k\n1mzUbGZbAAsJySedk4BTCP19H5vZVoTPXCr3WRiZ1M/CqLazyFx7zHXE1Ear1FFDT7r7oZleS1Ju\nBzacQJ9KdQLNd1RLrszsAcJkkv+K4loEdHH3o9KU2SpFTO+3R0zRMaZ46/HtSV9LeP96Qqdq4iS/\ndKNz3mHDSWotoWb3y9hJO8n2TYTf0wTPftTQroQmvViCWQSc6UlGANlGjDDLh5m9BmxDuLpdRYpZ\nqWY2hNBk8XGSfawfGpviGNeQuqlnf3c/OmH7rEYybSwzu5DQyboloVY7EXjG3d9Osf2XgSfcPeum\nFDO7jDD661BCrcMJE8NSjtePmvRWRhc32xJq6Y+6+5oU219NmBAaP8rqME8zPyifEVMbq6ISgZl1\nJ3yInwYOYkP7aB9CtXK7NGX/6u6nZ3otz7gOcfenEq4o10t3JRm3jwOJOtk8zdIOCVd4PQjjwGd5\n+qF63Qlt64mdZmclbLcPsC8hMcVf3fcBjnP3XdMcIzZUL35stXuaDrCon+dcwgnBCR2HN3uKDkAz\ne9Hd9zazqXGJ4NXEE2dCmVgndmyc9nKidvnYFXZ7sPyWMBma7HXfuMlKicdImdTTJdGokx1vh+GS\nGeLrRbgYuhgY4u6d0my7E22XZbgrxbY1wN7u/nz0vBvQPdPPE11sfJEwuu45QtPg6sQactz2yX6/\naT+TpVBpTUPnEE5SWxCaY2KJYCmhuphOqxNl1OlUn2xDM7vP3U9MUa0myR/5AMIElNjkLkv4mjIR\nWNux54MJV4hJJf7jWhjznnJ8f+SvhKuuLxOaiU4leedvV8IJszOtO3+XEjraksUfO9GOY8PPvD7c\nDHGNifYdm2B2ShTrCSm2n2lmpxCaL0YAPyD036TTED0eimI7FXgV+J6Z3e/u18T9LD9x92uSNA2G\nHybFRLdI1kuYmFkfDyPRMo2UaQ+JTT17kqGpx+JG2liKkUwby8x+R7gA6EXoT7mccCGQavufEy7+\ndiAMEjgSmESo7bXhYU2yPxA6cvGwvEo2M+vNwyjE7wB/jD4PrySJ5/uE/7utEwZe9CZFE2pC+X1p\nu8ZY0p+lPVRUIvAwSuIGM7vA3W/MpoyFZQJiQw6XsuFEtZrUt4i7MPqa7Xoiy6IT4gxanwwzTc1P\nHHrWhQxDzxK5+xQzS7mwWWQbdz/BzI5x9zFmdg9J/ul8w8iMO3O4Ko0ljJGE2aUPEn7+o8k8QmMn\nd98h7vnTUXNJK3E1t7cJJ6hVhNmijxM6zdMZAuzu7sujff2cMAroAMLFxDVx28ZO4o1kTmKJclnC\nJHGxtsTk2Z5NBN8B7oiuvCEkn+9ETSBXJm5s+Y20yccLhElUn2S5/fHAroQ5JN+2sDDg3RnKPGlm\n3yAMwMj272lRzfhUwu8Oks9Ev4cwwupKWi9XsixTW7+Z/ZUwM34arReBLFgiqKimoXi5VBOj7a90\n90sLFMvPo2+Tngzd/bQU5aYRDT3Lo6kDwge0njCi58tpyrzs7nua2TOEq5iPo7iSnnCittGLaXvF\nkq6Z5xlCG/Ky6Hlv4GF3T7lIn5ndTRii92L0fC/gPHc/I2G71wizux8lnKBaSfePZ2ZvADvH2nij\nJoJX3H27+CamhDJ7EC4ehrHh52/Tdh9tG2sOPJAwyiSXJUwKzsKS6MaGNYkWE36WpFf4sc9f3Nde\nhDbyLxYgtq8Rze8gdP6mnBMS9xluInwGlgGvZ2gOXkZIauvY8DfJ1Fx3IGH+wHPufrWFgRX/laE2\nmBMzex3YIYfktNEqqkYQk0s10cy2c/c3gPstySqOyTozrfU6Iq3eIskHyTeszfMM4eozdjK8gnD1\nmUrWY8/jroovZ0P7/VpCk0ym1UdvsTAe/jJCE0kv0i9wFRvnfCvZj3MeRKhlxayOXkunnjByJtbR\nvRUwK9YkF3fi/RPwJOFKuTGufMbljgnDYF8ys9gErKOBe6LfdarRVncTJvxMJ/2igbH9xTQTOg1j\nkjYLJvscxkv2mdwIsY775YSLpq+Sfr2s2IiqXEba5MzMriSMAord++IHZraPu/93iiKNZtaPMCKn\nifDzJC6Yl+hBwkznZ7OtrcVqxXHP5xCaINvTDMJFw7x23m9KFVkjiE4UsWrirrFqorsfnmTbWzys\nR5Q4izVjZ2Yecc0iTFxbFT3vRhhTnHRIqIWltEcQVqy8kjD07J5kzV5xV8WPEZJgK5mqo7kwsyZ3\nT9p/kqbMzwhjvB+IXjoW+Ie7t2l+iCuTtLM0JrF5ysxudvfv5xJXVK6BDc1tz7l7Y4btJ7n7/rke\nJ4d4nk7zdrt+JpMcuxvwuLsflOL9nEfa5BnHq8Aoj4aARn12U7PpZLUwqa6PZ1776WBCx+8XCU0x\nUwhJ4YY0ZXKd7Z6z6BijCE1u8bXHdMOgN+6YFZoIYotc5VJNzGmESp5x5XQytLB0wDzClZER/kGT\njj2Ptv0+YZTQR/FvkWLomeV+A5jYuP4fECaS5TTOObrSjTUhPOPuU9NtX67M7FDCujaJq7Wm6/TP\nZwmToovimuzu22SxbVYjbfKM41XgoNhnKvrsTUhMBBtbe4oSzB6E88T3gM8znCfiL4DWz3Z395+k\nO04uouanNjz7Wdk5q8imIWByHtXEZCNU7mLD0gMbzd1/bWaPsuFk+O0MJ8PNCCfdKYSx20+k2ffv\nCTcWyeWqONdlHxI7L+OXUc7YiRn9U7Zns0apfJswfrwLG5qG0o7+IoclTCzFMOO4su3Wr2CtR751\nIqykmnIEUHSC/jvhAuZtsr+HRa6uBKZGV8dG6CtIdo+I3yV5Lf7qNl2/1ZOEmb4vEC789nD3+emC\n8rZ3L3vOzNq7s3wXQgvGonbeb0qVWiO4mw0LVq0ku2riawkjVJK+VmxmZoR25W8TRhDdR1j0LOnE\nGik8M5uVqjkvTZlXCFe48WtATfQk4/StiBPXEprf1hJGN6WcIRxtf1L0aCFMELzP23HCYtyx6thw\nG9eXPcmEubhtTyTMsVkaNV/tDvwqXY3AwiTHekIye47QX/CCuyedWR6VSTbb/YZcPw/pWLhz3cls\nuAB8vNAdx5WaCBLb/qYSmiLStf1lNUKlFCzMfv02YaG6pwlLZ4xvr+poNPLhhmi/TrhC+mHUEZZs\n+2RXrEsIa66kvaKqBNGJ+reeeemO+DJnEEYatVrCxAs4NrzQLMzVuAw41dNM9NqI/Q+m7QqcSVfe\njRvFtD9hyPC1hGU8Mg2djo1g+xZhJNzm7t4tzbY5zXbPV7EvACsyEUD2bX9xVeMuhOGd70fPhwJv\nlLJGYGGa/RmENVduBf7j7mssunuRuw9vp+O8SOj4uzd66WTC2i5J/4nM7GHCTcJjnZoHEZqNvkD4\np/hrsnKVIhreN5wMSz8kKZfVEiYJZXJdJrngEmoF6wjNRMmaaDbmGFdH+59JXPNbqg5Ti4b6RqON\nprv7PZZi+G9cmfMJF4v1hBP6s4TO4qfSlCl4X2LcsQp6AdjqWJWYCJK0/U1KdaWa68iUYjKzXxBu\nKtMmBjPbPtshb1kcp83cBDN7xVMsGWFmjwNneDTZJxqVdRehA/UZd091P4aKkOozk+6zYnksYWIp\nJm95mqXLC83MXiJcNN1PSABJa43tcJxWI+yy2H4c4ZaOhxOahT4n/K7SLXtyMeH80JSuOSyhzH2E\nvsTYsNZTCDeCSjXbPWfFugBsdcwKTQQ5t/1Vs+jqaxGhE9AJV2L9iVZVTBwNlNh3ElVjZ7r7Dpmu\nwqqVJaw5E9VYp6ercVoRJ29ly8xGepJbYhbgOI8CJ3g04zuL7WsJV87T3X121L+ws7v/XzvHVfC+\nRAvzi+4o9AVgvIocNeTRzSvi2v7uIEzQSNn2V+ViI6POofWicCeTfDTQhOgKLNbe/Y3otZ6EmakS\nsfyWMIkpyuStXLj7rCI1VzUD06LaffwQ3aSTt9y9mbhRW+4+j8JMyJpiZnsn9CWmnXeSi+gC4WR3\nvyLZ+4VIAlChiSBJ29/tpFmwSvgpuY24OI9w8o9NwroL+Fc0sqHNEg/VzMMckSstvyVMxkXDoK8h\n9MFA5juuFVSq5qoCHOoFwiz3ePnc5a69ZTvbPS8elreeZWZbFWIkViqV2jSUc9tfNduYEReSnkVL\nmKSa+JRheGMPwiTBL1LgjslsFau5ysymEPqhZkTPv0lY06ekn8li9ClaWIpmN0KCjb93R8FmFldk\njcDd870fa7WKrRf0FcJyAQ9HY5mTstZrLXUldB6u8DSLdVWxi4DRhIlPbZYwIf39p8cQZsUXbJJj\nHhLv6vUZhWmuOp5w7+VTCInwDFqv01QSRRo8ElvzKcYI97IomIpMBJKzuRZup3c4cLWFpQOSLa0L\ngLuvr6JHHcXHEIa2SQJ3Hx19exRJhh1mKJ7VMtxFNjZqrvotYcKTk/nWizlz9zlmdjJhtdb3gS9V\n0WCPzp6wnERUOyzcAQu5c+kwTiSMuLjW3RdHIy5+nKEMEBpFgf9YWPE12RIAEuSzhElBOybz9Aaw\nzt3/Fc2L2J1wsm4X1vZmTwMIS1+8ZOF+4GV1Z6/2ZBt5M5uNOnYl9hFIYSXMLI5Nsz/Q3fcpUUhl\nL5dhh2U+ybGg/UnlPK+n0Czc/rM/edzMZmOpRiD5iF9jPzbN/pjShNJh5HJ1n+2d70ohp/6kXFXy\niT4TD6u4LiFMzCwq1QhECqicr+7zkc8MXil/SgSSMzPrTrhfa+KkorJaW78cVFpTR7Fm8EpxKRFI\nzszsfkKn4SmEtetPJdz458KSBiYieVEikJzFrfQY6zjsQli1UUNIRTqglGPFRdJYE31dbGY7AX0J\nd1MTkQ5Io4YkH7dYuLft/xDWg+lFuEGJiHRAahqSrFnym93HVtN0T7jZvYh0DKoRSC5iS0uMJNz9\nLbY65NEUZgVKESkC1QgkZ9HqiF9x92XR897Aw+5+QGkjE5F8qLNY8jGIcGOVmNXRayLSAalpSPJx\nF/CymT0QPT8WuLN04YjIxlDTkOQlutFK7GYkz7j71FLGIyL5UyIQEaly6iMQEalySgQiIlVOiUCq\njpn9zMxmmtmrZjYtujdAoY41wcwaCrV/kfagUUNSVcxsH8KNX3Z391VmNhDoWuKwREpKNQKpNnXA\nAndfBeDuC9z9IzO73Mwmm9kMM7vFzAzWX9Ffb2aNZva6me1hZv82s9mxO3OZ2TAze8PM/hZt889o\n3f5WzOxLZvaCmU0xs/vNrFf0+lVm9lpUQ7m2iL8LEUCJQKrP/wFbmtmbZvZHMzswev0md9/D3XcC\netD6dpGr3b0B+BPwIHAesBPwLTPbJNpmJPBHd9+ecJP6c+MPGtU8/gc4zN13J9ym8qKo/HHAjtGN\n2dvtto8i2VIikKri7suBemA08CnwDzP7FnCwmb0U3VryEMLd12JiaypNB2a6+7yoRjEH2DJ67wN3\nfy76/m5g/4RD7w3sADxnZtOAMwm3q1wCrARuM7OvA83t9sOKZEl9BFJ13H0dMAGYEJ34zwF2ARrc\n/QMzu4K4W3ACq6KvLXHfx57H/ocSJ+QkPjdgvLu3uTG5me0JHAocD5xPSEQiRaMagVQVMxtpZiPi\nXhoFzIq+XxC12x+fx663ijqiIdzCc1LC+y8C+5nZNlEcPc1s2+h4fd39EeCHgG4CL0WnGoFUm17A\njWbWD1gLvEVoJloMzAA+Bibnsd9ZwHlmdjvwGnBz/Jvu/mnUBHWvmXWLXv4fYBnwoJl1J9Qakt3z\nQaSgtMSEyEYys2HAuKijWaTDUdOQiEiVU41ARKTKqUYgIlLllAhERKqcEoGISJVTIhARqXJKBCIi\nVU6JQESkyv1/KNgxYm0cyMMAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2f67bb4080>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0.2185430463576159"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"song_title_to_analyze = 'Water'\n",
"\n",
"lyrics = songs['lyrics'].where(songs['title'] == song_title_to_analyze, '').max()\n",
"print(lyrics)\n",
"words = nltk.tokenize.word_tokenize(lyrics)\n",
"clean_words = [w.lower() for w in remove_extra_junk(remove_stop_words(words))]\n",
"water_dist = nltk.FreqDist(clean_words)\n",
"water_dist.plot(25)\n",
"\n",
"water_dist.freq(song_title_to_analyze.lower())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Looking at swear word distribution\n",
"\n",
"Let's look at the distribution of swear words..."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f2f66e27ef0>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEkCAYAAAAvoUY9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkgQYCJKkxkCQJAHw/wE0bGbuhrfvSQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2f6690bc50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sws = []\n",
"\n",
"for sw in set(corpus.swear_words()):\n",
" sws.append({'word': sw,\n",
" 'dist': freq_dist.freq(sw)})\n",
" \n",
"sw_df = pd.DataFrame.from_dict(sws)\n",
"sw_df.nlargest(10, 'dist').plot(x='word', kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
AGHPythonCourse2017/zad3-chudy1997 | zad3ex.ipynb | 1 | 1311 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Installation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"!pip install git+https://github.com/AGHPythonCourse2017/zad3-chudy1997.git"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Usage"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from song_singer.main import check\n",
"check('Leonard Cohen : Hallelujah')\n",
"check('John Lennon : Hallelujah')\n",
"check('Metallica: One')\n",
"check('Ariana Grande : One')\n",
"check('Adele : Hello')\n",
"check('Linkin Park : Hello')\n",
"check('John Lennon : Imagine')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3.0
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | gpl-3.0 |
karissa/pyeda | ipynb/SymPy_Comparison.ipynb | 5 | 10460 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this notebook, we will demonstrate some of the differences between SymPy's `logic` module, and PyEDA's logic expressions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sympy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pyeda.boolalg.expr\n",
"import pyeda.boolalg.bfarray"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create Variables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `xs` array is a tuple of SymPy symbolic variables,\n",
"and the `ys` array is a PyEDA function array."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"xs = sympy.symbols(\",\".join(\"x%d\" % i for i in range(64)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ys = pyeda.boolalg.bfarray.exprvars('y', 64)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Basic Boolean Functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a SymPy XOR function:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"f = sympy.Xor(*xs[:4])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a PyEDA `XOR` function:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"g = pyeda.boolalg.expr.Xor(*ys[:4])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"SymPy `atoms` method is similar to PyEDA's `support` property:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"f.atoms()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"g.support"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"SymPy's `subs` method is similar to PyEDA's `restrict` method:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"f.subs({xs[0]: 0, xs[1]: 1})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"g.restrict({ys[0]: 0, ys[1]: 1})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conversion to NNF"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Conversion to negation normal form is also similar. One difference is that SymPy inverts the variables by applying a `Not` operator, but PyEDA converts inverted variables to *complements* (a negative *literal*)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sympy.to_nnf(f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"type(sympy.Not(xs[0]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"g.to_nnf()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"type(~ys[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conversion to DNF"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Conversion to disjunctive normal form, on the other hand, has some differences. With only four input variables, `SymPy` takes a couple seconds to do the calculation. The output is large, with unsimplified values and redundant clauses."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sympy.to_dnf(f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"PyEDA's DNF conversion is minimal:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"g.to_dnf()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's a little hard to do an apples-to-apples comparison, because 1) SymPy is pure Python and 2) the algorithms are probably different.\n",
"\n",
"The `simplify_logic` function actually looks better for comparison:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sympy.logic import simplify_logic"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"simplify_logic(f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"simplify_logic(f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Running this experiment from `N=2` to `N=6` shows that PyEDA's runtime grows significantly slower."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"N = 5\n",
"\n",
"sympy_times = (.000485, .000957, .00202, .00426, .0103)\n",
"pyeda_times = (.0000609, .000104, .000147, .00027, .000451)\n",
"\n",
"ind = np.arange(N) # the x locations for the groups\n",
"width = 0.35 # the width of the bars\n",
"\n",
"fig, ax = plt.subplots()\n",
"\n",
"rects1 = ax.bar(ind, sympy_times, width, color='r')\n",
"rects2 = ax.bar(ind + width, pyeda_times, width, color='y')\n",
"\n",
"# add some text for labels, title and axes ticks\n",
"ax.set_ylabel('Time (s)')\n",
"ax.set_title('SymPy vs. PyEDA: Xor(x[0], x[1], ..., x[n-1]) to DNF')\n",
"ax.set_xticks(ind + width)\n",
"ax.set_xticklabels(('N=2', 'N=3', 'N=4', 'N=5', 'N=6'))\n",
"\n",
"ax.legend((rects1[0], rects2[0]), ('SymPy', 'PyEDA'))\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Going a bit further, things get worse.\n",
"\n",
"These numbers are from my laptop:\n",
"\n",
"| N | sympy | pyeda | ratio |\n",
"|----|----------|----------|--------|\n",
"| 2 | .000485 | .0000609 | 7.96 |\n",
"| 3 | .000957 | .000104 | 9.20 |\n",
"| 4 | .00202 | .000147 | 13.74 |\n",
"| 5 | .00426 | .00027 | 15.78 |\n",
"| 6 | .0103 | .000451 | 22.84 |\n",
"| 7 | .0231 | .000761 | 30.35 |\n",
"| 8 | .0623 | .00144 | 43.26 |\n",
"| 9 | .162 | .00389 | 41.65 |\n",
"| 10 | .565 | .00477 | 118.45 |\n",
"| 11 | 1.78 | .012 | 148.33 |\n",
"| 12 | 6.46 | .0309 | 209.06 |"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Simplification"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"SymPy supports some obvious simplifications, but PyEDA supports more. Here are a few examples."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sympy.Equivalent(xs[0], xs[1], 0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pyeda.boolalg.expr.Equal(ys[0], ys[1], 0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sympy.ITE(xs[0], 0, xs[1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pyeda.boolalg.expr.ITE(ys[0], 0, ys[1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sympy.Or(xs[0], sympy.Or(xs[1], xs[2]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pyeda.boolalg.expr.Or(ys[0], pyeda.boolalg.expr.Or(ys[1], ys[2]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sympy.Xor(xs[0], sympy.Not(sympy.Xor(xs[1], xs[2])))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pyeda.boolalg.expr.Xor(ys[0], pyeda.boolalg.expr.Xnor(ys[1], ys[2]))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-2-clause |
azogue/esiosdata | notebooks/esiosdata - PVPC data.ipynb | 1 | 3585853 | null | mit |
aidiary/notebooks | keras/170704-imdb-cnn-lstm.ipynb | 1 | 4009 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from keras.preprocessing import sequence\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Dropout, Activation\n",
"from keras.layers import Embedding\n",
"from keras.layers import LSTM\n",
"from keras.layers import Conv1D, MaxPooling1D\n",
"from keras.datasets import imdb"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Embedding\n",
"max_features = 20000\n",
"maxlen = 100\n",
"embedding_size = 128\n",
"\n",
"# Convolution\n",
"kernel_size = 5\n",
"filters = 64\n",
"pool_size = 4\n",
"\n",
"# LSTM\n",
"lstm_output_size = 70\n",
"\n",
"# Training\n",
"batch_size = 30\n",
"epochs = 2"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading data...\n",
"25000 train sequences\n",
"25000 test sequences\n"
]
}
],
"source": [
"print('Loading data...')\n",
"(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)\n",
"print(len(x_train), 'train sequences')\n",
"print(len(x_test), 'test sequences')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pad sequences (samples x time)\n",
"x_train shape: (25000, 100)\n",
"x_test shape: (25000, 100)\n"
]
}
],
"source": [
"# 系列長を長さmaxlen=100にそろえる\n",
"print('Pad sequences (samples x time)')\n",
"x_train = sequence.pad_sequences(x_train, maxlen=maxlen)\n",
"x_test = sequence.pad_sequences(x_test, maxlen=maxlen)\n",
"print('x_train shape:', x_train.shape)\n",
"print('x_test shape:', x_test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Build model...\n"
]
}
],
"source": [
"print('Build model...')\n",
"model = Sequential()\n",
"model.add(Embedding(max_features, embedding_size, input_length=maxlen))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"embedding_1 (Embedding) (None, 100, 128) 2560000 \n",
"=================================================================\n",
"Total params: 2,560,000\n",
"Trainable params: 2,560,000\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/migration/UJ11 HyperParameter Tuning Training Job with TensorFlow.ipynb | 1 | 45115 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "copyright"
},
"outputs": [],
"source": [
"# Copyright 2021 Google LLC\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "title:migration,new"
},
"source": [
"# Vertex SDK: Submit a HyperParameter tuning training job with TensorFlow\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "install_aip"
},
"source": [
"## Installation\n",
"\n",
"Install the latest (preview) version of Vertex SDK.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qXVD8TE-iBAZ"
},
"outputs": [],
"source": [
"! pip3 install -U google-cloud-aiplatform --user"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "install_storage"
},
"source": [
"Install the Google *cloud-storage* library as well.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-JOoJeejiBAa"
},
"outputs": [],
"source": [
"! pip3 install google-cloud-storage"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "restart"
},
"source": [
"### Restart the Kernel\n",
"\n",
"Once you've installed the Vertex SDK and Google *cloud-storage*, you need to restart the notebook kernel so it can find the packages.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "SlJHybHWiBAa"
},
"outputs": [],
"source": [
"import os\n",
"\n",
"if not os.getenv(\"AUTORUN\"):\n",
" # Automatically restart kernel after installs\n",
" import IPython\n",
"\n",
" app = IPython.Application.instance()\n",
" app.kernel.do_shutdown(True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "before_you_begin"
},
"source": [
"## Before you begin\n",
"\n",
"### GPU run-time\n",
"\n",
"*Make sure you're running this notebook in a GPU runtime if you have that option. In Colab, select* **Runtime > Change Runtime Type > GPU**\n",
"\n",
"### Set up your GCP project\n",
"\n",
"**The following steps are required, regardless of your notebook environment.**\n",
"\n",
"1. [Select or create a GCP project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
"\n",
"2. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\n",
"\n",
"3. [Enable the Vertex APIs and Compute Engine APIs.](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component)\n",
"\n",
"4. [Google Cloud SDK](https://cloud.google.com/sdk) is already installed in Google Cloud Notebooks.\n",
"\n",
"5. Enter your project ID in the cell below. Then run the cell to make sure the\n",
"Cloud SDK uses the right project for all the commands in this notebook.\n",
"\n",
"**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "set_project_id"
},
"outputs": [],
"source": [
"PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "autoset_project_id"
},
"outputs": [],
"source": [
"if PROJECT_ID == \"\" or PROJECT_ID is None or PROJECT_ID == \"[your-project-id]\":\n",
" # Get your GCP project id from gcloud\n",
" shell_output = !gcloud config list --format 'value(core.project)' 2>/dev/null\n",
" PROJECT_ID = shell_output[0]\n",
" print(\"Project ID:\", PROJECT_ID)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "set_gcloud_project_id"
},
"outputs": [],
"source": [
"! gcloud config set project $PROJECT_ID"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "region"
},
"source": [
"#### Region\n",
"\n",
"You can also change the `REGION` variable, which is used for operations\n",
"throughout the rest of this notebook. Below are regions supported for Vertex AI. We recommend when possible, to choose the region closest to you.\n",
"\n",
"- Americas: `us-central1`\n",
"- Europe: `europe-west4`\n",
"- Asia Pacific: `asia-east1`\n",
"\n",
"You cannot use a Multi-Regional Storage bucket for training with Vertex. Not all regions provide support for all Vertex services. For the latest support per region, see [Region support for Vertex AI services](https://cloud.google.com/vertex-ai/docs/general/locations)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wkzC9Mn5iBAd"
},
"outputs": [],
"source": [
"REGION = \"us-central1\" # @param {type: \"string\"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "timestamp"
},
"source": [
"#### Timestamp\n",
"\n",
"If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append onto the name of resources which will be created in this tutorial.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jX9n6pVLiBAd"
},
"outputs": [],
"source": [
"from datetime import datetime\n",
"\n",
"TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gcp_authenticate"
},
"source": [
"### Authenticate your GCP account\n",
"\n",
"**If you are using Google Cloud Notebooks**, your environment is already\n",
"authenticated. Skip this step.\n",
"\n",
"*Note: If you are on an Vertex notebook and run the cell, the cell knows to skip executing the authentication steps.*\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "BH7LjNZTiBAe"
},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"\n",
"# If you are running this notebook in Colab, run this cell and follow the\n",
"# instructions to authenticate your Google Cloud account. This provides access\n",
"# to your Cloud Storage bucket and lets you submit training jobs and prediction\n",
"# requests.\n",
"\n",
"# If on Vertex, then don't execute this code\n",
"if not os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n",
" if \"google.colab\" in sys.modules:\n",
" from google.colab import auth as google_auth\n",
"\n",
" google_auth.authenticate_user()\n",
"\n",
" # If you are running this tutorial in a notebook locally, replace the string\n",
" # below with the path to your service account key and run this cell to\n",
" # authenticate your Google Cloud account.\n",
" else:\n",
" %env GOOGLE_APPLICATION_CREDENTIALS your_path_to_credentials.json\n",
"\n",
" # Log in to your account on Google Cloud\n",
" ! gcloud auth login"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bucket:batch_prediction"
},
"source": [
"### Create a Cloud Storage bucket\n",
"\n",
"**The following steps are required, regardless of your notebook environment.**\n",
"\n",
"This tutorial is designed to use training data that is in a public Cloud Storage bucket and a local Cloud Storage bucket for your batch predictions. You may alternatively use your own training data that you have stored in a local Cloud Storage bucket.\n",
"\n",
"Set the name of your Cloud Storage bucket below. It must be unique across all Cloud Storage buckets.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bucket"
},
"outputs": [],
"source": [
"BUCKET_NAME = \"[your-bucket-name]\" # @param {type:\"string\"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "autoset_bucket"
},
"outputs": [],
"source": [
"if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
" BUCKET_NAME = PROJECT_ID + \"aip-\" + TIMESTAMP"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "create_bucket"
},
"source": [
"**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "IdMuD9HViBAf"
},
"outputs": [],
"source": [
"! gsutil mb -l $REGION gs://$BUCKET_NAME"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "validate_bucket"
},
"source": [
"Finally, validate access to your Cloud Storage bucket by examining its contents:\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tRs6mJDwiBAg"
},
"outputs": [],
"source": [
"! gsutil ls -al gs://$BUCKET_NAME"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "setup_vars"
},
"source": [
"### Set up variables\n",
"\n",
"Next, set up some variables used throughout the tutorial.\n",
"### Import libraries and define constants\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "import_aip"
},
"source": [
"#### Import Vertex SDK\n",
"\n",
"Import the Vertex SDK into our Python environment.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "UCREN4OMiBAg"
},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"import time\n",
"\n",
"from google.cloud.aiplatform import gapic as aip\n",
"from google.protobuf import json_format\n",
"from google.protobuf.json_format import MessageToJson, ParseDict\n",
"from google.protobuf.struct_pb2 import Struct, Value"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aip_constants"
},
"source": [
"#### Vertex AI constants\n",
"\n",
"Setup up the following constants for Vertex AI:\n",
"\n",
"- `API_ENDPOINT`: The Vertex AI API service endpoint for dataset, model, job, pipeline and endpoint services.\n",
"- `API_PREDICT_ENDPOINT`: The Vertex AI API service endpoint for prediction.\n",
"- `PARENT`: The Vertex AI location root path for dataset, model and endpoint resources.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "A5-uS7XSiBAh"
},
"outputs": [],
"source": [
"# API Endpoint\n",
"API_ENDPOINT = \"{}-aiplatform.googleapis.com\".format(REGION)\n",
"\n",
"# Vertex AI location root path for your dataset, model and endpoint resources\n",
"PARENT = \"projects/\" + PROJECT_ID + \"/locations/\" + REGION"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "clients"
},
"source": [
"## Clients\n",
"\n",
"The Vertex SDK works as a client/server model. On your side (the Python script) you will create a client that sends requests and receives responses from the server (Vertex).\n",
"\n",
"You will use several clients in this tutorial, so set them all up upfront.\n",
"\n",
"- Dataset Service for managed datasets.\n",
"- Model Service for managed models.\n",
"- Pipeline Service for training.\n",
"- Endpoint Service for deployment.\n",
"- Job Service for batch jobs and custom training.\n",
"- Prediction Service for serving. *Note*: Prediction has a different service endpoint.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "5DhFs5vNiBAi"
},
"outputs": [],
"source": [
"# client options same for all services\n",
"client_options = {\"api_endpoint\": API_ENDPOINT}\n",
"\n",
"\n",
"def create_model_client():\n",
" client = aip.ModelServiceClient(client_options=client_options)\n",
" return client\n",
"\n",
"\n",
"def create_endpoint_client():\n",
" client = aip.EndpointServiceClient(client_options=client_options)\n",
" return client\n",
"\n",
"\n",
"def create_prediction_client():\n",
" client = aip.PredictionServiceClient(client_options=client_options)\n",
" return client\n",
"\n",
"\n",
"def create_job_client():\n",
" client = aip.JobServiceClient(client_options=client_options)\n",
" return client\n",
"\n",
"\n",
"clients = {}\n",
"clients[\"model\"] = create_model_client()\n",
"clients[\"endpoint\"] = create_endpoint_client()\n",
"clients[\"prediction\"] = create_prediction_client()\n",
"clients[\"job\"] = create_job_client()\n",
"\n",
"for client in clients.items():\n",
" print(client)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7NnFMX6QiBAi"
},
"source": [
"## Prepare a trainer script"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BrRN2X0DiBAi"
},
"source": [
"### Package assembly"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "rMP5OyIYiBAj"
},
"outputs": [],
"source": [
"# Make folder for python training script\n",
"! rm -rf custom\n",
"! mkdir custom\n",
"\n",
"# Add package information\n",
"! touch custom/README.md\n",
"\n",
"setup_cfg = \"[egg_info]\\n\\\n",
"tag_build =\\n\\\n",
"tag_date = 0\"\n",
"! echo \"$setup_cfg\" > custom/setup.cfg\n",
"\n",
"setup_py = \"import setuptools\\n\\\n",
"# Requires TensorFlow Datasets\\n\\\n",
"setuptools.setup(\\n\\\n",
" install_requires=[\\n\\\n",
" 'tensorflow_datasets==1.3.0',\\n\\\n",
" ],\\n\\\n",
" packages=setuptools.find_packages())\"\n",
"! echo \"$setup_py\" > custom/setup.py\n",
"\n",
"pkg_info = \"Metadata-Version: 1.0\\n\\\n",
"Name: Hyperparameter Tuning - Boston Housing\\n\\\n",
"Version: 0.0.0\\n\\\n",
"Summary: Demonstration hyperparameter tuning script\\n\\\n",
"Home-page: www.google.com\\n\\\n",
"Author: Google\\n\\\n",
"Author-email: [email protected]\\n\\\n",
"License: Public\\n\\\n",
"Description: Demo\\n\\\n",
"Platform: Vertex AI\"\n",
"! echo \"$pkg_info\" > custom/PKG-INFO\n",
"\n",
"# Make the training subfolder\n",
"! mkdir custom/trainer\n",
"! touch custom/trainer/__init__.py"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nZLqlZ2OiBAj"
},
"source": [
"### Task.py contents"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "uDCJ3DXmiBAj"
},
"outputs": [],
"source": [
"%%writefile custom/trainer/task.py\n",
"# hyperparameter tuningfor Boston Housing\n",
" \n",
"import tensorflow_datasets as tfds\n",
"import tensorflow as tf\n",
"from tensorflow.python.client import device_lib\n",
"from hypertune import HyperTune\n",
"import numpy as np\n",
"import argparse\n",
"import os\n",
"import sys\n",
"tfds.disable_progress_bar()\n",
"\n",
"parser = argparse.ArgumentParser()\n",
"parser.add_argument('--model-dir', dest='model_dir',\n",
" default='/tmp/saved_model', type=str, help='Model dir.')\n",
"parser.add_argument('--lr', dest='lr',\n",
" default=0.001, type=float,\n",
" help='Learning rate.')\n",
"parser.add_argument('--units', dest='units',\n",
" default=64, type=int,\n",
" help='Number of units.')\n",
"parser.add_argument('--epochs', dest='epochs',\n",
" default=20, type=int,\n",
" help='Number of epochs.')\n",
"parser.add_argument('--param-file', dest='param_file',\n",
" default='/tmp/param.txt', type=str,\n",
" help='Output file for parameters')\n",
"args = parser.parse_args()\n",
"\n",
"print('Python Version = {}'.format(sys.version))\n",
"print('TensorFlow Version = {}'.format(tf.__version__))\n",
"print('TF_CONFIG = {}'.format(os.environ.get('TF_CONFIG', 'Not found')))\n",
"\n",
"def make_dataset():\n",
" # Scaling Boston Housing data features\n",
" def scale(feature):\n",
" max = np.max(feature)\n",
" feature = (feature / max).astype(np.float)\n",
" return feature, max\n",
"\n",
" (x_train, y_train), (x_test, y_test) = tf.keras.datasets.boston_housing.load_data(\n",
" path=\"boston_housing.npz\", test_split=0.2, seed=113\n",
" )\n",
" params = []\n",
" for _ in range(13):\n",
" x_train[_], max = scale(x_train[_])\n",
" x_test[_], _ = scale(x_test[_])\n",
" params.append(max)\n",
" \n",
" # store the normalization (max) value for each feature\n",
" with tf.io.gfile.GFile(args.param_file, 'w') as f:\n",
" f.write(str(params))\n",
" return (x_train, y_train), (x_test, y_test)\n",
"\n",
"# Build the Keras model\n",
"def build_and_compile_dnn_model():\n",
" model = tf.keras.Sequential([\n",
" tf.keras.layers.Dense(args.units, activation='relu', input_shape=(13,)),\n",
" tf.keras.layers.Dense(args.units, activation='relu'),\n",
" tf.keras.layers.Dense(1, activation='linear')\n",
" ])\n",
" model.compile(\n",
" loss='mse',\n",
" optimizer=tf.keras.optimizers.RMSprop(learning_rate=args.lr))\n",
" return model\n",
"\n",
"model = build_and_compile_dnn_model()\n",
"\n",
"# Instantiate the HyperTune reporting object\n",
"hpt = HyperTune()\n",
"\n",
"# Reporting callback\n",
"class HPTCallback(tf.keras.callbacks.Callback):\n",
"\n",
" def on_epoch_end(self, epoch, logs=None):\n",
" global hpt\n",
" hpt.report_hyperparameter_tuning_metric(\n",
" hyperparameter_metric_tag='val_loss',\n",
" metric_value=logs['val_loss'],\n",
" global_step=epoch)\n",
"\n",
"# Train the model\n",
"BATCH_SIZE = 16\n",
"(x_train, y_train), (x_test, y_test) = make_dataset()\n",
"model.fit(x_train, y_train, epochs=args.epochs, batch_size=BATCH_SIZE, validation_split=0.1, callbacks=[HPTCallback()])\n",
"model.save(args.model_dir)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CDd81Xt8iBAj"
},
"source": [
"### Store training script on your Cloud Storage bucket"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "8CofVaX6iBAk"
},
"outputs": [],
"source": [
"! rm -f custom.tar custom.tar.gz\n",
"! tar cvf custom.tar custom\n",
"! gzip custom.tar\n",
"! gsutil cp custom.tar.gz gs://$BUCKET_NAME/hpt_boston_housing.tar.gz"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "text_create_and_deploy_model:migration"
},
"source": [
"## Train a model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0oqIBOSnJjkW"
},
"source": [
"### [projects.locations.hyperparameterTuningJob.create](https://cloud.google.com/vertex-ai/docs/reference/rest/v1beta1/projects.locations.hyperparameterTuningJobs/create)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EILzZZ4biBAk"
},
"source": [
"#### Request"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "O2KyGzoYiBAk"
},
"outputs": [],
"source": [
"JOB_NAME = \"hyperparameter_tuning_\" + TIMESTAMP\n",
"\n",
"WORKER_POOL_SPEC = [\n",
" {\n",
" \"replica_count\": 1,\n",
" \"machine_spec\": {\"machine_type\": \"n1-standard-4\", \"accelerator_count\": 0},\n",
" \"python_package_spec\": {\n",
" \"executor_image_uri\": \"gcr.io/cloud-aiplatform/training/tf-cpu.2-1:latest\",\n",
" \"package_uris\": [\"gs://\" + BUCKET_NAME + \"/hpt_boston_housing.tar.gz\"],\n",
" \"python_module\": \"trainer.task\",\n",
" \"args\": [\"--model-dir=\" + \"gs://{}/{}\".format(BUCKET_NAME, JOB_NAME)],\n",
" },\n",
" }\n",
"]\n",
"\n",
"STUDY_SPEC = {\n",
" \"metrics\": [\n",
" {\"metric_id\": \"val_loss\", \"goal\": aip.StudySpec.MetricSpec.GoalType.MINIMIZE}\n",
" ],\n",
" \"parameters\": [\n",
" {\n",
" \"parameter_id\": \"lr\",\n",
" \"discrete_value_spec\": {\"values\": [0.001, 0.01, 0.1]},\n",
" \"scale_type\": aip.StudySpec.ParameterSpec.ScaleType.UNIT_LINEAR_SCALE,\n",
" },\n",
" {\n",
" \"parameter_id\": \"units\",\n",
" \"integer_value_spec\": {\"min_value\": 32, \"max_value\": 256},\n",
" \"scale_type\": aip.StudySpec.ParameterSpec.ScaleType.UNIT_LINEAR_SCALE,\n",
" },\n",
" ],\n",
" \"algorithm\": aip.StudySpec.Algorithm.RANDOM_SEARCH,\n",
"}\n",
"\n",
"hyperparameter_tuning_job = aip.HyperparameterTuningJob(\n",
" display_name=JOB_NAME,\n",
" trial_job_spec={\"worker_pool_specs\": WORKER_POOL_SPEC},\n",
" study_spec=STUDY_SPEC,\n",
" max_trial_count=6,\n",
" parallel_trial_count=1,\n",
")\n",
"\n",
"print(\n",
" MessageToJson(\n",
" aip.CreateHyperparameterTuningJobRequest(\n",
" parent=PARENT, hyperparameter_tuning_job=hyperparameter_tuning_job\n",
" ).__dict__[\"_pb\"]\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "datasets_import:migration,new,request"
},
"source": [
"*Example output*:\n",
"```\n",
"{\n",
" \"parent\": \"projects/migration-ucaip-training/locations/us-central1\",\n",
" \"hyperparameterTuningJob\": {\n",
" \"displayName\": \"hyperparameter_tuning_20210226020029\",\n",
" \"studySpec\": {\n",
" \"metrics\": [\n",
" {\n",
" \"metricId\": \"val_loss\",\n",
" \"goal\": \"MINIMIZE\"\n",
" }\n",
" ],\n",
" \"parameters\": [\n",
" {\n",
" \"parameterId\": \"lr\",\n",
" \"discreteValueSpec\": {\n",
" \"values\": [\n",
" 0.001,\n",
" 0.01,\n",
" 0.1\n",
" ]\n",
" },\n",
" \"scaleType\": \"UNIT_LINEAR_SCALE\"\n",
" },\n",
" {\n",
" \"parameterId\": \"units\",\n",
" \"integerValueSpec\": {\n",
" \"minValue\": \"32\",\n",
" \"maxValue\": \"256\"\n",
" },\n",
" \"scaleType\": \"UNIT_LINEAR_SCALE\"\n",
" }\n",
" ],\n",
" \"algorithm\": \"RANDOM_SEARCH\"\n",
" },\n",
" \"maxTrialCount\": 6,\n",
" \"parallelTrialCount\": 1,\n",
" \"trialJobSpec\": {\n",
" \"workerPoolSpecs\": [\n",
" {\n",
" \"machineSpec\": {\n",
" \"machineType\": \"n1-standard-4\"\n",
" },\n",
" \"replicaCount\": \"1\",\n",
" \"pythonPackageSpec\": {\n",
" \"executorImageUri\": \"gcr.io/cloud-aiplatform/training/tf-cpu.2-1:latest\",\n",
" \"packageUris\": [\n",
" \"gs://migration-ucaip-trainingaip-20210226020029/hpt_boston_housing.tar.gz\"\n",
" ],\n",
" \"pythonModule\": \"trainer.task\",\n",
" \"args\": [\n",
" \"--model-dir=gs://migration-ucaip-trainingaip-20210226020029/hyperparameter_tuning_20210226020029\"\n",
" ]\n",
" }\n",
" }\n",
" ]\n",
" }\n",
" }\n",
"}\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "94Kfzi6UiBAm"
},
"source": [
"#### Call"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0NeORIoMiBAm"
},
"outputs": [],
"source": [
"request = clients[\"job\"].create_hyperparameter_tuning_job(\n",
" parent=PARENT, hyperparameter_tuning_job=hyperparameter_tuning_job\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "baTCgkvoiBAm"
},
"source": [
"#### Response"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cLTfBHC2iBAn"
},
"outputs": [],
"source": [
"print(MessageToJson(request.__dict__[\"_pb\"]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5ZY0QyWbiBAn"
},
"source": [
"*Example output*:\n",
"```\n",
"{\n",
" \"name\": \"projects/116273516712/locations/us-central1/hyperparameterTuningJobs/5264408897233354752\",\n",
" \"displayName\": \"hyperparameter_tuning_20210226020029\",\n",
" \"studySpec\": {\n",
" \"metrics\": [\n",
" {\n",
" \"metricId\": \"val_loss\",\n",
" \"goal\": \"MINIMIZE\"\n",
" }\n",
" ],\n",
" \"parameters\": [\n",
" {\n",
" \"parameterId\": \"lr\",\n",
" \"discreteValueSpec\": {\n",
" \"values\": [\n",
" 0.001,\n",
" 0.01,\n",
" 0.1\n",
" ]\n",
" },\n",
" \"scaleType\": \"UNIT_LINEAR_SCALE\"\n",
" },\n",
" {\n",
" \"parameterId\": \"units\",\n",
" \"integerValueSpec\": {\n",
" \"minValue\": \"32\",\n",
" \"maxValue\": \"256\"\n",
" },\n",
" \"scaleType\": \"UNIT_LINEAR_SCALE\"\n",
" }\n",
" ],\n",
" \"algorithm\": \"RANDOM_SEARCH\"\n",
" },\n",
" \"maxTrialCount\": 6,\n",
" \"parallelTrialCount\": 1,\n",
" \"trialJobSpec\": {\n",
" \"workerPoolSpecs\": [\n",
" {\n",
" \"machineSpec\": {\n",
" \"machineType\": \"n1-standard-4\"\n",
" },\n",
" \"replicaCount\": \"1\",\n",
" \"diskSpec\": {\n",
" \"bootDiskType\": \"pd-ssd\",\n",
" \"bootDiskSizeGb\": 100\n",
" },\n",
" \"pythonPackageSpec\": {\n",
" \"executorImageUri\": \"gcr.io/cloud-aiplatform/training/tf-cpu.2-1:latest\",\n",
" \"packageUris\": [\n",
" \"gs://migration-ucaip-trainingaip-20210226020029/hpt_boston_housing.tar.gz\"\n",
" ],\n",
" \"pythonModule\": \"trainer.task\",\n",
" \"args\": [\n",
" \"--model-dir=gs://migration-ucaip-trainingaip-20210226020029/hyperparameter_tuning_20210226020029\"\n",
" ]\n",
" }\n",
" }\n",
" ]\n",
" },\n",
" \"state\": \"JOB_STATE_PENDING\",\n",
" \"createTime\": \"2021-02-26T02:02:02.787187Z\",\n",
" \"updateTime\": \"2021-02-26T02:02:02.787187Z\"\n",
"}\n",
"```\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "training_pipeline_id:migration,new,response"
},
"outputs": [],
"source": [
"# The full unique ID for the hyperparameter tuningjob\n",
"hyperparameter_tuning_id = request.name\n",
"# The short numeric ID for the hyperparameter tuningjob\n",
"hyperparameter_tuning_short_id = hyperparameter_tuning_id.split(\"/\")[-1]\n",
"\n",
"print(hyperparameter_tuning_id)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uNbWmXHziBAo"
},
"source": [
"### [projects.locations.hyperparameterTuningJob.get](https://cloud.google.com/vertex-ai/docs/reference/rest/v1beta1/projects.locations.hyperparameterTuningJobs/get)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TOP1v7ybiBAo"
},
"source": [
"#### Call"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "CKH3m0NTiBAo"
},
"outputs": [],
"source": [
"request = clients[\"job\"].get_hyperparameter_tuning_job(name=hyperparameter_tuning_id)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FVgPQi7MiBAo"
},
"source": [
"#### Response"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "P0EdlOAeiBAp"
},
"outputs": [],
"source": [
"print(MessageToJson(request.__dict__[\"_pb\"]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zJ7nxJ2OiBAp"
},
"source": [
"*Example output*:\n",
"```\n",
"{\n",
" \"name\": \"projects/116273516712/locations/us-central1/hyperparameterTuningJobs/5264408897233354752\",\n",
" \"displayName\": \"hyperparameter_tuning_20210226020029\",\n",
" \"studySpec\": {\n",
" \"metrics\": [\n",
" {\n",
" \"metricId\": \"val_loss\",\n",
" \"goal\": \"MINIMIZE\"\n",
" }\n",
" ],\n",
" \"parameters\": [\n",
" {\n",
" \"parameterId\": \"lr\",\n",
" \"discreteValueSpec\": {\n",
" \"values\": [\n",
" 0.001,\n",
" 0.01,\n",
" 0.1\n",
" ]\n",
" },\n",
" \"scaleType\": \"UNIT_LINEAR_SCALE\"\n",
" },\n",
" {\n",
" \"parameterId\": \"units\",\n",
" \"integerValueSpec\": {\n",
" \"minValue\": \"32\",\n",
" \"maxValue\": \"256\"\n",
" },\n",
" \"scaleType\": \"UNIT_LINEAR_SCALE\"\n",
" }\n",
" ],\n",
" \"algorithm\": \"RANDOM_SEARCH\"\n",
" },\n",
" \"maxTrialCount\": 6,\n",
" \"parallelTrialCount\": 1,\n",
" \"trialJobSpec\": {\n",
" \"workerPoolSpecs\": [\n",
" {\n",
" \"machineSpec\": {\n",
" \"machineType\": \"n1-standard-4\"\n",
" },\n",
" \"replicaCount\": \"1\",\n",
" \"diskSpec\": {\n",
" \"bootDiskType\": \"pd-ssd\",\n",
" \"bootDiskSizeGb\": 100\n",
" },\n",
" \"pythonPackageSpec\": {\n",
" \"executorImageUri\": \"gcr.io/cloud-aiplatform/training/tf-cpu.2-1:latest\",\n",
" \"packageUris\": [\n",
" \"gs://migration-ucaip-trainingaip-20210226020029/hpt_boston_housing.tar.gz\"\n",
" ],\n",
" \"pythonModule\": \"trainer.task\",\n",
" \"args\": [\n",
" \"--model-dir=gs://migration-ucaip-trainingaip-20210226020029/hyperparameter_tuning_20210226020029\"\n",
" ]\n",
" }\n",
" }\n",
" ]\n",
" },\n",
" \"state\": \"JOB_STATE_PENDING\",\n",
" \"createTime\": \"2021-02-26T02:02:02.787187Z\",\n",
" \"updateTime\": \"2021-02-26T02:02:02.787187Z\"\n",
"}\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QflEzRwkiBAp"
},
"source": [
"## Wait for the study to complete"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "trainingpipelines_get:migration,new,wait"
},
"outputs": [],
"source": [
"while True:\n",
" response = clients[\"job\"].get_hyperparameter_tuning_job(\n",
" name=hyperparameter_tuning_id\n",
" )\n",
" if response.state != aip.PipelineState.PIPELINE_STATE_SUCCEEDED:\n",
" print(\"Study trials have not completed:\", response.state)\n",
" if response.state == aip.PipelineState.PIPELINE_STATE_FAILED:\n",
" break\n",
" else:\n",
" print(\"Study trials have completed:\", response.end_time - response.start_time)\n",
" break\n",
" time.sleep(20)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cL8hxrQ6iBAp"
},
"source": [
"## Review the results of the study"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "blbKukdkiBAq"
},
"outputs": [],
"source": [
"best = (None, None, None, 0.0)\n",
"response = clients[\"job\"].get_hyperparameter_tuning_job(name=hyperparameter_tuning_id)\n",
"for trial in response.trials:\n",
" print(MessageToJson(trial.__dict__[\"_pb\"]))\n",
" # Keep track of the best outcome\n",
" try:\n",
" if float(trial.final_measurement.metrics[0].value) > best[3]:\n",
" best = (\n",
" trial.id,\n",
" float(trial.parameters[0].value),\n",
" float(trial.parameters[1].value),\n",
" float(trial.final_measurement.metrics[0].value),\n",
" )\n",
" except:\n",
" pass\n",
"\n",
"print()\n",
"print(\"ID\", best[0])\n",
"print(\"Decay\", best[1])\n",
"print(\"Learning Rate\", best[2])\n",
"print(\"Validation Accuracy\", best[3])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4ONNEM15iBAq"
},
"source": [
"*Example output*:\n",
"```\n",
"{\n",
" \"id\": \"1\",\n",
" \"state\": \"SUCCEEDED\",\n",
" \"parameters\": [\n",
" {\n",
" \"parameterId\": \"lr\",\n",
" \"value\": 0.1\n",
" },\n",
" {\n",
" \"parameterId\": \"units\",\n",
" \"value\": 80.0\n",
" }\n",
" ],\n",
" \"finalMeasurement\": {\n",
" \"stepCount\": \"19\",\n",
" \"metrics\": [\n",
" {\n",
" \"metricId\": \"val_loss\",\n",
" \"value\": 46.61515110294993\n",
" }\n",
" ]\n",
" },\n",
" \"startTime\": \"2021-02-26T02:05:16.935353384Z\",\n",
" \"endTime\": \"2021-02-26T02:12:44Z\"\n",
"}\n",
"{\n",
" \"id\": \"2\",\n",
" \"state\": \"SUCCEEDED\",\n",
" \"parameters\": [\n",
" {\n",
" \"parameterId\": \"lr\",\n",
" \"value\": 0.01\n",
" },\n",
" {\n",
" \"parameterId\": \"units\",\n",
" \"value\": 45.0\n",
" }\n",
" ],\n",
" \"finalMeasurement\": {\n",
" \"stepCount\": \"19\",\n",
" \"metrics\": [\n",
" {\n",
" \"metricId\": \"val_loss\",\n",
" \"value\": 32.55313952376203\n",
" }\n",
" ]\n",
" },\n",
" \"startTime\": \"2021-02-26T02:15:31.357856840Z\",\n",
" \"endTime\": \"2021-02-26T02:24:18Z\"\n",
"}\n",
"{\n",
" \"id\": \"3\",\n",
" \"state\": \"SUCCEEDED\",\n",
" \"parameters\": [\n",
" {\n",
" \"parameterId\": \"lr\",\n",
" \"value\": 0.1\n",
" },\n",
" {\n",
" \"parameterId\": \"units\",\n",
" \"value\": 70.0\n",
" }\n",
" ],\n",
" \"finalMeasurement\": {\n",
" \"stepCount\": \"19\",\n",
" \"metrics\": [\n",
" {\n",
" \"metricId\": \"val_loss\",\n",
" \"value\": 42.709188321741614\n",
" }\n",
" ]\n",
" },\n",
" \"startTime\": \"2021-02-26T02:26:40.704476222Z\",\n",
" \"endTime\": \"2021-02-26T02:34:21Z\"\n",
"}\n",
"{\n",
" \"id\": \"4\",\n",
" \"state\": \"SUCCEEDED\",\n",
" \"parameters\": [\n",
" {\n",
" \"parameterId\": \"lr\",\n",
" \"value\": 0.01\n",
" },\n",
" {\n",
" \"parameterId\": \"units\",\n",
" \"value\": 173.0\n",
" }\n",
" ],\n",
" \"finalMeasurement\": {\n",
" \"stepCount\": \"17\",\n",
" \"metrics\": [\n",
" {\n",
" \"metricId\": \"val_loss\",\n",
" \"value\": 46.12480219399057\n",
" }\n",
" ]\n",
" },\n",
" \"startTime\": \"2021-02-26T02:37:45.275581053Z\",\n",
" \"endTime\": \"2021-02-26T02:51:07Z\"\n",
"}\n",
"{\n",
" \"id\": \"5\",\n",
" \"state\": \"SUCCEEDED\",\n",
" \"parameters\": [\n",
" {\n",
" \"parameterId\": \"lr\",\n",
" \"value\": 0.01\n",
" },\n",
" {\n",
" \"parameterId\": \"units\",\n",
" \"value\": 223.0\n",
" }\n",
" ],\n",
" \"finalMeasurement\": {\n",
" \"stepCount\": \"19\",\n",
" \"metrics\": [\n",
" {\n",
" \"metricId\": \"val_loss\",\n",
" \"value\": 24.875632611716664\n",
" }\n",
" ]\n",
" },\n",
" \"startTime\": \"2021-02-26T02:53:32.612612421Z\",\n",
" \"endTime\": \"2021-02-26T02:54:19Z\"\n",
"}\n",
"{\n",
" \"id\": \"6\",\n",
" \"state\": \"SUCCEEDED\",\n",
" \"parameters\": [\n",
" {\n",
" \"parameterId\": \"lr\",\n",
" \"value\": 0.1\n",
" },\n",
" {\n",
" \"parameterId\": \"units\",\n",
" \"value\": 123.0\n",
" }\n",
" ],\n",
" \"finalMeasurement\": {\n",
" \"stepCount\": \"13\",\n",
" \"metrics\": [\n",
" {\n",
" \"metricId\": \"val_loss\",\n",
" \"value\": 43.352300690441595\n",
" }\n",
" ]\n",
" },\n",
" \"startTime\": \"2021-02-26T02:56:47.323707459Z\",\n",
" \"endTime\": \"2021-02-26T03:03:49Z\"\n",
"}\n",
"\n",
"ID 1\n",
"Decay 0.1\n",
"Learning Rate 80.0\n",
"Validation Accuracy 46.61515110294993\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cleanup:migration,new"
},
"source": [
"# Cleaning up\n",
"\n",
"To clean up all GCP resources used in this project, you can [delete the GCP\n",
"project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
"\n",
"Otherwise, you can delete the individual resources you created in this tutorial.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "QACczMumiBAq"
},
"outputs": [],
"source": [
"delete_hpt_job = True\n",
"delete_bucket = True\n",
"\n",
"# Delete the hyperparameter tuningusing the Vertex AI fully qualified identifier for the custome training\n",
"try:\n",
" if delete_hpt_job:\n",
" clients[\"job\"].delete_hyperparameter_tuning_job(name=hyperparameter_tuning_id)\n",
"except Exception as e:\n",
" print(e)\n",
"\n",
"if delete_bucket and \"BUCKET_NAME\" in globals():\n",
" ! gsutil rm -r gs://$BUCKET_NAME"
]
}
],
"metadata": {
"colab": {
"name": "UJ11 unified HyperParameter Tuning Training Job with TensorFlow.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
helgeho/ArchiveSpark | notebooks/Demo.ipynb | 1 | 2374 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import org.archive.archivespark._\n",
"import org.archive.archivespark.functions._\n",
"import org.archive.archivespark.specific.warc._"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"val records = ArchiveSpark.load(WarcSpec.fromFilesWithCdx(\"/data/helgeholzmann-de.warc.gz\"))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"val html = records.filter(r => r.mime == \"text/html\" && r.status == 200)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"val Title = HtmlText.of(Html.first(\"title\"))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{\n",
" \"record\" : {\n",
" \"redirectUrl\" : \"-\",\n",
" \"timestamp\" : \"20190528152652\",\n",
" \"digest\" : \"sha1:HCHVDRUSN7WDGNZFJES2Y4KZADQ6KINN\",\n",
" \"originalUrl\" : \"https://www.helgeholzmann.de/\",\n",
" \"surtUrl\" : \"de,helgeholzmann)/\",\n",
" \"mime\" : \"text/html\",\n",
" \"compressedSize\" : 2087,\n",
" \"meta\" : \"-\",\n",
" \"status\" : 200\n",
" },\n",
" \"payload\" : {\n",
" \"string\" : {\n",
" \"html\" : {\n",
" \"title\" : {\n",
" \"text\" : \"Helge Holzmann - @helgeho\"\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"html.enrich(Title).peekJson"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "ArchiveSpark",
"language": "",
"name": "archivespark"
},
"language_info": {
"name": "scala",
"version": "2.11.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
ponderousmad/pyndent | notMNIST_setup.ipynb | 1 | 165564 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"deletable": true,
"editable": true,
"id": "5hIbr52I7Z7U"
},
"source": [
"notMINST Data Setup\n",
"===================\n",
"\n",
"This notebook sets up the the [notMNIST](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html) dataset. This dataset is designed to look like the classic [MNIST](http://yann.lecun.com/exdb/mnist/) dataset, while looking a little more like real data: it's a harder task, and the data is a lot less 'clean' than MNIST.\n",
"\n",
"This notebook is derived from the [Udacity Tensorflow Course Assignment 1](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/udacity/1_notmnist.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"collapsed": false,
"deletable": true,
"editable": true,
"id": "apJbCsBHl-2A"
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from __future__ import print_function\n",
"\n",
"import gzip\n",
"import os\n",
"import sys\n",
"import tarfile\n",
"import urllib.request\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"from IPython.display import display, Image\n",
"from scipy import ndimage\n",
"from six.moves import cPickle as pickle\n",
"\n",
"import outputer"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"deletable": true,
"editable": true,
"id": "jNWGtZaXn-5j"
},
"source": [
"Download the dataset of characters 'A' to 'J' rendered in various fonts as 28x28 images.\n",
"\n",
"There is training set of about 500k images and a test set of about 19000 images."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{
"item_id": 1
}
]
},
"colab_type": "code",
"collapsed": false,
"deletable": true,
"editable": true,
"executionInfo": {
"elapsed": 186058,
"status": "ok",
"timestamp": 1444485672507,
"user": {
"color": "#1FA15D",
"displayName": "Vincent Vanhoucke",
"isAnonymous": false,
"isMe": true,
"permissionId": "05076109866853157986",
"photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
"sessionId": "2a0a5e044bb03b66",
"userId": "102167687554210253930"
},
"user_tz": 420
},
"id": "EYRJ4ICW6-da",
"outputId": "0d0f85df-155f-4a89-8e7e-ee32df36ec8d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found notMNIST/notMNIST_large.tar.gz with correct size.\n",
"Found notMNIST/notMNIST_small.tar.gz with correct size.\n"
]
}
],
"source": [
"url = \"http://yaroslavvb.com/upload/notMNIST/\"\n",
"data_path = outputer.setup_directory(\"notMNIST\")\n",
"\n",
"def maybe_download(path, filename, expected_bytes):\n",
" \"\"\"Download a file if not present, and make sure it's the right size.\"\"\"\n",
" file_path = os.path.join(path, filename)\n",
" if not os.path.exists(file_path):\n",
" file_path, _ = urllib.request.urlretrieve(url + filename, file_path)\n",
" statinfo = os.stat(file_path)\n",
" if statinfo.st_size == expected_bytes:\n",
" print(\"Found\", file_path, \"with correct size.\")\n",
" else:\n",
" raise Exception(\"Error downloading \" + filename)\n",
" return file_path\n",
"\n",
"train_filename = maybe_download(data_path, \"notMNIST_large.tar.gz\", 247336696)\n",
"test_filename = maybe_download(data_path, \"notMNIST_small.tar.gz\", 8458043)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"deletable": true,
"editable": true,
"id": "cC3p0oEyF8QT"
},
"source": [
"Extract the dataset from the compressed .tar.gz file.\n",
"This should give you a set of directories, labelled A through J."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{
"item_id": 1
}
]
},
"colab_type": "code",
"collapsed": false,
"deletable": true,
"editable": true,
"executionInfo": {
"elapsed": 186055,
"status": "ok",
"timestamp": 1444485672525,
"user": {
"color": "#1FA15D",
"displayName": "Vincent Vanhoucke",
"isAnonymous": false,
"isMe": true,
"permissionId": "05076109866853157986",
"photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
"sessionId": "2a0a5e044bb03b66",
"userId": "102167687554210253930"
},
"user_tz": 420
},
"id": "H8CBE-WZ8nmj",
"outputId": "ef6c790c-2513-4b09-962e-27c79390c762"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Checking notMNIST/.DS_Store\n",
"Checking notMNIST/full.pickle\n",
"Checking notMNIST/notMNIST_large\n",
"Found ['notMNIST/notMNIST_large/A', 'notMNIST/notMNIST_large/B', 'notMNIST/notMNIST_large/C', 'notMNIST/notMNIST_large/D', 'notMNIST/notMNIST_large/E', 'notMNIST/notMNIST_large/F', 'notMNIST/notMNIST_large/G', 'notMNIST/notMNIST_large/H', 'notMNIST/notMNIST_large/I', 'notMNIST/notMNIST_large/J']\n",
"Checking notMNIST/notMNIST_large.tar.gz\n",
"Checking notMNIST/notMNIST_small\n",
"Found ['notMNIST/notMNIST_small/A', 'notMNIST/notMNIST_small/B', 'notMNIST/notMNIST_small/C', 'notMNIST/notMNIST_small/D', 'notMNIST/notMNIST_small/E', 'notMNIST/notMNIST_small/F', 'notMNIST/notMNIST_small/G', 'notMNIST/notMNIST_small/H', 'notMNIST/notMNIST_small/I', 'notMNIST/notMNIST_small/J']\n",
"Checking notMNIST/notMNIST_small.tar.gz\n"
]
}
],
"source": [
"def extract(filename, root, class_count):\n",
" # remove path and .tar.gz\n",
" dir_name = os.path.splitext(os.path.splitext(os.path.basename(filename))[0])[0]\n",
" path = os.path.join(root, dir_name)\n",
" print(\"Extracting\", filename, \"to\", path)\n",
" tar = tarfile.open(filename)\n",
" tar.extractall(path=root)\n",
" tar.close()\n",
" data_folders = [os.path.join(path, d) for d in sorted(os.listdir(path))]\n",
" if len(data_folders) != class_count:\n",
" raise Exception(\"Expected %d folders, one per class. Found %d instead.\" %\n",
" (class_count, len(data_folders)))\n",
" print(data_folders)\n",
" return data_folders\n",
"\n",
"train_folders = []\n",
"test_folders = []\n",
"\n",
"for name in os.listdir(data_path):\n",
" path = os.path.join(data_path, name)\n",
" target = None\n",
" print(\"Checking\", path)\n",
" if path.endswith(\"_small\"):\n",
" target = test_folders\n",
" elif path.endswith(\"_large\"):\n",
" target = train_folders\n",
" if target is not None:\n",
" target.extend([os.path.join(path, name) for name in os.listdir(path)])\n",
" print(\"Found\", target)\n",
"\n",
"expected_classes = 10\n",
"\n",
"if len(train_folders) < expected_classes:\n",
" train_folders = extract(train_filename, data_path, expected_classes)\n",
"\n",
"if len(test_folders) < expected_classes:\n",
" test_folders = extract(test_filename, data_path, expected_classes)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"deletable": true,
"editable": true,
"id": "4riXK3IoHgx6"
},
"source": [
"# Inspect Data\n",
"\n",
"Verify that the images contain rendered glyphs."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAACRUlEQVR4nG2STUiUYRDH//O8z368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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename=\"notMNIST/notMNIST_small/A/MDEtMDEtMDAudHRm.png\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABZ0lEQVR4nMWSPWuUQRSFn7u7UUHW\nZAV7wSqoYGVtYaMEbWxsBBGx8w9YqAj+AS3sREhtaxUNWlgIC4looRaSJiBidoWI2fed+1i82Q9k\nsfV0M885c5mZE0DYXj5/7uSxIwH48/vH9bUPdQhAh1NPh6YTpcPVM3QCIlq3fljqMqFZ6trB7Q4B\n7ftWddGcSLOufNgBblgVtZ4eW6s58iZxfLNrQLaqwR4AB5cWsgWwe5q7FjVdu9ADgN7FV6ZavEdf\ntfiyC0REAN11i2qfgerIFTrRJKPDiiPVndYigGyTza0x2UaApRb7fmY0Xoyhs9C/4Fz9Txj/gs6F\nwyZ3dC782uSvH8oYa/pCLxDaXn10YvLZkxmx/J42AIPdZi+/PPv0dnSgwXesUrNMa/Jro+liwuFV\n92pnC1YaZ0qw+KTOmWaaxSxa3IKI9pV3lTMavflsml4DokXv8uP+t33D740HC2efb+28vsQfU/It\nPYCuJtMAAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename=\"notMNIST/notMNIST_large/A/a2F6b28udHRm.png\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAABpUlEQVR4nHWSv0vbURTFz733q4mD\nECxWVHRRpODQJWtjh1J/jXYogri4SpcK/gNVHFqKQoeSyaFTaQehg1MlpSCCoA5Kl/qz1aQtxUrb\nb5LvOx3yQkz55vB4y+fdc8+77wm8VCJ0jNwd7ElF5/mPbzYjc6wx3M5esKoodwcqnhnaX5ZI8vJo\nb/vgko5c9kgMo2eMmH/1sK/NkOif3mLJUzE8ZsSrZ50QQAQA5spFPgIgiiUWuTEAUVMRiJphynER\ngOIJQ75IINBqPIhhdDYJGGYYch5muC6tbOmrMpcQCOplgQCaI1drl6rXJHl2ExrLNEfOwGIZMkXu\nJxFvquNNePvXGA/TwLt4TwAn/NbbIA7wm59bGrSECsI/iG8J/YFka8PKU3QN/A/Vz1I/oOl+fSBV\nV/Z9MiV+6bg2ITHB0HybN3tNrgUwE0DUTNCZDbnqjw9+J9e7Iais1NwFi3xagYaRkCwspFOClu7h\nlWNGjs8Db2u495WOpcOd7U8FRzrujtUSGnqzv+j8d/659iBRfXoBYHS3JjL9N5rzhf3196dQiSpl\n/wCOobI6iFNaJwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename=\"notMNIST/notMNIST_large/C/ZXVyb2Z1cmVuY2UgaXRhbGljLnR0Zg==.png\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAAFklEQVR4nGP8z4AbMOGRG5UclRxh\nkgCD/gE3CChK/QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This I is all white\n",
"Image(filename=\"notMNIST/notMNIST_small/I/SVRDIEZyYW5rbGluIEdvdGhpYyBEZW1pLnBmYg==.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"deletable": true,
"editable": true,
"id": "PBdkjESPK8tw"
},
"source": [
"Convert the data into an array of normalized grayscale floating point images, and an array of classification labels.\n",
"\n",
"Unreadable images are skipped."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{
"item_id": 30
}
]
},
"colab_type": "code",
"collapsed": false,
"deletable": true,
"editable": true,
"executionInfo": {
"elapsed": 399874,
"status": "ok",
"timestamp": 1444485886378,
"user": {
"color": "#1FA15D",
"displayName": "Vincent Vanhoucke",
"isAnonymous": false,
"isMe": true,
"permissionId": "05076109866853157986",
"photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg",
"sessionId": "2a0a5e044bb03b66",
"userId": "102167687554210253930"
},
"user_tz": 420
},
"id": "h7q0XhG3MJdf",
"outputId": "92c391bb-86ff-431d-9ada-315568a19e59"
},
"outputs": [],
"source": [
"\n",
"def normalize_separator(path):\n",
" return path.replace(\"\\\\\", \"/\")\n",
"\n",
"def load(data_folders, set_id, min_count, max_count):\n",
" # Create arrays large enough for maximum expected data.\n",
" dataset = np.ndarray(shape=(max_count, image_size, image_size), dtype=np.float32)\n",
" labels = np.ndarray(shape=(max_count), dtype=np.int32)\n",
" label_index = 0\n",
" image_index = 0\n",
" \n",
" solid_blacks = []\n",
" solid_whites = []\n",
" \n",
" for folder in sorted(data_folders):\n",
" print(folder)\n",
" for image in os.listdir(folder):\n",
" if image_index >= max_count:\n",
" raise Exception(\"More than %d images!\" % (max_count,))\n",
" image_file = os.path.join(folder, image)\n",
" if normalize_separator(image_file) in skip_list:\n",
" continue\n",
" try:\n",
" raw_data = ndimage.imread(image_file)\n",
" \n",
" # Keep track of images a that are solid white or solid black.\n",
" if np.all(raw_data == 0):\n",
" solid_blacks.append(image_file)\n",
" if np.all(raw_data == int(pixel_depth)):\n",
" solid_whites.append(image_file)\n",
" \n",
" # Convert to float and normalize.\n",
" image_data = (raw_data.astype(float) - pixel_depth / 2) / pixel_depth\n",
"\n",
" if image_data.shape != (image_size, image_size):\n",
" raise Exception(\"Unexpected image shape: %s\" % str(image_data.shape))\n",
"\n",
" # Capture the image data and label.\n",
" dataset[image_index, :, :] = image_data\n",
" labels[image_index] = label_index\n",
" image_index += 1\n",
" except IOError as e:\n",
" skip_list.append(normalize_separator(image_file))\n",
" print(\"Could not read:\", image_file, ':', e, \"skipping.\")\n",
" label_index += 1\n",
" image_count = image_index\n",
" # Trim down to just the used portion of the arrays.\n",
" dataset = dataset[0:image_count, :, :]\n",
" labels = labels[0:image_count]\n",
" if image_count < min_count:\n",
" raise Exception('Many fewer images than expected: %d < %d' %\n",
" (num_images, min_num_images))\n",
" print(\"Input data shape:\", dataset.shape)\n",
" print(\"Mean of all normalized pixels:\", np.mean(dataset))\n",
" print(\"Standard deviation of normalized pixels:\", np.std(dataset))\n",
" print('Labels shape:', labels.shape)\n",
" print(\"Found\", len(solid_whites), \"solid white images, and\",\n",
" len(solid_blacks), \"solid black images.\")\n",
" return dataset, labels"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"notMNIST/notMNIST_large/A\n",
"notMNIST/notMNIST_large/B\n",
"notMNIST/notMNIST_large/C\n",
"notMNIST/notMNIST_large/D\n",
"notMNIST/notMNIST_large/E\n",
"notMNIST/notMNIST_large/F\n",
"notMNIST/notMNIST_large/G\n",
"notMNIST/notMNIST_large/H\n",
"notMNIST/notMNIST_large/I\n",
"notMNIST/notMNIST_large/J\n",
"Input data shape: (529114, 28, 28)\n",
"Mean of all normalized pixels: -0.0816596\n",
"Standard deviation of normalized pixels: 0.454233\n",
"Labels shape: (529114,)\n",
"Found 5552 solid white images, and 0 solid black images.\n",
"notMNIST/notMNIST_small/A\n",
"notMNIST/notMNIST_small/B\n",
"notMNIST/notMNIST_small/C\n",
"notMNIST/notMNIST_small/D\n",
"notMNIST/notMNIST_small/E\n",
"notMNIST/notMNIST_small/F\n",
"notMNIST/notMNIST_small/G\n",
"notMNIST/notMNIST_small/H\n",
"notMNIST/notMNIST_small/I\n",
"notMNIST/notMNIST_small/J\n",
"Input data shape: (18724, 28, 28)\n",
"Mean of all normalized pixels: -0.0746362\n",
"Standard deviation of normalized pixels: 0.458622\n",
"Labels shape: (18724,)\n",
"Found 254 solid white images, and 0 solid black images.\n"
]
},
{
"data": {
"text/plain": [
"['notMNIST/notMNIST_large/A/SG90IE11c3RhcmQgQlROIFBvc3Rlci50dGY=.png',\n",
" 'notMNIST/notMNIST_large/A/RnJlaWdodERpc3BCb29rSXRhbGljLnR0Zg==.png',\n",
" 'notMNIST/notMNIST_large/A/Um9tYW5hIEJvbGQucGZi.png',\n",
" 'notMNIST/notMNIST_large/B/TmlraXNFRi1TZW1pQm9sZEl0YWxpYy5vdGY=.png',\n",
" 'notMNIST/notMNIST_large/D/VHJhbnNpdCBCb2xkLnR0Zg==.png',\n",
" 'notMNIST/notMNIST_small/A/RGVtb2NyYXRpY2FCb2xkT2xkc3R5bGUgQm9sZC50dGY=.png',\n",
" 'notMNIST/notMNIST_small/F/Q3Jvc3NvdmVyIEJvbGRPYmxpcXVlLnR0Zg==.png']"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_dataset, train_labels = load(train_folders, \"train\", 450000, 550000)\n",
"test_dataset, test_labels = load(test_folders, 'test', 18000, 20000)\n",
"\n",
"skip_list"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"deletable": true,
"editable": true,
"id": "vUdbskYE2d87"
},
"source": [
"# Verify Proccessed Data"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nO4gj8Y844h3EkfhHHPEO4v8DxHX+mMpbsLwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1041f2438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"exemplar = plt.imshow(train_dataset[0])\n",
"train_labels[0]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0rnuAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10339dbe0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"exemplar = plt.imshow(train_dataset[373])\n",
"train_labels[373]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"9"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x187742d68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"exemplar = plt.imshow(test_dataset[18169])\n",
"test_labels[18169]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"9"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x18796c278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"exemplar = plt.imshow(train_dataset[-9])\n",
"train_labels[-9]"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"deletable": true,
"editable": true,
"id": "GPTCnjIcyuKN"
},
"source": [
"# Compress and Store Data"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Compressed pickle size: 248903840\n"
]
}
],
"source": [
"pickle_file = 'notMNIST/full.pickle'\n",
"\n",
"try:\n",
" f = gzip.open(pickle_file, 'wb')\n",
" save = {\n",
" 'train_dataset': train_dataset,\n",
" 'train_labels': train_labels,\n",
" 'test_dataset': test_dataset,\n",
" 'test_labels': test_labels\n",
" }\n",
" pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)\n",
" f.close()\n",
"except Exception as e:\n",
" print('Unable to save data to', pickle_file, ':', e)\n",
" raise\n",
"\n",
"statinfo = os.stat(pickle_file)\n",
"print('Compressed pickle size:', statinfo.st_size)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"colabVersion": "0.3.2",
"colab_default_view": {},
"colab_views": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
JAmarel/QLab | FranckHertz/DataAndAnalysis.ipynb | 2 | 1049272 | null | mit |
romeokienzler/uhack | projects/bosch/SparkMLPython.ipynb | 1 | 12464 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import ibmos2spark\n",
"\n",
"# @hidden_cell\n",
"credentials = {\n",
" 'auth_url': 'https://identity.open.softlayer.com',\n",
" 'project_id': '6aaf54352357483486ee2d4981f8ef15',\n",
" 'region': 'dallas',\n",
" 'user_id': 'b160340071b3407ca50c6b9a46b0bb25',\n",
" 'username': 'member_b092a5c6f5c11f819059a83dfbd5d922b8a2299b',\n",
" 'password': 'qwN4Y5EM*0KuZck['\n",
"}\n",
"\n",
"configuration_name = 'os_d3bd5b94a9334de59a55a7fed2bedeaa_configs'\n",
"bmos = ibmos2spark.bluemix(sc, credentials, configuration_name)\n",
"\n",
"from pyspark.sql import SparkSession\n",
"spark = SparkSession.builder.getOrCreate()\n",
"# Please read the documentation of PySpark to learn more about the possibilities to load data files.\n",
"# PySpark documentation: https://spark.apache.org/docs/2.0.1/api/python/pyspark.sql.html#pyspark.sql.SparkSession\n",
"# The SparkSession object is already initalized for you.\n",
"# The following variable contains the path to your file on your Object Storage.\n",
"path_1 = bmos.url('dwlive', 'part-00000-ddf48c42-d9ea-4d72-aa4e-0b879587b372.snappy.parquet')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"path_2 = bmos.url('dwlive', 'part-00000-abf604d6-bc0a-4ea3-8ffa-7838c5912fa2.snappy.parquet')\n",
"df_categorical = spark.read.parquet(path_2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_numeric = spark.read.parquet(path_1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_categorical.printSchema()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_numeric.printSchema()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_numeric.first()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_categorical.createOrReplaceTempView(\"dfcat\")\n",
"dfcat = spark.sql(\"select Id, L0_S22_F545 from dfcat\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_numeric.createOrReplaceTempView(\"dfnum\")\n",
"dfnum = spark.sql(\"select Id,L0_S0_F0,L0_S0_F2,L0_S0_F4,Response from dfnum\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = dfcat.join(dfnum,\"Id\")\n",
"df.createOrReplaceTempView(\"df\")\n",
"\n",
"df_notnull = spark.sql(\"\"\"\n",
"select\n",
" Response as label,\n",
" case \n",
" when L0_S22_F545 is null then 'NA'\n",
" when L0_S22_F545 = '' then 'NA' \n",
" else L0_S22_F545 end as L0_S22_F545, \n",
" case\n",
" when L0_S0_F0 is null then 0.0 \n",
" else L0_S0_F0 end as L0_S0_F0, \n",
" case\n",
" when L0_S0_F2 is null then 0.0 \n",
" else L0_S0_F2 end as L0_S0_F2,\n",
" case\n",
" when L0_S0_F4 is null then 0.0 \n",
" else L0_S0_F4 end as L0_S0_F4\n",
"from df\n",
"\"\"\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pyspark.ml.feature import OneHotEncoder\n",
"from pyspark.ml.feature import StringIndexer\n",
"\n",
"indexer = StringIndexer() \\\n",
" .setInputCol(\"L0_S22_F545\") \\\n",
" .setOutputCol(\"L0_S22_F545Index\")\n",
"\n",
"indexed = indexer.setHandleInvalid(\"skip\").fit(df_notnull).transform(df_notnull)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"indexed.printSchema()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"indexed.first()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"indexed.select(\"L0_S22_F545Index\").show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"indexed.select(\"L0_S22_F545Index\").distinct().show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"encoder = OneHotEncoder().setInputCol(\"L0_S22_F545Index\").setOutputCol(\"L0_S22_F545Vec\")\n",
"encoded = encoder.transform(indexed)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"encoded.printSchema()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"encoded.first().L0_S22_F545Vec"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pyspark.ml import Pipeline\n",
"from pyspark.ml.linalg import Vector\n",
"from pyspark.ml.feature import VectorAssembler\n",
"\n",
"transformers = [\n",
" indexer,\n",
" encoder,\n",
" VectorAssembler()\n",
" .setInputCols([\"L0_S22_F545Vec\", \"L0_S0_F0\", \"L0_S0_F2\",\"L0_S0_F4\"])\n",
" .setOutputCol(\"features\")\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pipeline = Pipeline().setStages(transformers).fit(df_notnull)\n",
"\n",
"transformed = pipeline.transform(df_notnull)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"transformed.printSchema()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"transformed.first().features"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pyspark.ml.classification import RandomForestClassifier\n",
"rf =RandomForestClassifier() \\\n",
" .setLabelCol(\"label\") \\\n",
" .setFeaturesCol(\"features\")\n",
"\n",
"model = Pipeline().setStages([rf]).fit(transformed)\n",
"\n",
"result = model.transform(transformed)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"pixiedust": {
"displayParams": {
"handlerId": "dataframe"
}
}
},
"outputs": [],
"source": [
"import pixiedust\n",
"display(result)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pyspark.ml.evaluation import BinaryClassificationEvaluator\n",
"\n",
"# Evaluate model\n",
"evaluator = BinaryClassificationEvaluator(rawPredictionCol=\"rawPrediction\")\n",
"evaluation = evaluator.evaluate(result)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"evaluation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pyspark.ml.tuning import ParamGridBuilder, CrossValidator\n",
"\n",
"paramGrid = ParamGridBuilder() \\\n",
" .addGrid(rf.numTrees, [3,5]) \\\n",
" .addGrid(rf.featureSubsetStrategy, [\"auto\",\"all\"]) \\\n",
" .addGrid(rf.impurity, [\"gini\",\"entropy\"]) \\\n",
" .addGrid(rf.maxBins, [2,5]) \\\n",
" .addGrid(rf.maxDepth, [3,5]) \\\n",
" .build()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"transformed_sampled = transformed.sample(False,0.001)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"transformed_sampled.count()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create 5-fold CrossValidator\n",
"cv = CrossValidator(estimator=rf, estimatorParamMaps=paramGrid, evaluator=evaluator, numFolds=5)\n",
"\n",
"# Run cross validations\n",
"cvModel = cv.fit(transformed)\n",
"# this will likely take a fair amount of time because of the amount of models that we're creating and testing\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import org.apache.spark.ml.tuning.{CrossValidator, ParamGridBuilder}\n",
"/**var paramGrid = new ParamGridBuilder()\n",
" .addGrid(rf.numTrees, 3 :: 5 :: 10 :: 30 :: 50 :: 70 :: 100 :: 150 :: Nil)\n",
" .addGrid(rf.featureSubsetStrategy, \"auto\" :: \"all\" :: \"sqrt\" :: \"log2\" :: \"onethird\" :: Nil)\n",
" .addGrid(rf.impurity, \"gini\" :: \"entropy\" :: Nil) \n",
" .addGrid(rf.maxBins, 2 :: 5 :: 10 :: 15 :: 20 :: 25 :: 30 :: Nil)\n",
" .addGrid(rf.maxDepth, 3 :: 5 :: 10 :: 15 :: 20 :: 25 :: 30 :: Nil)\n",
" .build()*/\n",
"\n",
"var paramGrid = new ParamGridBuilder().\n",
" addGrid(rf.numTrees, 3 :: 5 :: 10 :: Nil).\n",
" addGrid(rf.featureSubsetStrategy, \"auto\" :: \"all\" :: Nil).\n",
" addGrid(rf.impurity, \"gini\" :: \"entropy\" :: Nil). \n",
" addGrid(rf.maxBins, 2 :: 5 :: Nil).\n",
" addGrid(rf.maxDepth, 3 :: 5 :: Nil).\n",
" build()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"//Model is created\n",
" var crossValidatorModel = crossValidator.fit(df_notnull)\n",
" //Model used to Predict\n",
" var newPredictions = crossValidatorModel.transform(df_notnull)\n",
"\n",
"\n",
" var newAucTest = evaluator.evaluate(newPredictions, evaluatorParamMap)\n",
" println(\"new AUC (with Cross Validation) \" + newAucTest)\n",
" var bestModel = crossValidatorModel.bestModel\n",
"\n",
" //Understand the Model selected\n",
" println()\n",
" println(\"Parameters for Best Model:\")\n",
"\n",
" var bestPipelineModel = crossValidatorModel.bestModel.asInstanceOf[PipelineModel]\n",
" var stages = bestPipelineModel.stages"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import org.apache.spark.ml.classification.RandomForestClassificationModel\n",
" val rfStage = stages(stages.length-1).asInstanceOf[RandomForestClassificationModel]\n",
"rfStage.getNumTrees\n",
"rfStage.getFeatureSubsetStrategy\n",
"rfStage.getImpurity\n",
"rfStage.getMaxBins\n",
"rfStage.getMaxDepth"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2 with Spark 2.1",
"language": "python",
"name": "python2-spark21"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| apache-2.0 |
hunterherrin/phys202-2015-work | assignments/assignment07/AlgorithmsEx02.ipynb | 1 | 23542 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# Algorithms Exercise 2"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"nbgrader": {}
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from matplotlib import pyplot as plt\n",
"import seaborn as sns\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Peak finding"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Write a function `find_peaks` that finds and returns the indices of the local maxima in a sequence. Your function should:\n",
"\n",
"* Properly handle local maxima at the endpoints of the input array.\n",
"* Return a Numpy array of integer indices.\n",
"* Handle any Python iterable as input."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0, 2, 4, 6]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array(range(5)).max()\n",
"\n",
"list(range(1,5))\n",
"find_peaks([2,0,1,0,2,0,1])"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false,
"nbgrader": {
"checksum": "16e8d0e271e2b778e4e94f95fe3933a2",
"solution": true
}
},
"outputs": [],
"source": [
"def find_peaks(a):\n",
" \"\"\"Find the indices of the local maxima in a sequence.\"\"\"\n",
" b=[]\n",
" c=np.array(a)\n",
" if c[0]>c[1]:\n",
" b.append(0)\n",
" for i in range(1,len(c)-1):\n",
" if c[i]>c[i-1] and c[i]>c[i+1]:\n",
" b.append(i)\n",
" if c[len(c)-1]>c[len(c)-2]:\n",
" b.append(len(c)-1)\n",
" return b"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "10f9a6cd367de8fdeafe16e190f7db83",
"grade": true,
"grade_id": "algorithmsex02a",
"points": 5
}
},
"outputs": [],
"source": [
"p1 = find_peaks([2,0,1,0,2,0,1])\n",
"assert np.allclose(p1, np.array([0,2,4,6]))\n",
"p2 = find_peaks(np.array([0,1,2,3]))\n",
"assert np.allclose(p2, np.array([3]))\n",
"p3 = find_peaks([3,2,1,0])\n",
"assert np.allclose(p3, np.array([0]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Here is a string with the first 10000 digits of $\\pi$ (after the decimal). Write code to perform the following:\n",
"\n",
"* Convert that string to a Numpy array of integers.\n",
"* Find the indices of the local maxima in the digits of $\\pi$.\n",
"* Use `np.diff` to find the distances between consequtive local maxima.\n",
"* Visualize that distribution using an appropriately customized histogram."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true,
"nbgrader": {}
},
"outputs": [],
"source": [
"from sympy import pi, N\n",
"pi_digits_str = str(N(pi, 10001))[2:]"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
}
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAALKCAYAAAAvY6d9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSdLAAlmSJEkaWCBLkiRJAwtkSZIkafAflqYHcM7djQ0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7a1c059550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"first_10000=np.array(list(pi_digits_str), dtype=int)\n",
"peaks=find_peaks(first_10000)\n",
"differences=np.diff(peaks)\n",
"plt.figure(figsize=(10,10))\n",
"plt.hist(differences, 20, (1,20))\n",
"plt.title('Hoe Far Apart the Local Maxima of the First 10,0000 Digits of $\\pi$ Are')\n",
"plt.ylabel('Number of Occurences')\n",
"plt.xlabel('Distance Apart')\n",
"plt.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "140552b7e8017eddb99806fbeaf8d8a0",
"grade": true,
"grade_id": "algorithmsex02b",
"points": 5
}
},
"outputs": [],
"source": [
"assert True # use this for grading the pi digits histogram"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.